ctbnrle.1.1.1/0000775000175000017500000000000012251376074013375 5ustar csheltoncsheltonctbnrle.1.1.1/INSTALL0000664000175000017500000000444412115753610014426 0ustar csheltoncsheltonHow to compile: =============== This code has been tested under Unix with the Gnu tools and Windows with Visual C++. You may want to read the "Installing Graphviz" section first if you plan to use the visualization portion of the libraries. UNIX: The Makefile in src/ will make ctbn.a (a library of all of the object files). "make" in demo/ will make the demos (including making the objects in src/ if necessary), and "make" tst/ will make all of the unit/regression tests. Running the python script tst/unit_test.py will run each of the test scripts to confirm that the calculations are being performed correctly. Windows: Under Visual C++, you must create your own project. Add all of the source files in src/ and all of the header files in hdr/. Adding one "main" file (from tst/ or demo/) should allow the entire project to compile. You may have a number of compile warnings about conversions from "size_t" to "int." These may be safely ignored. Using CMake: ============ Starting with release 1.1.0, we have added support for cmake. To use it with make (on Unix), look at the build.sh script in the base directory. It shows how to configure glpk and then run cmake followed by make (from the directory ctbn_build_dir). All resulting executables will be in the ctbn_build_dir under different directories. We hope to improve cmake support in future releases. Installing Graphviz: ==================== graphiviz is a graph layout program available from www.graphviz.org. The CTBNRLE uses the "dot" executable from it. We have successfully tested CTBNRLE with graphviz version 2.14.1 (Oct 20, 2007) and version 2.26.3 (Jan 26, 2010). Install graphviz as per the instructions on their website. If the executable is not in your path, or you would like to explicitly state where to find it, you should edit src/graphic.cc and change the definition of DOTCOMMAND to the string containing the executable (with path) to be executed to run the "dot" program of graphviz. Tests: ====== "make test" from the cmake build directory or "python ./unit_test.py" from the tst/ directory will run a bank of tests. Note that the SEM test will take a few minutes (much longer if compiler speed optimizations are not on). Very occasionally one of the sampling tests will fail. Rerunning (ie with a different random seed) will fix it. ctbnrle.1.1.1/ctbn_build_dir/0000775000175000017500000000000012115751237016335 5ustar csheltoncsheltonctbnrle.1.1.1/doc/0000775000175000017500000000000012251377341014140 5ustar csheltoncsheltonctbnrle.1.1.1/doc/AUTHORS0000664000175000017500000000063112107032454015201 0ustar csheltoncsheltonThis project is managed by Christian Shelton and development is done in R-LAIR, Riverside Lab for Artificial Intelligence Research at the University of California, Riverside The following people have contributed significant code to the project Busra Celikkaya Yu Fan Dave Gomboc William Lam Joon Lee Christian Shelton Jing Xu The following people have contributed bug reports and fixes Asela Gunawardana ctbnrle.1.1.1/doc/readme.txt0000664000175000017500000000316612115753603016142 0ustar csheltoncsheltonHow to cite: ============ If you wish to cite this code, please cite the JMLR paper Christian R. Shelton, Yu Fan, William Lam, Joon Lee, and Jing Xu. Continuous Time Bayesian Network Reasoning and Learning Engine. Journal of Machine Learning Research. 11(Mar), 1137-1140, 2010. a suitable bibtex entry would be @article{CTBNRLE, author = "Christian R. Shelton and Yu Fan and William Lam and Joon Lee and Jing Xu", title = "Continuous Time {B}ayesian Network Reasoning and Learning Engine", journal = "Journal of Machine Learning Research", volume = 11, number = "Mar', pages = "1137--1140", year = 2010 } How to compile: =============== see the file "INSTALL" in the root directory Structure: ========== The base directory is split into multiple directorys: demo/ This contains example programs on how to use the libraries. They are often useful on their own (e.g. sample from a CTBN, learn a CTBN from samples, etc). The README.txt file also has information on the file extensions used for data files. doc/ This directory contains information on how to read the code and use it, literature to read, and funding sources that made this code base possible. src/ This directory contains all of the non-header files. hdr/ This directory contains all of the header files. This, plus the ctbn.a created in /src are all that is necessary to link a new program to the libraries here tst/ This directory contains regression tests that are used to verify that the code is working eigen/ and glpk/ These directories contain the source trees for Eigen (a matrix package -- headers only) and GLPK (a linear program solver) ctbnrle.1.1.1/doc/sponsorship.txt0000664000175000017500000000040412107032454017257 0ustar csheltoncsheltonThis code base was made possible by two grants: Continuous Time Structured Stochastic Processes AFOSR Award No FA9550-07-1-0076 Reasoning in Dynamic Real Time Systems DARPA Award No HR0011-09-1-0030 We thank them for their generous support of this project. ctbnrle.1.1.1/doc/ctbn_papers.txt0000664000175000017500000002063512107032454017200 0ustar csheltoncsheltonThe following are the academic papers we know about that pertain to continuous time Bayesian networks. The code assumes knowledge of at least the first paper which is included in this directory. BASIC FORMULATION: Uri Nodelman, Christian R. Shelton, and Daphne Koller (2002). "Continuous Time Bayesian Networks." Proceedings of the Eighteenth International Conference on Uncertainty in Artificial Intelligence (pp. 378-387). http://www.cs.ucr.edu/~cshelton/papers/index.cgi?NodSheKol02 [First paper formulating CTBNs. It defines a CTBN's syntax and semantics and briefly discusses an approximate inference method.] Tal El-Hay, Nir Friedman, Daphne Koller and Raz Kupferman (2006). "Continuous Time Markov Networks", Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (pp. 155-164). http://ai.stanford.edu/~koller/Papers/El-Hay+al:UAI06.pdf [An undirected version of CTBNs that represents the subset of reversable Markov processes.] LEARNING: Uri Nodelman, Christian R. Shelton, and Daphne Koller (2003). "Learning Continuous Time Bayesian Networks." Proceedings of the Nineteenth International Conference on Uncertainty in Artificial Intelligence (pp. 451-458). http://www.cs.ucr.edu/~cshelton/papers/index.cgi?NodSheKol03 [Introduces the sufficient statistics for a CTBN and formulates the maximum likelihood and maximum a posteriori parameter learning formulas for the complete data case. It also formulates a structure learning method based on a Bayesian scoring function.] Uri Nodelman, Christian R. Shelton, and Daphne Koller (2005). "Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks." Proceedings of the Twenty-First International Conference on Uncertainty in Artificial Intelligence (pp. 421-430). http://www.cs.ucr.edu/~cshelton/papers/index.cgi?NodSheKol05 [Introduces expectation maximization for CTBNs to extend the parameter and structure learning methods of the previous paper to the incomplete data case.] INFERENCE: (The first paper from 2002 also discusses this to some degree) Uri Nodelman, Daphne Koller, and Christian R. Shelton (2005). "Expectation Propagation for Continuous Time Bayesian Networks." Proceedings of the Twenty-First International Conference on Uncertainty in Artificial Intelligence (pp. 431-440). http://www.cs.ucr.edu/~cshelton/papers/index.cgi?NodKolShe05 [An approximate inference method based on expectation propagation.] Brenda Ng, Avi Pfeffer, and Richard Dearden (2005). "Continuous Time Particle Filtering." Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (pp. 1360-1365). [A particle filter for the case of an unobserved CTBN that is driving stochastic differential equations whose values are observed at discrete time points.] Nir Friedman and Rza Kupferman (2006). "Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian Networks." Proceedings of the Twenty-Second International Conference on Uncertainty in Artificial Intelligence. http://www.ma.huji.ac.il/~razk/Publications/PDF/FK06.pdf [Demonstrates that a separation of time scales between variables can lead to a simpler inference problem.] Suchi Saria, Uri Nodelman, and Daphne Koller (2007). "Reasoning at the Right Time Granularity." Proceedings of the Twenty-third Conference on Uncertainty in Artificial Intelligence" (pp. 421-430). http://ai.stanford.edu/~nodelman/papers/dynamic-EP.pdf [An improved approximate inference method based on expectation propagation.] Yu Fan and Christian R. Shelton (2008). "Sampling for Approximate Inference in Continuous Time Bayesian Networks." Proceedings of the Tenth International Symposium on Artificial Intelligence and Mathematics. http://www.cs.ucr.edu/~cshelton/papers/index.cgi?FanShe08 [An importance sampling method and its extension to particle filtering and smoothing.] Tal El-Hay, Nir Friedman, and Raz Kupferman (2008). "Gibbs Sampling in Factorized Continuous-Time Markov Processes." Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (pp. 169-178). http://uai2008.cs.helsinki.fi/UAI_camera_ready/el-hay.pdf [A method for Gibbs sampling for a CTBN.] Ido Cohn, Tal El-Hay, Raz Kupferman, and Nir Friedman (2009). "Mean Field Variational Approximation for Continuous-Time Bayesian Networks." Proceedings of the Twenty-Fifth International Conference on Uncertainty in Artificial Intelligence. http://www.cs.mcgill.ca/~uai2009/papers/ UAI2009_0134_c9c16e478d8e82ecc3848c5dc76b4925.pdf [A mean field variational method for a CTBN.] Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman (2010). "Continuous-Time Belief Propagation." Proceedings of the Twenty-Seventh International Conference on Machine Learning. http://www.cs.huji.ac.il/~nir/Papers/ElHayICML10.pdf [A BP method for a CTBN based on a Bethe energy approximation.] Vinayak Rao, Yee Whye Teh (2011). "Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks." Proceedings of the Twenty-Seventh International Conference on Uncertainty in Artificial Intelligence. http://www.gatsby.ucl.ac.uk/~ywteh/research/compstats/RaoTeh2011a.pdf [An MCMC method using uniformization.] E. Busra Celikkaya, Christian R. Shelton, and William Lam (2011). "Factored Filtering of Continuous-Time Systems." Proceedings of the Twenty-Seventh International Conference on Uncertainty in Artificial Intelligence. http://www.cs.ucr.edu/~cshelton/papers/index.cgi?CelSheLam11 [A factored matrix-exponential method using uniformization.] OTHER EXTENSIONS: Karthik Gopalratnam, Henry Kautz, and Daniel S. Weld (2005). "Extending Continuous Time Bayesian Networks." Proceedings of 20th National Conference on Artificial Intelligence-AAAI 2005 (pp. 981-986). http://www.cs.washington.edu/homes/weld/papers/aaai05_CTBN.pdf [This presents an alternative to exponential duration distributions.] Shyamsundar Rajaram, Thore Graepel, and Ralf Herbrich (2005). "Poisson-Networks: A Model for Structured Point Processes" Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (pp. 277-284). http://www.gatsby.ucl.ac.uk/aistats/fullpapers/200.pdf [This presents a CTBN-like network for counting variables.] Kin Fai Kan and Christian R. Shelton (2008). "Solving Structured Continuous-Time Markov Decision Processes." Tenth International Symposium on Artificial Intelligence and Mathematics. http://www.cs.ucr.edu/~cshelton/papers/index.cgi?KanShe08 [This presents an approximate continuous Markov decision process planning solution using CTBN.] Luigi Portinale and Daniele Codetta-Raiteri (2009). "Generalizing Continuous Time Bayesian Networks with Immediate Nodes." Proceedings on the Workshop on Graph Structured for Knowledge Representation and Reasoning. http://web.unipmn.it/%7Eraiteri/papers/gkr.pdf [Presents an extension for describing simultaneous transitions in a CTBN and links it to generalized stochastic Petri nets.] APPLICATIONS: (most of the papers above have at least one application of a CTBN. These papers' main contributions are in applications, rather than theory.) Uri Nodelman and Eric Horvitz (2003). "Continuous Time Bayesian Networks for Inferring Users' Presence and Activities with Extensions for Modeling and Evaluation." MSR-TR-2003-97, Microsoft Research. http://research.microsoft.com/en-us/um/people/horvitz/uri_eh.pdf [Applies CTBNs to modeling user's use of desktop applications.] Ralf Herbrich, Thore Graepel, and Brendan Murphy (2007). "Structure from Failure." SYSML '07: Proceedings of the 2nd USENIX workshop on tackling computer system problems with machine learning. pp. 1-6. http://www.usenix.org/event/sysml07/tech/full_papers/herbrich/herbrich.pdf [Applies CTBNs to modeling failures in server farms.] Jing Xu and Christian R. Shelton (2008). "Continuous Time Bayesian Networks for Host Level Network Intrusion Detection." Machine Learning and Knowledge Discovery in Databases (ECML/PKDD) (LNAI, vol 5212) (pp. 613-627). http://www.cs.ucr.edu/~cshelton/papers/index.cgi?XuShe08 [Applies CTBNs to host-level computer network intrusion detection. Introduces "toggle" variables.] Yu Fan and Christian R. Shelton (2009). "Learning Continuous-Time Social Network Dynamics." Proceedings of the Twenty-Fifth International Conference on Uncertainty in Artificial Intelligence. http://www.cs.ucr.edu/~cshelton/papers/index.cgi?YuShe09 [Applies CTBNs to social network dynamics. Introduces MCMC inference method.] ctbnrle.1.1.1/doc/CHANGES0000664000175000017500000000175512251377341015143 0ustar csheltoncsheltonLog of changes Version 1.1.1 ------------- Fixed compilation errors Version 1.1.0 ------------- Markov decision processes added (Kan & Shelton 2008) [ctmdp.h] Mean field inference added (Cohn, El-Hay, Friedman, & Kupferman 2009) [meanfieldinf.h] Factored uniformization for filtering added (Celikkaya & Shelton 2011) [uniformizedfactoredinf.h] Auxilliary Gibbs sampling added (Rao & Teh 2011) [gibbsauxsampler.h] Multi-threading for EM added many speed improvements unit testing improved [tst/] new demo "main" programs added [demo/] file format changed to XML-ish format [hdr/serial/] (with old methods still there for conversion) matrix switched to Eigen package (with option to return to old matrix) glpk now included initial cmake functionality minor bug fixes Version 1.0.2 ------------- a few minor changes to make more compiler-compliant Version 1.0.1 ------------- ctbn namespace added updating demos and tests added Trajectory::AddPointEvidence minor other organizational changes ctbnrle.1.1.1/doc/NOTES0000664000175000017500000000103712115742156014753 0ustar csheltoncsheltonNotes: 1. The code will probably move to C++11 standards on the next release. If this will pose a problem for you, please let us know. 2. We hope to add BP on the next release 3. We would like to encorporate non-CTBN structured representations of continuous-time processes. If you have one and want to contribute, please let us know. 4. There are some additional speed increases for exact inference that can be realized if the number of unobserved variables is always very small. If this would be useful to you, please let us know. ctbnrle.1.1.1/doc/code_intro.txt0000664000175000017500000002542712107032454017031 0ustar csheltoncsheltonThis file serves as an introduction to the code. The first section is on how to use the code as a black box. The second is a brief introduction to the logic behind the class organization. Throughout this document, it is assumed that the reader is familiar with continuous time Bayesian networks (CTBNs) in at least their formulation and basic theory. See the file ctbn_papers.txt for a list of reading on the subject. HOW TO USE: ----------- In general, the header files (in .hdr) explain what the class' methods do. This is a general primer for orientation. Context & Instantiation (in context.h) A context is a collection of variables. Each is identified by a number (variable 0, 1, etc.) but need not be consecutively ordered. Each variable has a size (number of values it can take on). An instantiation is a context plus an assignment to each of the variables. However, the assignment -1 indicates that the value is unknown. If the assignment is complete (no -1 values), an instantiation also has an index which maps the assignment to a unique integer. The indexes for a particular context are consecutive, starting at 0. Inc() moves an instantiation to the next consecutive index's assignment. context.h also contains marcos for iterating over all assignments (instantiations) to a particular set of variables -- see the end of the file Trajectory (in trajectory.h) This class stores temporal data. It has a set of variables, a beginning and ending time, and a set of events. Each event is the assignment of a value to one variable. The value -1 is used to indicate that the variable is unobserved. Therefore a "transition" of a variable *to* -1 means that it has gone from being observed to being unobserved. Conversely a "transition" of a variable *from* -1 (to a non-negative value) means that the variable has started to be observed at this instant. Notable is the inner class Index (and RIndex) which allow for the iteration through a trajectories events (or a subset of them). They are like iterators from STL (but not exactly the same, hence the different name). Process (in process.h) An abstract class, all processes are subclasses of it Dynamics (in dynamics.h) An abstract class, all dynamics are subclasses of it. A "dynamics" is the part of a Markov process describing how it changes (as compared to its starting distribution at time 0). RV (in rv.h) An abstract class, all random variables are subclasses of it. An RV is the part of a Markov process describing how it starts (at time 0). It can also be used for other purposes. We make a distinction between random variables and processes. Markov (in markov.h) Markov is a dynamic process with a starting distribution and a dynamics. CTBNDyn (in ctbndyn.h) This is a "factored" dynamics class, the essence of a CTBN BN (in bn.h) This is a "factored" RV, the starting distribution for a CTBN CTBN (in ctbn.h) A really small class purely for notational convenience. It is a Markov class with CTBNDyn as the dynamics and BN as the starting distribution -- How to make a CTBN: The demo/ directory has a number of examples (makedrug.cc and makechain.cc for example) on how to construct a CTBN by hand. They involve creating MarkovDyn and MultiRV for each variable, describing the conditional dynamics and starting distribution for each (conditioned on other variables -- its parents) and then adding them all into a CTBNDyn variable and a BN variable and then placing the two into a CTBN. -- How to sample from a CTBN: The method Sample will generate a sample trajectory, provided the trajectory input already has a starting and ending time set. -- How to learn the parameters of a CTBN from data: The demo/ directory has an example learnparams.cc of how to learn parameters. It takes a CTBN as input, samples from it, and then learns the parameters back again (hopefully similar to the original ones). For more general case, you would replace the sampled trajectories with real data. The essential method is to feed the data into the SuffStats method for the CTBN, thus generating the sufficient statistics necessary for learning parameters (the CTBN need only have the correct structure, the parameters are ignored). Then, these sufficient statistics are fed into the CTBN's Maximize method which calculates the maximum likelihood parameters. If you have incomplete data, you will want to run the EM function in em.h. It requires the data, the process to optimize, and an inference method (for the E-step). inference.h has the abstract superclass for inference. exactmarkovinf.h has ExactMarkovInf which will perform exact inference. samplinginf.h has SamplingInf which performs sampling-based inference. It takes in a Sampler (see sampler.h) as the method for generating samples from the posterior distribution. GibbsSampler (gibbssampler.h), ImportanceSampler (importancesampler.h), and GibbsAuxSampler (gibbsauxsampler.h) are all possible sampling methods. If you also do not know the structure, you will want to run the SEM function in em.h. It requires the data, the process to optimize, and an inference method (just as EM). -- How to perform inference for a CTBN: exactquery (and others) in the demo/ directory demonstrate the basics of inference. They read in a trajectory (a partial trajectory with missing values for some variables for some periods of time) and query the expected amount of time a variable spent in a particular state and the expected number of transitions from one state to another state. Other queries are possible (like the distribution of a variable at a point -- either smoothed or filtered -- see inference.h). CODING NOTES: ------------- Unless stated in the header file, all pointers passed into a function or method are still "owned" by the calling function afterward. All returned pointers are owned by the calling function. A few constructors ask for pointers and do not own them, but assume that the pointers are valid for the lifetime of the object. This is noted in the comments in the header file. Comments describing the purpose and usage of each method are described once at the most base class's header file. STL is assumed, but no other external libraries are required. As of version 1.1, Eigen is used for matrices, but this can be reverted to the old matrix code by uncommenting the "NOUSEEIGEN" definition in matrix.h GLPK is used for linear programming. Both Eigen and GLPK are supplied. Almost all classes implement Clone() as a virtual copy constructor. The code is contained in the ctbn namespace, with the exception of a few items that cannot be placed there (iostream related items that must be in std, for instance). CLASS ORGANIZATION: ------------------- The basic organization of the classes is reasonably straight-forward. There are a few small self-contained modules: random.h/.cc have code for drawing random numbers, matrix.h/.cc hold matrix operations, define.h holds differences between compilation environments, streamextra.h/.cc contains stream operations (it has extensive comments) extramath.h has random math functions (like log-gamma) serial/serial.h has a template meta-programming solution to automatic serialization of objects in an XML-like format rk.h/.cc contain the Runga-Kutta methods for computing the alpha and beta (forward and backward) passes for inference, for computing expected sufficient statistics of a flat Markov process, and for solving the ODEs in mean field inference clique.h/.cc hold the code for building clique trees (not needed by anything currently) graphic.h/.cc hold the code for drawing trajectories or CTBNs graphically samplequeue.h/.cc is an internal class for sampling searchqueue.h/.cc is an internal class for structure search contfunction.h/.tcc is a class for approximating continuous functions with linear interpolation In general, the class Process holds any process. It has a subclass Markov that holds any Markov process. Markov consists of two variables: an initial distribution and a "dynamics." The former is of type RV (or rather a subclass of RV) and the latter describes the evolution of the system (and is a Dynamics or subclass). All of these classes also hold a Context (see above) describing the set of variables in the event space (and the variables' sizes) as well as a second Context describing the set of variable upon which the variable or process is conditioned. If the conditional context is not empty, then any requests (for sampling, probability, etc) to the class must specify all values for these conditioning variables. Working from the "bottom up," classes ending with "simple" represent random variables or processes with no conditioning and where the event space is "flat" (just the first n non-negative integers). They are used in the lower levels of all of the algorithms to describe things when the event space is known or implied and need not be "carried" by all variables. Classes ending with "comp" make a composition of "simple" classes to construct a full RV, Process, or Dynamics. That is, they have conditioning contexts and event contexts and translate their method calls to those of "simple" classes that perform the computations. They are usually templates (where the template argument is the simple class out of which to construct a full version). All processes, dynamics, and random variables have their own sufficient statistics class. Each sufficient statistics class is a subclass of the (empty) class SS. With these distinctions understood, the rest of the organization is pretty easily read from the code itself. Here is a (partial) class inheritance diagram: SS -+- MarkovSS +- CTBNDynSS +- DynCompSS +- MarkovSimpleSS +- MultiSimpleSS +- BNSS \- BNCompSS Process --- Markov Sampler -+- GibbsSampler \- ImpBase -+- ImportanceSampler \- NodeSampler VarSample --- ExpMethod --- LookAheadMethod DynSimple --- MarkovSimple --- MarkovSimpleToggle Dynamics -+- CTBNDyn \- DynComp --- MarkovDyn RV -+- BN \- RVComp --- MultiRV Inference -+- ExactMarkovInf |- SamplingInfAbs -+- SamplingInf | \- SamplingPreInf +- MeanFieldInf \- BFInf -+- ExactMarkovInf \- UniformizedFactoredInf QueryCalculator -+- QueryTransition |- QueryTime \- QueryProb RVSimple -+- MultiLogSimple +- MultiSimple \- MultiZSimple --- SparseMultiZSimple RVCondSimple -+- RVCondSimpleComp +- SparseCondTransQ2 +- CondTransQ2 +- SparseCondTransQ +- CondTransQ StructureSearch -+- BruteStructureSearch \- GraphEditSearch LinearProgram --- GLPKSolver Other Questions =============== If questions arise while reading the code, please contact the authors at cshelton@cs.ucr.edu. ctbnrle.1.1.1/doc/Nodelman-etal-2002.pdf0000664000175000017500000067437612107032454017714 0ustar csheltoncshelton%PDF-1.3 %쏢 5 0 obj <> stream x\YsƑޘ|JCu-W4 t-#w7ʬ=Z1ˣb7/iV֕MSkVwot~oonx ωvǛkLVB*~mXuq~8<㭖ԍ{~]hRET6W틿(*n]jMҝUVFxu> eQJH8VVqV&&b^0z"q;ö)*-;%Op?N{_U)S7Ae0,c(iO^ ϗ!vs5WUWWQJ+ϧ"+Ck"p7ԕfMlVnyƵ}>=ކES)֤Mٛ4NnޱsRum$+q}_m-a7xxޗwijwp?7`y~@p[gط! 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0000169005 00000 n 0000170148 00000 n 0000170726 00000 n 0000171693 00000 n 0000187181 00000 n 0000197753 00000 n 0000215123 00000 n trailer << /Size 194 /Root 1 0 R /Info 2 0 R >> startxref 223620 %%EOF ctbnrle.1.1.1/build.sh0000775000175000017500000000032012115746433015025 0ustar csheltoncshelton#!/bin/bash cd glpk/glpk-4.47 ./configure make cd ../../ctbn_build_dir cmake .. # use -jx to run x compiles at the same time (for multiple processors) # eg: replace below with "make -j4" make make test cd .. ctbnrle.1.1.1/src/0000775000175000017500000000000012251375763014170 5ustar csheltoncsheltonctbnrle.1.1.1/src/impbase.cc0000664000175000017500000001705012107032416016103 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "impbase.h" #include "nullptr03.h" namespace ctbn { using namespace std; void ImpBase::SetProcess(const Process *pr) { p = pr; const CTBNDyn *ctbndyn = dynamic_cast(dynamic_cast(pr)->GetDynamics()); ctbndyn->GetStructure(str); int n = ctbndyn->NumofNodes(); nodes.clear(); for (int i=0; i(ctbndyn->Node(i))); } double ImpBase::SampleTime(const Instantiation &curri, int id, int parindex, int nodeval, int evidencetype, Trajectory::Index &nextindex, int e, double t, Random &rand) { double ret = 0.0; const matrix &Q = (*nodes[id])(parindex)->Intensity(); if (evidencetype==0) { //no upcoming evidence ret = t + method->SampleTime(Q, id, nodeval, curri, t, evid, -1, -1, rand); } else if (evidencetype==1 && e!=-1) { //there is an upcoming evidence double te = nextindex.Time(); ret = t + method->SampleTime(Q, id, nodeval, curri, t, evid, te, e, rand); if (ret>te&&nodeval==e) { ret = te; changeindex[id] = 1; } } else { //evidence change value ret = nextindex.Time(); changeindex[id] = 1; } return ret; } double ImpBase::SampleTimeWeight(const Instantiation &curri, int nodeid, int transitionid, int parindex, int nodeval, int evidencetype, Trajectory::Index &nextindex, int e, double currt, double nextt) { const matrix &Q = (*nodes[nodeid])(parindex)->Intensity(); double te = nextindex.Time(); if (evidencetype==2 || (evidencetype==1 && e==-1)) { //node is forced to agree the evidence, //calculate the likelihood return Q[nodeval][nodeval] * (nextt-currt); } else if (evidencetype==1 && e!=-1) { //there is an upcoming evidence return method->TimeWeight(Q, curri, nodeid, transitionid, nodeval, currt, nextt-currt, evid, te, e); } else { return method->TimeWeight(Q, curri, nodeid, transitionid, nodeval, currt, nextt-currt, evid, -1, -1); } } double ImpBase::SampleInitial(Instantiation &initval, Random &rand) { const BN* bn = dynamic_cast(dynamic_cast(p)->GetStartDist()); return bn->ImportanceSample(initval, true,rand); } int ImpBase::SampleTransition(Instantiation &inst, int id, int parindex, int nodeval, int evidencetype, Trajectory::Index &nextindex, int e, double t, double &weight, Random &rand) { forceflip = 0; int retval; const matrix &Q = (*nodes[id])(parindex)->Intensity(); if (evidencetype==2) { //force a transition according to the evidence retval = e; //matrix Q = (*nodes[id])(parindex)->Intensity(); //weight += log(Q[nodeval][retval]) - log(-(Q[nodeval][nodeval])); // cshelton 8/20/12: removed second part of term above // (fixes weight calculation) -- also removed similar below in // corner case of too close to evidence weight += log(Q[nodeval][retval]); } else if (evidencetype==1) { if (e!=-1) { //there is an upcoming evidence double te = nextindex.Time(); if (te>t) { //matrix Q = (*nodes[id])(parindex)->Intensity(); if (te-t>1e-6 || nodeval==e) { //sample next transition according to the evidence retval = method->SampleTransition(Q, id, nodeval, inst, t, evid, te, e, weight, rand); } else { //too close to the upcoming evidence, //force the variable transition to the //value of the upcoming evidence and //update the correponding weight retval = e; forceflip = 1; //matrix Q = (*nodes[transitionid])(parindex)->Intensity(); weight += log(Q[nodeval][e]); // - log(-(Q[nodeval][nodeval])); forceflip = 0; } } else { //we are at the time where evidence is a transition that //the value changes from unknown to known. At this time, //the current value of the variable should agree with the //evidence. So we do nothing. retval = nodeval; } } else { //the evidence changes from known value to unknow value retval = nodeval; } } else { //there is no upcoming evidence //matrix Q = (*nodes[id])(parindex)->Intensity(); retval = method->SampleTransition(Q, id, nodeval, inst, t, evid, -1, -1, weight, rand); } //update the instantiation if (nodeval != retval) // below should probably be left to caller, as std.Node2Var(id) // might already have been computed there inst.SetVal(str.Node2Var(id), retval); return retval; } /* double ImpBase::SampleTransitionWeight(const Instantiation &nextinst, int transitionid, int oldval, int parindex, int nodeval, int evidencetype, Trajectory::Index &nextindex, int e, double currt, double nextt) { double retweight = 0.0; if (evidencetype==2) {//force a transition according to the evidence int val = oldval; int nextval = nodeval; matrix Q = (*nodes[transitionid])(parindex)->Intensity(); retweight = log(Q[val][nextval]) - log(-(Q[val][val])); } else if (evidencetype==1&&e==-1) //evidence change to known value to unknown retweight = 0; else if (evidencetype==1&&e!=-1) { //retweight = method->TransitionWeight(); if (forceflip==1) { int val = oldval; int nextval = nodeval; matrix Q = (*nodes[transitionid])(parindex)->Intensity(); retweight = log(Q[oldval][e]) - log(-(Q[val][val])); forceflip = 0; //cout << "force flip weight: " << retweight << endl; } else { double te = nextindex.Time(); matrix Q = (*nodes[transitionid])(parindex)->Intensity(); retweight = method->TransitionWeight(Q, transitionid, oldval, nextinst, nextinst, currt, nextt-currt, evid, te, e); } } else { retweight = 0; } return retweight; } */ void ImpBase::UndefineTime(int id, vector &undefinelist) { undefinelist.clear(); if (id!=-1) { changeindex[id] = 0; undefinelist = str.GetChildren(id); undefinelist.push_back(id); for (unsigned int i=0; i(p)->GetDynamics()->CondDomain(); vector varlist = conddomain.VarList(); vector clist(nodes.size(), false); for (unsigned int i=0; i c = str.GetChildren(str.Var2Node(varlist[i])); for (unsigned int j=0; jDomain(); Trajectory::Index it = evid->Begin(nodecontext); evidindex.push_back(it); } //Add index of conddomain of the whole process if has one Context conddomain = dynamic_cast(p)->GetDynamics()->CondDomain(); if (conddomain.NumVars()!=0) { Trajectory::Index it = evid->Begin(conddomain); evidindex.push_back(it); } } } // end of ctbn namespace ctbnrle.1.1.1/src/ctbndyn.cc0000664000175000017500000005247412107032416016135 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "ctbndyn.h" #include "markovdyn.h" #include "markovsimple.h" #include #include "extramath.h" #include "samplequeue.h" #include "searchqueue.h" #include #include "params.h" namespace ctbn { using namespace std; SOBJCLASSDEF(CTBNDyn) CTBNDyn::CTBNDyn() : Dynamics(Context(),Context()) { joint = NULL; } CTBNDyn::CTBNDyn(std::istream &is) : Dynamics(is) { int n; is >> n; nodes.resize(n); for(int i=0;i(StreamObj::LoadOldPtr(is)); joint = NULL; } CTBNDyn::CTBNDyn(const CTBNDyn &ctbn) : Dynamics(ctbn) { int n = ctbn.nodes.size(); nodes.resize(n); for(int i=0;iClone(); joint = NULL; } CTBNDyn::CTBNDyn(const Structure &s, const Context &vars) : Dynamics(Context(), Context()) { joint = NULL; vector > parentlist = s.parentlist; int n = parentlist.size(); for(int i=0;i currentParentlist = parentlist[i]; Context v; Context cv; if(vars.HasId(i)) v.AddVar(i,vars.Cardinality(i)); else continue; for(unsigned int j=0;jdyn = NULL; n = ctbn.nodes.size(); nodes.resize(n); for(int i=0;iClone(); ClearNode2IdMaps(); } return *this; } CTBNDyn *CTBNDyn::Clone() const { return new CTBNDyn(*this); } CTBNDyn::~CTBNDyn() { if (joint) delete joint; int n = nodes.size(); for(int i=0;idyn = NULL; } void CTBNDyn::LoadOld(istream &is) { if (joint) delete joint; joint = NULL; Dynamics::LoadOld(is); int n = nodes.size(); for(int i=0;idyn = NULL; assocss.resize(0); is >> n; for(int i=0;i(StreamObj::LoadOldPtr(is)); ClearNode2IdMaps(); } void CTBNDyn::serial_preload() { if (joint) delete joint; joint = NULL; int n = nodes.size(); for(int i=0;idyn = NULL; assocss.resize(0); ClearNode2IdMaps(); } void CTBNDyn::SaveOld(ostream &os) const { Dynamics::SaveOld(os); int n = nodes.size(); os << os.fill() << n; for(int i=0;iSaveOldPtr(os); } } void CTBNDyn::Normalize() { int n = nodes.size(); if (joint) delete joint; joint = NULL; for(int i=0;iNormalize(); } void CTBNDyn::Restrict(const Instantiation &x) { int n = nodes.size(); if (joint) delete joint; joint = NULL; for(int i=0;iRestrict(x); ClearNode2IdMaps(); // could change... } void CTBNDyn::Mult(const Dynamics *x) { // not really sure how to do this... assert(false); } RVCondSimple *CTBNDyn::Cond(double t0, double t1, const Instantiation &x) const { static bool usesparse = ParamInt("AvoidJoint",1); if (!usesparse) return Joint()->Cond(t0,t1,x); Instantiation knownx(x.KnownVars(),x); // assumes that only one variable can change at a time Context missing = Domain()+CondDomain()-knownx; int nmiss = missing.Size(); matrix subQ(nmiss,nmiss,0.0); vector ix(nmiss); map rev; int ii = 0; forallassign_subset(Instantiation &i1,missing,knownx) { ix[ii] = i1.Index(); rev[i1.Index()] = ii; ii++; } ii = 0; forallassign_subset(Instantiation &i1,missing,knownx) { for(vector::const_iterator n=nodes.begin(); n!=nodes.end();++n) { const DynComp *dyn = dynamic_cast(*n); if (dyn==NULL) return Joint()->Cond(t0,t1,x); const MarkovSimple *simp = dynamic_cast((*dyn)[i1]); if (simp==NULL) return Joint()->Cond(t0,t1,x); const matrix &localQ = simp->Intensity(); const Context &locald = dyn->Domain(); int lii1 = locald.Index(i1); subQ[ii][ii] += localQ[lii1][lii1]; Context ch = locald / missing; foralldiffone_subset(Instantiation &i2,i1,ch) { int lii2 = locald.Index(i2); subQ[ii][rev[i2.Index()]] += localQ[lii1][lii2]; } } ii++; } return new SparseCondTransQ(subQ,t1-t0,ix); } RVCondSimple *CTBNDyn::Cond(double t, const Instantiation &from, const Instantiation &to, bool transition) const { static bool usesparse = ParamInt("AvoidJoint",1); if (!usesparse) return Joint()->Cond(t,from,to,transition); vector fromind,toind; Context all = Domain()+CondDomain(); all.ConsistentIndexes(fromind,from); all.ConsistentIndexes(toind,to); matrix subQ(fromind.size(),toind.size(),0.0); // assumes that only one variable can change at a time if (transition) { Instantiation knownfrom(from.KnownVars(),from); Instantiation knownto(to.KnownVars(),to); vector inbothvar = (knownfrom / knownto).VarList(); int chvar = -1; for(vector::iterator v=inbothvar.begin();v!=inbothvar.end();++v) if (knownfrom.Value(*v)!=-1 && knownto.Value(*v)!=-1 && knownfrom.Value(*v) != knownto.Value(*v)) { chvar = *v; break; } assert(chvar != -1); PrepNode2IdMaps(); const DynComp *dyn = dynamic_cast(nodes[var2node[chvar]]); if (dyn==NULL) return Joint()->Cond(t,from,to,transition); const Context &locald = dyn->Domain(); map torev; for(unsigned int i=0;i((*dyn)[ifrom]); if (simp==NULL) return Joint()->Cond(t,from,to,transition); const matrix &localQ = simp->Intensity(); Instantiation ito(ifrom); ito.SetVal(chvar,newv); int liito = locald.Index(ito); int liifrom = locald.Index(ifrom); subQ[ii][torev[ito.Index()]] += localQ[liifrom][liito]; ii++; } } else { // ripped straight from markovsimple.cc (should perhaps merge): unsigned int i=0,j=0; while(i(subQ,fromind,toind); } CTBNDynSS *CTBNDyn::BlankSS() const { CTBNDynSS *ret = new CTBNDynSS(this); assocss.push_back(ret); int n = nodes.size(); ret->dynss.resize(n); for(int i=0;idynss[i] = nodes[i]->BlankSS(); ret->jss = NULL; return ret; } void CTBNDyn::AddSS(const Instantiation &x, double t0, double deltat, SS *ss, double w) const { CTBNDynSS *css = dynamic_cast(ss); int n = nodes.size(); for(int i=0;iAddSS(x,t0,deltat,css->dynss[i],w); } void CTBNDyn::AddTransSS(const Instantiation &x1, const Instantiation &x2, double t, SS *ss, double w) const { CTBNDynSS *css = dynamic_cast(ss); int n = nodes.size(); for(int i=0;iAddTransSS(x1,x2,t,css->dynss[i],w); } void CTBNDyn::AddExpSS(const RV *alpha, const RV *beta, double t0, double deltat, SS *ss, double w) const { CTBNDynSS *css = dynamic_cast(ss); int n = nodes.size(); for(int i=0;iAddExpSS(alpha,beta,t0,deltat,css->dynss[i],w); } void CTBNDyn::AddExpTransSS(const RV *x1, const RV *x2, const Context &changevar, double t, SS *ss, double w) const { if (!changevar.IsOverlap(Domain())) return; CTBNDynSS *css = dynamic_cast(ss); int n = nodes.size(); for(int i=0;iAddExpTransSS(x1,x2,changevar,t,css->dynss[i],w); } void CTBNDyn::AddExpSS(const Instantiation &condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w) const { CTBNDynSS *css = dynamic_cast(ss); if (NULL == css->jss) css->jss = Joint()->BlankSS(); Joint()->AddExpSS(condi, alpha, beta, t0, deltat, css->jss, w); /* SS *allss = Joint()->BlankSS(); Joint()->AddExpSS(condi,alpha,beta,t0,deltat,allss,w); CTBNDynSS *css = dynamic_cast(ss); int n = nodes.size(); for(int i=0;iAddSS(allss,Joint(),css->dynss[i]); delete allss; */ } void CTBNDyn::AddSS(const SS *toadd, const Dynamics *dyn, SS *ss, double w) const { int n = nodes.size(); CTBNDynSS *ctbndynss = dynamic_cast(ss); for(int i=0;iAddSS(toadd,dyn,ctbndynss->dynss[i],w); } void CTBNDyn::AddExpSS(int condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w) const { Instantiation i(CondDomain()); i.SetIndex(condi); AddExpSS(i,alpha,beta,t0,deltat,ss,w); } void CTBNDyn::AddExpTransSS(const Instantiation &condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w) const { RVSimple *myx1 = x1->Clone(); RVSimple *myx2 = x2->Clone(); myx1->Normalize(); myx2->Normalize(); CTBNDynSS *css = dynamic_cast(ss); int n = nodes.size(); for(int i=0;iDomain().IsOverlap(changevar)) continue; Context cond = nodes[i]->CondDomain()-CondDomain(); vectr condprob(nodes[i]->CondDomain().Size(), 0.0); forallassign_subset(Instantiation &transii,cond,condi) { vector > mapping(nodes[i]->Domain().Size()); forallassign_subset(Instantiation &ii,nodes[i]->Domain(),transii) Domain().ConsistentIndexes( mapping[nodes[i]->Domain().Index(ii)],ii); RVSimple *t1 = myx1->Clone(); t1->Reindex(mapping); RVCondSimple *p1 = nodes[i]->Cond(t, transii, transii, true); p1->Mult(t1); RVSimple *t2 = myx2->Clone(); t2->Reindex(mapping); t1->MultBy(t2); condprob[cond.Index(transii)] = t1->Sum(); delete t2; delete p1; delete t1; } condprob.normalize(); forallassign_subset(Instantiation &transii,cond,condi) { vector > mapping(nodes[i]->Domain().Size()); forallassign_subset(Instantiation &ii,nodes[i]->Domain(),transii) Domain().ConsistentIndexes( mapping[nodes[i]->Domain().Index(ii)],ii); RVSimple *t1 = myx1->Clone(); t1->Reindex(mapping); t1->Normalize(); RVSimple *t2 = myx2->Clone(); t2->Reindex(mapping); t2->Normalize(); double condw = w*condprob[cond.Index(transii)]; if (condw>0.0) nodes[i]->AddExpTransSS(transii,t1,t2,changevar,t, css->dynss[i],condw); delete t2; delete t1; } } delete myx1; delete myx2; } void CTBNDyn::AddExpTransSS(int condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w) const { Instantiation i(CondDomain()); i.SetIndex(condi); AddExpTransSS(i,x1,x2,changevar,t,ss,w); } void CTBNDyn::SampleNextEvent(const Instantiation &i, double t, double &nextt, Instantiation &nexti, Random &rand) const { int n = nodes.size(); nextt = INFINITY; for(int j=0;jSampleNextEvent(i,t,tt,ni,rand); if (finite(tt) && tt(ss); int n = nodes.size(); css->Compact(); for(int i=0;iMaximize(css->dynss[i]); if (joint) delete joint; joint = NULL; } void CTBNDyn::PrepNode2IdMaps() const { if (node2var.size() == nodes.size()) return; ClearNode2IdMaps(); int m = nodes.size(); node2var.resize(m); for(int j=0;jDomain().VarList()[0]; var2node[node2var[j]] = j; vector par = nodes[j]->CondDomain().VarList(); for(unsigned int k=0;k())); } } void CTBNDyn::ClearNode2IdMaps() const { node2var.clear(); var2node.clear(); children.clear(); } // We could just keep the default SampleTrajectory. However that has // a running time O(nm) where n is the number of samples and m is the number // of variables. This method is O(m+nkln(m)) where k is the maximal number of // parents in the graph. // This currently makes the assumption that each node only corresponds to // one variable void CTBNDyn::SampleTrajectory(Trajectory &tr, double t, Random &rand) const { int m = nodes.size(); SampleQueue events(m); double endt = tr.TimeEnd(); Instantiation i(tr.Values(Domain(),t)); PrepNode2IdMaps(); for(int j=0;jSampleNextEvent(i,t,newt,newv,rand); events.Add(SampleQueue::Event(j,newv.Value(node2var[j]), newt)); } while(1) { SampleQueue::Event e; if (!events.Head(e) || e.time>endt) return; int varid = node2var[e.var]; tr.AddTransition(varid, e.time, e.value); i.SetVal(varid,e.value); t = e.time; events.Remove(e.var); map >::iterator ci = children.find(node2var[e.var]); vector c; if (ci != children.end() && ci->first == node2var[e.var]) c = ci->second; for(unsigned int j=0;jSampleNextEvent(i,t,newt,newv,rand); events.Add(SampleQueue::Event(e.var, newv.Value(node2var[e.var]),newt)); for(unsigned int j=0;jSampleNextEvent(i,t,newt,newv,rand); events.Add(SampleQueue::Event(c[j], newv.Value(node2var[c[j]]),newt)); } } } void CTBNDyn::AddNode(Dynamics *d) { nodes.push_back(d); v = v+d->Domain(); cv = (cv+d->CondDomain())-v; if (joint!=NULL) { delete joint; joint = NULL; } ClearNode2IdMaps(); } const Dynamics *CTBNDyn::Joint() const { if (!joint) { int n = nodes.size(); if (n==0) return NULL; joint = nodes[0]->Clone(); for(int i=1;iMult(nodes[i]); } } return joint; } void CTBNDyn::GetStructure(Structure &s) const { PrepNode2IdMaps(); s.var2node = var2node; s.node2var = node2var; s.children = children; s.parentlist.clear(); int n = nodes.size(); for (int i=0; iCondDomain(); s.parentlist.push_back(c.VarList()); } } void CTBNDyn::Scramble(double a, double b, double alpha, double degree, Random &rand) { //Added by Yu int nc = nodes.size(); for(int i=0;iScramble(a, b, alpha, degree, rand); } double CTBNDyn::LLH(const SS *ss) const { const CTBNDynSS *css = dynamic_cast(ss); css->Compact(); int nc = nodes.size(); double llh = 0.0; for(int i=0; iLLH(css->dynss[i]); return llh; } matrix CTBNDyn::JointMatrix() const { const MarkovDyn *jointp = dynamic_cast(Joint()); matrix m = (*jointp)(0)->Intensity(); return m; } double CTBNDyn::GetScore(double numTrans, double amtTime, const SS *ss) const { const CTBNDynSS *css = dynamic_cast(ss); css->Compact(); double score(0); int vars = css->dynss.size(); double localscore; // Sum over each variable for(int i=0;i GetScore(numTrans, amtTime, css->dynss[i]); score += localscore; } return score; } // Assumes the source CTBNDyn has the same organization of nodes. void CTBNDyn::FillParams(const CTBNDyn &src, bool useRandom) { for(int i = 0; i < NumofNodes(); i++) { const MarkovDyn *csdyn = dynamic_cast(src.nodes[i]); MarkovDyn *cdyn = dynamic_cast(nodes[i]); const Context &csrc = csdyn->CondDomain(); const Context &cur = cdyn->CondDomain(); if (useRandom) { forallassign(Instantiation &curins,cur) { int idx = randomizer.RandInt(csrc.Size()); (*cdyn)(curins)->Intensity() = (*csdyn)(idx)->Intensity(); } } else { int nstates = cdyn->Domain().Size(); forallassign(Instantiation &curins,cur) { matrix AIM(nstates,nstates,0.0); // Compute average IM over extra vars int count = 0; forallassign_except(Instantiation &ins,csrc,curins) { AIM += (*csdyn)(ins)->Intensity(); count++; } AIM /= count; (*cdyn)(curins)->Intensity() = AIM; } } } } const Dynamics *CTBNDyn::NodeByVar(int i) const { PrepNode2IdMaps(); int idx = var2node[i]; return nodes[idx]; } const SS *CTBNDyn::NodeSSByVar(int i, SS* ss) const { PrepNode2IdMaps(); int idx = var2node[i]; const CTBNDynSS *css = dynamic_cast(ss); css->Compact(); return css->dynss[idx]; } vector CTBNDyn::GetChildrenByVar(int varid) const { PrepNode2IdMaps(); vector ret; map >::iterator it = children.find(varid); if(it == children.end()) return ret; ret = it->second; for(unsigned int i = 0; i < ret.size(); i++) ret[i] = node2var[ret[i]]; return ret; } SOBJCLASSDEF(CTBNDynSS) CTBNDynSS::CTBNDynSS(istream &is) { int n; is >> n; dynss.resize(n); for(int i=0;i(StreamObj::LoadOldPtr(is)); bool hasjss; is >> hasjss; if (hasjss) jss = dynamic_cast(StreamObj::LoadOldPtr(is)); else jss = NULL; dyn = NULL; } CTBNDynSS::CTBNDynSS(const CTBNDyn *d) { jss = NULL; dyn = d; } CTBNDynSS::CTBNDynSS(const CTBNDynSS &ctbnss) : SS() { int n = ctbnss.dynss.size(); dynss.resize(n); for(int i=0;iClone(); if (NULL == ctbnss.jss) { jss = NULL; } else { jss = ctbnss.jss->Clone(); } dyn = ctbnss.dyn; if (dyn) dyn->assocss.push_back(this); } CTBNDynSS::~CTBNDynSS() { int n = dynss.size(); for(int i=0;iassocss.erase(remove(dyn->assocss.begin(),dyn->assocss.end(),this),dyn->assocss.end()); } CTBNDynSS &CTBNDynSS::operator=(const CTBNDynSS &ctbnss) { if (&ctbnss!=this) { int n = dynss.size(); for(int i=0;iClone(); if (ctbnss.jss == NULL) jss = NULL; else jss = ctbnss.jss->Clone(); if (dyn) dyn->assocss.erase(remove(dyn->assocss.begin(),dyn->assocss.end(),this),dyn->assocss.end()); dyn = ctbnss.dyn; if (dyn) dyn->assocss.push_back(this); } return *this; } CTBNDynSS *CTBNDynSS::Clone() const { return new CTBNDynSS(*this); } void CTBNDynSS::Compact() const { if (jss != NULL) { assert(dyn!=NULL); // cerr << "compacting jss: " << endl; // jss->SaveV(cerr); int n = dyn->nodes.size(); for(int i=0;inodes[i]->AddSS(jss,dyn->Joint(),dynss[i]); // cerr << i << ": " << endl; // dynss[i]->SaveV(cerr); } delete jss; jss = NULL; } } void CTBNDynSS::LoadOld(istream &is) { int n = dynss.size(); for(int i=0;iassocss.erase(remove(dyn->assocss.begin(),dyn->assocss.end(),this),dyn->assocss.end()); is >> n; dynss.resize(n); for(int i=0;i(StreamObj::LoadOldPtr(is)); bool hasjss; is >> hasjss; if (hasjss) jss = dynamic_cast(StreamObj::LoadOldPtr(is)); else jss = NULL; dyn = NULL; } void CTBNDynSS::serial_preload() { int n = dynss.size(); for(int i=0;iassocss.erase(remove(dyn->assocss.begin(), dyn->assocss.end(),this),dyn->assocss.end()); dyn = NULL; } void CTBNDynSS::serial_presave() const { Compact(); } void CTBNDynSS::SaveOld(ostream &os) const { int n = dynss.size(); os << n; for(int i=0;iSaveOldPtr(os); } bool hasjss = (jss!=NULL); os << os.fill() << hasjss; if (hasjss) { os << os.fill(); jss->SaveOldPtr(os); } } void CTBNDynSS::Scale(double w) { int n = dynss.size(); for (int i = 0; iScale(w); if (jss) { jss->Scale(w); } } double CTBNDynSS::NodeSS(int id, int val1, int val2, const Instantiation &cond) const { vector index; Compact(); //cond.ConsistentIndexes(index); assert(dyn!=NULL); dyn->PrepNode2IdMaps(); return dynamic_cast(dynss[dyn->var2node[id]])-> Element(index, val1, val2); } void CTBNDynSS::AddSS(const SS* nss, double w) { int n = dynss.size(); const CTBNDynSS *css = dynamic_cast(nss); for(int i=0; iAddSS(css->dynss[i], w); if (css->jss!=NULL) { if (!jss) jss = dyn->Joint()->BlankSS(); jss->AddSS(css->jss,w); } } } // end of ctbn namespace ctbnrle.1.1.1/src/samplinginf.cc0000664000175000017500000001730712107032416016777 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "samplinginf.h" #include "markov.h" #include "markovdyn.h" #include "multirv.h" #include "ctbndyn.h" #include "utils.h" namespace ctbn { using namespace std; SamplingInfAbs::SamplingInfAbs() { p = NULL; numofsamples = 100; timelimit = 0.0; } SamplingInf::SamplingInf() { } SamplingPreInf::SamplingPreInf(const vector &queries, int workingsetsize) : qs(queries), ans(queries.size(),0) { maxlnw = -INFINITY; ttlw = 0; sampled = false; setsize = workingsetsize; } SamplingInfAbs::~SamplingInfAbs() { if(sampler!=NULL) delete sampler; } SamplingInf::~SamplingInf() { } SamplingPreInf::~SamplingPreInf() { } SamplingInf* SamplingInf::Clone() const { SamplingInf* siClone = new SamplingInf(*this); siClone->SetSampler(this->sampler->Clone()); return siClone; } SamplingPreInf *SamplingPreInf::Clone() const { SamplingPreInf* siClone = new SamplingPreInf(*this); siClone->SetSampler(this->sampler->Clone()); return siClone; } void SamplingInfAbs::SetProcess(const Process *pr) { if(sampler==NULL) { cout << "Sampler not set!" << endl; exit(1); } sampler->SetProcess(pr); p = dynamic_cast(pr); Reset(); } void SamplingInfAbs::SetSampler(const Sampler *s) { sampler = s->Clone(); if(p!=NULL) sampler->SetProcess(p); Reset(); } void SamplingInfAbs::SetTrajectory(const Trajectory *tr) { if(sampler==NULL) { cout << "Sampler not set!" << endl; exit(1); } if(p==NULL) { cout << "Process not set!" << endl; exit(1); } sampler->SetTrajectory(tr); Reset(); } void SamplingInfAbs::SetNumSample(int n) { numofsamples = n; } void SamplingInfAbs::SetTimeLimit(double t, int batchsize) { timelimit = t; bsize = batchsize; } // Sample completions of given evidence trajectory (stored in sampler), and // associate them with their weights. void SamplingInfAbs::Sample() { if(sampler==NULL || p==NULL) { cout << "Sampler or process not set!" << endl; exit(1); } if (timelimit == 0.0) { AddSamples(numofsamples,true); } else { double begin_time = getcputime(); double end_time = begin_time + timelimit; do { AddSamples(bsize,false); } while (getcputime() < end_time); } } double SamplingInfAbs::NormalizeSet(vector &w) { double maxweight = w[0]; for(unsigned int i=1; imaxweight) maxweight = w[i]; for(unsigned int i=0; iSampleTrajectories(traj, weights, numofsamples); else { vector t; vector w; sampler->SampleTrajectories(t, w, nsamp); traj.insert(traj.end(),t.begin(),t.end()); weights.insert(weights.end(),w.begin(),w.end()); } } void SamplingPreInf::AddSamples(int nsamp, bool all) { while(nsamp) { vector t; vector w; int n = nsamp>setsize ? setsize : nsamp; sampler->SampleTrajectories(t,w,n); nsamp -= n; double lnw = NormalizeSet(w); double f; if (lnw > maxlnw) { f = 1.0; double oldf = exp(maxlnw-lnw); ttlw *= oldf; maxlnw = lnw; for(size_t i=0;iCalculate(t[j])*wf; } } } void SamplingInf::Reset() { traj.clear(); weights.clear(); } void SamplingPreInf::Reset() { ans = vector(0,qs.size()); maxlnw = -INFINITY; ttlw = 0.0; sampled = false; } double SamplingInf::Smooth(const Instantiation &x, double t, bool log) { QueryProb query(x,t); double ret = CalcQuery(query); return log ? ::log(ret) : ret; } double SamplingPreInf::Smooth(const Instantiation &,double,bool) { cout << "not implemented" << endl; return 0; } double SamplingInf::Filter(const Instantiation &x, double t, bool log) { cout << "not implemented" << endl; return 0; } double SamplingPreInf::Filter(const Instantiation &x, double t, bool log) { cout << "not implemented" << endl; return 0; } double SamplingInf::Prob(double t, bool log) { cout << "not implemented" << endl; return 0; } double SamplingPreInf::Prob(double t, bool log) { cout << "not implemented" << endl; return 0; } void SamplingInf::AddExpSuffStats(SS *ss, double w) { if(sampler==NULL) { cout << "Sampler not set!" << endl; exit(1); } if(traj.size()==0) Sample(); double sumweights = 0.0; for(unsigned int i=0; iSuffStats(traj, weights); newss->Scale(sumweights); //by Yu ss->AddSS(newss); delete newss; } void SamplingPreInf::AddExpSuffStats(SS *ss, double w) { assert(0); // not implementable } void SamplingInf::AddExpSuffStats(const Dynamics *dyn, SS *ss, double w) { if(sampler==NULL) { cout << "Sampler not set!" << endl; exit(1); } if(traj.size()==0) Sample(); double sumweights = 0.0; for(unsigned int i=0; iSuffStats(traj, weights); newdynss->Scale(sumweights); ss->AddSS(newdynss); delete newdynss; } void SamplingPreInf::AddExpSuffStats(const Dynamics *dyn, SS *ss, double w) { assert(0); // not implementable } void SamplingInf::AddExpSuffStats(const RV *p0, SS *ss, double w) { if(sampler==NULL) { cout << "Sampler not set!" << endl; exit(1); } if(traj.size()==0) Sample(); double sumweights = 0.0; for(unsigned int i=0; iSuffStats(traj, weights); newp0ss->Scale(sumweights); ss->AddSS(newp0ss); delete newp0ss; } void SamplingPreInf::AddExpSuffStats(const RV *p0, SS *ss, double w) { assert(0); } double SamplingInf::CalcQuery(QueryCalculator &calc) { if(sampler==NULL) { cout << "Sampler not set!" << endl; exit(1); } if(traj.size()==0) Sample(); double sumweights = 0; for(unsigned int i=0; i. */ #include "inference.h" #include "markov.h" #include namespace ctbn { using namespace std; QueryTime::QueryTime(const Instantiation &c) { condi = Instantiation(c.KnownVars()); vector varlist = condi.VarList(); for (unsigned int i=0; i varlist = condi.VarList(); int id = varlist[0]; return dynamic_cast(ss)->NodeSS(id, condi.Value(id), condi.Value(id), condi); } QueryTransition::QueryTransition(const Context &c, int from, int to) { queryfrom = from; queryto = to; querycontext = c; } double QueryTransition::Calculate(const SS *ss) { vector varlist = querycontext.VarList(); int id = varlist[0]; // condi is not used here. Just a place-holder to call ss.NodeSS. Instantiation condi = Instantiation(querycontext); return dynamic_cast(ss)->NodeSS(id, queryfrom, queryto, condi); } double QueryTransition::Calculate(const Trajectory &tr) { Trajectory::Index i = tr.Begin(querycontext); double retval = 0.0; int previndex = i.Values().Index(); ++i; //tr.Draw(cout); for(; !i.Done(); ++i) { int currindex = i.Values().Index(); //cout << currindex << ' ' << previndex << " retval=" // << retval << endl; if( (currindex==queryto && previndex==queryfrom) || (currindex==queryto && queryfrom==-1) || (queryto==-1 && queryfrom==previndex)) retval += 1.0; previndex = currindex; } return retval; } QueryProb::QueryProb(const Instantiation &val, double time) : x(val.KnownVars()), t(time) { xind = x.Index(val); } double QueryProb::Calculate(const Trajectory &tr) { return tr.Values(x,t).Index()==xind ? 1.0 : 0.0; } double QueryProb::Calculate(const SS *ss) { assert(0); // not implementable -- see .h file } //SOBJCLASSDEF(Inference) Inference::~Inference() { } } // end of ctbn namespace ctbnrle.1.1.1/src/structure.cc0000664000175000017500000001070212107032416016520 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "structure.h" #include "extramath.h" #include namespace ctbn { using namespace std; Structure::Structure() { } Structure::~Structure() { } const vector &Structure::GetChildren(int parent, bool isVarID) const { if(!isVarID) parent = node2var[parent]; map >::const_iterator ci = children.find(parent); if (ci != children.end()) { return ci->second; } else { static vector empty(0); return empty; } } const vector &Structure::GetParents(int child, bool isVarID) const { if(isVarID) child = var2node.find(child)->second; if(static_cast(child) >= parentlist.size()) { cerr << "incorrect node index" << endl; cerr << "child: " << child << endl; exit(1); } return parentlist[child]; } const vector > &Structure::GetAdjLists() const { return parentlist; } int Structure::HammingDist(const Structure &other) { const vector > &listA = this->GetAdjLists(); const vector > &listB = other.GetAdjLists(); vector vlA = this->GetVarList(); vector vlB = other.GetVarList(); set allvarid; for(unsigned int i=0;i::const_iterator it; for(it = allvarid.begin(); it != allvarid.end(); ++it) { int cur = *it; if(this->children.find(cur) == this->children.end()) { ret += listB[other.var2node.find(cur)->second].size(); continue; } if(other.children.find(cur) == other.children.end()) { ret += listA[this->var2node.find(cur)->second].size(); continue; } ret += listDiff(listA[this->var2node.find(cur)->second], listB[other.var2node.find(cur)->second]); } return ret; } vector Structure::GetVarList() const { vector ret; for(unsigned int i=0;i zeroInDeg; vector > pl(parentlist); map > cl(children); for(unsigned int i = 0; i < pl.size(); i++) if(pl[i].size() == 0) zeroInDeg.push_back(i); while(zeroInDeg.size() != 0) { int rem = zeroInDeg.back(); rem = node2var[rem]; zeroInDeg.pop_back(); map >::iterator ci = cl.find(rem); if(ci == cl.end()) continue; for(unsigned int i = 0; i < ci->second.size(); i++) { int rem2 = ci->second[i]; vector::iterator it = find(pl[rem2].begin(),pl[rem2].end(), rem); pl[rem2].erase(it); if(pl[rem2].size() == 0) zeroInDeg.push_back(rem2); } } int totalsize = 0; for(unsigned int i = 0; i < pl.size(); i++) totalsize += pl[i].size(); return totalsize != 0; } int Structure::Node2Var(int index) const { return node2var[index]; } int Structure::Var2Node(int varid) const { return var2node.find(varid)->second; } void Structure::Print(ostream &os) const { os << "Parent list: " << endl; for(unsigned int i=0; i. */ #include #include "gibbsbase.h" #include "ctbndyn.h" #include "markov.h" #include "markovdyn.h" #include "multisimple.h" #include "bn.h" #include "rk.h" #include "utils.h" #include "params.h" namespace ctbn { using namespace std; // "process" is a pointer to a (subclass of ) Process object. // "evidence" is a pointer to Trajectory object that holds the context. // Variables with value -1 means no evidence. // Interval evidence X(t:t+dt)=x is represented as X(t)=x, X(t+dt)=-1. // Point evidence X(t)=x is represented as X(t)=x, X(t+epsilon)=-1. GibbsBase::GibbsBase(const Process *process, const Trajectory *evidence, int burnin) { p = process; init_traj = NULL; evid = evidence; numBurninIter = burnin; Initialize(); } void GibbsBase::Initialize() { const Markov *ctbn = dynamic_cast(p); const CTBNDyn *ctbndyn = dynamic_cast (ctbn->GetDynamics()); own_var_list = ctbndyn->Domain().VarList(); context = ctbndyn->Domain() + ctbndyn->CondDomain(); p0 = ctbn->GetStartDist(); vector var_list = context.VarList(); for (size_t j=0; j!=var_list.size(); ++j) { // markovdyn for each variable const MarkovDyn *markov_dyn = dynamic_cast (ctbndyn->NodeByVar(var_list[j])); Context mb = markov_dyn->Domain(); // *will* become Markov Blanket vector domain_var_list = mb.VarList(); mb = mb + markov_dyn->CondDomain(); // domain_var_list should have exactly one variable mds[domain_var_list[0]] = markov_dyn; // parents and children set for each variable Context parent_context = Context(); Context children_context = Context(); for (int i=0; iNumofNodes(); ++i) { Context c = ctbndyn->Node(i)->Domain(); Context cc = ctbndyn->Node(i)->CondDomain(); if (c.HasId(var_list[j])) parent_context = parent_context + cc; if (cc.HasId(var_list[j])) { children_context = children_context + c; mb = mb + c + cc; } } parents[var_list[j]] = parent_context; children[var_list[j]] = children_context; markovblanket[var_list[j]] = mb; } burntin = false; } void GibbsBase::SampleTrajectories(vector &traj, vector &w, int numsamples, Random &rand) { BurnIn(rand); for (int i=0; iTimeBegin()); tr.SetEndTime(evid->TimeEnd()); // Sample a start distribution from BN. The start distribution should // be consistent with the initial values in evidence. // NOTE: start with the next event after tr.Timebegin(), // since the first one is already handled in BN's sample; Trajectory::Index i = evid->Begin(context); Instantiation inst(i.Values()); Instantiation myinst(inst); p0->Sample(myinst, rand); tr.AddTransition(myinst, tr.TimeBegin()); double dt = i.DeltaT(); // loop over evidence for (++i; !i.Done(); ++i) { Instantiation newinst(i.Values()); Instantiation myoldinst(myinst); myinst.SetVal(Instantiation(newinst.KnownVars(),newinst),true); Context diff(myinst, myoldinst); vector diff_vars = diff.VarList(); for (vector::iterator varid=diff_vars.begin(); varid!=diff_vars.end();++varid) { double event_time; if (inst.Value(*varid)==-1) event_time = i.Time() - randomizer.RandReal() * dt; else event_time = i.Time(); // observed transition! tr.AddTransition(*varid, event_time, newinst.Value(*varid)); } inst = newinst; dt = i.DeltaT(); } } void GibbsBase::SampleVariable(int v, Random &rand) const { // Remove all v's transitions from tr. oldtr = tr; tr.SetUnknown(v,true); vector x0, xT; vector t0, tT; SplitFreeIntervals(v, x0, xT, t0, tT, tr.Values(context, tr.TimeBegin()), rand); if (x0.empty()) // no free interval for v return; for (size_t i=0; i!=x0.size(); ++i) SampleVariableInterval(v, x0[i], xT[i], t0[i], tT[i], rand); } void GibbsBase::SplitFreeIntervals(int v, vector &x0, vector &xT, vector &t0, vector &tT, const Instantiation &inst0, Random &rand) const { // NOTE: if no evidence for v specifies the value of v at // tr.TimeBegin() and tr.TimeEnd(), then we must sample // x0 and xT for them. Context vcontext; vcontext.AddVar(v,context.Cardinality(v)); Trajectory::Index i = evid->Begin(vcontext); Instantiation inst(i.Values()); int oldval = inst.Value(v); if (oldval == -1) { // open free interval t0.push_back(i.Time()); x0.push_back(-1); } else { tr.AddTransition(v, tr.TimeBegin(), oldval); } for (++i; !i.Done(); ++i) { inst = i.Values(); int newval = inst.Value(v); // "&& oldval!=-1" added (cshelton 9/18/12) b/c I think that // will be fixed when sampling if (newval!=oldval && newval!=-1 && oldval!=-1) tr.AddTransition(v, i.Time(), newval); if (oldval==-1 && newval!=-1) { // close free interval tT.push_back(i.Time()); xT.push_back(newval); } else if (oldval!=-1 && newval==-1) { // open free interval t0.push_back(i.Time()); x0.push_back(oldval); } oldval = newval; } if (oldval == -1) { // close free interval tT.push_back(evid->TimeEnd()); xT.push_back(-1); } } void GibbsBase::GetTau(const Trajectory &t, double begin_time, double end_time, int v, const Context &varset, vector &tau, vector &inst) { Trajectory::Index index = t.Begin(varset, begin_time, end_time); tau.push_back(begin_time); inst.push_back(index.Values()); for (++index; !index.Done(); ++index) { Instantiation newinst(index.Values()); inst.push_back(newinst); tau.push_back(index.Time()); } tau.push_back(end_time); } // assumes input vectr T is right sized bool GibbsBase::GetTMatrix(int v, const Instantiation &oldinst, const Instantiation &newinst, vectr &T) const { int vdim = context.Cardinality(v); Context diff(oldinst, newinst); // NOTE: assume there is exactly one variable changed int changeid = diff.VarList()[0]; // if the changed variable is not a child of v; // simply return an identity matrix; if (!children[v].HasId(changeid)) return false; // o.w. return a diagonal matrix whose T[i][i] = q(a->a')|i int a = oldinst.Value(changeid); int b = newinst.Value(changeid); const MarkovDyn *md = mds[changeid]; Instantiation inst = oldinst; for (int i=0; iIntensity()[a][b]; //get the CIM } return true; } } // end of ctbn namespace ctbnrle.1.1.1/src/multirv.cc0000664000175000017500000000240512107032416016163 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "multirv.h" #include "multisimple.h" namespace ctbn { SOBJCLASSDEF(MultiRV) MultiRV::MultiRV(const Context &var, const Context &cvar) : RVComp(var,cvar, new RVCondSimpleComp(var.Size(),cvar.Size())) { } MultiRV::MultiRV(std::istream &is) : RVComp(is) { } MultiRV::~MultiRV() { } MultiRV *MultiRV::Clone() const { return new MultiRV(*this); } template class RVCondSimpleComp; } // end of ctbn namespace ctbnrle.1.1.1/src/rvcomp.cc0000664000175000017500000001410012107032416015762 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "rvcomp.h" #include "multisimple.h" namespace ctbn { using namespace std; SOBJCLASSDEF(RVComp) RVComp::RVComp(const Context &var, const Context &cvar, RVCondSimple *touse) : RV(var,cvar) { impl = touse; } RVComp::RVComp(istream &is) : RV(is) { impl = dynamic_cast(StreamObj::LoadOldPtr(is)); } RVComp::RVComp(const RVComp &rvc) : RV(rvc) { impl = rvc.impl->Clone(); } RVComp &RVComp::operator=(const RVComp &rvc) { if (&rvc != this) { if (impl!=NULL) delete impl; impl = rvc.impl->Clone(); } return *this; } RVComp::~RVComp() { if (impl!=NULL) delete impl; } void RVComp::LoadOld(std::istream &is) { RV::LoadOld(is); if (impl!=NULL) delete impl; impl = dynamic_cast(StreamObj::LoadOldPtr(is)); } void RVComp::serial_preload() { if (impl!=NULL) delete impl; impl = NULL; } void RVComp::SaveOld(std::ostream &os) const { RV::SaveOld(os); os << os.fill(); impl->SaveOldPtr(os); } RVComp *RVComp::Clone() const { return new RVComp(*this); } void RVComp::Normalize() { impl->Normalize(); } double RVComp::Prob(const Instantiation &x, bool log) const { int condi = CondDomain().Index(x); if (condi==-1) return log ? -INFINITY : 0.0; RVSimple *condrv = impl->Condition(condi); vector ids; Domain().ConsistentIndexes(ids,x); condrv->Restrict(ids); double ret = condrv->Sum(log); delete condrv; return ret; } void RVComp::Restrict(const Instantiation &x) { vector i,ci; Domain().ConsistentIndexes(i,x); CondDomain().ConsistentIndexes(ci,x); impl->Restrict(i,ci); } void RVComp::Project(const Context &c, const Context &cc) { Context all = c+cc+Domain()+CondDomain(); Context condc = cc+CondDomain(); Context nonc = all-condc; RVCondSimple::RemapT ind; forallassign(Instantiation &vcond,condc) { int oldci = CondDomain().Index(vcond); int newci = cc.Index(vcond); vector > remap(c.Size()); forallassign_subset(Instantiation &v,nonc,vcond) { int oldi = Domain().Index(v); int newi = c.Index(v); remap[newi].push_back(oldi); } ind[make_pair(newci,oldci)] = remap; } impl->Reindex(c.Size(),cc.Size(),ind); RV::Project(c,cc); } void RVComp::MultBy(const RV *x) { const RVComp *xx = dynamic_cast(x); Context newc(Domain(),xx->Domain()); Context newcc(CondDomain(),xx->CondDomain()); newcc = newcc - newc; if (newc.Size()!=Domain().Size() || newcc.Size()!=CondDomain().Size()) Project(newc,newcc); if (newc.Size()!=xx->Domain().Size() || newcc.Size()!=xx->CondDomain().Size()) { RVComp *cpy = xx->Clone(); cpy->Project(newc,newcc); impl->MultBy(cpy->impl); delete cpy; } else { impl->MultBy(xx->impl); } } SS *RVComp::BlankSS() const { return impl->BlankSS(); } void RVComp::AddSS(const Instantiation &x, SS *ss, double w) const { int condi = CondDomain().Index(x); int i = Domain().Index(x); impl->AddSS(condi,i,ss,w); } void RVComp::AddExpSS(const Instantiation &x, SS *ss, double w) const { int condi = CondDomain().Index(x); vector ids,cids; Domain().ConsistentIndexes(ids,x); cids.push_back(condi); if (ids.size()==static_cast(Domain().Size())) { impl->AddExpSS(condi,ss,w); } else { RVCondSimple *condrv = impl->Clone(); condrv->Restrict(ids,cids); condrv->Normalize(); condrv->AddExpSS(condi,ss,w); delete condrv; } } void RVComp::AddExpSS(const Instantiation &x, const RVSimple *rvs, SS *ss, double w) const { // cerr << "here" << endl; // cerr << rvs << endl; int condi = CondDomain().Index(x); // cerr << condi << endl; // cerr << "impl of this "; // cerr << impl << endl; impl->AddExpSS(condi,rvs,ss,w); } void RVComp::AddSS(const SS *toadd, const RV* rv, SS *ss, double w) const { const RVComp *rvc = dynamic_cast(rv); assert(rvc!=NULL); assert(CondDomain().IsSubset(rvc->CondDomain())); Instantiation i(Domain()+CondDomain(),0); forallassign(Instantiation &condi,CondDomain()) { vector > mapping(Domain().Size()); forallassign_subset(Instantiation &i,Domain(),condi) rvc->Domain().ConsistentIndexes(mapping[Domain().Index(i)],i); impl->AddSS(toadd,rvc->impl,mapping,condi.Index(), rvc->CondDomain().Index(condi),ss); } } void RVComp::Sample(Instantiation &x, Random &rand) const { int condi = CondDomain().Index(x); vector ids,cids; Domain().ConsistentIndexes(ids,x); cids.push_back(condi); int i; if (ids.size()==static_cast(Domain().Size())) { // sample all... i = impl->Sample(condi,rand); } else { RVCondSimple *condrv = impl->Clone(); condrv->Restrict(ids,cids); condrv->Normalize(); i = condrv->Sample(condi,rand); delete condrv; } Instantiation newi(Domain()); newi.SetIndex(i); x.SetVal(newi,true); } void RVComp::Maximize(const SS *ss) { impl->Maximize(ss); } RVSimple * RVComp::MakeSimple(const Instantiation &x, bool normalize) const { RVSimple *ret = impl->Condition(CondDomain().Index(x)); vector ids; Domain().ConsistentIndexes(ids,x); ret->Restrict(ids); if (normalize) ret->Normalize(); return ret; } void RVComp::Scramble(double alpha, double degree, Random &rand) { impl->Scramble(alpha, degree, rand); } double RVComp::LLH(const SS *ss) const { return impl->LLH(ss); } double RVComp::GetScore(double numTrans, const SS *ss) const { return impl->GetScore(numTrans,ss); } } // end of ctbn namespace ctbnrle.1.1.1/src/matrix.cc0000664000175000017500000000163012107032416015764 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef NOUSEEIGEN #include "matrix-eigen.cc" #else #include "matrix-noeigen.cc" #endif ctbnrle.1.1.1/src/markov.cc0000664000175000017500000001557212107032416015771 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markov.h" #include "ctbndyn.h" #include "markovdyn.h" #include "params.h" #include namespace ctbn { using namespace std; SOBJCLASSDEF(Markov) Markov::Markov(RV *startdist, Dynamics *dyn) : Process() { p0 = startdist; d = dyn; } Markov::Markov(istream &is) : Process() { p0 = NULL; d = NULL; LoadOld(is); } Markov::Markov(const Markov &m) : Process() { p0 = m.p0->Clone(); d = m.d->Clone(); } Markov::~Markov() { if (p0!=NULL) delete p0; if (d!=NULL) delete d; } Markov &Markov::operator=(const Markov &m) { if (&m==this) return *this; if (p0!=NULL) delete p0; if (d!=NULL) delete d; p0 = m.p0->Clone(); d = m.d->Clone(); return *this; } Markov *Markov::Clone() const { return new Markov(*this); } void Markov::Mult(const Process *p) { const Markov *m = dynamic_cast(p); p0->MultBy(m->p0); d->Mult(m->d); } SS *Markov::BlankSS() const { return new MarkovSS(p0->BlankSS(),d->BlankSS()); } void Markov::Maximize(const SS *ss) { const MarkovSS *mss = dynamic_cast(ss); p0->Maximize(mss->p0ss); d->Maximize(mss->dss); } void Markov::LoadOld(istream &is) { if (p0!=NULL) delete p0; if (d!=NULL) delete d; p0 = dynamic_cast(StreamObj::LoadOldPtr(is)); d = dynamic_cast(StreamObj::LoadOldPtr(is)); } void Markov::serial_preload() { if (p0!=NULL) delete p0; if (d!=NULL) delete d; p0 = NULL; d = NULL; } void Markov::SaveOld(ostream &os) const { p0->SaveOldPtr(os); os << os.fill(); d->SaveOldPtr(os); } void Markov::Sample(Trajectory &tr, Random &rand) const { Instantiation x0; p0->Sample(x0,rand); tr.AddTransition(x0,tr.TimeBegin()); d->SampleTrajectory(tr,tr.TimeBegin(),rand); //dynamic_cast (d)->SampleTrajectory(tr,tr.TimeBegin(),rand); } // return value is the total llh on a single trajectory // also pass out the p0llh and dynllh seperately double Markov::LLH(const Trajectory &tr, double w, double &p0llh, double &dynllh) const { MarkovSS *mss = dynamic_cast(BlankSS()); const Context &c = d->Domain() + d->CondDomain(); const Context &owndomain = d->Domain(); Instantiation curri = tr.Values(c, tr.TimeBegin()); p0->AddSS(curri, mss->p0ss, w); Trajectory::Index index = tr.Begin(c); while(!index.Done()) { double t = index.Time(); double deltat = index.DeltaT(); d->AddSS(curri, t, deltat, mss->dss, w); int sign = index.TestInc(owndomain); if (!index.Done()) { Instantiation nexti = index.Values(); t = index.Time(); if (sign==2) d->AddTransSS(curri, nexti, t, mss->dss, w); curri = nexti; } } mss->Scale(w); p0llh = p0->LLH(mss->p0ss); dynllh = d->LLH(mss->dss); delete mss; return p0llh + dynllh; } double Markov::LLH(const vector &tr, const vector &w) const { MarkovSS *mss = dynamic_cast(BlankSS()); const Context &c = d->Domain() + d->CondDomain(); const Context &owndomain = d->Domain(); double sumw = 0.0; for(unsigned int i=0; iAddSS(curri, mss->p0ss, w[i]); Trajectory::Index index = tr[i].Begin(c); while(!index.Done()) { double t = index.Time(); double deltat = index.DeltaT(); d->AddSS(curri, t, deltat, mss->dss, w[i]); int sign = index.TestInc(owndomain); if(!index.Done()) { Instantiation nexti = index.Values(); t = index.Time(); if (sign==2) d->AddTransSS(curri, nexti, t, mss->dss, w[i]); curri = nexti; } } sumw += w[i]; } mss->Scale(sumw); double llh = LLH(mss); delete mss; return llh; } double Markov::LLH(const SS *ss) const { const MarkovSS *mss = dynamic_cast(ss); return p0->LLH(mss->p0ss) + d->LLH(mss->dss); } SS* Markov::SuffStats(const vector &tr, const vector &w) const { MarkovSS *mss = dynamic_cast(BlankSS()); mss->p0ss = p0->SuffStats(tr,w); mss->dss = d->SuffStats(tr,w); for(unsigned int i=0; iScramble(a, b, alpha, degree, rand); p0->Scramble(alpha, degree, rand); } double Markov::GetScore(double numTrans, double amtTime, const SS* ss) const { double p0Score = p0->GetScore(numTrans, dynamic_cast (ss)->p0ss); double dynScore = d->GetScore(numTrans, amtTime, dynamic_cast (ss)->dss); return p0Score + dynScore; } double MarkovSS::NodeSS(int id, int val1, int val2, const Instantiation &cond) const { return dynamic_cast(dss)-> NodeSS(id, val1, val2, cond); } SOBJCLASSDEF(MarkovSS) MarkovSS::MarkovSS(std::istream &is) { p0ss = dynamic_cast(StreamObj::LoadOldPtr(is)); dss = dynamic_cast(StreamObj::LoadOldPtr(is)); } MarkovSS::MarkovSS(SS *rvss, SS *dynss) { p0ss = rvss; dss = dynss; } MarkovSS::MarkovSS(const MarkovSS &mss) : SS() { p0ss = mss.p0ss->Clone(); dss = mss.dss->Clone(); } MarkovSS &MarkovSS::operator=(const MarkovSS &mss) { if (&mss!=this) { if (p0ss != NULL) delete p0ss; if (dss != NULL) delete dss; p0ss = mss.p0ss->Clone(); dss = mss.dss->Clone(); } return *this; } MarkovSS::~MarkovSS() { if (p0ss != NULL) delete p0ss; if (dss != NULL) delete dss; } MarkovSS *MarkovSS::Clone() const { return new MarkovSS(*this); } void MarkovSS::LoadOld(istream &is) { if (p0ss != NULL) delete p0ss; if (dss != NULL) delete dss; p0ss = dynamic_cast(StreamObj::LoadOldPtr(is)); dss = dynamic_cast(StreamObj::LoadOldPtr(is)); } void MarkovSS::serial_preload() { if (p0ss != NULL) delete p0ss; if (dss != NULL) delete dss; p0ss = NULL; dss = NULL; } void MarkovSS::SaveOld(ostream &os) const { p0ss->SaveOldPtr(os); os << os.fill(); dss->SaveOldPtr(os); } void MarkovSS::Scale(double w) { p0ss->Scale(w); dss->Scale(w); } void MarkovSS::AddSS(const SS* nss, double w) { const MarkovSS* mss = dynamic_cast(nss); p0ss->AddSS(mss->p0ss, w); dss->AddSS(mss->dss, w); } } // end of ctbn namespace ctbnrle.1.1.1/src/exactmarkovinf.cc0000664000175000017500000001076512107032416017512 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "exactmarkovinf.h" namespace ctbn { ExactMarkovInf::ExactMarkovInf(): FBInf() { // Except for invoking the ancestor's constructor, there is nothing // else to do. } ExactMarkovInf::~ExactMarkovInf() throw() { // There is no cleanup required above and beyond what the ancestor's // class destructor will already clean up for us. } ExactMarkovInf * ExactMarkovInf::Clone() const { ExactMarkovInf * the_clone = new ExactMarkovInf(*this); CloneTo(the_clone); // This class has no additional data members to be cloned. return the_clone; } void ExactMarkovInf::Restrict(RVSimple * a, Instantiation const & variable_assignment) const { vector ind; the_markov_process_dynamics()->Domain().ConsistentIndexes(ind, variable_assignment); a->Restrict(ind); } RVSimple * ExactMarkovInf::MakeSimple(RV * prior, Instantiation const &, bool normalize) const { Trajectory const * const the_trajectory(getTrajectory()); Instantiation const trajectory_values( the_trajectory->Values(prior->Domain() + prior->CondDomain(), the_trajectory->TimeBegin())); RVSimple * ret = prior->MakeSimple(trajectory_values); return ret; } RVCondSimple * ExactMarkovInf::IntervalPropagator(Dynamics * const the_markov_process_dynamics, double start_time_point, double past_end_time_point, Instantiation const & current_evidence) const { RVCondSimple * const ret(the_markov_process_dynamics->Cond(start_time_point, past_end_time_point, current_evidence)); return ret; } RVCondSimple * ExactMarkovInf::PointTransitionPropagator(Dynamics * const the_markov_process_dynamics, double time_point, Instantiation const & prior_evidence, Instantiation const & current_evidence) const { RVCondSimple * const ret(the_markov_process_dynamics->Cond(time_point, prior_evidence, current_evidence)); return ret; } RVCondSimple * ExactMarkovInf::PointChangeEvidencePropagator(Dynamics * const the_markov_process_dynamics, double time_point, Instantiation const & prior_evidence, Instantiation const & current_evidence) const { RVCondSimple * const ret(the_markov_process_dynamics->Cond(time_point, prior_evidence, current_evidence, false)); return ret; } void ExactMarkovInf::AddExpectedSufficientStatistics(Dynamics const * const dynamics, unsigned int const trajectory_index, SS * const sufficient_statistics, double const weight) const { unsigned int const & i(trajectory_index); dynamics->AddExpSS(condi_at(i), alpha_at(i), beta_at(i+1), time_points_at(i), time_points_at(i+1) - time_points_at(i), sufficient_statistics, weight); } void ExactMarkovInf::AddExpectedTransitionSufficientStatistics(Dynamics const * const dynamics, unsigned int const trajectory_index, SS * const sufficient_statistics, double const weight) const { unsigned int const & i(trajectory_index); dynamics->AddExpTransSS(condi_at(i), alpha_at(i), beta_at(i+1), evidence_variables_whose_observed_values_changed_at(i), time_points_at(i), sufficient_statistics, weight); } void ExactMarkovInf::AddExpectedInitialSufficientStatistics(Trajectory const * const trajectory, RVSimple * initial_prob_dist, const RV *ssrv, SS * const sufficient_statistics, double const weight) const { ssrv->AddExpSS(trajectory->Values(ssrv->CondDomain(), trajectory->TimeBegin()), initial_prob_dist, sufficient_statistics, weight); } bool ExactMarkovInf::IsAlphaElementValid(RVSimple * const alpha_sub_x) { SparseMultiZSimple * as_sparse_multi_z_simple = dynamic_cast(alpha_sub_x); bool ret = ((nullptr03 != as_sparse_multi_z_simple) && (as_sparse_multi_z_simple->Dist().isvalid())); return ret; } } // end of ctbn namespace ctbnrle.1.1.1/src/ss.cc0000664000175000017500000000210512107032416015103 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "ss.h" namespace ctbn { SS::SS() : StreamObj() { } SS::SS(std::istream &is) : StreamObj() { } SS::~SS() { } void SS::LoadOld(std::istream &is) { } void SS::SaveOld(std::ostream &os) const { StreamObj::SaveOld(os); } } // end of ctbn namespace ctbnrle.1.1.1/src/gibbsauxsampler.cc0000664000175000017500000001012112107032416017643 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include #include "gibbsauxsampler.h" #include "ctbndyn.h" #include "markov.h" #include "markovdyn.h" #include "multisimple.h" #include "bn.h" #include "rk.h" #include "utils.h" #include "params.h" namespace ctbn { using namespace std; // "process" is a pointer to a (subclass of ) Process object. // "evidence" is a pointer to Trajectory object that holds the context. // Variables with value -1 means no evidence. // Interval evidence X(t:t+dt)=x is represented as X(t)=x, X(t+dt)=-1. // Point evidence X(t)=x is represented as X(t)=x, X(t+epsilon)=-1. GibbsAuxSampler::GibbsAuxSampler(const Process *process, const Trajectory *evidence, int burnin) : GibbsBase(process,evidence,burnin) { } GibbsAuxSampler::~GibbsAuxSampler() { } void GibbsAuxSampler::SampleVariableInterval(int v, int x0, int xT, double t0, double tT, Random &rand) const { const MarkovDyn *md = mds[v]; int nv = context.Cardinality(v); vector tau; vector inst; GetTau(oldtr, t0, tT, v, markovblanket[v], tau, inst); vector newtau; vector prop; vector lhood; matrix eye(nv,nv,0.0); for(int i=0;i chvar = children[v].VarList(); vector chmd; for(size_t j=0;jIntensity(); double alpha = 2*Q.diag().absmax(); double auxrate = alpha+Q[val][val]; matrix M = eye+Q/alpha; vectr qs(nv,0.0); for(int k=0;kIntensity()[chval][chval]; } } inst[i].SetVal(v,val); newtau.push_back(tau[i]); if (i==0) prop.push_back(eye); else if (inst[i-1].Value(v)!=val) prop.push_back(M); else if (GetTMatrix(v,inst[i-1],inst[i],Tvec)) prop.push_back(matrix(nv,nv,Tvec)); else prop.push_back(eye); double dt,currt=tau[i],lastt=tau[i]; while((currt+=(dt=rand.SampleExp(auxrate))) alpha; if (x0!=-1) curra[x0] = 1.0; else { double mlp = -INFINITY; Instantiation i0 = tr.Values(context,t0); for(int k=0;kProb(i0,true); if (curra[k]>mlp) mlp = curra[k]; } for(int k=0;k0;i--) { vectr w(alpha[i-1]); for(int k=0;k. */ #include "defines.h" #if defined __linux__ || __CYGWIN__ #include TIME_STRUCT GetLinuxTime() { struct timeval t; struct timezone z; gettimeofday(&t, &z); return (TIME_STRUCT)t.tv_sec * (TIME_STRUCT)1000000000 + (TIME_STRUCT)t.tv_usec * (TIME_STRUCT)1000; } #endif #if defined(_WIN32) longlong GetWindowsTime() { SYSTEMTIME syst; GetSystemTime(&syst); FILETIME filet; SystemTimeToFileTime(&syst,&filet); ULARGE_INTEGER larget; larget.LowPart = filet.dwLowDateTime; larget.HighPart = filet.dwHighDateTime; return larget.QuadPart/10; } const unsigned long win32nan[2]={0xffffff, 0x7ffffff}; double nan(const char *) { return *((double *)win32nan);} float nanf(const char *) { return (float)(*((double *)win32nan));} long double nanl(const char *) { return (long double)(*((double *)win32nan));} #endif ctbnrle.1.1.1/src/importancesampler.cc0000664000175000017500000001654512107032416020220 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "importancesampler.h" #include "ctbn.h" #include "utils.h" #include "samplequeue.h" namespace ctbn { //#define DEBUG using namespace std; ImportanceSampler::ImportanceSampler(const Process *pr, const Trajectory *traj, const VarSample *m) { SetProcess(pr); SetTrajectory(traj); SetMethod(m); discard_bn_weight = false; } ImportanceSampler::~ImportanceSampler() {} ImportanceSampler *ImportanceSampler::Clone() const { return new ImportanceSampler(*this); } void ImportanceSampler::SampleTrajectories(vector &tr, vector &w, int numsamples, Random &rand) { MakeIndex(); const Markov* pr = dynamic_cast(p); Instantiation x = evid->Values(pr->GetDynamics()->Domain() + pr->GetDynamics()->CondDomain(), begintime); for (int i=0; i &nextevid) { nodeindex.clear(); nodeval.clear(); nextevidval.clear(); for (unsigned int i=0; iCondDomain().Index(inst)); nodeval.push_back(inst.Value(str.Node2Var(i))); nextevidval.push_back(nextevid[i].Values().Value(str.Node2Var(i))); } } double ImportanceSampler::SampleDyn(Trajectory &traj, Random &rand) { unsigned int numofnodes = nodes.size(); vector nextindex = evidindex; //index for each node changeindex = vector(numofnodes, 0); int * nextevidtype = new int [numofnodes]; double weight = 0; double currt = begintime; const Dynamics* dyn = dynamic_cast(p)->GetDynamics(); //Get initial values Instantiation curri = traj.Values(dyn->Domain()+dyn->CondDomain(), currt); for (unsigned int i=0; iDomain()); //check conddomain if (nextindex.size()>numofnodes) ++nextindex[numofnodes]; MakeCache(curri, nextindex); int count = 1; int movecount = 0; vector undefinelist; //save the nodes that need to be resampled for (unsigned int i=0; iCondDomain().Index(curri); double newt = SampleTime(curri, i, nodeindex[i], nodeval[i], nextevidtype[i], nextindex[i], nextevidval[i], currt, rand); events.Add(SampleQueue::Event(i, 0, newt)); } SampleQueue::Event e; events.Head(e); //Get the next transition variable int nextid = e.var; double nextt = e.time; //check transitions in conddomain of the process if (nextindex.size()>numofnodes && nextt>nextindex[numofnodes].Time()) { nextt = nextindex[numofnodes].Time(); nextid = -1; } //############################################## //This part checks if the sampling process falls into //some state that it will never be able to jump out if (fabs(nextt-currt)<1e-12) movecount++; else movecount = 0; if (movecount>100) exit(1); //############################################## if (nextt>=endtime) { nextt = endtime; for (unsigned int i=0; iDomain()); // switch from below to next line reported as bug by Asela Gunawardana //nextevidval[nextid] = nextindex[nextid].Values().Value(nextid); nextevidval[nextid] = nextindex[nextid].Values().Value(str.Node2Var(nextid)); #ifdef DEBUG cout << "Change index: nextid: " << nextid << endl; cout << "next evid type: " << nextevidtype[nextid] << endl; cout << "nextevid val: " << nextevidval[nextid] << endl; #endif } } else { //Add the transition of the conddomain to the trajectory //we have to assume that the evidence of conddomain is fully observed //otherwise, we can't sample any trajectory traj.AddTransition(nextindex[numofnodes].Values(), nextindex[numofnodes].Time()); ++nextindex[numofnodes]; currt = nextt; } UndefineTime(nextid, undefinelist); for (unsigned int k=0; kValues( dynamic_cast(p)->GetDynamics()->Domain(), currt); //Sample initial values // notes by Jing: for network application: only use the dynamic's weight double weight = 0.0; if (discard_bn_weight) SampleInitial(curri); else weight = SampleInitial(curri); traj.AddTransition(curri, begintime); weight += SampleDyn(traj); //cout << weight << endl; return logscale ? weight : exp(weight); } } // end of ctbn namespace ctbnrle.1.1.1/src/markovdyn.cc0000664000175000017500000000250312107032416016472 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" namespace ctbn { SOBJCLASSDEF(MarkovDyn) MarkovDyn::MarkovDyn(const Context &var, const Context &cvar, bool is_toggle) : DynComp(var,cvar,(is_toggle? tmbase:mbase)) { } MarkovDyn::MarkovDyn(std::istream &is) : DynComp(is) { } MarkovDyn::~MarkovDyn() { } MarkovDyn *MarkovDyn::Clone() const { return new MarkovDyn(*this); } const MarkovSimple *MarkovDyn::mbase = new MarkovSimple(); const MarkovSimpleToggle *MarkovDyn::tmbase = new MarkovSimpleToggle(); } // end of ctbn namespace ctbnrle.1.1.1/src/dyncomp.cc0000664000175000017500000003016212107032416016133 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "dyncomp.h" #include "markovsimple.h" namespace ctbn { using namespace std; SOBJCLASSDEF(DynCompSS) DynCompSS::DynCompSS(int n) : SS(), ss(n) { } DynCompSS::DynCompSS(istream &is) : SS(is) { LoadOld(is); } DynCompSS::DynCompSS(const DynCompSS &dcss) : SS(dcss) { int n = dcss.ss.size(); for(int i=0;iClone()); } DynCompSS &DynCompSS::operator=(const DynCompSS &dcss) { if (&dcss==this) return *this; SS::operator=(dcss); int n = ss.size(); for(int i=0;iClone(); return *this; } DynCompSS::~DynCompSS() { int n = ss.size(); for(int i=0;i> n; for(int i=0;i(StreamObj::LoadOldPtr(is)); ss.push_back(ssptr); } } void DynCompSS::SaveOld(ostream &os) const { int n = ss.size(); os << n; for(int i=0;iSaveOldPtr(os); } } void DynCompSS::Scale(double w) { int n = ss.size(); for(int i=0; iScale(w); } void DynCompSS::AddSS(const SS *nss, double w) { int n = ss.size(); const DynCompSS *dynss = dynamic_cast(nss); for(int i=0; iAddSS(dynss->ss[i], w); } double DynCompSS::Element(std::vector index, int s1, int s2) const { double ret = 0.0; int n = ss.size(); for(int i=0; i(ss[i])-> Element(s1, s2); } return ret; } SOBJCLASSDEF(DynComp) DynComp::DynComp(const Context &var, const Context &cvar, const DynSimple *base) : Dynamics(var,cvar), impl(cvar.Size()) { for(int i=0;iMakeNew(var.Size()); } DynComp::DynComp(istream &is) : Dynamics(is), impl(CondDomain().Size()) { int n = impl.size(); for(int i=0;i(StreamObj::LoadOldPtr(is)); } DynComp::DynComp(const DynComp &dc) : Dynamics(dc), impl(dc.impl.size()) { for(unsigned int i=0;iClone(); } DynComp::~DynComp() { for(unsigned int i=0;i(StreamObj::LoadOldPtr(is)); } void DynComp::SaveOld(ostream &os) const { Dynamics::SaveOld(os); int n = impl.size(); for(int i=0;iSaveOldPtr(os); } } DynComp &DynComp::operator=(const DynComp &dc) { if (&dc != this) { for(unsigned int i=0;iClone(); } return *this; } DynComp *DynComp::Clone() const { return new DynComp(*this); } void DynComp::Normalize() { int n = impl.size(); for(int i=0;iNormalize(); } void DynComp::Restrict(const Instantiation &x) { if (impl.size()==1) { vector ids; Domain().ConsistentIndexes(ids,x); impl[0]->Restrict(ids); } else { vector condi; CondDomain().ConsistentIndexes(condi,x); int ci=0; int n = impl.size(); for(int i=0;i ids; Domain().ConsistentIndexes(ids,x); impl[i]->Restrict(ids); ++ci; } else { vector ids; impl[i]->Restrict(ids); } } } } void DynComp::Mult(const Dynamics *x) { const DynComp *xx = dynamic_cast(x); assert(xx != NULL); // otherwise, I'm not sure what to do // update domains (and save old ones): Context oldv(Domain()); Context oldcv(CondDomain()); Dynamics::Mult(x); vector newimpl(CondDomain().Size()); for(unsigned int i=0;iMakeNew(Domain().Size()); Instantiation v(Domain()+CondDomain(),0); Context anycond(oldcv,xx->CondDomain(),Context::UNION); Context otherc = CondDomain() - oldcv; Context notc = v - oldcv; do { int myci = oldcv.Index(v); vector > myind(oldv.Size()); do { int myi = oldv.Index(v); int i = Domain().Index(v); myind[myi].push_back(i); } while(v.Inc(notc)); impl[myci]->Expand(myind,Domain().Size()); do { int ci = CondDomain().Index(v); newimpl[ci]->Mult(impl[myci]); } while(v.Inc(otherc)); } while(v.Inc(oldcv)); otherc = CondDomain() - xx->CondDomain(); notc = v - xx->CondDomain(); do { int xci = xx->CondDomain().Index(v); vector > xind(xx->Domain().Size()); do { int xi = xx->Domain().Index(v); int i = Domain().Index(v); xind[xi].push_back(i); } while(v.Inc(notc)); DynSimple *ds = xx->impl[xci]->Clone(); ds->Expand(xind,Domain().Size()); do { int ci = CondDomain().Index(v); newimpl[ci]->Mult(ds); } while(v.Inc(otherc)); delete ds; } while(v.Inc(xx->CondDomain())); for(unsigned int i=0;i i; Domain().ConsistentIndexes(i,x); return impl[condi]->CondRestrict(t0,t1,i); } RVCondSimple *DynComp::Cond(double t, const Instantiation &from, const Instantiation &to, bool transition) const { int condi = CondIndex(from); vector fromi,toi; Domain().ConsistentIndexes(fromi,from); Domain().ConsistentIndexes(toi,to); return impl[condi]->CondRestrict(t,fromi,toi,transition); } DynCompSS *DynComp::BlankSS() const { int nc = impl.size(); DynCompSS *ret = new DynCompSS(nc); for(int i=0;iss[i] = impl[i]->BlankSS(); return ret; } void DynComp::AddSS(const Instantiation &x, double t0, double deltat, SS *ss, double w) const { DynCompSS *dcss = dynamic_cast(ss); int condi = CondIndex(x); int i = Domain().Index(x); impl[condi]->AddSS(i,t0,deltat,dcss->ss[condi],w); } void DynComp::AddTransSS(const Instantiation &x1, const Instantiation &x2, double t, SS *ss, double w) const { DynCompSS *dcss = dynamic_cast(ss); // assume conditioning variables are the same... int condi = CondIndex(x1); int i1 = Domain().Index(x1), i2 = Domain().Index(x2); // cout << "condi: " << condi << endl; // cout << "i1: " << i1 << endl; // cout << "i2: " << i2 << endl; // x1.SaveOld(cout); cout << endl; // x2.SaveOld(cout); cout << endl; // cout << endl; if(i1!=i2) //add by Yu impl[condi]->AddTransSS(i1,i2,t,dcss->ss[condi],w); } void DynComp::AddExpSS(const RV *alpha, const RV *beta, double t0, double deltat, SS *ss, double w) const { DynCompSS *dcss = dynamic_cast(ss); RV *aa = alpha->Clone(); aa->Project(Domain(),CondDomain()); RV *bb = beta->Clone(); bb->Project(Domain(),CondDomain()); Instantiation ci(CondDomain(),0); do { // assuming that a and b have the same distribution over // the conditioning variables... RVSimple *a = aa->MakeSimple(ci,false); double ww = w*a->Sum(); if (ww<=0.0) continue; a->Normalize(); RVSimple *b = bb->MakeSimple(ci,true); int cind = ci.Index(); impl[cind]->AddExpSS(a,b,t0,deltat,dcss->ss[cind],ww); delete a; delete b; } while(ci.Inc()); delete aa; delete bb; } void DynComp::AddExpTransSS(const RV *x1, const RV *x2, const Context &changevar, double t, SS *ss, double w) const { if (!changevar.IsOverlap(Domain())) return; DynCompSS *dcss = dynamic_cast(ss); RV *aa = x1->Clone(); aa->Project(Domain(),CondDomain()); RV *bb = x2->Clone(); bb->Project(Domain(),CondDomain()); RV *acond = x1->Clone(); acond->Project(CondDomain(),Context()); RV *bcond = x2->Clone(); bcond->Project(CondDomain(),Context()); acond->MultBy(bcond); acond->Normalize(); Instantiation ci(CondDomain(),0); do { RVSimple *a = aa->MakeSimple(ci,false); double ww = w*acond->Prob(ci); if (ww<=0.0) continue; a->Normalize(); RVSimple *b = bb->MakeSimple(ci,true); int cind = ci.Index(); impl[cind]->AddExpTransSS(a,b,t,dcss->ss[cind],ww); delete a; delete b; } while(ci.Inc()); delete acond; delete bcond; delete aa; delete bb; } void DynComp::AddExpSS(const Instantiation &condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w) const { AddExpSS(CondDomain().Index(condi),alpha,beta,t0,deltat,ss,w); } void DynComp::AddExpTransSS(const Instantiation &condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w) const { AddExpTransSS(CondDomain().Index(condi),x1,x2,changevar,t,ss,w); } void DynComp::AddExpSS(int condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w) const { DynCompSS *dcss = dynamic_cast(ss); impl[condi]->AddExpSS(alpha,beta,t0,deltat,dcss->ss[condi],w); } void DynComp::AddExpTransSS(int condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w) const { if (!changevar.IsOverlap(Domain())) return; DynCompSS *dcss = dynamic_cast(ss); impl[condi]->AddExpTransSS(x1,x2,t,dcss->ss[condi],w); } void DynComp::AddSS(const SS *toadd, const Dynamics *dyn, SS *ss, double w) const { const DynComp *d = dynamic_cast(dyn); assert(d!=NULL); assert(CondDomain().IsSubset(d->CondDomain())); DynCompSS *dcss = dynamic_cast(ss); const DynCompSS *toadddcss = dynamic_cast(toadd); Instantiation i(Domain()+CondDomain(),0); do { int mycondi = CondDomain().Index(i); int dcondi = d->CondDomain().Index(i); vector > mapping(Domain().Size()); do { d->Domain().ConsistentIndexes(mapping[Domain().Index(i)],i); } while(i.Inc(Domain())); impl[mycondi]->AddSS(toadddcss->ss[dcondi],d->impl[dcondi], mapping,dcss->ss[mycondi],w); } while(i.Inc(CondDomain())); } void DynComp::SampleNextEvent(const Instantiation &i, double t, double &nextt, Instantiation &nexti, Random &rand) const { int condi = CondIndex(i); int ii = Domain().Index(i); int nextii; impl[condi]->SampleNextEvent(ii,t,nextt,nextii,rand); Instantiation newi(Domain()); newi.SetIndex(nextii); nexti.SetVal(newi,true); } void DynComp::Maximize(const SS *ss) { const DynCompSS *dcss = dynamic_cast(ss); int nc = impl.size(); for(int i=0;iMaximize(dcss->ss[i]); } void DynComp::Scramble(double a, double b, double alpha, double degree, Random &rand) { //by Yu int nc = impl.size(); for(int i=0;iScramble(a, b, alpha, degree, rand); } double DynComp::LLH(const SS *ss) const { const DynCompSS *dynss = dynamic_cast(ss); double llh = 0.0; int nc = impl.size(); for(int i=0; iLLH(dynss->ss[i]); return llh; } double DynComp::GetScore(double numTrans, double amtTime, const SS* ss) const { const DynCompSS *dynss = dynamic_cast (ss); double localScore(0); int nc = impl.size(); // Sum over all possible parent instantiations for(int i = 0; i < nc; i++) localScore += impl[i]->GetScore(nc, numTrans, amtTime, dynss->ss[i]); return localScore; } } // end of ctbn namespace ctbnrle.1.1.1/src/uniformizedfactoredinf.cc0000664000175000017500000001070112107032416021217 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "uniformizedfactoredinf.h" #include namespace ctbn { using namespace std; UniformizedFactoredInf::UniformizedFactoredInf() : FBInf() { lval = 10; thval = 10; } UniformizedFactoredInf::~UniformizedFactoredInf() throw() {} UniformizedFactoredInf * UniformizedFactoredInf::Clone() const { UniformizedFactoredInf * cloneInf = new UniformizedFactoredInf(*this); CloneTo(cloneInf); cloneInf->lval = lval; cloneInf->thval = thval; return cloneInf; } FactoredMatrix * UniformizedFactoredInf::IntervalPropagator(Dynamics * const the_markov_process_dynamics, double start_time_point, double past_end_time_point, Instantiation const & current_evidence) const { FactoredMatrix * m = new FactoredMatrix(the_markov_process_dynamics); m->SetL(lval); m->SetTheta(thval); m->Cond(start_time_point, past_end_time_point, current_evidence); return m; } FactoredMatrix * UniformizedFactoredInf::PointTransitionPropagator(Dynamics * const the_markov_process_dynamics, double time_point, const Instantiation & prior_evidence, const Instantiation & current_evidence) const { FactoredMatrix * m = new FactoredMatrix(the_markov_process_dynamics); m->SetL(lval); m->SetTheta(thval); m->Cond(time_point, prior_evidence, current_evidence, true); return m; } FactoredMatrix * UniformizedFactoredInf::PointChangeEvidencePropagator(Dynamics * const the_markov_process_dynamics, double time_point, const Instantiation & prior_evidence, const Instantiation & current_evidence) const { FactoredMatrix * m = new FactoredMatrix(the_markov_process_dynamics); m->SetL(lval); m->SetTheta(thval); m->Cond(time_point, prior_evidence, current_evidence, false); return m; } void UniformizedFactoredInf::Restrict(RVSimple * a, Instantiation const & variable_assignment) const { vector cind; CTBNDyn *cdyn = dynamic_cast(the_markov_process_dynamics()); for (int i=0; iNumofNodes(); i++){ cind.clear(); (cdyn->Node(i)->Domain()).ConsistentIndexes(cind, variable_assignment); //dynamic_cast(a)->Restrict(i,cind); if(cind.size() < (unsigned int)cdyn->Node(i)->Domain().Size()) { for(unsigned int j=0; j(a)->RestrictById(i, cind[j]); } } } } //FactoredVector * UniformizedFactoredInf::MakeSimple(Instantiation const & x, bool normalize) { RVSimple * UniformizedFactoredInf::MakeSimple(RV * prior, Instantiation const & x, bool normalize) const { // FactoredVector * ret = new FactoredVector(the_initial_distribution_of_the_markov_process()); FactoredVector * ret = new FactoredVector(prior); FactoredVector * & ref_to_ptr_to_approximation_vector(ret); Restrict(ref_to_ptr_to_approximation_vector, x); if (normalize){ ret->Normalize(); } return ret; } RVSimple * UniformizedFactoredInf::Convert(RVCondSimple *&rvcond, RVSimple *&rv){ return rv->Clone(); } void UniformizedFactoredInf::Restrict(FactoredVector * & rv, Instantiation const & x) const { CTBNDyn * ctbndyn = dynamic_cast(the_markov_process_dynamics()); int n = ctbndyn->NumofNodes(); for (int i=0; i ind; ctbndyn->NodeByVar(i)->Domain().ConsistentIndexes(ind, x); if (ind.size() == 1){ rv->RestrictById(i, ind.at(0)); } } //dynamic_cast(rv)->Print(cout, nullptr03); } bool UniformizedFactoredInf::IsAlphaElementValid(RVSimple * const alpha_sub_x) { FactoredVector * as_factored_vector(dynamic_cast(alpha_sub_x)); // TODO: consider if there is a tighter validity condition to be checked here. bool ret = (nullptr03 != as_factored_vector); return ret; } } // end of ctbn namespace ctbnrle.1.1.1/src/matrix-noeigen.cc0000664000175000017500000003722112107032416017413 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "matrix.h" #include #include namespace ctbn { using namespace std; // adopted from NRiC, pg 45 matrix matrix::inv(bool &worked) const { if (m!=n) { cerr << "invalid matrix inverse" << endl; exit(1); } matrix temp(n,n); int *ix = new int[n]; if (!LUdecomp(temp,ix)) { worked = false; delete []ix; return matrix(n,n,0,false); } worked = true; matrix ret(n,n); int i,j; double *col = new double[n]; for(j=0;j=0); if ((detsign=LUdecomp(temp,ix))==0) { delete []ix; return matrix(n,n,0,false); } logdet = 0.0; if (dodet) { for(int i=0;i(e+1)?0:(e+1)); temp /= (pow(2.0,s)); matrix X(temp); matrix cX(n,n); double c = 0.5; matrix D(eye-temp*c); E=eye+temp*c; q = 6; p = 1; for (k=2;k<=q;k++) { c = c*(q-k+1)/(k*(2*q-k+1)); X = temp*X; cX = X*c; E += cX; if(p) D += cX; else D -= cX; p = !p; } //solve for F in DF = E matrix F = E * D.inv(); for (k=1;k<=s;k++) { F = F*F; } return F; } void matrix::sparsepremult(vectr &v) const { vectr c(v); v = 0.0; for(int i=0,s=0;i=big) { big = dum; imax = i; } } if (j!=imax) { for(k=0;k=0;i--) { sum = b[i]; for(int j=i+1;j<=n-1;j++) sum -= x[i*n+j]*b[j]; b[i] = sum/x[i*n+i]; } } double *matrix::solve(const double *b, bool &worked) const { if (m!=n) { cerr << "invalid matrix in solve" << endl; exit(1); } double *ret = new double[n]; for(int i=0;iabsb) { double sqr = absb/absa; return absa*sqrt(1.0+sqr*sqr); } else { if (absb==0.0) return 0; double sqr = absa/absb; return absb*sqrt(1.0+sqr*sqr); } } #define SIGN(a,b) ((b) > 0.0 ? fabs(a) : -fabs(a)) // adopted from pg 67 of NRiC void matrix::svd(matrix &u, matrix &v, vectr &w) const { u = *this; if (v.n!=n || v.m!=n) { delete []v.x; v.n = n; v.m = n; v.s = n*n; v.x = new double[v.s]; } if (w.m!=n) { delete []w.x; w.m = n; w.x = new double[n]; } int flag,i,its,j,jj,k,l,nm; double anorm,c,f,g,h,s,scale,x,y,z,*rv1; rv1 = new double[n]; g=scale=anorm=(double)0; for(i=0;itemp ? anorm : temp; } for(i=n-1;i>=0;i--) { if (in?n-1:m-1;i>=0;i--) { l=i+1; g=w[i]; for(j=l;j=0;k--) { for(its=1;its<=30;its++) { flag = 1; for(l=k;l>=0;l--) { nm = l-1; if ((double)(fabs(rv1[l])+anorm)==anorm) { flag = 0; break; } if ((double)(fabs(w[nm])+anorm)==anorm) break; } if (flag) { c = (double)0; s = (double)1; for(i=l;i<=k;i++) { f = s*rv1[i]; rv1[i] = c*rv1[i]; if ((double)(fabs(f)+anorm)==anorm) break; g = w[i]; h = pythag(f,g); w[i] = h; h = 1/h; c = g*h; s = -f*h; for(j=0;j max) { max = fabs(retval[i][j]); maxi = i; } } if(j != lastj && max == 0.0) { // leave this column for last lastj = j; if(j != m-1) continue; } if(maxi == -1) { cout << "oops"; // NaN input return 0; } i = maxi; pivot = retval[i][j]; for(i0 = 0; i0 < m; i0++) { if(i0 != i) { for(j0 = 0; j0 < m; j0++) { if(j0 != j) { retval[i0][j0] *= pivot; retval[i0][j0] -= retval[i0][j] * retval[i][j0]; retval[i0][j0] /= D; } } } } for(i0 = 0; i0 < m; i0++) { retval[i0][j] = -retval[i0][j]; } retval[i][j] = D; D = pivot; r[i] = j; c[j] = i; if(j == lastj) break; if(j == m-1 && lastj >= 0) // go back and do the column we left for last j = lastj - 1; } int s=0; i = 0; memcpy(r2, r, m * sizeof(int)); while(i < m-1) { // find sign of permutation (i just hacked this up, might be done quicker) j = r2[i]; if(i != j) { s++; r2[i] = r2[j]; r2[j] = j; } else i++; } //unpermute rows and columns for(i = 0; i < m; i++) for(j = 0; j < m; j++) x[i*n+j] = retval[c[i]][r[j]]; if(s%2 == 1) { negate(); D = -D; } delete [] r; delete [] r2; delete [] c; return D; } double matrix::trace() { assert(n == m); double sum = 0.0; for(int i = 0; i < n; i++) { sum += x[i*n+i]; } return sum; } // from NRiC, pg 474 void matrix::tridiag(vectr &d, vectr &e) { // matrix must also be symmetric, but we aren't going to check that if (n!=m) { cerr << "must be square matrix in tridiag" << endl; exit(1); } if (d.m != n) { if (d.x) delete []d.x; d.x = new double[n]; d.m = n; } if (e.m != n) { if (e.x) delete []e.x; e.x = new double[n]; e.m = n; } double *xi = x+(n-1)*n; for (int i=n-1;i>=1;i--,xi-=n) { int l = i-1; double h=0.0,scale=0.0; if (l>0) { for(int k=0;k<=l;k++) scale += fabs(xi[k]); if (scale==0.0) e.x[i] = xi[l]; else { for (int k=0;k<=l;k++) { xi[k] /= scale; h += xi[k]*xi[k]; } double f = xi[l]; double g = (f>=0.0 ? -sqrt(h) : sqrt(h)); e.x[i] = scale*g; h -= f*g; xi[l] = f-g; f = 0.0; double *xj = x; for(int j=0;j<=l;j++,xj+=n) { // next line can be omitted if evectors not needed xj[i] = xi[j]/h; g = 0.0; for(int k=0;k<=j;k++) g += xj[k]*xi[k]; double *xk = x+(j+1)*n; for(int k=j+1;k<=l;k++,xk+=n) g += xk[j]*xi[k]; e.x[j] = g/h; f += e.x[j]*xi[j]; } double hh = f/(h+h); xj = x; for(int j=0;j<=l;j++,xj+=n) { f = xi[j]; g = e.x[j]-hh*f; e.x[j]=g; for(int k=0;k<=j;k++) xj[k] -= (f*e.x[k]+g*xi[k]); } } } else e.x[i] = xi[l]; d.x[i] = h; } // next line can be omitted if evectors not needed d.x[0]=0.0; e.x[0]=0.0; xi = x; // the whole loop (excepting noted line) can be omitted // if evectors not needed for(int i=0;i=l;i--) { double f = s*e.x[i]; double b = c*e.x[i]; e.x[i+1] = (r=pythag(f,g)); if (r==0.0) { d.x[i+1] -= p; e.x[mm] = 0.0; break; } s = f/r; c = g/r; g = d.x[i+1]-p; r =(d.x[i]-g)*s+2.0*c*b; d.x[i+1] = g+(p=s*r); g = c*r-b; // this loop may be omitted if evectors not needed double *xk = x; for (int k=0;k=l) continue; d.x[l] -= p; e.x[l] = g; e.x[mm] = 0.0; } } while(mm!=l); //cout << l << ": " << iter << endl; } } void matrix::permutebydiag(vector &revperm, matrix &out) const { if (n!=m) { cerr << "permutebydiag currently requires a square matrix" << endl; exit(1); } vector perm(n); revperm.resize(n); double *diag = new double[n]; int *dind = new int[n]; double *xii = x; for(int i=0;i rperm; matrix permcpy; permutebydiag(rperm,permcpy); vectr d,e; permcpy.tridiag(d,e); permcpy.tridiagev(d,e); evectr = matrix(permcpy,rperm); return vectr(d,rperm); } } // end of ctbn namespace ctbnrle.1.1.1/src/clique.cc0000664000175000017500000003401312107032416015743 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "clique.h" #include "nullptr03.h" #include "params.h" #include namespace ctbn { using namespace std; CliqueTree::CliqueTree() : Head_compare(nullptr03), numrandrestarts(ParamInt("CliqueTreeRestarts",100)), Head(nullptr03), unit_test(false) { } CliqueTree::CliqueNode::CliqueNode() { prev = nullptr03; } CliqueTree::CliqueNode::~CliqueNode() { } void CliqueTree::CliqueNode::AddVar(const int &varid) { connected.insert(varid); } void CliqueTree::CliqueNode:: print(ostream &os) { set ::iterator it; for(it = connected.begin(); it != connected.end(); it ++) { os << *it << " "; } os << endl; } void CliqueTree::CliqueNode::serial_preload() { prev = nullptr03; } void CliqueTree::CliqueNode::serial_postload() { for(unsigned int i=0;iprev = this; } //destructor CliqueTree::~CliqueTree() { Destroy(Head); Destroy(Head_compare); } void CliqueTree::BuildClique(const Markov &process, Random &rand) { vars = Context(); data.clear(); Destroy(Head_compare); Head_compare = nullptr03; AddFromBN(dynamic_cast(process.GetStartDist())); AddFromCTBNDyn(dynamic_cast(process.GetDynamics())); BuildIt(rand); } void CliqueTree::BuildClique(const BN *bn, Random &rand) { vars = Context(); data.clear(); Destroy(Head_compare); Head_compare = nullptr03; AddFromBN(bn); BuildIt(rand); } void CliqueTree::BuildClique(const CTBNDyn *dyn, Random &rand) { vars = Context(); data.clear(); Destroy(Head_compare); Head_compare = nullptr03; AddFromCTBNDyn(dyn); BuildIt(rand); } void CliqueTree::BuildClique(const vector & Basis, Random & rand) { vars = Context(); data.clear(); Destroy(Head_compare); Head_compare = nullptr03; AddFromContext(Basis); BuildIt(rand); } void CliqueTree::AddFromContext(const vector & Basis) { for(unsigned int j = 0; j < Basis.size(); j ++) { CliqueNode newitem; vector varids = Basis[j].VarList(); if (varids.size()==0) continue; for(vector::iterator i=varids.begin(); i!=varids.end();++i) { newitem.AddVar(*i); vars.AddVarCheck(*i,Basis[j].Cardinality(*i)); } data.push_back(newitem); } } void CliqueTree::AddFromBN(const BN *bn) { int n = bn->NumofNodes(); for(int i = 0; i < n; i++) { CliqueNode newitem; Context allvar = bn->Node(i)->Domain() + bn->Node(i)->CondDomain(); vector varids = allvar.VarList(); if (varids.empty()) continue; for(vector::iterator i=varids.begin(); i!=varids.end();++i) { newitem.AddVar(*i); // AddVarSize(*i,allvar.Cardinality(*i)); vars.AddVarCheck(*i,allvar.Cardinality(*i)); } data.push_back(newitem); } } void CliqueTree::AddFromCTBNDyn(const CTBNDyn *dyn) { int n = dyn->NumofNodes(); for(int i = 0; i < n; i++) { CliqueNode newitem; Context allvar = dyn->Node(i)->Domain() + dyn->Node(i)->CondDomain(); vector varids = allvar.VarList(); for(vector::iterator i=varids.begin(); i!=varids.end();++i) { newitem.AddVar(*i); // AddVarSize(*i,allvar.Cardinality(*i)); vars.AddVarCheck(*i,allvar.Cardinality(*i)); } data.push_back(newitem); } } /* int CliqueTree::VarSize(int varid) const { map::const_iterator loc = context_size.find(varid); if (loc==context_size.end()) return 0; return loc->second; } void CliqueTree::AddVarSize(int varid, int size) { map::iterator loc = context_size.lower_bound(varid); if (loc != context_size.end() && loc->first==varid) { if (loc->second==size) return; cerr << "inconsistent model input to CliqueTree: variable sizes do not agree" << endl; exit(1); } context_size.insert(loc,make_pair(varid,size)); } */ void CliqueTree::BuildIt(Random &rand) { save_data = data; double max_compare = 0; double maximum; //run the clique tree build on random arrangement as it set for(int i = 0; i < numrandrestarts; i++) { CliqueTrees(rand); maximum = MaxSize(); //get the better clique tree if(maximum < max_compare || i == 0) { Destroy(Head_compare); Head_compare = Head; Head = nullptr03; max_compare = maximum; } else { Destroy(Head); Head = nullptr03; } } } //remove all the database; void CliqueTree::Destroy(CliqueNode * place) { if(place) { for(unsigned int i = 0; i < place->next.size(); i++) { Destroy(place->next[i]); } delete place; } } //remove the item from the vector while generating void CliqueTree::RemoveElem(vector & random_clique, int remove) { if((random_clique.size()-1) != (unsigned)remove ) { random_clique[remove] = random_clique[random_clique.size()-1]; } random_clique.pop_back(); } //building a clique_tree base on the node with same //context connected and combine into the tree //eliminate the used node from the list //run it til all the node in the data vector is gone or //all the number has been called void CliqueTree::CliqueTrees(Random &rand) { data = save_data; clique_built.clear(); int i = 0; vector random_clique = vars.VarList(); /* vector random_clique; for(map::iterator i=context_size.begin(); i!=context_size.end();++i) { random_clique.push_back(i->first); } */ while (data.size() > 0) { i = unit_test ? 0 : rand.RandInt(random_clique.size()); CliqueTrees(random_clique[i]); RemoveElem(random_clique, i); } Eliminate(); } void CliqueTree::CliqueTrees(int search) { vector node_exist; set extend; set extend_out; extend.insert(search); set ::iterator it; for(unsigned int j = 0; j < data.size(); j++ ) { if(data[j].connected.find(search) != data[j].connected.end()) { node_exist.push_back(j); } } if(node_exist.size() > 0) { for(unsigned int j = 0; j < node_exist.size(); j ++) { set ::iterator it; for(it = data[node_exist[j]].connected.begin(); it != data[node_exist[j]].connected.end(); it ++) { if(extend.find(*it) == extend.end()) { extend.insert(*it); extend_out.insert(*it); } } } if(extend_out.size() != 0) { CliqueNode * newitem = new CliqueNode(); newitem->connected = extend; Dequeue(node_exist, newitem); if(data.size() > 0) { CliqueNode newitem2; newitem2.connected = extend_out; newitem2.prev = newitem; data.push_back(newitem2); } clique_built.push_back(newitem); } else { CliqueNode * newitem = new CliqueNode(); newitem->connected = extend; Dequeue(node_exist, newitem); } } } //set up the head and remove all the duplicate node within the tree void CliqueTree::Eliminate() { if(clique_built.size() > 0) { CliqueNode * find_head = clique_built[0]; while(find_head->prev != nullptr03) { find_head = find_head->prev; } Head = find_head; EliminateDups(Head); clique_built.clear(); data = save_data; } } //eliminate the duplicate with in clique tree // returns whether the previous node was eliminated // (because if it was, then the calling function needs to know!) bool CliqueTree::EliminateDups(CliqueNode * checking) { bool ret = false; if(checking != Head) { if(checking->connected.size() >= checking->prev->connected.size()) { if(includes(checking->connected.begin(), checking->connected.end(), checking->prev->connected.begin(), checking->prev->connected.end())) { EliminatePrev(checking); ret = true; } } } for(unsigned int i = 0; i < checking->next.size(); i++) { // if checking was removed, then I cannot continue // (after removal, other "nexts" will have been checked // by recursive call, so it is okay) if (EliminateDups(checking->next[i])) return ret; } return ret; } void CliqueTree::EliminatePrev(CliqueNode * current) { CliqueNode * delet = current->prev; if(delet == Head) { Head = current; for(unsigned int i = 0;i < delet->next.size(); i++) { if(delet->next[i] != current) { delet->next[i]->prev = current; current->next.push_back(delet->next[i]); } } current->prev = nullptr03; delete delet; } else { for(unsigned int i = 0;i < delet->next.size(); i++) { if(delet->next[i] != current) { current->next.push_back(delet->next[i]); delet->next[i]->prev = current; } } current->prev = delet->prev; //current->prev->next.push_back(current); for(unsigned int i = 0;i < current->prev->next.size(); i++) { if(current->prev->next[i] == delet) { current->prev->next[i] = current; } } delete delet; } } //remove the unnecessary node from the clique tree void CliqueTree::Dequeue(vector remove, CliqueNode * newitem) { for(int i = remove.size()-1; i > -1; i--) { if(data[remove[i]].prev != nullptr03) { newitem->next.push_back(data[remove[i]].prev); data[remove[i]].prev->prev = newitem; } if(&data[remove[i]].connected != &data[data.size()-1].connected) { data[remove[i]] = data[data.size()-1]; } data.pop_back(); } } //return the largest value to get the best tree double CliqueTree::MaxSize() const { return MaxSize(Head); } //return the maximum size in the tree by looking into the each node double CliqueTree::MaxSize(CliqueNode * place) const { double ret = 0; if(place) { int compare_size = 1; for(set ::iterator i = place->connected.begin(); i != place->connected.end(); ++i) { // compare_size = compare_size * VarSize(*i); compare_size = compare_size * vars.Cardinality(*i); } ret = compare_size; for(unsigned int i = 0; i < place->next.size(); ++i) { double temp = MaxSize(place->next[i]); if (temp>ret) ret = temp; } } return ret; } //print the clique tree to stream os void CliqueTree::CliquePrint(ostream &os) { CliqueNodePrint(Head_compare, 0, os); } void CliqueTree::CliqueNodePrint(CliqueNode * print, int size, ostream &os) { int a = size; if(print) { size ++; for(unsigned int i = 0; i < print->next.size(); ++i) { CliqueNodePrint(print->next[i], size, os); } while(a > 0) { os << " "; a--; } if(print->prev != nullptr03) { prints(print->connected,os); os << "("; prints(print->prev->connected,os); os << ")" << endl << endl; } else { prints(print->connected,os); os << endl << endl; } } } void CliqueTree::prints(set print_out, ostream &os) const { for(set ::iterator i = print_out.begin(); i != print_out.end(); ++i) { os << *i << " "; } } //return the CliqueTree tree by CliqueTree::cliques; vector CliqueTree::ReturnCliques() const { vector ret; ReturnClique(Head_compare, 0, ret); return ret; } void CliqueTree::ReturnClique(CliqueNode * place, int parent, vector &ret) const { if(place) { InsertCliqueNode(place, parent, ret); int myspot = ret.size(); for(unsigned int i = 0; i < place->next.size(); ++i) { ReturnClique(place->next[i], myspot, ret); } } } void CliqueTree::InsertCliqueNode(CliqueNode * place, int parent, vector &ret) const { Node new_node; new_node.vars = Clique2Context(place); if(parent>0) { new_node.adj.push_back(parent-1); ret[parent-1].adj.push_back(ret.size()); } ret.push_back(new_node); } Context CliqueTree::Clique2Context(CliqueNode *node) const { Context ret; set::const_iterator e = node->connected.end(); for(set::const_iterator i=node->connected.begin();i!=e;++i) // ret.AddVar(*i,VarSize(*i)); ret.AddVar(*i,vars.Cardinality(*i)); return ret; } void CliqueTree::SaveOld(std::ostream &os) const { set::const_iterator it; for(unsigned int i = 0; i < data.size(); ++i) { for(it = data[i].connected.begin(); it != data[i].connected.end(); it++) { os << *it << os.fill(); } os << -1 <::iterator it; os << size << os.fill(); for(it = print->connected.begin(); it != print->connected.end(); it++) { os << *it << os.fill(); } os << -1 << os.fill(); size ++; for(unsigned int i = 0; i < print->next.size(); ++i) { CliqueSave(os, print->next[i], size); } } } void CliqueTree::LoadOld(std::istream &is) { int get_out; data.clear(); is >> get_out; while (get_out != -100) { CliqueNode newitem; while(get_out != -1 && get_out != -100) { newitem.AddVar(get_out); is >> get_out; } if(get_out != -100) { data.push_back(newitem); is >> get_out; } } CliqueNode * curr_pos = NULL; int get_before = 0; Destroy(Head_compare); int move_back; while(is >> get_out) { if(get_out != -1) { CliqueNode * newitem = new CliqueNode(); if(get_out == 0) { get_before = get_out; while(is >> get_out) { if(get_out == -1) break; newitem->AddVar(get_out); } Head_compare = newitem; curr_pos = Head_compare; } else { if (curr_pos==NULL) exit(1); // file format error if(get_before >= get_out) { move_back = get_before - get_out + 1; for(int i = 0; i < move_back; i++) { curr_pos = curr_pos ->prev; } } get_before = get_out; while(is >> get_out) { if(get_out == -1) break; newitem->AddVar(get_out); } curr_pos->next.push_back(newitem); newitem ->prev = curr_pos; curr_pos = newitem; } } } } void CliqueTree::serial_preload() { Destroy(Head_compare); clique_built.clear(); save_data.clear(); } } // end of ctbn namespace ctbnrle.1.1.1/src/expsuffstatsquery.cc0000664000175000017500000000502612107032416020310 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "expsuffstatsquery.h" #include "nullptr03.h" namespace ctbn { using namespace std; ExpSuffStatsQuery::ExpSuffStatsQuery(Inference* i, bool cloneInf) : SuffStatsQuery(), inf(i), useInfCache(cloneInf) { } void ExpSuffStatsQuery::InitInfCache() { if(infCache.size() == tr->size()) return; for(unsigned int i = 0; i < tr->size(); i++) { infCache.push_back(inf->Clone()); infCache[i]->SetTrajectory(&(*tr)[i]); } } void ExpSuffStatsQuery::SetProcess(const Process* p) { inf->SetProcess(p); for(unsigned int i=0;iBlankSS(); if(useInfCache) { InitInfCache(); for(unsigned int i = 0; i < infCache.size(); i++) { infCache[i]->AddExpSuffStats(p0,ss); } } else { for(unsigned int i = 0; i < tr->size(); i++) { inf->SetTrajectory(&(*tr)[i]); inf->AddExpSuffStats(p0,ss); } } return ss; } else { cerr << "p0 and/or data are not initialized" << endl; exit(0); } } SS* ExpSuffStatsQuery::GetDSS() { if(d && tr) { SS* ss = d->BlankSS(); if(useInfCache) { InitInfCache(); for(unsigned int i = 0; i < infCache.size(); i++) { infCache[i]->AddExpSuffStats(d,ss); } } else { const vector &trj = *tr; for(unsigned int i = 0; i < trj.size(); i++) { inf->SetTrajectory(&trj[i]); inf->AddExpSuffStats(d,ss); } } return ss; } else { cerr << "d and/or data are not initialized" << endl; exit(0); } } ExpSuffStatsQuery::~ExpSuffStatsQuery() { for(unsigned int i = 0; i < infCache.size(); i++) if (infCache[i] != nullptr03) delete infCache[i]; infCache.clear(); delete inf; } } // end of ctbn namespace ctbnrle.1.1.1/src/dynsimple.cc0000664000175000017500000000216212107032416016465 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "dynsimple.h" namespace ctbn { using namespace std; DynSimple::DynSimple() : StreamObj() { } DynSimple::DynSimple(istream &is) : StreamObj() { } DynSimple::~DynSimple() { } void DynSimple::LoadOld(istream &is) { } void DynSimple::SaveOld(ostream &os) const { } } // end of ctbn namespace ctbnrle.1.1.1/src/streamextra.cc0000664000175000017500000000262512107032416017024 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "streamextra.h" #include namespace { char infstring[] = "inf"; char nanstring[] = "NaN"; // the "C" means classic, and it should really be based // on the current locale, but currently the "C" is ignored anyway! nannum_put *putfacet = new nannum_put("C"); nannum_get *getfacet = new nannum_get("C"); } void addnan(std::basic_ios &str) { std::locale withread(str.getloc(),putfacet); std::locale withboth(withread,getfacet); str.imbue(withboth); } ctbnrle.1.1.1/src/ctmdp.cc0000664000175000017500000002622312107032416015574 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "ctmdp.h" #include "params.h" namespace ctbn { using namespace std; CTMDP::CTMDP() { beta = 1.0; resetlpvarmap(); } CTMDP::~CTMDP() { for(unsigned int i=0;i &r) { ActionInfo a; a.dyn = dyn; a.rs = r; actions.push_back(a); } void CTMDP::AddBasis(const Factor &phi) { basis.push_back(phi); } // expands factor h by the dynamics dyn (see paper) CTMDP::Factor CTMDP::ExpandFactor(const CTMDP::Factor &h, const Dynamics *dyn) const { Factor newfactor; const vector &basisVar = h.var.VarList(); // get the union of all the contexts in ctbndyn // which associate with factor h for(unsigned int i = 0; i < basisVar.size(); i++) { const MarkovDyn *node = dynamic_cast (dynamic_cast(dyn) ->NodeByVar(basisVar[i])); newfactor.var = newfactor.var + node->Domain()+node->CondDomain(); } //initiate the instantiation for union of the context vector basisAdj = h.val; forallassign(Instantiation &inst,newfactor.var) { double valueG = 0.0; int placeH, placeH2 = 0; //h_i (x) place placeH = h.var.Index(inst); //sum all the Q which x_j to x_j^' and h_i(x <- x_j^') for(unsigned int k = 0; k < basisVar.size(); k ++) { const MarkovDyn *node = dynamic_cast (dynamic_cast(dyn) ->NodeByVar(basisVar[k])); const matrix &Q = (*node)(inst)->Intensity(); for (int m = 0; m < Q.getn(); m ++) { if(inst.Value(basisVar[k]) != m) { Instantiation newinst(inst); newinst.SetVal(basisVar[k],m); placeH2 = h.var.Index(newinst); int i1 = node->Domain().Index(inst); int i2 = node->Domain().Index(newinst); valueG += Q[i1][i2]*(h.val[placeH2]-h.val[placeH]); } } } valueG -= beta*h.val[placeH]; newfactor.val.push_back(valueG); valueG = 0.0; } return newfactor; } int findClique(const vector &c, const Context &v) { for(unsigned int i=0;i CTMDP::SolveApprox(LinearProgram *lpsolver) const { lpsolver->ClearProblem(); bool norandom = ParamInt("CTMDP_NoRandom",0); // first first set are reserved for the basis function weights: maxvar = basis.size(); set DoneBF; for(unsigned int a = 0; a < actions.size(); a++ ) { // build clique tree for expanded basis factors and reward factors: vector fcon; vector g; for(unsigned int i=0;i cliqueAct = clique_build.ReturnCliques(); /* for(int i=0;i > gloc(cliqueAct.size()); vector > rloc(cliqueAct.size()); for(unsigned int i=0;iDomain().Size(),lpsolver); double value; vector sol = lpsolver->Solve(value); vector ret; for(unsigned int i = 0; i < basis.size(); i++) ret.push_back(sol[i]); resetlpvarmap(); return ret; } void CTMDP::SetApproxOptWeights(int totalsize,LinearProgram *lpsolver) const { vector obji; vector objw; for(unsigned int k=0; k < basis.size(); k++) { obji.push_back(k); double alpha = 0.0; for(unsigned int i=0;iSetObjective(obji,objw); } void CTMDP::writeConstraints(const vector & cliqueAct, const int &a, const int &ci, const int &parent, const std::vector &gs, const std::vector > &gloc, const std::vector > &rloc, LinearProgram *lp) const { writeNodeConstraints(cliqueAct,a,ci,parent,gs,gloc,rloc,lp); for(unsigned int i=0;i & cliqueAct, const int &a, const int &ci, const int &parent, const std::vector &gs, const std::vector > &gloc, const std::vector > &rloc, LinearProgram *lp) const { forallassign(Instantiation &inst,cliqueAct[ci].vars) { vector ix; vector coeff; double cons = 0.0; for(unsigned int j=0;jAddGrEqConstraint(ix,coeff,cons); } } void CTMDP::SetBeta(const double & beta) { this -> beta = beta; } vector CTMDP::SolveExact(LinearProgram *lpsolver) const { const Context &allvars = actions[0].dyn->Domain(); const vector varlist = allvars.VarList(); lpsolver->SetObjective(vector(allvars.Size(),1.0)); for(unsigned int a=0;a ws; vector xs; double qa = 0.0; // we assume that only one variable can change at a // time (if not true, then we need to change this to a // complete loop over all newinst). // We also assume it is a CTBN with MarkovDyn nodes, so // that would also need to be changed foralldiffone_diff(Instantiation &newinst,int chvar,inst) { const MarkovDyn *node = dynamic_cast (dynamic_cast(actions[a].dyn) ->NodeByVar(chvar)); const matrix &Q = (*node)(inst)->Intensity(); xs.push_back(newinst.Index()); double negq = -Q[inst.Value(chvar)][newinst.Value(chvar)]; ws.push_back(negq); qa -= negq; } xs.push_back(i); ws.push_back(beta+qa); lpsolver->AddGrEqConstraint(xs,ws,cons); } } double value; return lpsolver->Solve(value); } vector CTMDP::Policy(const vector &sol, bool isapprox) const { vector ret; forallassign(Instantiation &x,actions[0].dyn->Domain()) ret.push_back(Policy(x,sol,isapprox)); return ret; } int CTMDP::Policy(const Instantiation &x, const vector &sol, bool isapprox) const { int ret=-1; double val=-INFINITY; const Context &allvars = actions[0].dyn->Domain(); const vector varlist = allvars.VarList(); for(unsigned int a=0;a (dynamic_cast(actions[a].dyn) ->NodeByVar(chvar)); const matrix &Q = (*node)(x)->Intensity(); int oldval = x.Value(chvar), newval=xp.Value(chvar); double q = Q[oldval][newval]; qa += q; double vfn = 0; if (!isapprox) vfn = sol[xp.Index()]; else for(unsigned int j=0;jval) { val = cval; ret = a; } } return ret; } vector CTMDP::SolveApprox_exp(LinearProgram * lpsolver) const { lpsolver->ClearProblem(); const Context &allvars = actions[0].dyn->Domain(); const vector varlist = allvars.VarList(); for(unsigned int a=0;a ws(basis.size(),0.0); double qa = 0; // we assume that only one variable can change at a // time (if not true, then we need to change this to a // complete loop over all newinst). // We also assume it is a CTBN with MarkovDyn nodes, so // that would also need to be changed foralldiffone_diff(Instantiation &newinst,int chvar,inst) { const MarkovDyn *node = dynamic_cast (dynamic_cast(actions[a].dyn) ->NodeByVar(chvar)); const matrix &Q = (*node)(inst)->Intensity(); double negq = -Q[inst.Value(chvar)][newinst.Value(chvar)]; qa -= negq; for(unsigned int j=0;jAddGrEqConstraint(ws,cons); } } SetApproxOptWeights(allvars.Size(),lpsolver); double value; return lpsolver->Solve(value); } int CTMDP::getLPVar(int action, int node, int connect, int value) const { IDDP x(action,node,connect,value); map::iterator i = lpvarmap.find(x); if (i==lpvarmap.end()) i = lpvarmap.insert(make_pair(x,maxvar++)).first; return i->second; } void CTMDP::resetlpvarmap() const { lpvarmap.clear(); maxvar = 0; } } ctbnrle.1.1.1/src/samplequeue.cc0000664000175000017500000000460612107032416017014 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "samplequeue.h" #include "defines.h" #include namespace ctbn { SampleQueue::SampleQueue(int maxn) { heap = new Event[maxn]; places = new int[maxn]; n = 0; } SampleQueue::~SampleQueue() { delete []heap; delete []places; } bool SampleQueue::Head(Event &e) { if (n<=0) return false; e = heap[0]; return true; } void SampleQueue::Add(const Event &e) { heap[n] = e; ++n; double key = e.time; int i; for(i=n-1;i>0 && key < heap[(i-1)/2].time;i = (i-1)/2) { heap[i] = heap[(i-1)/2]; places[heap[i].var] = i; } heap[i] = e; places[e.var] = i; } void SampleQueue::Remove(int var) { int i = places[var]; if (i==0) { --n; if (n>0) { heap[0] = heap[n]; places[heap[0].var] = 0; Heapify(0,heap[0].time); } } else { i = Heapify(i,INFINITY); --n; if (i==n) return; Event temp = heap[n]; while(i>0 && temp.time < heap[(i-1)/2].time) { heap[i] = heap[(i-1)/2]; places[heap[i].var] = i; i = (i-1)/2; } heap[i] = temp; places[temp.var] = i; } } int SampleQueue::Heapify(int i, double tempk) { int l,r,small; double smallt; while(i. */ #include "matrix.h" ctbnrle.1.1.1/src/factoredvector.cc0000664000175000017500000003031612107032416017475 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "factoredvector.h" #include "matrix.h" #include "ctbn.h" #include "ctbndyn.h" #include "context.h" #include "multirv.h" #include "notyetimplementederror.h" #include "nullptr03.h" #include namespace ctbn { using namespace std; static const string the_class_name("FactoredMatrix"); FactoredVector::FactoredVector() : RVSimple() { logf = 0.0; } FactoredVector::FactoredVector(const FactoredVector &v) : RVSimple() { unsigned int n = v.dists.size(); dists.resize(n); for (unsigned int i = 0; i != n; ++i) { dists[i] = v.dists.at(i); } logf = v.logf; } FactoredVector::FactoredVector(RV *rv) : RVSimple() { BN *bn = dynamic_cast(rv); int n = bn->NumofNodes(); dists.resize(n); int domainsize; logf = 0.0; for (int i=0; iNodeByVar(i); domainsize = newrv->Domain().Size(); vectr d(domainsize, -1.0); double logfactor; newrv->operator[](0).GetDist(d, logfactor); dists[i] = d; logf += logfactor; } } FactoredVector::~FactoredVector() { } FactoredVector * FactoredVector::Clone() const { return new FactoredVector(*this); } void FactoredVector::Condition(const map &ev) { map::const_iterator it; int id, val; double tmp; for (it = (ev).begin(); it != ev.end(); it++){ id = (*it).first; val = (*it).second; RestrictById(id, val); } } void FactoredVector::Restrict(const std::vector >&) { cout << "FactoredVector::Restrict not implemented" << endl; } void FactoredVector::RestrictById(int id, int val) { double tmp = dists.at(id)[val]; dists.at(id) = 0.0; dists.at(id)[val] = tmp; } void FactoredVector::RestrictById(int id, int val, double x) { dists.at(id) = 0.0; dists.at(id)[val] = x; } void FactoredVector::MultBy(const RVSimple *x) { const FactoredVector *v = dynamic_cast(x); for(unsigned int i=0; idists[i]); } } void FactoredVector::MakeUniform() { for (unsigned int i=0; i - eps))){ mofsum *= tmp; } } double ret= exp(logf)*mofsum; logf = 0.0; return ret; /* double tmp = 1.0; for (unsigned int i=0; i v[i]) min = v[i]; } for (int i=0; i(&v); const FactoredVector *minfv, *maxfv; double difflogf, maxlogf; double alogf = a->logf + log(x); if(logf < alogf){ minfv = this; maxfv = a; difflogf = alogf - logf; maxlogf = alogf; } else{ minfv = a; maxfv = this; difflogf = logf - alogf; maxlogf = logf; } double expf = exp(difflogf); for (unsigned int i=0; idists.at(i))/expf + maxfv->dists.at(i); dists.at(i) = dists.at(i)/(1+1/expf); } logf = maxlogf + log(1+1/expf); */ /* if (logf!=0.0) { double m = exp(logf); for(unsigned int i=0;i sums(dists.size()); double sprod = 1.0; for(unsigned int i=0;i sums(dists.size()); for(unsigned int i=0;i(x); const FactoredVector *minfv, *maxfv; double difflogf, maxlogf; if(logf < a->logf){ minfv = this; maxfv = a; difflogf = a->logf - logf; maxlogf = a->logf; } else{ minfv = a; maxfv = this; difflogf = logf - a->logf; maxlogf = logf; } double expf = exp(difflogf); for (unsigned int i=0; idists.at(i))/expf + maxfv->dists.at(i); dists.at(i) = dists.at(i)/(1+1/expf); } logf = maxlogf + log(1+1/expf); } void FactoredVector::Mult(double x) { if(x > 0){ logf += log(x); } else if( x == 0){ logf = 0; for (unsigned int i=0; i= 0 &&*/ varid_as_uint < dists.size()) { v = dists[varid_as_uint]; logfactor = logf; } } void FactoredVector::SetDist(const vectr &v, int varid, double logfactor) { unsigned varid_as_uint(varid); if ( varid_as_uint < dists.size()){ dists[varid_as_uint] = v; logf = logfactor; } // cout << __LINE__ << " logf " << logf << endl; /* v.niceprint(cout); double vsum = v.sum(); bool allzero = false; if (vsum == 0){ allzero = true; cout << "sum zero" << endl; for(int j=0; j 0) allzero = false; } } if ( varid_as_uint < dists.size()) {//varid_as_uint >= 0 && if (allzero){ dists[varid_as_uint] = v+1e-10; vsum = 2*(1e-10); } else dists[varid_as_uint] = v; logf = logfactor; if(vsum > 0){ for(uint i=0; i 0) return (dists[varid])[index]/dsum; else return (dists[varid])[index]; } double FactoredVector::GetDistInst(int varid, int index) { return (dists[varid])[index]; } void FactoredVector::SetDistInst(int varid, int index, double value) { (dists[varid])[index] = value; } double FactoredVector::GetMargMin() { double minval = GetMin(0); double tmp = 0.0; for(unsigned int i=1; i tmp) minval = tmp; } return minval; } double FactoredVector::GetJointMin() { double minval = 1.0; for (unsigned int i=0; i fabs((dists[varid])[i])) maxval = (dists[varid])[i]; } return maxval; } int FactoredVector::GetDistSize(int varid) { return (dists[varid]).length(); } int FactoredVector::Size() { return dists.size(); } std::ostream & FactoredVector::Print(std::ostream & out, RV * ignored = nullptr03) const { for (unsigned int i=0; iGetLogf(); out << "FactoredVector logf: " << logf << endl; this->Print(out); return out; } //From RVSimple void FactoredVector::GetDist(vectr &d, double &logfactor) const { throw not_yet_implemented_error(the_class_name, "GetDist"); } void FactoredVector::SetDist(const vectr &d, double logfactor) { throw not_yet_implemented_error(the_class_name, "SetDist"); } double FactoredVector::Prob(int ind, bool log) const{ throw not_yet_implemented_error(the_class_name, "Prob"); } void FactoredVector::Load(std::istream &is) { throw not_yet_implemented_error(the_class_name, "Load"); } void FactoredVector::Save(std::ostream &os) const { throw not_yet_implemented_error(the_class_name, "Save"); } void FactoredVector::Reindex(const std::vector > &ind) { throw not_yet_implemented_error(the_class_name, "Reindex"); } void FactoredVector::Add(const RVSimple *x, double w) { throw not_yet_implemented_error(the_class_name, "Add"); } SS *FactoredVector::BlankSS() const { throw not_yet_implemented_error(the_class_name, "BlankSS"); } void FactoredVector::AddSS(int x, SS *ss, double w) const { throw not_yet_implemented_error(the_class_name, "AddSS"); } void FactoredVector::AddExpSS(SS *ss, double w) const { throw not_yet_implemented_error(the_class_name, "AddExpSS"); } void FactoredVector::AddSS(const SS *toadd, const RVSimple* rvs, const std::vector > &mapping, SS *ss, double w) const { throw not_yet_implemented_error(the_class_name, "AddSS"); } int FactoredVector::Sample(Random &rand) const { throw not_yet_implemented_error(the_class_name, "Sample"); } void FactoredVector::Maximize(const SS *ss) { throw not_yet_implemented_error(the_class_name, "Maximize"); } void FactoredVector::Scramble(double alpha, double degree, Random &rand) { throw not_yet_implemented_error(the_class_name, "Scramble"); } double FactoredVector::LLH(const SS *ss) const { throw not_yet_implemented_error(the_class_name, "LLH"); } double FactoredVector::GetScore(double numTrans, const SS* ss) const { throw not_yet_implemented_error(the_class_name, "GetScore"); } } ctbnrle.1.1.1/src/nonexpsuffstatsquery.cc0000664000175000017500000000265512107032416021030 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "nonexpsuffstatsquery.h" namespace ctbn { using namespace std; NonExpSuffStatsQuery::NonExpSuffStatsQuery() : SuffStatsQuery() { } SS* NonExpSuffStatsQuery::GetP0SS() { if(p0 && tr) { return p0->SuffStats(*tr,vector(tr->size(),1)); } else { cerr << "p0 and/or data are not initialized" << endl; return NULL; } } SS* NonExpSuffStatsQuery::GetDSS() { if(d && tr) { return d->SuffStats(*tr,vector(tr->size(),1)); } else { cerr << "d and/or data are not initialized" << endl; return NULL; } } NonExpSuffStatsQuery::~NonExpSuffStatsQuery() { } } // end of ctbn namespace ctbnrle.1.1.1/src/gibbssampler.cc0000664000175000017500000002001612107032416017131 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include #include "gibbssampler.h" #include "ctbndyn.h" #include "markov.h" #include "markovdyn.h" #include "multisimple.h" #include "bn.h" #include "rk.h" #include "utils.h" #include "params.h" namespace ctbn { using namespace std; // "process" is a pointer to a (subclass of ) Process object. // "evidence" is a pointer to Trajectory object that holds the context. // Variables with value -1 means no evidence. // Interval evidence X(t:t+dt)=x is represented as X(t)=x, X(t+dt)=-1. // Point evidence X(t)=x is represented as X(t)=x, X(t+epsilon)=-1. GibbsSampler::GibbsSampler(const Process *process, const Trajectory *evidence, int burnin) : GibbsBase(process,evidence,burnin) { } GibbsSampler::~GibbsSampler() { } void GibbsSampler::SampleVariableInterval(int v, int x0, int xT, double t0, double tT, Random &rand) const { static double kEpsilon = ParamDouble("GibbsEpsilon",1e-5); if (x0==-1) { // must be initial interval and var not known at beginning Instantiation init_val = SampleConditionalRv(tr.Values(context,t0), t0,v,rand); x0 = init_val.Value(v); tr.AddTransition(v,t0,x0); } // interval too small if (fabs(tT-t0) < 2.0*kEpsilon) { if (x0==xT || xT==-1) // already consistent return; tr.AddTransition(v, (t0+tT)/2.0, xT); return; } vector tau; vector inst; GetTau(tr, t0, tT, v, markovblanket[v], tau, inst); // Sample the time to stay at state x0. // Compute P(future) vectors for {tau} from K back to 0. vector p_future; // normalized distribution vectors vector log_z; // log of the normalization factors of p_future double logfactor; // required by GetDist but not used int tau_size = static_cast(tau.size()); int vdim = context.Cardinality(v); for (int i=0; i!=tau_size; ++i) { p_future.push_back(MultiSimple(vdim)); log_z.push_back(0.0); } vectr dist(vdim, 0.0); if (xT == -1) dist = 1.0; else dist[xT] = 1.0; p_future[tau_size-1].SetDist(dist); vectr Tvec(vdim); for (int i=tau_size - 2; i>=0; --i) { // compute p_future[i] from p_future[i+1] matrix R = GetRMatrix(v, inst[i]); p_future[i+1].GetDist(dist, logfactor); logfactor = expmtv(dist, R, tau[i+1] - tau[i]); if (i > 0 && GetTMatrix(v,inst[i-1],inst[i],Tvec)) dist = dist.dotstar(Tvec); p_future[i].SetDist(dist); logfactor += log(p_future[i].Normalize()); log_z[i] = logfactor + log_z[i+1]; } double log_p_future0 = p_future[0].Prob(x0, true) + log_z[0]; double ksi = rand.RandReal(); double log_p_past = 0.0; int which_interval; for (which_interval=0; which_interval= tau_size - 1) return; matrix R = GetRMatrix(v, inst[which_interval]); double t = SearchPoint(ksi, x0, R, log_p_future0, log_p_past, p_future[which_interval+1], log_z[which_interval+1], tau[which_interval], tau[which_interval+1], kEpsilon); // stay till the end of trajectory if (fabs(tT-t) < 2.0*kEpsilon) { if (x0==xT || xT==-1) // already consistent return; tr.AddTransition(v, (t+tT)/2.0, xT); return; } // Sample the next state to transition to. p_future[which_interval+1].GetDist(dist, logfactor); expmtv(dist, R, tau[which_interval+1] - t); for (int i=0; iIntensity(); vector children_id = children[v].VarList(); for (size_t i=0; i!=children_id.size(); ++i) { int val = inst.Value(children_id[i]); Instantiation pinst = inst; const MarkovDyn *cmd = mds[children_id[i]]; for (int j=0; jIntensity()[val][val]; } } return R; } double GibbsSampler::SearchPoint(double ksi, int x0, const matrix &R, double log_p_future0, double log_p_past, MultiSimple p_future, double log_z, double t0, double t1, double epsilon) const { // This loop is guaranteed to terminate since each iteration we will // reduce (t1 - t0) by a half. for (;;) { double t = (t0+t1)/2.0; double dt = (t1-t0)/2.0; if (dt < epsilon) return t; // Compute F(t). vectr dist; double logfactor; p_future.GetDist(dist, logfactor); logfactor = expmtv(dist, R, dt); logfactor += log(dist.normalize()); logfactor += log_z; double new_log_p_past = log_p_past; new_log_p_past += dt*R[x0][x0]; MultiSimple new_p_future = p_future; new_p_future.SetDist(dist); double Ft = 1.0 - exp(new_log_p_past + log(dist[x0]) + logfactor - log_p_future0); if (fabs(Ft-ksi) < epsilon) return t; if (Ft < ksi) { // search (t, t1) t0 = t; log_p_past = new_log_p_past; } else { t1 = t; // search (t0, t) p_future = new_p_future; log_z = logfactor; } } } Instantiation GibbsSampler::SampleConditionalRv(const Instantiation &partial, double t, int v, Random &rand) const { double log_prob_e = p0->Prob(partial, true); int card_v = context.Cardinality(v); vector posterior_prob(card_v); double max_log_prob = -1e20; for (int val=0; valProb(inst, true); if (posterior_prob[val] > max_log_prob) max_log_prob = posterior_prob[val]; } // now do "beta pass" for evidence after this time (on this var // or others) Context vc; vc.AddVar(v,context.Cardinality(v)); Trajectory::Index vi = evid->Begin(vc); double t0 = vi.Time(); assert(vi.Values().Value(v)==-1); // otherwise why are we here? ++vi; vectr beta(card_v); if (vi.Done() || vi.Values().Value(v)==-1) { beta = 1.0; } else { beta = 0.0; beta[vi.Values().Value(v)] = 1.0; } vector tau; vector tauinst; GetTau(tr,t0,vi.Time(),v,markovblanket[v],tau,tauinst); vectr Tvec(card_v); for(int i=tau.size()-2;i>=0;--i) { expmtv(beta,GetRMatrix(v,tauinst[i]),tau[i+1]-tau[i]); if (i>0 && GetTMatrix(v,tauinst[i-1],tauinst[i],Tvec)) beta = beta.dotstar(Tvec); } // put the two together: double summation = 0.0; for (int val=0; val. */ #include "suffstatsquery.h" namespace ctbn { using namespace std; SuffStatsQuery::SuffStatsQuery() : p0(NULL), d(NULL), tr(new vector()) { } // Nothing to delete; none of the pointers are owned by this class. SuffStatsQuery::~SuffStatsQuery() { } } // end of ctbn namespace ctbnrle.1.1.1/src/Makefile0000664000175000017500000141625012251373455015635 0ustar csheltoncsheltonEXCLUDE = $(foreach v,$(2),$(if $(findstring $(1),$(v)),,$(v))) SRC := $(call EXCLUDE,rk_modified.cc,$(call EXCLUDE,matrix-noeigen.cc,$(call EXCLUDE,matrix-eigen.cc,$(wildcard *.cc)))) OBJ := $(patsubst %.cc,%.o,$(SRC)) CXX = g++ #OFLAGS = -O0 #OFLAGS = -O2 OFLAGS = -O3 #OFLAGS = -march=core2 -msse -msse2 -mfpmath=sse #OFLAGS = -mno-sse -mno-sse2 #OFLAGS = -g -O2 #-O0 CXXFLAGS += $(OFLAGS) CXXFLAGS += -pedantic -Wall -Wextra -Wno-long-long -Wno-unused-local-typedefs -Wno-unused-parameter -Wno-unused-variable CXXFLAGS += -std=c++03 CXXFLAGS += -Werror -pedantic-errors #CXXFLAGS += -g -fpermissive CXXFLAGS += -I../hdr CXXFLAGS += -I../glpk/glpk-4.47/include -I../glpk/glpk-4.47/src CXXFLAGS += -DUSE_PTHREADS LDFLAGS += -lpthread GCC_BASE_DIR = /usr/csshare/pkgs/gcc-4.7.0 GCC_BASE_INCLUDE_DIR = $(GCC_BASE_DIR)/lib/gcc/i686-pc-linux-gnu/4.7.0/include GPP_BASE_INCLUDE_DIR = $(GCC_BASE_DIR)/include/c++/4.7.0 DEPEND = makedepend -- $(CXXFLAGS) -- -w20 -Y -I$(GCC_BASE_INCLUDE_DIR) -I$(GPP_BASE_INCLUDE_DIR) -I$(GPP_BASE_INCLUDE_DIR)/tr1 -I$(GPP_INCLUDE_BASE_DIR)/i686-pc-linux-gnu -I/usr/include/asm -I../hdr -I../glpk/glpk-4.47/include .PHONY: local local: ctbn.a .PHONY: all all: ctbn.a ctbn.a: $(OBJ) ar rvu $@ $(OBJ) ranlib $@ depend: $(SRC) $(DEPEND) $(SRC) .PHONY: clean clean: rm -rf *.o *~ *.a # DO NOT DELETE THIS LINE -- make depend depends on it. varsample.o: ../hdr/varsample.h varsample.o: ../hdr/dynamics.h varsample.o: ../hdr/context.h varsample.o: ../hdr/streamserial.h varsample.o: ../hdr/serial/serial.h varsample.o: ../hdr/nullptr03.h varsample.o: ../hdr/streamserial-old.h varsample.o: ../hdr/defines.h varsample.o: ../hdr/streamextra.h varsample.o: ../hdr/trajectory.h varsample.o: ../hdr/rvsimple.h varsample.o: ../hdr/matrix.h varsample.o: ../hdr/matrix-eigen.h varsample.o: ../hdr/Eigen/Core varsample.o: ../hdr/Eigen/src/Core/util/DisableStupidWarnings.h varsample.o: ../hdr/Eigen/src/Core/util/Macros.h varsample.o: ../hdr/Eigen/src/Core/util/MKL_support.h varsample.o: ../hdr/Eigen/src/Core/util/Constants.h varsample.o: ../hdr/Eigen/src/Core/util/ForwardDeclarations.h varsample.o: ../hdr/Eigen/src/Core/util/Meta.h varsample.o: ../hdr/Eigen/src/Core/util/XprHelper.h varsample.o: ../hdr/Eigen/src/Core/util/StaticAssert.h varsample.o: ../hdr/Eigen/src/Core/util/Memory.h varsample.o: ../hdr/Eigen/src/Core/NumTraits.h varsample.o: ../hdr/Eigen/src/Core/MathFunctions.h varsample.o: ../hdr/Eigen/src/Core/GenericPacketMath.h varsample.o: ../hdr/Eigen/src/Core/arch/Default/Settings.h varsample.o: ../hdr/Eigen/src/Core/Functors.h varsample.o: ../hdr/Eigen/src/Core/DenseCoeffsBase.h varsample.o: ../hdr/Eigen/src/Core/DenseBase.h varsample.o: ../hdr/Eigen/src/plugins/BlockMethods.h varsample.o: ../hdr/Eigen/src/Core/MatrixBase.h varsample.o: 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factoredvector.o: ../hdr/Eigen/src/SparseCore/SparsePermutation.h factoredvector.o: ../hdr/Eigen/src/SparseCore/SparseAssign.h factoredvector.o: ../hdr/Eigen/src/SparseCore/SparseRedux.h factoredvector.o: ../hdr/Eigen/src/SparseCore/SparseFuzzy.h factoredvector.o: ../hdr/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h factoredvector.o: ../hdr/Eigen/src/SparseCore/SparseSparseProductWithPruning.h factoredvector.o: ../hdr/Eigen/src/SparseCore/SparseProduct.h factoredvector.o: ../hdr/Eigen/src/SparseCore/SparseDenseProduct.h factoredvector.o: ../hdr/Eigen/src/SparseCore/SparseDiagonalProduct.h factoredvector.o: ../hdr/Eigen/src/SparseCore/SparseTriangularView.h factoredvector.o: ../hdr/Eigen/src/SparseCore/SparseSelfAdjointView.h factoredvector.o: ../hdr/Eigen/src/SparseCore/TriangularSolver.h factoredvector.o: ../hdr/Eigen/src/SparseCore/SparseView.h factoredvector.o: ../hdr/Eigen/OrderingMethods factoredvector.o: ../hdr/Eigen/src/OrderingMethods/Amd.h factoredvector.o: 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fbinf.o: ../hdr/Eigen/src/Core/util/ForwardDeclarations.h fbinf.o: ../hdr/Eigen/src/Core/util/Meta.h fbinf.o: ../hdr/Eigen/src/Core/util/XprHelper.h fbinf.o: ../hdr/Eigen/src/Core/util/StaticAssert.h fbinf.o: ../hdr/Eigen/src/Core/util/Memory.h fbinf.o: ../hdr/Eigen/src/Core/NumTraits.h fbinf.o: ../hdr/Eigen/src/Core/MathFunctions.h fbinf.o: ../hdr/Eigen/src/Core/GenericPacketMath.h fbinf.o: ../hdr/Eigen/src/Core/arch/Default/Settings.h fbinf.o: ../hdr/Eigen/src/Core/Functors.h fbinf.o: ../hdr/Eigen/src/Core/DenseCoeffsBase.h fbinf.o: ../hdr/Eigen/src/Core/DenseBase.h fbinf.o: ../hdr/Eigen/src/plugins/BlockMethods.h fbinf.o: ../hdr/Eigen/src/Core/MatrixBase.h fbinf.o: ../hdr/Eigen/src/plugins/CommonCwiseUnaryOps.h fbinf.o: ../hdr/Eigen/src/plugins/CommonCwiseBinaryOps.h fbinf.o: ../hdr/Eigen/src/plugins/MatrixCwiseUnaryOps.h fbinf.o: ../hdr/Eigen/src/plugins/MatrixCwiseBinaryOps.h fbinf.o: ../hdr/matrix-eigenext.h fbinf.o: ../hdr/Eigen/src/Core/EigenBase.h fbinf.o: 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fbinf.o: ../hdr/Eigen/SparseCholesky fbinf.o: ../hdr/Eigen/src/misc/Solve.h fbinf.o: ../hdr/Eigen/src/misc/SparseSolve.h fbinf.o: ../hdr/Eigen/src/SparseCholesky/SimplicialCholesky.h fbinf.o: ../hdr/Eigen/IterativeLinearSolvers fbinf.o: ../hdr/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h fbinf.o: ../hdr/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h fbinf.o: ../hdr/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h fbinf.o: ../hdr/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h fbinf.o: ../hdr/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h fbinf.o: ../hdr/matrix-stream.h fbinf.o: ../hdr/structure.h fbinf.o: ../hdr/ctbndyn.h fbinf.o: ../hdr/dynamics.h fbinf.o: ../hdr/rvsimple.h fbinf.o: ../hdr/ss.h fbinf.o: ../hdr/suffstatsquery.h fbinf.o: ../hdr/process.h fbinf.o: ../hdr/markov.h fbinf.o: ../hdr/extramath.h fbinf.o: ../hdr/factoredvector.h fbinf.o: ../hdr/factoredmatrix.h fbinf.o: ../hdr/expmcache.h fbinf.o: ../hdr/contfunction.h fbinf.o: ../hdr/contfunction.tcc fbinf.o: ../hdr/rk.h fbinf.o: ../hdr/meanfieldinf.h fbinf.o: ../hdr/dyncomp.h fbinf.o: ../hdr/dynsimple.h fbinf.o: ../hdr/multisimple.h fbinf.o: ../hdr/rvcomp.h markov.o: ../hdr/markov.h markov.o: ../hdr/process.h markov.o: ../hdr/dynamics.h markov.o: ../hdr/context.h markov.o: ../hdr/streamserial.h markov.o: ../hdr/serial/serial.h markov.o: ../hdr/nullptr03.h markov.o: ../hdr/streamserial-old.h markov.o: ../hdr/defines.h markov.o: ../hdr/streamextra.h markov.o: ../hdr/trajectory.h markov.o: ../hdr/rvsimple.h markov.o: ../hdr/matrix.h markov.o: ../hdr/matrix-eigen.h markov.o: ../hdr/Eigen/Core markov.o: ../hdr/Eigen/src/Core/util/DisableStupidWarnings.h markov.o: ../hdr/Eigen/src/Core/util/Macros.h markov.o: ../hdr/Eigen/src/Core/util/MKL_support.h markov.o: ../hdr/Eigen/src/Core/util/Constants.h markov.o: ../hdr/Eigen/src/Core/util/ForwardDeclarations.h markov.o: ../hdr/Eigen/src/Core/util/Meta.h markov.o: ../hdr/Eigen/src/Core/util/XprHelper.h markov.o: 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../hdr/Eigen/src/Core/NestByValue.h markov.o: ../hdr/Eigen/src/Core/ForceAlignedAccess.h markov.o: ../hdr/Eigen/src/Core/ReturnByValue.h markov.o: ../hdr/Eigen/src/Core/NoAlias.h markov.o: ../hdr/Eigen/src/Core/PlainObjectBase.h markov.o: ../hdr/Eigen/src/Core/Matrix.h markov.o: ../hdr/Eigen/src/Core/Array.h markov.o: ../hdr/Eigen/src/Core/CwiseBinaryOp.h markov.o: ../hdr/Eigen/src/Core/CwiseUnaryOp.h markov.o: ../hdr/Eigen/src/Core/CwiseNullaryOp.h markov.o: ../hdr/Eigen/src/Core/CwiseUnaryView.h markov.o: ../hdr/Eigen/src/Core/SelfCwiseBinaryOp.h markov.o: ../hdr/Eigen/src/Core/Dot.h markov.o: ../hdr/Eigen/src/Core/StableNorm.h markov.o: ../hdr/Eigen/src/Core/MapBase.h markov.o: ../hdr/Eigen/src/Core/Stride.h markov.o: ../hdr/Eigen/src/Core/Map.h markov.o: ../hdr/Eigen/src/Core/Block.h markov.o: ../hdr/Eigen/src/Core/VectorBlock.h markov.o: ../hdr/Eigen/src/Core/Transpose.h markov.o: ../hdr/Eigen/src/Core/DiagonalMatrix.h markov.o: ../hdr/Eigen/src/Core/Diagonal.h markov.o: 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../hdr/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h markov.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixVector.h markov.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixMatrix.h markov.o: ../hdr/Eigen/src/Core/products/SelfadjointProduct.h markov.o: ../hdr/Eigen/src/Core/products/SelfadjointRank2Update.h markov.o: ../hdr/Eigen/src/Core/products/TriangularMatrixVector.h markov.o: ../hdr/Eigen/src/Core/products/TriangularMatrixMatrix.h markov.o: ../hdr/Eigen/src/Core/products/TriangularSolverMatrix.h markov.o: ../hdr/Eigen/src/Core/products/TriangularSolverVector.h markov.o: ../hdr/Eigen/src/Core/BandMatrix.h markov.o: ../hdr/Eigen/src/Core/BooleanRedux.h markov.o: ../hdr/Eigen/src/Core/Select.h markov.o: ../hdr/Eigen/src/Core/VectorwiseOp.h markov.o: ../hdr/Eigen/src/Core/Random.h markov.o: ../hdr/Eigen/src/Core/Replicate.h markov.o: ../hdr/Eigen/src/Core/Reverse.h markov.o: ../hdr/Eigen/src/Core/ArrayBase.h markov.o: ../hdr/Eigen/src/plugins/ArrayCwiseUnaryOps.h 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brutestructuresearch.o: ../hdr/Eigen/src/Core/NumTraits.h brutestructuresearch.o: ../hdr/Eigen/src/Core/MathFunctions.h brutestructuresearch.o: ../hdr/Eigen/src/Core/GenericPacketMath.h brutestructuresearch.o: ../hdr/Eigen/src/Core/arch/Default/Settings.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Functors.h brutestructuresearch.o: ../hdr/Eigen/src/Core/DenseCoeffsBase.h brutestructuresearch.o: ../hdr/Eigen/src/Core/DenseBase.h brutestructuresearch.o: ../hdr/Eigen/src/plugins/BlockMethods.h brutestructuresearch.o: ../hdr/Eigen/src/Core/MatrixBase.h brutestructuresearch.o: ../hdr/Eigen/src/plugins/CommonCwiseUnaryOps.h brutestructuresearch.o: ../hdr/Eigen/src/plugins/CommonCwiseBinaryOps.h brutestructuresearch.o: ../hdr/Eigen/src/plugins/MatrixCwiseUnaryOps.h brutestructuresearch.o: ../hdr/Eigen/src/plugins/MatrixCwiseBinaryOps.h brutestructuresearch.o: ../hdr/matrix-eigenext.h brutestructuresearch.o: ../hdr/Eigen/src/Core/EigenBase.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Assign.h brutestructuresearch.o: ../hdr/Eigen/src/Core/util/BlasUtil.h brutestructuresearch.o: ../hdr/Eigen/src/Core/DenseStorage.h brutestructuresearch.o: ../hdr/Eigen/src/Core/NestByValue.h brutestructuresearch.o: ../hdr/Eigen/src/Core/ForceAlignedAccess.h brutestructuresearch.o: ../hdr/Eigen/src/Core/ReturnByValue.h brutestructuresearch.o: ../hdr/Eigen/src/Core/NoAlias.h brutestructuresearch.o: ../hdr/Eigen/src/Core/PlainObjectBase.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Matrix.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Array.h brutestructuresearch.o: ../hdr/Eigen/src/Core/CwiseBinaryOp.h brutestructuresearch.o: ../hdr/Eigen/src/Core/CwiseUnaryOp.h brutestructuresearch.o: ../hdr/Eigen/src/Core/CwiseNullaryOp.h brutestructuresearch.o: ../hdr/Eigen/src/Core/CwiseUnaryView.h brutestructuresearch.o: ../hdr/Eigen/src/Core/SelfCwiseBinaryOp.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Dot.h brutestructuresearch.o: ../hdr/Eigen/src/Core/StableNorm.h brutestructuresearch.o: ../hdr/Eigen/src/Core/MapBase.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Stride.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Map.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Block.h brutestructuresearch.o: ../hdr/Eigen/src/Core/VectorBlock.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Transpose.h brutestructuresearch.o: ../hdr/Eigen/src/Core/DiagonalMatrix.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Diagonal.h brutestructuresearch.o: ../hdr/Eigen/src/Core/DiagonalProduct.h brutestructuresearch.o: ../hdr/Eigen/src/Core/PermutationMatrix.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Transpositions.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Redux.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Visitor.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Fuzzy.h brutestructuresearch.o: ../hdr/Eigen/src/Core/IO.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Swap.h brutestructuresearch.o: ../hdr/Eigen/src/Core/CommaInitializer.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Flagged.h brutestructuresearch.o: ../hdr/Eigen/src/Core/ProductBase.h brutestructuresearch.o: ../hdr/Eigen/src/Core/GeneralProduct.h brutestructuresearch.o: ../hdr/Eigen/src/Core/TriangularMatrix.h brutestructuresearch.o: ../hdr/Eigen/src/Core/SelfAdjointView.h brutestructuresearch.o: ../hdr/Eigen/src/Core/SolveTriangular.h brutestructuresearch.o: ../hdr/Eigen/src/Core/products/Parallelizer.h brutestructuresearch.o: ../hdr/Eigen/src/Core/products/CoeffBasedProduct.h brutestructuresearch.o: ../hdr/Eigen/src/Core/products/GeneralBlockPanelKernel.h brutestructuresearch.o: ../hdr/Eigen/src/Core/products/GeneralMatrixVector.h brutestructuresearch.o: ../hdr/Eigen/src/Core/products/GeneralMatrixMatrix.h brutestructuresearch.o: ../hdr/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h brutestructuresearch.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixVector.h brutestructuresearch.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixMatrix.h brutestructuresearch.o: ../hdr/Eigen/src/Core/products/SelfadjointProduct.h brutestructuresearch.o: ../hdr/Eigen/src/Core/products/SelfadjointRank2Update.h brutestructuresearch.o: ../hdr/Eigen/src/Core/products/TriangularMatrixVector.h brutestructuresearch.o: ../hdr/Eigen/src/Core/products/TriangularMatrixMatrix.h brutestructuresearch.o: ../hdr/Eigen/src/Core/products/TriangularSolverMatrix.h brutestructuresearch.o: ../hdr/Eigen/src/Core/products/TriangularSolverVector.h brutestructuresearch.o: ../hdr/Eigen/src/Core/BandMatrix.h brutestructuresearch.o: ../hdr/Eigen/src/Core/BooleanRedux.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Select.h brutestructuresearch.o: ../hdr/Eigen/src/Core/VectorwiseOp.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Random.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Replicate.h brutestructuresearch.o: ../hdr/Eigen/src/Core/Reverse.h brutestructuresearch.o: ../hdr/Eigen/src/Core/ArrayBase.h brutestructuresearch.o: 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brutestructuresearch.o: ../hdr/Eigen/src/SparseCore/SparseBlock.h brutestructuresearch.o: ../hdr/Eigen/src/SparseCore/SparseTranspose.h brutestructuresearch.o: ../hdr/Eigen/src/SparseCore/SparseCwiseUnaryOp.h brutestructuresearch.o: ../hdr/Eigen/src/SparseCore/SparseCwiseBinaryOp.h brutestructuresearch.o: ../hdr/Eigen/src/SparseCore/SparseDot.h brutestructuresearch.o: ../hdr/Eigen/src/SparseCore/SparsePermutation.h brutestructuresearch.o: ../hdr/Eigen/src/SparseCore/SparseAssign.h brutestructuresearch.o: ../hdr/Eigen/src/SparseCore/SparseRedux.h brutestructuresearch.o: ../hdr/Eigen/src/SparseCore/SparseFuzzy.h brutestructuresearch.o: ../hdr/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h brutestructuresearch.o: ../hdr/Eigen/src/SparseCore/SparseSparseProductWithPruning.h brutestructuresearch.o: ../hdr/Eigen/src/SparseCore/SparseProduct.h brutestructuresearch.o: ../hdr/Eigen/src/SparseCore/SparseDenseProduct.h brutestructuresearch.o: 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clique.o: ../hdr/Eigen/src/Core/util/Macros.h clique.o: ../hdr/Eigen/src/Core/util/MKL_support.h clique.o: ../hdr/Eigen/src/Core/util/Constants.h clique.o: ../hdr/Eigen/src/Core/util/ForwardDeclarations.h clique.o: ../hdr/Eigen/src/Core/util/Meta.h clique.o: ../hdr/Eigen/src/Core/util/XprHelper.h clique.o: ../hdr/Eigen/src/Core/util/StaticAssert.h clique.o: ../hdr/Eigen/src/Core/util/Memory.h clique.o: ../hdr/Eigen/src/Core/NumTraits.h clique.o: ../hdr/Eigen/src/Core/MathFunctions.h clique.o: ../hdr/Eigen/src/Core/GenericPacketMath.h clique.o: ../hdr/Eigen/src/Core/arch/Default/Settings.h clique.o: ../hdr/Eigen/src/Core/Functors.h clique.o: ../hdr/Eigen/src/Core/DenseCoeffsBase.h clique.o: ../hdr/Eigen/src/Core/DenseBase.h clique.o: ../hdr/Eigen/src/plugins/BlockMethods.h clique.o: ../hdr/Eigen/src/Core/MatrixBase.h clique.o: ../hdr/Eigen/src/plugins/CommonCwiseUnaryOps.h clique.o: ../hdr/Eigen/src/plugins/CommonCwiseBinaryOps.h clique.o: ../hdr/Eigen/src/plugins/MatrixCwiseUnaryOps.h clique.o: ../hdr/Eigen/src/plugins/MatrixCwiseBinaryOps.h clique.o: ../hdr/matrix-eigenext.h clique.o: ../hdr/Eigen/src/Core/EigenBase.h clique.o: ../hdr/Eigen/src/Core/Assign.h clique.o: ../hdr/Eigen/src/Core/util/BlasUtil.h clique.o: ../hdr/Eigen/src/Core/DenseStorage.h clique.o: ../hdr/Eigen/src/Core/NestByValue.h clique.o: ../hdr/Eigen/src/Core/ForceAlignedAccess.h clique.o: ../hdr/Eigen/src/Core/ReturnByValue.h clique.o: ../hdr/Eigen/src/Core/NoAlias.h clique.o: ../hdr/Eigen/src/Core/PlainObjectBase.h clique.o: ../hdr/Eigen/src/Core/Matrix.h clique.o: ../hdr/Eigen/src/Core/Array.h clique.o: ../hdr/Eigen/src/Core/CwiseBinaryOp.h clique.o: ../hdr/Eigen/src/Core/CwiseUnaryOp.h clique.o: ../hdr/Eigen/src/Core/CwiseNullaryOp.h clique.o: ../hdr/Eigen/src/Core/CwiseUnaryView.h clique.o: ../hdr/Eigen/src/Core/SelfCwiseBinaryOp.h clique.o: ../hdr/Eigen/src/Core/Dot.h clique.o: ../hdr/Eigen/src/Core/StableNorm.h clique.o: ../hdr/Eigen/src/Core/MapBase.h clique.o: ../hdr/Eigen/src/Core/Stride.h clique.o: ../hdr/Eigen/src/Core/Map.h clique.o: ../hdr/Eigen/src/Core/Block.h clique.o: ../hdr/Eigen/src/Core/VectorBlock.h clique.o: ../hdr/Eigen/src/Core/Transpose.h clique.o: ../hdr/Eigen/src/Core/DiagonalMatrix.h clique.o: ../hdr/Eigen/src/Core/Diagonal.h clique.o: ../hdr/Eigen/src/Core/DiagonalProduct.h clique.o: ../hdr/Eigen/src/Core/PermutationMatrix.h clique.o: ../hdr/Eigen/src/Core/Transpositions.h clique.o: ../hdr/Eigen/src/Core/Redux.h clique.o: ../hdr/Eigen/src/Core/Visitor.h clique.o: ../hdr/Eigen/src/Core/Fuzzy.h clique.o: ../hdr/Eigen/src/Core/IO.h clique.o: ../hdr/Eigen/src/Core/Swap.h clique.o: ../hdr/Eigen/src/Core/CommaInitializer.h clique.o: ../hdr/Eigen/src/Core/Flagged.h clique.o: ../hdr/Eigen/src/Core/ProductBase.h clique.o: ../hdr/Eigen/src/Core/GeneralProduct.h clique.o: ../hdr/Eigen/src/Core/TriangularMatrix.h clique.o: ../hdr/Eigen/src/Core/SelfAdjointView.h clique.o: ../hdr/Eigen/src/Core/SolveTriangular.h clique.o: ../hdr/Eigen/src/Core/products/Parallelizer.h clique.o: ../hdr/Eigen/src/Core/products/CoeffBasedProduct.h clique.o: ../hdr/Eigen/src/Core/products/GeneralBlockPanelKernel.h clique.o: ../hdr/Eigen/src/Core/products/GeneralMatrixVector.h clique.o: ../hdr/Eigen/src/Core/products/GeneralMatrixMatrix.h clique.o: ../hdr/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h clique.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixVector.h clique.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixMatrix.h clique.o: ../hdr/Eigen/src/Core/products/SelfadjointProduct.h clique.o: ../hdr/Eigen/src/Core/products/SelfadjointRank2Update.h clique.o: ../hdr/Eigen/src/Core/products/TriangularMatrixVector.h clique.o: ../hdr/Eigen/src/Core/products/TriangularMatrixMatrix.h clique.o: ../hdr/Eigen/src/Core/products/TriangularSolverMatrix.h clique.o: ../hdr/Eigen/src/Core/products/TriangularSolverVector.h clique.o: ../hdr/Eigen/src/Core/BandMatrix.h clique.o: ../hdr/Eigen/src/Core/BooleanRedux.h clique.o: ../hdr/Eigen/src/Core/Select.h clique.o: ../hdr/Eigen/src/Core/VectorwiseOp.h clique.o: ../hdr/Eigen/src/Core/Random.h clique.o: ../hdr/Eigen/src/Core/Replicate.h clique.o: ../hdr/Eigen/src/Core/Reverse.h clique.o: ../hdr/Eigen/src/Core/ArrayBase.h clique.o: ../hdr/Eigen/src/plugins/ArrayCwiseUnaryOps.h clique.o: ../hdr/Eigen/src/plugins/ArrayCwiseBinaryOps.h clique.o: ../hdr/Eigen/src/Core/ArrayWrapper.h clique.o: ../hdr/Eigen/src/Core/GlobalFunctions.h clique.o: ../hdr/Eigen/src/Core/util/ReenableStupidWarnings.h clique.o: ../hdr/Eigen/Sparse clique.o: ../hdr/Eigen/SparseCore clique.o: ../hdr/Eigen/Core clique.o: ../hdr/Eigen/src/SparseCore/SparseUtil.h clique.o: ../hdr/Eigen/src/SparseCore/SparseMatrixBase.h clique.o: ../hdr/Eigen/src/SparseCore/CompressedStorage.h clique.o: ../hdr/Eigen/src/SparseCore/AmbiVector.h clique.o: ../hdr/Eigen/src/SparseCore/SparseMatrix.h clique.o: ../hdr/Eigen/src/SparseCore/MappedSparseMatrix.h clique.o: ../hdr/Eigen/src/SparseCore/SparseVector.h clique.o: ../hdr/Eigen/src/SparseCore/CoreIterators.h clique.o: ../hdr/Eigen/src/SparseCore/SparseBlock.h clique.o: ../hdr/Eigen/src/SparseCore/SparseTranspose.h clique.o: ../hdr/Eigen/src/SparseCore/SparseCwiseUnaryOp.h clique.o: ../hdr/Eigen/src/SparseCore/SparseCwiseBinaryOp.h clique.o: ../hdr/Eigen/src/SparseCore/SparseDot.h clique.o: ../hdr/Eigen/src/SparseCore/SparsePermutation.h clique.o: ../hdr/Eigen/src/SparseCore/SparseAssign.h clique.o: ../hdr/Eigen/src/SparseCore/SparseRedux.h clique.o: ../hdr/Eigen/src/SparseCore/SparseFuzzy.h clique.o: ../hdr/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h clique.o: ../hdr/Eigen/src/SparseCore/SparseSparseProductWithPruning.h clique.o: ../hdr/Eigen/src/SparseCore/SparseProduct.h clique.o: ../hdr/Eigen/src/SparseCore/SparseDenseProduct.h clique.o: ../hdr/Eigen/src/SparseCore/SparseDiagonalProduct.h clique.o: ../hdr/Eigen/src/SparseCore/SparseTriangularView.h clique.o: ../hdr/Eigen/src/SparseCore/SparseSelfAdjointView.h clique.o: ../hdr/Eigen/src/SparseCore/TriangularSolver.h clique.o: ../hdr/Eigen/src/SparseCore/SparseView.h clique.o: ../hdr/Eigen/OrderingMethods clique.o: ../hdr/Eigen/src/OrderingMethods/Amd.h clique.o: ../hdr/Eigen/SparseCholesky clique.o: ../hdr/Eigen/src/misc/Solve.h clique.o: ../hdr/Eigen/src/misc/SparseSolve.h clique.o: ../hdr/Eigen/src/SparseCholesky/SimplicialCholesky.h clique.o: ../hdr/Eigen/IterativeLinearSolvers clique.o: ../hdr/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h clique.o: ../hdr/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h clique.o: ../hdr/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h clique.o: ../hdr/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h clique.o: ../hdr/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h clique.o: ../hdr/matrix-stream.h clique.o: ../hdr/structure.h clique.o: ../hdr/ctbndyn.h clique.o: ../hdr/dynamics.h clique.o: ../hdr/rvsimple.h clique.o: ../hdr/ss.h clique.o: ../hdr/suffstatsquery.h clique.o: ../hdr/markov.h clique.o: ../hdr/process.h clique.o: ../hdr/extramath.h clique.o: ../hdr/markovdyn.h clique.o: ../hdr/markovsimple.h clique.o: ../hdr/dynsimple.h clique.o: ../hdr/dyncomp.h clique.o: ../hdr/params.h ctbnrle.1.1.1/src/nodesampler.cc0000664000175000017500000002375212107032416017002 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "nodesampler.h" #define DEBUG namespace ctbn { using namespace std; NodeSampler::NodeSampler(const Process *pr, const Trajectory *traj, const VarSample *m) { SetProcess(pr); SetTrajectory(traj); SetMethod(m); GetChildrenContext(); MakeIndex(); } NodeSampler *NodeSampler::Clone() const { return new NodeSampler(*this); } double NodeSampler::SampleNodeTrajectory(const Trajectory &tr, Trajectory &nodetraj, int nodeid, Random &rand) { Trajectory::Index nextindex = evidindex[nodeid]; changeindex = vector(nodes.size(), 0); double currt = tr.TimeBegin(); nodetraj.SetBeginTime(begintime); nodetraj.SetEndTime(endtime); double weight = 0.0; //remove the content of nodeid in tr //get the index of it's parent for sampling //cout << "begin remove" << endl; //tr.SetUnknown(nodeid); //cout << "remove node" << endl; //tr.Draw(cout); Context c = nodes[nodeid]->CondDomain(); Trajectory::Index parenttraj = tr.Begin(c); Instantiation curri = tr.Values(dynamic_cast(p)->GetDynamics()->Domain(), currt); //set evidence of initial value of nodeid curri.SetVal(nodeid, nextindex.Values().Value(nodeid)); //sample initial value; const BN *bn=dynamic_cast(dynamic_cast(p)->GetStartDist()); bn->Sample(curri, rand); //At this monent, Let's just use the network that //the initial distribution of each node is independent //we only consider the weight contribution of the sampling node Instantiation tmpi(curri, -1); tmpi.SetVal(nodeid, nextindex.Values().Value(nodeid)); //weight += bn->ImportanceSampleWeight(curri, tmpi, true); //Feb-12 comment only for now int nextevidtype = nextindex.TestInc(nodes[nodeid]->Domain()); int nextevidval = nextindex.Values().Value(nodeid); int nodeval = curri.Value(nodeid); nodetraj.AddTransition(nodeid, currt, nodeval); //++parenttraj; double nextt = currt; int nodeparindex = parenttraj.Values().Index(); while (currttrajtime) nextt = trajtime; else nextt = newt; int nextvar = parenttraj.NextVar(); if (nextt>=endtime) { currt = nextt; continue; } if (newt>trajtime) { ++parenttraj; nodeparindex = parenttraj.Values().Index(); curri.SetVal(nextvar, parenttraj.Values().Value(nextvar)); continue; } int oldval = nodeval; int newval = SampleTransition(curri, nodeid, nodeparindex, nodeval, nextevidtype, nextindex, nextevidval, newt, weight, rand); if (oldval!=newval) { nodetraj.AddTransition(nodeid, newt, newval); nodeval = newval; //comment only for now //weight += SampleTransitionWeight(curri, nodeid, oldval, nodeparindex, nodeval, nextevidtype, nextindex, nextevidval, currt, nextt); } currt = nextt; if (currt>=nextindex.Time()) { nextevidtype = nextindex.TestInc(nodes[nodeid]->Domain()); nextevidval = nextindex.Values().Value(nodeid); } } //tr.Draw(cout); //calculate weight contribution of nodeid's children //weight += ChildrenSampleWeight(tr, nodeid); //Feb-12, comment only for now weight = 0.0; //For now, it only works for Jing's special case. return weight; } double NodeSampler::ChildrenSampleWeight(const Trajectory &tr, int nodeid) const { double retweight = 0.0; vector childrenid = str.GetChildren(nodeid); int numchildren = childrenid.size(); if (numchildren==0) return 0.0; Trajectory::Index trajindex = tr.Begin(childrencontext[nodeid]); //llh of initial values const BN *bn=dynamic_cast(dynamic_cast(p)->GetStartDist()); Instantiation inst = trajindex.Values(); for (int i=0; iNode(childrenid[i])->Prob(inst, true); map nodess; for (int i=0; iBlankSS())); double currt = trajindex.Time(); while (!trajindex.Done()) { Instantiation curri = trajindex.Values(); double deltat = trajindex.DeltaT(); int nextvar = trajindex.NextVar(); for (int i=0; iAddSS(curri, currt, deltat, nodess[childrenid[i]]); ++trajindex; map::const_iterator vit = nodess.find(nextvar); currt = trajindex.Time(); if (vit!=nodess.end()) { Instantiation nexti = trajindex.Values(); nodes[nextvar]->AddTransSS(curri, nexti, currt, vit->second); } } for (map::const_iterator i=nodess.begin(); i!=nodess.end(); ++i) { retweight += nodes[i->first]->LLH(i->second); delete i->second; } return retweight; } // void NodeSampler::SampleNodeTrajectory(Trajectory &tr, int nodeid, Random &rand) // { // Trajectory::Index nextindex = evidindex[nodeid]; // int nextevidtype = nextindex.TestInc(nodes[nodeid]->Domain()); // int nextevidval = nextindex.Values().Value(nodeid); // double currt = tr.TimeBegin(); // //remove the content of nodeid in tr // //get the index of it's parent for sampling // tr.SetUnknown(nodeid); // Context c = nodes[nodeid]->CondDomain(); // Trajectory::Index parenttraj = tr.Begin(c); // Instantiatoin curri = tr.Values(dynamic_cast(p)->Domain(), currt); // //sample initial value; // SampleInitial(curri, rand); // int nodeval = curri.Value(nodeid); // tr.AddTransition(nodeid, currt, nodeval); // parenttraj.TestInc(); // while (currtendtime) return; // if (newt>parenttraj.Time()) continue; // int oldval = nodeval; // int newval = SampleTransition(curri, nodeid, nodeindex, nodeval, nextevidtype, nextindex, nextevidval, newt, rand); // if (oldval!=newval) // { // tr.AddTransition(nodeid, newt, newval); // nodeval = newval; // } // currt = newt; // if (currt>=nextindex.Time()) // { // nextevidtpe = nextindex.TestInc(nodes[nodeid]->Domain()); // nextevidval = nextindex.Values().Value(nodeid); // } // } // } // double NodeSampler::SampleWeight(const Trajectory &tr, int nodeid, bool logscale) // { // double retwegiht = 0.0; // Context condcontext = nodes[nodeid]->CondDomain(); // Context nodecontext = nodes[nodeid]->Domain(); // vector childrenid = str.GetChildren(nodeid); // // map childrenval; // // map childrenindex; // vector childrenval; // vector childrenindex; // int numchild = childrenid.size(); // Trajectory::Index trajindex = tr.Begin(mbcontext); // Trajectory::Index nodeindex = evidindex[nodeid]; // Instantiation curri = tr.Values(globalcontext, tr.TimeBegin()); // int nodeval = curri.Value(nodeid); // int nextevidtype = nodeindex.TestInc(nodecontext); // int nextevidval = nextindex.Values().Value(nodeid); // for (int i=0; iCondDomain().Index(curri)); // } // // for (int i=0; inodeindex.Time()+nodeindex.DeltaT()) // //evidence begin before next transition // { // nextt = nodeindex.Time()+nodeindex.DeltaT(); // inc = true; // } // //calculate weight contribution of nodeid // retweight += SampleTimeWeight(curri, nodeid, nextvar, parindex, nodeval, nextevidtype, nodeindex, nextevidval, currt, nextt); // if (inc) // nodeindex.TestInc(nodes[nodeid]->Domain()); // else // { // //contribution of nodeid's children // for (int i=0; i child = str.GetChildren(i); for (unsigned int j=0; jDomain(); //add children c = c + nodes[child[j]]->CondDomain(); //add children's parents } childrencontext.push_back(c); } } } // end of ctbn namespace ctbnrle.1.1.1/src/extramath.cc0000664000175000017500000000446712107032416016470 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "extramath.h" #include #include #include namespace ctbn { using namespace std; double lngamma(double x) { double y,tmp,ser; static double cof[6]={76.18009172947146,-86.50532032941677, 24.01409824083091,-1.231739572450155, 0.1208650973866179e-2,-0.5395239384953e-5}; y=x; tmp=x+5.5; tmp -= (x+0.5)*log(tmp); ser=1.000000000190015; for (int j=0;j<6;j++) ser += cof[j]/++y; return -tmp+log(2.5066282746310005*ser/x); } vector > combo(int nItems, int nChoose) { vector > ret(0); ret.push_back(vector (0)); vector vec; for(int i=0;i= 1) { vector > temp = ret; for(int i=0; i > current(0); for(unsigned int j=0; j element = temp[j]; int end = element.size()-1; for(int k=end<0 ? 0 : element[end]+1;k temp2 = element; temp2.push_back(k); current.push_back(temp2); } } temp = current; ret.insert(ret.end(),current.begin(),current.end()); } } return ret; } int listDiff(const vector &listA, const vector &listB) { int ret = 0; for( vector::const_iterator it = listB.begin(); it != listB.end(); it++) { if(find(listA.begin(),listA.end(),*it) != listA.end()) ret++; } return listA.size() + listB.size() - (2*ret); } } // end of ctbn namespace ctbnrle.1.1.1/src/structuresearch.cc0000664000175000017500000000376312107032416017717 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "structuresearch.h" #include "matrix.h" namespace ctbn { using namespace std; StructureSearch::StructureSearch(const Context &c, FamScore* fs) : fScore(fs) { vector varL = c.VarList(); for(unsigned int i=0; i > children; int n = nodes.size(); for(int j=0;j par = nodes[j]->CondDomain().VarList(); for(unsigned int k=0;k())); } s.children = children; s.parentlist.clear(); for (int i=0; iCondDomain(); s.parentlist.push_back(c.VarList()); } } } // end of ctbn namespace ctbnrle.1.1.1/src/rk.cc0000664000175000017500000011015412107032416015076 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "rk.h" #include "matrix.h" #include "params.h" #include "utils.h" #include #include #include #include using namespace std; namespace ctbn { const double MU_END_PRECISION = 1e-2; #define USE_SPARSE_SS // This version has a calculation cache // comment out the USECACHE definition to remove the caching //#define USECACHE #define MAXCACHE 1000 #ifdef USECACHE static int reuse = 0; static int tries = 0; #endif // used by all RKF methods, placed here for easy initialization static double a2=0.2, a3=0.3, a4=0.6, a5=1.0, a6=0.875, b21=0.2, b31=3.0/40.0, b32=9.0/40.0, b41=0.3, b42=-0.9, b43=1.2, b51=-11.0/54.0, b52=2.5, b53=-70.0/27.0, b54=35.0/27.0, b61=1631.0/55296.0, b62=175.0/512.0, b63=575.0/13824.0, b64=44275.0/110592.0, b65=253.0/4096.0, c1=37.0/378.0, c3=250.0/621.0, c4=125.0/594.0, c6=512.0/1771.0, dc5=-277.00/14336.0; static double dc1=c1-2825.0/27648.0, dc3=c3-18575.0/48384.0, dc4=c4-13525.0/55296.0, dc6=c6-0.25; //static double defaulteps = ParamDouble("RKEPS",RKEPS); // computes a * exp(Qt), via integration with desired error of eps // (h, if non-negative, gives a starting estimate of step size) // return value replaces a // This is a version based on adaptive step sizes (and therefore // a fifth-order Runge-Kutta method with an embedded 4th order method // -- see Numerical Recipes in C) double vexpmt(vectr &a, const matrix &Q, double t, double eps, double h, vector *timesteps) { //double vexpmt(vectr &a, const matrix &Q, double t, double eps, double h) { double ret = 0.0; if (t<=0.0) return ret; static double defaulteps = ParamDouble("RKEPS",RKEPS); if (eps<0.0) eps = defaulteps; #ifdef USECACHE static vector cachea(MAXCACHE); static vector cacheQ(MAXCACHE); static vector cachet(MAXCACHE); static vector cacheret(MAXCACHE); static vector cachellh(MAXCACHE); static int cachepos = 0; tries++; //if (tries%1000==0) cout << "cache: " << reuse << '/' << tries << endl; for(int i=0;i0) h = 0.1/Q.diag().absmax(); else h = 0.1; } vectr ka1(p),ka2(p),ka3(p),ka4(p),ka5(p),ka6(p); //static double a2=0.2, a3=0.3, a4=0.6, a5=1.0, a6=0.875, /* static double b21=0.2, b31=3.0/40.0, b32=9.0/40.0, b41=0.3, b42=-0.9, b43=1.2, b51=-11.0/54.0, b52=2.5, b53=-70.0/27.0, b54=35.0/27.0, b61=1631.0/55296.0, b62=175.0/512.0, b63=575.0/13824.0, b64=44275.0/110592.0, b65=253.0/4096.0, c1=37.0/378.0, c3=250.0/621.0, c4=125.0/594.0, c6=512.0/1771.0, dc5=-277.00/14336.0; static double dc1=c1-2825.0/27648.0, dc3=c3-18575.0/48384.0, dc4=c4-13525.0/55296.0, dc6=c6-0.25; */ if(timesteps) timesteps->clear(); double currt = 0; while(currt=t) h = t-currt; // don't overshoot! if(timesteps) timesteps->push_back(currt); double err; for(;;) { // find stepsize // take 5th-order step: //iter++; vectr temp; Q.leftmult(a,ka1); ka1 *= h; //ka1 = (a*Q)*h; temp = a; temp.add(ka1,b21); //temp = ka1*b21+a; Q.leftmult(temp,ka2); ka2 *= h; //ka2 = (temp*Q)*h; temp = a; temp.add(ka1,b31); temp.add(ka2,b32); //temp = ka1*b31+ka2*b32+a; Q.leftmult(temp,ka3); ka3 *= h; //ka3 = (temp*Q)*h; temp = a; temp.add(ka1,b41); temp.add(ka2,b42); temp.add(ka3,b43); //temp = ka1*b41+ka2*b42+ka3*b43+a; Q.leftmult(temp,ka4); ka4 *= h; //ka4 = (temp*Q)*h; temp = a; temp.add(ka1,b51); temp.add(ka2,b52); temp.add(ka3,b53); temp.add(ka4,b54); //temp = ka1*b51+ka2*b52+ka3*b53+ka4*b54+a; Q.leftmult(temp,ka5); ka5 *= h; //ka5 = (temp*Q)*h; temp = a; temp.add(ka1,b61); temp.add(ka2,b62); temp.add(ka3,b63); temp.add(ka4,b64); temp.add(ka5,b65); //temp = ka1*b61+ka2*b62+ka3*b63+ka4*b64+ka5*b65+a; Q.leftmult(temp,ka6); ka6 *= h; //ka6 = (temp*Q)*h; vectr erra = ka1; erra *= dc1; erra.add(ka3,dc3); erra.add(ka4,dc4); erra.add(ka5,dc5); erra.add(ka6,dc6); //vectr erra = dc1*ka1+dc3*ka3+dc4*ka4+dc5*ka5+dc6*ka6; // check error: /*err = erra.absmax(); err /= eps; if (err <= 1.0) break;*/ double amin = a.absmin(); if(amin<1.0) amin = 1.0; err = erra.absmax()/amin; err /= eps; if(err<=1.0) break; // adjust step: double htemp = 0.9*h*pow(err,-0.25); if (htemp>h/10) h = htemp; else h /= 10; if (currt+h == currt) { /*cerr << "step size underflow in rk2" << endl; cerr << "in alpha pass:" << endl; cerr << Q << endl; cerr << a << endl; cerr << t << ' ' << currt << endl; exit(1);*/ return ret; } } currt += h; if (err > 1.89e-4) h = 0.9*h*pow(err,-0.2); else h *= 5; a.add(ka1,c1); a.add(ka3,c3); a.add(ka4,c4); a.add(ka6,c6); //a += c1*ka1+c3*ka3+c4*ka4+c6*ka6; double ss = a.sum(); if (ss>0.0) { ret += log(ss); a /= ss; } //ret += log(a.normalize()); } #ifdef USECACHE cacheret[cachepos] = a; cachellh[cachepos] = ret; cachepos++; //if (cachepos>MAXCACHE) { cerr << "cache overflow" << endl; cachepos=0;} if (cachepos>=MAXCACHE) cachepos=0; #endif return ret; } // computes exp(Qt) * b, via integration with desired error of eps // (h, if non-negative, gives a starting estimate of step size) // return value replaces b // This is a version based on adaptive step sizes (and therefore // a fifth-order Runge-Kutta method with an embedded 4th order-method // -- see Numerical Recipes in C) double expmtv(vectr &b, const matrix &Q, double t, double eps, double h) { double ret = 0.0; if (t<=0.0) return ret; static double defaulteps = ParamDouble("RKEPS",RKEPS); if (eps<0.0) eps = defaulteps; #ifdef USECACHE static vector cacheb(MAXCACHE); static vector cacheQ(MAXCACHE); static vector cachet(MAXCACHE); static vector cacheret(MAXCACHE); static vector cachellh(MAXCACHE); static int cachepos = 0; tries++; //if (tries%1000==0) cout << "cache: " << reuse << '/' << tries << endl; for(int i=0;i0) h = 0.1/Q.diag().absmax(); else h = 0.1; } vectr kb1(p),kb2(p),kb3(p),kb4(p),kb5(p),kb6(p); //static double a2=0.2, a3=0.3, a4=0.6, a5=1.0, a6=0.875, /* static double b21=0.2, b31=3.0/40.0, b32=9.0/40.0, b41=0.3, b42=-0.9, b43=1.2, b51=-11.0/54.0, b52=2.5, b53=-70.0/27.0, b54=35.0/27.0, b61=1631.0/55296.0, b62=175.0/512.0, b63=575.0/13824.0, b64=44275.0/110592.0, b65=253.0/4096.0, c1=37.0/378.0, c3=250.0/621.0, c4=125.0/594.0, c6=512.0/1771.0, dc5=-277.00/14336.0; static double dc1=c1-2825.0/27648.0, dc3=c3-18575.0/48384.0, dc4=c4-13525.0/55296.0, dc6=c6-0.25; */ double currt = 0; while(currt=t) h = t-currt; // don't overshoot! double err; for(;;) { // find stepsize // take 5th-order step: vectr temp; Q.rightmult(b,kb1); kb1 *= h; //kb1 = (Q*b)*h; temp = b; temp.add(kb1,b21); //temp = kb1*b21+b; Q.rightmult(temp,kb2); kb2 *= h; //kb2 = (Q*temp)*h; temp = b; temp.add(kb1,b31); temp.add(kb2,b32); //temp = kb1*b31+kb2*b32+b; Q.rightmult(temp,kb3); kb3 *= h; //kb3 = (Q*temp)*h; temp = b; temp.add(kb1,b41); temp.add(kb2,b42); temp.add(kb3,b43); //temp = kb1*b41+kb2*b42+kb3*b43+b; Q.rightmult(temp,kb4); kb4 *= h; //kb4 = (Q*temp)*h; temp = b; temp.add(kb1,b51); temp.add(kb2,b52); temp.add(kb3,b53); temp.add(kb4,b54); //temp = kb1*b51+kb2*b52+kb3*b53+kb4*b54+b; Q.rightmult(temp,kb5); kb5 *= h; //kb5 = (Q*temp)*h; temp = b; temp.add(kb1,b61); temp.add(kb2,b62); temp.add(kb3,b63); temp.add(kb4,b64); temp.add(kb5,b65); //temp = kb1*b61+kb2*b62+kb3*b63+kb4*b64+kb5*b65+b; Q.rightmult(temp,kb6); kb6 *= h; //kb6 = (Q*temp)*h; vectr errb = kb1; errb *= dc1; errb.add(kb3,dc3); errb.add(kb4,dc4); errb.add(kb5,dc5); errb.add(kb6,dc6); //vectr errb = dc1*kb1+dc3*kb3+dc4*kb4+dc5*kb5+dc6*kb6; // check error: /*err = errb.absmax(); err /= eps; if (err <= 1.0) break;*/ double bmin = b.absmin(); if(bmin<1.0) bmin = 1.0; err = errb.absmax()/bmin; err /= eps; if(err<=1.0) break; // adjust step: double htemp = 0.9*h*pow(err,-0.25); if (htemp>h/10) h = htemp; else h /= 10; if (currt+h == currt) { return ret; /*cerr << "step size underflow in rk2" << endl; cerr << "in beta pass:" << endl; cerr << Q << endl; cerr << b << endl; cerr << t << ' ' << currt << endl; exit(1);*/ } } currt += h; if (err > 1.89e-4) h = 0.9*h*pow(err,-0.2); else h *= 5; b.add(kb1,c1); b.add(kb3,c3); b.add(kb4,c4); b.add(kb6,c6); //b += c1*kb1+c3*kb3+c4*kb4+c6*kb6; double ss = b.sum(); if (ss>0.0) { ret += log(ss); b /= ss; } //ret += log(b.normalize()); } #ifdef USECACHE cacheret[cachepos] = b; cachellh[cachepos] = ret; cachepos++; //if (cachepos>MAXCACHE) { cerr << "cache overflow" << endl; cachepos=0;} if (cachepos>=MAXCACHE) cachepos=0; #endif return ret; } // calculates \int_u=0^t a*exp(Q*u)*e_i e_j*exp(Q*(t-u))*b du // [this is a matrix indexed by (i,j)] // the output is returned on c // (actually, it returns the transpose...) // This is a version based on adaptive step sizes (and therefore // a fifth-order Runge-Kutta method with an embedded 4th order method // -- see Numerical Recipes in C) double suffstat(const vectr &a, const vectr &b, matrix &c, const matrix &Q, double t, double eps, double h) { double bgtime = getcputime(); double ret = 0.0; if (t<=0.0) return ret; static double defaulteps = ParamDouble("RKEPS",RKEPS); if (eps<0.0) eps = defaulteps; #ifdef USECACHE static vector cachea(MAXCACHE); static vector cacheb(MAXCACHE); static vector cacheQ(MAXCACHE); static vector cachet(MAXCACHE); static vector cacheret(MAXCACHE); static vector cachellh(MAXCACHE); static int cachepos = 0; tries++; //if (tries%1000==0) cout << "cache: " << reuse << '/' << tries << endl; for(int i=0;i0) h = 0.1/Q.diag().absmax(); else h = 0.1; } double maxh = h, minh = h; //int evals = 0; #ifdef USE_SPARSE_SS spmatrix spQ(Q); #else const matrix &spQ = Q; #endif vectr mya(a); c = matrix(p,p,0.0); vectr ka1(p),ka2(p),ka3(p),ka4(p),ka5(p),ka6(p); matrix kc1(p,p),kc2(p,p),kc3(p,p),kc4(p,p),kc5(p,p),kc6(p,p); //static double a2=0.2, a3=0.3, a4=0.6, a5=1.0, a6=0.875, /* static double b21=0.2, b31=3.0/40.0, b32=9.0/40.0, b41=0.3, b42=-0.9, b43=1.2, b51=-11.0/54.0, b52=2.5, b53=-70.0/27.0, b54=35.0/27.0, b61=1631.0/55296.0, b62=175.0/512.0, b63=575.0/13824.0, b64=44275.0/110592.0, b65=253.0/4096.0, c1=37.0/378.0, c3=250.0/621.0, c4=125.0/594.0, c6=512.0/1771.0, dc5=-277.00/14336.0; static double dc1=c1-2825.0/27648.0, dc3=c3-18575.0/48384.0, dc4=c4-13525.0/55296.0, dc6=c6-0.25; */ double currt = 0; while(currtmaxh) maxh = h; if (h=t) h = t-currt; // don't overshoot! double err; for(;;) { // find stepsize //evals++; //if(evals>MAX_INT) return; // take 5th-order step: vectr temp; //ka1 = (mya*Q)*h; spQ.leftmult(mya, ka1); ka1 *= h; //kc1 = (matrix(b,mya)+ Q*c)*h; kc1 = spQ.rightmult(c); kc1.add(b,mya); kc1 *= h; //temp = ka1*b21+mya; temp = mya; temp.add(ka1, b21); //ka2 = (temp*Q)*h; spQ.leftmult(temp,ka2); ka2 *= h; //kc2 = (matrix(b,temp) + Q*(kc1*b21+c))*h; kc2 = c; kc2.add(kc1, b21); kc2 = spQ.rightmult(kc2); kc2.add(b, temp); kc2 *= h; //temp = ka1*b31+ka2*b32+mya; temp = mya; temp.add(ka1, b31); temp.add(ka2, b32); //ka3 = (temp*Q)*h; spQ.leftmult(temp, ka3); ka3 *= h; //kc3 = (matrix(b,temp) + Q*(kc1*b31+kc2*b32+c))*h; kc3 = c; kc3.add(kc1, b31); kc3.add(kc2, b32); kc3 = spQ.rightmult(kc3); kc3.add(b,temp); kc3 *= h; //temp = ka1*b41+ka2*b42+ka3*b43+mya; temp = mya; temp.add(ka1, b41); temp.add(ka2, b42); temp.add(ka3, b43); //ka4 = (temp*Q)*h; spQ.leftmult(temp, ka4); ka4 *= h; //kc4 = (matrix(b,temp) + Q*(kc1*b41+kc2*b42+kc3*b43+c))*h; kc4 = c; kc4.add(kc1, b41); kc4.add(kc2, b42); kc4.add(kc3, b43); kc4 = spQ.rightmult(kc4); kc4.add(b, temp); kc4 *= h; //temp = ka1*b51+ka2*b52+ka3*b53+ka4*b54+mya; temp = mya; temp.add(ka1, b51); temp.add(ka2, b52); temp.add(ka3, b53); temp.add(ka4, b54); //ka5 = (temp*Q)*h; spQ.leftmult(temp,ka5); ka5 *= h; //kc5 = (matrix(b,temp) + Q*(kc1*b51+kc2*b52+kc3*b53+kc4*b54+c))*h; kc5 = c; kc5.add(kc1, b51); kc5.add(kc2, b52); kc5.add(kc3, b53); kc5.add(kc4, b54); kc5 = spQ.rightmult(kc5); kc5.add(b,temp); kc5 *= h; //temp = ka1*b61+ka2*b62+ka3*b63+ka4*b64+ka5*b65+mya; temp = mya; temp.add(ka1, b61); temp.add(ka2, b62); temp.add(ka3, b63); temp.add(ka4, b64); temp.add(ka5, b65); //ka6 = (temp*Q)*h; spQ.leftmult(temp, ka6); ka6 *= h; //kc6 = (matrix(b,temp) + Q*(kc1*b61+kc2*b62+kc3*b63+kc4*b64+kc5*b65+c))*h; kc6 = c; kc6.add(kc1, b61); kc6.add(kc2, b62); kc6.add(kc3, b63); kc6.add(kc4, b64); kc6.add(kc5, b65); kc6 = spQ.rightmult(kc6); kc6.add(b, temp); kc6 *= h; //vectr erra = dc1*ka1+dc3*ka3+dc4*ka4+dc5*ka5+dc6*ka6; vectr erra = ka1; erra *= dc1; erra.add(ka3, dc3); erra.add(ka4, dc4); erra.add(ka5, dc5); erra.add(ka6, dc6); //matrix errc = dc1*kc1+dc3*kc3+dc4*kc4+dc5*kc5+dc6*kc6; matrix errc = kc1; errc *= dc1; errc.add(kc3, dc3); errc.add(kc4, dc4); errc.add(kc5, dc5); errc.add(kc6, dc6); // check error: /*err = erra.absmax(); double terr = errc.absmax(); if (erramin) amin = bmin; err = erra.absmax()/amin; double terr = errc.absmax()/amin; if(err 1.0) amax = 1.0; double cmax = errc.absmax(); if (cmax > 1.0) cmax = 1.0; err = amax > cmax ? amax : cmax; err /= eps; if(err <= 1.0) break; */ // adjust step: double htemp = 0.9*h*pow(err,-0.25); if (htemp>h/10) h = htemp; else h /= 10; if (currt+h == currt) { return ret; /*cerr << "step size underflow in rk2" << endl; cerr << "in ss pass:" << endl; cerr << Q << endl; cerr << a << endl; cerr << b << endl; cerr << t << ' ' << currt << endl; exit(1);*/ } } currt += h; if (err > 1.89e-4) h = 0.9*h*pow(err,-0.2); else h *= 5; //mya += c1*ka1+c3*ka3+c4*ka4+c6*ka6; mya.add(ka1, c1); mya.add(ka3, c3); mya.add(ka4, c4); mya.add(ka6, c6); //c += c1*kc1+c3*kc3+c4*kc4+c6*kc6; c.add(kc1, c1); c.add(kc3, c3); c.add(kc4, c4); c.add(kc6, c6); double ss = mya.sum(); if (ss>0.0) { ret += log(ss); c /= ss; mya /= ss; } //double z=mya.normalize(); //ret += log(z); //c/=z; } #ifdef USECACHE cacheret[cachepos] = c; cachellh[cachepos] = ret; cachepos++; //if (cachepos>MAXCACHE) { cerr << "cache overflow" << endl; cachepos=0;} if (cachepos>=MAXCACHE) cachepos=0; #endif return ret; } // these are versions for 2-by-2 matrices that are often faster double vexpmt2(vectr &a, const matrix &Q, double t) { double del = Q[0][0] - Q[1][1]; double det = del*del + 4*Q[0][1]*Q[1][0]; double a0 = a[0]; double a1 = a[1]; double ch,sh; double logfactor = t*(Q[0][0]+Q[1][1])/2; if (det>=0.0) { det = sqrt(det); double ex1 = exp(-det*t); logfactor += det*t/2.0; ch = (1.0 + ex1)/2.0; sh = (1.0 - ex1)/2.0/det; if (isnan(sh)) sh = 0.5*exp(-det*t/2.0); } else { // should not need for rate matrices det = sqrt(-det); ch = cos(det*t/2.0); sh = sin(det*t/2.0)/det; } double delsh = del*sh; a[0] = a0*(ch + delsh) + 2.0*a1*Q[1][0]*sh; a[1] = a1*(ch - delsh) + 2.0*a0*Q[0][1]*sh; //if (a[0]<0) a[0] = 0.0; //if (a[1]<0) a[1] = 0.0; double s = a[0]+a[1]; if (s<0.0) s = 1.0; a[0] /= s; a[1] /= s; return log(s)+logfactor; //t*(Q[0][0]+Q[1][1])/2; } double expmtv2(vectr &b, const matrix &Q, double t) { double del = Q[0][0] - Q[1][1]; double det = del*del + 4*Q[0][1]*Q[1][0]; double b0 = b[0]; double b1 = b[1]; double ch,sh; double logfactor = t*(Q[0][0]+Q[1][1])/2; if (det>=0.0) { det = sqrt(det); double ex1 = exp(-det*t); logfactor += det*t/2.0; ch = (1.0 + ex1)/2.0; sh = (1.0 - ex1)/2.0/det; if (isnan(sh)) sh = 0.5*exp(-det*t/2.0); } else { // should not need for rate matrices det = sqrt(-det); ch = cos(det*t/2.0); sh = sin(det*t/2.0)/det; } double delsh = del*sh; b[0] = b0*(ch + delsh) + 2.0*b1*Q[0][1]*sh; b[1] = b1*(ch - delsh) + 2.0*b0*Q[1][0]*sh; //if (b[0]<0) b[0] = 0.0; //if (b[1]<0) b[1] = 0.0; double s = b[0]+b[1]; if (s<0.0) s = 1.0; b[0] /= s; b[1] /= s; return log(s)+logfactor; //t*(Q[0][0]+Q[1][1])/2; } double suffstat2(const vectr &a, const vectr &b, matrix &c, const matrix &Q, double t) { double ret = 0.0; double del = Q[0][0] - Q[1][1]; double det = sqrt(del*del + 4*Q[0][1]*Q[1][0]); double sa = a.sum(); double sb = b.sum(); double a0 = a[0]/sa; double a1 = a[1]/sa; double b0 = b[0]/sb; double b1 = b[1]/sb; double ex = exp(det*t/2.0); double ch = (ex + 1.0/ex)/2.0; double sh = (ex - 1.0/ex)/2.0/det; double integral_shsh = (-sh + t/2.0 * ch)/(det*det); if (isnan(sh)) { sh = 0.5; integral_shsh = 0.0; } double integral_chch = sh + t/2.0 * ch; double integral_chsh = t/2.0 * sh; double integral_shch = t/2.0 * sh; double s00, s01, s10, s11; double tmp; tmp = integral_chch; tmp += del * 2 * integral_chsh; tmp += del * del * integral_shsh; s00 = tmp * a0 * b0; //---------------- tmp = integral_chsh + del * integral_shsh; s00 += tmp * 2 * Q[0][1] * a0 * b1; //---------------- s00 += tmp * 2 * Q[1][0] * a1 * b0; //---------------- s00 += 4 * Q[0][1] * Q[1][0] * integral_shsh * a1 * b1; //========================================================= tmp = integral_chsh + del * integral_shsh; s01 = tmp * 2 * Q[1][0] * a0 * b0; //---------------- tmp = integral_chch - del * del * integral_shsh; s01 += tmp * a0 * b1; //---------------- s01 += 4 * Q[1][0] * Q[1][0] * integral_shsh * a1 * b0; //---------------- tmp = integral_shch - del * integral_shsh; s01 += tmp * 2 * Q[1][0] * a1 * b1; //========================================================= tmp = integral_shch + del * integral_shsh; s10 = tmp * 2 * Q[0][1] * a0 * b0; //---------------- s10 += 4 * Q[0][1] * Q[0][1] * integral_shsh * a0 * b1; //---------------- tmp = integral_chch - del * del * integral_shsh; s10 += tmp * a1 * b0; //---------------- tmp = integral_chsh - del * integral_shsh; s10 += tmp * 2 * Q[0][1] * a1 * b1; //========================================================= s11 = 4 * Q[0][1] * Q[1][0] * integral_shsh * a0 * b0; //---------------- tmp = integral_shch - del * integral_shsh; s11 += tmp * 2 * Q[0][1] * a0 * b1; //---------------- s11 += tmp * 2 * Q[1][0] * a1 * b0; //---------------- tmp = integral_chch - 2 * del * integral_shch + del * del * integral_shsh; s11 += tmp * a1 * b1; c[0][0] = s00; c[0][1] = s10; c[1][0] = s01; c[1][1] = s11; c *= exp(t*(Q[0][0]+Q[1][1])/2) * sb; ret = log(sa); return ret; } void mfBackward(MeanFieldInf *inf, int varid, double t1, double t0, double eps, double h) { static double defaulteps = ParamDouble("RKEPS",RKEPS); if(eps<0.0) eps = defaulteps; if(h<=0.0) h = 0.05; int card = inf->GetNumStates(varid); vectr kd2(card),kd3(card),kd4(card),kd5(card),kd6(card), stepres(card), temp(card), reserr(card); /* static double a2=0.2, a3=0.3, a4=0.6, a5=1.0, a6=0.875, b21=0.2, b31=3.0/40.0, b32=9.0/40.0, b41=0.3, b42=-0.9, b43=1.2, b51=-11.0/54.0, b52=2.5, b53=-70.0/27.0, b54=35.0/27.0, b61=1631.0/55296.0, b62=175.0/512.0, b63=575.0/13824.0, b64=44275.0/110592.0, b65=253.0/4096.0, c1=37.0/378.0, c3=250.0/621.0, c4=125.0/594.0, c6=512.0/1771.0, dc5=-277.00/14336.0; static double dc1=c1-2825.0/27648.0, dc3=c3-18575.0/48384.0, dc4=c4-13525.0/55296.0, dc6=c6-0.25; */ double currt = t1; double endt = t0; vectr curRho(card,0.0); while(currt>endt) { if(currt-h<=endt) h = currt-endt; double err = 0; double hstep = -h; for( ; ; ) { hstep = -h; inf->GetRhoTempVal(currt,curRho); vectr rhoGrad(card,0.0); inf->CalcRhoGrad(varid,currt,curRho,rhoGrad); rhoGrad *= hstep; temp = curRho; temp.add(rhoGrad,b21); // temp = curRho+b21*rhoGrad; inf->CalcRhoGrad(varid,currt+a2*hstep,temp,kd2); kd2 *= hstep; temp = curRho; temp.add(rhoGrad,b31); temp.add(kd2,b32); // temp = curRho+(b31*rhoGrad+b32*kd2); inf->CalcRhoGrad(varid,currt+a3*hstep,temp,kd3); kd3 *= hstep; temp = curRho; temp.add(rhoGrad,b41); temp.add(kd2,b42); temp.add(kd3,b43); // temp = curRho+(b41*rhoGrad+b42*kd2+b43*kd3); inf->CalcRhoGrad(varid,currt+a4*hstep,temp,kd4); kd4 *= hstep; temp = curRho; temp.add(rhoGrad,b51); temp.add(kd2,b52); temp.add(kd3,b53); temp.add(kd4,b54); // temp = curRho+(b51*rhoGrad+b52*kd2+b53*kd3+b54*kd4); inf->CalcRhoGrad(varid,currt+a5*hstep,temp,kd5); kd5 *= hstep; temp = curRho; temp.add(rhoGrad,b61); temp.add(kd2,b62); temp.add(kd3,b63); temp.add(kd4,b64); temp.add(kd5,b65); // temp = curRho+(b61*rhoGrad+b62*kd2+b63*kd3+b64*kd4 // +b65*kd5); inf->CalcRhoGrad(varid,currt+a6*hstep,temp,kd6); kd6 *= hstep; stepres = curRho; stepres.add(rhoGrad,c1); stepres.add(kd3,c3); stepres.add(kd4,c4); stepres.add(kd6,c6); // stepres = curRho+(c1*rhoGrad+c3*kd3+c4*kd4+c6*kd6); for(int i=0; ih/10) h = htemp; else h /= 10; if(currt+h == currt) { cout << "Bad exit" << endl; return; } } hstep = -h; currt += hstep; if(err > 1.89e-4) h = 0.9*h*pow(err,-0.2); else h *= 5; curRho = stepres; inf->AddRhoTempVal(currt, curRho); } } void mfForward(MeanFieldInf *inf, int varid, double t0, double t1, double eps, double h) { static double defaulteps = ParamDouble("RKEPS",RKEPS); if(eps<0.0) eps = defaulteps; if(h<=0.0) h = 0.05; int card = inf->GetNumStates(varid); vectr kd2(card),kd3(card),kd4(card),kd5(card),kd6(card), stepres(card), temp(card), rhoTemp(card), reserr(card); matrix gammaTemp(card,card,0.0); /* static double a2=0.2, a3=0.3, a4=0.6, a5=1.0, a6=0.875, b21=0.2, b31=3.0/40.0, b32=9.0/40.0, b41=0.3, b42=-0.9, b43=1.2, b51=-11.0/54.0, b52=2.5, b53=-70.0/27.0, b54=35.0/27.0, b61=1631.0/55296.0, b62=175.0/512.0, b63=575.0/13824.0, b64=44275.0/110592.0, b65=253.0/4096.0, c1=37.0/378.0, c3=250.0/621.0, c4=125.0/594.0, c6=512.0/1771.0, dc5=-277.00/14336.0; static double dc1=c1-2825.0/27648.0, dc3=c3-18575.0/48384.0, dc4=c4-13525.0/55296.0, dc6=c6-0.25; */ double currt = t0; double endt = t1-1e-11; vectr curMu(card,0.0); matrix curGamma(card,card,0.0); bool reachedEnd=false; while(currt=endt) { h = endt-currt; } if(abs(endt-currt) < MU_END_PRECISION) { h = endt-currt; vectr endTemp(card,0.0); matrix endGamma(card,card,0.0); inf->GetRhoTempVal(endt,endTemp); int endState = -1; if(endTemp.sum() > 1) { inf->GetMuTempVal(currt,endTemp); } else { for(int i=0;iAddMuTempVal(endt,endTemp); inf->AddGammaTempVal(endt,endGamma); return; } } double err = 0; double hstep; for( ; ; ) { hstep = h; inf->GetMuTempVal(currt,curMu); inf->GetGammaTempVal(currt,curGamma); vectr muGrad(card,0.0); inf->CalcMuGrad(varid,currt, curGamma,muGrad); muGrad *= hstep; temp = curMu; temp.add(muGrad,b21); // temp = curMu+b21*muGrad; inf->GetRhoTempVal(currt+a2*hstep,rhoTemp); inf->CalcGamma(varid,currt+a2*hstep, temp,rhoTemp,gammaTemp); inf->CalcMuGrad(varid,currt+a2*hstep, gammaTemp,kd2); kd2 *= hstep; temp = curMu; temp.add(muGrad,b31); temp.add(kd2,b32); // temp = curMu+(b31*muGrad+b32*kd2); inf->GetRhoTempVal(currt+a3*hstep,rhoTemp); inf->CalcGamma(varid,currt+a3*hstep, temp,rhoTemp,gammaTemp); inf->CalcMuGrad(varid,currt+a3*hstep, gammaTemp,kd3); kd3 *= hstep; temp = curMu; temp.add(muGrad,b41); temp.add(kd2,b42); temp.add(kd3,b43); // temp = curMu+(b41*muGrad+b42*kd2+b43*kd3); inf->GetRhoTempVal(currt+a4*hstep,rhoTemp); inf->CalcGamma(varid,currt+a4*hstep, temp,rhoTemp,gammaTemp); inf->CalcMuGrad(varid,currt+a4*hstep, gammaTemp,kd4); kd4 *= hstep; temp = curMu; temp.add(muGrad,b51); temp.add(kd2,b52); temp.add(kd3,b53); temp.add(kd4,b54); // temp = curMu+(b51*muGrad+b52*kd2+b53*kd3+b54*kd4); inf->GetRhoTempVal(currt+a5*hstep,rhoTemp); inf->CalcGamma(varid,currt+a5*hstep, temp,rhoTemp,gammaTemp); inf->CalcMuGrad(varid,currt+a5*hstep, gammaTemp,kd5); kd5 *= hstep; temp = curMu; temp.add(muGrad,b61); temp.add(kd2,b62); temp.add(kd3,b63); temp.add(kd4,b64); temp.add(kd5,b65); // temp = curMu+(b61*muGrad+b62*kd2+b63*kd3+b64*kd4 // +b65*kd5); inf->GetRhoTempVal(currt+a6*hstep,rhoTemp); inf->CalcGamma(varid,currt+a6*hstep, temp,rhoTemp,gammaTemp); inf->CalcMuGrad(varid,currt+a6*hstep, gammaTemp,kd6); kd6 *= hstep; stepres = curMu; stepres.add(muGrad,c1); stepres.add(kd3,c3); stepres.add(kd4,c4); stepres.add(kd6,c6); // stepres = curMu+(c1*muGrad+c3*kd3+c4*kd4+c6*kd6); for(int i=0; i 1.0) stepres[i] = 1.0; } reserr = vectr(card,0.0); reserr.add(muGrad,dc1); reserr.add(kd3,dc3); reserr.add(kd4,dc4); reserr.add(kd5,dc5); reserr.add(kd6,dc6); // reserr = dc1*muGrad+dc3*kd3+dc4*kd4+dc5*kd+dc6*kd6); double mmin = curMu.absmin(); if(mmin<1.0) mmin = 1.0; err = reserr.absmax()/mmin; err /= eps; // cout << "err=" << err << endl; if(err<=1.0) break; double htemp = 0.9*h*pow(err,-0.25); htemp = abs(htemp); if(htemp>h/10) h = htemp; else h /= 10; if(currt+h == currt) { return; } } hstep = h; currt += hstep; if(err > 1.89e-4) h = 0.9*h*pow(err,-0.2); else h *= 5; curMu = stepres; curMu.normalize(); inf->GetRhoTempVal(currt,rhoTemp); inf->CalcGamma(varid,currt,curMu,rhoTemp,curGamma); inf->AddMuTempVal(currt, curMu); inf->AddGammaTempVal(currt, curGamma); if(reachedEnd && currt >= endt) break; } } double mfEnergy(MeanFieldInf *inf, int varid, double t0, double t1, double eps, double h) { static double defaulteps = ParamDouble("RKEPS",RKEPS); if(abs(t1-t0)< defaulteps) return 0; if(eps<0.0) eps = defaulteps; if(h<=0.0) h = 0.05; int card = inf->GetNumStates(varid); double /*kd2(0.0),*/kd3(0.0),kd4(0.0),kd5(0.0),kd6(0.0), stepres(0.0), temp(0.0), reserr(0.0); double k1(0.0), k2(0.0), k3(0.0), k4(0.0); /* static double a2=0.2, a3=0.3, a4=0.6, a5=1.0, a6=0.875, b21=0.2, b31=3.0/40.0, b32=9.0/40.0, b41=0.3, b42=-0.9, b43=1.2, b51=-11.0/54.0, b52=2.5, b53=-70.0/27.0, b54=35.0/27.0, b61=1631.0/55296.0, b62=175.0/512.0, b63=575.0/13824.0, b64=44275.0/110592.0, b65=253.0/4096.0, c1=37.0/378.0, c3=250.0/621.0, c4=125.0/594.0, c6=512.0/1771.0, dc5=-277.00/14336.0; static double dc1=c1-2825.0/27648.0, dc3=c3-18575.0/48384.0, dc4=c4-13525.0/55296.0, dc6=c6-0.25; */ double currt = t0; double endt = t1; double curEn = 0.0; while(currt=endt) h = endt-currt; double err = 0; double hstep; for( ; ; ) { hstep = h; double enIntegrand = inf->CalcPointCompEnergy(varid,currt); // kd2 = inf->CalcPointCompEnergy(varid,currt+a2*hstep); kd3 = inf->CalcPointCompEnergy(varid,currt+a3*hstep); kd4 = inf->CalcPointCompEnergy(varid,currt+a4*hstep); kd5 = inf->CalcPointCompEnergy(varid,currt+a5*hstep); kd6 = inf->CalcPointCompEnergy(varid,currt+a6*hstep); stepres = curEn + hstep*(c1*enIntegrand +c3*kd3 +c4*kd4 +c6*kd6); reserr = hstep*(dc1*enIntegrand +dc3*kd3 +dc4*kd4 +dc5*kd5 +dc6*kd6); double mmin = abs(curEn); if(mmin<1.0) mmin = 1.0; err = reserr/mmin; err = reserr; err /= eps; if(err<=1.0) break; double htemp = 0.9*h*pow(err,-0.25); htemp = htemp > 0 ? htemp : -htemp; if(htemp>h/10) h = htemp; else h /= 10; if(currt+h == currt) { return 0; } } hstep = h; currt += hstep; if(err > 1.89e-4) h = 0.9*h*pow(err,-0.2); else h *= 5; curEn = stepres; } return curEn; } double mfSuffStat(MeanFieldInf *inf, const Instantiation &x, double t0, double t1, double eps, double h) { static double defaulteps = ParamDouble("RKEPS",RKEPS); if(abs(t1-t0)< defaulteps) { return 0; } if(eps<0.0) eps = defaulteps; if(h<=0.0) h = 0.05; // int card = inf->GetNumStates(varid); double /*kd2(0.0),*/kd3(0.0),kd4(0.0),kd5(0.0),kd6(0.0), stepres(0.0), temp(0.0), reserr(0.0); double k1(0.0), k2(0.0), k3(0.0), k4(0.0); double currt = t0; double endt = t1; double curTimeSS = 0.0; while(currt=endt) h = endt-currt; double err = 0; double hstep; for( ; ; ) { hstep = h; double integrand = inf->Smooth(x,currt); // kd2 = inf->Smooth(x,currt+a2*hstep); kd3 = inf->Smooth(x,currt+a3*hstep); kd4 = inf->Smooth(x,currt+a4*hstep); kd5 = inf->Smooth(x,currt+a5*hstep); kd6 = inf->Smooth(x,currt+a6*hstep); stepres = curTimeSS + hstep*(c1*integrand +c3*kd3 +c4*kd4 +c6*kd6); reserr = hstep*(dc1*integrand +dc3*kd3 +dc4*kd4 +dc5*kd5 +dc6*kd6); double mmin = abs(curTimeSS); if(mmin<1.0) mmin = 1.0; err = reserr/mmin; err = reserr; err /= eps; if(err<=1.0) break; double htemp = 0.9*h*pow(err,-0.25); htemp = htemp > 0 ? htemp : -htemp; if(htemp>h/10) h = htemp; else h /= 10; if(currt+h == currt) { return 0; } } hstep = h; currt += hstep; if(err > 1.89e-4) h = 0.9*h*pow(err,-0.2); else h *= 5; curTimeSS = stepres; } return curTimeSS; } double mfTransSuffStat(MeanFieldInf *inf, int varid, const Instantiation &x1, const Instantiation &x2, double t0, double t1, double eps, double h) { static double defaulteps = ParamDouble("RKEPS",RKEPS); if(abs(t1-t0)< defaulteps) return 0; if(eps<0.0) eps = defaulteps; if(h<=0.0) h = 0.05; // int card = inf->GetNumStates(varid); double /*kd2(0.0),*/kd3(0.0),kd4(0.0),kd5(0.0),kd6(0.0), stepres(0.0), temp(0.0), reserr(0.0); double k1(0.0), k2(0.0), k3(0.0), k4(0.0); /* static double a2=0.2, a3=0.3, a4=0.6, a5=1.0, a6=0.875, b21=0.2, b31=3.0/40.0, b32=9.0/40.0, b41=0.3, b42=-0.9, b43=1.2, b51=-11.0/54.0, b52=2.5, b53=-70.0/27.0, b54=35.0/27.0, b61=1631.0/55296.0, b62=175.0/512.0, b63=575.0/13824.0, b64=44275.0/110592.0, b65=253.0/4096.0, c1=37.0/378.0, c3=250.0/621.0, c4=125.0/594.0, c6=512.0/1771.0, dc5=-277.00/14336.0; static double dc1=c1-2825.0/27648.0, dc3=c3-18575.0/48384.0, dc4=c4-13525.0/55296.0, dc6=c6-0.25; */ double currt = t0; double endt = t1; double curTimeSS = 0.0; while(currt=endt) h = endt-currt; double err = 0; double hstep; for( ; ; ) { hstep = h; double integrand = inf->PointTransSuffStat(varid,x1,x2,currt); // kd2 = inf->PointTransSuffStat(varid,x1,x2,currt+a2*hstep); kd3 = inf->PointTransSuffStat(varid,x1,x2,currt+a3*hstep); kd4 = inf->PointTransSuffStat(varid,x1,x2,currt+a4*hstep); kd5 = inf->PointTransSuffStat(varid,x1,x2,currt+a5*hstep); kd6 = inf->PointTransSuffStat(varid,x1,x2,currt+a6*hstep); stepres = curTimeSS + hstep*(c1*integrand +c3*kd3 +c4*kd4 +c6*kd6); reserr = hstep*(dc1*integrand +dc3*kd3 +dc4*kd4 +dc5*kd5 +dc6*kd6); double mmin = abs(curTimeSS); if(mmin<1.0) mmin = 1.0; err = reserr/mmin; err = reserr; err /= eps; if(err<=1.0) break; double htemp = 0.9*h*pow(err,-0.25); htemp = htemp > 0 ? htemp : -htemp; if(htemp>h/10) h = htemp; else h /= 10; if(currt+h == currt) { return 0; } } hstep = h; currt += hstep; if(err > 1.89e-4) h = 0.9*h*pow(err,-0.2); else h *= 5; curTimeSS = stepres; } return curTimeSS; } } // end of ctbn namespace ctbnrle.1.1.1/src/rvsimple.cc0000664000175000017500000001221612107032416016323 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "bn.h" #include "nullptr03.h" #include "rvsimple.h" namespace ctbn { using namespace std; RVSimple::RVSimple() : StreamObj() { } RVSimple::RVSimple(istream &is) : StreamObj() { } RVSimple::~RVSimple() { } void RVSimple::LoadOld(istream &is) { } void RVSimple::SaveOld(ostream &os) const { } std::ostream & RVSimple::PrintJoint(std::ostream & out, RV * the_initial_distribution_of_the_markov_process) const { vectr v(the_initial_distribution_of_the_markov_process->Domain().Size(), 0.0); double logf; this->GetDist(v, logf); //Printing scaled to when max = 1 vectr scaled = v/v.max(); out <<"\nJoint Distribution:\t"; for(int i=0; iDomain().Size(), 0.0); double logf; this->GetDist(v, logf); out << "Exact Logf: " << logf << endl; BN const * const initial_distribution(dynamic_cast(the_initial_distribution_of_the_markov_process)); int nn = (nullptr03 == initial_distribution) ? 0 : initial_distribution->NumofNodes(); // int nn = dynamic_cast(the_initial_distribution_of_the_markov_process)->NumofNodes(); int div = 1; int ds, ids; vector mdist; mdist.resize(nn); for(int i=0; i(the_initial_distribution_of_the_markov_process)->NodeByVar(i)->Domain().Size(); mdist.at(i) = vectr(ds, 0.0); } for (int ind=0; indClone(); } RVCSCompSS::RVCSCompSS(istream &is) { int nc; is >> nc; for(int i=0;i(StreamObj::LoadOldPtr(is)); ss.push_back(sptr); } } RVCSCompSS::~RVCSCompSS() { int nc = ss.size(); for(int i=0;i> nc; ss.resize(nc); for(int i=0;i(StreamObj::LoadOldPtr(is)); } void RVCSCompSS::SaveOld(ostream &os) const { int nc = ss.size(); os << nc; for(int i=0;iSaveOldPtr(os); } } RVCSCompSS *RVCSCompSS::Clone() const { return new RVCSCompSS(*this); } RVCSCompSS &RVCSCompSS::operator=(const RVCSCompSS &x) { if (&x!=this) { int nc = ss.size(); for(int i=0;iClone(); } return *this; } void RVCSCompSS::Scale(double w) { int n = ss.size(); for(int i=0; iScale(w); } void RVCSCompSS::AddSS(const SS* nss, double w) { const RVCSCompSS* rss = dynamic_cast(nss); int n = ss.size(); for(int i=0; iAddSS(rss->ss[i], w); } } // end of ctbn namespace ctbnrle.1.1.1/src/fbinf.cc0000664000175000017500000004072412107032416015553 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "fbinf.h" #include "factoredvector.h" #include "factoredmatrix.h" #include "dyncomp.h" #include "multisimple.h" #include "nullptr03.h" #include "rvcomp.h" #include using namespace std; namespace ctbn { double ZeroProb(bool use_log_probs); SparseMultiZSimple * dynamic_cast_to_SparseMultiZSimple(RVSimple * const ptr); FBInf::FBInf(): the_markov_process(nullptr03), the_trajectory(nullptr03), jointP0SS(nullptr03), jointDSS(nullptr03) {} FBInf::FBInf(FBInf const * const copy_from): the_markov_process(nullptr03 == copy_from->the_markov_process ? nullptr03 : copy_from->the_markov_process->Clone()), the_trajectory(copy_from->the_trajectory), time_points(copy_from->time_points), alpha(copy_from->alpha), beta(copy_from->beta), prop(copy_from->prop), condi(copy_from->condi), transition_kind(copy_from->transition_kind), evidence_variables_whose_observed_values_changed(copy_from->evidence_variables_whose_observed_values_changed), jointP0SS(nullptr03 == copy_from->jointP0SS ? nullptr03 : copy_from->jointP0SS->Clone()), jointDSS(nullptr03 == copy_from->jointDSS ? nullptr03 : copy_from->jointDSS->Clone()) {} FBInf::~FBInf() throw() { ClearCache(); // Do not delete the_trajectory: we don't own it (see inference.h). // Do not delete the_markov_process: we don't own it (see inference.h). } void FBInf::CloneTo(FBInf * const copy_to) const { copy_to->alpha.clear(); copy_to->beta.clear(); copy_to->prop.clear(); for (unsigned int i(0); i != alpha.size(); ++i) copy_to->alpha.push_back(alpha[i]->Clone()); for (unsigned int i(0); i != beta.size(); ++i) copy_to->beta.push_back(beta[i]->Clone()); for (unsigned int i(0); i != prop.size(); ++i) copy_to->prop.push_back(prop[i]->Clone()); if (jointP0SS) copy_to->jointP0SS = jointP0SS->Clone(); if (jointDSS) copy_to->jointDSS = jointDSS->Clone(); } void FBInf::SetProcess(Process const * const some_markov_process) { if (this->the_markov_process != some_markov_process) { ClearCache(); this->the_markov_process=nullptr03; this->the_markov_process = dynamic_cast(some_markov_process); } } void FBInf::SetTrajectory(const Trajectory * some_trajectory) { if (this->the_trajectory != some_trajectory) { ClearCache(); this->the_trajectory = some_trajectory; } } double FBInf::Prob(double time_point_of_interest, bool use_log_probs) { return FilterFuncImpl(Instantiation(), time_point_of_interest, use_log_probs, false); } double FBInf::Filter(const Instantiation & variable_assignment, double time_point_of_interest, bool use_log_probs) { return FilterFuncImpl(variable_assignment, time_point_of_interest, use_log_probs); } double FBInf::FilterFuncImpl(Instantiation const & variable_assignment, double const time_point_of_interest, bool const use_log_probs, bool const normalize_probs) { if (the_markov_process == nullptr03 || the_trajectory == nullptr03) { return ZeroProb(use_log_probs); } EnsurePassesPerformedAndResultsCached(); unsigned int const first_index_where_time_point_is_not_less_than_time_point_of_interest = lower_bound(time_points.begin(), time_points.end(), time_point_of_interest) - time_points.begin(); unsigned int const & i = first_index_where_time_point_is_not_less_than_time_point_of_interest; RVSimple *a; if (time_points[i] == time_point_of_interest) { a = alpha[i]->Clone(); } else { if (i == 0) { return ZeroProb(use_log_probs); } a = alpha[i-1]->Clone(); /*RVCondSimple *trans = the_markov_process_dynamics()->Cond(time_points[i-1], time_point_of_interest, the_trajectory->Values(the_markov_process_dynamics()->Domain() + the_markov_process_dynamics()->CondDomain(), time_point_of_interest)); */ RVCondSimple *trans = IntervalPropagator(the_markov_process_dynamics(), time_points[i-1], time_point_of_interest, the_trajectory->Values(the_markov_process_dynamics()->Domain() + the_markov_process_dynamics()->CondDomain(), time_point_of_interest)); trans->Mult(a); delete trans; } if (normalize_probs) a->Normalize(); double ret; if (variable_assignment.NumVars() == 0) ret = a->Sum(use_log_probs); else { Restrict(a, variable_assignment); /* vector ind; the_markov_process_dynamics()->Domain().ConsistentIndexes(ind, variable_assignment); a->Restrict(ind); */ ret = a->Sum(use_log_probs); } delete a; return ret; } double FBInf::Smooth(const Instantiation & variable_assignment, double time_point_of_interest, bool use_log_probs) { if (the_markov_process == nullptr03 || the_trajectory == nullptr03) { return ZeroProb(use_log_probs); } EnsurePassesPerformedAndResultsCached(); unsigned int const first_index_where_time_point_is_not_less_than_time_point_of_interest = lower_bound(time_points.begin(), time_points.end(), time_point_of_interest) - time_points.begin(); unsigned int const & i = first_index_where_time_point_is_not_less_than_time_point_of_interest; RVSimple *a; if (time_points[i] == time_point_of_interest) { a = alpha[i]->Clone(); a->MultBy(beta[i]); } else { if (i==0) { return ZeroProb(use_log_probs); } a = alpha[i-1]->Clone(); Instantiation inst = the_trajectory->Values(the_markov_process_dynamics()->Domain() + the_markov_process_dynamics()->CondDomain(), time_point_of_interest); //RVCondSimple *trans = the_markov_process_dynamics()->Cond(time_points[i-1], time_point_of_interest, inst); RVCondSimple *trans = IntervalPropagator(the_markov_process_dynamics(),time_points[i-1], time_point_of_interest, inst); trans->Mult(a); delete trans; RVSimple *b = (i==time_points.size() ? beta[i-1]->Clone() : beta[i]->Clone()); //trans = the_markov_process_dynamics()->Cond(time_point_of_interest, time_points[i], inst); trans = IntervalPropagator(the_markov_process_dynamics(), time_point_of_interest, time_points[i], inst); trans->RMult(b); delete trans; a->MultBy(b); delete b; } a->Normalize(); double ret; if (variable_assignment.NumVars()==0) ret = a->Sum(use_log_probs); else { vector ind; the_markov_process_dynamics()->Domain().ConsistentIndexes(ind,variable_assignment); a->Restrict(ind); ret = a->Sum(use_log_probs); } delete a; return ret; } void FBInf::ClearCache() { delete_each(alpha); free_memory(alpha); delete_each(beta); free_memory(beta); delete_each(prop); free_memory(prop); free_memory(time_points); free_memory(condi); free_memory(transition_kind); free_memory(evidence_variables_whose_observed_values_changed); if (jointP0SS) { delete jointP0SS; jointP0SS = nullptr03; } if (jointDSS) { delete jointDSS; jointDSS = nullptr03; } } void FBInf::EnsurePassesPerformedAndResultsCached() { if (time_points.empty()) { FetchInformationRequiredForPasses(); PerformForwardAlphaPass(); PerformBackwardBetaPass(); } } /* This method retrieves the entire sequence of probability distributions * that are conditioned on the evidence in the trajectory. This includes * not only transitions in time from one event to the next, but also * transitions where time does not elapse but an event occurs (for example, * the new availability or unavailability of part of the evidence). */ void FBInf::FetchInformationRequiredForPasses() { time_points.push_back(the_trajectory->TimeBegin()); Context const initial_context(the_markov_process_dynamics()->Domain() + the_markov_process_dynamics()->CondDomain()); Trajectory::Index i = the_trajectory->Begin(initial_context); while (!i.Done()) { double const start_time_point(i.Time()); double const past_end_time_point(start_time_point + i.DeltaT()); Instantiation const current_evidence(i.Values()); // RVCondSimple * prob(the_markov_process_dynamics()->Cond(start_time_point, past_end_time_point, current_evidence)); RVCondSimple * prob(IntervalPropagator( the_markov_process_dynamics(), start_time_point, past_end_time_point, current_evidence)); prop.push_back(prob); transition_kind.push_back(unchanged_with_respect_to_context); evidence_variables_whose_observed_values_changed.push_back(Context()); condi.push_back(the_markov_process_dynamics()->CondDomain().Index(current_evidence)); EvidenceChangeEnum const varchange(i.TestInc(the_markov_process_dynamics()->Domain())); double const double_equality_comparison_epsilon = 1.0e-6; assert(i.Time() - past_end_time_point < double_equality_comparison_epsilon); time_points.push_back(past_end_time_point); if (i.Done()) break; Instantiation const new_evidence(i.Values()); switch (varchange) { case unchanged_with_respect_to_context: // The conditioning set has changed, but nothing else has changed. // Therefore, nothing need be done at this moment (pun intended). // We merely want to proceed to the next iteration of the loop. continue; case value_of_an_observed_variable_changed: { // prob = the_markov_process_dynamics()->Cond(i.Time(), current_evidence, i.Values()); prob = PointTransitionPropagator(the_markov_process_dynamics(), past_end_time_point, current_evidence, new_evidence); evidence_variables_whose_observed_values_changed.push_back(Context(current_evidence, new_evidence)); } break; case only_variable_observability_changed: { // prob = the_markov_process_dynamics()->Cond(i.Time(), current_evidence, new_evidence, false); prob = PointChangeEvidencePropagator(the_markov_process_dynamics(), past_end_time_point, current_evidence, new_evidence); evidence_variables_whose_observed_values_changed.push_back(Context()); } break; default: // Unanticipated circumstance. throw; } prop.push_back(prob); condi.push_back(the_markov_process_dynamics()->CondDomain().Index(new_evidence)); time_points.push_back(past_end_time_point); transition_kind.push_back(varchange); } } // This method propagates the classic "alpha pass" starting with the initial // distribution indicated by the Markov process's starting distribution and // running it forward through the conditional distributions set up by // FetchInformationRequiredForPasses. They are saved for easy access later. void FBInf::PerformForwardAlphaPass() { unsigned int n = prop.size(); alpha.resize(n+1); RV * prior = the_initial_distribution_of_the_markov_process(); Instantiation const trajectory_values( the_trajectory->Values(prior->Domain() + prior->CondDomain(), the_trajectory->TimeBegin())); // TODO: replace with descendent's MakeSimple // RVSimple *curr = prior->MakeSimple(trajectory_values); RVSimple * curr = MakeSimple(prior, trajectory_values, true); /* if (dynamic_cast(curr)) { cout << "yay, curr is a factored vector." << endl; } else { cout << "warning, curr is not a factored vector." << endl; } if (dynamic_cast(prop[0])) { cout << "yay, prop[0] is a factored matrix." << endl; } else { cout << "warning, prop[0] is not a factored matrix." << endl; } */ if (n > 0) { alpha[0] = prop[0]->Convert(curr); delete curr; curr = alpha[0]->Clone(); } else { alpha[0] = curr->Clone(); } if (!IsAlphaElementValid(alpha[0])) { cerr << "alpha[0] is invalid" << endl; } for (unsigned int i = 0; i != n; ++i) { prop[i]->Mult(curr); alpha[i + 1] = curr->Clone(); if (!IsAlphaElementValid(alpha[i + 1])) { cerr << "alpha[" << i + 1 << "] is invalid" << endl; } } delete curr; } // This is the same as PerformForwardAlphaPass, but running the // distribution (starting with a uniform one) backward through the // conditional distributions. void FBInf::PerformBackwardBetaPass() { int i = prop.size(); beta.resize(i+1); RVSimple *curr = alpha[i]->Clone(); curr->MakeUniform(); // set to all 1s beta[i] = curr->Clone(); for(i--;i>=0;i--) { prop[i]->RMult(curr); beta[i] = curr->Clone(); } delete curr; } void FBInf::AddExpSuffStats(SS *ss, double w) { MarkovSS * mss = dynamic_cast(ss); EnsurePassesPerformedAndResultsCached(); int const n(prop.size()); for(int i = 0; i != n; ++i) { // we could construct the correct full RV and call AddExpSS on the // markov process dynamics, but that's a lot of extra unneeded // work given that we know the underlying structure switch (transition_kind[i]) { case unchanged_with_respect_to_context: /* the_markov_process_dynamics()->AddExpSS(condi[i], alpha[i], beta[i+1], time_points[i], time_points[i+1] - time_points[i], mss->dss, w); */ AddExpectedSufficientStatistics( the_markov_process_dynamics(), i, mss->dss, w); break; case value_of_an_observed_variable_changed: /* the_markov_process_dynamics()->AddExpTransSS(condi[i], alpha[i], beta[i+1], evidence_variables_whose_observed_values_changed[i], time_points[i], mss->dss, w); */ AddExpectedTransitionSufficientStatistics( the_markov_process_dynamics(), i, mss->dss, w); break; case only_variable_observability_changed: break; default: throw; } } if (!alpha.empty()) { // add suffstats for initial distribution RVSimple * initial_prob_dist = alpha[0]->Clone(); initial_prob_dist->MultBy(beta[0]); initial_prob_dist->Normalize(); RV * prior = the_initial_distribution_of_the_markov_process(); /* prior->AddExpSS(the_trajectory->Values(prior->CondDomain(), the_trajectory->TimeBegin()), initial_prob_dist, mss->p0ss, w); */ AddExpectedInitialSufficientStatistics( the_trajectory, initial_prob_dist, prior, mss->p0ss, w); delete initial_prob_dist; } } void FBInf::AddExpSuffStats(const Dynamics *dyn, SS *ss, double w) { CTBNDyn const * const cdyn = dynamic_cast(the_markov_process_dynamics()); DynComp const * const jcdyn = dynamic_cast(cdyn->GetJoint()); if(!jointDSS) { DynCompSS * const dcss = dynamic_cast(jcdyn->BlankSS()); EnsurePassesPerformedAndResultsCached(); int const n(prop.size()); for(int i(0); i != n; ++i) { if (transition_kind[i]==0) /* jcdyn->AddExpSS(condi[i],alpha[i],beta[i+1], time_points[i],time_points[i+1]-time_points[i],dcss,w); */ AddExpectedSufficientStatistics(jcdyn, i, dcss, w); else if (transition_kind[i]==2) /* jcdyn->AddExpTransSS(condi[i],alpha[i],beta[i+1], evidence_variables_whose_observed_values_changed[i], time_points[i],dcss,w); */ AddExpectedTransitionSufficientStatistics(jcdyn, i, dcss, w); } jointDSS = dcss; } dyn->AddSS(jointDSS,jcdyn,ss,w); } void FBInf::AddExpSuffStats(const RV * p0, SS * ss, double w) { const BN* bn = dynamic_cast( the_initial_distribution_of_the_markov_process()); RVComp *jbn = dynamic_cast(bn->Joint( Instantiation(bn->Domain()+bn->CondDomain()))); if(!jointP0SS) { RVCSCompSS* rvcscss = dynamic_cast(jbn->BlankSS()); EnsurePassesPerformedAndResultsCached(); if (!alpha.empty()) { RVSimple * initial_prob_dist = alpha[0]->Clone(); initial_prob_dist->MultBy(beta[0]); initial_prob_dist->Normalize(); /* jbn->AddExpSS(the_trajectory->Values(jbn->CondDomain(), the_trajectory->TimeBegin()), init, rvcscss, w); */ AddExpectedInitialSufficientStatistics( the_trajectory, initial_prob_dist, jbn, rvcscss, w); delete initial_prob_dist; } jointP0SS = rvcscss; } p0->AddSS(jointP0SS, jbn, ss, w); } double FBInf::CalcQuery(QueryCalculator &calc) { SS *ss = the_markov_process->BlankSS(); //ClearCache(); AddExpSuffStats(ss); double ret = calc.Calculate(ss); delete ss; return ret; } inline double ZeroProb(bool use_log_probs) { return use_log_probs ? -INFINITY : 0.0; } } // end of ctbn namespace ctbnrle.1.1.1/src/grapheditsearch.cc0000664000175000017500000001343612107032416017624 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "grapheditsearch.h" namespace ctbn { using namespace std; GraphEditSearch::GraphEditSearch(const Context& c, FamScore* fs, bool noCyc) : StructureSearch(c, fs), noCycles(noCyc), learnCalled(false) { int numNodes = vars.size(); sQ = new SearchQueue(numNodes*2*(numNodes-1), numNodes); Initialize(); } void GraphEditSearch::SetStructure(const Structure& s) { for(unsigned int i=0; i& curParList = s.parentlist[i]; int curvarid = s.node2var[i]; map::iterator it = var2node.find(curvarid); int ssnodeid = it != var2node.end() ? it->second : -1; if(ssnodeid == -1) continue; delete nodes[ssnodeid]; Context cv; for(unsigned int j=0; jsecond : -1; if(pnodeid == -1) continue; cv = cv + vars[pnodeid]; } double s = fScore->GetScore(vars[ssnodeid], cv); nodes[ssnodeid] = new Node(vars[ssnodeid], cv); scores[ssnodeid] = s; } } void GraphEditSearch::Initialize() { scores.clear(); for(unsigned int i=0; iGetScore(vars[i],nullc); nodes.push_back(new Node(vars[i],nullc)); scores.push_back(s); } } void GraphEditSearch::EnqueueOps(int i) { vector varLvid = nodes[i]->CondDomain().VarList(); vector varL; // Map the VarList into a new vector using node ids instead. for(unsigned int j=0; j(i) == j || find(varL.begin(),varL.end(),j) != varL.end()) continue; if(noCycles && ProducesCycle(i,j)) continue; double s = fScore->GetScore(nodes[i]->Domain(), nodes[i]->CondDomain() + vars[j]); double deltas = s - scores[i]; sQ->Add(SearchQueue::Action(i,j, SearchQueue::ADD,deltas,0)); } // Enqueue all REMOVE and REVERSE operations for(unsigned int j=0; jGetScore(nodes[i]->Domain(), nodes[i]->CondDomain() - vars[k]); double deltasrem = srem - scores[i]; sQ->Add(SearchQueue::Action(i,k, SearchQueue::REMOVE,deltasrem,0)); if(noCycles && ProducesCycleRev(k,i)) continue; double saddr = fScore->GetScore(nodes[k]->Domain(), nodes[k]->CondDomain() + vars[i]); double deltasaddr = saddr - scores[k]; sQ->Add(SearchQueue::Action(i,k, SearchQueue::REVERSE, deltasrem,deltasaddr)); } } bool GraphEditSearch::ProducesCycle(int i, int j) { Node* temp = nodes[i]; nodes[i] = new Node(nodes[i]->Domain(), nodes[i]->CondDomain() + vars[j]); Structure sCycleCheck; GetStructure(sCycleCheck); delete nodes[i]; nodes[i] = temp; return sCycleCheck.IsCyclic(); } bool GraphEditSearch::ProducesCycleRev(int i, int j) { Node* tempi = nodes[i]; Node* tempj = nodes[j]; nodes[i] = new Node(nodes[i]->Domain(), nodes[i]->CondDomain() + vars[j]); nodes[j] = new Node(nodes[j]->Domain(), nodes[j]->CondDomain() - vars[i]); Structure sCycleCheck; GetStructure(sCycleCheck); delete nodes[i]; delete nodes[j]; nodes[i] = tempi; nodes[j] = tempj; return sCycleCheck.IsCyclic(); } Structure GraphEditSearch::LearnStructure() { Context nullc; int numNodes = vars.size(); if(learnCalled) Initialize(); for(int i=0; iHead(nextAct); while(nextAct.gain + nextAct.gainrev > 0) { int ch = nextAct.child; int pa = nextAct.parent; SearchQueue::searchop op = nextAct.oper; if(op == SearchQueue::ADD) { Node* newNode = new Node(nodes[ch]->Domain(), nodes[ch]->CondDomain() + vars[pa]); delete nodes[ch]; nodes[ch] = newNode; scores[ch] += nextAct.gain; } else if(op == SearchQueue::REMOVE) { Node* newNode = new Node(nodes[ch]->Domain(), nodes[ch]->CondDomain() - vars[pa]); delete nodes[ch]; nodes[ch] = newNode; scores[ch] += nextAct.gain; } else if(op == SearchQueue::REVERSE) { Node* newNode = new Node(nodes[ch]->Domain(), nodes[ch]->CondDomain() - vars[pa]); delete nodes[ch]; nodes[ch] = newNode; scores[ch] += nextAct.gain; newNode = new Node(nodes[pa]->Domain(), nodes[pa]->CondDomain() + vars[ch]); delete nodes[pa]; nodes[pa] = newNode; scores[pa] += nextAct.gainrev; } if(noCycles) { sQ->Clear(); // complicated to determine what would // now be allowed (or now disallowed) due to // cycles... for now, just recalculate everything for(unsigned int i = 0; i < vars.size(); i++) EnqueueOps(i); } else { sQ->Remove(ch); if (op == SearchQueue::REVERSE) sQ->Remove(pa); EnqueueOps(ch); if(op == SearchQueue::REVERSE) EnqueueOps(pa); } sQ->Head(nextAct); } Structure s; GetStructure(s); learnCalled = true; return s; } } // end of ctbn namespace ctbnrle.1.1.1/src/sampler.cc0000664000175000017500000000216312107032416016125 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "sampler.h" namespace ctbn { using namespace std; Sampler::~Sampler() { } void Sampler::SetMethod(const VarSample *m) { method = m; } void Sampler::SetTrajectory(const Trajectory *traj) { evid = traj; begintime = evid->TimeBegin(); endtime = evid->TimeEnd(); } } // end of ctbn namespace ctbnrle.1.1.1/src/process.cc0000664000175000017500000000163512107032416016143 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "process.h" namespace ctbn { Process::~Process() { } } // end of ctbn namespace ctbnrle.1.1.1/src/meanfieldinf.cc0000664000175000017500000006326112107032416017111 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "meanfieldinf.h" #include "markovdyn.h" #include "rk.h" #include "params.h" #include "nullptr03.h" #include #include #include namespace ctbn { using namespace std; // The precision for the energy convergence accuracy const double PRECISION = 1e-5; MeanFieldInf::MeanFieldInf() : Inference(), factored(nullptr03), initialized(false), optimized(false), eps(1e-5) { } MeanFieldInf::~MeanFieldInf() { mus.clear(); gammas.clear(); delete factored; } MeanFieldInf* MeanFieldInf::Clone() const { MeanFieldInf* cloneInf = new MeanFieldInf(*this); if(this->factored) cloneInf->factored = this->factored->Clone(); else cloneInf->factored = nullptr03; return cloneInf; } void MeanFieldInf::SetProcess(const Process *p) { Reset(); this->p = nullptr03; this->p = dynamic_cast(p); } void MeanFieldInf::SetTrajectory(const Trajectory *tr) { Reset(); this->tr = nullptr03; this->tr = tr; } double MeanFieldInf::Filter(const Instantiation &x, double t, bool log) { return 0.0; } double MeanFieldInf::Smooth(const Instantiation &x, double t, bool log) { if(!optimized) Optimize(); return GetProb(x,t,log); } double MeanFieldInf::Prob(double t, bool log) { cout << "not implemented" << endl; return 0.0; } void MeanFieldInf::AddExpSuffStats(SS *ss, double w) { if(!optimized) Optimize(); MarkovSS *mss = dynamic_cast(ss); CTBNDynSS *css = dynamic_cast(mss->dss); const BN* bn = dynamic_cast(p->GetStartDist()); Instantiation b(bn->Domain()+bn->CondDomain()); b.SetAllVal(0); do { double startprob = Smooth(b, tr->TimeBegin()); if(startprob>0) bn->AddSS(b, mss->p0ss, startprob*w); } while(b.Inc()); const CTBNDyn* cdyn = dynamic_cast(p->GetDynamics()); Context c = cdyn->Domain(); vector varL = c.VarList(); vector cutPoints; for(unsigned int i=0; i varCP = mus.find(varL[i])->second.GetCutPoints(); for(unsigned int j=0; j::iterator itv; itv = unique(cutPoints.begin(),cutPoints.end()); cutPoints.resize(itv - cutPoints.begin()); for(unsigned int i=0; i( cdyn->NodeByVar(varL[i])); const Context& v = node->Domain(); const Context& cv = node->CondDomain(); Instantiation idx(v+cv); idx.SetAllVal(0); do { double timeSS = 0; for(unsigned int j=0; jAddSS(idx, 0, w, css->dynss[i],timeSS); } while(idx.Inc()); Instantiation v1(v); Instantiation v2(v); Instantiation parents(cv); v1.SetAllVal(0); v2.SetAllVal(0); parents.SetAllVal(0); Instantiation idx1(v+cv); Instantiation idx2(v+cv); Instantiation curri = tr->Values(v, tr->TimeBegin()); Instantiation nexti; Trajectory::Index index = tr->Begin(v); while(!index.Done()) { double t = index.Time(); int sign = index.TestInc(c); if(!index.Done()) { nexti = index.Values(); t = index.Time(); if(sign==2) { do { v1.SetVal(curri); v2.SetVal(nexti); idx1.SetVal(v1); idx1.SetVal(parents); idx2.SetVal(v2); idx2.SetVal(parents); double transSS = Smooth(parents, t); node->AddTransSS(idx1, idx2,t, css->dynss[i], transSS*w); } while(parents.Inc()); } curri = nexti; } } v1.SetAllVal(0); v2.SetAllVal(0); parents.SetAllVal(0); do { idx1.SetVal(parents); idx2.SetVal(parents); do { idx1.SetVal(v1); do { idx2.SetVal(v2); double transSS = 0; for(unsigned int j=0; jAddTransSS(idx1,idx2, 0, css->dynss[i],transSS*w); } while(v2.Inc()); } while(v1.Inc()); } while(parents.Inc()); } } void MeanFieldInf::AddExpSuffStats(const Dynamics *dyn, SS *ss, double w) { if(!optimized) Optimize(); const Context &v = dyn->Domain(); int vid = v.MinVar(); const Context &cv = dyn->CondDomain(); Context allv(v+cv); vector varL = allv.VarList(); vector cutPoints; for(unsigned int i=0; i varCP = mus.find(varL[i])->second.GetCutPoints(); for(unsigned int j=0; j::iterator itv; itv = unique(cutPoints.begin(),cutPoints.end()); cutPoints.resize(itv - cutPoints.begin()); Instantiation idx(v+cv); idx.SetAllVal(0); do { double timeSS = 0; for(unsigned int j=0; jAddSS(idx, 0, timeSS, ss, w); } while(idx.Inc()); Instantiation v1(v); Instantiation v2(v); Instantiation parents(cv); v1.SetAllVal(0); v2.SetAllVal(0); parents.SetAllVal(0); Instantiation idx1(v+cv); Instantiation idx2(v+cv); Instantiation curri = tr->Values(v, tr->TimeBegin()); Instantiation nexti; Trajectory::Index index = tr->Begin(v); while(!index.Done()) { double t = index.Time(); int sign = index.TestInc(allv); if(!index.Done()) { nexti = index.Values(); t = index.Time(); if(sign==2) { do { v1.SetVal(curri); v2.SetVal(nexti); idx1.SetVal(v1); idx1.SetVal(parents); idx2.SetVal(v2); idx2.SetVal(parents); double transSS = Smooth(parents, t); dyn->AddTransSS(idx1, idx2,t, ss, transSS*w); } while(parents.Inc()); } curri = nexti; } } v1.SetAllVal(0); v2.SetAllVal(0); parents.SetAllVal(0); do { idx1.SetVal(parents); idx2.SetVal(parents); do { idx1.SetVal(v1); do { idx2.SetVal(v2); double transSS = 0; for(unsigned int j=0; jAddTransSS(idx1,idx2, 0, ss,transSS*w); } while(v2.Inc()); } while(v1.Inc()); } while(parents.Inc()); } void MeanFieldInf::AddExpSuffStats(const RV *p0, SS *ss, double w) { Instantiation i(p0->Domain()+p0->CondDomain()); i.SetAllVal(0); do { double startprob = Smooth(i, tr->TimeBegin()); if(startprob>0) p0->AddSS(i, ss, startprob*w); } while(i.Inc()); } void MeanFieldInf::TransToFactored() { if(!p || factored) return; const CTBNDyn* cdyn = dynamic_cast (p->GetDynamics()); Context c = cdyn->Domain(); vector varL = c.VarList(); //Create a factored version of the dynamics CTBNDyn* factoredCDyn = new CTBNDyn(); Context nullc; for(unsigned int i=0; iAddNode(new MarkovDyn(cur, nullc)); } factoredCDyn->FillParams(*cdyn,true); factored = new Markov(p->GetStartDist()->Clone(),factoredCDyn); } void MeanFieldInf::Initialize() { if(!p || !tr || initialized) return; TransToFactored(); const Context &c = factored->GetDynamics()->Domain(); vector varL = c.VarList(); for(unsigned int ii=0; ii( factored->GetDynamics())-> NodeByVar(curVarId); const Context &nodeCon = curNode->Domain(); const MarkovDyn *curmdyn = dynamic_cast(curNode); const matrix &Q = (*curmdyn)(0)->Intensity(); int states = nodeCon.Size(); Trajectory trNode = tr->ExtractNodeTraj(curVarId); double ts = trNode.TimeBegin(); double te = trNode.TimeEnd(); Instantiation curri = trNode.Values(nodeCon, trNode.TimeBegin()); Trajectory::Index i = trNode.Begin(nodeCon); vector times; vector vals; vector transtype; times.push_back(trNode.TimeBegin()); vectr e0(states,0.0); int val = i.Values().Value(curVarId); bool startHidden = false; if(val == -1) { startHidden = true; e0 = vectr(states,1.0/states); } else { e0[val] = 1; } transtype.push_back(0); vals.push_back(e0); while(!i.Done()) { int trans = i.TestInc(nodeCon); times.push_back(i.Time()); int state = i.Values().Value(curVarId); vectr evid(states,0.0); if(state >= 0) evid[state] = 1.0; if(i.Done() && state == -1) { evid = vectr(states,1.0); } transtype.push_back(trans); vals.push_back(evid); } AddVar(curVarId); transTimes.insert(make_pair >( curVarId,times)); bool inHidden = false; for(unsigned int i=0; i alpha; deque beta; vectr curAlpha = alphastart; vectr curBeta = betastart; alpha.push_back(curAlpha); beta.push_front(curBeta); for(int j=0; j 1e-11) t -= 1e-11; AddMuVal(curVarId,t,dist); AddGammaVal(curVarId,t,gammaVal); } } } initialized = true; } void MeanFieldInf::Optimize() { // A bound on the maximum number of iterations for the optimization of the // ODEs const int KMAX = ParamInt("KMAX",30); if(!initialized) { PrintMu(); Initialize(); } if(optimized) return; const Context &c = factored->GetDynamics()->Domain(); vector varL = c.VarList(); bool converged = false; int runNumber = 1; double prevEnergy = -INT_MAX; double curEnergy = -INT_MAX; vectr deltaEnergies(3,INT_MAX); while(!converged) { prevEnergy = curEnergy; for(unsigned int i=0; i &curMu = mus.find(curVarId)->second; ContFunction &curGamma = gammas.find(curVarId)->second; const vector &hiddenPairs = hiddenIntervals.find(curVarId)->second; int states = GetNumStates(curVarId); for(unsigned int j=0; j childTrans = GetChildTransTimes(curVarId,t0,t1); map::reverse_iterator rit = childTrans.rbegin(); rhoTemp.Clear(); if(j==hiddenPairs.size()-1 && tr->Value(varL[i],tr->TimeEnd())) { rhoTemp.AddVal(t1,vectr(states,1)); } else { rhoTemp.AddVal(t1,curMu.GetVal(t1)); } // Solve rho backward // Integrate from t1 to t0 double t1temp = t1; // Take care of observed child transitions while(rit != childTrans.rend() && rit->first > t0) { double tPlus = rit->first+1e-11; int chVarId = rit->second; int chstates = GetNumStates(chVarId); matrix ceQ(chstates,chstates,0.0); mfBackward(this,curVarId, t1,tPlus,eps); pair trans = GetTrans(chVarId, rit->first); int y0 = trans.first; int y1 = trans.second; vectr rhotPlus = rhoTemp.GetVal(tPlus); for(int k=0; kfirst, ceQ,curVarId,k, false); rhotPlus[k] *= ceQ[y0][y1]; } t1=rit->first; rhoTemp.AddVal(t1,rhotPlus); ++rit; } // Finish up the backward integration mfBackward(this,curVarId,t1,t0,eps); t1=hiddenPairs[j].second; muTemp.Clear(); // Run mean field for BNs on t_0 if unobserved if(t0 == tr->TimeBegin() && tr->Value(curVarId, tr->TimeBegin()) == -1) { vectr mut0(states,0.0); vectr rhot0 = rhoTemp.GetVal(t0); CalcMuStart(curVarId,rhot0,mut0); muTemp.AddVal(t0,mut0); } else { muTemp.AddVal(t0,curMu.GetVal(t0)); } gammaTemp.Clear(); matrix gammaVal(states,states,0.0); CalcGamma(curVarId,t0, muTemp.GetVal(t0), rhoTemp.GetVal(t0),gammaVal); gammaTemp.AddVal(t0,gammaVal); // Solve mu forward // Integrate from t0 to t1 mfForward(this,curVarId,t0,t1,eps); if(j==hiddenPairs.size()-1 && tr->Value(curVarId, tr->TimeEnd()) == -1) { muTemp.AddVal(t1, muTemp.GetVal(t1-1e-11)); muTemp.EraseVal(t1-1e-11); gammaTemp.AddVal(t1, gammaTemp.GetVal(t1-1e-11)); gammaTemp.EraseVal(t1-1e-11); } else { muTemp.AddVal(t1,curMu.GetVal(t1)); gammaVal *= 0; gammaTemp.AddVal(t1,gammaVal); } curMu.Replace(muTemp); curGamma.Replace(gammaTemp); } } curEnergy = CalcEnergy(); deltaEnergies[runNumber%3] = curEnergy - prevEnergy; converged = runNumber >= KMAX || abs(deltaEnergies.sum() / (3 * prevEnergy)) < 0.01 || curEnergy == 0 || abs(curEnergy - prevEnergy) < PRECISION; runNumber++; } optimized = true; } void MeanFieldInf::CalcRhoGrad(int varid, double t, const vectr &rhoVal, vectr &rhoGrad) { int states = GetNumStates(varid); assert(rhoGrad.getm() == states); matrix qBack(states,states,0.0); CalcExpectQ(varid,t,qBack); vectr psi(states,0.0); CalcPsi(varid,t,psi); for(int xi=0; xi(p->GetDynamics()); const MarkovDyn* node = dynamic_cast( cdyn->NodeByVar(varid)); const Context& v = node->Domain(); const Context& cv = node->CondDomain(); int states = v.Size(); // expectQ = matrix(states,states,0.0); // expectQ *= 0; Context jv; bool cond = false; if(jvarid != -1 && xj != -1) { jv = cdyn->NodeByVar(jvarid)->Domain(); cond = true; } Instantiation i(cv-jv); vector paList = i.VarList(); i.SetAllVal(0); Instantiation jvi(jv); if(cond) jvi.SetVal(jvarid, xj); Instantiation idx(cv); for(int xi=0;xi second.GetVal(t) [i.Value(paList[k])]; muprod *= thismu; } if (muprod==0.0) continue; idx.SetVal(i); idx.SetVal(jvi); double rate = (*node)(idx)->Intensity()[xi][yi]; if(xi!=yi && log) rate = ::log(rate); val += muprod*rate; } while(i.Inc()); expectQ[xi][yi] = (xi!=yi && log ? exp(val) : val); } } } // Needs mu, gamma, eQ updated void MeanFieldInf::CalcPsi(int varid, double t, vectr &psi) { int states = GetNumStates(varid); psi *= 0; const CTBNDyn* cdyn = dynamic_cast(p->GetDynamics()); vector children = cdyn->GetChildrenByVar(varid); for(unsigned int j=0; jNodeByVar(chvarid)->Domain().Size(); vectr muVal = mus.find(chvarid)->second.GetVal(t); matrix gammaVal = gammas.find(chvarid)->second.GetVal(t); for(int xi=0; xisecond.GetVal(t); matrix gammaVal = gammas.find(varid)->second.GetVal(t); double res = 0; for(int x=0; x( p->GetDynamics()); const MarkovDyn* node = dynamic_cast( cdyn->NodeByVar(varid)); const Context& v = node->Domain(); const Context& cv = node->CondDomain(); int i1 = v.Index(x1); int i2 = v.Index(x2); Instantiation parents(x1-v); parents.SetVal(x1); double ret = 1; if(i1!=i2) { ret *= gammas.find(varid)->second.GetVal(t)[i1][i2]; ret *= Smooth(parents,t); } else ret = 0; return ret; } double MeanFieldInf::CalcCompEnergy(int varid) { ContFunction &curMu = mus.find(varid)->second; map >::iterator it = transTimes.find(varid); double res = 0; double finalres = 0; vector cutPoints = curMu.GetCutPoints(); for(unsigned int i=0; iGetDynamics()->Domain(); vector varL = c.VarList(); for(unsigned int i=0; i >::const_iterator it = mus.begin(); for(; it!=mus.end(); ++it) { cout << "var: " << it->first << endl; it->second.Print(); cout << endl << "====================" << endl << endl; } } void MeanFieldInf::PrintGamma() const { map >::const_iterator it = gammas.begin(); for(; it!=gammas.end(); ++it) { cout << "var: " << it->first << endl; it->second.Print(); cout << endl << "====================" << endl << endl; } } void MeanFieldInf::PrintHidden() const { map >::const_iterator it = hiddenIntervals.begin(); for(; it!=hiddenIntervals.end(); ++it) { cout << "var: " << it->first << endl; cout << it->second; cout << endl << "====================" << endl << endl; } } double MeanFieldInf::CalcQuery(QueryCalculator &calc) { SS *ss = p->BlankSS(); AddExpSuffStats(ss); double ret = calc.Calculate(ss); delete ss; return ret; } pair MeanFieldInf::GetTrans(int varid, double t) const { int from = tr->Value(varid,t-1e-11); int to = tr->Value(varid,t+1e-11); return make_pair(from,to); } map MeanFieldInf::GetChildTransTimes(int varid, double start, double end) const { const CTBNDyn* cdyn = dynamic_cast( p->GetDynamics()); vector children = cdyn->GetChildrenByVar(varid); map ret; for(unsigned int i=0; i transTimes = GetObservedTransTimes(children[i],start,end); for(unsigned int j=0; j( transTimes[j],children[i])); } } return ret; } vector MeanFieldInf::GetObservedTransTimes(int varid, double start, double end) const { const Context &nodeCon = dynamic_cast( p->GetDynamics())-> NodeByVar(varid)->Domain(); Trajectory trNode = tr->ExtractNodeTraj(varid); Instantiation curri = trNode.Values(nodeCon, trNode.TimeBegin()); Trajectory::Index i = trNode.Begin(nodeCon); vector times; while(!i.Done()) { if(i.TestInc(nodeCon)!=2) continue; double t = i.Time(); if(t>start && t(p->GetStartDist()); const RV *node = bn->NodeByVar(varid); const Context &v = bn->NodeByVar(varid)->Domain(); vector varL = bn->GetChildrenByVar(varid); varL.push_back(varid); int states = v.Size(); vectr factor(states,0.0); for(unsigned int i=0; iNodeByVar(varL[i]); Instantiation x(curNode->Domain()+curNode->CondDomain()); x.SetAllVal(0); Instantiation nx(x-v); do { nx.SetVal(x); double prob = GetProb(nx,tr->TimeBegin()); if (prob>0) factor[x.Value(varid)] += prob*curNode->Prob(x,true); } while(x.Inc()); } for(int i=0; i vList = x.VarList(); for(unsigned int i=0; isecond.GetVal(t)[val]; if(ret==0.0) break; } return !log ? ret : ::log(ret); } void MeanFieldInf::AddVar(int varid) { mus.insert(make_pair >( varid, ContFunction())); gammas.insert(make_pair >( varid, ContFunction())); hiddenIntervals.insert(make_pair >( varid, vector())); } void MeanFieldInf::AddMuVal(int varid, double t, const vectr &vals) { map >::iterator it = mus.find(varid); if(it == mus.end()) return; it->second.AddVal(t, vals); } void MeanFieldInf::AddGammaVal(int varid, double t, const matrix &vals) { map >::iterator it = gammas.find(varid); if(it == gammas.end()) return; it->second.AddVal(t, vals); } void MeanFieldInf::AddHiddenInterval(int varid, double start, double end) { map >::iterator it = hiddenIntervals.find(varid); if(it == hiddenIntervals.end()) return; it->second.push_back(double_pair(start,end)); } void MeanFieldInf::Reset() { initialized = false; optimized = false; if(factored) delete factored; factored = nullptr03; mus.clear(); gammas.clear(); hiddenIntervals.clear(); transTimes.clear(); } void MeanFieldInf::SetEPS(double neweps) { Reset(); eps = neweps; } } // end of ctbn namespace ctbnrle.1.1.1/src/random.cc0000664000175000017500000001726412107032416015752 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "random.h" #include "defines.h" #include #include namespace ctbn { // Define a constant to be 2 * sqrt(2/e); used in SampleNormal() const double TWICE_A = 1.7155277699214135; using namespace std; const double Random::Conv32Double = 1.0/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2/2; const double Random::Conv64Double = Random::Conv32Double*Random::Conv32Double; Random randomizer(GET_TIME % (1 << 24)); Random::Random (Random::uint32 in_seed) { static int init_seed = 5489; if (in_seed > 0) seed = in_seed; else seed = init_seed; init_seed++; Reset(seed); } void Random::Reset (Random::uint32 in_seed) { if (in_seed > 0) seed = in_seed; MT[0] = seed; for(int i=1;i<624;i++) MT[i] = (1812433253 * (MT[i-1] ^ (MT[i-1]>>30))+i) & 0xffffffff; loc = 0; } Random::uint32 Random::Rand32() { if (loc==0) generateMore(); uint32 ret = MT[loc]; ret ^= ret>11; ret ^= (ret<<7) & 0x9d2c5680; ret ^= (ret<<15) & 0xefc60000; ret ^= ret>>18; loc++; if (loc==624) loc=0; return ret&0xffffffff; } void Random::generateMore() { for(int i=0;i<624;i++) { int i1 = i==623 ? 0 : i+1; int i397 = i>226 ? i-227 : i+397; uint32 t = (MT[i]&0x80000000) + (MT[i1]&0x7fffffff); MT[i] = MT[i397] ^ (t>>1); if (t & 1) MT[i] = MT[i] ^ 0x9908b0df; MT[i] &= 0xffffffff; } } double Random::RandRealFast() { return Rand32()*Conv32Double; } double Random::RandReal() { return Rand32()*Conv32Double+Rand32()*Conv64Double; } int Random::RandInt(int range) { // for small range, this works fine return int(RandRealFast()*range); } // The algorithm is taken from "Non-Uniform Random Variate Generation" by // Luc Devroye pages 194-199 double Random::SampleNormal (void) { double u, v, x, sqr_x; while (true) { u = RandReal(); if (u == 0.0) continue; v = (RandReal() - 0.5) * TWICE_A; x = v / u; sqr_x = x*x; if (sqr_x <= 6 - 8*u + 2*u*u) return x; if (sqr_x >= 2 / u - 2 * u) continue; if (sqr_x <= -4 * ::log(u)) return x; } } // The algorithm is taken from Cheng, Generating Beta Variates // with Nonintegral Shape Parameters. Communications of the ACM // 1978 Vol 21 No 4 p317 - p322 double Random::SampleBeta (double a, double b) { double s, lambda, u, u1, u2, v, y; s = a+b; double minab = a= ::log(u1*u1*u2)) { return (y/(y+b)); } } } double Random::SampleGamma (double a, double b) { if( a < 1.0 ) return DblGammaLessThanOne(a)/b; else if( a > 1.0 ) return DblGammaGreaterThanOne(a)/b; return -::log(RandReal()); } // Code adopted from Nir who adapted it from David Heckerman //----------------------------------------------------------- // DblGammaGreaterThanOne(dblAlpha) // // routine to generate a gamma random variable with unit scale and // alpha > 1 // reference: Ripley, Stochastic Simulation, p.90 // Chang and Feast, Appl.Stat. (28) p.290 //----------------------------------------------------------- double Random::DblGammaGreaterThanOne(double dblAlpha) { double rgdbl[6]; rgdbl[1] = dblAlpha - 1.0; rgdbl[2] = (dblAlpha - (1.0 / (6.0 * dblAlpha))) / rgdbl[1]; rgdbl[3] = 2.0 / rgdbl[1]; rgdbl[4] = rgdbl[3] + 2.0; rgdbl[5] = 1.0 / sqrt(dblAlpha); for (;;) { double dblRand1; double dblRand2; do { dblRand1 = RandReal(); dblRand2 = RandReal(); if (dblAlpha > 2.5) dblRand1 = dblRand2 + rgdbl[5] * (1.0 - 1.86 * dblRand1); } while (!(0.0 < dblRand1 && dblRand1 < 1.0)); double dblTemp = rgdbl[2] * dblRand2 / dblRand1; if (rgdbl[3] * dblRand1 + dblTemp + 1.0 / dblTemp <= rgdbl[4] || rgdbl[3] * ::log(dblRand1) + dblTemp - ::log(dblTemp) < 1.0) { return dblTemp * rgdbl[1]; } } assert(false); return 0.0; } /* routine to generate a gamma random variable with unit scale and * alpha < 1 reference: Ripley, Stochastic Simulation, p.88 */ double Random::DblGammaLessThanOne(double dblAlpha) { double dblTemp; const double dblexp = exp(1.0); for (;;) { double dblRand0 = RandReal(); double dblRand1 = RandReal(); if (dblRand0 <= (dblexp / (dblAlpha + dblexp))) { dblTemp = pow(((dblAlpha + dblexp) * dblRand0) / dblexp, 1.0 / dblAlpha); if (dblRand1 <= exp(-1.0 * dblTemp)) return dblTemp; } else { dblTemp = -1.0 * ::log((dblAlpha + dblexp) * (1.0 - dblRand0) / (dblAlpha * dblexp)); if (dblRand1 <= pow(dblTemp,dblAlpha - 1.0)) return dblTemp; } } assert(false); return 0.0; } /* DblGammaLessThanOne */ void Random::SampleDirichlet(double const *alpha, int n, double *theta) { double sum = 0; for(int i =0; i < n; i++) sum += (theta[i] = SampleGamma(alpha[i], 1)); // normalize for(int i =0; i < n; i++) theta[i] /= sum; } double Random::SampleExp (double a) { // where a = intensity = 1/mean ///// ///// Need to fix so it never samples a 0? if(isfinite(1/a)) return (-::log(1.0 - RandReal()) / a); else return INFINITY; } double Random::SampleTruncateExp(double q, double t) { double u1 = 1 - exp(-q * t); double u = RandReal() * u1; double ret = (-::log(1.0 - u)/q); return ret; } int Random::SampleAddWeights (const vector& additive_weights) { double val = RandReal() * additive_weights[additive_weights.size()-1]; return lower_bound(additive_weights.begin(),additive_weights.end(),val) - additive_weights.begin(); } int Random::SampleMultinomial(const double *weights, int n, double sum) { double j = RandReal()*sum; for(int i=0;i0) { // added so that an intensity row can be used // directly j -= weights[i]; if (j<=0) return i; } } return n-1; } int Random::SampleMultinomial(const vectr &weights, double sum) { double j= RandReal()*sum; int n = weights.length(); for(int i=0;i0) { // added so that an intensity row can be used // directly j -= weights[i]; if (j<=0) return i; } } return weights.length()-1; } int Random::SampleMultinomial(const vector &weights, double sum) { double j= RandReal()*sum; int n = weights.size(); for(int i=0;i0) { // added so that an intensity row can be used // directly j -= weights[i]; if (j<=0) return i; } } return weights.size()-1; } //sample n samples from multinomial void Random::SampleMultinomial(const vector &weights, vector &samples, int n) { int length = weights.size(); double * cdf = new double [length]; cdf[0] = weights[0]; for(int i=1; icdf[idx]) idx++; samples.push_back(idx); } delete [] cdf; } } // end of ctbn namespace ctbnrle.1.1.1/src/rv.cc0000664000175000017500000000313612107032416015112 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "rv.h" namespace ctbn { using namespace std; RV::RV(const Context &var, const Context &cvar) : StreamObj(), v(var), cv(cvar) { } RV::RV(istream &is) : StreamObj(), v(is), cv(is) { } RV::~RV() { } void RV::LoadOld(istream &is) { v.LoadOld(is); cv.LoadOld(is); } void RV::SaveOld(ostream &os) const { v.SaveOld(os); os << os.fill(); cv.SaveOld(os); } SS* RV::SuffStats(const vector &tr, const vector &w) const { SS* ss = this->BlankSS(); const Context &c = this->Domain() + this->CondDomain(); for(unsigned int i=0; iAddSS(curri, ss, w[i]); } return ss; } } // end of ctbn namespace ctbnrle.1.1.1/src/famscore.cc0000664000175000017500000000377112107032416016267 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "famscore.h" #include "markovdyn.h" #include "ss.h" #include "multirv.h" #include namespace ctbn { using namespace std; FamScore::FamScore(double nTrans, double aTime, SuffStatsQuery* ssQ) : numTrans(nTrans), amtTime(aTime), ssQuery(ssQ) { } FamScore::~FamScore() { } // Subclasses below // ================ BNFamScore::BNFamScore(double nTrans, SuffStatsQuery* ssQ) : FamScore(nTrans,0,ssQ) { } CTBNFamScore::CTBNFamScore(double nTrans, double aTime, SuffStatsQuery* ssQ) : FamScore(nTrans,aTime,ssQ) { } //--- BNFamScore::~BNFamScore() { } CTBNFamScore::~CTBNFamScore() { } //--- double BNFamScore::GetScore(const Context& v, const Context& cv) { MultiRV* test = new MultiRV(v, cv); ssQuery->SetInitRV(test); SS* ss = ssQuery->GetP0SS(); double ret = test->GetScore(numTrans,ss); delete ss; ssQuery->SetInitRV(NULL); delete test; return ret; } double CTBNFamScore::GetScore(const Context& v, const Context& cv) { MarkovDyn* test = new MarkovDyn(v, cv); ssQuery->SetDynamics(test); SS* ss = ssQuery->GetDSS(); double ret = test->GetScore(numTrans,amtTime,ss); delete ss; ssQuery->SetDynamics(NULL); delete test; return ret; } } // end of ctbn namespace ctbnrle.1.1.1/src/trajsimple.cc0000664000175000017500000001657412107032416016647 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "trajsimple.h" #include namespace ctbn { using namespace std; TrajSimple::~TrajSimple() { } TrajSimple::Index::~Index() { } TrajSimpleMap::TrajSimpleMap() { te = ts = 0.0; } TrajSimpleMap::~TrajSimpleMap() { } int TrajSimpleMap::Value(double t, bool inclusive) const { if (t>te || (t==te && inclusive)) return -1; if (tfirst>t || (!inclusive && i->first==t))) ++i; return i==tr.rend() ? -1 : i->second; } void TrajSimpleMap::AddTransition(int val, double t) { if (ts==te && tr.empty()) ts=te=t; else if (ts>t) ts = t; else if (tesecond; done = false; } TrajSimpleMap::RIndex::RIndex(const TrajSimpleMap &tsm, double t0, double tstart) : loc(tsm.tr.lower_bound(t0)) { startt = tstart; t = t0; end = tsm.tr.rend(); val = loc.base()->second; done = false; } TrajSimpleMap::Index::~Index() { } TrajSimpleMap::RIndex::~RIndex() { } void TrajSimpleMap::Index::Next() { if (loc!=end) { t = loc->first; val = loc->second; ++loc; if (loc->first>=endt) loc=end; } else { done = true; t = endt; } } void TrajSimpleMap::RIndex::Next() { if (loc!=end) { t = loc->first; ++loc; val = loc->second; if (loc->firstfirst-t; } double TrajSimpleMap::RIndex::DeltaT() const { return loc==end ? t-startt : t-loc->first; } int TrajSimpleMap::Index::Value() const { return done ? -1 : val; } int TrajSimpleMap::RIndex::Value() const { return done ? -1 : val; } bool TrajSimpleMap::Index::Done() const { return done; } bool TrajSimpleMap::RIndex::Done() const { return done; } TrajSimpleMap::Index *TrajSimpleMap::Begin() const { return new TrajSimpleMap::Index(*this,ts,te); } TrajSimpleMap::Index *TrajSimpleMap::Begin(double startt, double endt) const { return new TrajSimpleMap::Index(*this,startt,endt); } TrajSimpleMap::RIndex *TrajSimpleMap::End() const { return new TrajSimpleMap::RIndex(*this,te,ts); } TrajSimpleMap::RIndex *TrajSimpleMap::End(double startt, double endt) const { return new TrajSimpleMap::RIndex(*this,endt,startt); } //---------- ConstFlatTraj::ConstFlatTraj(const Trajectory &traj, const Context &vars) : tr(traj), c(vars) { } ConstFlatTraj::~ConstFlatTraj() { } double ConstFlatTraj::TimeBegin() const { return tr.TimeBegin(); } double ConstFlatTraj::TimeEnd() const { return tr.TimeEnd(); } void ConstFlatTraj::SetBeginTime(double t) { assert(0); // not possible } void ConstFlatTraj::SetEndTime(double t) { assert(0); // not possible } int ConstFlatTraj::Value(double t, bool inclusive) const { return tr.Values(c,t,inclusive).Index(); } void ConstFlatTraj::AddTransition(int val, double t) { assert(0); // not possible } ConstFlatTraj::Index::Index(const ConstFlatTraj &ft, double tstart, double tend) : impl(ft.tr,ft.c,tstart,tend) { } ConstFlatTraj::RIndex::RIndex(const ConstFlatTraj &ft, double tstart, double tend) : impl(ft.tr,ft.c,tend,tstart) { } ConstFlatTraj::Index::~Index() { } ConstFlatTraj::RIndex::~RIndex() { } void ConstFlatTraj::Index::Next() { ++impl; } void ConstFlatTraj::RIndex::Next() { --impl; } double ConstFlatTraj::Index::Time() const { return impl.Time(); } double ConstFlatTraj::RIndex::Time() const { return impl.Time(); } double ConstFlatTraj::Index::DeltaT() const { return impl.DeltaT(); } double ConstFlatTraj::RIndex::DeltaT() const { return impl.DeltaT(); } int ConstFlatTraj::Index::Value() const { return impl.Values().Index(); } int ConstFlatTraj::RIndex::Value() const { return impl.Values().Index(); } bool ConstFlatTraj::Index::Done() const { return impl.Done(); } bool ConstFlatTraj::RIndex::Done() const { return impl.Done(); } ConstFlatTraj::Index *ConstFlatTraj::Begin() const { return new ConstFlatTraj::Index(*this,TimeBegin(),TimeEnd()); } ConstFlatTraj::Index *ConstFlatTraj::Begin(double startt, double endt) const { return new ConstFlatTraj::Index(*this,startt,endt); } ConstFlatTraj::RIndex *ConstFlatTraj::End() const { return new ConstFlatTraj::RIndex(*this,TimeBegin(),TimeEnd()); } ConstFlatTraj::RIndex *ConstFlatTraj::End(double startt, double endt) const { return new ConstFlatTraj::RIndex(*this,startt,endt); } //-------- FlatTraj::FlatTraj(Trajectory &traj, const Context &vars) : tr(traj), c(vars) { } FlatTraj::~FlatTraj() { } double FlatTraj::TimeBegin() const { return tr.TimeBegin(); } double FlatTraj::TimeEnd() const { return tr.TimeEnd(); } void FlatTraj::SetBeginTime(double t) { tr.SetBeginTime(t); } void FlatTraj::SetEndTime(double t) { tr.SetEndTime(t); } int FlatTraj::Value(double t, bool inclusive) const { return tr.Values(c,t,inclusive).Index(); } void FlatTraj::AddTransition(int val, double t) { Instantiation i(c); i.SetIndex(val); tr.AddTransition(i,t); } FlatTraj::Index::Index(const FlatTraj &ft, double tstart, double tend) : impl(ft.tr,ft.c,tstart,tend) { } FlatTraj::RIndex::RIndex(const FlatTraj &ft, double tstart, double tend) : impl(ft.tr,ft.c,tend,tstart) { } FlatTraj::Index::~Index() { } FlatTraj::RIndex::~RIndex() { } void FlatTraj::Index::Next() { ++impl; } void FlatTraj::RIndex::Next() { --impl; } double FlatTraj::Index::Time() const { return impl.Time(); } double FlatTraj::RIndex::Time() const { return impl.Time(); } double FlatTraj::Index::DeltaT() const { return impl.DeltaT(); } double FlatTraj::RIndex::DeltaT() const { return impl.DeltaT(); } int FlatTraj::Index::Value() const { return impl.Values().Index(); } int FlatTraj::RIndex::Value() const { return impl.Values().Index(); } bool FlatTraj::Index::Done() const { return impl.Done(); } bool FlatTraj::RIndex::Done() const { return impl.Done(); } FlatTraj::Index *FlatTraj::Begin() const { return new FlatTraj::Index(*this,TimeBegin(),TimeEnd()); } FlatTraj::Index *FlatTraj::Begin(double startt, double endt) const { return new FlatTraj::Index(*this,startt,endt); } FlatTraj::RIndex *FlatTraj::End() const { return new FlatTraj::RIndex(*this,TimeBegin(),TimeEnd()); } FlatTraj::RIndex *FlatTraj::End(double startt, double endt) const { return new FlatTraj::RIndex(*this,startt,endt); } } //end of ctbn namespace ctbnrle.1.1.1/src/trajectory.cc0000664000175000017500000001715212107032416016654 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "trajectory.h" namespace ctbn { using namespace std; Trajectory::Trajectory() { ts = te = 0; } Trajectory::~Trajectory() { } int Trajectory::Value(int varid, double time, bool inclusive) const { if (time>te || (time==te && inclusive)) return -1; if (timesecond.upper_bound(time)); while (i!=vari->second.rend() && (i->first>time || (!inclusive && i->first==time))) { ++i; } return i==vari->second.rend() ? -1 : i->second; } Instantiation Trajectory::Values(const Context &c, double time, bool inclusive) const { Instantiation ret(c); if (time>te || (time==te && inclusive)) return ret; if (timefirst,Value(vari,time,inclusive)); return ret; } void Trajectory::AddTransition(int varid, double time, int newval) { if (ts==te && traj.empty()) ts=te=time; else if (ts>time) ts = time; else if (tesecond.insert(make_pair(time,newval)); } void Trajectory::AddTransition(const Instantiation &i, double time) { Instantiation oldi = Values(i,time); vector varlist = i.VarList(); for(unsigned int j=0;j> ts >> te >> traj; } ostream &operator<<(ostream &os, const Trajectory &tr) { tr.SaveOld(os); return os; } istream &operator>>(istream &is, Trajectory &tr) { tr.LoadOld(is); return is; } Trajectory::Index::~Index() { } Trajectory::Index::Index(const Trajectory &tr, const Context &c, double t0, double tend) { done = false; t = t0; endt = tend; v = tr.Values(c,t0); for(ivmap::const_iterator vi = tr.traj.begin(); vi != tr.traj.end(); vi++) { if(!c.HasId(vi->first)) continue; VarTrajectory::const_iterator i = vi->second.upper_bound(t0); if (i!=vi->second.end()) { vari.insert(make_pair(vi->first,make_pair(i,vi->second.end()))); if (i->firstfirst,vi->first)); } } } EvidenceChangeEnum Trajectory::Index::TestInc(const Context & c) { EvidenceChangeEnum ret(unchanged_with_respect_to_context); if (nextvar.empty()) { done = true; t = endt; if (-1 != v.Index()) { ret = only_variable_observability_changed; } v.SetAllVal(-1); return ret; } double newt = -nextvar.top().first; while(!nextvar.empty() && -nextvar.top().first == newt) { int vi = nextvar.top().second; nextvar.pop(); std::map >::iterator spot = vari.find(vi); VarTrajectory::const_iterator &vti = spot->second.first; VarTrajectory::const_iterator &vtiend = spot->second.second; if (c.HasId(vi)) { if (vti->second != -1 && v.Value(vi) != -1) { ret = value_of_an_observed_variable_changed; } else if (ret == unchanged_with_respect_to_context) { ret = only_variable_observability_changed; } } v.SetVal(vi, vti->second); ++vti; if (vti!=vtiend && vti->firstfirst, vi)); } } t = newt; return ret; } Trajectory::RIndex::~RIndex() { } Trajectory::RIndex::RIndex(const Trajectory &tr, const Context &c, double t0, double tstart) { done = false; t = t0; startt = tstart; v = tr.Values(c,t0,false); for(ivmap::const_iterator vi = tr.traj.begin(); vi != tr.traj.end(); vi++) { if(!c.HasId(vi->first)) continue; VarTrajectory::const_reverse_iterator i(vi->second.lower_bound(t0)); if (i!=vi->second.rend()) { vari.insert(make_pair(vi->first,make_pair(i,vi->second.rend()))); if (i->first>startt) nextvar.push(make_pair(i->first,vi->first)); } } } int Trajectory::RIndex::TestDec(const Context &c) { if (nextvar.empty()) { done = true; t = startt; int retval; if(v.Index()==-1) retval = 0; else retval = 1; v.SetAllVal(-1); return retval; } int ret = 0; double newt = nextvar.top().first; while(!nextvar.empty() && nextvar.top().first==newt) { int vi = nextvar.top().second; nextvar.pop(); std::map >::iterator spot = vari.find(vi); VarTrajectory::const_reverse_iterator &vti = spot->second.first; VarTrajectory::const_reverse_iterator &vtiend = spot->second.second; ++vti; if (c.HasId(vi)) { if (vti->second != -1 && v.Value(vi)!=-1) ret = 2; else if (ret==0) ret = 1; } v.SetVal(vi,vti->second); if (vti!=vtiend && vti->first>startt) { nextvar.push(make_pair(vti->first,vi)); } } t = newt; return ret; } void Trajectory::Draw(ostream &os) const { ios::fmtflags oldflags = os.flags(); os.setf(ios::fixed); Context all; os << "time \\ var "; for(ivmap::const_iterator i=traj.begin();i!=traj.end();++i) { os.width(5); os << i->first << " "; all.AddVar(i->first,2); } os << endl; Instantiation oldv(all,-1); for(Index ii = Begin(all);!ii.Done();++ii) { //os << "_____________________________________________________________" << endl; os << ii.Time() << " "; for(ivmap::const_iterator i=traj.begin();i!=traj.end();++i) { if (oldv.Value(i->first)!=ii.Values().Value(i->first)) { os.width(5); os << ii.Values().Value(i->first) << " "; } else { os << " "; } } os << endl; oldv = ii.Values(); } os.flags(oldflags); } void Trajectory::SetUnknown(int varid, bool noinit) { traj.erase(varid); if (!noinit) AddTransition(varid, ts, -1); } void Trajectory::RemoveNodeTraj(int varid) { traj.erase(varid); } void Trajectory::ReplaceNodeTraj(int varid, const Trajectory &tr) { map::const_iterator iter = tr.traj.find(varid); if (iter != tr.traj.end()) traj[varid] = iter->second; } Trajectory Trajectory::ExtractNodeTraj(int varid) const { map::const_iterator iter = traj.find(varid); Trajectory tr; tr.ts = this->ts; tr.te = this->te; if(iter != traj.end()) tr.traj[varid] = iter->second; return tr; } } // end of ctbn namespace ctbnrle.1.1.1/src/dynamics.cc0000664000175000017500000000467312107032416016301 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "dynamics.h" namespace ctbn { using namespace std; Dynamics::Dynamics(const Context &var, const Context &cvar) : StreamObj(), v(var), cv(cvar) { } Dynamics::Dynamics(istream &is) : StreamObj(), v(is), cv(is) { } Dynamics::~Dynamics() { } void Dynamics::LoadOld(istream &is) { v.LoadOld(is); cv.LoadOld(is); } void Dynamics::SaveOld(ostream &os) const { v.SaveOld(os); os << os.fill(); cv.SaveOld(os); } void Dynamics::SampleTrajectory(Trajectory &tr, double t, Random &rand) const { double endt = tr.TimeEnd(); Instantiation newi(Domain()); Instantiation i(tr.Values(Domain(),t)); while(1) { double newt; SampleNextEvent(i,t,newt,newi,rand); if (newt>=endt) break; tr.AddTransition(newi,newt); t = newt; i = newi; } } void Dynamics::Mult(const Dynamics *x) { v = v+x->v; cv = (cv+x->cv)-v; } SS* Dynamics::SuffStats(const vector &tr, const vector &w) const { SS *ss = BlankSS(); const Context &c = this->Domain() + this->CondDomain(); const Context &owndomain = Domain(); for(unsigned int i=0; iAddSS(curri, t, deltat, ss, w[i]); int sign = index.TestInc(owndomain); if(!index.Done()) { Instantiation nexti = index.Values(); t = index.Time(); if (sign==2) this->AddTransSS(curri,nexti, t,ss,w[i]); curri = nexti; } } } return ss; } } // end of ctbn namespace ctbnrle.1.1.1/src/serial.cc0000664000175000017500000001400512107032416015737 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "streamserial.h" #include "nullptr03.h" #include #include namespace SERIALNAMESPACE { using ::ctbn::nullptr03; // helpful formatting fn void Indent(std::ostream &os, int indent) { for(int i=0;i=0;i--) is.unget(); return false; } char ReadAmpChar(std::istream &is) { if (ConsumeToken(is,"quot;")) return '"'; if (ConsumeToken(is,"amp;")) return '&'; if (ConsumeToken(is,"apos;")) return '\''; if (ConsumeToken(is,"lt;")) return '<'; if (ConsumeToken(is,"gt;")) return '>'; return '&'; } void ReadToken(std::istream &is, std::string &ret) { ret.clear(); IgnoreWS(is); bool isquoted = is.peek()=='"'; if (isquoted) is.get(); ReadStr(is,ret,isquoted ? "\"" : " \t\n\r\v>\\="); if (isquoted) is.get(); } inline bool charin(const char *endchar,char c) { return strchr(endchar,c) != nullptr03; } void ReadStr(std::istream &is, std::string &ret,const char *endchar) { ret.clear(); while(char c=is.peek()) { if (charin(endchar,c)) return; is.get(); if (is.fail()) return; if (c=='\\') c = ReadEscChar(is); else if (c=='&') c = ReadAmpChar(is); ret.push_back(c); } } void WriteStr(std::ostream &os, const std::string &s,bool escape) { for(std::string::const_iterator i=s.begin();i!=s.end();++i) { switch(*i) { case '"': os << """; break; case '&': os << "&"; break; case '\'': os << "'"; break; case '<': os << "<"; break; case '>': os << ">"; break; case '\\': os << "\\\\"; break; case '\a': (escape ? (os << '\\' << 'a') : (os << '\a')); break; case '\b': (escape ? (os << '\\' << 'b') : (os << '\b')); break; case '\f': (escape ? (os << '\\' << 'f') : (os << '\f')); break; case '\n': (escape ? (os << '\\' << 'n') : (os << '\n')); break; case '\r': (escape ? (os << '\\' << 'r') : (os << '\r')); break; case '\t': (escape ? (os << '\\' << 't') : (os << '\t')); break; case '\v': (escape ? (os << '\\' << 'v') : (os << '\v')); break; default: os << *i; break; } } } void ReadTag(std::istream &is,XMLTagInfo &info) { info.attr.clear(); IgnoreWS(is); char c = is.get(); if (is.fail() || c!='<') throw streamexception("Stream Input Format Error: expected <"); if (is.peek()=='\\') { info.isstart=false; info.isend=true; is.get(); } else { info.isstart=true; info.isend=false; } ReadToken(is,info.name); IgnoreWS(is); if (info.isend) { if (is.get()=='>') return; throw streamexception("Stream Input Format Error: expected >"); } while(!is.fail()) { char c = is.peek(); if (c=='\\') { is.get(); info.isend=true; if (is.get()=='>') return; throw streamexception("Stream Input Format Error: expected >"); } if (c=='>') { is.get(); return; } std::string aname; ReadToken(is,aname); if (aname.empty()) throw streamexception("Stream Input Format Error: tag missing name"); IgnoreWS(is); if (is.peek()=='=') { is.get(); std::string aval; ReadToken(is,aval); info.attr[aname] = aval; } else { info.attr[aname] = "1"; } IgnoreWS(is); } throw streamexception("Stream Input Format Error: unexpected stream end"); } void ReadEndTag(std::istream &is,const char *ename) { XMLTagInfo einfo; ReadTag(is,einfo); if (!einfo.isend || einfo.name!=ename) throw streamexception(std::string("Stream Input Format Error: expected end tag for ")+ename+", received "+(einfo.isend ? "end" : "start")+" tag for "+einfo.name); } // How to construct a name for a template class char *TName(const char *cname, int n, ...) { int s = strlen(cname)+1; va_list ap; va_start(ap,n); for(int i=0;i allocmaptype; allocmaptype &alloctable() { static allocmaptype table; return table; } const char *AddAlloc(const char *name, createfntype fn) { alloctable()[std::string(name)] = fn; return name; } void *AllocByName(const std::string &name) { allocmaptype::iterator i = alloctable().find(name); if (i==alloctable().end()) { return nullptr03; } else return i->second(); } } ctbnrle.1.1.1/src/context.cc0000664000175000017500000002455512107032416016157 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "context.h" #include #include #include namespace ctbn { using namespace std; ostream& operator<< (ostream& os, const Instantiation& inst) { return(inst.SaveOld(os)); } istream& operator>> (istream& is, Instantiation& inst) { return(inst.LoadOld(is)); } ostream& operator<< (ostream& os, const Context& con) { return(con.SaveOld(os)); } istream& operator>> (istream& is, Context& con) { return(con.LoadOld(is)); } Context::Context() { size = 1; } Context::Context(Context const & other): card(other.card), size(other.size) {} Context::Context(istream &is) { LoadOld(is); } Context::~Context() { } Context::Context(const Context &c1, const Context &c2, setop op) { switch(op) { case UNION: set_union(c1.card.begin(),c1.card.end(), c2.card.begin(),c2.card.end(), inserter(card,card.begin())); break; case INTERSECTION: set_intersection(c1.card.begin(),c1.card.end(), c2.card.begin(),c2.card.end(), inserter(card,card.begin())); break; case DIFFERENCE: set_difference(c1.card.begin(),c1.card.end(), c2.card.begin(),c2.card.end(), inserter(card,card.begin())); break; } size = 1; for(map::iterator i=card.begin();i!=card.end();++i) size *= i->second; } Context::Context(const Instantiation &i1, const Instantiation &i2) { set_intersection(i1.card.begin(),i1.card.end(), i2.card.begin(),i2.card.end(), inserter(card,card.begin())); size = 1; for(map::iterator i=card.begin();i!=card.end();) { int v1 = i1.Value(i->first); int v2 = i2.Value(i->first); if (v1 == -1 || v2==-1 || v1==v2) card.erase(i++); else { size *= i->second; ++i; } } } vector Context::VarList() const { vector ret; for(map::const_iterator i=card.begin();i!=card.end();++i) ret.push_back(i->first); return ret; } int Context::Index(const Instantiation &ind) const { int ret = 0; int inc = 1; for(map::const_iterator i=card.begin();i!=card.end();++i) { int v = ind.Value(i->first); if (v==-1) return -1; ret += inc*v; inc *= i->second; } return ret; } Instantiation Context::Index(int i) const { Instantiation ret(*this); ret.SetIndex(i); return ret; } istream &Context::LoadOld(istream &is) { return is >> size >> card; } void Context::serial_postload() { size = 1; for(map::iterator i=card.begin();i!=card.end();++i) size *= i->second; } ostream &Context::SaveOld(ostream &os) const { return os << size << os.fill() << card; } void Context::ConsistentIndexes(vector &l, const Instantiation &v) const { l.clear(); vector maxind,incind; int iinc = 1; int index = 0; for(map::const_iterator j=card.begin();j!=card.end();++j) { int vv = v.Value(j->first); if (vv == -1) { maxind.push_back(j->second); incind.push_back(iinc); } else { index += vv*iinc; } iinc *= j->second; } CConsistentIndexes(l,maxind,incind,index); } void Context::CConsistentIndexes(vector &l, const vector &maxind, const vector &incind, int startindex) const { int n = maxind.size(); vector v(n,0); int i=0; do { l.push_back(startindex); for(i=0;i &l, const Instantiation &v) const { l.clear(); vector lneg; ConsistentIndexes(lneg,v); CInconsistentIndexes(l,lneg); } void Context::CInconsistentIndexes(vector &l, const vector &lneg) const { int s = Size(); vector::const_iterator negi = lneg.begin(),negend = lneg.end(); int i = 0; while(negi!=negend && i::const_iterator i=c.card.begin();i!=c.card.end();++i) if (card.find(i->first)==card.end()) return false; return true; } bool Context::IsOverlap(const Context &c) const { for(map::const_iterator i=c.card.begin();i!=c.card.end();++i) if (card.find(i->first)!=card.end()) return true; return false; } // ---------------------------------------------------- Instantiation::Instantiation() : Context() { index = 0; numunknown = 0; } Instantiation::Instantiation(const Context &c, int defaultval) : Context(c) { index = 0; numunknown = defaultval == -1 ? c.NumVars() : 0; int accum = 1; for(map::iterator i=card.begin();i!=card.end();++i) { vals.insert(make_pair(i->first,defaultval)); indexinc.insert(make_pair(i->first,accum)); if (defaultval > 0) index += accum*defaultval; accum *= i->second; } } Instantiation::Instantiation(const Context &c, const Instantiation &v) : Context(c) { index = 0; numunknown = 0; int accum = 1; for(map::iterator i=card.begin();i!=card.end();++i) { int vv = v.Value(i->first); vals.insert(make_pair(i->first,vv)); indexinc.insert(make_pair(i->first,accum)); if (vv!=-1) index += accum*vv; else numunknown++; accum *= i->second; } } Instantiation::~Instantiation() { } bool Instantiation::Inc(int varid) { map::iterator i=vals.find(varid); map::iterator j=card.find(varid); map::iterator k=indexinc.find(varid); if (i!=vals.end() && j!=card.end() && k!=indexinc.end() && i->second != -1) { i->second++; index += k->second; if (i->second==j->second) { index -= k->second*j->second; i->second = 0; return false; } else { return true; } } return false; // I guess... } bool Instantiation::Inc() { index++; for(map::iterator i=vals.begin(),j=card.begin(); i!=vals.end();++i,++j) { i->second++; if (i->second == j->second) i->second = 0; else return true; } index = 0; return false; } bool Instantiation::Inc(const Context &c) { for(map::iterator i=vals.begin(),j=card.begin(),k=indexinc.begin(); i!=vals.end();++i,++j,++k) { if (!c.HasId(i->first)) continue; i->second++; index += k->second; if (i->second == j->second) { index -= k->second*j->second; i->second = 0; } else return true; } return false; } void Instantiation::AddVar(int varid, int cardinality, int val) { if (HasId(varid)) SetVal(varid,val); else { map::iterator ii = indexinc.lower_bound(varid); if (ii==indexinc.end()) { // if no other varids after me... indexinc.insert(make_pair(varid,size)); index += size*(val==-1 ? 0 : val); } else { // otherwise we need to adjust the index values int iinc = ii->second; if (val!=-1) index += iinc*val; // for all (lexographically) later indexs, // adjust the index accordinly and adjust the indexinc map::iterator vi = vals.find(ii->first); for(;ii != indexinc.end(); ++ii,++vi) { index += (cardinality-1)*ii->second*vi->second; ii->second *= cardinality; } indexinc.insert(make_pair(varid,iinc)); } Context::AddVar(varid,cardinality); vals.insert(make_pair(varid,val)); } } bool Instantiation::operator==(const Instantiation &i) const { for(iimap::const_iterator vi = vals.begin();vi!=vals.end();++vi) if (vi->second != i.Value(vi->first)) return false; for(iimap::const_iterator vi = i.vals.begin();vi!=i.vals.end();++vi) if (vi->second != Value(vi->first)) return false; return true; } bool Instantiation::operator!=(const Instantiation &i) const { return !(*this==i); } ostream& Instantiation::SaveOld(ostream &os) const { return Context::SaveOld(os) << os.fill() << vals; } istream& Instantiation::LoadOld(istream &is) { Context::LoadOld(is) >> vals; Recalc(); return is; } void Instantiation::serial_postload() { Recalc(); } void Instantiation::SetIndex(int i) { numunknown = 0; index = i; for(map::iterator ci=card.begin(); ci!=card.end();++ci) { vals[ci->first] = i%(ci->second); i /= ci->second; } } void Instantiation::Recalc() { indexinc.clear(); index = 0; numunknown = 0; int acc = 1; for(map::iterator i=card.begin(),j=vals.begin(); i!=card.end();++i,++j) { indexinc.insert(make_pair(i->first,acc)); if (j->second != -1) index += acc*j->second; else numunknown++; acc *= i->second; } } Context Instantiation::MissingVars() const { Context c; for(map::const_iterator i=vals.begin(),j=card.begin(); i!=vals.end();++i,++j) if (i->second==-1) c.AddVar(j->first,j->second); return c; } Context Instantiation::KnownVars() const { Context c; for(map::const_iterator i=vals.begin(),j=card.begin(); i!=vals.end();++i,++j) if (i->second!=-1) c.AddVar(j->first,j->second); return c; } void Instantiation::ConsistentIndexes(vector &l) const { l.clear(); vector maxind,incind,v; int n = 0; for(map::const_iterator i=vals.begin(),j=card.begin(),k=indexinc.begin(); i!=vals.end();++i,++j,++k) if (i->second == -1) { v.push_back(0); maxind.push_back(j->second); incind.push_back(k->second); n++; } CConsistentIndexes(l,maxind,incind,index); } void Instantiation::InconsistentIndexes(vector &l) const { l.clear(); vector lneg; CInconsistentIndexes(l,lneg); } void Instantiation::SetAllVal(int val) { for(map::iterator i=vals.begin(),j=indexinc.begin(); i!=vals.end();++i,++j) { index += ((val==-1?0:val)-(i->second==-1?0:i->second))*j->second; i->second = val; } numunknown = val==-1 ? vals.size() : 0; } void Instantiation::PrintVal(ostream &os) const { for(map::const_iterator i=vals.begin();i!=vals.end();++i) os << i->second << ' '; os << endl; } // --------------------------------------------- } // end of ctbn namespace ctbnrle.1.1.1/src/varsample.cc0000664000175000017500000000172312107032416016455 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "varsample.h" namespace ctbn { using namespace std; VarSample::VarSample() {} VarSample::~VarSample() {} } // end of ctbn namespace ctbnrle.1.1.1/src/params.cc0000664000175000017500000000612212107032416015744 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "params.h" #include #include #include #include #include #include namespace ctbn { using namespace std; map Params; void ReadParams(istream &pf) { while(!pf.eof()) { string line; getline(pf,line); if (line.empty() || line[0] == ';' || line[0] == '#') continue; string::size_type nstart = line.find_first_not_of(" \t\n\r",0); if (nstart==string::npos) continue; string::size_type nend = line.find_first_of(" \t\n\r",nstart); if (nend==string::npos) continue; string::size_type valstart = line.find_first_not_of(" \t\n\r",nend); if (valstart==string::npos) continue; string::size_type valend = line.size(); if (valend==string::npos) continue; Params[line.substr(nstart,nend-nstart)] = line.substr(valstart,valend-valstart); } } void SetParam(const string &name, const string &val) { Params[name] = val; } void InitParams(int argc, char **argv) { ifstream pf("params"); if (pf.good()) { ReadParams(pf); pf.close(); } pf.open((string(argv[0]) + string(".params")).c_str()); if (pf.good()) { ReadParams(pf); pf.close(); } bool listem = false; for(int i=1;i=argc) { cerr << "invalid parameters: missing value for " << argv[i]+2 << endl; exit(-1); } Params[argv[i]+2] = argv[i+1]; i++; } else if (strcmp(argv[i],"-params")==0) { pf.open(argv[i+1]); if (pf.good()) { ReadParams(pf); i++; pf.close(); } } else if (strcmp(argv[i],"-listparams")==0) listem = true; } if (listem) { for(map::iterator i = Params.begin(); i != Params.end(); ++i) { cout << i->first << " => " << i->second << endl; } exit(0); } } string ParamStr(const string &name, const string &dflt) { map::iterator i = Params.find(name); if (i==Params.end()) return dflt; return i->second; } int ParamInt(const string &name, int dflt) { map::iterator i = Params.find(name); if (i==Params.end()) return dflt; return atoi(i->second.c_str()); } double ParamDouble(const string &name, double dflt) { map::iterator i = Params.find(name); if (i==Params.end()) return dflt; return atof(i->second.c_str()); } } // end of ctbn namespace ctbnrle.1.1.1/src/searchqueue.cc0000664000175000017500000001013312107032416016770 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "searchqueue.h" #include "defines.h" #include #include namespace ctbn { using namespace std; SearchQueue::SearchQueue(int maxn, int nvars) { heap = new Action[maxn]; places = new deque[nvars]; n = 0; nv = nvars; } SearchQueue::~SearchQueue() { delete []heap; delete []places; } bool SearchQueue::Head(Action &a) { if (n<=0) return false; a = heap[0]; return true; } void SearchQueue::Add(const Action &a) { heap[n] = a; ++n; double key = a.gain + a.gainrev; int i; for(i=n-1;i>0 && key > heap[(i-1)/2].gain + heap[(i-1)/2].gainrev; i = (i-1)/2) { heap[i] = heap[(i-1)/2]; deque &list = places[heap[i].child]; list.erase(find(list.begin(),list.end(),(i-1)/2)); list.push_front(i); } heap[i] = a; places[a.child].push_front(i); } void SearchQueue::Remove(int child) { while(places[child].size() != 0) { int i = places[child].front(); places[child].pop_front(); if (i==0) { --n; if (n>0) { heap[0] = heap[n]; deque &list = places[heap[0].child]; list.erase(find(list.begin(),list.end(),n)); list.push_front(0); Heapify(0,heap[0].gain + heap[0].gainrev); } } else { i = Heapify(i,INFINITY); --n; deque &list = places[heap[i].child]; list.erase(find(list.begin(),list.end(),i)); if (i==n) continue; Action temp = heap[n]; while(i>0 && temp.gain + temp.gainrev > heap[(i-1)/2].gain + heap[(i-1)/2].gainrev) { heap[i] = heap[(i-1)/2]; deque &list2 = places[heap[i].child]; list2.erase(find(list2.begin(),list2.end(), (i-1)/2)); list2.push_front(i); i = (i-1)/2; } heap[i] = temp; deque &list2 = places[temp.child]; list2.erase(find(list2.begin(),list2.end(),n)); list2.push_front(i); } } } int SearchQueue::Heapify(int i, double tempk) { int l,r,large,oldlarge; double larget; oldlarge = -1; while(i tempk || isinf(tempk))) { large = l; larget = heap[l].gain + heap[l].gainrev; } else { large = i; larget = tempk; } if (r < n && heap[r].gain + heap[r].gainrev > larget) { large = r; larget = heap[r].gain + heap[r].gainrev; } if (large==i) { deque &list = places[heap[i].child]; deque::iterator it = find(list.begin(),list.end(),oldlarge); if(it != list.end()) list.erase(it); list.push_front(i); return i; } Action temp = heap[i]; heap[i] = heap[large]; heap[large] = temp; deque &list = places[temp.child]; deque::iterator it = find(list.begin(),list.end(),i); if(it != list.end()) list.erase(it); list.push_front(large); deque &list2 = places[heap[i].child]; it = find(list2.begin(),list2.end(),large); if(it != list2.end()) list2.erase(it); list2.push_front(i); i = large; oldlarge = large; } return i; // shouldn't get here } void SearchQueue::Clear() { n = 0; for(int i = 0; i < nv; i++) { places[i].clear(); } } void SearchQueue::Print() { for(int i = 0; i < n; i++) { cout << i << ": " << heap[i] << endl; } } void SearchQueue::PrintPlaceList() { for(int i = 0; i < nv; i++) { cout << i << ": "; for(unsigned int j = 0; j < places[i].size(); j++) cout << places[i][j] << " "; cout << endl; } } } // end of ctbn namespace ctbnrle.1.1.1/src/linearprogram.cc0000664000175000017500000001601112107032416017321 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "linearprogram.h" #include "defines.h" #include #include #define USE_SIMPLEX // could be duplicated from defines.h, but this makes this package // more independent #ifndef INFINITY #define INFINITY (std::numeric_limits::infinity()) #endif using namespace std; namespace ctbn { GLPKSolver::GLPKSolver() : LinearProgram() { p = glp_create_prob(); nconstr = 0; } GLPKSolver::GLPKSolver(const GLPKSolver &lp) : mins(lp.mins), maxs(lp.maxs) { p = glp_create_prob(); glp_copy_prob(p,lp.p,GLP_OFF); nconstr = lp.nconstr; } GLPKSolver::~GLPKSolver() throw() { glp_delete_prob(p); } void GLPKSolver::ClearProblem() { glp_delete_prob(p); p = glp_create_prob(); nconstr = 0; mins.clear(); maxs.clear(); } GLPKSolver &GLPKSolver::operator=(const GLPKSolver &lp) { if (this == &lp) return *this; glp_delete_prob(p); glp_copy_prob(p,lp.p,GLP_OFF); nconstr = lp.nconstr; mins = lp.mins; maxs = lp.maxs; return *this; } void GLPKSolver::SetObjective(const vector &coeff) { ResizeVars(coeff.size()-1); for(unsigned int i=0;i &ix, const vector &coeff) { ResizeVars(ix); for(unsigned int i=0;i &coeff, double val) { int ncoeff = coeff.size(); ResizeVars(ncoeff-1); // indices in GLPK are 1-indexed (hence the +1 everywhere) int *ixs = new int[ncoeff+1]; for(int i=1;i<=ncoeff;i++) ixs[i] = i; AddGenRow(coeff,ixs,val,GLP_FX); delete []ixs; } void GLPKSolver::AddEqConstraint(const vector &ix, const vector &coeff, double val) { int ncoeff = coeff.size(); ResizeVars(ix); // indices in GLPK are 1-indexed (hence the +1 everywhere) int *ixs = new int[ncoeff+1]; for(int i=0;i &coeff, double val) { int ncoeff = coeff.size(); ResizeVars(ncoeff-1); // indices in GLPK are 1-indexed (hence the +1 everywhere) int *ixs = new int[ncoeff+1]; for(int i=1;i<=ncoeff;i++) ixs[i] = i; AddGenRow(coeff,ixs,val,GLP_LO); delete []ixs; } void GLPKSolver::AddGrEqConstraint(const vector &ix, const vector &coeff, double val) { int ncoeff = coeff.size(); ResizeVars(ix); // indices in GLPK are 1-indexed (hence the +1 everywhere) int *ixs = new int[ncoeff+1]; for(int i=0;i &coeff, int *ix, double val, int type) { if (coeff.size()==1) { // just place bounds directly on variable int varid = ix[1]-1; double realval = val/coeff[0]; int newtype = type; if (coeff[0]<0.0) { if (type==GLP_LO) { newtype = GLP_UP; } else { if (type==GLP_UP) newtype = GLP_LO; } } switch (newtype) { case GLP_LO: if (realval > mins[varid]) { if (realval < maxs[varid]) { mins[varid] = realval; ReassertVarBounds(varid); return; } else break; // we have inconsistent... so add as normal // so that the solver will report it (same below) } return; // bound is subsumed by current bound (same below) case GLP_UP: if (realval < maxs[varid]) { if (realval > mins[varid]) { maxs[varid] = realval; ReassertVarBounds(varid); return; } else break; } return; case GLP_FX: if (realval < maxs[varid] && realval > mins[varid]) { mins[varid] = maxs[varid] = realval; ReassertVarBounds(varid); return; } else break; } } glp_add_rows(p,1); nconstr++; int ncoeff = coeff.size(); // indices in GLPK are 1-indexed (hence the +1 everywhere) double *c = new double[ncoeff+1]; for(int i=0;i0) { glp_add_cols(p,toadd); for(int i=0;i &ix) { int maxv = -1; for(vector::const_iterator i=ix.begin();i!=ix.end();++i) if (*i>maxv) maxv = *i; ResizeVars(maxv); } vector GLPKSolver::Solve(double &val) { vector ret; glp_set_obj_dir(p,GLP_MIN); //glp_write_lp(p,NULL,"debug.lp"); #ifdef USE_SIMPLEX glp_smcp param; glp_init_smcp(¶m); param.meth = GLP_PRIMAL; // GLP_DUAL; // GLP_DUALP; param.presolve = GLP_OFF; // GLP_ON; param.msg_lev = GLP_MSG_OFF; // GLP_MSG_ON; int retcode = glp_simplex(p,¶m); int status = glp_get_status(p); if (retcode || status==GLP_INFEAS) { val = nan(""); return ret; } if (status==GLP_UNBND || status==GLP_NOFEAS) { val = -INFINITY; return ret; } ret.resize(mins.size()); for(unsigned int i=0;i. */ #include "expmcache.h" #include "params.h" #include #include #include #include using namespace std; namespace ctbn { void vexpmt(vectr &a, const matrix &Q, double t, double eps, double h, ContFunction &output); void vexpmt(matrix &a, const matrix &Q, double t, double eps, double h, ContFunction &output); ExpMCache::ExpMCache(const matrix &Q, double maxt, double eps) //: expm(Q.getm()) { int m = Q.getm(); /* version 1: for(int i=0;i &output) { if (t<=0.0) return; static double defaulteps = ParamDouble("RKEPS",RKEPS); if (eps<0.0) eps = defaulteps; int p = Q.getm(); if (h<=0) { double dd = Q.diag().absmax(); if (dd>0) h = 0.1/Q.diag().absmax(); else h = 0.1; } vectr ka1(p),ka2(p),ka3(p),ka4(p),ka5(p),ka6(p); double currt = 0; while(currt=t) h = t-currt; // don't overshoot! double err; for(;;) { // find stepsize // take 5th-order step: vectr temp; Q.leftmult(a,ka1); ka1 *= h; //ka1 = (a*Q)*h; temp = a; temp.add(ka1,b21); //temp = ka1*b21+a; Q.leftmult(temp,ka2); ka2 *= h; //ka2 = (temp*Q)*h; temp = a; temp.add(ka1,b31); temp.add(ka2,b32); //temp = ka1*b31+ka2*b32+a; Q.leftmult(temp,ka3); ka3 *= h; //ka3 = (temp*Q)*h; temp = a; temp.add(ka1,b41); temp.add(ka2,b42); temp.add(ka3,b43); //temp = ka1*b41+ka2*b42+ka3*b43+a; Q.leftmult(temp,ka4); ka4 *= h; //ka4 = (temp*Q)*h; temp = a; temp.add(ka1,b51); temp.add(ka2,b52); temp.add(ka3,b53); temp.add(ka4,b54); //temp = ka1*b51+ka2*b52+ka3*b53+ka4*b54+a; Q.leftmult(temp,ka5); ka5 *= h; //ka5 = (temp*Q)*h; temp = a; temp.add(ka1,b61); temp.add(ka2,b62); temp.add(ka3,b63); temp.add(ka4,b64); temp.add(ka5,b65); //temp = ka1*b61+ka2*b62+ka3*b63+ka4*b64+ka5*b65+a; Q.leftmult(temp,ka6); ka6 *= h; //ka6 = (temp*Q)*h; vectr erra = ka1; erra *= dc1; erra.add(ka3,dc3); erra.add(ka4,dc4); erra.add(ka5,dc5); erra.add(ka6,dc6); //vectr erra = dc1*ka1+dc3*ka3+dc4*ka4+dc5*ka5+dc6*ka6; // check error: /*err = erra.absmax(); err /= eps; if (err <= 1.0) break;*/ double amin = a.absmin(); if(amin<1.0) amin = 1.0; err = erra.absmax()/amin; err /= eps; if(err<=1.0) break; // adjust step: double htemp = 0.9*h*pow(err,-0.25); if (htemp>h/10) h = htemp; else h /= 10; if (currt+h == currt) return; // should throw exception? } currt += h; if (err > 1.89e-4) h = 0.9*h*pow(err,-0.2); else h *= 5; a.add(ka1,c1); a.add(ka3,c3); a.add(ka4,c4); a.add(ka6,c6); //a += c1*ka1+c3*ka3+c4*ka4+c6*ka6; } output.AddVal(currt,a); } // could probably be made a template function (matrix or vectr) // if some way of initializing ka1, ka2, ... could be arranged void vexpmt(matrix &a, const matrix &Q, double t, double eps, double h, ContFunction &output) { if (t<=0.0) return; static double defaulteps = ParamDouble("RKEPS",RKEPS); if (eps<0.0) eps = defaulteps; int p = Q.getm(); if (h<=0) { double dd = Q.diag().absmax(); if (dd>0) h = 0.1/Q.diag().absmax(); else h = 0.1; } matrix ka1(p,p),ka2(p,p),ka3(p,p),ka4(p,p),ka5(p,p),ka6(p,p); double currt = 0; while(currt=t) h = t-currt; // don't overshoot! double err; for(;;) { // find stepsize // take 5th-order step: matrix temp; //Q.leftmult(a,ka1); ka1 = a*Q; ka1 *= h; //ka1 = (a*Q)*h; temp = a; temp.add(ka1,b21); //temp = ka1*b21+a; //Q.leftmult(temp,ka2); ka2 = temp*Q; ka2 *= h; //ka2 = (temp*Q)*h; temp = a; temp.add(ka1,b31); temp.add(ka2,b32); //temp = ka1*b31+ka2*b32+a; //Q.leftmult(temp,ka3); ka3 = temp*Q; ka3 *= h; //ka3 = (temp*Q)*h; temp = a; temp.add(ka1,b41); temp.add(ka2,b42); temp.add(ka3,b43); //temp = ka1*b41+ka2*b42+ka3*b43+a; //Q.leftmult(temp,ka4); ka4 = temp*Q; ka4 *= h; //ka4 = (temp*Q)*h; temp = a; temp.add(ka1,b51); temp.add(ka2,b52); temp.add(ka3,b53); temp.add(ka4,b54); //temp = ka1*b51+ka2*b52+ka3*b53+ka4*b54+a; //Q.leftmult(temp,ka5); ka5 = temp*Q; ka5 *= h; //ka5 = (temp*Q)*h; temp = a; temp.add(ka1,b61); temp.add(ka2,b62); temp.add(ka3,b63); temp.add(ka4,b64); temp.add(ka5,b65); //temp = ka1*b61+ka2*b62+ka3*b63+ka4*b64+ka5*b65+a; //Q.leftmult(temp,ka6); ka6 = temp*Q; ka6 *= h; //ka6 = (temp*Q)*h; matrix erra = ka1; erra *= dc1; erra.add(ka3,dc3); erra.add(ka4,dc4); erra.add(ka5,dc5); erra.add(ka6,dc6); //matrix erra = dc1*ka1+dc3*ka3+dc4*ka4+dc5*ka5+dc6*ka6; // check error: /*err = erra.absmax(); err /= eps; if (err <= 1.0) break;*/ double amin = a.absmin(); if(amin<1.0) amin = 1.0; err = erra.absmax()/amin; err /= eps; if(err<=1.0) break; // adjust step: double htemp = 0.9*h*pow(err,-0.25); if (htemp>h/10) h = htemp; else h /= 10; if (currt+h == currt) return; // should throw exception? } currt += h; if (err > 1.89e-4) h = 0.9*h*pow(err,-0.2); else h *= 5; a.add(ka1,c1); a.add(ka3,c3); a.add(ka4,c4); a.add(ka6,c6); //a += c1*ka1+c3*ka3+c4*ka4+c6*ka6; } output.AddVal(currt,a); } } ctbnrle.1.1.1/src/streamserial.cc0000664000175000017500000000617012107032416017157 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "streamserial.h" #include "nullptr03.h" #include namespace ctbn { // the (few) definitions for that in streamserial.h std::map &StreamObj::loadtable() { static std::map *_loadtable = new std::map(); return *_loadtable; } StreamObj::StreamObj() { } StreamObj::~StreamObj() { } const char *StreamObj::myID() const { return "StreamObj"; } const char *StreamObj::IDname() { static const char *ret = StreamObj::addID("StreamObj",StreamObj::Create); return ret; } void StreamObj::SaveOld(std::ostream &os) const { } void StreamObj::SaveOldPtr(std::ostream &os) const { os << myID() << os.fill(); SaveOld(os); } StreamObj *StreamObj::LoadOldPtr(std::istream &is) { std::string name; is >> name; std::map::iterator rec = loadtable().find(name); if (rec == loadtable().end()) return nullptr03; else return rec->second(is); } StreamObj *StreamObj::Create(std::istream &is) { return new StreamObj(); } const char *StreamObj::addID(const char *classname, CreatorFn fn) { loadtable()[std::string(classname)] = fn; return classname; } const char *StreamObj::addID(const char *classname, const char *classname2, CreatorFn fn) { char *newname = new char[strlen(classname)+strlen(classname2)+3]; strcpy(newname,classname); strcat(newname,"<"); strcat(newname,classname2); strcat(newname,">"); loadtable()[std::string(newname)] = fn; return newname; } char *striptypename(const char *name) { if (name && name[0]=='N') { int i = atoi(name+1); int j = 1; for(;name[j]<='9' && name[j]>='0';j++) ; char *ret = new char[strlen(name)-j-i+1]; i +=j; int b = 1,k=0; for(;name[i];i++) { if (name[i]=='I' && name[i+1]=='N' && name[i+2]=='S' && name[i+3]=='_') { ret[k++] = 'I'; b++; i += 3; } else ret[k++] = name[i]; } ret[k-b] = 0; return ret; } else return strcpy(new char[strlen(name)+1],name); } const char *StreamObj::_forcename = StreamObj::IDname(); /* char *StreamObj::killSpaces(const char *classname) { char *ret = new char[strlen(classname)+1]; strcpy(ret,classname); for(int i=0;ret[i];i++) if (ret[i]==' ') ret[i]='_'; return ret; } */ } // end of ctbn namespace ctbnrle.1.1.1/src/bn.cc0000664000175000017500000003211512107032416015061 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "bn.h" #include "multirv.h" #include "params.h" #include "multisimple.h" namespace ctbn { using namespace std; SOBJCLASSDEF(BN) BN::BN() : RV(Context(),Context()) { } BN::BN(istream &is) : RV(is) { int n; is >> n; nodes.resize(n); for(int i=0;i(StreamObj::LoadOldPtr(is)); } BN::BN(const BN &bn) : RV(bn), nodes(bn.nodes.size()) { int n = nodes.size(); for(int i=0;iClone(); } BN::BN(const Structure &s, const Context &vars) : RV(Context(), Context()) { vector > parentlist = s.parentlist; int n = parentlist.size(); for(int i=0;i currentParentlist = parentlist[i]; Context v; Context cv; if(vars.HasId(i)) v.AddVar(i,vars.Cardinality(i)); else continue; for(unsigned int j=0;jClone(); sampleorder = bn.sampleorder; } return *this; } BN *BN::Clone() const { return new BN(*this); } BN::~BN() { int n = nodes.size(); for(int i=0;i> n; nodes.resize(n); for(int i=0;i(StreamObj::LoadOldPtr(is)); sampleorder.clear(); } void BN::serial_preload() { int n = nodes.size(); for(int i=0;iSaveOldPtr(os); } } void BN::Normalize() { assert(0); // not implemented (requires changing structure of network) // below would work, but would create one large node: RV *v = Joint(Context()); // don't condition on anything v->Normalize(); sampleorder.clear(); int n = nodes.size(); for(int i=0;iClone(); ans->Restrict(x); //ans->Project(ans->Domain()/rest, ans->CondDomain()/rest); int n = nodes.size(); for(int i=1;iClone(); v->Restrict(x); //v->Project(v->Domain()/rest, v->CondDomain()/rest); ans->MultBy(v); delete v; } return ans; } // This is the variable elimination that does // not sum out until the very end (not efficient) double BN::Prob(const Instantiation &x, bool log) const { RVSimple *v = MakeSimple(x,false); double ret = v->Sum(log); delete v; return ret; } void BN::Restrict(const Instantiation &x) { int n= nodes.size(); for(int i=0;iRestrict(x); } void BN::Project(const Context &c, const Context &cc) { assert(0); // not yet implemented RV::Project(c,cc); } void BN::MultBy(const RV *x) { assert(0); // again, not yet implemented } BNSS *BN::BlankSS() const { BNSS *ret = new BNSS(); int n = nodes.size(); ret->rvss.resize(n); for(int i=0;irvss[i] = nodes[i]->BlankSS(); return ret; } void BN::AddSS(const Instantiation &x, SS *ss, double w) const { BNSS *bnss = dynamic_cast(ss); // x should be a complete instantiation, so this should work // fine (and it not complex at all) int n = nodes.size(); for(int i=0;iAddSS(x,bnss->rvss[i],w); } void BN::AddExpSS(const Instantiation &x, SS *ss, double w) const { RVSimple *rvs = MakeSimple(x); AddExpSS(x,rvs,ss,w); delete rvs; } // Again, this one is correct (hopefully) but in no way efficient! void BN::AddExpSS(const Instantiation &x, const RVSimple *rvs, SS *ss, double w) const { BNSS *bnss = dynamic_cast(ss); int n = nodes.size(); Instantiation knowncond(CondDomain(),x); for(int i=0;iDomain()+nodes[i]->CondDomain(); Instantiation inst(var,-1); Context conditt = nodes[i]->CondDomain() - CondDomain(); inst.SetVal(knowncond); forallassign_subset(Instantiation &instcond, nodes[i]->CondDomain()-CondDomain(),knowncond) { vector > reind; forallassign_subset(Instantiation &inst, nodes[i]->Domain(),instcond) { vector vv; Domain().ConsistentIndexes(vv,inst); reind.push_back(vv); } RVSimple *subrv = rvs->Clone(); subrv->Reindex(reind); nodes[i]->AddExpSS(instcond,subrv,bnss->rvss[i],w); delete subrv; } } } void BN::Sample(Instantiation &x, Random &rand) const { // Added for sampling more efficiently with no evidence: if (x.NumVars()==x.NumMissingVars()) ImportanceSample(x,false,rand); else { RV *v = Joint(x); v->Normalize(); Instantiation sample(v->Domain()); v->Sample(sample,rand); x.SetVal(sample,true); delete v; } } void BN::Maximize(const SS *ss) { const BNSS *bnss = dynamic_cast(ss); int n = nodes.size(); for(int i=0;iMaximize(bnss->rvss[i]); } RVSimple *BN::MakeSimple(const Instantiation &x, bool normalize) const { const bool usesparse = ParamInt("AvoidJoint",1); if (usesparse) return MakeSimpleSparse(x,normalize); RV *v = Joint(x); RVSimple *ret = v->MakeSimple(x,normalize); delete v; return ret; } RVSimple *BN::MakeSimpleSparse(const Instantiation &x, bool normalize) const { Instantiation knownx(x.KnownVars(),x); Context missing = Domain() - knownx; vectr p(missing.Size()); vector ix(missing.Size()); Instantiation i(Domain()+CondDomain(),0); i.SetVal(knownx); int ii = 0; forallassign_subset(Instantiation &i, missing, knownx) { p[ii] = 1.0; for(vector::const_iterator n=nodes.begin();n!=nodes.end();++n) p[ii] *= (*n)->Prob(i); ix[ii] = i.Index(); ii++; } SparseMultiZSimple *ret = new SparseMultiZSimple(p,ix); if (normalize) ret->Normalize(); return ret; } void BN::AddNode(RV *rv) { nodes.push_back(rv); v = v+rv->Domain(); cv = (cv+rv->CondDomain())-v; sampleorder.clear(); } void BN::SetSampleOrder() const { sampleorder.clear(); unsigned int numnode = nodes.size(); bool *mat = new bool[numnode*numnode]; bool *added = new bool[numnode]; int *numpar = new int[numnode]; for(unsigned int i=0; iCondDomain(); if(conddomain.VarList().size()==0) continue; for(unsigned int j=0; jDomain(); if(conddomain.IsSubset(nodedomain)) { mat[j*numnode+i] = 1; numpar[i]++; } } } bool change = true; while (change) { change = false; for(unsigned int i=0; i::const_iterator i=sampleorder.begin(); i!=sampleorder.end();++i) { // If there is only one variable per node, this is faster; if(nodes[*i]->Domain().NumVars()==1) { if (x.Value(nodes[*i]->Domain().VarList()[0])!=-1) { if (log) ret += nodes[*i]->Prob(x,true); else ret *= nodes[*i]->Prob(x,false); } else { nodes[*i]->Sample(x,rand); } } else { // If not, this is necessary: if (log) ret += nodes[*i]->Prob(x,true); else ret *= nodes[*i]->Prob(x,false); nodes[*i]->Sample(x,rand); } } return ret; } void BN::Scramble(double alpha, double degree, Random &rand) { int n = nodes.size(); for(int i=0; iScramble(alpha, degree, rand); } double BN::LLH(const SS *ss) const { double llh = 0.0; const BNSS *bnss = dynamic_cast(ss); int n = nodes.size(); for(int i=0; iLLH(bnss->rvss[i]); } return llh; } double BN::GetScore(double numTrans, const SS* ss) const { const BNSS *bnss = dynamic_cast(ss); double score(0); int numNodes = NumofNodes(); for(int i = 0; i < numNodes; i++) score += nodes[i]->GetScore(numTrans, bnss->rvss[i]); return score; } // Assumes the source BN has the same organization of nodes. void BN::FillParams(const BN &src) { for(int i = 0; i < NumofNodes(); i++) { const MultiRV *csrv = dynamic_cast(src.nodes[i]); MultiRV *crv = dynamic_cast(nodes[i]); const Context &csrc = csrv->CondDomain(); const Context &cur = crv->CondDomain(); Instantiation ins(csrc); ins.SetAllVal(0); int nstates = crv->Domain().Size(); forallassign(Instantiation &curins, cur) { vectr avgDist(nstates,0.0); int count = 0; // Compute average over extra conditioning vars forallassign_subset(Instantiation &ins, csrc-cur ,curins) { vectr thisDist(nstates,0.0); double logfactor = 0.0; (*csrv)[ins].GetDist(thisDist,logfactor); avgDist += thisDist; count++; } avgDist /= count; (*crv)[curins].SetDist(avgDist); } } } void BN::PrepNode2IdMaps() const { if (node2var.size() == nodes.size()) return; ClearNode2IdMaps(); int m = nodes.size(); node2var.resize(m); for(int j=0;jDomain().VarList()[0]; var2node[node2var[j]] = j; vector par = nodes[j]->CondDomain().VarList(); for(unsigned int k=0;k())); } } void BN::ClearNode2IdMaps() const { node2var.clear(); var2node.clear(); children.clear(); } const RV* BN::NodeByVar(int i) const { PrepNode2IdMaps(); int idx = var2node[i]; return nodes[idx]; } vector BN::GetChildrenByVar(int varid) const { PrepNode2IdMaps(); vector ret; map >::iterator it = children.find(varid); if(it == children.end()) return ret; ret = it->second; for(unsigned int i=0; iCondDomain(); s.parentlist.push_back(c.VarList()); } } SOBJCLASSDEF(BNSS) BNSS::BNSS() { } BNSS::BNSS(istream &is) { int n; is >> n; rvss.resize(n); for(int i=0;i(StreamObj::LoadOldPtr(is)); } BNSS::BNSS(const BNSS &bnss) : SS() { int n = rvss.size(); for(int i=0;iClone(); } BNSS &BNSS::operator=(const BNSS &bnss) { if (&bnss!=this) { int n = rvss.size(); for(int i=0;iClone(); } return *this; } BNSS *BNSS::Clone() const { return new BNSS(*this); } BNSS::~BNSS() { int n = rvss.size(); for(int i=0;i> n; rvss.resize(n); for(int i=0;i(StreamObj::LoadOldPtr(is)); } void BNSS::serial_preload() { int n = rvss.size(); for(int i=0;iSaveOldPtr(os); } } void BNSS::Scale(double w) { int n = rvss.size(); for(int i=0; iScale(w); } void BNSS::AddSS(const SS* nss, double w) { int n = rvss.size(); const BNSS *bss = dynamic_cast(nss); for(int i=0; iAddSS(bss->rvss[i], w); } } // end of ctbn namespace ctbnrle.1.1.1/src/utils.cc0000664000175000017500000001405212107036364015630 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "utils.h" #include "trajectory.h" #include "context.h" #include "random.h" #if defined __linux__ || __CYGWIN__ #include #include #endif #include #include "params.h" namespace ctbn { using namespace std; bool IsTrajSame(const Trajectory &tr1, const Trajectory &tr2, const Context &c) { Trajectory::Index i1 = tr1.Begin(c); Trajectory::Index i2 = tr2.Begin(c); Instantiation inst1, inst2; for (;;) { if (i1.Done() || i2.Done()) break; inst1 = i1.Values(); inst2 = i2.Values(); Context diff(inst1, inst2); if (diff.VarList().size() > 0) return false; i1++; i2++; } return true; } bool IsTrajValid(const Trajectory &tr, const Context &c) { Trajectory::Index i = tr.Begin(c); Instantiation oldinst, newinst; oldinst = i.Values(); for (;;) { ++i; if (i.Done()) break; newinst = i.Values(); Context diff(oldinst, newinst); oldinst = newinst; if (diff.VarList().size() != 1) return false; } return true; } void RemoveInformation(Trajectory &tr, const Context &c, int var, double start, double end) { Trajectory newtr; newtr.SetBeginTime(tr.TimeBegin()); newtr.SetEndTime(tr.TimeEnd()); for(Trajectory::Index i = tr.Begin(c);!i.Done();++i) { Instantiation inst(i.Values()); if (i.Time()>=start && i.Time()start) { inst.SetVal(var,-1); newtr.AddTransition(inst,start); } if (i.Time()>start && i.Time()=end) { inst.SetVal(var,i.Values().Value(var)); newtr.AddTransition(inst,end); } } tr = newtr; } void RemoveInformation(Trajectory &tr, const Context &c, int nvar, int nit, double frac) { double len = tr.TimeEnd()-tr.TimeBegin(); for(int i=0;i select; double sum = SelectTime(select, frac, begint, endt, thresh); double res = sum - (endt-begint)*thresh; double seg = (endt-begint)*frac; while(!select.empty()) { double st = -select.top(); double nextt = st + seg; select.pop(); if(select.empty()) nextt -= res; RemoveInformation(tr, c, var, st, nextt); } } } double SelectTime(priority_queue &select, double ratio, double begint, double endt, double thresh) { priority_queue Q; double sum=0; double len = endt-begint; double seg = ratio*len; while(sum tmpq(Q); double top = -tmpq.top(); tmpq.pop(); while(!tmpq.empty()) { double next = -tmpq.top(); if(next-top> Q; } MarkovSimple::~MarkovSimple() { } void MarkovSimple::LoadOld(istream &is) { DynSimple::LoadOld(is); is >> Q; } void MarkovSimple::SaveOld(ostream &os) const { DynSimple::SaveOld(os); os << os.fill() << Q; } MarkovSimple *MarkovSimple::Clone() const { return new MarkovSimple(*this); } MarkovSimple *MarkovSimple::MakeNew(int nstates) const { return new MarkovSimple(nstates); } void MarkovSimple::Normalize() { int n = Q.getm(); for(int i=0;i &ind) { int n = ind.size(); int m = Q.getm(); int ii = 0; for(int i=0;i > &ind, int newn) { matrix newQ(newn,newn,0.0); const int n = Q.getm(); for (int i=0; i *MarkovSimple::Cond(double t0, double t1) const { return new CondTransQ(Q,t1-t0); } SparseCondTransQ *MarkovSimple::CondRestrict(double t0, double t1, const vector &ind) const { return new SparseCondTransQ(matrix(Q,ind),t1-t0,ind); } CondTransQ2 *MarkovSimple::Cond(double t) const { matrix newQ(Q); int n = Q.getm(); for(int i=0;i(newQ); } SparseCondTransQ2 *MarkovSimple::CondRestrict(double t, const vector &fromind, const vector &toind, bool transition) const { if (transition) return new SparseCondTransQ2(matrix(Q,fromind,toind), fromind,toind); else { matrix m(fromind.size(),toind.size(),0.0); unsigned int i=0, j=0; while(i(m,fromind,toind); } } MarkovSimpleSS *MarkovSimple::BlankSS() const { return new MarkovSimpleSS(Q.getm()); } void MarkovSimple::Mult(const DynSimple *x) { const MarkovSimple *xx = dynamic_cast(x); assert(xx!=NULL); // otherwise, not sure what to do... if (Q.getm()!=xx->Q.getm()) Q = xx->Q; else Q += xx->Q; } void MarkovSimple::AddSS(int x, double t0, double deltat, SS *ss, double w) const { MarkovSimpleSS *msss = dynamic_cast(ss); msss->c[x][x] += deltat*w; } void MarkovSimple::AddTransSS(int x1,int x2, double t, SS *ss,double w) const { MarkovSimpleSS *msss = dynamic_cast(ss); if (x1 != x2) msss->c[x1][x2] += w; } void MarkovSimple::AddExpSS(const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w) const { MarkovSimpleSS *msss = dynamic_cast(ss); const SparseMultiZSimple *a = dynamic_cast(alpha); assert(a!=NULL); // otherwise, need to implement more code const SparseMultiZSimple *b = dynamic_cast(beta); assert(b!=NULL); // ditto // a->Indexes() should be the same as b->Indexes()! const vector &ind = a->Indexes(); int n = ind.size(); matrix c; double z = suffstat(a->Dist(),b->Dist(),c,matrix(Q,ind),deltat); double sum = 0.0; for(int i=0;ic[ind[j]][ind[i]] += c[i][j]; } void MarkovSimple::AddExpTransSS(const RVSimple *x1, const RVSimple *x2, double t, SS *ss, double w) const { MarkovSimpleSS *msss = dynamic_cast(ss); const SparseMultiZSimple *a = dynamic_cast(x1); assert(a!=NULL); // otherwise, need to implement more code const SparseMultiZSimple *b = dynamic_cast(x2); assert(b!=NULL); // ditto const vector &aind = a->Indexes(); const vector &bind = b->Indexes(); matrix c(Q,aind,bind); c.multbycol(a->Dist()); c.multbyrow(b->Dist()); int mm = c.getm(); int nn = c.getn(); double csum = 0.0; for(int i=0;ic[aind[i]][bind[j]] += c[i][j]; } void MarkovSimple::AddSS(const SS *toadd, const DynSimple *dyn, const std::vector > &mapping, SS *ss, double w) const { const MarkovSimple *m = dynamic_cast(dyn); assert(m!=NULL); // otherwise, need double-dispatch and more code MarkovSimpleSS *mss = dynamic_cast(ss); const MarkovSimpleSS *addmss = dynamic_cast(toadd); int n = Q.getm(); for(int i=0;ic[i][i] += w*addmss->c[mapping[i][ki]][mapping[i][ki]]; // new version that assumes this comes from a process in which two // variables do not change at exactly the same time: for(int j=0;jc[i][j] += w*addmss->c[mapping[i][ki]][mapping[j][ki]]; /* old version: for(int j=0;jc[i][j] += w*addmss->c[mapping[i][ki]][mapping[j][kj]]; */ } } } } void MarkovSimple::SampleNextEvent(int ind, double t, double &newt, int &newind, Random &rand) const { newt = t + rand.SampleExp(-Q[ind][ind]); double sum = 0.0; int n = Q.getn(); for(int i=0;i(ss); int m = Q.getm(), n = Q.getn(); for(int i=0;ic[i][j]; double tottime = msss->c[i][i]; if (tottime<=0) { for(int j=0;jc[i][j]/tottime; } } for (int i = 0; i < m; ++i) { if (fabs(Q[i][i]) < divzeroq) { for (int j = 0; j < n; ++j) Q[i][j] = divzeroq; Q[i][i] = -divzeroq*(n-1); } else{ for (int j = 0; j < n; ++j) if(i!=j && fabs(Q[i][j]) < divzeroq) { Q[i][j] = divzeroq; Q[i][i] -= divzeroq; } } } /* //DEBUG for(int i=0; i 0) { cout << "stopped" << endl; cin.get(); } } */ } //by Yu void MarkovSimple::Scramble(double a, double b, double alpha, double degree, Random &rand) { int fix = ParamInt("FixScramble", -1); if(fix!=-1) { int n = Q.getn(); int m = Q.getm(); for (int i = 0; i < m; ++i) for (int j = 0; j < n; ++j) if(i==j) Q[i][j] = -a; else Q[i][j] = a/(n-1); return; } int n = Q.getn(); int m = Q.getm(); double *in = new double[n-1]; matrix original(Q); for(int i=0;i(ss); double llh = 0.0; matrix c = mss->c; unsigned int n = c.getn(); unsigned int m = c.getm(); for (unsigned int i=0; i0.0) { if (i!=j) { llh += c[i][j] * log(Q[i][j]); } else { llh += c[i][j] * Q[i][j]; } } return llh; } // Score over a single matrix. (sum over states) double MarkovSimple::GetScore(int parentCard, double numTrans, double amtTime, const SS* ss) const { double localScore(0); const MarkovSimpleSS *mss = dynamic_cast(ss); const matrix& c = mss->c; int size = c.getm(); double alpha_xx_u(numTrans / ((size-1)*size*parentCard)); double alpha_x_u((size-1)*alpha_xx_u); double tau_x_u(amtTime / (size*parentCard)); /* cout << "size=" << size << endl; cout << "amtTime=" << amtTime << endl; cout << "numTrans=" << numTrans << endl; cout << "parentCard=" << parentCard << endl; cout << "alpha_xx_u=" << alpha_xx_u << endl; cout << "alpha_x_u=" << alpha_x_u << endl; cout << "tau_x_u=" << tau_x_u << endl; cout << "c=" << c << endl; */ for(int i = 0; i < size; i++) { // Sum over each state of the variable double m_x_u(0); double t_x_u = c[i][i]; // Theta score (transitions) for(int j = 0; j < size; j++) { if(i == j) continue; m_x_u += c[i][j]; localScore += lngamma(alpha_xx_u + c[i][j]); } localScore += lngamma(alpha_x_u) - lngamma(alpha_x_u + m_x_u) - (size-1)*lngamma(alpha_xx_u); // q score (time) localScore += lngamma(alpha_x_u + m_x_u + 1) + (alpha_x_u + 1)*log(tau_x_u) - lngamma(alpha_x_u + 1) - (alpha_x_u + m_x_u + 1) * log(tau_x_u + t_x_u); } //cout << "\nlocalScore=" << localScore << endl; return localScore; } //----- SOBJCLASSDEF(MarkovSimpleSS) MarkovSimpleSS::MarkovSimpleSS(int n) : c(n,n,0.0) { } MarkovSimpleSS::MarkovSimpleSS(istream &is) { LoadOld(is); } MarkovSimpleSS::~MarkovSimpleSS() { } MarkovSimpleSS *MarkovSimpleSS::Clone() const { return new MarkovSimpleSS(*this); } void MarkovSimpleSS::LoadOld(istream &is) { is >> c; } void MarkovSimpleSS::SaveOld(ostream &os) const { os << c; } void MarkovSimpleSS::Scale(double w) { for(int i=0; i(nss); for(int i=0; ic[i][j] * w; } MarkovSimpleToggle::MarkovSimpleToggle(int n) : MarkovSimple(n) { } MarkovSimpleToggle::MarkovSimpleToggle(istream &is) : MarkovSimple(is) { } MarkovSimpleToggle::~MarkovSimpleToggle() { } MarkovSimpleToggle *MarkovSimpleToggle::Clone() const { return new MarkovSimpleToggle(*this); } MarkovSimpleToggle *MarkovSimpleToggle::MakeNew(int nstates) const { return new MarkovSimpleToggle(nstates); } void MarkovSimpleToggle::Maximize(const SS *ss) { static const double divzeroq = ParamDouble("DivZeroQ",1e-6); const MarkovSimpleSS *msss = dynamic_cast(ss); matrix &Q = Intensity(); int m = Q.getm(), n = Q.getn(); double tottr = 0.0, tottime = 0.0; for (int i = 0; i < m; ++i) for (int j = 0; j < n; ++j) if (j == i) tottime += msss->c[i][i]; else tottr += msss->c[i][j]; double toggleq = divzeroq; if (tottime > 0) toggleq = tottr/tottime; for (int i = 0; i < m; ++i) for (int j = 0; j < n; ++j) { if (j == i) Q[i][i] = -(n-1)*toggleq; else Q[i][j] = toggleq; } for (int i = 0; i < m; ++i) { if (fabs(Q[i][i]) < divzeroq) { for (int j = 0; j < n; ++j) Q[i][j] = divzeroq; Q[i][i] = -divzeroq*(n-1); } else{ for (int j = 0; j < n; ++j) if(i!=j && fabs(Q[i][j]) < divzeroq) { Q[i][j] = divzeroq; Q[i][i] -= divzeroq; } } } } void MarkovSimpleToggle::Scramble(double a, double b, double alpha, double degree, Random &rand) { int fix = ParamInt("FixScramble", -1); if(fix!=-1) { int n = Q.getn(); int m = Q.getm(); for (int i = 0; i < m; ++i) for (int j = 0; j < n; ++j) if(i==j) Q[i][j] = -a; else Q[i][j] = a/(n-1); return; } int n = Q.getn(); int m = Q.getm(); matrix original(Q); double q = rand.SampleGamma(a,b); for(int i=0;i. */ #include "em.h" #include "params.h" #include "utils.h" #include "markov.h" #include "structure.h" #include "ctbndyn.h" #include "dynamics.h" #include "expsuffstatsquery.h" #include "nonexpsuffstatsquery.h" #include "brutestructuresearch.h" #include "grapheditsearch.h" #include "famscore.h" #include "nullptr03.h" #ifdef USE_PTHREADS #include #endif namespace ctbn { using namespace std; SS *EStep(const vector &data, int si, int ei, Process *dist, Inference *inf) { SS *ss = dist->BlankSS(); inf->SetProcess(dist); for(int j=si;jSetTrajectory(&data[j]); inf->AddExpSuffStats(ss); } return ss; } #ifdef USE_PTHREADS struct EThreadInfo { int si,ei; Process *dist; Inference *inf; const vector *data; pthread_t thread; }; void *RunEThread(void *iptr) { EThreadInfo *tinfo = (EThreadInfo *)iptr; SS *ret = EStep(*(tinfo->data),tinfo->si,tinfo->ei,tinfo->dist,tinfo->inf); return ret; } #endif void EM(const vector &data, Process *dist, Inference *inf) { // max # of iterations int nitt = ParamInt("EMItt", 5); // LLH improvement (per example) below which loop will terminate early // (negative value means never terminate early) double llhimprov = ParamDouble("EMLLHImprov",-1); int verbose = ParamInt("VerboseEM",0); int ndata = data.size(); #ifdef USE_PTHREADS int nthreads = ParamInt("EMNumThreads",1); EThreadInfo *tinfo = nthreads<=1 ? nullptr03 : new EThreadInfo[nthreads-1]; pthread_attr_t tattr; pthread_attr_init(&tattr); pthread_attr_setdetachstate(&tattr,PTHREAD_CREATE_JOINABLE); for(int i=0;iClone(); } #endif double oldllh = -INFINITY; for(int i = 0; i < nitt; i++) { if (verbose) cout << "EMItt=" << i+1 << endl; SS *ss; #ifdef USE_PTHREADS if (nthreads<=1) { // single thread... #endif ss = EStep(data,0,ndata,dist,inf); #ifdef USE_PTHREADS } else { // multiple threads... for(int i=0;iClone(); int rc = pthread_create(&(tinfo[i].thread),&tattr, RunEThread,(void *)(&(tinfo[i]))); if (rc!=0) { cerr << "Error # " << rc << " in starting thread in EM" << endl; exit(1); } } inf->SetProcess(dist); // run first batch in main thread ss = EStep(data,0,ndata/nthreads,dist,inf); for(int i=0;iAddSS(nss); delete nss; } } #endif if (verbose>2) ss->SaveV(cout); if (verbose>1) cout << "\tLLH/example=" << dist->LLH(ss)/ndata << endl << "...maximize..." << endl; dist->Maximize(ss); if (llhimprov>=0) { double newllh = dist->LLH(ss)/ndata; if (verbose) cout << "\tLLH/example=" << newllh << endl; if (newllh-oldllhLLH(ss)/ndata; if (verbose) cout << "\tLLH/example=" << newllh << endl; } if (verbose>1) dist->SaveV(cout); delete ss; } #ifdef USE_PTHREADS for(int i=0;i &data, Process *&dist, Inference *inf) { int nitt = ParamInt("SEMItt", 10); double nTrans = ParamDouble("alpha", 1.0); double nTime = ParamDouble("tau", 1.0); int verbose = ParamInt("VerboseSEM",ParamInt("VerboseEM",0)); Structure s; // Initial parameter update EM(data,dist,inf); int strmod = 1; for(int i = 0; i < nitt && strmod>0; i++) { if (verbose) cout << "SEMItt=" << i+1 << endl; ExpSuffStatsQuery* essQuery = new ExpSuffStatsQuery(inf->Clone(),true); essQuery->SetData(&data); essQuery->SetProcess(dist); // Structure modification step Markov* m = dynamic_cast(dist); const BN* bnet = dynamic_cast(m->GetStartDist()); const CTBNDyn* cdyn = dynamic_cast(m->GetDynamics()); BNFamScore* bfs = new BNFamScore(nTrans,essQuery); GraphEditSearch gesBN(bnet->Domain(),bfs,true); if (verbose) cout << "Learning BN..." << endl; s = gesBN.LearnStructure(); delete bfs; RV* newrv = new BN(s, bnet->Domain()); dynamic_cast(newrv)->FillParams(*bnet); Structure olds; bnet->GetStructure(olds); strmod = olds.HammingDist(s); CTBNFamScore* cfs = new CTBNFamScore(nTrans,nTime,essQuery); GraphEditSearch ges(cdyn->Domain(),cfs); if (verbose) cout << "Learning CTBN..." << endl; s = ges.LearnStructure(); delete cfs; delete essQuery; Dynamics* newdyn = new CTBNDyn(s, cdyn->Domain()); dynamic_cast(newdyn)->FillParams(*cdyn); if (strmod==0) { cdyn->GetStructure(olds); strmod = olds.HammingDist(s); } Process* newdist = new Markov(newrv, newdyn); delete dist; dist = newdist; // Inference and parameter update step EM(data, dist, inf); } } } // end of ctbn namespace ctbnrle.1.1.1/src/brutestructuresearch.cc0000664000175000017500000000474012107032416020755 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "brutestructuresearch.h" #include "extramath.h" #include namespace ctbn { using namespace std; BruteStructureSearch::BruteStructureSearch(const Context &c, FamScore* fs, int mp) : StructureSearch(c, fs), maxParents(mp) { } Structure BruteStructureSearch::LearnStructure() { int numNodes = vars.size(); Context nullc; vector > exhaustIdx = combo(numNodes-1,maxParents); unsigned int numTests = exhaustIdx.size(); for(int i=0; i parentSet; parentSet.insert(parentSet.end(), vars.begin(),vars.begin() + i); parentSet.insert(parentSet.end(), vars.begin() + (i+1),vars.end()); double score; double bestScore = 1; Node* bestnode = NULL; for(unsigned int j=0; jGetScore(vars[i],cvTest); if(bestScore == 1 || score > bestScore) { if(bestScore != 1 && bestnode!=NULL) delete bestnode; bestScore = score; bestnode = new Node(vars[i],cvTest); } } nodes.push_back(bestnode->Clone()); vector tmp = bestnode->CondDomain().VarList(); for (unsigned int k=0; k < tmp.size(); k++) cout << tmp[k] << " "; cout << endl; delete bestnode; } Structure s; GetStructure(s); return s; } } // end of ctbn namespace ctbnrle.1.1.1/src/graphic.cc0000664000175000017500000010331712251373743016115 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "graphic.h" #include "params.h" namespace ctbn { using namespace std; // If the "dot" command (from graphviz) is not in your // path, or if you wish to define an explicit path to it, // change the string below accordingly #define DOTCOMMAND "/usr/bin/dot" /* * GraphicEvent is class for generating trajectory graph */ //return the difference between two Instantiation vector diffvalues(const Instantiation &i1, const Instantiation &i2) { Context both(i1,i2,Context::UNION); vector vars = both.VarList(); vector ret; for(vector::iterator i=vars.begin();i!=vars.end();++i) if (i1.Value(*i) != i2.Value(*i)) ret.push_back(*i); return ret; } Graphic::GraphicEvent::GraphicEvent() { // Auto-generated constructor stub change_node = 0; time = 0; state.clear(); } void Graphic::GraphicEvent::SetChanges(int node, double change_time, const vector &change_state) { change_node = node; time = change_time; state = change_state; } double Graphic::GraphicEvent::GetTime() const { return time; } int Graphic::GraphicEvent::GetChange() const { return change_node; } vector Graphic::GraphicEvent::GetState() const { return state; } void Graphic::GraphicEvent::SetName(string insertname) { name = insertname; } string Graphic::GraphicEvent::GetName() const{ return name; } /* * GraphicNode class is for drawing the CTBN graph. * this class will save all the data need to be generate the CTBN graph */ Graphic::GraphicNode::GraphicNode() { a = ""; b = 0; check_string = false; } string Graphic::GraphicNode :: GetName() { return a; } int Graphic::GraphicNode :: GetSize() { return matching.size(); } void Graphic::GraphicNode::Add(const string & name) { a = name; } void Graphic::GraphicNode::Add(const int & name) { exist = false; check_string = true; stringstream ss; ss << name; ss >> a; ss.clear(stringstream::goodbit); } void Graphic::GraphicNode::SetMatrix(const matrix & matrix, int & conn, int & locate) { dyn.push_back(matrix); matching.push_back(conn); place.push_back(locate); } vector Graphic::GraphicNode::GetPlace() { return place; } vector Graphic::GraphicNode::GetMatching() { return matching; } void Graphic::GraphicNode::SetMatching(vector matching) { exist = true; this->matching = matching; } void Graphic::GraphicNode::SetPlace(int location) { place.push_back(location); } void Graphic::GraphicNode::SetMatrix(const matrix & matrix) { dyn.push_back(matrix); } void Graphic::GraphicNode::SetBool() { exist = true; } bool Graphic::GraphicNode::GetExist() { return exist; } matrix Graphic::GraphicNode::GetMatrix(const int & i) { return dyn[i]; } bool Graphic::GraphicNode::CheckStrings() { return check_string; } void Graphic::GraphicNode::SetName(const string & name) { a = name; check_string = true; } //initialize graphic class Graphic :: Graphic() { d = NULL; size = 0; //size of x coordinate the trajectory visualization size_x = ParamInt("traj_win_size", 800); //size of the font on trajectory change_point = ParamInt("traj_font", 15); //size of changed time mark in trajectory graph size_distance = ParamInt("traj_ruler", 5); //size of the font on ctbn graph size_font = ParamInt("ctbn_font", 16); //start time of the trajectory start = 0; //ending time of the trajectory finish = 2; largest_tr = 0; change_name = false; } //outputing both graph representation of Markov and trajectory void Graphic::DrawBoth(const Markov & A, Trajectory & traj, ofstream & ls, string & name, ofstream & lb) { this->start_point = traj.TimeBegin(); this->ending_point = traj.TimeEnd(); const CTBNDyn * B = dynamic_cast(A.GetDynamics()); size = B->NumofNodes(); Context context = Context(); for (int i = 0; i < B->NumofNodes(); ++i) context = context + B->Node(i)->Domain(); Trajectory::Index i = traj.Begin(context); ExtractInfo(A); Changes(); BuildRank(ls); Create(i); large_point = 0; getlocation.clear(); string command2 = string(DOTCOMMAND) + " -Tdot " + name + " > temp.dot"; const char * commend; commend = command2.c_str(); if (system(commend)<0) return; // report error? (assume >0 not error code) GetLocation("temp.dot"); BuildStateRank(lb); } //extracting location of each placement in graph void Graphic::GetLocation(string name) { ifstream obtainlocation; double get_distance; obtainlocation.open(name.c_str()); if(!obtainlocation.is_open()) return; int count = size; vector location; string textmatch = "rects="; string::iterator it = textmatch.begin(); while(!obtainlocation.eof()) { if( it == textmatch.end()) { obtainlocation.get(); obtainlocation.get(); obtainlocation >> get_distance; obtainlocation.get(); obtainlocation >> get_distance; if(large_point < get_distance) { large_point = get_distance; } location.push_back(get_distance); obtainlocation.get(); obtainlocation >> get_distance; obtainlocation.get(); obtainlocation >> get_distance; if(large_point < get_distance) { large_point = get_distance; } location.push_back(get_distance); count--; getlocation.push_back(location); location.clear(); if(count == 0) { break; } it = textmatch.begin(); } else if(* it == obtainlocation.get()) it ++; else it = textmatch.begin(); } obtainlocation.close(); } //output Markov graph in one arrange line void Graphic::DrawCTBNRank(const Markov & A, ofstream & ls, string & name, ofstream & lb) { ExtractInfo(A); Changes(); BuildRank(ls); large_point = 0; getlocation.clear(); string command2 = string(DOTCOMMAND) + " -Tdot " + name + " > temp.dot"; const char * commend; commend = command2.c_str(); if (system(commend)<0) return; // report error? (assume >0 not error code) GetLocation("temp.dot"); Create(A, start, finish); BuildStateRank(lb); } void Graphic::DrawCTBN(const Markov & A, ofstream & ls) { ExtractInfo(A); Changes(); Build(ls); } void Graphic::DrawCTBNMatrix(const Markov & A, ofstream & ls) { ExtractInfo(A); Changes(); BuildM(ls); } void Graphic ::SetSizeTrajectory(const int & size_x) { this -> size_x = size_x; } void Graphic :: SetFontTrajectory(const int & change_font, const int & change_font1) { if(change_font > 0 && change_font1 > 0) { change_point = change_font; size_distance = change_font1; } } void Graphic ::SetFontCTBN(const int & size_font) { this -> size_font = size_font; } //extract the data from Markov void Graphic :: ExtractInfo(const Markov & A) { graph.clear(); nodes.clear(); d = A.GetDynamics(); const CTBNDyn * B = dynamic_cast(d); int n = B->NumofNodes(); size = B->NumofNodes(); for(int i = 0; i < n; i++) { nodes.push_back(dynamic_cast (B->Node(i))); } Context x, y; vector domain; vector condomain; std::map::iterator it; for(int i = 0; i < n; i ++) { x = nodes[i]->Domain(); y = nodes[i]->CondDomain(); domain = x.VarList(); if(graph.find(domain[0]) != graph.end()) { it = graph.find(domain[0]); } else { GraphicNode newnode; newnode.Add(domain[0]); graph[domain[0]] = newnode; it = graph.find(domain[0]); } if(y.NumVars() > 0) { condomain = y.VarList(); it->second.SetMatching(condomain); Instantiation inst(y, 0); bool good = true; while(good) { matrix cim = (*nodes[i])(inst)->Intensity(); for(unsigned int j = 0; j < condomain.size(); j ++) { graph[domain[0]].SetPlace(inst.Value(condomain[j])); } it->second.SetMatrix(cim); good = inst.Inc(); } } else { it->second.SetBool(); Instantiation inst(y, 0); matrix cim = (*nodes[i])(inst)->Intensity(); it->second.SetMatrix(cim); it->second.SetMatching(domain); it->second.SetPlace(0); } } } void Graphic::Changes (vector new_name) { change_name = true; change_names = new_name; } void Graphic::ClearName() { change_names.clear(); change_name = false; } void Graphic::Changes() { if(change_name) { std::map::iterator it = graph.begin(); for(unsigned int i = 0; i < change_names.size() && it != graph.end(); i++, it++) { if(it->second.GetExist()) { it->second.SetName(change_names[i]); } } } } void Graphic ::DrawTrajectory(const Markov & A, ofstream & ls) { ExtractInfo(A); Create(A, start, finish); Changes(); BuildState(ls); } void Graphic::Largest(int big) { if (big > largest_tr) { largest_tr = big; } } void Graphic ::DrawTrajectory(Trajectory traj,const Context & context, ofstream & ls) { this->start_point = traj.TimeBegin(); this->ending_point = traj.TimeEnd(); Trajectory::Index i = traj.Begin(context); vector domain = context.VarList(); this ->size = domain.size(); graph.clear(); for(int k = 0; k < size; k ++) { GraphicNode newItem; newItem.Add(domain[k]); newItem.SetBool(); graph[domain[k]] = newItem; } Create(i); Changes(); BuildState(ls); } void Graphic ::Create(Trajectory::Index & i) { graph_change.clear(); stringstream ss; string name; Instantiation oldinst(i.Values()); vector state; for(int n = 0; n < size; n++) { state.push_back(oldinst.Value(n)); Largest(oldinst.Value(n)); } GraphicEvent newItem; newItem.SetChanges(size+1, i.Time(), state); newItem.SetName("0"); graph_change.push_back(newItem); int count = 1; while(!i.Done()) { ++i; GraphicEvent newItem; Instantiation newinst(i.Values()); vector change_id = diffvalues(newinst, oldinst); if(change_id.size() > 0 && ending_point != i.Time()) { for(unsigned int j = 0; j < change_id.size(); j ++ ) { state[change_id[j]] = newinst.Value(change_id[j]); newItem.SetChanges(change_id[j], i.Time(), state); Largest(newinst.Value(change_id[j])); ss << count; ss >> name; ss.clear(stringstream::goodbit); newItem.SetName(name); graph_change.push_back(newItem); count ++; } } oldinst = newinst; } } void Graphic ::Create(const Markov & A, double & start, double & finish) { largest_tr = 0; graph_change.clear(); ending_point = finish; stringstream ss; d = A.GetDynamics(); const CTBNDyn * B = dynamic_cast(d); size = B->NumofNodes(); Context context = Context(); for (int i = 0; i < B->NumofNodes(); ++i) context = context + B->Node(i)->Domain(); string name; Trajectory tr; tr.SetBeginTime(start); tr.SetEndTime(finish); A.Sample(tr); Trajectory::Index i = tr.Begin(context); Instantiation oldinst(i.Values()); vector state; for(int n = 0; n < size; n++) { state.push_back(oldinst.Value(n)); Largest(oldinst.Value(n)); } GraphicEvent newItem; newItem.SetChanges(size+1, i.Time(), state); newItem.SetName("0"); graph_change.push_back(newItem); int count = 1; while(!i.Done()) { ++i; GraphicEvent newItem; Instantiation newinst(i.Values()); vector change_id = diffvalues(newinst, oldinst); if(change_id.size() > 0 && ending_point != i.Time()) { for(unsigned int j = 0; j < change_id.size(); j ++ ) { state[change_id[j]] = newinst.Value(change_id[j]); newItem.SetChanges(change_id[j], i.Time(), state); Largest(newinst.Value(change_id[j])); ss << count; ss >> name; ss.clear(stringstream::goodbit); newItem.SetName(name); graph_change.push_back(newItem); count ++; } } oldinst = newinst; } } void Graphic::BuildMatrix(matrix cim, ofstream& ls) { int matrix_size = 0; matrix_size = cim.getm(); for(int m = 0; m < matrix_size; m++) { if(m != 0) { ls << "{"; } for (int n = 0; n < matrix_size; n++) { ls << cim[n][m]; ArrangeFind(cim); Arrange(ls,(int_size[n]- arrangement[m][n])); if(n +1 != matrix_size) ls<< " | "; } ls << "}"; if ((m+1) != matrix_size) ls << "|"; } } int Graphic::Largest(vector length) { int large = length[0]; for(unsigned int i = 1; i < length.size(); i++) { if(length[i] > large) large = length[i]; } return large; } void Graphic::Arrange(ofstream & ls, int space) { for(int i = 0; i < space; i++) ls << "\\ "; } void Graphic::ArrangeFind(matrix cim) { arrangement.clear(); int_size.clear(); vector length; int matrix_size = cim.getm(); string size; int size_count; for(int m = 0; m < matrix_size; m++) { length.clear(); for (int n = 0; n < matrix_size; n++) { stringstream ss; ss << cim[n][m]; ss >> size; size_count = 0; for(unsigned int i = 0; i < size.size(); i ++) { if(size[i] == '.') { size_count = i +1; } if(size_count == 0 && i +1 == size.size()) { size_count = i +1; } } length.push_back((size.size()-size_count)); ss.clear(stringstream::goodbit); } int_size.push_back(Largest(length)); arrangement.push_back(length); } } void Graphic::CTBNGraphicIntro(std::ofstream & ls) { ls << "digraph g {" << endl; ls << "ordering=out;" << endl; ls << "graph [ " << endl << "rankdir = " << "\"" << "LR" << "\"" << endl << "];" << endl; ls << "node [" << endl << "fontsize = " << "\"" << size_font << "\"" << "\n" << "shape = " << "\"" << "eclipse" << "\"" << "];" << endl; ls << "edge [" << endl << "];" << endl; } void Graphic::BuildM(ofstream& ls) { CTBNGraphicIntro(ls); string name = ""; string node = "node"; stringstream ss; vector temp; vector places; int count; std::map::iterator it; for(it = graph.begin(); it != graph.end(); it ++) { if(it->second.GetExist()) { node = node + it->second.GetName(); ls << "\"" << node << "\"" << "[" << endl; ls << "label = " << "\"" << it->second.GetName(); temp = it->second.GetMatching(); places = it->second.GetPlace(); count = 0; int matrix_count = 0; for(unsigned int x = 0; x < places.size(); x ++) { if (x ==0) { ls << "| " << "{"; } if(places.size() != 1) { if(count == 0) { ls << graph.find(temp[count])->second.GetName() << "= " << places[x] << " "; } else { ls << ", " << graph.find(temp[count])->second.GetName() << "= " << places[x] << " "; } } count ++; if((unsigned) count >= temp.size()) { count = 0; if((x+1) != places.size()) { ls << "| {"; matrix cim = it->second. GetMatrix(matrix_count); BuildMatrix(cim, ls); ls <<"} || {"; matrix_count++; } } } if(places.size() != 1) { ls << "| "; } ls << "{"; matrix cim = it->second.GetMatrix(matrix_count); BuildMatrix(cim, ls); ls <<"}"; ls << "\"" << endl; ls << "shape = " << "\"" << "record" << "\"" << endl; ls << "];" << endl; } node = "node"; } string nod1 ="node"; count = 0; temp.clear(); for(it = graph.begin(); it != graph.end(); it ++) { node = node + it->second.GetName(); temp = it->second.GetMatching(); for(unsigned int j = 0; j < temp.size(); j++) { if(it->second.CheckStrings()) { nod1 = nod1 + graph.find(temp[j])->second.GetName(); } else { nod1 = nod1 + name; } ss << count; ss >> name; if (nod1 != node) { ls << "\"" << nod1 << "\"" << "-> " << "\"" << node << "\"" << " [ id = " << name << "];" << endl; } nod1 = "node"; ss.clear(stringstream::goodbit); count ++; } node = "node"; } ls << "overlap=false" << endl <<"}" << endl; } void Graphic::Build(ofstream & ls) { CTBNGraphicIntro(ls); string name = ""; string node = "node"; stringstream ss; vector temp; vector places; std::map::iterator it; int count; for(it = graph.begin(); it != graph.end(); it ++) { if(it->second.GetExist()) { node = node + it->second.GetName(); ls << "\"" << node << "\"" << "[" << endl; ls << "label = " << "\"" << it->second.GetName(); temp = it->second.GetMatching(); places = it->second.GetPlace(); count = 0; ls << "\"" << endl; ls << "shape = " << "\"" << "record" << "\"" << endl; ls << "];" << endl; } node = "node"; } string nod1 ="node"; count = 0; temp.clear(); for(it = graph.begin(); it != graph.end(); it ++) { node = node + it->second.GetName(); temp = it->second.GetMatching(); for(unsigned int j = 0; j < temp.size(); j++) { if(it->second.CheckStrings()) { nod1 = nod1 + graph.find(temp[j])->second.GetName(); } else { nod1 = nod1 + name; } ss << count; ss >> name; if (nod1 != node) { ls << "\"" << node << "\"" << " -> " << "\"" << nod1 << "\"" << " [ dir=back, id = " << name << "];" << endl; } nod1 = "node"; ss.clear(stringstream::goodbit); count ++; } node = "node"; } ls << "overlap=false" << endl <<"}" << endl; } void Graphic::BuildRank(ofstream & ls) { CTBNGraphicIntro(ls); string name = ""; string node = "node"; stringstream ss; vector temp; vector places; int count; std::map::iterator it; for(it = graph.begin(); it != graph.end(); it ++) { if(it->second.GetExist()) { node = node + it->second.GetName(); ls << "\"" << node << "\"" << "[" << endl; ls << "label = " << "\"" << it->second.GetName(); temp = it->second.GetMatching(); places = it->second.GetPlace(); count = 0; ls << "\"" << endl; ls << "shape = " << "\"" << "record" << "\"" << endl; ls << "];" << endl; } node = "node"; } string nod1 ="node"; count = 0; temp.clear(); ls << "subgraph ctbn { " << endl; ls << "rank = same; " << endl; for(it = graph.begin(); it != graph.end(); it ++) { node = node + it->second.GetName(); temp = it->second.GetMatching(); for(unsigned int j = 0; j < temp.size(); j++) { if(it->second.CheckStrings()) { nod1 = nod1 + graph.find(temp[j])->second.GetName(); } else { nod1 = nod1 + name; } ss << count; ss >> name; if (nod1 != node) { ls << "\"" << node << "\"" << " -> " << "\"" << nod1 << "\"" << " [dir = back id = " << name << "];" << endl; } nod1 = "node"; ss.clear(stringstream::goodbit); count ++; } node = "node"; } ls << endl << "}"; ls << endl <<"}" << endl; } vector< vector > Graphic::setcolor() { int change; int count; vector< vector > color_set; vector color_sequence; for(int i = 0; (largest_tr - i) > -1; i ++) { change = largest_tr -i; count = 0; color_sequence.clear(); while(change > 7) { change = change -7; count++; } if(change >= 4) { change = change - 4; if(count >= 3 && count <= 5) { color_sequence.push_back( 9.00 - (.15 * count)); } else { color_sequence.push_back(1.00); } } else { if(count >= 3 && count <= 5) { color_sequence.push_back(.18 * count); } else { color_sequence.push_back(0.00); } } if(change >= 2) { change= change - 2; if(count >= 1 && count <= 3) { color_sequence.push_back( 1.00 - (.15 * count)); } else { color_sequence.push_back(1.00); } } else { if(count >= 1 && count <= 3) { color_sequence.push_back(.18 * count); } else { color_sequence.push_back(0.10); } } if(change >= 1) { change= change - 1; color_sequence.push_back(1.00 - (.15 * count)); } else { if(count >= 1) { color_sequence.push_back(.18 * count); } else { color_sequence.push_back(0.00); } } color_set.push_back(color_sequence); } return color_set; } int Graphic ::BiggestNumber() { int size_text = 0; int compare = size; while(compare >= 0) { compare = compare - 10; size_text ++; } return size_text; } int Graphic::BiggestText() { int size_text = graph.begin()->second.GetName().length(); std::map::iterator it; for(it = graph.begin()++; it != graph.end(); it ++) { if(it->second.GetName().length() > (unsigned) size_text) { size_text = it->second.GetName().length(); } } return size_text; } void Graphic::BuildState(ofstream & ls) { vector > space_place; vector space_inn; int size_text; if(graph.begin()->second.CheckStrings()) { size_text = BiggestText(); size_text = (int) ((size_text * 0.55) + 0.5); } else { size_text= BiggestNumber(); } space_inn.push_back(((change_point * size_text)) + change_point); space_inn.push_back(((size+size_distance) * change_point - ( change_point * 2.5 ))); int state_size = graph_change.size(); ls << "%!PS-Adobe-3.0 EPSF-3.0" << endl; ls << "%%Title: CTBN Graphic" << endl; ls << "%%Creator: UCR Research project" << endl; time_t timenow; struct tm * timeinfo; time ( &timenow ); timeinfo = localtime ( &timenow ); ls << "%%CreationDate: "<< asctime (timeinfo); ls << "%%BoundingBox: 0 0 " << (size_x + change_point) <<" " <<(size+size_distance)* change_point << endl; ls << "%%EndComments" << endl; ls << "%%Document-Fonts: Times-Roman" << endl; ls << "/Times-Roman findfont" << endl; ls << change_point << " scalefont" << endl; ls << "setfont \n" << endl; ls << "1 setlinewidth " << endl; int init_box = (change_point * 2); int count_line = 0; vector< vector > color_set = setcolor(); for(unsigned int i = 0; i < color_set.size(); i++) { if(init_box > size_x) { init_box = (change_point * 2); count_line++; } ls << init_box - change_point << " " << ((size+size_distance)* change_point) -(count_line * change_point ) << " " << "newpath moveto" << endl; ls << init_box << " " << ((size+size_distance)* change_point) - (count_line * change_point ) << " " << "lineto" << endl; ls << init_box << " " << ((size+size_distance)* change_point) - (count_line * change_point ) - change_point << " " << "lineto" << endl; ls << init_box - change_point<< " " << ((size+size_distance)* change_point) - (count_line * change_point ) - change_point << " " << "lineto" << endl; ls << "closepath" << endl; ls << "gsave" << endl; ls << color_set[color_set.size() - i -1][0] << " " << color_set[color_set.size() - i -1][1] << " " << color_set[color_set.size() - i -1][2] << " setrgbcolor fill" << endl; ls << "grestore" << endl; ls << "stroke" << endl; ls << init_box << " " << ((size+size_distance)* change_point) - (count_line * change_point )- change_point << " " << "newpath moveto" << endl; ls << "( =" << i << " )" << " show" << endl << endl; init_box = init_box + 4 * change_point; } for(int i = 0; i < size; i ++) { space_inn[1] = space_inn[1] - (change_point * i); space_place.push_back(space_inn); ls << space_inn[0] - (change_point * size_text) << " " << space_inn[1] << " " << "newpath moveto" << endl; ls << space_inn[0] << " " << space_inn[1] << " " << "lineto" << endl; ls << space_inn[0] << " " << space_inn[1] - change_point << " " << "lineto" << endl; ls << space_inn[0] - (change_point * size_text) << " " << space_inn[1] - change_point << " " << "lineto" << endl; ls << "closepath" << endl; ls << "stroke" << endl; ls << space_inn[0]- change_point* size_text +(change_point / 4) << " " << space_inn[1] - change_point+(change_point / 4) << " " << "newpath moveto" << endl; ls << "(" << graph[i].GetName() << ")" << " show" << endl << endl; space_inn[1] = ((size+size_distance)* change_point - ( change_point * 2.5 )); } int measure; vector check_value; for(int i = 0; i < size; i ++) { for(int j = 0; j < state_size; j ++) { if (graph_change[j].GetChange() == i) { measure = (int) (((size_x - space_inn[0])*(graph_change[j]. GetTime() / (ending_point - start_point))) + space_inn[0]); ls << space_place[i][0] << " " << space_place[i][1] << " " << "newpath moveto" << endl; ls << measure << " " << space_place[i][1] << " " << "lineto" << endl; ls << measure << " " << space_place[i][1] - change_point << " " << "lineto" << endl; ls << space_place[i][0]<< " " << space_place[i][1] - change_point << " " << "lineto" << endl; ls << "closepath" << endl; ls << "gsave" << endl; check_value = graph_change[j-1].GetState(); if(check_value[i] != -1) { ls << color_set[color_set.size() - 1 - check_value[i]][0] << " " << color_set[color_set.size() - 1 - check_value[i]][1] << " " << color_set[color_set.size() - 1 - check_value[i]][2] << " setrgbcolor fill" << endl; } ls << "grestore" << endl; ls << "stroke" << endl << endl; ls << "1 setlinewidth " << endl; space_place[i][0] = measure; } } ls << space_place[i][0] << " " << space_place[i][1] << " " << "newpath moveto" << endl; ls << size_x << " " << space_place[i][1] << " " << "lineto" << endl; ls << size_x << " " << space_place[i][1] - change_point << " " << "lineto" << endl; ls << space_place[i][0]<< " " << space_place[i][1] - change_point << " " << "lineto" << endl; ls << "closepath" << endl; ls << "gsave" << endl; check_value =graph_change[state_size-1].GetState(); if(check_value[i] != -1) { ls << color_set[color_set.size() - 1 - check_value[i]][0] << " " << color_set[color_set.size() - 1 - check_value[i]][1] << " " << color_set[color_set.size() - 1 - check_value[i]][2] << " setrgbcolor fill" << endl; } ls << "grestore" << endl; ls << "stroke" << endl << endl; } ls << "/Times-Roman findfont" << endl; ls << size_distance << " scalefont" << endl; ls << "setfont \n" << endl; int first_row = (int) (space_place[size-1][1] - (change_point + size_distance)); int next_row = size_distance; vector firstline_row; firstline_row.push_back(0); unsigned int line = 0; int present_column; int rejust; double justed; for(int i = 0; i < state_size; i ++) { if(i > 0 && graph_change[i].GetTime() != graph_change[i-1].GetTime()) { measure = (int) ((size_x - space_inn[0]) * (graph_change[i].GetTime() / (ending_point - start_point)) + space_inn[0]); line = 0; while(firstline_row[line] > measure - (size_distance * 2.5)) { line ++; if(line >= firstline_row.size()) firstline_row.push_back(0); } present_column = first_row -(next_row * line); ls << measure << " " << present_column << " " << "newpath moveto" << endl; firstline_row[line] = measure; rejust = (int) (graph_change[i].GetTime() * 1000); justed = rejust; justed = justed / 1000; ls << "(" << justed << ")" << " show" << endl << endl; } } ls << "showpage"<< endl; ls << "end"; } void Graphic::BuildStateRank(ofstream & ls) { vector space_inn; int size_text; if(graph[0].CheckStrings()) { size_text = BiggestText(); size_text = (int) ((size_text * 0.55) + 0.5); } else { size_text= BiggestNumber(); } int state_size = graph_change.size(); ls << "%!PS-Adobe-3.0 EPSF-3.0" << endl; ls << "%%Title: CTBN Graphic" << endl; ls << "%%Creator: UCR Research project" << endl; time_t timenow; struct tm * timeinfo; time ( &timenow ); timeinfo = localtime ( &timenow ); ls << "%%CreationDate: "<< asctime (timeinfo); ls << "%%BoundingBox: 0 0 " << (size_x + change_point) <<" " << large_point + (9 * change_point) << endl; ls << "%%EndComments" << endl; ls << "%%Document-Fonts: Times-Roman" << endl; ls << "/Times-Roman findfont" << endl; ls << change_point << " scalefont" << endl; ls << "setfont \n" << endl; ls << "1 setlinewidth " << endl; int init_box = (change_point* 2); double position_y = large_point + (9 * change_point); int count_line = 0; vector< vector > color_set = setcolor(); for(unsigned int i = 0; i < color_set.size(); i++) { if(init_box > size_x) { init_box = (change_point * 2); count_line++; } ls << init_box - change_point<< " " << position_y -(count_line * change_point ) << " " << "newpath moveto" << endl; ls << init_box << " " << position_y -(count_line * change_point ) << " " << "lineto" << endl; ls << init_box << " " << position_y - (count_line * change_point ) - change_point << " " << "lineto" << endl; ls << init_box - change_point << " " << position_y -(count_line * change_point ) - change_point << " " << "lineto" << endl; ls << "closepath" << endl; ls << "gsave" << endl; ls << color_set[color_set.size() - i -1][0] << " " << color_set[color_set.size() - i -1][1] << " " << color_set[color_set.size() - i -1][2] << " setrgbcolor fill" << endl; ls << "grestore" << endl; ls << "stroke" << endl; ls << init_box << " " << position_y -(count_line * change_point ) - change_point << " " << "newpath moveto" << endl; ls << "( =" << i << " )" << " show" << endl << endl; init_box = init_box + 4 * change_point; } init_box = ((change_point * size_text)) + change_point; for(int i = 0; i < size; i ++) { ls << init_box - (change_point * size_text) << " " << getlocation[i][1] + (5 * change_point) << " " << "newpath moveto" << endl; ls << init_box << " " << getlocation[i][1] + (5 * change_point) << " " << "lineto" << endl; ls << init_box << " " << getlocation[i][0] + (5 * change_point) << " " << "lineto" << endl; ls << init_box - (change_point * size_text) << " " << getlocation[i][0] + (5 * change_point) << " " << "lineto" << endl; ls << "closepath" << endl; ls << "stroke" << endl; ls << init_box - change_point * size_text +(change_point / 4) << " " << getlocation[i][0] + (5 * change_point) + ((getlocation[i][1] - getlocation[i][0]) / 4) << " " << "newpath moveto" << endl; ls << "(" << graph[i].GetName() << ")" << " show" << endl << endl; space_inn.push_back(init_box); } int measure; vector check_value; for(int i = 0; i < size; i ++) { for(int j = 0; j < state_size; j ++) { if (graph_change[j].GetChange() == i) { measure = (int) ((size_x - init_box)*(graph_change[j]. GetTime() / (ending_point - start_point)) + init_box); ls << space_inn[i] << " " << getlocation[i][1] + (5 * change_point) << " " << "newpath moveto" << endl; ls << measure << " " << getlocation[i][1] + (5 * change_point) << " " << "lineto" << endl; ls << measure << " " << getlocation[i][0] + (5 * change_point) << " " << "lineto" << endl; ls << space_inn[i] << " " << getlocation[i][0] + (5 * change_point) << " " << "lineto" << endl; ls << "closepath" << endl; ls << "gsave" << endl; check_value =graph_change[j-1].GetState(); if(check_value[i] != -1) { ls << color_set[color_set.size() - 1 - check_value[i]][0] << " " << color_set[color_set.size() - 1 - check_value[i]][1] << " " << color_set[color_set.size() - 1 - check_value[i]][2] << " setrgbcolor fill" << endl; } else { ls << 1 << " " << 1 << " " << 1 << " setrgbcolor fill" << endl; } ls << "grestore" << endl; ls << "stroke" << endl << endl; ls << "1 setlinewidth " << endl; space_inn[i] = measure; } } ls << space_inn[i]<< " " << getlocation[i][1] + (5 * change_point) << " " << "newpath moveto" << endl; ls << size_x << " " << getlocation[i][1] + (5 * change_point) << " " << "lineto" << endl; ls << size_x << " " << getlocation[i][0] + (5 * change_point) << " " << "lineto" << endl; ls << space_inn[i]<< " " << getlocation[i][0] + (5 * change_point) << " " << "lineto" << endl; ls << "closepath" << endl; ls << "gsave" << endl; check_value =graph_change[state_size-1].GetState(); if(check_value[i] != -1) { ls << color_set[color_set.size() - 1 - check_value[i]][0] << " " << color_set[color_set.size() - 1 - check_value[i]][1] << " " << color_set[color_set.size() - 1 - check_value[i]][2] << " setrgbcolor fill" << endl; } ls << "grestore" << endl; ls << "stroke" << endl << endl; } ls << "/Times-Roman findfont" << endl; ls << size_distance << " scalefont" << endl; ls << "setfont \n" << endl; int first_row = (int) getlocation[0][0] + size_distance; int next_row = size_distance; vector firstline_row; firstline_row.push_back(0); unsigned int line = 0; int present_column; int rejust; double justed; for(int i = 0; i < state_size; i ++) { if(i > 0 && graph_change[i].GetTime() != graph_change[i-1].GetTime()) { measure = (int) ((size_x - init_box) * (graph_change[i].GetTime() / (ending_point - start_point)) + init_box); line = 0; while(firstline_row[line] > measure - (size_distance * 2.5)) { line ++; if(line >= firstline_row.size()) firstline_row.push_back(0); } present_column = first_row -(next_row * line); ls << measure << " " << present_column << " " << "newpath moveto" << endl; firstline_row[line] = measure; rejust = (int) (graph_change[i].GetTime() * 1000); justed = rejust; justed = justed / 1000; ls << "(" << justed << ")" << " show" << endl << endl; } } ls << "showpage"<< endl; ls << "end"; } } // end of ctbn namespace ctbnrle.1.1.1/src/ctbn.cc0000664000175000017500000000305312107032416015407 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "ctbn.h" #include "nullptr03.h" namespace ctbn { using namespace std; SOBJCLASSDEF(CTBN) CTBN::CTBN(RV *startdist, Dynamics *dyn) : Markov(startdist, dyn) { } CTBN::CTBN(std::istream &is) : Markov(is) { } CTBN::CTBN(const Markov &m) : Markov(m) { } CTBN *CTBN::Clone() const { return new CTBN(*this); } CTBN &CTBN::operator=(const CTBN &ctbn) { if(&ctbn==this) return *this; if(p0 != nullptr03) delete p0; if(d != nullptr03) delete d; p0 = ctbn.p0->Clone(); d = ctbn.d->Clone(); return *this; } const MarkovDyn* CTBN::Node(int i) const { return dynamic_cast(CTBNDynamic()->Node(i)); } const CTBNDyn* CTBN::CTBNDynamic() const { return dynamic_cast(d); } } // end of ctbn namespace ctbnrle.1.1.1/src/expmethod.cc0000664000175000017500000000767112107032416016470 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "expmethod.h" #include "markovdyn.h" #include "rk.h" namespace ctbn { using namespace std; ExpMethod::ExpMethod() : VarSample() { } ExpMethod::~ExpMethod() { } double ExpMethod::SampleTime(const matrix &Q, int id, int val, const Instantiation &currinst, double currt, const Trajectory *evid, double te, int e, Random &rand) const { double deltat; if (e==-1 || val==e) { deltat = rand.SampleExp(-Q[val][val]); } else { deltat = rand.SampleTruncateExp(-Q[val][val], te-currt); } return deltat; } int ExpMethod::SampleTransition(const matrix &Q, int id, int val, const Instantiation &inst, double currt, const Trajectory *evid, double te, int e, double &weight, Random &rand) const { int newval = -1; //vectr prob = Q.row(val); //double sum = -prob[val]; // unnecessary -- SampleMultinormial ignores negative values //prob[val] = 0; //newval = rand.SampleMultinomial(prob, sum); double sum = -Q[val][val]; newval = rand.SampleMultinomial(&Q[val][0], Q.getn(), sum); return newval; } double ExpMethod::TimeWeight(const matrix &Q, const Instantiation &currinst, int id, int transitionid, int val, double currt, double deltat, const Trajectory *evid, double te, int e) const { if (e==-1||val==e) return 0; double negq = Q[val][val]; double weight = log(1-exp(negq * (te-currt))); if (id!=transitionid) weight -= log(1 - exp(negq*(te-currt-deltat))); if (!finite(weight)) exit(0); return weight; } /* double ExpMethod::TransitionWeight(const matrix &Q, int transitionid, int val, const Instantiation &currinst, const Instantiation &nextinst, double currt, double deltat, const Trajectory *evid, double te, int e, bool logscale) const { double weight = 0; // if (te-currt<1e-8&&Q.getm()>2) // if (nextinst.Value(transitionid)==e) // weight = Q[val][e]; return weight; } */ //******************************************************** LookAheadMethod::LookAheadMethod() : ExpMethod() { } int LookAheadMethod::SampleTransition(const matrix &Q, int id, int val, const Instantiation &inst, double currt, const Trajectory *evid, double te, int e, double &weight, Random &rand) const { if (e==-1) return ExpMethod::SampleTransition(Q,id,val,inst, currt,evid,te,e,weight,rand); transitionprob = 1; vectr prob = Q.row(val); double sum = -prob[val]; prob[val] = 0; int newval = -1; int size = prob.length(); if (size<3) { newval = rand.SampleMultinomial(prob, sum); transitionprob = 1.0; } else { vectr b(prob.length(), 0.0); b[e] = 1; expmtv(b, Q, te-currt); b = b.dotstar(Q.row(val)); b[val] = 0.0; double bsum = b.sum(); newval = randomizer.SampleMultinomial(b, bsum); transitionprob = prob[newval] * bsum / (b[newval] * sum); } weight += log(transitionprob); return newval; } /* double LookAheadMethod::TransitionWeight(const matrix &Q, int transitionid, int val, const Instantiation &currinst, const Instantiation &nextinst, double currt, double deltat, const Trajectory *evid, double te, int e, bool logscale) const { // vectr prob = Q.row(val); // if (prob.length()<3) return 0; // else return log(transitionprob); } */ } // end of ctbn namespace ctbnrle.1.1.1/src/multisimple.cc0000664000175000017500000005377112107032416017041 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "multisimple.h" #include "extramath.h" #include #include namespace ctbn { using namespace std; SOBJCLASSDEF(MultiSimple) SOBJCLASSDEF(MultiLogSimple) SOBJCLASSDEF(MultiZSimple) SOBJCLASSDEF(SparseMultiZSimple) SOBJCLASSDEF(MultiSimpleSS) MultiSimple::MultiSimple(int n) : RVSimple(), theta(n,0.0) { } MultiLogSimple::MultiLogSimple(int n) : RVSimple(), logtheta(n,0.0) { } MultiZSimple::MultiZSimple(int n) : RVSimple(), theta(n,0.0) { logz = 0.0; } SparseMultiZSimple::SparseMultiZSimple(int n) : MultiZSimple(n), ix(n) { //iota(ix.begin(),ix.end(),0); // set to 0, 1, 2, ... for(int i=0;i &indexes, double logfactor) : MultiZSimple(dist,logfactor), ix(indexes) { } //--- MultiSimple::MultiSimple(istream &is) : RVSimple(is) { is >> theta; } MultiLogSimple::MultiLogSimple(istream &is) : RVSimple(is) { is >> logtheta; } MultiZSimple::MultiZSimple(istream &is) : RVSimple(is) { is >> theta >> logz; } SparseMultiZSimple::SparseMultiZSimple(istream &is) : MultiZSimple(is) { is >> ix; } //--- MultiSimple::~MultiSimple() { } MultiLogSimple::~MultiLogSimple() { } MultiZSimple::~MultiZSimple() { } SparseMultiZSimple::~SparseMultiZSimple() { } //--- void MultiSimple::LoadOld(istream &is) { RVSimple::LoadOld(is); is >> theta; } void MultiLogSimple::LoadOld(istream &is) { RVSimple::LoadOld(is); is >> logtheta; } void MultiZSimple::LoadOld(istream &is) { RVSimple::LoadOld(is); is >> theta >> logz; } void SparseMultiZSimple::LoadOld(istream &is) { MultiZSimple::LoadOld(is); is >> ix; } //--- void MultiSimple::SaveOld(ostream &os) const { RVSimple::SaveOld(os); os << os.fill() << theta; } void MultiLogSimple::SaveOld(ostream &os) const { RVSimple::SaveOld(os); os << os.fill() << logtheta; } void MultiZSimple::SaveOld(ostream &os) const { RVSimple::SaveOld(os); os << os.fill() << theta << os.fill() << logz; } void SparseMultiZSimple::SaveOld(ostream &os) const { MultiZSimple::SaveOld(os); os << os.fill() << ix; } //--- MultiSimple *MultiSimple::Clone() const { return new MultiSimple(*this); } MultiLogSimple *MultiLogSimple::Clone() const { return new MultiLogSimple(*this); } MultiZSimple *MultiZSimple::Clone() const { return new MultiZSimple(*this); } SparseMultiZSimple *SparseMultiZSimple::Clone() const { return new SparseMultiZSimple(*this); } //--- double MultiSimple::Prob(int ind, bool log) const { if (log) return ::log(theta[ind]); else return theta[ind]; } double MultiLogSimple::Prob(int ind, bool log) const { if (log) return logtheta[ind]; else return exp(logtheta[ind]); } double MultiZSimple::Prob(int ind, bool log) const { if (log) return logz+::log(theta[ind]); else return exp(logz)*theta[ind]; } double SparseMultiZSimple::Prob(int ind, bool log) const { vector::const_iterator spot = lower_bound(ix.begin(),ix.end(),ind); if (spot==ix.end() || *spot!=ind) return log ? -INFINITY : 0; return MultiZSimple::Prob(spot-ix.begin(),log); } //--- double MultiSimple::Normalize() { return theta.normalize(); } double MultiLogSimple::Normalize() { double lmax = logtheta.max(); double sum = 0.0; int n = logtheta.getm(); for(int i=0;i &ind) { int n = theta.getm(); int indn = ind.size(); unsigned int j=0; for(int i=0;i &ind) { int n = logtheta.getm(); int indn = ind.size(); unsigned int j=0; for(int i=0;i &ind) { int n = theta.getm(); int indn = ind.size(); unsigned int j=0; for(int i=0;i &ind) { int n = ix.size(); int indn = ind.size(); vectr oldt(theta); theta.resize(indn); theta = 0.0; int i=0,j=0; while(i > &ind) { vectr oldt(theta); int n = ind.size(); theta.resize(n); for(int i=0;i > &ind) { double lmax = logtheta.max(); vectr oldt(logtheta); int n = ind.size(); logtheta.resize(n); for(int i=0;i > &ind) { vectr oldt(theta); int n = ind.size(); theta.resize(n); for(int i=0;i > &ind) { vectr oldt(theta); int n = ind.size(); int oldn = ix.size(); vector oldix(ix); int newn = 0; for(int i=0;i0) ++newn; theta.resize(newn); ix.resize(newn,0); int kk = 0; for(int k=0;k(x); theta.multby(ms->theta); } void MultiLogSimple::MultBy(const RVSimple *x) { const MultiLogSimple *ms = dynamic_cast(x); logtheta += ms->logtheta; } void MultiZSimple::MultBy(const RVSimple *x) { const MultiZSimple *ms = dynamic_cast(x); theta.multby(ms->theta); logz += ms->logz; SetZ(); } //--- void MultiSimple::Add(const RVSimple *x) { const MultiSimple *ms = dynamic_cast(x); theta += ms->theta; } void MultiLogSimple::Add(const RVSimple *x) { const MultiLogSimple *ms = dynamic_cast(x); int n = logtheta.getm(); for(int i=0;ilogtheta[i]) logtheta[i] = ms->logtheta[i] + ::log(exp(logtheta[i]-ms->logtheta[i]) + 1.0); else logtheta[i] = logtheta[i] + ::log(exp(ms->logtheta[i]-logtheta[i]) + 1.0); } } void MultiZSimple::Add(const RVSimple *x) { const MultiZSimple *ms = dynamic_cast(x); if (ms->logz>logz) { theta = theta*exp(logz-ms->logz) + ms->theta; logz = ms->logz; } else { theta += ms->theta*exp(ms->logz-logz); } SetZ(); } //--- void MultiSimple::Add(const RVSimple *x, double w) { const MultiSimple *ms = dynamic_cast(x); theta.add(ms->theta,w); } void MultiLogSimple::Add(const RVSimple *x, double w) { const MultiLogSimple *ms = dynamic_cast(x); int n = logtheta.getm(); double logw = ::log(w); for(int i=0;ilogtheta[i]+logw; if (logtheta[i](x); double xlz = ms->logz+::log(w); if (xlz>logz) { theta = theta*exp(logz-xlz) + ms->theta; logz = xlz; } else { theta += ms->theta*exp(xlz-logz); } SetZ(); } //--- void MultiSimple::Mult(double x) { theta *= x; } void MultiLogSimple::Mult(double x) { logtheta += log(x); } void MultiZSimple::Mult(double x) { logz += log(x); } //--- MultiSimpleSS *MultiSimple::BlankSS() const { return new MultiSimpleSS(theta.getm()); } MultiSimpleSS *MultiLogSimple::BlankSS() const { return new MultiSimpleSS(logtheta.getm()); } MultiSimpleSS *MultiZSimple::BlankSS() const { return new MultiSimpleSS(theta.getm()); } //--- void MultiSimple::AddSS(int x, SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); msss->c[x] += w; } void MultiLogSimple::AddSS(int x, SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); msss->c[x] += w; } void MultiZSimple::AddSS(int x, SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); msss->c[x] += w; } void SparseMultiZSimple::AddSS(int x, SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); msss->c[x] += w; } //--- void MultiSimple::AddExpSS(SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); msss->c += w*theta; } void MultiLogSimple::AddExpSS(SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); int n = logtheta.getm(); for(int i=0;ic[i] += w*exp(logtheta[i]); } void MultiZSimple::AddExpSS(SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); int n = theta.getm(); w *= exp(logz); for(int i=0;ic[i] += w*theta[i]; } void SparseMultiZSimple::AddExpSS(SS *ss, double w) const { MultiSimpleSS * msss = dynamic_cast(ss); int n = theta.getm(); w *= exp(logz); for(int i=0;ic[ix[i]] += w*theta[i]; } //--- void MultiSimple::AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w) const { const MultiSimple *ms = dynamic_cast(rvs); assert(ms!=NULL); // otherwise, need double-dispatch and more code MultiSimpleSS *msss = dynamic_cast(ss); const MultiSimpleSS *addmsss = dynamic_cast(toadd); int n = theta.getm(); for(int i=0;ic[i] += addmsss->c[mapping[i][ki]]; } } } void MultiLogSimple::AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w) const { const MultiLogSimple *ms = dynamic_cast(rvs); assert(ms!=NULL); // otherwise, need double-dispatch and more code MultiSimpleSS *msss = dynamic_cast(ss); const MultiSimpleSS *addmsss = dynamic_cast(toadd); int n = logtheta.getm(); for(int i=0;ic[i] += addmsss->c[mapping[i][ki]]; } } } void MultiZSimple::AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w) const { const MultiZSimple *ms = dynamic_cast(rvs); assert(ms!=NULL); // otherwise, need double-dispatch and more code MultiSimpleSS *msss = dynamic_cast(ss); const MultiSimpleSS *addmsss = dynamic_cast(toadd); int n = theta.getm(); for(int i=0;ic[i] += addmsss->c[mapping[i][ki]]; } } } void SparseMultiZSimple::AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w) const { const SparseMultiZSimple *ms = dynamic_cast(rvs); assert(ms!=NULL); // otherwise, need double-dispatch and more code MultiSimpleSS *msss = dynamic_cast(ss); const MultiSimpleSS *addmsss = dynamic_cast(toadd); int n = theta.getm(); for(int i=0;ic[ix[i]] += addmsss->c[mapping[ix[i]][ki]]; } } } //--- int MultiSimple::Sample(Random &rand) const { // return rand.SampleMultinomial(theta,theta.getm(),theta.sum()); return rand.SampleMultinomial(theta,theta.sum()); } int MultiLogSimple::Sample(Random &rand) const { int n = logtheta.getm(); vector addw(n,0.0); double lmax = logtheta.max(); double sum = 0.0; for(int i=0;i(ss); theta = msss->c; double sum = theta.sum(); if (sum>0) theta /= sum; else theta = 1.0/theta.getm(); } void MultiLogSimple::Maximize(const SS *ss) { const MultiSimpleSS *msss = dynamic_cast(ss); logtheta = msss->c; double sum = logtheta.sum(); if (sum>0) sum = log(sum); else logtheta = 1.0/logtheta.getm(); int n = logtheta.getm(); for(int i=0;i(ss); theta = msss->c; double sum = theta.sum(); if (sum>0) logz = -log(sum); else { theta = 1.0; logz = -log((double) theta.getm()); } } void SparseMultiZSimple::Maximize(const SS *ss) { MultiZSimple::Maximize(ss); int n = theta.getm(); ix.resize(n); // iota(ix.begin(),ix.end(),0); // set to 0, 1, 2, ... for(int i=0;ilogfactor) logfactor = d[i]; for(int i=0;inn) nn = ix[i]; if (d.getm()0) theta[i] = 1.0; } void MultiLogSimple::MakeUniform() { int n = logtheta.getm(); for(int i=0;i0) theta[i] = 1.0; logz = 0.0; } //--- void MultiSimple::Scramble(double alpha, double degree, Random &rand) { int n = theta.getm(); double *in = new double[n]; double *newtheta = new double[n]; for(int i=0;i(ss); double llh = 0.0; int n = theta.length(); for(int i=0; ic[i]!=0.0) llh += rvss->c[i] * log(theta[i]); return llh; } double MultiZSimple::LLH(const SS *ss) const { const MultiSimpleSS *rvss = dynamic_cast(ss); double llh = 0.0; int n = theta.length(); for(int i=0; ic[i]>0.0) llh += rvss->c[i] * (log(theta[i]) + logz); return llh; } double MultiLogSimple::LLH(const SS *ss) const { const MultiSimpleSS *rvss = dynamic_cast(ss); double llh = 0.0; int n = logtheta.length(); for(int i=0; ic[i]!=0.0) llh += rvss->c[i] * logtheta[i]; return llh; } double SparseMultiZSimple::LLH(const SS *ss) const { const MultiSimpleSS *rvss = dynamic_cast(ss); double llh = 0.0; int n = rvss->c.length(); //int m = ix.length(); int m = ix.size(); for(int i=0,j=0; ic[i]>0.0) { if (ix[j]==i) llh += rvss->c[i] * (log(theta[j]) + logz); else return log(0.0); } } return llh; } //double MultiLogSimple::LLH(const SS *ss) const //--- void SparseMultiZSimple::SetSparseDist(const vectr &d, double logfactor) { MultiZSimple::SetDist(d,logfactor); } void SparseMultiZSimple::SetSparseIndexes(const vector &ind) { ix = ind; } void SparseMultiZSimple::GetSparseDist(vectr &d, double &logfactor) const { MultiZSimple::GetDist(d,logfactor); } //--- void MultiZSimple::SetZ() { // set so that maximum is 1 (could set so that sum=1 instead, // but this seems to work better) double s = theta.max(); if (s>0) { theta /= s; logz += log(s); } } //--- double RVSimple::GetScore(double numTrans, const SS* ss) const { const MultiSimpleSS *rvss = dynamic_cast(ss); const vectr& c = rvss->c; unsigned int size = c.length(); /* double alpha_xx_u(numTrans / ((size-1)*size)); double alpha_x_u(alpha_xx_u*size); double localScore(0); localScore += lngamma(alpha_x_u) - lngamma(alpha_x_u + c.sum()); */ double alpha_xx_u(numTrans / size); double alpha_x_u(numTrans); double localScore = lngamma(alpha_x_u) - lngamma(alpha_x_u + c.sum()); for(unsigned int i = 0; i < size; i++) localScore += lngamma(alpha_xx_u + c[i]) - lngamma(alpha_xx_u); return localScore; } //--------- MultiSimpleSS::MultiSimpleSS(int n) : SS(), c(n,0.0) { } MultiSimpleSS::MultiSimpleSS(istream &is) : SS(is) { is >> c; } MultiSimpleSS::MultiSimpleSS(const MultiSimpleSS &msss) : SS(*this), c(msss.c) { } MultiSimpleSS::~MultiSimpleSS() { } MultiSimpleSS *MultiSimpleSS::Clone() const { return new MultiSimpleSS(*this); } void MultiSimpleSS::LoadOld(istream &is) { SS::LoadOld(is); is >> c; } void MultiSimpleSS::SaveOld(ostream &os) const { SS::SaveOld(os); os << os.fill() << c; } void MultiSimpleSS::Scale(double w) { int n = c.length(); for(int i=0; i(nss); int n = c.length(); for(int i=0; ic[i] * w; } } // end of ctbn namespace ctbnrle.1.1.1/src/factoredmatrix.cc0000664000175000017500000006045612107032416017507 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "factoredmatrix.h" #include "factoredvector.h" #include "bn.h" #include "ctbn.h" #include "context.h" #include "dynamics.h" #include "matrix.h" #include "notyetimplementederror.h" #include "nullptr03.h" #include "params.h" #include #include #include #include #include #define MAXCACHE 1000 namespace ctbn { #ifdef USECACHE static int reuse = 0; static int tries = 0; #endif using namespace std; static const string the_class_name("FactoredMatrix"); FactoredMatrix::FactoredMatrix() : RVCondSimple(), minrate(0.0), t(-1.0), trans(false), taylor_expansion_term_count(10), theta_value(10), cachemaxt(-1.0) {} FactoredMatrix::FactoredMatrix(const FactoredMatrix & copy_from) : RVCondSimple(), minrate(copy_from.minrate), t(copy_from.t), trans(copy_from.trans), taylor_expansion_term_count(copy_from.taylor_expansion_term_count), theta_value(copy_from.theta_value), nodes(), cachemaxt(copy_from.cachemaxt), rkcache(copy_from.rkcache) /*, condpars(copy_from.condpars), children_in_evidence(copy_from.children_in_evidence), condpars_evid(copy_from.condpars_evid), evid(copy_from.evid), trans_evid(copy_from.trans_evid), notrans_evid(copy_from.notrans_evid) */ { unsigned int const nodes_size(copy_from.nodes.size()); nodes.resize(nodes_size); for (unsigned int i(0); i != nodes_size; ++i) { // TODO: Ask Busra: should this actually be cloning the elements? nodes[i] = copy_from.nodes[i]; } //copy maps as well if (copy_from.condpars.size() > 0) condpars = copy_from.condpars; if (copy_from.children_in_evidence.size() > 0) children_in_evidence = copy_from.children_in_evidence; if (copy_from.condpars_evid.size() > 0) condpars_evid = copy_from.condpars_evid; if (copy_from.evid.size() > 0) evid = copy_from.evid; if (copy_from.trans_evid.size()>0) trans_evid = copy_from.trans_evid; if (copy_from.notrans_evid.size()>0) notrans_evid = copy_from.notrans_evid; } FactoredMatrix::FactoredMatrix(Dynamics * dyn) : RVCondSimple(), minrate(0.0), t(-1.0), trans(false), taylor_expansion_term_count(10), theta_value(10), cachemaxt(-1.0) { CTBNDyn *ctbndyn = dynamic_cast(dyn); int n = ctbndyn->NumofNodes(); nodes.clear(); nodes.resize(n); bnodes.clear(); bnodes.resize(n); amatrices.clear(); amatrices.resize(n); for (int i=0; iNodeByVar(i)->Clone(); minrate += FindMinVal(nodes[i]); bnodes[i] = ctbndyn->NodeByVar(i)->Clone(); amatrices[i] = MakeAB(bnodes[i]); } } FactoredMatrix::~FactoredMatrix(){ unsigned int const nodes_size(nodes.size()); for (unsigned int i=0; i != nodes_size; ++i){ if (nodes[i] != nullptr03) { delete nodes[i]; delete bnodes[i]; delete amatrices[i]; } } } RVCondSimple* FactoredMatrix::Clone() const{ return new FactoredMatrix(*this); } void FactoredMatrix::Uniformize(Dynamics *node){ int domainsize = ((MarkovDyn*)node)->Domain().Size(); // matrix eye(domainsize, domainsize, vectr(domainsize, 1.0)); matrix eye(domainsize, domainsize, 0.0); for (int i(0); i != domainsize; ++i) { eye[i][i] = 1.0; }; for (int i=0; i<((MarkovDyn*)node)->CondDomain().Size(); i++){ matrix &m = ((MarkovDyn*)node)->operator()(((MarkovDyn*)node)->CondDomain().Index(i))->Intensity(); m += eye; m = m/minrate; } } double FactoredMatrix::FindMinVal(Dynamics *node) const{ double min = 1.0; double tmp = 1.0; for (int i=0; i< ((MarkovDyn*)node)->CondDomain().Size(); i++){ matrix &m = ((MarkovDyn*)node)->operator()( ((MarkovDyn*)node)->CondDomain().Index(i))->Intensity(); tmp = m.min(); if ( min >= tmp) min = tmp; } return -min; } double FactoredMatrix::FindMinRate() const{ double ret=0.0; for (unsigned int i=0; i cind; std::map::iterator it; std::map >::iterator itv; for (unsigned int i=0; iDomain()).ConsistentIndexes(cind, x); if (cind.size()==1){ it = evid.begin(); evid.insert(it, pair(i, cind[0])); (nodes[i]->CondDomain()).ConsistentIndexes(condpars_evid[i],x); } //don't include observed parents else { (nodes[i]->CondDomain()).ConsistentIndexes( condpars[i], x); } } int id; for (it=evid.begin(); it!=evid.end(); it++){ id = (*it).first; MarkovDyn *evnode = dynamic_cast(nodes[id]); vector parlist = evnode->CondDomain().VarList(); for (unsigned int k=0; k fromind; nodes.at(i)->Domain().ConsistentIndexes(fromind,from); vector toind; nodes.at(i)->Domain().ConsistentIndexes(toind,to); int fromsize = fromind.size(); int tosize = toind.size(); if (fromsize == 1 && tosize == 1){ if (fromind.at(0) != toind.at(0)){ trans_evid[i].push_back(fromind.at(0)); trans_evid[i].push_back(toind.at(0)); nodes.at(i)->CondDomain().ConsistentIndexes(condpars_evid[i], from); } } } } else{ //No transition for (unsigned int i=0; i fromind; nodes.at(i)->Domain().ConsistentIndexes(fromind,from); vector toind; nodes.at(i)->Domain().ConsistentIndexes(toind,to); int fromsize = fromind.size(); int tosize = toind.size(); if (fromsize > 1 && tosize == 1){ notrans_evid[i] = toind.at(0); } } } } double FactoredMatrix::FindAvgRate(FactoredVector &v, const Dynamics *node, int from, int to, const vector &cind, bool transpose, double &maxval) const{ double par_sum=0.0; double tmp_mult; MarkovDyn const *nnode = dynamic_cast(node); vector parlist = nnode->CondDomain().VarList(); maxval = 0.0; for (unsigned int j=0; jCondDomain().Index(cind.at(j)).Value(parlist.at(k))); } if (transpose) tmp_mult *= nnode->operator()(nnode->CondDomain().Index(cind.at(j)))->Intensity()[to][from]; else tmp_mult *= nnode->operator()(nnode->CondDomain().Index(cind.at(j)))->Intensity()[from][to]; if (maxval < fabs(tmp_mult)) maxval = tmp_mult; par_sum += tmp_mult; } return par_sum; } //rewrites on v void FactoredMatrix::MultByVec(FactoredVector & v, bool transpose, double &maxval) const{ FactoredVector newv(v); maxval = 0.0; double tmpsum = 0.0; int numofnodes = nodes.size(); for (int id=0; idCondDomain(); vector parlist = pcontext.VarList(); double node_sum = 0.0; double tmp_mult = 1.0; double par_sum = 0.0; for (int xout=0; xoutDomain().Size(); xout++){ node_sum = 0.0; for (int xin=0; xinDomain().Size(); xin++){ par_sum = 0; for (int k=0; k(node)->operator()(pcontext.Index(k))->Intensity()[xout][xin]); else tmpsum = tmp_mult*(dynamic_cast(node)->operator()(pcontext.Index(k))->Intensity()[xin][xout]); if (maxval < fabs(tmpsum) ) maxval = tmpsum; par_sum += tmpsum; } par_sum = par_sum / minrate; if (xin == xout){ par_sum += 1.0; } node_sum += v.GetDistInst(id, xin)*par_sum; } newv.SetDistInst(id, xout, node_sum); } } v = newv; } void FactoredMatrix::TransMultByVec(FactoredVector & v, bool transpose) const{ FactoredVector newv(v); map >::const_iterator it; int id, from, to; double par_sum, node_sum; MarkovDyn const *node; if (trans_evid.size() > 0){ for(it=trans_evid.begin(); it!=trans_evid.end(); it++){ id = (*it).first; from = ((*it).second).at(0); to = ((*it).second).at(1); node = dynamic_cast(nodes[id]); const vector cind = (*condpars_evid.find(id)).second; //par_sum = FindAvgRate(v, node, from, to, transpose); double tmp_mult = 1.0; double tmp_pardist; par_sum = 0.0; vector parlist = node->CondDomain().VarList(); int parid, parval; //Initialize parent distributions to zero for (unsigned int k=0; kCondDomain().Index(cind.at(i)).Value(parid); tmp_mult *= v.GetDistInst(parid, parval); } if (transpose) tmp_mult *= node->operator()(node->CondDomain().Index(cind.at(i)))->Intensity()[to][from]; else tmp_mult *= node->operator()(node->CondDomain().Index(cind.at(i)))->Intensity()[from][to]; par_sum += tmp_mult; for (unsigned int k=0; k < parlist.size(); k++){ parid = parlist.at(k); parval = node->CondDomain().Index(cind.at(i)).Value(parid); tmp_pardist = newv.GetDistInst(parid, parval); newv.SetDistInst(parid, parval, (tmp_pardist+tmp_mult)); } } if (transpose){ int tmp = from; from = to; to = tmp; } node_sum = par_sum*v.GetDistInst(id, from); newv.RestrictById(id, to, node_sum); } } v = newv; } void FactoredMatrix::CondMultByVec(FactoredVector & v, bool transpose, double &maxval) const{ FactoredVector newv(v); vector leakp; leakp.resize(nodes.size()); vector cind; vector cpind; map::const_iterator it; int id, val; double tmp_mult; double leaksum = 0.0; std::vector parinst; maxval = 0.0; for (it=evid.begin(); it!=evid.end(); it++){ id = (*it).first; val = (*it).second; MarkovDyn *evnode = dynamic_cast(nodes[id]); vector parlist = evnode->CondDomain().VarList(); leakp[id] = 0.0; tmp_mult=1.0; parinst.clear(); parinst = condpars_evid.find(id)->second; for (unsigned int j=0; joperator()(evnode->CondDomain().Index(parinst.at(j)))->Intensity()[val][val]; for (unsigned int k=0; kCondDomain().Index(parinst.at(j)).Value(parlist.at(k))); } } leakp[id] += tmp_mult; } leakp[id] = leakp[id]/minrate; leaksum += leakp[id]; } map >::const_iterator itv; for (itv=condpars.begin(); itv!=condpars.end(); itv++){ id = (*itv).first; std::vector condparlist; std::vector chie; vector parlist; vectorparinst; condparlist = (*itv).second; MarkovDyn *nnode = dynamic_cast(nodes[id]); double par_sum, node_sum; for (int xout=0; xoutDomain().Size(); xout++){ node_sum=0.0; for (int xin=0; xinDomain().Size(); xin++){ if (v.GetDistInst(id, xin) > 0.0){ par_sum = FindAvgRate(v, nnode, xin, xout, condparlist, transpose, maxval); par_sum /= minrate; if (xin == xout){ double chsum = 0.0; double depsum = 0.0; double depmult = 1.0; par_sum = par_sum + leaksum + 1.0; chie.clear(); map >::const_iterator tmpit; tmpit = children_in_evidence.find(id); if (tmpit != children_in_evidence.end()) chie = tmpit->second; if (chie.size() >0 ){ chsum = 0.0; depsum = 0.0; for (unsigned int cc=0; cc(nodes[ch_id]); parlist.clear(); parlist = evnode->CondDomain().VarList(); parinst.clear(); parinst = condpars_evid.find(ch_id)->second; depmult=1.0; for (unsigned int ch=0; chCondDomain().Index(parinst.at(ch)).Value(id) == xin){ depmult = evnode->operator()(evnode->CondDomain().Index(parinst.at(ch)))->Intensity() [evid.find(ch_id)->second][evid.find(ch_id)->second]; for (unsigned int k=0; kCondDomain().Index(parinst.at(ch)).Value(parlist.at(k)) ); } } depsum += depmult; } } } depsum = depsum /minrate; par_sum = par_sum - chsum + depsum; if (maxval < (leaksum - chsum + depsum)) maxval = leaksum - chsum + depsum; } } node_sum += par_sum*v.GetDistInst(id, xin); } } newv.SetDistInst(id, xout, node_sum); } } v = newv; } void FactoredMatrix::TransMult(RVSimple *&x, bool transpose) const { FactoredVector *v = dynamic_cast(x); if (trans_evid.size()>0){ double res = log(v->Normalize()); TransMultByVec(*v, transpose); res += log(v->Normalize()); v->SetLogf(res); } else if (notrans_evid.size() >0){ v->Condition(notrans_evid); } } void FactoredMatrix::Mult(RVSimple *&x, bool transpose) const{ FactoredVector *v = dynamic_cast(x); assert (nullptr03 != v); bool conditioned; if (evid.size()>0){ v->Condition(evid); conditioned = true; // If some of the variables are not observed if (condpars.size()>0){ double res = Unifvexpmt(*v, conditioned, transpose); v->SetLogf(res); } } else{ //No evidence, calling normal multiplication conditioned = false; double res = Unifvexpmt(*v, conditioned, transpose); v->SetLogf(res); } } void FactoredMatrix::Mult(RVSimple *&x)const{ bool transpose = false; if (t != -1.0){ if (trans){ TransMult(x, transpose); } else{ Mult(x, transpose); } } else { cout << "Cannot multiply: Interval t not set" << endl; } } // this is the reverse void FactoredMatrix::RMult(RVSimple *&x) const{ bool transpose = true; if (t != -1.0){ if (trans){ TransMult(x, transpose); } else{ Mult(x, transpose); } } else { cout << "Cannot multiply: Interval t not set" << endl; } } double FactoredMatrix::Unifvexpmt(FactoredVector & v, bool conditioned, bool transpose) const { double a, theta, s, r, gamma, m, beta; double wnormf; double tnew; double tmpmaxval; FactoredVector f, w; //Compute norm of A a = minrate; //Loop parameters theta = theta_value; int l = taylor_expansion_term_count; //MAX_L; m = ceil(a*theta/l); //ceil(theta/l); //1,10,100 // ceil(a*t/theta); tnew = t/m; s = a*tnew; r = exp(-s); w = v; wnormf = 0.0; wnormf += log(w.Normalize()); for (int i=1; i<=m; i++){ f = w; gamma = 1.0; beta = 1.0; for (int k=1; k<=l; k++){ gamma *= (s/k); if (conditioned) CondMultByVec(f, transpose, tmpmaxval); else MultByVec(f, transpose, tmpmaxval); beta *= f.Normalize(); RVSimple *tmp = f.Clone(); tmp->Mult(exp(log(gamma)+log(beta))); w.Add(tmp); } w.Mult(r); //normalize w wnormf += log(w.Normalize()); } v = w; return wnormf; } // cshelton: added void FactoredMatrix::initRKcache(double maxtime, double eps) const { rkcache.clear(); for(uint id=0;id > *timesteps) const{ if (cachemaxt<0.0 || time>cachemaxt) { double logfactor; double ret = 0; for(uint id=0; idpush_back(vector()); vector *ts = &(timesteps->back()); ret = vexpmt(tmp, *amatrices[id], time, -1, -1, ts); } else ret = vexpmt(tmp, *amatrices[id], time); tmp = tmp*exp(ret); v.SetDist(tmp, id, logfactor); } return ret; // cshelton: added } else { // we have a cache to use... for(uint id=0;idpush_back(vector()); rkcache[id].vexpmt(tmp,time); v.SetDist(tmp,id,logfactor); } return 0.0; } } // Multiply by B matrix only!! void FactoredMatrix::BMultByVec(FactoredVector &v, int varid, int bno, bool transpose) const{ //FactoredVector newv(v); double tmp_mult = 1.0; double logfactor = 0; int numofnodes = bnodes.size(); if(varid < numofnodes){ int id = varid; MarkovDyn *node = (MarkovDyn *)(bnodes[id]); Context pcontext = node->CondDomain(); vector parlist = pcontext.VarList(); vectr nodedist(node->Domain().Size(), 0.0); v.GetDist(nodedist, id, logfactor); vectr newnodedist(node->Domain().Size(), 0.0); int bsize = pcontext.Size(); if(bno < bsize){ int k = bno; tmp_mult = 1.0; for (uint l=0; lIntensity()); // cshelton: this is much faster (removed most of the time // spent in this function) newnodedist = nodedist*((*node)(pcontext.Index(k))->Intensity()); newnodedist *= tmp_mult; //newv.SetDist( newnodedist,id, logfactor); v.SetDist( newnodedist,id, logfactor); } } //v = newv; } matrix* FactoredMatrix::MakeAB(Dynamics *bnode){ int ds = ((MarkovDyn*)bnode)->Domain().Size(); int cds = ((MarkovDyn*)bnode)->CondDomain().Size(); vector parlist = ((MarkovDyn*)bnode)->CondDomain().VarList(); vector avgdiags(ds, 0.0); matrix *amatrix = new matrix( ((MarkovDyn*)bnode)->operator()(((MarkovDyn*)bnode)-> \ CondDomain().Index(0))->Intensity() ); //calculate the matrix of minimal values among all rate matrices for (int i=1; ioperator()(((MarkovDyn*)bnode)-> \ CondDomain().Index(i))->Intensity(); for(int j=0; j m[j][k] ) (*amatrix)[j][k] = m[j][k]; if(j!=k) rsum += (*amatrix)[j][k]; } //(*amatrix)[j][j] = -1*rsum; } } //calculate the remaining values in each rate matrix for(int i=0; ioperator()(((MarkovDyn*)bnode)-> \ CondDomain().Index(i))->Intensity(); m -= (*amatrix); m.unfuzz(1e-15); } return amatrix; } void FactoredMatrix::SetL(int terms_in_taylor_expansion) { taylor_expansion_term_count = terms_in_taylor_expansion; } void FactoredMatrix::SetTheta(double theta) { theta_value = theta; } void FactoredMatrix::Load(std::istream &is) { throw not_yet_implemented_error(the_class_name, "Load"); } void FactoredMatrix::Save(std::ostream &os) const { throw not_yet_implemented_error(the_class_name, "Save"); } double FactoredMatrix::Prob(int ind, int cind, bool log) const { throw not_yet_implemented_error(the_class_name, "Prob"); } void FactoredMatrix::Normalize() { throw not_yet_implemented_error(the_class_name, "Normalize"); } RVSimple * FactoredMatrix::Condition(int ind) const { throw not_yet_implemented_error(the_class_name, "Condition"); } void FactoredMatrix::Restrict(const std::vector &ind, const std::vector &cind) { throw not_yet_implemented_error(the_class_name, "Restrict"); } typedef std::map, std::vector > > RemapT; void FactoredMatrix::Reindex(int n, int nc, const RemapT &ind) { throw not_yet_implemented_error(the_class_name, "Reindex"); } // Multiplies by another measure (point-wise) void FactoredMatrix::MultBy(const RVCondSimple *x) { throw not_yet_implemented_error(the_class_name, "MultBy"); } // return suitable sufficient statistics object SS* FactoredMatrix::BlankSS() const { throw not_yet_implemented_error(the_class_name, "BlankSS"); } // add independent draw of (cind,ind) (conditioning value, value) to ss void FactoredMatrix::AddSS(int cind, int ind, SS *ss, double w) const{ throw not_yet_implemented_error(the_class_name, "AddSS"); } void FactoredMatrix::AddSS(const SS *toadd, const RVCondSimple* rv, const std::vector > &mapping, int mycondi, int rvccondi, SS *ss, double w) const { throw not_yet_implemented_error(the_class_name, "AddSS"); } // add average of independent draws to ss void FactoredMatrix::AddExpSS(int cind, SS *ss, double w) const { throw not_yet_implemented_error(the_class_name, "AddExpSS"); } void FactoredMatrix::AddExpSS(int cind, const RVSimple *rvs, SS *ss, double w) const { throw not_yet_implemented_error(the_class_name, "AddExpSS"); } // returns a sample int FactoredMatrix::Sample(int cx, Random &rand ) const { throw not_yet_implemented_error(the_class_name, "Sample"); } // sets parameters to ML estimate void FactoredMatrix::Maximize(const SS *ss) { throw not_yet_implemented_error(the_class_name, "Maximize"); } // Returns a new object that can be multiplied into this // object (as per Mult and RMult) RVSimple * FactoredMatrix::Convert(const RVSimple *rvs) const { // FactoredVector * v = new FactoredVector(rvs); return rvs->Clone(); } // see above class for description void FactoredMatrix::Scramble(double alpha, double degree, Random &rand) { throw not_yet_implemented_error(the_class_name, "Scramble"); } double FactoredMatrix::LLH(const SS *ss) const{ throw not_yet_implemented_error(the_class_name, "LLH"); } double FactoredMatrix::GetScore(double numTrans, const SS *ss) const { throw not_yet_implemented_error(the_class_name, "GetScore"); } //////// } // end of ctbn namespace ctbnrle.1.1.1/tst/0000775000175000017500000000000012251376000014174 5ustar csheltoncsheltonctbnrle.1.1.1/tst/factoredunifquery.cc0000664000175000017500000000660612115732673020265 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "samplinginf.h" #include "uniformizedfactoredinf.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include #include "ensurectbn.h" using namespace std; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using uniformized factored sampling and compare the results with the true ones // if the error is within the threshold, this unittest successes, o.w.error message will pop out namespace { int testing_result = 0; double kEpsilon = 0.02; } #define EXPECT(cond) do { \ if (!(cond)) { \ std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \ << " in " << __func__ \ << ", failed condition is " #cond << std::endl; \ testing_result = 1; \ } \ } while (0) int main(int argc, char **argv) { const char *input_file = "queryinput-toroid.data"; addnan(cout); InitParams(argc, argv); ifstream fin(input_file); Markov m; try { m.Load(fin); } catch (const serial::streamexception &e) { cerr << e.what() << endl; exit(-1); } const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics()); Trajectory evid; evid.Load(fin); UniformizedFactoredInf inf; inf.SetTrajectory(&evid); inf.SetProcess(&m); inf.SetL(20); inf.SetTheta(0.5); while(1) { int qtype; fin >> qtype; if (qtype==-1 || fin.eof()) break; int var, val; double t1; fin >> var >> val; double compans; Instantiation x(ctbndyn->Domain()); x.SetVal(var,val); switch(qtype) { case 0: // filter distribution at point fin >> t1; compans = inf.Filter(x,t1); break; case 1: // smooth distribution at point fin >> t1 >> compans; // not implemented continue; case 2: // expected time query fin >> compans; // not implemented continue; //{ QueryTime query(x); compans = inf.CalcQuery(query); } //break; case 3: // expected # trans query fin >> t1 >> compans; // not implemented continue; /* { int val2; fin >> val2; QueryTransition query(x.KnownVars(),val,val2); compans=inf.CalcQuery(query); } break; */ } double ans; fin >> ans; cout << "ans = " << compans << "\texpected " << ans << endl; EXPECT(fabs(ans-compans)/ans < kEpsilon); } fin.close(); if (testing_result == 0) cout << "PASS factoredunifquery unittest." << endl; return testing_result!=0; } ctbnrle.1.1.1/tst/calcquery.m0000664000175000017500000000675412107032413016353 0ustar csheltoncsheltonfunction ans = calcquery(fn,dt) fid = fopen(fn,'rt'); p0 = readvec(fid); Q = readmat(fid); ns = size(Q,1); [times,types,inds] = readtraj(fid); tpts = times(1):dt:times(end); A = expm(Q*dt); alpha = genalpha(p0,Q,A,times,types,inds,tpts); beta = genbeta(p0,Q,A,times,types,inds,tpts); smooth = alpha.*beta; smoothsum = sum(smooth,1); smooth = smooth./repmat(smoothsum,[ns 1]); ans = []; while(1) qtype = fscanf(fid,'%d',1); if (qtype==-1 || feof(fid)) break; end; if (qtype==0) inds1 = readvec(fid); inds1 = inds1+1; t = fscanf(fid,'%f',1); a = findclose(compress(alpha,inds1),tpts,t); ans = [ans;a]; elseif (qtype==1) inds1 = readvec(fid); inds1 = inds1+1; t = fscanf(fid,'%f',1); a = findclose(compress(smooth,inds1),tpts,t); ans = [ans;a]; elseif (qtype==2) inds1 = readvec(fid); inds1 = inds1+1; a = sum(compress(smooth,inds1))... /length(tpts)*(times(end)-times(1)); ans = [ans;a]; elseif (qtype==3) inds1 = readvec(fid); inds2 = readvec(fid); inds1 = inds1+1; inds2 = inds2+1; bp = permute(beta(inds2,:,:),[3 1 2]); ap = alpha(inds1,:)./repmat(smoothsum,[size(inds1,1) 1]); ss = (ap'*Q(inds1,inds2))'; ss = ss.*beta(inds2,:); a = sum(sum(ss))... /length(tpts)*(times(end)-times(1)); for i=2:length(times) % assume which variable is known if (types(i) && ... any(ismember(inds{i-1},inds1)) && ... ~any(ismember(inds{i-1},inds2)) && ... any(ismember(inds{i},inds2)) && ... ~any(ismember(inds{i},inds1))) a = a+1; end; end; ans = [ans;a]; else return; end; end; end; function v = compress(vs,inds) v = sum(vs(inds,:),1); end; function x = findclose(vs,ts,t) [delta,i] = min(abs(ts-t)); x = vs(i); end; function alpha = genalpha(p0,Q,A,times,types,inds,tpts) ns = size(Q,1); nt = length(tpts); alpha = zeros(ns,nt); v = zeros(ns,1); v(inds{1}) = p0(inds{1}); v = v/sum(v); t = times(1); alpha(:,1) = v; ai = 1; ti = 1; while(ti0 && bi>1) while (ti>0 && times(ti)>tpts(bi-1)) if (types(ti)) % var change cQ = zeros(ns,ns); cQ(inds{ti-1},inds{ti}) = Q(inds{ti-1},inds{ti}); cQ = cQ-diag(diag(cQ)); v = cQ*v; end; ti = ti-1; end; if (ti==0) ti = 1; end; newv = A*v; v = zeros(ns,1); v(inds{ti}) = newv(inds{ti}); v = v/sum(v); bi = bi-1; beta(:,bi) = v; end; end; function [times,types,inds] = readtraj(fid) n = fscanf(fid,'%d',1); times = zeros(n+1,1); types = zeros(n+1,1); inds = cell(n,1); for i=1:n times(i) = fscanf(fid,'%f',1); types(i) = fscanf(fid,'%d',1); m = fscanf(fid,'%d',1); inds{i} = fscanf(fid,'%d',m); inds{i} = inds{i}+1; end; times(n+1) = fscanf(fid,'%f',1); end; function v = readvec(fid) n = fscanf(fid,'%f',1); v = fscanf(fid,'%f',n); end; function M = readmat(fid) m = fscanf(fid,'%f',1); n = fscanf(fid,'%f',1); M = fscanf(fid,'%f',m*n); M = reshape(M,m,n); M = M'; end; ctbnrle.1.1.1/tst/importanceprequery.cc0000664000175000017500000000716312115732751020460 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "samplinginf.h" #include "importancesampler.h" #include "expmethod.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include #include "ensurectbn.h" using namespace std; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using importance sampling and compare the results with the true ones // if the error is within the threshold, this unittest successes, o.w.error message will pop out namespace { int testing_result = 0; double kEpsilon = 0.01; } #define EXPECT(cond) do { \ if (!(cond)) { \ std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \ << " in " << __func__ \ << ", failed condition is " #cond << std::endl; \ testing_result = 1; \ } \ } while (0) int main(int argc, char **argv) { const char *input_file = "queryinput.data"; addnan(cout); InitParams(argc, argv); ifstream fin(input_file); Markov m; try { m.Load(fin); } catch (const serial::streamexception &e) { cerr << e.what() << endl; exit(-1); } const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics()); Trajectory evid; evid.Load(fin); vector queries; vector ans; while(1) { int qtype; fin >> qtype; if (qtype==-1 || fin.eof()) break; int var, val; double t1; fin >> var >> val; double compans; Instantiation x(ctbndyn->Domain()); x.SetVal(var,val); switch(qtype) { case 0: // filter distribution at point fin >> t1; fin >> compans; // Filtering not implemented! continue; case 1: // smooth distribution at point fin >> t1; queries.push_back(new QueryProb(x,t1)); break; case 2: // expected time query queries.push_back(new QueryTime(x)); break; case 3: // expected # trans query { int val2; fin >> val2; queries.push_back(new QueryTransition(x.KnownVars(),val,val2)); } break; } double a; fin >> a; ans.push_back(a); } fin.close(); SamplingPreInf inf(queries); int numofsamples = ParamInt("NumSamples",1000000); Sampler *sampler; ExpMethod method; sampler = new ImportanceSampler(&m, &evid, &method); inf.SetSampler(sampler); inf.SetProcess(&m); inf.SetNumSample(numofsamples); for(size_t i=0;i. sub main { #system("rm -fr bhps_query_results"); #system("mkdir bhps_query_results"); @nsample = (10,20,50,100,200,500,1000); #,2000,5000,10000,20000,50000,100000,200000,500000); for (my $i = 0; $i <= $#nsample; $i++) { system("./approxinf drug.ctbn drug.traj_q.txt $nsample[$i] >drug_query_results/gibbs.$nsample[$i].txt &"); } } &main; ctbnrle.1.1.1/tst/tope.pl0000664000175000017500000000111712107032413015475 0ustar csheltoncshelton#!/usr/bin/perl sub kldiv; $ln2 = log(2); open(F,"-|","./topetest",@ARGV); while() { chomp; if (/Exact Result/) { while() { chomp; last if (/^\s*$/); my @p = split /\s+/, $_; push @exact, [ @p ]; } } elsif (/Tope Result/) { my $i = 0; while() { chomp; last if (/^\s*$/); my @p = split /\s+/, $_; my $kl = kldiv($exact[$i],\@p); print "($kl) $_\n"; $i++; } } } sub kldiv { my ($p1,$p2) = @_; my $ret = 0; for(my $i=0;$i<@$p1;$i++) { if ($p1->[$i] > 0) { $ret += $p1->[$i]*log($p1->[$i]/$p2->[$i])/$ln2; } } return $ret; } ctbnrle.1.1.1/tst/testing_drug.clique0000664000175000017500000000476612107032413020110 0ustar csheltoncshelton <\map> <\Context> 0 <\set.int> <\vector> <\CliqueNode> 1 <\set.int> <\vector> <\CliqueNode> 2 <\set.int> <\vector> <\CliqueNode> 3 <\set.int> <\vector> <\CliqueNode> 4 <\set.int> <\vector> <\CliqueNode> 5 <\set.int> <\vector> <\CliqueNode> 6 <\set.int> <\vector> <\CliqueNode> 7 <\set.int> <\vector> <\CliqueNode> 0 2 <\set.int> <\vector> <\CliqueNode> 0 1 <\set.int> <\vector> <\CliqueNode> 1 2 <\set.int> <\vector> <\CliqueNode> 3 <\set.int> <\vector> <\CliqueNode> 1 3 4 <\set.int> <\vector> <\CliqueNode> 5 <\set.int> <\vector> <\CliqueNode> 4 5 6 <\set.int> <\vector> <\CliqueNode> 4 7 <\set.int> <\vector> <\CliqueNode> <\vector> <\CliqueTree> ctbnrle.1.1.1/tst/Makefile.bak0000664000175000017500000053163012107035537016407 0ustar csheltoncsheltonSRC := $(wildcard *.cc) #OBJ := $(patsubst %.cc,%.o,$(SRC)) LIBOBJ = ../src/ctbn.a ../glpk/glpk-4.47/src/.libs/libglpk.a CXX = g++ #OFLAGS = -O0 #OFLAGS = -O2 OFLAGS = -O3 CXXFLAGS += $(OFLAGS) CXXFLAGS += -pedantic -Wall -Wextra -Wno-long-long -Wno-unused-parameter -Wno-unused-variable CXXFLAGS += -std=c++03 CXXFLAGS += -Werror -pedantic-errors CXXFLAGS += -I../hdr CXXFLAGS += -I../glpk/glpk-4.47/include -I../glpk/glpk-4.47/src CXXFLAGS += -DUSE_PTHREADS LDFLAGS += -lpthread -L../glpk/glpk-4.47/src/.libs GCC_BASE_DIR = /usr/csshare/pkgs/gcc-4.7.0 GCC_BASE_INCLUDE_DIR = $(GCC_BASE_DIR)/lib/gcc/i686-pc-linux-gnu/4.7.0/include GPP_BASE_INCLUDE_DIR = $(GCC_BASE_DIR)/include/c++/4.7.0 DEPEND = makedepend -- $(CXXFLAGS) -- -w20 -Y -I$(GCC_BASE_INCLUDE_DIR) -I$(GPP_BASE_INCLUDE_DIR) -I$(GPP_BASE_INCLUDE_DIR)/tr1 -I$(GPP_INCLUDE_BASE_DIR)/i686-pc-linux-gnu -I/usr/include/asm -I../hdr -I../glpk/glpk-4.47/include # keep line below as "EXES=" and not "EXES =" so that python script parses # it correctly EXES= clique_unit_test unit_testing_graphic exactfbquery factoredunifquery importanceprequery gibbsquery gibbsauxprequery meanfieldquery glpktest structurescore structsearch sem gibbsprequery decision DATA = queryinput.data queryinput-toroid.data drug.ctbn drug.ctbndyn .PHONY: local local: $(EXES) $(DATA) .PHONY: all all: ../src/ctbn.a local (cd ../src; make all; cd ../tst) queryinput.data: makequeryinput (rm -f queryinput.data; ./makequeryinput > queryinput.data) queryinput-toroid.data: maketoroid (rm -f queryinput-toroid.data; ./maketoroid > queryinput-toroid.data) drug.ctbn: makedrugtst (rm -f drug.ctbn; ./makedrugtst > drug.ctbn) drug.ctbndyn: makedrugtst (rm -f drug.ctbndyn; ./makedrugtst -Dsavedyn 1 > drug.ctbndyn) decision: decision.o $(LIBOBJ) $(CXX) $(FXXFLAGS) $(LDFLAGS) -o decision decision.o $(LIBOBJ) -lglpk glpktest: glpktest.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o glpktest glpktest.o $(LIBOBJ) -lglpk unit_testing_graphic: unit_testing_graphic.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o unit_testing_graphic unit_testing_graphic.o $(LIBOBJ) clique_unit_test: clique_unit_test.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o clique_unit_test clique_unit_test.o $(LIBOBJ) genquerytxt: genquerytxt.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o genquerytxt genquerytxt.o $(LIBOBJ) makequeryinput: makequeryinput.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o makequeryinput makequeryinput.o $(LIBOBJ) structsearch: structsearch.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o structsearch structsearch.o $(LIBOBJ) sem: sem.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o sem sem.o $(LIBOBJ) structurescore: structurescore.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o structurescore structurescore.o $(LIBOBJ) exactfbquery: exactfbquery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o exactfbquery exactfbquery.o $(LIBOBJ) factoredunifquery: factoredunifquery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o factoredunifquery factoredunifquery.o $(LIBOBJ) gibbsquery: gibbsquery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o gibbsquery gibbsquery.o $(LIBOBJ) gibbsauxprequery: gibbsauxprequery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o gibbsauxprequery gibbsauxprequery.o $(LIBOBJ) gibbsprequery: gibbsprequery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o gibbsprequery gibbsprequery.o $(LIBOBJ) importanceprequery: importanceprequery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o importanceprequery importanceprequery.o $(LIBOBJ) importancequery: importancequery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o importancequery importancequery.o $(LIBOBJ) meanfieldquery: meanfieldquery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o meanfieldquery meanfieldquery.o $(LIBOBJ) makedrugtst: makedrugtst.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o makedrugtst makedrugtst.o $(LIBOBJ) maketoroid: maketoroid.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o maketoroid maketoroid.o $(LIBOBJ) depend: $(SRC) $(DEPEND) $(SRC) .PHONY: clean clean: rm -rf *.o *~ output2.eps output.dot output_test_ctbn.dot output_unit_test.eps makedrugtst makequeryinput genquerytxt $(EXES) $(DATA) .PHONY: realclean realclean: (cd ../src; make clean; cd ../tst) rm -rf *.o *~ output2.eps output.dot output_test_ctbn.dot output_unit_test.eps makedrugtst makequeryinput genquerytxt $(EXES) $(DATA) # DO NOT DELETE importanceprequery.o: ../hdr/markovdyn.h importanceprequery.o: ../hdr/markovsimple.h importanceprequery.o: ../hdr/dynsimple.h importanceprequery.o: ../hdr/random.h importanceprequery.o: ../hdr/matrix.h importanceprequery.o: ../hdr/matrix-eigen.h importanceprequery.o: ../hdr/streamserial.h importanceprequery.o: ../hdr/serial/serial.h importanceprequery.o: ../hdr/nullptr03.h importanceprequery.o: ../hdr/streamserial-old.h importanceprequery.o: ../hdr/defines.h importanceprequery.o: ../hdr/streamextra.h importanceprequery.o: ../hdr/Eigen/Core importanceprequery.o: ../hdr/Eigen/src/Core/util/DisableStupidWarnings.h importanceprequery.o: ../hdr/Eigen/src/Core/util/Macros.h importanceprequery.o: ../hdr/Eigen/src/Core/util/MKL_support.h importanceprequery.o: ../hdr/Eigen/src/Core/util/Constants.h importanceprequery.o: ../hdr/Eigen/src/Core/util/ForwardDeclarations.h importanceprequery.o: ../hdr/Eigen/src/Core/util/Meta.h importanceprequery.o: ../hdr/Eigen/src/Core/util/XprHelper.h importanceprequery.o: ../hdr/Eigen/src/Core/util/StaticAssert.h importanceprequery.o: ../hdr/Eigen/src/Core/util/Memory.h importanceprequery.o: ../hdr/Eigen/src/Core/NumTraits.h importanceprequery.o: ../hdr/Eigen/src/Core/MathFunctions.h importanceprequery.o: ../hdr/Eigen/src/Core/GenericPacketMath.h importanceprequery.o: ../hdr/Eigen/src/Core/arch/Default/Settings.h importanceprequery.o: ../hdr/Eigen/src/Core/Functors.h importanceprequery.o: ../hdr/Eigen/src/Core/DenseCoeffsBase.h importanceprequery.o: ../hdr/Eigen/src/Core/DenseBase.h importanceprequery.o: ../hdr/Eigen/src/plugins/BlockMethods.h importanceprequery.o: ../hdr/Eigen/src/Core/MatrixBase.h importanceprequery.o: ../hdr/Eigen/src/plugins/CommonCwiseUnaryOps.h importanceprequery.o: ../hdr/Eigen/src/plugins/CommonCwiseBinaryOps.h importanceprequery.o: ../hdr/Eigen/src/plugins/MatrixCwiseUnaryOps.h importanceprequery.o: ../hdr/Eigen/src/plugins/MatrixCwiseBinaryOps.h importanceprequery.o: ../hdr/matrix-eigenext.h importanceprequery.o: ../hdr/Eigen/src/Core/EigenBase.h importanceprequery.o: ../hdr/Eigen/src/Core/Assign.h importanceprequery.o: ../hdr/Eigen/src/Core/util/BlasUtil.h importanceprequery.o: ../hdr/Eigen/src/Core/DenseStorage.h importanceprequery.o: ../hdr/Eigen/src/Core/NestByValue.h importanceprequery.o: ../hdr/Eigen/src/Core/ForceAlignedAccess.h importanceprequery.o: ../hdr/Eigen/src/Core/ReturnByValue.h importanceprequery.o: ../hdr/Eigen/src/Core/NoAlias.h importanceprequery.o: ../hdr/Eigen/src/Core/PlainObjectBase.h importanceprequery.o: ../hdr/Eigen/src/Core/Matrix.h importanceprequery.o: ../hdr/Eigen/src/Core/Array.h importanceprequery.o: ../hdr/Eigen/src/Core/CwiseBinaryOp.h importanceprequery.o: ../hdr/Eigen/src/Core/CwiseUnaryOp.h importanceprequery.o: ../hdr/Eigen/src/Core/CwiseNullaryOp.h importanceprequery.o: ../hdr/Eigen/src/Core/CwiseUnaryView.h importanceprequery.o: ../hdr/Eigen/src/Core/SelfCwiseBinaryOp.h importanceprequery.o: ../hdr/Eigen/src/Core/Dot.h importanceprequery.o: ../hdr/Eigen/src/Core/StableNorm.h importanceprequery.o: ../hdr/Eigen/src/Core/MapBase.h importanceprequery.o: ../hdr/Eigen/src/Core/Stride.h importanceprequery.o: ../hdr/Eigen/src/Core/Map.h importanceprequery.o: ../hdr/Eigen/src/Core/Block.h importanceprequery.o: ../hdr/Eigen/src/Core/VectorBlock.h importanceprequery.o: ../hdr/Eigen/src/Core/Transpose.h importanceprequery.o: ../hdr/Eigen/src/Core/DiagonalMatrix.h importanceprequery.o: ../hdr/Eigen/src/Core/Diagonal.h importanceprequery.o: ../hdr/Eigen/src/Core/DiagonalProduct.h importanceprequery.o: ../hdr/Eigen/src/Core/PermutationMatrix.h importanceprequery.o: ../hdr/Eigen/src/Core/Transpositions.h importanceprequery.o: ../hdr/Eigen/src/Core/Redux.h importanceprequery.o: ../hdr/Eigen/src/Core/Visitor.h importanceprequery.o: ../hdr/Eigen/src/Core/Fuzzy.h importanceprequery.o: ../hdr/Eigen/src/Core/IO.h importanceprequery.o: ../hdr/Eigen/src/Core/Swap.h importanceprequery.o: ../hdr/Eigen/src/Core/CommaInitializer.h importanceprequery.o: ../hdr/Eigen/src/Core/Flagged.h importanceprequery.o: ../hdr/Eigen/src/Core/ProductBase.h importanceprequery.o: ../hdr/Eigen/src/Core/GeneralProduct.h importanceprequery.o: ../hdr/Eigen/src/Core/TriangularMatrix.h importanceprequery.o: ../hdr/Eigen/src/Core/SelfAdjointView.h importanceprequery.o: ../hdr/Eigen/src/Core/SolveTriangular.h importanceprequery.o: ../hdr/Eigen/src/Core/products/Parallelizer.h importanceprequery.o: ../hdr/Eigen/src/Core/products/CoeffBasedProduct.h importanceprequery.o: ../hdr/Eigen/src/Core/products/GeneralBlockPanelKernel.h importanceprequery.o: ../hdr/Eigen/src/Core/products/GeneralMatrixVector.h importanceprequery.o: ../hdr/Eigen/src/Core/products/GeneralMatrixMatrix.h importanceprequery.o: ../hdr/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h importanceprequery.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixVector.h importanceprequery.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixMatrix.h importanceprequery.o: ../hdr/Eigen/src/Core/products/SelfadjointProduct.h importanceprequery.o: ../hdr/Eigen/src/Core/products/SelfadjointRank2Update.h importanceprequery.o: ../hdr/Eigen/src/Core/products/TriangularMatrixVector.h importanceprequery.o: ../hdr/Eigen/src/Core/products/TriangularMatrixMatrix.h importanceprequery.o: ../hdr/Eigen/src/Core/products/TriangularSolverMatrix.h importanceprequery.o: ../hdr/Eigen/src/Core/products/TriangularSolverVector.h importanceprequery.o: ../hdr/Eigen/src/Core/BandMatrix.h importanceprequery.o: ../hdr/Eigen/src/Core/BooleanRedux.h importanceprequery.o: ../hdr/Eigen/src/Core/Select.h importanceprequery.o: ../hdr/Eigen/src/Core/VectorwiseOp.h importanceprequery.o: ../hdr/Eigen/src/Core/Random.h importanceprequery.o: ../hdr/Eigen/src/Core/Replicate.h importanceprequery.o: ../hdr/Eigen/src/Core/Reverse.h importanceprequery.o: ../hdr/Eigen/src/Core/ArrayBase.h importanceprequery.o: ../hdr/Eigen/src/plugins/ArrayCwiseUnaryOps.h importanceprequery.o: ../hdr/Eigen/src/plugins/ArrayCwiseBinaryOps.h importanceprequery.o: ../hdr/Eigen/src/Core/ArrayWrapper.h importanceprequery.o: ../hdr/Eigen/src/Core/GlobalFunctions.h importanceprequery.o: ../hdr/Eigen/src/Core/util/ReenableStupidWarnings.h importanceprequery.o: ../hdr/Eigen/Sparse importanceprequery.o: ../hdr/Eigen/SparseCore importanceprequery.o: ../hdr/Eigen/Core importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseUtil.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseMatrixBase.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/CompressedStorage.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/AmbiVector.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseMatrix.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/MappedSparseMatrix.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseVector.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/CoreIterators.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseBlock.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseTranspose.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseCwiseUnaryOp.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseCwiseBinaryOp.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseDot.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparsePermutation.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseAssign.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseRedux.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseFuzzy.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseSparseProductWithPruning.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseProduct.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseDenseProduct.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseDiagonalProduct.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseTriangularView.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseSelfAdjointView.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/TriangularSolver.h importanceprequery.o: ../hdr/Eigen/src/SparseCore/SparseView.h importanceprequery.o: ../hdr/Eigen/OrderingMethods importanceprequery.o: ../hdr/Eigen/src/OrderingMethods/Amd.h importanceprequery.o: ../hdr/Eigen/SparseCholesky importanceprequery.o: ../hdr/Eigen/src/misc/Solve.h importanceprequery.o: ../hdr/Eigen/src/misc/SparseSolve.h importanceprequery.o: ../hdr/Eigen/src/SparseCholesky/SimplicialCholesky.h 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topetest.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixVector.h topetest.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixMatrix.h topetest.o: ../hdr/Eigen/src/Core/products/SelfadjointProduct.h topetest.o: ../hdr/Eigen/src/Core/products/SelfadjointRank2Update.h topetest.o: ../hdr/Eigen/src/Core/products/TriangularMatrixVector.h topetest.o: ../hdr/Eigen/src/Core/products/TriangularMatrixMatrix.h topetest.o: ../hdr/Eigen/src/Core/products/TriangularSolverMatrix.h topetest.o: ../hdr/Eigen/src/Core/products/TriangularSolverVector.h topetest.o: ../hdr/Eigen/src/Core/BandMatrix.h topetest.o: ../hdr/Eigen/src/Core/BooleanRedux.h topetest.o: ../hdr/Eigen/src/Core/Select.h topetest.o: ../hdr/Eigen/src/Core/VectorwiseOp.h topetest.o: ../hdr/Eigen/src/Core/Random.h topetest.o: ../hdr/Eigen/src/Core/Replicate.h topetest.o: ../hdr/Eigen/src/Core/Reverse.h topetest.o: ../hdr/Eigen/src/Core/ArrayBase.h topetest.o: ../hdr/Eigen/src/plugins/ArrayCwiseUnaryOps.h topetest.o: 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topetest.o: ../hdr/dynsimple.h topetest.o: ../hdr/dyncomp.h topetest.o: ../hdr/factoredmatrix.h topetest.o: ../hdr/factoredvector.h topetest.o: ../hdr/expmcache.h topetest.o: ../hdr/contfunction.h topetest.o: ../hdr/contfunction.tcc topetest.o: ../hdr/rk.h topetest.o: ../hdr/meanfieldinf.h topetest.o: ../hdr/multirv.h topetest.o: ../hdr/rvcomp.h topetest.o: ../hdr/multisimple.h topetest.o: ../hdr/exactmarkovinf.h topetest.o: ../hdr/fbinf.h topetest.o: ../hdr/utils.h topetest.o: ../hdr/ensurectbn.h topetest.o: ../hdr/params.h makequeryinput.o: ../hdr/markovdyn.h makequeryinput.o: ../hdr/markovsimple.h makequeryinput.o: ../hdr/dynsimple.h makequeryinput.o: ../hdr/random.h makequeryinput.o: ../hdr/matrix.h makequeryinput.o: ../hdr/matrix-eigen.h makequeryinput.o: ../hdr/streamserial.h makequeryinput.o: ../hdr/serial/serial.h makequeryinput.o: ../hdr/nullptr03.h makequeryinput.o: ../hdr/streamserial-old.h makequeryinput.o: ../hdr/defines.h makequeryinput.o: ../hdr/streamextra.h makequeryinput.o: ../hdr/Eigen/Core makequeryinput.o: ../hdr/Eigen/src/Core/util/DisableStupidWarnings.h makequeryinput.o: ../hdr/Eigen/src/Core/util/Macros.h makequeryinput.o: ../hdr/Eigen/src/Core/util/MKL_support.h makequeryinput.o: ../hdr/Eigen/src/Core/util/Constants.h makequeryinput.o: ../hdr/Eigen/src/Core/util/ForwardDeclarations.h makequeryinput.o: ../hdr/Eigen/src/Core/util/Meta.h makequeryinput.o: ../hdr/Eigen/src/Core/util/XprHelper.h makequeryinput.o: ../hdr/Eigen/src/Core/util/StaticAssert.h makequeryinput.o: ../hdr/Eigen/src/Core/util/Memory.h makequeryinput.o: ../hdr/Eigen/src/Core/NumTraits.h makequeryinput.o: ../hdr/Eigen/src/Core/MathFunctions.h makequeryinput.o: ../hdr/Eigen/src/Core/GenericPacketMath.h makequeryinput.o: ../hdr/Eigen/src/Core/arch/Default/Settings.h makequeryinput.o: ../hdr/Eigen/src/Core/Functors.h makequeryinput.o: ../hdr/Eigen/src/Core/DenseCoeffsBase.h makequeryinput.o: ../hdr/Eigen/src/Core/DenseBase.h makequeryinput.o: 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makequeryinput.o: ../hdr/Eigen/src/Core/CwiseBinaryOp.h makequeryinput.o: ../hdr/Eigen/src/Core/CwiseUnaryOp.h makequeryinput.o: ../hdr/Eigen/src/Core/CwiseNullaryOp.h makequeryinput.o: ../hdr/Eigen/src/Core/CwiseUnaryView.h makequeryinput.o: ../hdr/Eigen/src/Core/SelfCwiseBinaryOp.h makequeryinput.o: ../hdr/Eigen/src/Core/Dot.h makequeryinput.o: ../hdr/Eigen/src/Core/StableNorm.h makequeryinput.o: ../hdr/Eigen/src/Core/MapBase.h makequeryinput.o: ../hdr/Eigen/src/Core/Stride.h makequeryinput.o: ../hdr/Eigen/src/Core/Map.h makequeryinput.o: ../hdr/Eigen/src/Core/Block.h makequeryinput.o: ../hdr/Eigen/src/Core/VectorBlock.h makequeryinput.o: ../hdr/Eigen/src/Core/Transpose.h makequeryinput.o: ../hdr/Eigen/src/Core/DiagonalMatrix.h makequeryinput.o: ../hdr/Eigen/src/Core/Diagonal.h makequeryinput.o: ../hdr/Eigen/src/Core/DiagonalProduct.h makequeryinput.o: ../hdr/Eigen/src/Core/PermutationMatrix.h makequeryinput.o: ../hdr/Eigen/src/Core/Transpositions.h makequeryinput.o: 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../hdr/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h makequeryinput.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixVector.h makequeryinput.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixMatrix.h makequeryinput.o: ../hdr/Eigen/src/Core/products/SelfadjointProduct.h makequeryinput.o: ../hdr/Eigen/src/Core/products/SelfadjointRank2Update.h makequeryinput.o: ../hdr/Eigen/src/Core/products/TriangularMatrixVector.h makequeryinput.o: ../hdr/Eigen/src/Core/products/TriangularMatrixMatrix.h makequeryinput.o: ../hdr/Eigen/src/Core/products/TriangularSolverMatrix.h makequeryinput.o: ../hdr/Eigen/src/Core/products/TriangularSolverVector.h makequeryinput.o: ../hdr/Eigen/src/Core/BandMatrix.h makequeryinput.o: ../hdr/Eigen/src/Core/BooleanRedux.h makequeryinput.o: ../hdr/Eigen/src/Core/Select.h makequeryinput.o: ../hdr/Eigen/src/Core/VectorwiseOp.h makequeryinput.o: ../hdr/Eigen/src/Core/Random.h makequeryinput.o: ../hdr/Eigen/src/Core/Replicate.h makequeryinput.o: ../hdr/Eigen/src/Core/Reverse.h makequeryinput.o: ../hdr/Eigen/src/Core/ArrayBase.h makequeryinput.o: ../hdr/Eigen/src/plugins/ArrayCwiseUnaryOps.h makequeryinput.o: ../hdr/Eigen/src/plugins/ArrayCwiseBinaryOps.h makequeryinput.o: ../hdr/Eigen/src/Core/ArrayWrapper.h makequeryinput.o: ../hdr/Eigen/src/Core/GlobalFunctions.h makequeryinput.o: ../hdr/Eigen/src/Core/util/ReenableStupidWarnings.h makequeryinput.o: ../hdr/Eigen/Sparse makequeryinput.o: ../hdr/Eigen/SparseCore makequeryinput.o: ../hdr/Eigen/Core makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseUtil.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseMatrixBase.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/CompressedStorage.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/AmbiVector.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseMatrix.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/MappedSparseMatrix.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseVector.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/CoreIterators.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseBlock.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseTranspose.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseCwiseUnaryOp.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseCwiseBinaryOp.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseDot.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparsePermutation.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseAssign.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseRedux.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseFuzzy.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseSparseProductWithPruning.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseProduct.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseDenseProduct.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseDiagonalProduct.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseTriangularView.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseSelfAdjointView.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/TriangularSolver.h makequeryinput.o: ../hdr/Eigen/src/SparseCore/SparseView.h makequeryinput.o: ../hdr/Eigen/OrderingMethods makequeryinput.o: ../hdr/Eigen/src/OrderingMethods/Amd.h makequeryinput.o: ../hdr/Eigen/SparseCholesky makequeryinput.o: ../hdr/Eigen/src/misc/Solve.h makequeryinput.o: ../hdr/Eigen/src/misc/SparseSolve.h makequeryinput.o: ../hdr/Eigen/src/SparseCholesky/SimplicialCholesky.h makequeryinput.o: ../hdr/Eigen/IterativeLinearSolvers makequeryinput.o: ../hdr/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h makequeryinput.o: ../hdr/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h makequeryinput.o: ../hdr/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h makequeryinput.o: ../hdr/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h makequeryinput.o: ../hdr/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h makequeryinput.o: ../hdr/matrix-stream.h makequeryinput.o: ../hdr/ss.h makequeryinput.o: ../hdr/rvsimple.h makequeryinput.o: ../hdr/rv.h makequeryinput.o: ../hdr/context.h makequeryinput.o: ../hdr/trajectory.h makequeryinput.o: ../hdr/dyncomp.h makequeryinput.o: ../hdr/dynamics.h makequeryinput.o: ../hdr/multirv.h makequeryinput.o: ../hdr/rvcomp.h makequeryinput.o: ../hdr/multisimple.h makequeryinput.o: ../hdr/params.h makequeryinput.o: ../hdr/bn.h makequeryinput.o: ../hdr/structure.h makequeryinput.o: ../hdr/ctbndyn.h makequeryinput.o: ../hdr/suffstatsquery.h makequeryinput.o: ../hdr/em.h makequeryinput.o: ../hdr/inference.h makequeryinput.o: ../hdr/process.h makequeryinput.o: ../hdr/utils.h makequeryinput.o: ../hdr/ensurectbn.h makequeryinput.o: ../hdr/ctbn.h makequeryinput.o: ../hdr/markov.h makequeryinput.o: ../hdr/extramath.h ctbnrle.1.1.1/tst/gibbsquery.cc0000664000175000017500000000663712115732743016704 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "samplinginf.h" #include "gibbssampler.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include #include "ensurectbn.h" using namespace std; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using gibbs sampling and compare the results with the true ones // if the error is within the threshold, this unittest successes, o.w.error message will pop out namespace { int testing_result = 0; double kEpsilon = 0.02; } #define EXPECT(cond) do { \ if (!(cond)) { \ std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \ << " in " << __func__ \ << ", failed condition is " #cond << std::endl; \ testing_result = 1; \ } \ } while (0) int main(int argc, char **argv) { const char *input_file = "queryinput.data"; addnan(cout); InitParams(argc, argv); ifstream fin(input_file); Markov m; try { m.Load(fin); } catch (const serial::streamexception &e) { cerr << e.what() << endl; exit(-1); } const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics()); Trajectory evid; evid.Load(fin); SamplingInf inf; int numofburniniter = ParamInt("Burnin",1000); int numofsamples = ParamInt("NumSamples",10000); Sampler *sampler; sampler = new GibbsSampler(&m, &evid, numofburniniter); inf.SetSampler(sampler); inf.SetProcess(&m); inf.SetNumSample(numofsamples); while(1) { int qtype; fin >> qtype; if (qtype==-1 || fin.eof()) break; int var, val; double t1; fin >> var >> val; double compans; Instantiation x(ctbndyn->Domain()); x.SetVal(var,val); switch(qtype) { case 0: // filter distribution at point fin >> t1; fin >> compans; // Filtering not implemented! continue; case 1: // smooth distribution at point fin >> t1; compans = inf.Smooth(x,t1); break; case 2: // expected time query { QueryTime query(x); compans = inf.CalcQuery(query); } break; case 3: // expected # trans query { int val2; fin >> val2; QueryTransition query(x.KnownVars(),val,val2); compans=inf.CalcQuery(query); } break; } double ans; fin >> ans; cout << "ans = " << compans << "\texpected " << ans << endl; EXPECT(fabs(ans-compans)/ans < kEpsilon); } fin.close(); if (testing_result == 0) cout << "PASS gibbsquery unittest." << endl; return testing_result!=0; } ctbnrle.1.1.1/tst/makedrugtst.cc0000664000175000017500000003357712115732760017064 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ // This code creates the drug effect network as first seen in // Continuous Time Bayesian Networks by Nodelman et al., 2002 and // save it to stdout // // Usage: makedrug [options] > // options: // // Set whether to save only the dynamics (as opposed to the full CTBN): // -Dsavedyn (default: 0 [=false]) // // Set whether to save as a pointer (which can be loaded into any superclass // -Dsaveptr (default: 0 [=false]) #include "bn.h" #include "em.h" #include "context.h" #include "ctbn.h" #include "ctbndyn.h" #include "factoredmatrix.h" #include "factoredvector.h" #include "markov.h" #include "markovdyn.h" #include "multirv.h" #include "params.h" #include "streamextra.h" #include "trajectory.h" #include "utils.h" #include #include #include #include using namespace std; using namespace ctbn; int main(int argc, char **argv) { InitParams(argc, argv); bool savedyn = ParamInt("savedyn",false); bool saveasptr = ParamInt("saveptr",false); cout << setprecision(4); //******************************************************* // Create contexts for each variable Context nullc; Context eatc, fullc, hungryc, uptakec, concentrationc, barometerc, painc, drowsyc; eatc.AddVar(0, 2); fullc.AddVar(1,3); hungryc.AddVar(2,2); uptakec.AddVar(3, 2); concentrationc.AddVar(4,3); barometerc.AddVar(5,3); painc.AddVar(6, 2); drowsyc.AddVar(7,2); // Define the connections between variables. For a variable with multiple // parents, the context used is the union of the two parent contexts MarkovDyn eat(eatc, hungryc); MarkovDyn full(fullc, eatc); MarkovDyn hungry(hungryc, fullc); MarkovDyn uptake(uptakec, nullc); Context full_and_uptakec(fullc, uptakec); MarkovDyn concentration(concentrationc, full_and_uptakec); MarkovDyn barometer(barometerc, nullc); MarkovDyn pain(painc, Context(barometerc, concentrationc)); MarkovDyn drowsy(drowsyc, concentrationc); // Generate starting distributions for each variable MultiRV eatrv(eatc, nullc); MultiRV fullrv(fullc, nullc); MultiRV hungryrv(hungryc, nullc); MultiRV uptakerv(uptakec, nullc); MultiRV concentrationrv(concentrationc, nullc); MultiRV barometerrv(barometerc, nullc); MultiRV painrv(painc, nullc); MultiRV drowsyrv(drowsyc, nullc); // The following fills the conditional intensity matrices for each // variable and the initial distributions (multinomial distributions). //------eat-------- eat(0)->Intensity()[0][0] = -0.01; eat(0)->Intensity()[0][1] = 0.01; eat(0)->Intensity()[1][0] = 10.0; eat(0)->Intensity()[1][1] = -10.0; eat(1)->Intensity()[0][0] = -2.0; eat(1)->Intensity()[0][1] = 2.0; eat(1)->Intensity()[1][0] = 1.0; eat(1)->Intensity()[1][1] = -1.0; vectr peat(2, 0.0); peat[0] = 0.1; peat[1] = 0.9; eatrv[0].SetDist(peat); //------full-------- full(0)->Intensity()[0][0] = -0.02; full(0)->Intensity()[0][1] = 0.01; full(0)->Intensity()[0][2] = 0.01; full(0)->Intensity()[1][0] = 0.3; full(0)->Intensity()[1][1] = -0.31; full(0)->Intensity()[1][2] = 0.01; full(0)->Intensity()[2][0] = 0.01; full(0)->Intensity()[2][1] = 1.0; full(0)->Intensity()[2][2] = -1.01; full(1)->Intensity()[0][0] = -6.01; full(1)->Intensity()[0][1] = 6.0; full(1)->Intensity()[0][2] = 0.01; full(1)->Intensity()[1][0] = 0.01; full(1)->Intensity()[1][1] = -3.01; full(1)->Intensity()[1][2] = 3.0; full(1)->Intensity()[2][0] = 0.01; full(1)->Intensity()[2][1] = 0.01; full(1)->Intensity()[2][2] = -0.02; vectr pfull(3, 0.0); pfull[0] = 0.7; pfull[1] = 0.2; pfull[2] = 0.1; fullrv[0].SetDist(pfull); //------hungry-------- hungry(0)->Intensity()[0][0] = -2.0; hungry(0)->Intensity()[0][1] = 2.0; hungry(0)->Intensity()[1][0] = 0.01; hungry(0)->Intensity()[1][1] = -0.01; hungry(1)->Intensity()[0][0] = -0.01; hungry(1)->Intensity()[0][1] = 0.01; hungry(1)->Intensity()[1][0] = 0.01; hungry(1)->Intensity()[1][1] = -0.01; hungry(2)->Intensity()[0][0] = -0.01; hungry(2)->Intensity()[0][1] = 0.01; hungry(2)->Intensity()[1][0] = 10.0; hungry(2)->Intensity()[1][1] = -10.0; vectr phungry(2, 0.0); phungry[0] = 0.6; phungry[1] = 0.4; hungryrv[0].SetDist(phungry); //------uptake-------- uptake(0)->Intensity()[0][0] = -0.0; uptake(0)->Intensity()[0][1] = 0.0; uptake(0)->Intensity()[1][0] = 0.5; uptake(0)->Intensity()[1][1] = -0.5; vectr puptake(2, 0.0); puptake[0] = 0.45; puptake[1] = 0.55; uptakerv[0].SetDist(puptake); // For this variable there is more than one parent. // The CIM is filled by creating an Instantiation object that represents // the current intensity matrix we are entering values for. The // MarkovDyn objects can be then indexed with Instantiation objects. //------concentration------ Instantiation i(Context(fullc, uptakec)); // Set uptake to state 0; full to state 0 i.SetVal(3,0); i.SetVal(1,0); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; // Set uptake to state 1; full to state 0 // (We repeat this process for each parent instantiation.) i.SetVal(3,1); i.SetVal(1,0); concentration(i)->Intensity()[0][0] = -2.01; concentration(i)->Intensity()[0][1] = 2.0; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -1.01; concentration(i)->Intensity()[1][2] = 1.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; i.SetVal(3,0); i.SetVal(1,1); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; i.SetVal(3,1); i.SetVal(1,1); concentration(i)->Intensity()[0][0] = -1.01; concentration(i)->Intensity()[0][1] = 1.0; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -2.01; concentration(i)->Intensity()[1][2] = 2.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; i.SetVal(3,0); i.SetVal(1,2); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; i.SetVal(3,1); i.SetVal(1,2); concentration(i)->Intensity()[0][0] = -0.51; concentration(i)->Intensity()[0][1] = 0.5; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -4.01; concentration(i)->Intensity()[1][2] = 4.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; vectr pconcentration(3, 0.0); pconcentration[0] = 0.7;//0.1; pconcentration[1] = 0.2;//0.2; pconcentration[2] = 0.1;//0.7; concentrationrv[0].SetDist(pconcentration); //------barometer------ barometer(0)->Intensity()[0][0] = -0.21; barometer(0)->Intensity()[0][1] = 0.2; barometer(0)->Intensity()[0][2] = 0.01; barometer(0)->Intensity()[1][0] = 0.05; barometer(0)->Intensity()[1][1] = -0.1; barometer(0)->Intensity()[1][2] = 0.05; barometer(0)->Intensity()[2][0] = 0.01; barometer(0)->Intensity()[2][1] = 0.2; barometer(0)->Intensity()[2][2] = -0.21; vectr pbarometer(3, 0.0); pbarometer[0] = 0.3; pbarometer[1] = 0.7; barometerrv[0].SetDist(pbarometer); //------pain------ Instantiation j(Context(barometerc, concentrationc)); j.SetVal(4,0); j.SetVal(5,0); pain(j)->Intensity()[0][0] = -6.0; pain(j)->Intensity()[0][1] = 6.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(4,1); j.SetVal(5,0); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(4,2); j.SetVal(5,0); pain(j)->Intensity()[0][0] = -6.0; pain(j)->Intensity()[0][1] = 6.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(4,0); j.SetVal(5,1); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(4,1); j.SetVal(5,1); pain(j)->Intensity()[0][0] = -0.3; pain(j)->Intensity()[0][1] = 0.3; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(4,2); j.SetVal(5,1); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(4,0); j.SetVal(5,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; j.SetVal(4,1); j.SetVal(5,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; j.SetVal(4,2); j.SetVal(5,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; vectr ppain(2, 0.0); ppain[0] = 0.1; ppain[1] = 0.9; painrv[0].SetDist(ppain); //------drowsy------ drowsy(0)->Intensity()[0][0] = -0.17; drowsy(0)->Intensity()[0][1] = 0.17; drowsy(0)->Intensity()[1][0] = 0.5; drowsy(0)->Intensity()[1][1] = -0.5; drowsy(1)->Intensity()[0][0] = -0.33; drowsy(1)->Intensity()[0][1] = 0.33; drowsy(1)->Intensity()[1][0] = 0.33; drowsy(1)->Intensity()[1][1] = -0.33; drowsy(2)->Intensity()[0][0] = -1.0; drowsy(2)->Intensity()[0][1] = 1.0; drowsy(2)->Intensity()[1][0] = 0.1; drowsy(2)->Intensity()[1][1] = -0.1; vectr pdrowsy(2, 0.0); pdrowsy[0] = 0.1; pdrowsy[1] = 0.9; drowsyrv[0].SetDist(pdrowsy); // The dynamics of a CTBN are defined by the dynamics of each conditional // Markov process. Here it is the MarkovDyn objects created above. CTBNDyn ctbndyn; ctbndyn.AddNode(eat.Clone()); ctbndyn.AddNode(full.Clone()); ctbndyn.AddNode(hungry.Clone()); BN bn; bn.AddNode(eatrv.Clone()); bn.AddNode(fullrv.Clone()); bn.AddNode(hungryrv.Clone()); //(ctbndyn.JointMatrix()).niceprint(cout); ctbndyn.AddNode(uptake.Clone()); ctbndyn.AddNode(concentration.Clone()); ctbndyn.AddNode(barometer.Clone()); ctbndyn.AddNode(pain.Clone()); ctbndyn.AddNode(drowsy.Clone()); // The initial distribution of a CTBN is defined by the underlying // Bayesian network containing a multinomial distribution for each // variable. bn.AddNode(uptakerv.Clone()); bn.AddNode(concentrationrv.Clone()); bn.AddNode(barometerrv.Clone()); bn.AddNode(painrv.Clone()); bn.AddNode(drowsyrv.Clone()); /* Dynamics *dyn = &ctbndyn; FactoredMatrix apprm(dyn); RV *rvbn = &bn; FactoredVector apprv(rvbn); apprv.Print(); //cout << apprv.GetDistInst(0,0) << endl; FactoredVector bbb = apprv; //cout << bbb.GetDistInst(0,0) << endl; //apprm.Mult(1, bbb); Context context = ctbndyn.Domain() + ctbndyn.CondDomain(); Instantiation x(context, -1); int querynode = ParamInt("QueryNode", 5); int querytimeval = ParamInt("QueryTimeVal", 1); x.SetVal(querynode, querytimeval); querynode = ParamInt("QueryNode", 1); querytimeval = ParamInt("QueryTimeVal", 0); x.SetVal(querynode, querytimeval); querynode = ParamInt("QueryNode", 2); querytimeval = ParamInt("QueryTimeVal", 0); x.SetVal(querynode, querytimeval); querynode = ParamInt("QueryNode",3); querytimeval = ParamInt("QueryTimeVal", 0); x.SetVal(querynode, querytimeval); querynode = ParamInt("QueryNode", 4); querytimeval = ParamInt("QueryTimeVal", 0); x.SetVal(querynode, querytimeval); RVSimple *v = &apprv; apprm.Cond(0.0, 1.0, x); apprm.Mult(v); */ // Construct the final object that represents the CTBN. Markov m(bn.Clone(), ctbndyn.Clone()); Context context = ctbndyn.Domain() + ctbndyn.CondDomain(); // sample a partial trajectory as the input evidence double begintime = ParamDouble("BeginTime", 0.0); double endtime = ParamDouble("EndTime", 10.0); Trajectory evid; evid.SetBeginTime(begintime); evid.SetEndTime(endtime); evid.AddPointEvidence(0, 0.5, 1); evid.AddTransition(1, 2.5, 2); evid.AddTransition(6, 5.0, 1); //m.Sample(evid); //RemoveNodesInformation(evid, context, ctbndyn.NumofNodes(), ctbndyn.NumofNodes()*6 , 0.3); m.Save(cout); cout << endl << evid << endl; // Output the CTBN to the cout strem /* if (savedyn) { //if (saveasptr) ctbndyn.SavePtr(cout); //else ctbndyn.Save(cout); ctbndyn.Save(cout); } else { //if (saveasptr) m.SavePtr(cout); //else m.Save(cout); m.Save(cout); } */ } ctbnrle.1.1.1/tst/glpktest.cc0000664000175000017500000000453712115732746016365 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "linearprogram.h" #include "streamextra.h" #include using namespace ctbn; using namespace std; namespace{ int testing_result = 0; } #define EXPECT(cond) do { \ if (!(cond)) { \ std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \ << " in " << __func__ \ << ", failed condition is " #cond << std::endl; \ testing_result = 1; \ } \ } while (0) int main(int argc, char **argv) { LinearProgram *lp = new GLPKSolver(); vector w(3); w[0] = -10; w[1] = -6; w[2] = -4; lp->SetObjective(w); w[0] = -1; w[1] = -1; w[2] = -1; lp->AddGrEqConstraint(w,-100); w[0] = -10; w[1] = -4; w[2] = -5; lp->AddGrEqConstraint(w,-600); w[0] = -2; w[1] = -2; w[2] = -6; lp->AddGrEqConstraint(w,-300); w.resize(1); w[0] = 2; vector i(1); i[0] = 0; lp->AddGrEqConstraint(i,w,-30); lp->AddGrEqConstraint(i,w,-20); i[0] = 1; lp->AddGrEqConstraint(i,w,-20); lp->AddGrEqConstraint(i,w,-30); i[0] = 2; w[0] = 1; lp->AddEqConstraint(i,w,-10); double val; w = lp->Solve(val); EXPECT(fabs(val+760)<0.0001); EXPECT(w.size()==3); double expw[3] = {35,75,-10}; EXPECT(fabs(expw[0]-w[0])<0.0001); EXPECT(fabs(expw[1]-w[1])<0.0001); EXPECT(fabs(expw[2]-w[2])<0.0001); /* output should be: 3 35 75 -10 -760 */ //cout << w << endl; //cout << val << endl; delete lp; if (testing_result == 0) cout << "PASS exactquery unit test." << endl; return testing_result!=0; } ctbnrle.1.1.1/tst/run_burnin_error.pl0000664000175000017500000000215212107032413020120 0ustar csheltoncshelton#!/usr/bin/perl -w # Continuous Time Bayesian Network Reasoning and Learning Engine # Copyright (C) 2009 The Regents of the University of California # # Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . sub main { system("rm -fr burnin_error_results"); system("mkdir burnin_error_results"); for (my $burnin = 100; $burnin <= 1000; $burnin += 100) { system("./burnin_error_n1000 chain.ctbn traj_q.txt $burnin >burnin_error_results/$burnin.txt &"); } } &main; ctbnrle.1.1.1/tst/maketoroid.cc0000664000175000017500000001521412115732770016655 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include #include #include #include #include #include #include "bn.h" #include "em.h" #include "context.h" #include "ctbn.h" #include "ctbndyn.h" #include "markov.h" #include "markovdyn.h" #include "multirv.h" #include "trajectory.h" #include "params.h" using namespace std; using namespace ctbn; double tao = 1; double beta = 0.5; void FillIntensityIsing(MarkovDyn &md, Instantiation &i, int pa1, int pa2){ int pv1, pv2; for (int v1=0; v1<2; v1++){ i.SetVal(pa1, v1); for (int v2=0; v2<2; v2++){ i.SetVal(pa2, v2); pv1 = (v1==0)?-1:1; pv2 = (v2==0)?-1:1; md(i)->Intensity()[1][0] = tao*pow((1+exp(2*beta*(pv1+pv2))), -1.0); md(i)->Intensity()[0][1] = tao*pow((1+exp(-2*beta*(pv1+pv2))), -1.0); md(i)->Intensity()[0][0] = -1*(md(i)->Intensity()[0][1]); md(i)->Intensity()[1][1] = -1*(md(i)->Intensity()[1][0]); } } } void FillIntensityIsingOp(MarkovDyn &md, Instantiation &i, int pa1, int pa2){ int pv1, pv2; for (int v1=0; v1<2; v1++){ i.SetVal(pa1, v1); for (int v2=0; v2<2; v2++){ i.SetVal(pa2, v2); pv1 = (v1==0)?-1:1; pv2 = (v2==0)?-1:1; md(i)->Intensity()[1][0] = tao*pow((1+exp(-2*beta*(pv1+pv2))), -1.0); md(i)->Intensity()[0][1] = tao*pow((1+exp(2*beta*(pv1+pv2))), -1.0); md(i)->Intensity()[0][0] = -1*(md(i)->Intensity()[0][1]); md(i)->Intensity()[1][1] = -1*(md(i)->Intensity()[1][0]); } } } void FillIntensityIsing(MarkovDyn &md, Instantiation &i, int pa1){ int pv1; for (int v1=0; v1<2; v1++){ i.SetVal(pa1, v1); pv1 = (v1==0)?-1:1; md(i)->Intensity()[1][0] = tao*pow((1+exp(2*beta*(pv1))), -1.0); md(i)->Intensity()[0][1] = tao*pow((1+exp(-2*beta*(pv1))), -1.0); md(i)->Intensity()[0][0] = -1*(md(i)->Intensity()[0][1]); md(i)->Intensity()[1][1] = -1*(md(i)->Intensity()[1][0]); } } void FillIntensityIsing(MarkovDyn &md){ double pv1 = 0; //xo and pv = {-1, +1} //tao*(1+exp(-2*xo*beta*sum(pv)))^-1 md(0)->Intensity()[1][0] = tao*pow((1+exp(2*beta*(pv1))), -1.0); md(0)->Intensity()[0][1] = tao*pow((1+exp(-2*beta*(pv1))), -1.0); md(0)->Intensity()[0][0] = -1*(md(0)->Intensity()[0][1]); md(0)->Intensity()[1][1] = -1*(md(0)->Intensity()[1][0]); } int main (int argc, char **argv) { if (argc > 2){ tao = atof(argv[1]); beta = atof(argv[2]); } Context x0, x1, x2, x3, x4,x5,x6,x7,x8,x9,x10, x11; Context Null; x0.AddVar(0,2); x1.AddVar(1,2); x2.AddVar(2,2); x3.AddVar(3,2); x4.AddVar(4,2); x5.AddVar(5,2); x6.AddVar(6,2); x7.AddVar(7,2); x8.AddVar(8,2); x9.AddVar(9,2); x10.AddVar(10,2); x11.AddVar(11,2); MarkovDyn PX0(x0,Context(x3,x8)); // process of X MarkovDyn PX1(x1,Context(x0,x9)); MarkovDyn PX2(x2,Context(x1,x10)); MarkovDyn PX3(x3,Context(x2,x11)); MarkovDyn PX4(x4,Context(x0,x7)); MarkovDyn PX5(x5,Context(x1,x4)); MarkovDyn PX6(x6,Context(x2, x5)); MarkovDyn PX7(x7,Context(x3, x6)); MarkovDyn PX8(x8,Context(x4, x11)); MarkovDyn PX9(x9,Context(x5, x8)); MarkovDyn PX10(x10,Context(x6, x9)); MarkovDyn PX11(x11,Context(x7, x10)); Instantiation i0(Context(x3,x8)); FillIntensityIsing(PX0, i0, 3, 8); Instantiation i1(Context(x0,x9)); FillIntensityIsing(PX1, i1, 0, 9); Instantiation i2(Context(x1,x10)); FillIntensityIsing(PX2, i2, 1, 10); Instantiation i3(Context(x2, x11)); FillIntensityIsing(PX3, i3, 2, 11); Instantiation i4(Context(x0, x7)); FillIntensityIsing(PX4, i4, 0, 7); Instantiation i5(Context(x1, x4)); FillIntensityIsing(PX5, i5, 1, 4); Instantiation i6(Context(x2, x5)); FillIntensityIsing(PX6, i6, 2, 5); Instantiation i7(Context(x3, x6)); FillIntensityIsing(PX7, i7, 3, 6); Instantiation i8(Context(x4, x11)); FillIntensityIsing(PX8, i8, 4, 11); Instantiation i9(Context(x5, x8)); FillIntensityIsing(PX9, i9, 5, 8); Instantiation i10(Context(x6, x9)); FillIntensityIsing(PX10, i10, 6, 9); Instantiation i11(Context(x7, x10)); FillIntensityIsing(PX11, i11, 7, 10); //Independent initial dist. MultiRV P0x0(x0,Null); MultiRV P0x1(x1,Null); MultiRV P0x2(x2,Null); MultiRV P0x3(x3,Null); MultiRV P0x4(x4,Null); MultiRV P0x5(x5,Null); MultiRV P0x6(x6,Null); MultiRV P0x7(x7,Null); MultiRV P0x8(x8,Null); MultiRV P0x9(x9,Null); MultiRV P0x10(x10,Null); MultiRV P0x11(x11,Null); vectr p0(2,0.0); p0[0] = 1; p0[1] = 0; P0x0[0].SetDist(p0); p0[0] = 1; p0[1] = 0; P0x1[0].SetDist(p0); p0[0] = 1; p0[1] = 0; P0x2[0].SetDist(p0); p0[0] = 1; p0[1] = 0; P0x3[0].SetDist(p0); p0[0] = 0; p0[1] = 1; P0x4[0].SetDist(p0); p0[0] = 0; p0[1] = 1; P0x5[0].SetDist(p0); p0[0] = 1; p0[1] = 0; P0x6[0].SetDist(p0); p0[0] = 1; p0[1] = 0; P0x7[0].SetDist(p0); p0[0] = 1; p0[1] = 0; P0x8[0].SetDist(p0); p0[0] = 1; p0[1] = 0; P0x9[0].SetDist(p0); p0[0] = 1; p0[1] = 0; P0x10[0].SetDist(p0); p0[0] = 1; p0[1] = 0; P0x11[0].SetDist(p0); // set up CTBN: CTBNDyn ctbndyn; ctbndyn.AddNode(PX0.Clone()); ctbndyn.AddNode(PX1.Clone()); ctbndyn.AddNode(PX2.Clone()); ctbndyn.AddNode(PX3.Clone()); ctbndyn.AddNode(PX4.Clone()); ctbndyn.AddNode(PX5.Clone()); ctbndyn.AddNode(PX6.Clone()); ctbndyn.AddNode(PX7.Clone()); ctbndyn.AddNode(PX8.Clone()); ctbndyn.AddNode(PX9.Clone()); ctbndyn.AddNode(PX10.Clone()); ctbndyn.AddNode(PX11.Clone()); BN bn; bn.AddNode(P0x0.Clone()); bn.AddNode(P0x1.Clone()); bn.AddNode(P0x2.Clone()); bn.AddNode(P0x3.Clone()); bn.AddNode(P0x4.Clone()); bn.AddNode(P0x5.Clone()); bn.AddNode(P0x6.Clone()); bn.AddNode(P0x7.Clone()); bn.AddNode(P0x8.Clone()); bn.AddNode(P0x9.Clone()); bn.AddNode(P0x10.Clone()); bn.AddNode(P0x11.Clone()); Markov ctbn(bn.Clone(),ctbndyn.Clone()); Context context = ctbndyn.Domain() + ctbndyn.CondDomain(); ctbn.Save(cout); // trajectory Trajectory tr; tr.SetBeginTime(0.0); tr.SetEndTime(1.0); tr.AddTransition(0,0.5,1); tr.AddTransition(1,0.5,0); tr.Save(cout); // query cout << "0 2 0 1.0 0.8983018202" << endl; return 0; } ctbnrle.1.1.1/tst/CMakeLists.txt0000664000175000017500000000305312115750431016740 0ustar csheltoncsheltoncmake_minimum_required(VERSION 2.6.4 FATAL_ERROR) project(ctbn-test-executables) include_directories(${CTBN_INCLUDE_DIRS}) set(TEST_EXECUTABLES clique_unit_test decision exactfbquery factoredunifquery gibbsauxprequery gibbsprequery gibbsquery glpktest importancequery meanfieldquery sem structsearch structurescore unit_testing_graphic) set(AUX_EXECUTABLES makequeryinput makedrugtst maketoroid) set(DATA_FILES unit_test_traj.graph unit_test_traj.eps) add_custom_command(OUTPUT queryinput.data COMMAND ./makequeryinput > queryinput.data DEPENDS makequeryinput ) add_custom_command(OUTPUT queryinput-toroid.data COMMAND ./maketoroid > queryinput-toroid.data DEPENDS maketoroid ) add_custom_command(OUTPUT drug.ctbn COMMAND ./makedrugtst > drug.ctbn DEPENDS makedrugtst ) add_custom_command(OUTPUT drug.ctbndyn COMMAND ./makedrugtst -Dsavedyn 1 > drug.ctbndyn DEPENDS makedrugtst ) add_custom_target(datafilestst ALL DEPENDS drug.ctbn drug.ctbndyn queryinput.data queryinput-toroid.data) foreach(data_file ${DATA_FILES}) configure_file(${CMAKE_CURRENT_SOURCE_DIR}/${data_file} ${CMAKE_CURRENT_BINARY_DIR}/${data_file} COPYONLY) endforeach() foreach(aux_executable ${AUX_EXECUTABLES}) add_executable(${aux_executable} ${aux_executable}.cc) target_link_libraries(${aux_executable} ${CTBN_LIBRARIES}) endforeach() foreach(test_executable ${TEST_EXECUTABLES}) add_executable(${test_executable} ${test_executable}.cc) target_link_libraries(${test_executable} ${CTBN_LIBRARIES}) add_test(${test_executable} ${test_executable}) endforeach() ctbnrle.1.1.1/tst/meanfieldquery.cc0000664000175000017500000000643712115732774017544 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "meanfieldinf.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include #include "ensurectbn.h" using namespace std; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using impotrance sampling and compare the results with the true ones // if the error is within the threshold, this unittest successes, o.w.error message will pop out namespace { int testing_result = 0; double kEpsilon = 0.2; } #define EXPECT(cond) do { \ if (!(cond)) { \ std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \ << " in " << __func__ \ << ", failed condition is " #cond << std::endl; \ testing_result = 1; \ } \ } while (0) int main(int argc, char **argv) { const char *input_file = "queryinput.data"; addnan(cout); InitParams(argc, argv); double epsparam = ParamDouble("EPS", 1e-5); ifstream fin(input_file); Markov m; try { m.Load(fin); } catch (const serial::streamexception &e) { cerr << e.what() << endl; exit(-1); } const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics()); Trajectory evid; evid.Load(fin); MeanFieldInf inf; inf.SetProcess(&m); inf.SetTrajectory(&evid); inf.SetEPS(epsparam); while(1) { int qtype; fin >> qtype; if (qtype==-1 || fin.eof()) break; int var, val; double t1; fin >> var >> val; double compans; Instantiation x(ctbndyn->Domain()); x.SetVal(var,val); double eps = kEpsilon; switch(qtype) { case 0: // filter distribution at point fin >> t1; fin >> t1; // not implemented (remove answer) continue; case 1: // smooth distribution at point fin >> t1; compans = inf.Smooth(x,t1); eps *= 10; break; case 2: // expected time query { QueryTime query(x); compans = inf.CalcQuery(query); } break; case 3: // expected # trans query { int val2; fin >> val2; QueryTransition query(x.KnownVars(),val,val2); compans=inf.CalcQuery(query); } break; } double ans; fin >> ans; cout << "ans = " << compans << "\texpected " << ans << endl; EXPECT(fabs(ans-compans)/ans < eps); } fin.close(); if (testing_result == 0) cout << "PASS meanfieldquery unittest." << endl; return testing_result!=0; } ctbnrle.1.1.1/tst/Makefile0000664000175000017500000051126112251374017015650 0ustar csheltoncsheltonSRC := $(wildcard *.cc) #OBJ := $(patsubst %.cc,%.o,$(SRC)) LIBOBJ = ../src/ctbn.a ../glpk/glpk-4.47/src/.libs/libglpk.a CXX = g++ #OFLAGS = -O0 #OFLAGS = -O2 OFLAGS = -O3 CXXFLAGS += $(OFLAGS) CXXFLAGS += -pedantic -Wall -Wextra -Wno-unused-local-typedefs -Wno-long-long -Wno-unused-parameter -Wno-unused-variable CXXFLAGS += -std=c++03 CXXFLAGS += -Werror -pedantic-errors CXXFLAGS += -I../hdr CXXFLAGS += -I../glpk/glpk-4.47/include -I../glpk/glpk-4.47/src CXXFLAGS += -DUSE_PTHREADS LDFLAGS += -lpthread -L../glpk/glpk-4.47/src/.libs GCC_BASE_DIR = /usr/csshare/pkgs/gcc-4.7.0 GCC_BASE_INCLUDE_DIR = $(GCC_BASE_DIR)/lib/gcc/i686-pc-linux-gnu/4.7.0/include GPP_BASE_INCLUDE_DIR = $(GCC_BASE_DIR)/include/c++/4.7.0 DEPEND = makedepend -- $(CXXFLAGS) -- -w20 -Y -I$(GCC_BASE_INCLUDE_DIR) -I$(GPP_BASE_INCLUDE_DIR) -I$(GPP_BASE_INCLUDE_DIR)/tr1 -I$(GPP_INCLUDE_BASE_DIR)/i686-pc-linux-gnu -I/usr/include/asm -I../hdr -I../glpk/glpk-4.47/include # keep line below as "EXES=" and not "EXES =" so that python script parses # it correctly EXES= clique_unit_test unit_testing_graphic exactfbquery factoredunifquery importanceprequery gibbsquery gibbsauxprequery meanfieldquery glpktest structurescore structsearch sem gibbsprequery decision DATA = queryinput.data queryinput-toroid.data drug.ctbn drug.ctbndyn .PHONY: local local: $(EXES) $(DATA) .PHONY: all all: ../src/ctbn.a local (cd ../src; make all; cd ../tst) queryinput.data: makequeryinput (rm -f queryinput.data; ./makequeryinput > queryinput.data) queryinput-toroid.data: maketoroid (rm -f queryinput-toroid.data; ./maketoroid > queryinput-toroid.data) drug.ctbn: makedrugtst (rm -f drug.ctbn; ./makedrugtst > drug.ctbn) drug.ctbndyn: makedrugtst (rm -f drug.ctbndyn; ./makedrugtst -Dsavedyn 1 > drug.ctbndyn) decision: decision.o $(LIBOBJ) $(CXX) $(FXXFLAGS) $(LDFLAGS) -o decision decision.o $(LIBOBJ) -lglpk glpktest: glpktest.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o glpktest glpktest.o $(LIBOBJ) -lglpk unit_testing_graphic: unit_testing_graphic.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o unit_testing_graphic unit_testing_graphic.o $(LIBOBJ) clique_unit_test: clique_unit_test.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o clique_unit_test clique_unit_test.o $(LIBOBJ) genquerytxt: genquerytxt.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o genquerytxt genquerytxt.o $(LIBOBJ) makequeryinput: makequeryinput.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o makequeryinput makequeryinput.o $(LIBOBJ) structsearch: structsearch.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o structsearch structsearch.o $(LIBOBJ) sem: sem.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o sem sem.o $(LIBOBJ) structurescore: structurescore.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o structurescore structurescore.o $(LIBOBJ) exactfbquery: exactfbquery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o exactfbquery exactfbquery.o $(LIBOBJ) factoredunifquery: factoredunifquery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o factoredunifquery factoredunifquery.o $(LIBOBJ) gibbsquery: gibbsquery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o gibbsquery gibbsquery.o $(LIBOBJ) gibbsauxprequery: gibbsauxprequery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o gibbsauxprequery gibbsauxprequery.o $(LIBOBJ) gibbsprequery: gibbsprequery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o gibbsprequery gibbsprequery.o $(LIBOBJ) importanceprequery: importanceprequery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o importanceprequery importanceprequery.o $(LIBOBJ) importancequery: importancequery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o importancequery importancequery.o $(LIBOBJ) meanfieldquery: meanfieldquery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o meanfieldquery meanfieldquery.o $(LIBOBJ) makedrugtst: makedrugtst.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o makedrugtst makedrugtst.o $(LIBOBJ) maketoroid: maketoroid.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o maketoroid maketoroid.o $(LIBOBJ) depend: $(SRC) $(DEPEND) $(SRC) .PHONY: clean clean: rm -rf *.o *~ output2.eps output.dot output_test_ctbn.dot output_unit_test.eps makedrugtst makequeryinput genquerytxt $(EXES) $(DATA) .PHONY: realclean realclean: (cd ../src; make clean; cd ../tst) rm -rf *.o *~ output2.eps output.dot output_test_ctbn.dot output_unit_test.eps makedrugtst makequeryinput genquerytxt $(EXES) $(DATA) # DO NOT DELETE importanceprequery.o: ../hdr/markovdyn.h importanceprequery.o: ../hdr/markovsimple.h importanceprequery.o: ../hdr/dynsimple.h importanceprequery.o: ../hdr/random.h importanceprequery.o: ../hdr/matrix.h importanceprequery.o: ../hdr/matrix-eigen.h importanceprequery.o: ../hdr/streamserial.h importanceprequery.o: ../hdr/serial/serial.h importanceprequery.o: ../hdr/nullptr03.h importanceprequery.o: ../hdr/streamserial-old.h importanceprequery.o: ../hdr/defines.h importanceprequery.o: ../hdr/streamextra.h importanceprequery.o: ../hdr/Eigen/Core importanceprequery.o: ../hdr/Eigen/src/Core/util/DisableStupidWarnings.h importanceprequery.o: ../hdr/Eigen/src/Core/util/Macros.h importanceprequery.o: ../hdr/Eigen/src/Core/util/MKL_support.h importanceprequery.o: ../hdr/Eigen/src/Core/util/Constants.h importanceprequery.o: ../hdr/Eigen/src/Core/util/ForwardDeclarations.h importanceprequery.o: ../hdr/Eigen/src/Core/util/Meta.h importanceprequery.o: ../hdr/Eigen/src/Core/util/XprHelper.h importanceprequery.o: ../hdr/Eigen/src/Core/util/StaticAssert.h importanceprequery.o: ../hdr/Eigen/src/Core/util/Memory.h importanceprequery.o: ../hdr/Eigen/src/Core/NumTraits.h importanceprequery.o: ../hdr/Eigen/src/Core/MathFunctions.h importanceprequery.o: ../hdr/Eigen/src/Core/GenericPacketMath.h importanceprequery.o: ../hdr/Eigen/src/Core/arch/Default/Settings.h importanceprequery.o: ../hdr/Eigen/src/Core/Functors.h importanceprequery.o: ../hdr/Eigen/src/Core/DenseCoeffsBase.h importanceprequery.o: ../hdr/Eigen/src/Core/DenseBase.h importanceprequery.o: ../hdr/Eigen/src/plugins/BlockMethods.h importanceprequery.o: ../hdr/Eigen/src/Core/MatrixBase.h importanceprequery.o: ../hdr/Eigen/src/plugins/CommonCwiseUnaryOps.h importanceprequery.o: ../hdr/Eigen/src/plugins/CommonCwiseBinaryOps.h importanceprequery.o: ../hdr/Eigen/src/plugins/MatrixCwiseUnaryOps.h importanceprequery.o: ../hdr/Eigen/src/plugins/MatrixCwiseBinaryOps.h importanceprequery.o: ../hdr/matrix-eigenext.h importanceprequery.o: ../hdr/Eigen/src/Core/EigenBase.h importanceprequery.o: ../hdr/Eigen/src/Core/Assign.h importanceprequery.o: ../hdr/Eigen/src/Core/util/BlasUtil.h importanceprequery.o: ../hdr/Eigen/src/Core/DenseStorage.h importanceprequery.o: ../hdr/Eigen/src/Core/NestByValue.h importanceprequery.o: ../hdr/Eigen/src/Core/ForceAlignedAccess.h importanceprequery.o: ../hdr/Eigen/src/Core/ReturnByValue.h importanceprequery.o: ../hdr/Eigen/src/Core/NoAlias.h importanceprequery.o: ../hdr/Eigen/src/Core/PlainObjectBase.h importanceprequery.o: ../hdr/Eigen/src/Core/Matrix.h importanceprequery.o: ../hdr/Eigen/src/Core/Array.h importanceprequery.o: ../hdr/Eigen/src/Core/CwiseBinaryOp.h importanceprequery.o: ../hdr/Eigen/src/Core/CwiseUnaryOp.h importanceprequery.o: ../hdr/Eigen/src/Core/CwiseNullaryOp.h importanceprequery.o: ../hdr/Eigen/src/Core/CwiseUnaryView.h importanceprequery.o: ../hdr/Eigen/src/Core/SelfCwiseBinaryOp.h importanceprequery.o: ../hdr/Eigen/src/Core/Dot.h importanceprequery.o: ../hdr/Eigen/src/Core/StableNorm.h importanceprequery.o: ../hdr/Eigen/src/Core/MapBase.h importanceprequery.o: ../hdr/Eigen/src/Core/Stride.h importanceprequery.o: ../hdr/Eigen/src/Core/Map.h importanceprequery.o: ../hdr/Eigen/src/Core/Block.h importanceprequery.o: ../hdr/Eigen/src/Core/VectorBlock.h importanceprequery.o: ../hdr/Eigen/src/Core/Transpose.h importanceprequery.o: ../hdr/Eigen/src/Core/DiagonalMatrix.h importanceprequery.o: ../hdr/Eigen/src/Core/Diagonal.h importanceprequery.o: ../hdr/Eigen/src/Core/DiagonalProduct.h importanceprequery.o: ../hdr/Eigen/src/Core/PermutationMatrix.h importanceprequery.o: ../hdr/Eigen/src/Core/Transpositions.h importanceprequery.o: ../hdr/Eigen/src/Core/Redux.h importanceprequery.o: ../hdr/Eigen/src/Core/Visitor.h importanceprequery.o: ../hdr/Eigen/src/Core/Fuzzy.h importanceprequery.o: ../hdr/Eigen/src/Core/IO.h importanceprequery.o: ../hdr/Eigen/src/Core/Swap.h importanceprequery.o: ../hdr/Eigen/src/Core/CommaInitializer.h importanceprequery.o: ../hdr/Eigen/src/Core/Flagged.h importanceprequery.o: ../hdr/Eigen/src/Core/ProductBase.h importanceprequery.o: ../hdr/Eigen/src/Core/GeneralProduct.h importanceprequery.o: ../hdr/Eigen/src/Core/TriangularMatrix.h importanceprequery.o: ../hdr/Eigen/src/Core/SelfAdjointView.h importanceprequery.o: ../hdr/Eigen/src/Core/SolveTriangular.h importanceprequery.o: ../hdr/Eigen/src/Core/products/Parallelizer.h importanceprequery.o: ../hdr/Eigen/src/Core/products/CoeffBasedProduct.h importanceprequery.o: ../hdr/Eigen/src/Core/products/GeneralBlockPanelKernel.h importanceprequery.o: ../hdr/Eigen/src/Core/products/GeneralMatrixVector.h importanceprequery.o: ../hdr/Eigen/src/Core/products/GeneralMatrixMatrix.h importanceprequery.o: ../hdr/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h importanceprequery.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixVector.h importanceprequery.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixMatrix.h importanceprequery.o: ../hdr/Eigen/src/Core/products/SelfadjointProduct.h importanceprequery.o: ../hdr/Eigen/src/Core/products/SelfadjointRank2Update.h importanceprequery.o: 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../hdr/Eigen/src/Core/MatrixBase.h makequeryinput.o: ../hdr/Eigen/src/plugins/CommonCwiseUnaryOps.h makequeryinput.o: ../hdr/Eigen/src/plugins/CommonCwiseBinaryOps.h makequeryinput.o: ../hdr/Eigen/src/plugins/MatrixCwiseUnaryOps.h makequeryinput.o: ../hdr/Eigen/src/plugins/MatrixCwiseBinaryOps.h makequeryinput.o: ../hdr/matrix-eigenext.h makequeryinput.o: ../hdr/Eigen/src/Core/EigenBase.h makequeryinput.o: ../hdr/Eigen/src/Core/Assign.h makequeryinput.o: ../hdr/Eigen/src/Core/util/BlasUtil.h makequeryinput.o: ../hdr/Eigen/src/Core/DenseStorage.h makequeryinput.o: ../hdr/Eigen/src/Core/NestByValue.h makequeryinput.o: ../hdr/Eigen/src/Core/ForceAlignedAccess.h makequeryinput.o: ../hdr/Eigen/src/Core/ReturnByValue.h makequeryinput.o: ../hdr/Eigen/src/Core/NoAlias.h makequeryinput.o: ../hdr/Eigen/src/Core/PlainObjectBase.h makequeryinput.o: ../hdr/Eigen/src/Core/Matrix.h makequeryinput.o: ../hdr/Eigen/src/Core/Array.h makequeryinput.o: ../hdr/Eigen/src/Core/CwiseBinaryOp.h makequeryinput.o: ../hdr/Eigen/src/Core/CwiseUnaryOp.h makequeryinput.o: ../hdr/Eigen/src/Core/CwiseNullaryOp.h makequeryinput.o: ../hdr/Eigen/src/Core/CwiseUnaryView.h makequeryinput.o: ../hdr/Eigen/src/Core/SelfCwiseBinaryOp.h makequeryinput.o: ../hdr/Eigen/src/Core/Dot.h makequeryinput.o: ../hdr/Eigen/src/Core/StableNorm.h makequeryinput.o: ../hdr/Eigen/src/Core/MapBase.h makequeryinput.o: ../hdr/Eigen/src/Core/Stride.h makequeryinput.o: ../hdr/Eigen/src/Core/Map.h makequeryinput.o: ../hdr/Eigen/src/Core/Block.h makequeryinput.o: ../hdr/Eigen/src/Core/VectorBlock.h makequeryinput.o: ../hdr/Eigen/src/Core/Transpose.h makequeryinput.o: ../hdr/Eigen/src/Core/DiagonalMatrix.h makequeryinput.o: ../hdr/Eigen/src/Core/Diagonal.h makequeryinput.o: ../hdr/Eigen/src/Core/DiagonalProduct.h makequeryinput.o: ../hdr/Eigen/src/Core/PermutationMatrix.h makequeryinput.o: ../hdr/Eigen/src/Core/Transpositions.h makequeryinput.o: ../hdr/Eigen/src/Core/Redux.h makequeryinput.o: 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makequeryinput.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixVector.h makequeryinput.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixMatrix.h makequeryinput.o: ../hdr/Eigen/src/Core/products/SelfadjointProduct.h makequeryinput.o: ../hdr/Eigen/src/Core/products/SelfadjointRank2Update.h makequeryinput.o: ../hdr/Eigen/src/Core/products/TriangularMatrixVector.h makequeryinput.o: ../hdr/Eigen/src/Core/products/TriangularMatrixMatrix.h makequeryinput.o: ../hdr/Eigen/src/Core/products/TriangularSolverMatrix.h makequeryinput.o: ../hdr/Eigen/src/Core/products/TriangularSolverVector.h makequeryinput.o: ../hdr/Eigen/src/Core/BandMatrix.h makequeryinput.o: ../hdr/Eigen/src/Core/BooleanRedux.h makequeryinput.o: ../hdr/Eigen/src/Core/Select.h makequeryinput.o: ../hdr/Eigen/src/Core/VectorwiseOp.h makequeryinput.o: ../hdr/Eigen/src/Core/Random.h makequeryinput.o: ../hdr/Eigen/src/Core/Replicate.h makequeryinput.o: ../hdr/Eigen/src/Core/Reverse.h makequeryinput.o: 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../hdr/ss.h makequeryinput.o: ../hdr/rvsimple.h makequeryinput.o: ../hdr/rv.h makequeryinput.o: ../hdr/context.h makequeryinput.o: ../hdr/trajectory.h makequeryinput.o: ../hdr/dyncomp.h makequeryinput.o: ../hdr/dynamics.h makequeryinput.o: ../hdr/multirv.h makequeryinput.o: ../hdr/rvcomp.h makequeryinput.o: ../hdr/multisimple.h makequeryinput.o: ../hdr/params.h makequeryinput.o: ../hdr/bn.h makequeryinput.o: ../hdr/structure.h makequeryinput.o: ../hdr/ctbndyn.h makequeryinput.o: ../hdr/suffstatsquery.h makequeryinput.o: ../hdr/em.h makequeryinput.o: ../hdr/inference.h makequeryinput.o: ../hdr/process.h makequeryinput.o: ../hdr/utils.h makequeryinput.o: ../hdr/ensurectbn.h makequeryinput.o: ../hdr/ctbn.h makequeryinput.o: ../hdr/markov.h makequeryinput.o: ../hdr/extramath.h ctbnrle.1.1.1/tst/clique_unit_test.cc0000664000175000017500000001124512115732644020077 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "clique.h" #include "ensurectbn.h" #include #include using namespace std; using namespace ctbn; const int unit_test_check [4][3] = {{1}, {0, 2, 3} , {1} , {1}}; const int unit_test_check1 [4][3] = {{0,1,2}, {1, 3, 4}, {4,5,6}, {4,7}}; const int unit_test_save [3][80] = {{0, 2, -1, 0, 1, -1, 1, 2, -1, 3, -1, 1, 3, 4, -1, 5, -1, 4, 5, 6, -1, 4, 7, -1, -100, 0, 0, 1, 2, -1, 1, 1, 3, 4, -1, 2, 4, 5, 6, -1, 2, 4, 7, -1}, {0, 2, -1, 0, 1, -1, 1, 2, -1, 3, -1, 1, 3, 4, -1, 5, -1, 4, 5, 6, -1, 4, 7, -1, 0, -1, 0, 1, -1, 2, -1, 2, 3, -1, 4, -1, 5, -1, 4, 6, -1, 7, -1, -100, 0, 1, 2, 3, -1, 1, 1, 3, 4, -1, 2, 4, 5, 6, -1, 2, 4, 7, -1, 1, 0, 1, 2, -1 }}; bool checkingSAVE (ifstream &, const int &); int main(int argc, char **argv) { //to initialize the CTBNDyn ifstream ls("drug.ctbn"); if(!ls.is_open()) { cout << "error: can not find the drug.ctbn file"; return 1; } Markov ctbn; try { ctbn.Load(ls); } catch (const serial::streamexception &e) { cerr << e.what() << endl; exit(-1); } CliqueTree check; //set the random generated value to always be 0 check.IgnoreRand(true); const Dynamics * d = ctbn.GetDynamics(); const CTBNDyn * Dyn = dynamic_cast(d); //build the clique tree check.BuildClique(Dyn); //recieve the clique that been created by //vector of vector test2 = check.ReturnCliques(); //use the receive clique to test the correctness if(test2.size() != 4) { cout << "error: wrong number of clique item" << endl; cout << test2.size() << endl; return 2; } set ::iterator it; int count; for(unsigned int i = 0; i < test2.size(); i++) { vector varlist = test2[i].vars.VarList(); for (unsigned int j=0;j varlist = test2[i].vars.VarList(); for (unsigned int j=0;j> save_testing) { if(save_testing != unit_test_save[j][count_]) { cout << "error: wrong saved data been created" << endl; return false; } count_ ++; } return true; } ctbnrle.1.1.1/tst/maketoroid0000775000175000017500000321547212107036454016304 0ustar csheltoncsheltonELF>[@@ ( @8@@@@@@@@@@   j j,  j j@@DDPtd..H.H44Qtd/lib64/ld-linux-x86-64.so.2GNU GNUh~}_#OW (s3lv' !2az, 0vxz{}TqdTh$m C _Ɂ Ң%yRpyIk)ELPx LЍY@ms:BI?N? 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HuH;ut:HtDHHuXD9tDD$ LwH8[]A\A]A^A_HT$ H $T$j[T$ $HS1ǃH H1HHV,H1HH%H1HHH11A߰cfDʍWHcHcHTHD%HIIL3DtM1LDELDHHptou1ҍHS1ǃH H1HHV,H1HH%H1HH1H*Y5pHp.H@HHHxw JvHÐ@SHHp.HH[qHH\$Hl$HLd$Ll$IH(A0HžGH[tHHrHHHnHHSAE$L$H)XDHH9rHtb11fH4^H9wHt611DL$ȃA$tHHSA$H)HH9rH([]A\A]HHWH)HHtGfW1XHH9uHx H*^HƒHH H*X^fWHHWH)HHt\fW1X HH9uHx%H*^|\\fT^f(HƒHH H*XfWff.HHWf(H)HHfW1f(X HH9uHxEH*f(1^fD HH9\YXu^Qf.z,^HHHH H*XfWf(f(H,$r,$H^fff.AW1AVAUATUHSHxH|$H$1D$HD$PHD$`L$8HD$XT$(\$03+D$(T$0\D$8L$YD$(YfWT$0f.L$D$@^@9jD$ H\$XH;\$`D$(\D$YD$ XD$8fW/D$ AH$D$ Lt$PHH\$XHL)HHNH~'HFL$ HH?HHAf.jI<D$ HDH9H<pHLt$PHD$XIL)HHIXIJ;L@X@f(MM)IfW.IBII?IIM)1HHH HHHz H4f._HGHGL9H|AuILH?LHH9GHRL9fWf(fWh-f(\T$f.xXIMAM)AIIBII?IIM1HHH HHHz H4f._HGHGI9HAuILH?IIL9HL9f(Xd$Hd$ SiD$0f.D$ 4H|$HHt$PB&H|$PHt#iD$ Hx[]A\A]A^A_fDHHHBAIE1H)HHtL<HLLiH|$PKHH@Ht;X ~H@HuH$AH9t ;Z |Dj$H$ HL$IH@Ht;X ~H@HuH$I9A;\$ Et$$Au $tE1AEu $tE1H$PH$HHu-HHRHt;Z ~HRHuH$HH9t;X }!H$@HT$0HƉ\$0D$4E)Dx$D$pEl$$D$H$H$H$fMH|$@AHt$@H$ H|$@L$H$$H]H$E$H9Et|Hu(HH$HU HE(HE@HU0HU8HtGHHE(HfHHQHuHM0 fDHHPHuHE8H$HE@H$HĘ[]A\A]A^A_D1e$0\$0\7HH$lHMH|$@HKH$zH$HKH$H6$H$yKHHffff.AUAATI.HUHSHH|$PHT$ND$0L$8IH|$P+gH\$PHH,jnE1UED$ T$(1$1HD$`HD$pHD$h\$0H|$`T$(L$ D$8jD$H|$hHD$`D$(L$0\D$ T$H9YYD$8\D$T$L_L$Mf(I)gIfW IR\WII?IIMF1f.HqH6HHLBJ<f._IGHGI9HAuILH?IIL9t`HoL9&L\$hLHH|$hHD$`H9HtED9fHĈ[]A\A]@HTH HHJHf.w zDLHQH4II?IIHOBHf.wL9&L\$hA\L$6H1H|$`H^HJH{LMHt$OHyFoH|$PH'FHJffffff.AWI.HAVAUAATIUSHHH|$0HT$.D$FH|$0dH\$0AHH,jAD$1EA\D$D$~lf.ډA9tL@9jT$ <T$YD$LL\L$YL$YL$AXD$XD9uHH[]A\A]A^A_H|$0HEHjIH{KDHt$/H,E2AWAVAUAATUHSH(D$G\GD$~qYD$IA1D$fDD@9jȚ@9jA  CDYD$LH\L$YL$XEXD9uH([]A\A]A^A_fH\$Ld$HHl$Ll$ILt$H(H9tKHNL.H?HCHL)H)HHH9LCLH)HH9w5HHH{HHl$H$Ld$Ll$Lt$ H(DHItH)HDynamicsp0Markov<\pair." valuekeymappairtraj0 2 0 1.0 0.8983018202Trajectory+jGpGN4ctbn16RVCondSimpleCompINS_12MultiZSimpleEEE??time \ var N4ctbn10TrajectoryE)jG G@@Not yet implemented.../hdr/rvsimple.h m="MultiRVRVCondSimpleComp, received end tag for , received start tag for missing end tag for vectrlogz in tag has non-empty contentsRVCompRVSimpleRVCondSimpleisnull and name nelem=vectorimplcardcvnameExtra field of type in object vector::_M_fill_insertnelemContextStream Input Format Error: expected start tag for missing m-parameter in vectr tagStream Input Format Error: missing attribute Stream Input Format Error: tag Stream Input Format Error: expected end tag for Stream Input Format Error: expected start tag, received end tag for Stream Input Format Error: null pointer object for type Stream Input Format Error: expected start tag for subtype of , received start tag for type which is either unknown or not a subtypeStream Input Format Error: not all fields present for N4ctbn7MultiRVE+jG@!H`GA A`A ApA SA@@@@APkA@0A@@@@A@A0@A@@P@@XAAG`@@E@E@pE@E`EEPEEpEEEEEEEEпEE@cAA0GPAAP@+jG@+jvoid ctbn::RVCondSimpleComp::RMult(ctbn::RVSimple*&) const [with RVS = ctbn::MultiZSimple]N6serial15streamexceptionEMarkovDynDynSimpleDynCompN4ctbn9MarkovDynE+jGH GAAoCAC AbC`yCCwCuC{CrCsCkChCPhCpfCgCmCCCiD eCcCpdCcCABN4ctbn9DynSimpleE+j@G H`GBBBmDB2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@ B B0 RVCSCompSSExact Logf: vector::_M_range_check Joint Distribution: []=vector::_M_insert_auxNot yet implemented for this subtype of RVCondSimple.N4ctbn8RVSimpleE+jG HN4ctbn12RVCondSimpleE+jPG HN4ctbn10RVCSCompSSE+jG"H0G"B`$BP!BmD@!B2@%B1B)B2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@'FRBYBpG"B@$Bp!BmD`!B2@@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@2@SBPYBG@'B'B@#B&B(B`4B!B!B`QBpuBN4ctbn2RVE+j`H HpHuBPwBvBmDuB2@2@2@2@zB2@2@2@2@2@2@2@2@2@2@2@2@|BBctbndyn.ccfalseCTBNDynCTBNDynSSdyn!=__nulldynssnodesAvoidJointchvar != -1N4ctbn9CTBNDynSSE+jH"HN4ctbn7CTBNDynE+jH H H@BBЖBBBB BPBЏBpBBPBBPBBBBBBBBBBBBB`B`/CUCHPB0BPBB BB0BB`!CbCBvirtual void ctbn::CTBNDyn::Mult(const ctbn::Dynamics*)virtual ctbn::RVCondSimple* ctbn::CTBNDyn::Cond(double, const ctbn::Instantiation&, const ctbn::Instantiation&, bool) constvoid ctbn::CTBNDynSS::Compact() constvirtual double ctbn::CTBNDynSS::NodeSS(int, int, int, const ctbn::Instantiation&) constDynCompSSdyncomp.ccd!=__nullxx != __nullCondDomain().IsSubset(d->CondDomain())N4ctbn9DynCompSSE+jH"HN4ctbn7DynCompE+jH HHwCpxCpC uCCCPbC gC@CCeCHxC@yCoCuCC}CbC`yCCwCuC{CrCsCkChCPhCpfCgCmCCCiD eCcCpdCcCCCvirtual void ctbn::DynComp::Mult(const ctbn::Dynamics*)virtual void ctbn::DynComp::AddSS(const ctbn::SS*, const ctbn::Dynamics*, ctbn::SS*, double) constbn.ccBNBNSSrvssN4ctbn4BNSSE+j6H"HN4ctbn2BNE+jhHpHHCC@CCpC0CCШCCC`CCCC@C`CC C@C CpCCDDPH0CC`CCpCCCCD&Dvirtual void ctbn::BN::Normalize()virtual void ctbn::BN::Project(const ctbn::Context&, const ctbn::Context&)virtual void ctbn::BN::MultBy(const ctbn::RV*)N4ctbn7ContextE)j HN4ctbn13InstantiationE+j H H H 3D@3D0D@D Hp3D5D1DBDN4ctbn8DynamicsE+j H H H`D@aDbDmD`D2@2@2@cD2@2@2@2@2@2@2@2@2@2@2@2@2@iD2@2@2@2@PmD`mDStreamObjN4ctbn9StreamObjE)j H HmDmDmDmD >\="&'<>\\endstart, received DDDDDDDDDDDDDDDDDDDDDD DDDDD9D9D9D9D9DDD9D9D9DD9D9D9D9D9D9D9D D9D9D9DD9D!D9DDDDpD0DDDpDDDDDDDDDDDDDDDDDDDDDPDDDD0DDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDЇDStream Input Format Error: expected >Stream Input Format Error: tag missing nameStream Input Format Error: unexpected stream endStream Input Format Error: expected <markovsimple.ccm!=__nullDivZeroQ" n="MarkovSimpleMarkovSimpleSSmissing end tag for matrixMarkovSimpleToggleFixScramblexx!=__nulla!=__nullb!=__nullmissing m-parameter in matrix tagmissing n-parameter in matrix tagN4ctbn14MarkovSimpleSSE+jH"HN4ctbn12MarkovSimpleE+jH`GN4ctbn18MarkovSimpleToggleE+j HHHDD@D`DDpD0D@DDpDpDD DDD@DD`DDDD0D DDDжD`D EHDD0DD DD0D0D0D` ED@HDD@D`DDDD@DDpDpDD DDD@DD`DDDD0DDDDжDPDEHpDDDmDDD@DEDCEDD D0D@DPD`DpDMED;E;EDDDDSBPYBHD@DDmDDLE@GEDFEDD D0D@DPD`DpDDD0>Ep=EpEDDDDSBPYB0H`DDDmDD:E@DD0JEDDD D0D@DPD`DpDDBEAE@EDDDSBPYBvirtual void ctbn::MarkovSimple::Mult(const ctbn::DynSimple*)virtual void ctbn::MarkovSimple::AddExpSS(const ctbn::RVSimple*, const ctbn::RVSimple*, double, double, ctbn::SS*, double) constvirtual void ctbn::MarkovSimple::AddExpTransSS(const ctbn::RVSimple*, const ctbn::RVSimple*, double, ctbn::SS*, double) constvirtual void ctbn::MarkovSimple::AddSS(const ctbn::SS*, const ctbn::DynSimple*, const std::vector >&, ctbn::SS*, double) const+j`HpG+jHpG+jHpG+j HpGN4ctbn10CondTransQIRKNS_6matrixEEEN4ctbn16SparseCondTransQINS_6matrixEEEN4ctbn11CondTransQ2INS_6matrixEEEN4ctbn17SparseCondTransQ2INS_6matrixEEEư> basic_string::substr.params-params-listparams => invalid parameters: missing value for vector::_M_range_insert@ v S@ ?V6+WUL;8@͍4sS?־' @Suff Stats NaN:MarkovSSp0ssdssProcessN4ctbn6MarkovE+jbH0.HN4ctbn8MarkovSSE+jH"HH0EE@EE`Ep}E0E}E|EE@EEp~EzE{EHEpEEEE}EpzEzE`E E {Ervcomp.ccrvc!=__nullCondDomain().IsSubset(rvc->CondDomain())N4ctbn6RVCompE+j)!HpH@!H`EEE0EEEpE@E`EEPEEpEEEEEEEEпEE@EEvirtual void ctbn::RVComp::AddSS(const ctbn::SS*, const ctbn::RV*, ctbn::SS*, double) constN4ctbn2SSE+j"H H"HE@EEmD2@E2@2@EEmultisimple.ccms!=__nullMultiSimpleMultiLogSimpleSparseMultiZSimpleMultiSimpleSSlogthetaStream Input Format Error: ivector needs nelem attributeN4ctbn13MultiSimpleSSE+j#H"HN4ctbn11MultiSimpleE+j $H0GN4ctbn14MultiLogSimpleE+j`$H0GN4ctbn12MultiZSimpleE+j$H0GN4ctbn18SparseMultiZSimpleE+j$H$H@$HF0FPF`F#Fp;F%B1B)BAFF*F`E8F@CFFpFE =FF 'FAF0E@!F0GF@ FF'FcF@F$HF FFF#F ;F%B1B)B+FF*FE07F` >&, ctbn::SS*, double) constvirtual void ctbn::MultiLogSimple::AddSS(const ctbn::SS*, const ctbn::RVSimple*, const std::vector >&, ctbn::SS*, double) constvirtual void ctbn::MultiZSimple::AddSS(const ctbn::SS*, const ctbn::RVSimple*, const std::vector >&, ctbn::SS*, double) constvirtual void ctbn::SparseMultiZSimple::AddSS(const ctbn::SS*, const ctbn::RVSimple*, const std::vector >&, ctbn::SS*, double) const??RKEPSBad exit@?:0yE>333333?333333??$I$ɓп?$@àL(?ɿ@C?+.?)iv?څ?ؗҜ<ؗҜ?dy={Gz?MbP??_;==;3?r? @@@9B.?@(\?iW @N4ctbn7ProcessE+j .H H0.HGнGmDmD2@2@2@2@2@2@@GPGExcludeNode.A;455"S<pVzpp}XH 8&@Z 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0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "samplinginf.h" #include "gibbssampler.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include #include "ensurectbn.h" using namespace std; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using gibbs sampling and compare the results with the true ones // if the error is within the threshold, this unittest successes, o.w.error message will pop out namespace { int testing_result = 0; double kEpsilon = 0.02; } #define EXPECT(cond) do { \ if (!(cond)) { \ std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \ << " in " << __func__ \ << ", failed condition is " #cond << std::endl; \ testing_result = 1; \ } \ } while (0) int main(int argc, char **argv) { const char *input_file = "queryinput.data"; addnan(cout); InitParams(argc, argv); ifstream fin(input_file); Markov m; try { m.Load(fin); } catch (const serial::streamexception &e) { cerr << e.what() << endl; exit(-1); } const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics()); Trajectory evid; evid.Load(fin); vector queries; vector ans; while(1) { int qtype; fin >> qtype; if (qtype==-1 || fin.eof()) break; int var, val; double t1; fin >> var >> val; double compans; Instantiation x(ctbndyn->Domain()); x.SetVal(var,val); switch(qtype) { case 0: // filter distribution at point fin >> t1; fin >> compans; // Filtering not implemented! continue; case 1: // smooth distribution at point fin >> t1; queries.push_back(new QueryProb(x,t1)); break; case 2: // expected time query queries.push_back(new QueryTime(x)); break; case 3: // expected # trans query { int val2; fin >> val2; queries.push_back(new QueryTransition(x.KnownVars(),val,val2)); } break; } double a; fin >> a; ans.push_back(a); } fin.close(); SamplingPreInf inf(queries); int numofburniniter = ParamInt("Burnin",1000); int numofsamples = ParamInt("NumSamples",10000); Sampler *sampler; sampler = new GibbsSampler(&m, &evid, numofburniniter); inf.SetSampler(sampler); inf.SetProcess(&m); inf.SetNumSample(numofsamples); for(size_t i=0;i. */ #include #include "graphic.h" #include "markov.h" #include "params.h" #include "ensurectbn.h" #include "bn.h" #include "ctbn.h" #include "ensurectbn.h" using namespace std; using namespace ctbn; int main(int argc, char **argv) { //to initialize or use the params.h class InitParams(argc, argv); //to see if the file opens or not string File; ifstream ls("drug.ctbn"); if(!ls.is_open()) { cout << "drug.ctbn is not presented " << "in current directory. " << endl; return 1; } ifstream output1; ifstream output2; //building the Markov to use in graphic program Markov ctbn; ctbn.Load(ls); //several different file to be written //dot extention for graphviz and eps extention for post-script ofstream file("output_test_ctbn.dot"); //creating the Graphic class name test Graphic test; //setting the font for the trajectory graph test.SetFontTrajectory(16, 10); //drawing the CTBN graph with rank system intergrated test.DrawCTBN(ctbn, file); file.close(); char compare1, compare2; output1.open("unit_test_ctbn.dot"); output2.open("output_test_ctbn.dot"); if(output1.is_open() and output2.is_open()) { while(output1 >> compare1 and output2 >> compare2) { if(compare1 != compare2) { cout << "CTBN graphic it " << "generated is wrong. " << endl; return 2; } } output1.close(); output2.close(); } ofstream testoutput1("output.dot"); ofstream testoutput2("output2.eps"); string filename = "output.dot"; test.DrawCTBNRank(ctbn,testoutput1, filename, testoutput2); const CTBNDyn * B = dynamic_cast(ctbn.GetDynamics()); Context context = Context(); for (int i = 0; i < B->NumofNodes(); ++i) context = context + B->Node(i)->Domain(); //to create the Trajectory::Index Trajectory tr; ls.close(); ls.open("unit_test_traj.graph"); if(!ls.is_open()) { cout << "unit_test_traj.graph is" << "not presented in current directory. " << endl; return 3; } tr.Load(ls); ofstream file2("output_unit_test.eps"); //to draw the Index into trajectory graph test.DrawTrajectory(tr, context, file2); file2.close(); //end of the file output1.open("unit_test_traj.eps"); output2.open("output_unit_test.eps"); if(output1.is_open() and output2.is_open()) { string buffer; while(output1 >> compare1 and output2 >> compare2) { if(compare1 != compare2) { cout << compare1 << " " << compare2 << endl; cout << "Trajectory graphic " << "it generated is wrong. " << endl; return 4; } if(compare1 == '%') { getline(output1, buffer); getline(output2, buffer); } } output1.close(); output2.close(); } else { cout << "Error on trajectory graphic test." << "\ncouldn't find all the necessary file." << endl; return 5; } cout << "PASS" << endl; return 0; } ctbnrle.1.1.1/tst/unit_test_traj.graph0000664000175000017500000000362312107032413020256 0ustar csheltoncshelton <\map> <\pair> <\map> <\pair> <\map> <\pair> <\map> <\pair> <\map> <\pair> <\map> <\pair> <\map> <\pair> <\map> <\pair> <\map> <\Trajectory> ctbnrle.1.1.1/tst/unit_test_traj.eps0000664000175000017500000001311212107032413017736 0ustar csheltoncshelton%!PS-Adobe-3.0 EPSF-3.0 %%Title: CTBN Graphic %%Creator: UCR Research project %%CreationDate: Tue Jan 12 18:37:21 2010 %%BoundingBox: 0 0 816 288 %%EndComments %%Document-Fonts: Times-Roman /Times-Roman findfont 16 scalefont setfont 1 setlinewidth 16 288 newpath moveto 32 288 lineto 32 272 lineto 16 272 lineto closepath gsave 0 0.1 0 setrgbcolor 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newpath moveto (1.801) show 566 110 newpath moveto (2.088) show 599 110 newpath moveto (2.218) show 605 100 newpath moveto (2.238) show 609 90 newpath moveto (2.257) show 631 110 newpath moveto (2.342) show 715 110 newpath moveto (2.671) show 762 110 newpath moveto (2.854) show showpage end ctbnrle.1.1.1/tst/exactfbquery.cc0000664000175000017500000000635412115732667017233 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "exactmarkovinf.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include "ensurectbn.h" #include #include using namespace std; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using exact inference and compare the results with the true ones // if the error is within the threshold, this unittest successes, o.w.error message will pop out namespace { int testing_result = 0; double kEpsilon = 1e-4; } #define EXPECT(cond) do { \ if (!(cond)) { \ std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \ << " in " << __func__ \ << ", failed condition is " #cond << std::endl; \ testing_result = 1; \ } \ } while (0) int main(int argc, char **argv) { const char *input_file = "queryinput.data"; addnan(cout); InitParams(argc, argv); ifstream fin(input_file); Markov m; try { m.Load(fin); } catch (const serial::streamexception &e) { cerr << e.what() << endl; exit(-1); } const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics()); const BN *bn = dynamic_cast(m.GetStartDist()); Trajectory evid; evid.Load(fin); ExactMarkovInf inf; inf.SetProcess(&m); inf.SetTrajectory(&evid); while(1) { int qtype; fin >> qtype; if (qtype==-1 || fin.eof()) break; int var, val; double t1; fin >> var >> val; double compans; Instantiation x(ctbndyn->Domain()); x.SetVal(var,val); switch(qtype) { case 0: // filter distribution at point fin >> t1; compans = inf.Filter(x,t1); break; case 1: // smooth distribution at point fin >> t1; compans = inf.Smooth(x,t1); break; case 2: // expected time query { QueryTime query(x); compans = inf.CalcQuery(query); } break; case 3: // expected # trans query { int val2; fin >> val2; QueryTransition query(x.KnownVars(),val,val2); compans=inf.CalcQuery(query); } break; } double ans; fin >> ans; cout << "ans = " << compans << "\texpected " << ans << endl; EXPECT(fabs(ans-compans)/ans < kEpsilon); } fin.close(); if (testing_result == 0) cout << "PASS exactquery unit test." << endl; return testing_result!=0; } ctbnrle.1.1.1/tst/unit_test_ctbn.dot0000664000175000017500000000140212107032413017722 0ustar csheltoncsheltondigraph g { ordering=out; graph [ rankdir = "LR" ]; node [ fontsize = "16" shape = "eclipse"]; edge [ ]; "node0"[ label = "0" shape = "record" ]; "node1"[ label = "1" shape = "record" ]; "node2"[ label = "2" shape = "record" ]; "node3"[ label = "3" shape = "record" ]; "node4"[ label = "4" shape = "record" ]; "node5"[ label = "5" shape = "record" ]; "node6"[ label = "6" shape = "record" ]; "node7"[ label = "7" shape = "record" ]; "node0" -> "node2" [ dir=back, id = 0]; "node1" -> "node0" [ dir=back, id = 1]; "node2" -> "node1" [ dir=back, id = 2]; "node4" -> "node1" [ dir=back, id = 4]; "node4" -> "node3" [ dir=back, id = 5]; "node6" -> "node4" [ dir=back, id = 7]; "node6" -> "node5" [ dir=back, id = 8]; "node7" -> "node4" [ dir=back, id = 9]; overlap=false } ctbnrle.1.1.1/tst/importancequery.cc0000664000175000017500000000664412115732754017757 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "samplinginf.h" #include "importancesampler.h" #include "expmethod.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include #include "ensurectbn.h" using namespace std; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using impotrance sampling and compare the results with the true ones // if the error is within the threshold, this unittest successes, o.w.error message will pop out namespace { int testing_result = 0; double kEpsilon = 0.02; } #define EXPECT(cond) do { \ if (!(cond)) { \ std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \ << " in " << __func__ \ << ", failed condition is " #cond << std::endl; \ testing_result = 1; \ } \ } while (0) int main(int argc, char **argv) { const char *input_file = "queryinput.data"; addnan(cout); InitParams(argc, argv); ifstream fin(input_file); Markov m; try { m.Load(fin); } catch (const serial::streamexception &e) { cerr << e.what() << endl; exit(-1); } const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics()); Trajectory evid; evid.Load(fin); SamplingInf inf; int numofsamples = ParamInt("NumSamples",100000); Sampler *sampler; ExpMethod method; sampler = new ImportanceSampler(&m, &evid, &method); inf.SetSampler(sampler); inf.SetProcess(&m); inf.SetNumSample(numofsamples); while(1) { int qtype; fin >> qtype; if (qtype==-1 || fin.eof()) break; int var, val; double t1; fin >> var >> val; double compans; Instantiation x(ctbndyn->Domain()); x.SetVal(var,val); switch(qtype) { case 0: // filter distribution at point fin >> t1; fin >> compans; // Filtering not implemented! continue; case 1: // smooth distribution at point fin >> t1; compans = inf.Smooth(x,t1); break; case 2: // expected time query { QueryTime query(x); compans = inf.CalcQuery(query); } break; case 3: // expected # trans query { int val2; fin >> val2; QueryTransition query(x.KnownVars(),val,val2); compans=inf.CalcQuery(query); } break; } double ans; fin >> ans; cout << "ans = " << compans << "\texpected " << ans << endl; EXPECT(fabs(ans-compans)/ans < kEpsilon); } fin.close(); if (testing_result == 0) cout << "PASS importancequery unittest." << endl; return testing_result!=0; } ctbnrle.1.1.1/tst/gibbsauxprequery.cc0000664000175000017500000000712612115732733020122 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "samplinginf.h" #include "gibbsauxsampler.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include #include "ensurectbn.h" using namespace std; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using gibbs sampling and compare the results with the true ones // if the error is within the threshold, this unittest successes, o.w.error message will pop out namespace { int testing_result = 0; double kEpsilon = 0.02; } #define EXPECT(cond) do { \ if (!(cond)) { \ std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \ << " in " << __func__ \ << ", failed condition is " #cond << std::endl; \ testing_result = 1; \ } \ } while (0) int main(int argc, char **argv) { const char *input_file = "queryinput.data"; addnan(cout); InitParams(argc, argv); ifstream fin(input_file); Markov m; try { m.Load(fin); } catch (const serial::streamexception &e) { cerr << e.what() << endl; exit(-1); } const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics()); Trajectory evid; evid.Load(fin); vector queries; vector ans; while(1) { int qtype; fin >> qtype; if (qtype==-1 || fin.eof()) break; int var, val; double t1; fin >> var >> val; double compans; Instantiation x(ctbndyn->Domain()); x.SetVal(var,val); switch(qtype) { case 0: // filter distribution at point fin >> t1; fin >> compans; // Filtering not implemented! continue; case 1: // smooth distribution at point fin >> t1; queries.push_back(new QueryProb(x,t1)); break; case 2: // expected time query queries.push_back(new QueryTime(x)); break; case 3: // expected # trans query { int val2; fin >> val2; queries.push_back(new QueryTransition(x.KnownVars(),val,val2)); } break; } double a; fin >> a; ans.push_back(a); } fin.close(); SamplingPreInf inf(queries); int numofburniniter = ParamInt("Burnin",1000); int numofsamples = ParamInt("NumSamples",50000); Sampler *sampler; sampler = new GibbsAuxSampler(&m, &evid, numofburniniter); inf.SetSampler(sampler); inf.SetProcess(&m); inf.SetNumSample(numofsamples); for(size_t i=0;i. */ #include "markovdyn.h" #include "markov.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "trajectory.h" #include "exactmarkovinf.h" #include "params.h" #include "ctbndyn.h" #include "bn.h" #include "em.h" #include "structure.h" #include "suffstatsquery.h" #include "nonexpsuffstatsquery.h" #include "brutestructuresearch.h" #include "grapheditsearch.h" #include "utils.h" #include "samplinginf.h" #include "expmethod.h" #include "importancesampler.h" #include #include #include using namespace std; using namespace ctbn; namespace { int testing_result = 0; double kEpsilon = 0.1; } #define EXPECT(cond) do { \ if (!(cond)) { \ std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \ << " in " << __func__ \ << ", failed condition is " #cond << std::endl; \ testing_result = 1; \ } \ } while (0) int main(int argc, char **argv) { SetParam("EMLLHImprov","0.05"); SetParam("EMItt","10"); SetParam("VerboseEM","0"); InitParams(argc, argv); randomizer.Reset(11324432); // could get unlucky, but this one works (as do many others) Context nullc; Context eatc, fullc, hungryc, uptakec, concentrationc, barometerc, painc;//, drowsyc; eatc.AddVar(0,2); fullc.AddVar(1,2); hungryc.AddVar(2,2); uptakec.AddVar(3,2); concentrationc.AddVar(4,2); barometerc.AddVar(5,2); painc.AddVar(6,2); // drowsyc.AddVar(7,2); MarkovDyn eat(eatc, hungryc); MarkovDyn full(fullc, eatc); MarkovDyn hungry(hungryc, fullc); MarkovDyn uptake(uptakec, nullc); Context full_and_uptakec(fullc, uptakec); MarkovDyn concentration(concentrationc, full_and_uptakec); MarkovDyn barometer(barometerc, nullc); MarkovDyn pain(painc, Context(barometerc, concentrationc)); // MarkovDyn drowsy(drowsyc, concentrationc); // Generate starting distributions for each variable MultiRV eatrv(eatc, nullc); MultiRV fullrv(fullc, eatc); MultiRV hungryrv(hungryc, eatc); MultiRV uptakerv(uptakec, nullc); MultiRV concentrationrv(concentrationc, uptakec+hungryc); MultiRV barometerrv(barometerc, nullc); MultiRV painrv(painc, barometerc); // MultiRV drowsyrv(drowsyc, concentrationc+painc); //------eat-------- eat(0)->Intensity()[0][0] = -0.01; eat(0)->Intensity()[0][1] = 0.01; eat(0)->Intensity()[1][0] = 10.0; eat(0)->Intensity()[1][1] = -10.0; eat(1)->Intensity()[0][0] = -2.0; eat(1)->Intensity()[0][1] = 2.0; eat(1)->Intensity()[1][0] = 1.0; eat(1)->Intensity()[1][1] = -1.0; vectr peat(2, 0.0); peat[0] = 0.3; peat[1] = 0.7; eatrv[0].SetDist(peat); //------full-------- full(0)->Intensity()[0][0] = -0.02; full(0)->Intensity()[0][1] = 0.02; full(0)->Intensity()[1][0] = 0.3; full(0)->Intensity()[1][1] = -0.3; full(1)->Intensity()[0][0] = -6.0; full(1)->Intensity()[0][1] = 6.0; full(1)->Intensity()[1][0] = 3.01; full(1)->Intensity()[1][1] = -3.01; vectr pfull(2, 0.0); pfull[0] = 0.75; pfull[1] = 0.25; fullrv[0].SetDist(pfull); pfull[0] = 0.55; pfull[1] = 0.45; fullrv[1].SetDist(pfull); //------hungry-------- hungry(0)->Intensity()[0][0] = -2.0; hungry(0)->Intensity()[0][1] = 2.0; hungry(0)->Intensity()[1][0] = 0.01; hungry(0)->Intensity()[1][1] = -0.01; hungry(1)->Intensity()[0][0] = -0.01; hungry(1)->Intensity()[0][1] = 0.01; hungry(1)->Intensity()[1][0] = 0.01; hungry(1)->Intensity()[1][1] = -0.01; vectr phungry(2, 0.0); phungry[0] = 0.35; phungry[1] = 0.65; hungryrv[0].SetDist(phungry); phungry[0] = 0.45; phungry[1] = 0.55; hungryrv[1].SetDist(phungry); //------uptake-------- uptake(0)->Intensity()[0][0] = -0.0; uptake(0)->Intensity()[0][1] = 0.0; uptake(0)->Intensity()[1][0] = 0.5; uptake(0)->Intensity()[1][1] = -0.5; vectr puptake(2, 0.0); puptake[0] = 0.3; puptake[1] = 0.7; uptakerv[0].SetDist(puptake); //------concentration------ Instantiation i(Context(fullc, uptakec)); i.SetVal(3,0); i.SetVal(1,0); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.02; concentration(i)->Intensity()[1][0] = 0.26; concentration(i)->Intensity()[1][1] = -0.26; i.SetVal(3,1); i.SetVal(1,0); concentration(i)->Intensity()[0][0] = -2.01; concentration(i)->Intensity()[0][1] = 2.01; concentration(i)->Intensity()[1][0] = 1.01; concentration(i)->Intensity()[1][1] = -1.01; i.SetVal(3,0); i.SetVal(1,1); concentration(i)->Intensity()[0][0] = -0.01; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.25; i.SetVal(3,1); i.SetVal(1,1); concentration(i)->Intensity()[0][0] = -1.01; concentration(i)->Intensity()[0][1] = 1.01; concentration(i)->Intensity()[1][0] = 2.01; concentration(i)->Intensity()[1][1] = -2.01; vectr pconcentration(2, 0.0); pconcentration[0] = 0.7; pconcentration[1] = 0.3; concentrationrv[0].SetDist(pconcentration); pconcentration[0] = 0.6; pconcentration[1] = 0.4; concentrationrv[1].SetDist(pconcentration); pconcentration[0] = 0.8; pconcentration[1] = 0.2; concentrationrv[2].SetDist(pconcentration); pconcentration[0] = 0.5; pconcentration[1] = 0.5; concentrationrv[3].SetDist(pconcentration); //------barometer------ barometer(0)->Intensity()[0][0] = -0.2; barometer(0)->Intensity()[0][1] = 0.2; barometer(0)->Intensity()[1][0] = 0.1; barometer(0)->Intensity()[1][1] = -0.1; vectr pbarometer(2, 0.0); pbarometer[0] = 0.2; pbarometer[0] = 0.8; barometerrv[0].SetDist(pbarometer); //------pain------ Instantiation j(Context(barometerc, concentrationc)); j.SetVal(4,0); j.SetVal(5,0); pain(j)->Intensity()[0][0] = -6.0; pain(j)->Intensity()[0][1] = 6.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(4,1); j.SetVal(5,0); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(4,0); j.SetVal(5,1); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(4,1); j.SetVal(5,1); pain(j)->Intensity()[0][0] = -0.3; pain(j)->Intensity()[0][1] = 0.3; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; vectr ppain(2, 0.0); ppain[0] = 0.1; ppain[1] = 0.9; painrv[0].SetDist(ppain); ppain[0] = 0.3; ppain[1] = 0.7; painrv[1].SetDist(ppain); /* //------drowsy------ drowsy(0)->Intensity()[0][0] = -0.17; drowsy(0)->Intensity()[0][1] = 0.17; drowsy(0)->Intensity()[1][0] = 0.5; drowsy(0)->Intensity()[1][1] = -0.5; drowsy(1)->Intensity()[0][0] = -0.33; drowsy(1)->Intensity()[0][1] = 0.33; drowsy(1)->Intensity()[1][0] = 0.33; drowsy(1)->Intensity()[1][1] = -0.33; drowsy(2)->Intensity()[0][0] = -1.0; drowsy(2)->Intensity()[0][1] = 1.0; drowsy(2)->Intensity()[1][0] = 0.1; drowsy(2)->Intensity()[1][1] = -0.1; vectr pdrowsy(2, 0.0); pdrowsy[0] = 0.8; pdrowsy[1] = 0.2; drowsyrv[0].SetDist(pdrowsy); pdrowsy[0] = 0.7; pdrowsy[1] = 0.3; drowsyrv[1].SetDist(pdrowsy); pdrowsy[0] = 0.3; pdrowsy[1] = 0.7; drowsyrv[2].SetDist(pdrowsy); pdrowsy[0] = 0.4; pdrowsy[1] = 0.6; drowsyrv[3].SetDist(pdrowsy); */ CTBNDyn ctbndyn; ctbndyn.AddNode(eat.Clone()); ctbndyn.AddNode(full.Clone()); ctbndyn.AddNode(hungry.Clone()); ctbndyn.AddNode(uptake.Clone()); ctbndyn.AddNode(concentration.Clone()); ctbndyn.AddNode(barometer.Clone()); ctbndyn.AddNode(pain.Clone()); // ctbndyn.AddNode(drowsy.Clone()); BN bn; bn.AddNode(eatrv.Clone()); bn.AddNode(fullrv.Clone()); bn.AddNode(hungryrv.Clone()); bn.AddNode(uptakerv.Clone()); bn.AddNode(concentrationrv.Clone()); bn.AddNode(barometerrv.Clone()); bn.AddNode(painrv.Clone()); // bn.AddNode(drowsyrv.Clone()); Markov m(bn.Clone(), ctbndyn.Clone()); //Building independent structure MultiRV eatrvi(eatc, nullc); MultiRV fullrvi(fullc, nullc); MultiRV hungryrvi(hungryc, nullc); MultiRV uptakervi(uptakec, nullc); MultiRV concentrationrvi(concentrationc, nullc); MultiRV barometerrvi(barometerc, nullc); MultiRV painrvi(painc, nullc); // MultiRV drowsyrvi(drowsyc, nullc); BN bnindep; bnindep.AddNode(eatrvi.Clone()); bnindep.AddNode(fullrvi.Clone()); bnindep.AddNode(hungryrvi.Clone()); bnindep.AddNode(uptakervi.Clone()); bnindep.AddNode(concentrationrvi.Clone()); bnindep.AddNode(barometerrvi.Clone()); bnindep.AddNode(painrvi.Clone()); // bnindep.AddNode(drowsyrvi.Clone()); MarkovDyn eatindep(eatc, nullc); MarkovDyn fullindep(fullc, nullc); MarkovDyn hungryindep(hungryc, nullc); MarkovDyn uptakeindep(uptakec, nullc); MarkovDyn concentrationindep(concentrationc, nullc); MarkovDyn barometerindep(barometerc, nullc); MarkovDyn painindep(painc, nullc); // MarkovDyn drowsyindep(drowsyc, nullc); CTBNDyn ctbndynindep; ctbndynindep.AddNode(eatindep.Clone()); ctbndynindep.AddNode(fullindep.Clone()); ctbndynindep.AddNode(hungryindep.Clone()); ctbndynindep.AddNode(uptakeindep.Clone()); ctbndynindep.AddNode(concentrationindep.Clone()); ctbndynindep.AddNode(barometerindep.Clone()); ctbndynindep.AddNode(painindep.Clone()); // ctbndynindep.AddNode(drowsyindep.Clone()); Markov mindep(bnindep.Clone(), ctbndynindep.Clone()); // mindep.Scramble(); //============= //STRUCTURE SEARCH TESTING //============= int NUM_TRAJ = ParamInt("numtraj", 500); double TRAJ_LEN = ParamDouble("tlen", 10.0); double loss = ParamDouble("loss", 0.25); double factor = loss/0.25; const Context& con = ctbndyn.Domain(); int numNodes = ctbndyn.NumofNodes(); //Generate trajectories for training vector trVec; if (ParamInt("TrajFromInput",0)) { cin >> trVec; } else { for(int i=0; i(sem_ctbn)->Scramble(); Structure sABN; Structure sACTBN; Structure sLBN; Structure sLCTBN; bn.GetStructure(sABN); ctbndyn.GetStructure(sACTBN); ExactMarkovInf inf; SEM(trVec,sem_ctbn,&inf); Markov* learned = dynamic_cast (sem_ctbn); const CTBNDyn* learnedCTBN = dynamic_cast (learned->GetDynamics()); learnedCTBN->GetStructure(sLCTBN); sACTBN.Print(cout); cout << endl; sLCTBN.Print(cout); cout << endl; EXPECT(sACTBN.HammingDist(sLCTBN)==0); if (testing_result==0) cout << "PASS sem unittest." << endl; return testing_result!=0; } ctbnrle.1.1.1/tst/unit_test.py0000664000175000017500000000435412107032413016567 0ustar csheltoncshelton#! /usr/bin/python # Continuous Time Bayesian Network Reasoning and Learning Engine # Copyright (C) 2009 The Regents of the University of California # # Authored by Yu Fan, William Lam, Joon Lee, Christian Shelton, and Jing Xu # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . import os; # execute the Makefile os.system("make") # Run the EXES the file from EXES= in Makefile #open the Makefile file to read filename = "Makefile" FILE = open(filename, "r") #start the loop while 1: #read one line from the file sLine = FILE.readline() #check if the EXES= is in the line just being passed in if sLine.startswith("EXES="): while 1: #seperate the every word by space thisline = sLine.split(" ") #go through the array of string for element in thisline: # if it isn't EXES= run those file if "EXES=" not in element: if '\\' not in element: if " " not in element: #take out new line oPipe = os.popen("./" + element.strip('\n')) #start other loop while 1: #read the out put of the file osLine = oPipe.read() #check if pass is in the output first line if "PASS" in osLine: print "\n" + element.strip('\n') + " has PASSED." break else: print "\n" + element.strip('\n') + " has FAILED." print osLine #output everything form the program for osLine in oPipe.readlines(): print osLine break #read one more time if the sLine end with \\ if '\\' not in sLine: sline = "" break else: sLine = FILE.readline() #same as eof() if len(sLine) is 0: break #new line print "\n" #os.system("make clean") ctbnrle.1.1.1/tst/queries.txt0000664000175000017500000053167412107032413016427 0ustar csheltoncshelton288 0.014112 0.000392 0.015876 0.000588 0.003528 0.000196 0.000756 0.00042 0.013608 0.00126 0.002268 8.4e-05 0.000252 8.4e-05 0.000756 0.000336 0.00126 0.000224 0.000672 0.001512 0.000756 0.002268 0.000168 0.000756 3.6e-05 0.00162 0.000648 0.00486 0.000108 0.000324 1.2e-05 0.000324 3.6e-05 0.001296 6e-05 0.000864 0.002016 5.6e-05 0.002268 8.4e-05 0.000504 2.8e-05 0.001008 0.00056 0.018144 0.00168 0.003024 0.000112 0.000504 0.000168 0.001512 0.000672 0.00252 0.000448 9.6e-05 0.000216 0.000108 0.000324 2.4e-05 0.000108 4.8e-05 0.00216 0.000864 0.00648 0.000144 0.000432 2.4e-05 0.000648 7.2e-05 0.002592 0.00012 0.001728 0.002016 5.6e-05 0.002268 8.4e-05 0.000504 2.8e-05 0.000504 0.00028 0.009072 0.00084 0.001512 5.6e-05 0.000756 0.000252 0.002268 0.001008 0.00378 0.000672 9.6e-05 0.000216 0.000108 0.000324 2.4e-05 0.000108 2.4e-05 0.00108 0.000432 0.00324 7.2e-05 0.000216 3.6e-05 0.000972 0.000108 0.003888 0.00018 0.002592 0.002016 5.6e-05 0.002268 8.4e-05 0.000504 2.8e-05 0.000252 0.00014 0.004536 0.00042 0.000756 2.8e-05 0.001008 0.000336 0.003024 0.001344 0.00504 0.000896 9.6e-05 0.000216 0.000108 0.000324 2.4e-05 0.000108 1.2e-05 0.00054 0.000216 0.00162 3.6e-05 0.000108 4.8e-05 0.001296 0.000144 0.005184 0.00024 0.003456 0.008064 0.000224 0.009072 0.000336 0.002016 0.000112 0.001008 0.00056 0.018144 0.00168 0.003024 0.000112 0.001008 0.000336 0.003024 0.001344 0.00504 0.000896 0.000384 0.000864 0.000432 0.001296 9.6e-05 0.000432 4.8e-05 0.00216 0.000864 0.00648 0.000144 0.000432 4.8e-05 0.001296 0.000144 0.005184 0.00024 0.003456 0.016128 0.000448 0.018144 0.000672 0.004032 0.000224 0.002016 0.00112 0.036288 0.00336 0.006048 0.000224 0.001008 0.000336 0.003024 0.001344 0.00504 0.000896 0.000768 0.001728 0.000864 0.002592 0.000192 0.000864 9.6e-05 0.00432 0.001728 0.01296 0.000288 0.000864 4.8e-05 0.001296 0.000144 0.005184 0.00024 0.003456 0.04032 0.00112 0.04536 0.00168 0.01008 0.00056 0.004032 0.00224 0.072576 0.00672 0.012096 0.000448 0.001008 0.000336 0.003024 0.001344 0.00504 0.000896 0.00192 0.00432 0.00216 0.00648 0.00048 0.00216 0.000192 0.00864 0.003456 0.02592 0.000576 0.001728 4.8e-05 0.001296 0.000144 0.005184 0.00024 0.003456 0.016128 0.000448 0.018144 0.000672 0.004032 0.000224 0.003024 0.00168 0.054432 0.00504 0.009072 0.000336 0.007056 0.002352 0.021168 0.009408 0.03528 0.006272 0.000768 0.001728 0.000864 0.002592 0.000192 0.000864 0.000144 0.00648 0.002592 0.01944 0.000432 0.001296 0.000336 0.009072 0.001008 0.036288 0.00168 0.024192 288 288 -13.35 2 2 0 3 0 0.5 0 0 0 0 0 1.5 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 0 0 0 0 0 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warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include "ensurectbn.h" #include #include using namespace std; using namespace ctbn; class myBN : public BN { public: myBN(const BN &b) : BN(b) { } RV *J() { return Joint(Context()); } }; int main(int argc, char **argv) { const char *input_file = "queryinput.data"; ifstream fin(input_file); Markov m; try { m.Load(fin); } catch (const serial::streamexception &e) { cerr << e.what() << endl; exit(-1); } const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics()); const BN *bn = dynamic_cast(m.GetStartDist()); Trajectory evid; evid.Load(fin); myBN mybn(*bn); vectr p0; double lf; const MultiZSimple &dist = (dynamic_cast(mybn.J()))->operator[](0); dist.GetDist(p0,lf); p0 *= exp(lf); cout << p0 << endl; cout << ctbndyn->JointMatrix() << endl; int c = 0; for(Trajectory::Index i=evid.Begin(ctbndyn->Domain());!i.Done();++i) c++; cout << c << endl; Trajectory::Index i=evid.Begin(ctbndyn->Domain()); int type =0; for(;!i.Done();type=i.TestInc(ctbndyn->Domain())) { cout << i.Time() << ' ' << (type>1) << ' '; vector l; ctbndyn->Domain().ConsistentIndexes(l,i.Values()); cout << l << endl; } cout << i.Time() << endl; // one last time at the end while(1) { int qtype; fin >> qtype; if (qtype==-1 || fin.eof()) break; int var, val; double t1; fin >> var >> val; double compans; Instantiation x(ctbndyn->Domain()); x.SetVal(var,val); vector l; ctbndyn->Domain().ConsistentIndexes(l,x); switch(qtype) { case 0: // filter distribution at point fin >> t1; cout << "0 " << l << t1 << endl; break; case 1: // smooth distribution at point fin >> t1; cout << "1 " << l << t1 << endl; break; case 2: // expected time query cout << "2 " << l << endl; break; case 3: // expected # trans query { int val2; fin >> val2; vector l2; Instantiation x2(x); x2.SetVal(var,val2); ctbndyn->Domain().ConsistentIndexes(l2,x2); cout << "3 " << l << l2 << endl; } break; } double ans; fin >> ans; } fin.close(); return 0; } ctbnrle.1.1.1/tst/decision.cc0000664000175000017500000004011612115732663016314 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "ctmdp.h" #include "params.h" #include using namespace std; using namespace ctbn; namespace { int testing_result = 0; double kEpsilon = 0.1; } #define EXPECT(cond,extra) do { \ if (!(cond)) { \ std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \ << " in " << __func__ \ << ", failed condition is " #cond << extra << std::endl; \ testing_result = 1; \ } \ } while (0) const int N = 8; // num nodes double ansexactw[256] = {9.63254, 7.78596, 8.56606, 6.92004, 8.68593, 6.84863, 7.79335, 6.21898, 8.71317, 6.84798, 7.66402, 6.04073, 7.94439, 6.17403, 7.11453, 5.56483, 8.71934, 6.85008, 7.63544, 5.99726, 7.79371, 5.9798, 6.91784, 5.35789, 7.98393, 6.18008, 6.99364, 5.39153, 7.28304, 5.5265, 6.46227, 4.92072, 8.71317, 6.84798, 7.62066, 5.98461, 7.74831, 5.92406, 6.86627, 5.29874, 7.8158, 5.9776, 6.78361, 5.17858, 7.08911, 5.31502, 6.24866, 4.70518, 7.98393, 6.18008, 6.95698, 5.34555, 7.11126, 5.32542, 6.25229, 4.70858, 7.31803, 5.52945, 6.33366, 4.7445, 6.61752, 4.87583, 5.80069, 4.27223, 8.68593, 6.84863, 7.58533, 5.9816, 7.70154, 5.90771, 6.82691, 5.2833, 7.74831, 5.92406, 6.69831, 5.1208, 7.02011, 5.25996, 6.17874, 4.64948, 7.79371, 5.9798, 6.72206, 5.13547, 6.87472, 5.11288, 6.01472, 4.49543, 7.11126, 5.32542, 6.09684, 4.53429, 6.41022, 4.6711, 5.57471, 4.0628, 7.94439, 6.17403, 6.90709, 5.3291, 7.02011, 5.25996, 6.14704, 4.63857, 7.08911, 5.31502, 6.0813, 4.52592, 6.34305, 4.64726, 5.53194, 4.04623, 7.28304, 5.5265, 6.26612, 4.69796, 6.41022, 4.6711, 5.55738, 4.05688, 6.61752, 4.87583, 5.64269, 4.09637, 5.91752, 4.22173, 5.10641, 3.6207, 8.56606, 6.92004, 7.45872, 6.04905, 7.58533, 5.9816, 6.74717, 5.36902, 7.62066, 5.98461, 6.57816, 5.18286, 6.90709, 5.3291, 6.09544, 4.72711, 7.63544, 5.99726, 6.54549, 5.14626, 6.72206, 5.13547, 5.89556, 4.52845, 6.95698, 5.34555, 5.93673, 4.55201, 6.26612, 4.69796, 5.45883, 4.0977, 7.66402, 6.04073, 6.57816, 5.18286, 6.69831, 5.1208, 5.86724, 4.51148, 6.78361, 5.17858, 5.75636, 4.38422, 6.0813, 4.52592, 5.27214, 3.92601, 6.99364, 5.39153, 5.93673, 4.55201, 6.09684, 4.53429, 5.27727, 3.92971, 6.33366, 4.7445, 5.32255, 3.95552, 5.64269, 4.09637, 4.84004, 3.49823, 7.79335, 6.21898, 6.74717, 5.36902, 6.82691, 5.2833, 5.97464, 4.66736, 6.86627, 5.29874, 5.86724, 4.51148, 6.14704, 4.63857, 5.33914, 4.03847, 6.91784, 5.35789, 5.89556, 4.52845, 6.01472, 4.49543, 5.18629, 3.88829, 6.25229, 4.70858, 5.27727, 3.92971, 5.55738, 4.05688, 4.75246, 3.45796, 7.11453, 5.56483, 6.09544, 4.72711, 6.17874, 4.64948, 5.33914, 4.03847, 6.24866, 4.70518, 5.27214, 3.92601, 5.53194, 4.04623, 4.73588, 3.45034, 6.46227, 4.92072, 5.45883, 4.0977, 5.57471, 4.0628, 4.75246, 3.45796, 5.80069, 4.27223, 4.84004, 3.49823, 5.10641, 3.6207, 4.31035, 3.02481}; double ansapproxw[9] = {1.33261, 0.87038, 0.842329, 0.842329, 0.842329, 0.842329, 0.842329, 0.87038, 3.13556}; double ansapprox2w[25] = {1.30476, 0.627514, 0.563126, 0.463021, 0.425532, 0.463021, 0.563126, 0.627514, 3.07002, 0.210335, 0.0389265, 0.122746, 0.0364177, 0.17467, 0.0477463, 0.189393, 0.0349903, 0.189393, 0.0477463, 0.17467, 0.0364177, 0.122746, 0.0389265, 0.210335, 0.0539879}; int ansexactpi[256][2] = { {8,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,-1}, {0,-1}, {3,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,-1}, {0,-1}, {4,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,4}, {0,-1}, {8,-1}, {0,-1}, {1,-1}, {0,-1}, {8,-1}, {0,-1}, {1,-1}, {0,-1}, {5,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {5,-1}, {0,-1}, {3,5}, {0,-1}, {1,-1}, {0,-1}, {5,-1}, {0,-1}, {5,-1}, {0,-1}, {8,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,-1}, {0,-1}, {8,-1}, {0,-1}, {1,-1}, {0,-1}, {8,-1}, {0,-1}, {1,-1}, {0,-1}, {6,-1}, {0,-1}, {1,-1}, {0,-1}, {2,6}, {0,-1}, {6,-1}, {0,-1}, {6,-1}, {0,-1}, {1,-1}, {0,-1}, {6,-1}, {0,-1}, {6,-1}, {0,-1}, {6,-1}, {0,-1}, {1,-1}, {0,-1}, {2,6}, {0,-1}, {6,-1}, {0,-1}, {6,-1}, {0,-1}, {1,-1}, {0,-1}, {6,-1}, {0,-1}, {6,-1}, {0,-1}, {6,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,-1}, {0,-1}, {3,-1}, {0,-1}, {1,-1}, {0,-1}, {8,-1}, {0,-1}, {1,-1}, {0,-1}, {8,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,-1}, {0,-1}, {8,-1}, {0,-1}, {1,-1}, {0,-1}, {8,-1}, {0,-1}, {1,-1}, {0,-1}, {7,-1}, {0,-1}, {1,7}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {1,7}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {1,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {1,7}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {1,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {1,7}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,7}, {0,-1}, {3,-1}, {0,-1}, {1,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {4,7}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,7}, {0,-1}, {7,-1}, {0,-1}, {1,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,-1}, {0,-1}, {3,-1}, {0,-1}, {1,-1}, {0,-1}, {7,-1}, {0,-1}, {1,7}, {0,-1}, {7,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,-1}, {0,-1}, {7,-1}, {0,-1}, {1,-1}, {0,-1}, {7,-1}, {0,-1}, {1,7}, {0,-1} }; int ansapproxpi[256][5] = { {8,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {3,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,3,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {4,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,4,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {3,4,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,3,4,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {5,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,5,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {3,5,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,3,5,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {4,5,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,4,5,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {3,4,5,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,3,4,5,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {6,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,6,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {3,6,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,3,6,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {4,6,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,4,6,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {3,4,6,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,3,4,6,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {5,6,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,5,6,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {3,5,6,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,3,5,6,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {4,5,6,-1,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,4,5,6,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {3,4,5,6,-1}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {2,3,4,5,6}, {0,-1,-1,-1,-1}, {1,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1}, {7,-1,-1,-1,-1}, {0,-1,-1,-1,-1}, {1,7,-1,-1,-1}, {0,-1,-1,-1,-1} }; int ansapprox2pi[256][2] = { {8,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,-1}, {0,-1}, {3,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,-1}, {0,-1}, {4,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,-1}, {0,-1}, {8,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,-1}, {0,-1}, {5,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {5,-1}, {0,-1}, {3,5}, {0,-1}, {1,-1}, {0,-1}, {5,-1}, {0,-1}, {5,-1}, {0,-1}, {8,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,-1}, {0,-1}, {3,5}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,-1}, {0,-1}, {6,-1}, {0,-1}, {1,-1}, {0,-1}, {2,6}, {0,-1}, {6,-1}, {0,-1}, {6,-1}, {0,-1}, {1,-1}, {0,-1}, {6,-1}, {0,-1}, {6,-1}, {0,-1}, {6,-1}, {0,-1}, {1,-1}, {0,-1}, {2,6}, {0,-1}, {6,-1}, {0,-1}, {6,-1}, {0,-1}, {1,-1}, {0,-1}, {6,-1}, {0,-1}, {6,-1}, {0,-1}, {6,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,-1}, {0,-1}, {3,-1}, {0,-1}, {1,-1}, {0,-1}, {2,6}, {0,-1}, {1,-1}, {0,-1}, {6,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,-1}, {0,-1}, {6,-1}, {0,-1}, {1,-1}, {0,-1}, {2,6}, {0,-1}, {1,-1}, {0,-1}, {7,-1}, {0,-1}, {1,7}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {1,7}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {1,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {1,7}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {1,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {1,7}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {7,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,7}, {0,-1}, {3,-1}, {0,-1}, {1,-1}, {0,-1}, {7,-1}, {0,-1}, {1,7}, {0,-1}, {7,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {4,-1}, {0,-1}, {7,-1}, {0,-1}, {1,-1}, {0,-1}, {7,-1}, {0,-1}, {1,7}, {0,-1}, {7,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,7}, {0,-1}, {3,-1}, {0,-1}, {1,-1}, {0,-1}, {7,-1}, {0,-1}, {1,7}, {0,-1}, {7,-1}, {0,-1}, {1,-1}, {0,-1}, {2,-1}, {0,-1}, {1,7}, {0,-1}, {7,-1}, {0,-1}, {1,-1}, {0,-1}, {7,-1}, {0,-1}, {1,7}, {0,-1} }; vector fullvalues(const vector &w, const vector &bases, const Context &allvars) { vector ret; Instantiation x(allvars,0); do { double v = 0; for(unsigned int i=0;i> ret; return ret; } template void check(const vector &res, int ans[][N], size_t len1, string extra) { EXPECT(len1==res.size(),extra); for(size_t i=0;i &res, double *ans, size_t len, string extra) { EXPECT(len==res.size(),extra); for(size_t i=0;i con; Context allvars; for(int i=0;i r; for(int i=0;i2?4:2);p++) { double q = 0.5+0.5*(((p&1)!=0) + ((p&2)!=0)); x(p)->Intensity()[1][1] = (i==n) ? -2 : 0; x(p)->Intensity()[1][0] = (i==n) ? 2 : 0; x(p)->Intensity()[0][1] = q; x(p)->Intensity()[0][0] = -q; } dyn.AddNode(x.Clone()); } if (n==N) { // no-op action gets extra reward CTMDP::Factor f; f.var = Context(); f.val.push_back(1.5); r.push_back(f); } prob.AddAction(dyn.Clone(),r); } vector bases; for(int i=0;i exact = prob.SolveExact(&lpsolver); check(exact,ansexactw,256," exactw"); vector exactpi = prob.Policy(exact); check(exactpi,ansexactpi,256," exactpi"); vector approxslow = prob.SolveApprox_exp(&lpsolver); check(approxslow,ansapproxw,9," approxw (slow)"); vector approxslowpi = prob.Policy(approxslow,true); check(approxslowpi,ansapproxpi,256," approxpi (slow)"); vector approx = prob.SolveApprox(&lpsolver); check(approx,ansapproxw,9," approxw"); vector approxpi = prob.Policy(approx,true); check(approxpi,ansapproxpi,256," approxpi"); for(int i=0;i approx2slow = prob.SolveApprox_exp(&lpsolver); check(approx2slow,ansapprox2w,25," approx2w (slow)"); vector approx2slowpi = prob.Policy(approx2slow,true); check(approx2slowpi,ansapprox2pi,256," approx2pi (slow)"); vector approx2 = prob.SolveApprox(&lpsolver); check(approx2,ansapprox2w,25," approx2w"); vector approx2pi = prob.Policy(approx2,true); check(approx2pi,ansapprox2pi,256," approx2pi"); if (testing_result==0) cout << "PASS decision unittest." << endl; return testing_result!=0; } ctbnrle.1.1.1/tst/makequeryinput.cc0000664000175000017500000002411512115732765017606 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include #include "ensurectbn.h" #include using namespace std; using namespace ctbn; // this file is used to create an example CTBN model and a partial trajectory sampled from the model // the model, the evidence, and the true results of queries will be saved to a file "queryinput.data" // "queryinput.data" will be feeded as an input to: // exactquery.cc, importancequery.cc and gibbsquery.cc // the above three .cc file will do the queries, and compare the results with the true ones in "queryinput.data" // the queries will be specified in these three files // In case you want to change the example model, you can modify this file and rewrite the output to "queryinput.data" // and then do the exactquery.cc/importancequery.cc/gibbsquery.cc /* * Network of variable A-G (with non-consecutive numbers to test code) * * D<-\ * | | * v | * A->C<-B * | * v * F->E->G * * Initial BN looks like * D * | * v * A->C<-B * | * v * F->E->G * */ const int avar=11,bvar=14,cvar=32,dvar=33,evar=34,fvar=45,gvar=50; //const int avar=0,bvar=1,cvar=2,dvar=3,evar=4,fvar=5,gvar=6; Instantiation assign(const Context &c, ...) { Instantiation ret(c); va_list args; va_start(args,c); int vnum; while((vnum=va_arg(args,int))>=0) { int val = va_arg(args,int); ret.SetVal(vnum,val); } va_end(args); return ret; } vectr v(int nel, ...) { vectr ret(nel); va_list args; va_start(args,nel); for(int i=0;iIntensity() << -2, 2, 1, -1; ADyn(assign(D,dvar,1,-1))->Intensity() << -0.5, 0.5, 3, -3; BDyn(0)->Intensity() << -5, 2, 3, 1, -2, 1, 0.5, 0.5, -1; CDyn(assign(A+B,avar,0,bvar,0,-1))->Intensity() << -2, 0.5, 1.5, 0.2, -0.3, 0.1, 0.1, 0.3, -0.4; CDyn(assign(A+B,avar,1,bvar,0,-1))->Intensity() << -3, 0.5, 2.5, 0.2, -1.3, 1.1, 1.1, 0.3, -1.4; CDyn(assign(A+B,avar,0,bvar,1,-1))->Intensity() << -0.5, 0.2, 0.3, 0.1, -0.2, 0.1, .1, 0.1, -0.2; CDyn(assign(A+B,avar,1,bvar,1,-1))->Intensity() << -1.5, 0.2, 1.3, 0.1, -1.2, 1.1, 1.1, 0.1, -1.2; CDyn(assign(A+B,avar,0,bvar,2,-1))->Intensity() << -1, 0.5, 0.5, 0.1, -0.2, 0.1, 1.0, 1.0, -2.0; CDyn(assign(A+B,avar,1,bvar,2,-1))->Intensity() << -2, 0.5, 1.5, 0.1, -1.2, 1.1, 2.0, 1.0, -3.0; DDyn(assign(C,cvar,0,-1))->Intensity() << -4, 4, 0.1, -0.1; DDyn(assign(C,cvar,1,-1))->Intensity() << -2, 2, 1.5, -1.5; DDyn(assign(C,cvar,2,-1))->Intensity() << -0.1, 0.1, 4.5, -4.5; EDyn(assign(C+F,cvar,0,fvar,0,-1))->Intensity() << -0.3, 0.1, 0.1, 0.1, 0.1, -0.3, 0.1, 0.1, 0.1, 0.1, -0.3, 0.1, 0.1, 0.1, 0.1, -0.3; EDyn(assign(C+F,cvar,1,fvar,0,-1))->Intensity() << -0.7, 0.3, 0.3, 0.1, 0.1, -0.6, 0.4, 0.1, 0.1, 0.4, -0.6, 0.1, 0.1, 0.3, 0.3, -0.7; EDyn(assign(C+F,cvar,2,fvar,0,-1))->Intensity() << -0.6, 0.1, 0.1, 0.4, 0.3, -0.7, 0.1, 0.3, 0.3, 0.1, -0.7, 0.3, 0.4, 0.1, 0.1, -0.6; EDyn(assign(C+F,cvar,0,fvar,1,-1))->Intensity() << -0.6, 0.1, 0.1, 0.4, 0.1, -0.6, 0.1, 0.4, 0.1, 0.1, -0.6, 0.4, 0.1, 0.1, 0.1, -0.3; EDyn(assign(C+F,cvar,1,fvar,1,-1))->Intensity() << -0.7, 0.3, 0.3, 0.1, 0.4, -0.9, 0.4, 0.1, 0.4, 0.4, -0.9, 0.1, 0.4, 0.3, 0.3, -1.0; EDyn(assign(C+F,cvar,2,fvar,1,-1))->Intensity() << -1.0, 0.3, 0.3, 0.4, 0.3, -0.7, 0.1, 0.3, 0.3, 0.1, -0.7, 0.3, 0.4, 0.3, 0.3, -1.0; FDyn(0)->Intensity() << -0.05, 0.05, 0.07, -0.07; GDyn(assign(E,evar,0,-1))->Intensity() << -3, 3, 0.5, -0.5; GDyn(assign(E,evar,1,-1))->Intensity() << -2, 2, 1, -1; GDyn(assign(E,evar,2,-1))->Intensity() << -1, 1, 2, -2; GDyn(assign(E,evar,3,-1))->Intensity() << -0.5, 0.5, 3, -3; Ap0[assign(D,dvar,0,-1)].SetDist(v(2,0.9,0.1)); Ap0[assign(D,dvar,1,-1)].SetDist(v(2,0.1,0.9)); Bp0[0].SetDist(v(3,0.2,0.6,0.2)); Cp0[assign(A+B,avar,0,bvar,0,-1)].SetDist(v(3,0.8,0.1,0.1)); Cp0[assign(A+B,avar,1,bvar,0,-1)].SetDist(v(3,0.2,0.5,0.3)); Cp0[assign(A+B,avar,0,bvar,1,-1)].SetDist(v(3,0.3,0.6,0.1)); Cp0[assign(A+B,avar,1,bvar,1,-1)].SetDist(v(3,0.1,0.5,0.4)); Cp0[assign(A+B,avar,0,bvar,2,-1)].SetDist(v(3,0.2,0.3,0.5)); Cp0[assign(A+B,avar,1,bvar,2,-1)].SetDist(v(3,0.1,0.1,0.8)); Dp0[0].SetDist(v(2,0.7,0.3)); Ep0[assign(C+F,cvar,0,fvar,0,-1)].SetDist(v(4,0.7,0.1,0.1,0.1)); Ep0[assign(C+F,cvar,1,fvar,0,-1)].SetDist(v(4,0.3,0.4,0.2,0.1)); Ep0[assign(C+F,cvar,2,fvar,0,-1)].SetDist(v(4,0.1,0.2,0.3,0.4)); Ep0[assign(C+F,cvar,0,fvar,1,-1)].SetDist(v(4,0.1,0.2,0.5,0.2)); Ep0[assign(C+F,cvar,1,fvar,1,-1)].SetDist(v(4,0.1,0.2,0.4,0.3)); Ep0[assign(C+F,cvar,2,fvar,1,-1)].SetDist(v(4,0.1,0.1,0.1,0.7)); Fp0[0].SetDist(v(2,0.2,0.8)); Gp0[assign(E,evar,0,-1)].SetDist(v(2,0.9,0.1)); Gp0[assign(E,evar,1,-1)].SetDist(v(2,0.7,0.3)); Gp0[assign(E,evar,2,-1)].SetDist(v(2,0.3,0.7)); Gp0[assign(E,evar,3,-1)].SetDist(v(2,0.1,0.9)); // set up CTBN: CTBNDyn ctbndyn; ctbndyn.AddNode(ADyn.Clone()); ctbndyn.AddNode(BDyn.Clone()); ctbndyn.AddNode(CDyn.Clone()); ctbndyn.AddNode(DDyn.Clone()); ctbndyn.AddNode(EDyn.Clone()); ctbndyn.AddNode(FDyn.Clone()); //ctbndyn.AddNode(GDyn.Clone()); BN bn; bn.AddNode(Ap0.Clone()); bn.AddNode(Bp0.Clone()); bn.AddNode(Cp0.Clone()); bn.AddNode(Dp0.Clone()); bn.AddNode(Ep0.Clone()); bn.AddNode(Fp0.Clone()); //bn.AddNode(Gp0.Clone()); Markov ctbn(bn.Clone(),ctbndyn.Clone()); // set trajectory to something we know the answer (via Matlab script) Trajectory evid; evid.SetBeginTime(0.0); evid.SetEndTime(2.0); evid.AddTransition(avar,0.0,-1); evid.AddTransition(bvar,0.0,2); evid.AddTransition(cvar,0.0,1); evid.AddTransition(dvar,0.0,-1); evid.AddTransition(evar,0.0,0); evid.AddTransition(fvar,0.0,-1); evid.AddTransition(evar,0.05,1); evid.AddTransition(cvar,0.3,0); evid.AddTransition(bvar,0.4,1); evid.AddTransition(cvar,0.5,1); evid.AddTransition(avar,0.7,1); evid.AddTransition(bvar,1.0,-1); evid.AddTransition(fvar,1.0,0); evid.AddTransition(avar,1.1,0); evid.AddTransition(dvar,1.5,1); evid.AddTransition(dvar,1.6,0); ctbn.Save(cout); evid.Save(cout); // "true" values (those at the end of the lines) // were calculated using octave from calcquery.m & queries.txt // (queries.txt is generated from the output of this program // using genquerytxt) with a delta=0.00001 // query filtered prob of D=0 at time 0.9 cout << "0 " << dvar << " 0 0.9 0.546618" << endl; // query smoothed prob of D=0 at time 0.9 cout << "1 " << dvar << " 0 0.9 0.432647" << endl; // query expected total amount of time D spent in state 1 cout << "2 " << dvar << " 1 0.940300" << endl; // query expected total # of trans of D from 2 to 0 cout << "3 " << dvar << " 1 0 1.76552" << endl; return 0; } ctbnrle.1.1.1/tst/structsearch.cc0000664000175000017500000003121612115733005017221 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "markov.h" #include "streamextra.h" #include "context.h" #include #include "multirv.h" #include "trajectory.h" #include "params.h" #include "ctbndyn.h" #include "bn.h" #include "em.h" #include "structure.h" #include "suffstatsquery.h" #include "nonexpsuffstatsquery.h" #include "brutestructuresearch.h" #include "grapheditsearch.h" #include "famscore.h" #include #include using namespace std; using namespace ctbn; //const int TRIALS(10); double PRIOR_TRANS(1); double PRIOR_TRANSBN(1); double PRIOR_TIME(1); int NUM_TRAJ(2000); double TRAJ_LEN(10); double BNFUZZ(0.2); namespace { int testing_result = 0; double kEpsilon = 0.1; } #define EXPECT(cond) do { \ if (!(cond)) { \ std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \ << " in " << __func__ \ << ", failed condition is " #cond << std::endl; \ testing_result = 1; \ } \ } while (0) int main(int argc, char **argv) { InitParams(argc, argv); Context nullc; Context eatc, fullc, hungryc, uptakec, concentrationc, barometerc, painc, drowsyc; eatc.AddVar(120,2); fullc.AddVar(241,3); hungryc.AddVar(312,2); uptakec.AddVar(463,2); concentrationc.AddVar(594,3); barometerc.AddVar(685,3); painc.AddVar(776,2); drowsyc.AddVar(837,2); MarkovDyn eat(eatc, hungryc); MarkovDyn full(fullc, eatc); MarkovDyn hungry(hungryc, fullc); MarkovDyn uptake(uptakec, nullc); Context full_and_uptakec(fullc, uptakec); MarkovDyn concentration(concentrationc, full_and_uptakec); MarkovDyn barometer(barometerc, nullc); MarkovDyn pain(painc, Context(barometerc, concentrationc)); MarkovDyn drowsy(drowsyc, concentrationc); MultiRV eatrv(eatc, nullc); MultiRV fullrv(fullc, nullc); MultiRV hungryrv(hungryc, nullc); MultiRV uptakerv(uptakec, nullc); MultiRV concentrationrv(concentrationc, nullc); MultiRV barometerrv(barometerc, nullc); MultiRV painrv(painc, nullc); MultiRV drowsyrv(drowsyc, nullc); //------eat-------- eat(0)->Intensity()[0][0] = -0.01; eat(0)->Intensity()[0][1] = 0.01; eat(0)->Intensity()[1][0] = 10.0; eat(0)->Intensity()[1][1] = -10.0; eat(1)->Intensity()[0][0] = -2.0; eat(1)->Intensity()[0][1] = 2.0; eat(1)->Intensity()[1][0] = 1.0; eat(1)->Intensity()[1][1] = -1.0; vectr peat(2, BNFUZZ); peat[0] = 1-BNFUZZ; eatrv[0].SetDist(peat); //------full-------- full(0)->Intensity()[0][0] = -0.02; full(0)->Intensity()[0][1] = 0.01; full(0)->Intensity()[0][2] = 0.01; full(0)->Intensity()[1][0] = 0.3; full(0)->Intensity()[1][1] = -0.31; full(0)->Intensity()[1][2] = 0.01; full(0)->Intensity()[2][0] = 0.01; full(0)->Intensity()[2][1] = 1.0; full(0)->Intensity()[2][2] = -1.01; full(1)->Intensity()[0][0] = -6.01; full(1)->Intensity()[0][1] = 6.0; full(1)->Intensity()[0][2] = 0.01; full(1)->Intensity()[1][0] = 0.01; full(1)->Intensity()[1][1] = -3.01; full(1)->Intensity()[1][2] = 3.0; full(1)->Intensity()[2][0] = 0.01; full(1)->Intensity()[2][1] = 0.01; full(1)->Intensity()[2][2] = -0.02; vectr pfull(3, BNFUZZ); pfull[0] = 1-BNFUZZ*2; fullrv[0].SetDist(pfull); //------hungry-------- hungry(0)->Intensity()[0][0] = -2.0; hungry(0)->Intensity()[0][1] = 2.0; hungry(0)->Intensity()[1][0] = 0.01; hungry(0)->Intensity()[1][1] = -0.01; hungry(1)->Intensity()[0][0] = -0.01; hungry(1)->Intensity()[0][1] = 0.01; hungry(1)->Intensity()[1][0] = 0.01; hungry(1)->Intensity()[1][1] = -0.01; hungry(2)->Intensity()[0][0] = -0.01; hungry(2)->Intensity()[0][1] = 0.01; hungry(2)->Intensity()[1][0] = 10.0; hungry(2)->Intensity()[1][1] = -10.0; vectr phungry(2, BNFUZZ); phungry[0] = 1-BNFUZZ; hungryrv[0].SetDist(phungry); //------uptake-------- uptake(0)->Intensity()[0][0] = -0.0; uptake(0)->Intensity()[0][1] = 0.0; uptake(0)->Intensity()[1][0] = 0.5; uptake(0)->Intensity()[1][1] = -0.5; vectr puptake(2, BNFUZZ); puptake[1] = 1-BNFUZZ; uptakerv[0].SetDist(puptake); //------concentration------ Instantiation i(Context(fullc, uptakec)); i.SetVal(463,0); i.SetVal(241,0); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; i.SetVal(463,1); i.SetVal(241,0); concentration(i)->Intensity()[0][0] = -2.01; concentration(i)->Intensity()[0][1] = 2.0; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -1.01; concentration(i)->Intensity()[1][2] = 1.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; i.SetVal(463,0); i.SetVal(241,1); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; i.SetVal(463,1); i.SetVal(241,1); concentration(i)->Intensity()[0][0] = -1.01; concentration(i)->Intensity()[0][1] = 1.0; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -2.01; concentration(i)->Intensity()[1][2] = 2.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; i.SetVal(463,0); i.SetVal(241,2); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; i.SetVal(463,1); i.SetVal(241,2); concentration(i)->Intensity()[0][0] = -0.51; concentration(i)->Intensity()[0][1] = 0.5; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -4.01; concentration(i)->Intensity()[1][2] = 4.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; vectr pconcentration(3, BNFUZZ); pconcentration[0] = 1-2*BNFUZZ; concentrationrv[0].SetDist(pconcentration); //------barometer------ barometer(0)->Intensity()[0][0] = -0.21; barometer(0)->Intensity()[0][1] = 0.2; barometer(0)->Intensity()[0][2] = 0.01; barometer(0)->Intensity()[1][0] = 0.05; barometer(0)->Intensity()[1][1] = -0.1; barometer(0)->Intensity()[1][2] = 0.05; barometer(0)->Intensity()[2][0] = 0.01; barometer(0)->Intensity()[2][1] = 0.2; barometer(0)->Intensity()[2][2] = -0.21; vectr pbarometer(3, BNFUZZ); pbarometer[1] = 1-2*BNFUZZ; barometerrv[0].SetDist(pbarometer); //------pain------ Instantiation j(Context(barometerc, concentrationc)); j.SetVal(594,0); j.SetVal(685,0); pain(j)->Intensity()[0][0] = -6.0; pain(j)->Intensity()[0][1] = 6.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(594,1); j.SetVal(685,0); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(594,2); j.SetVal(685,0); pain(j)->Intensity()[0][0] = -6.0; pain(j)->Intensity()[0][1] = 6.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(594,0); j.SetVal(685,1); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(594,1); j.SetVal(685,1); pain(j)->Intensity()[0][0] = -0.3; pain(j)->Intensity()[0][1] = 0.3; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(594,2); j.SetVal(685,1); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(594,0); j.SetVal(685,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; j.SetVal(594,1); j.SetVal(685,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; j.SetVal(594,2); j.SetVal(685,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; vectr ppain(2, BNFUZZ); ppain[1] = 1-BNFUZZ; painrv[0].SetDist(ppain); //------drowsy------ drowsy(0)->Intensity()[0][0] = -0.17; drowsy(0)->Intensity()[0][1] = 0.17; drowsy(0)->Intensity()[1][0] = 0.5; drowsy(0)->Intensity()[1][1] = -0.5; drowsy(1)->Intensity()[0][0] = -0.33; drowsy(1)->Intensity()[0][1] = 0.33; drowsy(1)->Intensity()[1][0] = 0.33; drowsy(1)->Intensity()[1][1] = -0.33; drowsy(2)->Intensity()[0][0] = -1.0; drowsy(2)->Intensity()[0][1] = 1.0; drowsy(2)->Intensity()[1][0] = 0.1; drowsy(2)->Intensity()[1][1] = -0.1; vectr pdrowsy(2, BNFUZZ); pdrowsy[0] = 1-BNFUZZ; drowsyrv[0].SetDist(pdrowsy); CTBNDyn ctbndyn; ctbndyn.AddNode(hungry.Clone()); ctbndyn.AddNode(eat.Clone()); ctbndyn.AddNode(drowsy.Clone()); ctbndyn.AddNode(full.Clone()); ctbndyn.AddNode(uptake.Clone()); ctbndyn.AddNode(pain.Clone()); ctbndyn.AddNode(barometer.Clone()); ctbndyn.AddNode(concentration.Clone()); BN bn; bn.AddNode(hungryrv.Clone()); bn.AddNode(eatrv.Clone()); bn.AddNode(drowsyrv.Clone()); bn.AddNode(fullrv.Clone()); bn.AddNode(uptakerv.Clone()); bn.AddNode(painrv.Clone()); bn.AddNode(barometerrv.Clone()); bn.AddNode(concentrationrv.Clone()); Markov m(bn.Clone(), ctbndyn.Clone()); //============= //STRUCTURE SEARCH TESTING //============= //Generate trajectories vector trVec; vector w(NUM_TRAJ,1); for(int i=0; iSetData(&trVec); CTBNFamScore* cfs = new CTBNFamScore(PRIOR_TRANS,PRIOR_TIME,ssQuery); BruteStructureSearch bss(ctbndyn.Domain(),cfs,3); GraphEditSearch ges(ctbndyn.Domain(),cfs); s = ges.LearnStructure(); EXPECT(sA.HammingDist(s)==0); s = bss.LearnStructure(); EXPECT(sA.HammingDist(s)==0); delete cfs; BNFamScore* bfs = new BNFamScore(PRIOR_TRANSBN,ssQuery); GraphEditSearch gesBN(bn.Domain(),bfs,true); s = gesBN.LearnStructure(); Structure sBN; bn.GetStructure(sBN); // should be empty (no parents) EXPECT(sBN.HammingDist(s)==0); delete bfs; delete ssQuery; if (testing_result==0) cout << "PASS structsearch unittest." << endl; return testing_result!=0; } ctbnrle.1.1.1/tst/structurescore.cc0000664000175000017500000003747212115733010017611 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "markov.h" #include "streamextra.h" #include "context.h" #include #include "multirv.h" #include "trajectory.h" #include "params.h" #include "ctbndyn.h" #include "bn.h" #include "em.h" #include "ctbn.h" #include #include using namespace std; using namespace ctbn; const int TRIALS(50); const double NUM_TRAJ(40); const double TRAJ_LEN(2); double PRIOR_TRANS(1); double PRIOR_TIME(1); void score(const Markov& m, const vector & trVec, double &results); namespace { int testing_result = 0; double kEpsilon = 0.1; } #define EXPECT(cond) do { \ if (!(cond)) { \ std::cerr << "Test failure at " << __FILE__ << ":" << __LINE__ \ << " in " << __func__ \ << ", failed condition is " #cond << std::endl; \ testing_result = 1; \ } \ } while (0) int main(int argc, char **argv) { InitParams(argc, argv); Context nullc; Context eatc, fullc, hungryc, uptakec, concentrationc, barometerc, painc, drowsyc; eatc.AddVar(20,2); fullc.AddVar(41,3); hungryc.AddVar(12,2); uptakec.AddVar(63,2); concentrationc.AddVar(94,3); barometerc.AddVar(85,3); painc.AddVar(76,2); drowsyc.AddVar(37,2); MarkovDyn eat(eatc, hungryc); MarkovDyn full(fullc, eatc); MarkovDyn hungry(hungryc, fullc); MarkovDyn uptake(uptakec, nullc); Context full_and_uptakec(fullc, uptakec); MarkovDyn concentration(concentrationc, full_and_uptakec); MarkovDyn barometer(barometerc, nullc); MarkovDyn pain(painc, Context(barometerc, concentrationc)); MarkovDyn drowsy(drowsyc, concentrationc); MultiRV eatrv(eatc, nullc); MultiRV fullrv(fullc, nullc); MultiRV hungryrv(hungryc, nullc); MultiRV uptakerv(uptakec, nullc); MultiRV concentrationrv(concentrationc, nullc); MultiRV barometerrv(barometerc, nullc); MultiRV painrv(painc, nullc); MultiRV drowsyrv(drowsyc, nullc); //------eat-------- eat(0)->Intensity()[0][0] = -0.01; eat(0)->Intensity()[0][1] = 0.01; eat(0)->Intensity()[1][0] = 10.0; eat(0)->Intensity()[1][1] = -10.0; eat(1)->Intensity()[0][0] = -2.0; eat(1)->Intensity()[0][1] = 2.0; eat(1)->Intensity()[1][0] = 1.0; eat(1)->Intensity()[1][1] = -1.0; vectr peat(2, 0.0); peat[0] = 1; peat[1] = 0; eatrv[0].SetDist(peat); //------full-------- full(0)->Intensity()[0][0] = -0.02; full(0)->Intensity()[0][1] = 0.01; full(0)->Intensity()[0][2] = 0.01; full(0)->Intensity()[1][0] = 0.3; full(0)->Intensity()[1][1] = -0.31; full(0)->Intensity()[1][2] = 0.01; full(0)->Intensity()[2][0] = 0.01; full(0)->Intensity()[2][1] = 1.0; full(0)->Intensity()[2][2] = -1.01; full(1)->Intensity()[0][0] = -6.01; full(1)->Intensity()[0][1] = 6.0; full(1)->Intensity()[0][2] = 0.01; full(1)->Intensity()[1][0] = 0.01; full(1)->Intensity()[1][1] = -3.01; full(1)->Intensity()[1][2] = 3.0; full(1)->Intensity()[2][0] = 0.01; full(1)->Intensity()[2][1] = 0.01; full(1)->Intensity()[2][2] = -0.02; vectr pfull(3, 0.0); pfull[0] = 1; //pfull[1] = 0.25; //pfull[2] = 0.25; fullrv[0].SetDist(pfull); //------hungry-------- hungry(0)->Intensity()[0][0] = -2.0; hungry(0)->Intensity()[0][1] = 2.0; hungry(0)->Intensity()[1][0] = 0.01; hungry(0)->Intensity()[1][1] = -0.01; hungry(1)->Intensity()[0][0] = -0.01; hungry(1)->Intensity()[0][1] = 0.01; hungry(1)->Intensity()[1][0] = 0.01; hungry(1)->Intensity()[1][1] = -0.01; hungry(2)->Intensity()[0][0] = -0.01; hungry(2)->Intensity()[0][1] = 0.01; hungry(2)->Intensity()[1][0] = 10.0; hungry(2)->Intensity()[1][1] = -10.0; vectr phungry(2, 0.0); phungry[0] = 1; hungryrv[0].SetDist(phungry); //------uptake-------- uptake(0)->Intensity()[0][0] = -0.0; uptake(0)->Intensity()[0][1] = 0.0; uptake(0)->Intensity()[1][0] = 0.5; uptake(0)->Intensity()[1][1] = -0.5; vectr puptake(2, 0.0); puptake[1] = 1; uptakerv[0].SetDist(puptake); //------concentration------ Instantiation i(Context(fullc, uptakec)); i.SetVal(63,0); i.SetVal(41,0); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; i.SetVal(63,1); i.SetVal(41,0); concentration(i)->Intensity()[0][0] = -2.01; concentration(i)->Intensity()[0][1] = 2.0; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -1.01; concentration(i)->Intensity()[1][2] = 1.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; i.SetVal(63,0); i.SetVal(41,1); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; i.SetVal(63,1); i.SetVal(41,1); concentration(i)->Intensity()[0][0] = -1.01; concentration(i)->Intensity()[0][1] = 1.0; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -2.01; concentration(i)->Intensity()[1][2] = 2.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; i.SetVal(63,0); i.SetVal(41,2); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; i.SetVal(63,1); i.SetVal(41,2); concentration(i)->Intensity()[0][0] = -0.51; concentration(i)->Intensity()[0][1] = 0.5; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -4.01; concentration(i)->Intensity()[1][2] = 4.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; vectr pconcentration(3, 0.0); pconcentration[0] = 1; concentrationrv[0].SetDist(pconcentration); //------barometer------ barometer(0)->Intensity()[0][0] = -0.21; barometer(0)->Intensity()[0][1] = 0.2; barometer(0)->Intensity()[0][2] = 0.01; barometer(0)->Intensity()[1][0] = 0.05; barometer(0)->Intensity()[1][1] = -0.1; barometer(0)->Intensity()[1][2] = 0.05; barometer(0)->Intensity()[2][0] = 0.01; barometer(0)->Intensity()[2][1] = 0.2; barometer(0)->Intensity()[2][2] = -0.21; vectr pbarometer(3, 0.0); pbarometer[1] = 1; barometerrv[0].SetDist(pbarometer); //------pain------ Instantiation j(Context(barometerc, concentrationc)); j.SetVal(94,0); j.SetVal(85,0); pain(j)->Intensity()[0][0] = -6.0; pain(j)->Intensity()[0][1] = 6.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(94,1); j.SetVal(85,0); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(94,2); j.SetVal(85,0); pain(j)->Intensity()[0][0] = -6.0; pain(j)->Intensity()[0][1] = 6.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(94,0); j.SetVal(85,1); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(94,1); j.SetVal(85,1); pain(j)->Intensity()[0][0] = -0.3; pain(j)->Intensity()[0][1] = 0.3; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(94,2); j.SetVal(85,1); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(94,0); j.SetVal(85,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; j.SetVal(94,1); j.SetVal(85,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; j.SetVal(94,2); j.SetVal(85,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; //------drowsy------ drowsy(0)->Intensity()[0][0] = -0.17; drowsy(0)->Intensity()[0][1] = 0.17; drowsy(0)->Intensity()[1][0] = 0.5; drowsy(0)->Intensity()[1][1] = -0.5; drowsy(1)->Intensity()[0][0] = -0.33; drowsy(1)->Intensity()[0][1] = 0.33; drowsy(1)->Intensity()[1][0] = 0.33; drowsy(1)->Intensity()[1][1] = -0.33; drowsy(2)->Intensity()[0][0] = -1.0; drowsy(2)->Intensity()[0][1] = 1.0; drowsy(2)->Intensity()[1][0] = 0.1; drowsy(2)->Intensity()[1][1] = -0.1; vectr pdrowsy(2, 0.0); pdrowsy[0] = 1; drowsyrv[0].SetDist(pdrowsy); CTBNDyn ctbndyn; ctbndyn.AddNode(eat.Clone()); ctbndyn.AddNode(full.Clone()); ctbndyn.AddNode(hungry.Clone()); ctbndyn.AddNode(uptake.Clone()); ctbndyn.AddNode(concentration.Clone()); ctbndyn.AddNode(barometer.Clone()); ctbndyn.AddNode(pain.Clone()); ctbndyn.AddNode(drowsy.Clone()); BN bn; bn.AddNode(eatrv.Clone()); bn.AddNode(fullrv.Clone()); bn.AddNode(hungryrv.Clone()); bn.AddNode(uptakerv.Clone()); bn.AddNode(concentrationrv.Clone()); bn.AddNode(barometerrv.Clone()); bn.AddNode(painrv.Clone()); bn.AddNode(drowsyrv.Clone()); Markov m(bn.Clone(), ctbndyn.Clone()); // ========================== // Building simpler structure MarkovDyn eatsimp(eatc, nullc); MarkovDyn fullsimp(fullc, eatc); MarkovDyn hungrysimp(hungryc, fullc); MarkovDyn uptakesimp(uptakec, nullc); MarkovDyn concentrationsimp(concentrationc, uptakec); MarkovDyn barometersimp(barometerc, nullc); MarkovDyn painsimp(painc, Context(barometerc, concentrationc) ); MarkovDyn drowsysimp(drowsyc, nullc); CTBNDyn ctbndynsimp; ctbndynsimp.AddNode(eatsimp.Clone()); ctbndynsimp.AddNode(fullsimp.Clone()); ctbndynsimp.AddNode(hungrysimp.Clone()); ctbndynsimp.AddNode(uptakesimp.Clone()); ctbndynsimp.AddNode(concentrationsimp.Clone()); ctbndynsimp.AddNode(barometersimp.Clone()); ctbndynsimp.AddNode(painsimp.Clone()); ctbndynsimp.AddNode(drowsysimp.Clone()); Markov msimple(bn.Clone(), ctbndynsimp.Clone()); //============================== //Building independent structure MarkovDyn eatindep(eatc, nullc); MarkovDyn fullindep(fullc, nullc); MarkovDyn hungryindep(hungryc, nullc); MarkovDyn uptakeindep(uptakec, nullc); MarkovDyn concentrationindep(concentrationc, nullc); MarkovDyn barometerindep(barometerc, nullc); MarkovDyn painindep(painc, nullc); MarkovDyn drowsyindep(drowsyc, nullc); CTBNDyn ctbndynindep; ctbndynindep.AddNode(eatindep.Clone()); ctbndynindep.AddNode(fullindep.Clone()); ctbndynindep.AddNode(hungryindep.Clone()); ctbndynindep.AddNode(uptakeindep.Clone()); ctbndynindep.AddNode(concentrationindep.Clone()); ctbndynindep.AddNode(barometerindep.Clone()); ctbndynindep.AddNode(painindep.Clone()); ctbndynindep.AddNode(drowsyindep.Clone()); Markov mindep(bn.Clone(), ctbndynindep.Clone()); //============================== //Building complex structure MarkovDyn eatcomplex(eatc, hungryc); MarkovDyn fullcomplex(fullc, eatc); MarkovDyn hungrycomplex(hungryc, fullc); MarkovDyn uptakecomplex(uptakec, eatc); Context fubhc(Context(fullc,uptakec),Context(barometerc,hungryc)); MarkovDyn concentrationcomplex(concentrationc, fubhc); MarkovDyn barometercomplex(barometerc, nullc); Context bcdc(Context(barometerc,concentrationc),drowsyc); MarkovDyn paincomplex(painc, bcdc); MarkovDyn drowsycomplex(drowsyc, concentrationc); CTBNDyn ctbndyncomplex; ctbndyncomplex.AddNode(eatcomplex.Clone()); ctbndyncomplex.AddNode(fullcomplex.Clone()); ctbndyncomplex.AddNode(hungrycomplex.Clone()); ctbndyncomplex.AddNode(uptakecomplex.Clone()); ctbndyncomplex.AddNode(concentrationcomplex.Clone()); ctbndyncomplex.AddNode(barometercomplex.Clone()); ctbndyncomplex.AddNode(paincomplex.Clone()); ctbndyncomplex.AddNode(drowsycomplex.Clone()); Markov mcomplex(bn.Clone(), ctbndyncomplex.Clone()); //============================== //Building reverse structure MarkovDyn eatrev(eatc, fullc); MarkovDyn fullrev(fullc, hungryc); MarkovDyn hungryrev(hungryc, eatc); MarkovDyn uptakerev(uptakec, concentrationc); MarkovDyn concentrationrev(concentrationc, Context(painc,drowsyc)); MarkovDyn barometerrev(barometerc, painc); MarkovDyn painrev(painc, nullc); MarkovDyn drowsyrev(drowsyc, nullc); CTBNDyn ctbndynrev; ctbndynrev.AddNode(eatrev.Clone()); ctbndynrev.AddNode(fullrev.Clone()); ctbndynrev.AddNode(hungryrev.Clone()); ctbndynrev.AddNode(uptakerev.Clone()); ctbndynrev.AddNode(concentrationrev.Clone()); ctbndynrev.AddNode(barometerrev.Clone()); ctbndynrev.AddNode(painrev.Clone()); ctbndynrev.AddNode(drowsyrev.Clone()); Markov mrev(bn.Clone(), ctbndynrev.Clone()); //============= //SCORE TESTING //============= vector zeros; for(int i=0; i<=NUM_TRAJ; i++) zeros.push_back(0.0); double scores[5]; for(int k=0; k trVec; for(int i=0; iscores[1]); EXPECT(scores[0]>scores[2]); EXPECT(scores[0]>scores[3]); EXPECT(scores[0]>scores[4]); if (testing_result==0) cout << "PASS structurescore unittest." << endl; return testing_result!=0; } void normalizeVec(vector &results) { for(unsigned int i=0; i& trVec, double &results) { SS *ss; vector weights(trVec.size(),1.0); ss = m.SuffStats(trVec,weights); results += m.GetScore(PRIOR_TRANS,PRIOR_TIME,ss)/trVec.size(); delete ss; } ctbnrle.1.1.1/CMakeLists.txt0000664000175000017500000000125312115751566016140 0ustar csheltoncsheltoncmake_minimum_required(VERSION 2.6.4 FATAL_ERROR) IF(DEFINED CMAKE_BUILD_TYPE) SET(CMAKE_BUILD_TYPE ${CMAKE_BUILD_TYPE} CACHE STRING "Choose the type of build, options are: None(CMAKE_CXX_FLAGS or CMAKE_C_FLAGS used) Debug Release RelWithDebInfo MinSizeRel.") ELSE() SET(CMAKE_BUILD_TYPE Release CACHE STRING "Choose the type of build, options are: None(CMAKE_CXX_FLAGS or CMAKE_C_FLAGS used) Debug Release RelWithDebInfo MinSizeRel.") ENDIF() project(CTBNRLE) enable_testing() add_subdirectory(glpk) set(CTBN_INCLUDE_DIRS ${GLPK_INCLUDE_DIRS} ${EIGEN_INCLUDE_DIRS} ${CMAKE_SOURCE_DIR}/hdr ) add_subdirectory(src) add_subdirectory(tst) add_subdirectory(demo) ctbnrle.1.1.1/demo/0000775000175000017500000000000012251374045014315 5ustar csheltoncsheltonctbnrle.1.1.1/demo/exactquery.cc0000664000175000017500000000531212107032410017002 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "exactmarkovinf.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include #include "ensurectbn.h" using namespace std; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using exact inference // Usage: // no inputs to run: ./exactquery >output_file int main(int argc, char **argv) { // load the input model, evidence and query answers in "queryinput.data" const char *input_file = "queryinput.data"; addnan(cout); InitParams(argc, argv); ifstream fin(input_file); // load model Markov m; m.Load(fin); const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics()); const BN *bn = dynamic_cast(m.GetStartDist()); Context context = ctbndyn->Domain() + ctbndyn->CondDomain(); // load evidence Trajectory evid; evid.Load(fin); // load true query answers double expect_q1, expect_q2; fin >> expect_q1 >> expect_q2; fin.close(); // construct the queries Instantiation x(context, -1); int querynode = ParamInt("QueryNode", 1); int querytimeval = ParamInt("QueryTimeVal", 1); Context c = ctbndyn->Node(querynode)->Domain(); x.SetVal(querynode, querytimeval); // query the amount of time node 1 spends on state 1 QueryTime query1(x); // query the number of times node 1 transition from state 0 to state 1 QueryTransition query2(c, 0, 1); // set up the exact Markov inference engine ExactMarkovInf einf; einf.SetProcess(&m); einf.SetTrajectory(&evid); // output the results cout << "the expected query answer is:" << endl; cout << expect_q1 << " " << expect_q2 << endl; cout << "the exact inference answer to the query is:" << endl; cout << einf.CalcQuery(query1) << " " << einf.CalcQuery(query2) << endl; return 0; } ctbnrle.1.1.1/demo/makedrug.cc0000664000175000017500000003357712107032410016425 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ // This code creates the drug effect network as first seen in // Continuous Time Bayesian Networks by Nodelman et al., 2002 and // save it to stdout // // Usage: makedrug [options] > // options: // // Set whether to save only the dynamics (as opposed to the full CTBN): // -Dsavedyn (default: 0 [=false]) // // Set whether to save as a pointer (which can be loaded into any superclass // -Dsaveptr (default: 0 [=false]) #include "bn.h" #include "em.h" #include "context.h" #include "ctbn.h" #include "ctbndyn.h" #include "factoredmatrix.h" #include "factoredvector.h" #include "markov.h" #include "markovdyn.h" #include "multirv.h" #include "params.h" #include "streamextra.h" #include "trajectory.h" #include "utils.h" #include #include #include #include using namespace std; using namespace ctbn; int main(int argc, char **argv) { InitParams(argc, argv); bool savedyn = ParamInt("savedyn",false); bool saveasptr = ParamInt("saveptr",false); cout << setprecision(4); //******************************************************* // Create contexts for each variable Context nullc; Context eatc, fullc, hungryc, uptakec, concentrationc, barometerc, painc, drowsyc; eatc.AddVar(0, 2); fullc.AddVar(1,3); hungryc.AddVar(2,2); uptakec.AddVar(3, 2); concentrationc.AddVar(4,3); barometerc.AddVar(5,3); painc.AddVar(6, 2); drowsyc.AddVar(7,2); // Define the connections between variables. For a variable with multiple // parents, the context used is the union of the two parent contexts MarkovDyn eat(eatc, hungryc); MarkovDyn full(fullc, eatc); MarkovDyn hungry(hungryc, fullc); MarkovDyn uptake(uptakec, nullc); Context full_and_uptakec(fullc, uptakec); MarkovDyn concentration(concentrationc, full_and_uptakec); MarkovDyn barometer(barometerc, nullc); MarkovDyn pain(painc, Context(barometerc, concentrationc)); MarkovDyn drowsy(drowsyc, concentrationc); // Generate starting distributions for each variable MultiRV eatrv(eatc, nullc); MultiRV fullrv(fullc, nullc); MultiRV hungryrv(hungryc, nullc); MultiRV uptakerv(uptakec, nullc); MultiRV concentrationrv(concentrationc, nullc); MultiRV barometerrv(barometerc, nullc); MultiRV painrv(painc, nullc); MultiRV drowsyrv(drowsyc, nullc); // The following fills the conditional intensity matrices for each // variable and the initial distributions (multinomial distributions). //------eat-------- eat(0)->Intensity()[0][0] = -0.01; eat(0)->Intensity()[0][1] = 0.01; eat(0)->Intensity()[1][0] = 10.0; eat(0)->Intensity()[1][1] = -10.0; eat(1)->Intensity()[0][0] = -2.0; eat(1)->Intensity()[0][1] = 2.0; eat(1)->Intensity()[1][0] = 1.0; eat(1)->Intensity()[1][1] = -1.0; vectr peat(2, 0.0); peat[0] = 0.1; peat[1] = 0.9; eatrv[0].SetDist(peat); //------full-------- full(0)->Intensity()[0][0] = -0.02; full(0)->Intensity()[0][1] = 0.01; full(0)->Intensity()[0][2] = 0.01; full(0)->Intensity()[1][0] = 0.3; full(0)->Intensity()[1][1] = -0.31; full(0)->Intensity()[1][2] = 0.01; full(0)->Intensity()[2][0] = 0.01; full(0)->Intensity()[2][1] = 1.0; full(0)->Intensity()[2][2] = -1.01; full(1)->Intensity()[0][0] = -6.01; full(1)->Intensity()[0][1] = 6.0; full(1)->Intensity()[0][2] = 0.01; full(1)->Intensity()[1][0] = 0.01; full(1)->Intensity()[1][1] = -3.01; full(1)->Intensity()[1][2] = 3.0; full(1)->Intensity()[2][0] = 0.01; full(1)->Intensity()[2][1] = 0.01; full(1)->Intensity()[2][2] = -0.02; vectr pfull(3, 0.0); pfull[0] = 0.7; pfull[1] = 0.2; pfull[2] = 0.1; fullrv[0].SetDist(pfull); //------hungry-------- hungry(0)->Intensity()[0][0] = -2.0; hungry(0)->Intensity()[0][1] = 2.0; hungry(0)->Intensity()[1][0] = 0.01; hungry(0)->Intensity()[1][1] = -0.01; hungry(1)->Intensity()[0][0] = -0.01; hungry(1)->Intensity()[0][1] = 0.01; hungry(1)->Intensity()[1][0] = 0.01; hungry(1)->Intensity()[1][1] = -0.01; hungry(2)->Intensity()[0][0] = -0.01; hungry(2)->Intensity()[0][1] = 0.01; hungry(2)->Intensity()[1][0] = 10.0; hungry(2)->Intensity()[1][1] = -10.0; vectr phungry(2, 0.0); phungry[0] = 0.6; phungry[1] = 0.4; hungryrv[0].SetDist(phungry); //------uptake-------- uptake(0)->Intensity()[0][0] = -0.0; uptake(0)->Intensity()[0][1] = 0.0; uptake(0)->Intensity()[1][0] = 0.5; uptake(0)->Intensity()[1][1] = -0.5; vectr puptake(2, 0.0); puptake[0] = 0.45; puptake[1] = 0.55; uptakerv[0].SetDist(puptake); // For this variable there is more than one parent. // The CIM is filled by creating an Instantiation object that represents // the current intensity matrix we are entering values for. The // MarkovDyn objects can be then indexed with Instantiation objects. //------concentration------ Instantiation i(Context(fullc, uptakec)); // Set uptake to state 0; full to state 0 i.SetVal(3,0); i.SetVal(1,0); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; // Set uptake to state 1; full to state 0 // (We repeat this process for each parent instantiation.) i.SetVal(3,1); i.SetVal(1,0); concentration(i)->Intensity()[0][0] = -2.01; concentration(i)->Intensity()[0][1] = 2.0; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -1.01; concentration(i)->Intensity()[1][2] = 1.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; i.SetVal(3,0); i.SetVal(1,1); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; i.SetVal(3,1); i.SetVal(1,1); concentration(i)->Intensity()[0][0] = -1.01; concentration(i)->Intensity()[0][1] = 1.0; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -2.01; concentration(i)->Intensity()[1][2] = 2.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; i.SetVal(3,0); i.SetVal(1,2); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; i.SetVal(3,1); i.SetVal(1,2); concentration(i)->Intensity()[0][0] = -0.51; concentration(i)->Intensity()[0][1] = 0.5; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -4.01; concentration(i)->Intensity()[1][2] = 4.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; vectr pconcentration(3, 0.0); pconcentration[0] = 0.7;//0.1; pconcentration[1] = 0.2;//0.2; pconcentration[2] = 0.1;//0.7; concentrationrv[0].SetDist(pconcentration); //------barometer------ barometer(0)->Intensity()[0][0] = -0.21; barometer(0)->Intensity()[0][1] = 0.2; barometer(0)->Intensity()[0][2] = 0.01; barometer(0)->Intensity()[1][0] = 0.05; barometer(0)->Intensity()[1][1] = -0.1; barometer(0)->Intensity()[1][2] = 0.05; barometer(0)->Intensity()[2][0] = 0.01; barometer(0)->Intensity()[2][1] = 0.2; barometer(0)->Intensity()[2][2] = -0.21; vectr pbarometer(3, 0.0); pbarometer[0] = 0.3; pbarometer[1] = 0.7; barometerrv[0].SetDist(pbarometer); //------pain------ Instantiation j(Context(barometerc, concentrationc)); j.SetVal(4,0); j.SetVal(5,0); pain(j)->Intensity()[0][0] = -6.0; pain(j)->Intensity()[0][1] = 6.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(4,1); j.SetVal(5,0); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(4,2); j.SetVal(5,0); pain(j)->Intensity()[0][0] = -6.0; pain(j)->Intensity()[0][1] = 6.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(4,0); j.SetVal(5,1); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(4,1); j.SetVal(5,1); pain(j)->Intensity()[0][0] = -0.3; pain(j)->Intensity()[0][1] = 0.3; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(4,2); j.SetVal(5,1); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(4,0); j.SetVal(5,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; j.SetVal(4,1); j.SetVal(5,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; j.SetVal(4,2); j.SetVal(5,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; vectr ppain(2, 0.0); ppain[0] = 0.1; ppain[1] = 0.9; painrv[0].SetDist(ppain); //------drowsy------ drowsy(0)->Intensity()[0][0] = -0.17; drowsy(0)->Intensity()[0][1] = 0.17; drowsy(0)->Intensity()[1][0] = 0.5; drowsy(0)->Intensity()[1][1] = -0.5; drowsy(1)->Intensity()[0][0] = -0.33; drowsy(1)->Intensity()[0][1] = 0.33; drowsy(1)->Intensity()[1][0] = 0.33; drowsy(1)->Intensity()[1][1] = -0.33; drowsy(2)->Intensity()[0][0] = -1.0; drowsy(2)->Intensity()[0][1] = 1.0; drowsy(2)->Intensity()[1][0] = 0.1; drowsy(2)->Intensity()[1][1] = -0.1; vectr pdrowsy(2, 0.0); pdrowsy[0] = 0.1; pdrowsy[1] = 0.9; drowsyrv[0].SetDist(pdrowsy); // The dynamics of a CTBN are defined by the dynamics of each conditional // Markov process. Here it is the MarkovDyn objects created above. CTBNDyn ctbndyn; ctbndyn.AddNode(eat.Clone()); ctbndyn.AddNode(full.Clone()); ctbndyn.AddNode(hungry.Clone()); BN bn; bn.AddNode(eatrv.Clone()); bn.AddNode(fullrv.Clone()); bn.AddNode(hungryrv.Clone()); //(ctbndyn.JointMatrix()).niceprint(cout); ctbndyn.AddNode(uptake.Clone()); ctbndyn.AddNode(concentration.Clone()); ctbndyn.AddNode(barometer.Clone()); ctbndyn.AddNode(pain.Clone()); ctbndyn.AddNode(drowsy.Clone()); // The initial distribution of a CTBN is defined by the underlying // Bayesian network containing a multinomial distribution for each // variable. bn.AddNode(uptakerv.Clone()); bn.AddNode(concentrationrv.Clone()); bn.AddNode(barometerrv.Clone()); bn.AddNode(painrv.Clone()); bn.AddNode(drowsyrv.Clone()); /* Dynamics *dyn = &ctbndyn; FactoredMatrix apprm(dyn); RV *rvbn = &bn; FactoredVector apprv(rvbn); apprv.Print(); //cout << apprv.GetDistInst(0,0) << endl; FactoredVector bbb = apprv; //cout << bbb.GetDistInst(0,0) << endl; //apprm.Mult(1, bbb); Context context = ctbndyn.Domain() + ctbndyn.CondDomain(); Instantiation x(context, -1); int querynode = ParamInt("QueryNode", 5); int querytimeval = ParamInt("QueryTimeVal", 1); x.SetVal(querynode, querytimeval); querynode = ParamInt("QueryNode", 1); querytimeval = ParamInt("QueryTimeVal", 0); x.SetVal(querynode, querytimeval); querynode = ParamInt("QueryNode", 2); querytimeval = ParamInt("QueryTimeVal", 0); x.SetVal(querynode, querytimeval); querynode = ParamInt("QueryNode",3); querytimeval = ParamInt("QueryTimeVal", 0); x.SetVal(querynode, querytimeval); querynode = ParamInt("QueryNode", 4); querytimeval = ParamInt("QueryTimeVal", 0); x.SetVal(querynode, querytimeval); RVSimple *v = &apprv; apprm.Cond(0.0, 1.0, x); apprm.Mult(v); */ // Construct the final object that represents the CTBN. Markov m(bn.Clone(), ctbndyn.Clone()); Context context = ctbndyn.Domain() + ctbndyn.CondDomain(); // sample a partial trajectory as the input evidence double begintime = ParamDouble("BeginTime", 0.0); double endtime = ParamDouble("EndTime", 10.0); Trajectory evid; evid.SetBeginTime(begintime); evid.SetEndTime(endtime); evid.AddPointEvidence(0, 0.5, 1); evid.AddTransition(1, 2.5, 2); evid.AddTransition(6, 5.0, 1); //m.Sample(evid); //RemoveNodesInformation(evid, context, ctbndyn.NumofNodes(), ctbndyn.NumofNodes()*6 , 0.3); m.Save(cout); cout << endl << evid << endl; // Output the CTBN to the cout strem /* if (savedyn) { //if (saveasptr) ctbndyn.SavePtr(cout); //else ctbndyn.Save(cout); ctbndyn.Save(cout); } else { //if (saveasptr) m.SavePtr(cout); //else m.Save(cout); m.Save(cout); } */ } ctbnrle.1.1.1/demo/learnparams.cc0000664000175000017500000000401012107032410017107 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ //This code shows how to learn the paramters of a CTBN //given fully observed data. Data are n trajectories //saved in datafile. They can be generated using //gen_fulldata #include "markov.h" #include "trajectory.h" #include "params.h" #include #include "ensurectbn.h" using namespace std; using namespace ctbn; int main(int argc, char**argv) { InitParams(argc, argv); if (argc<3) { cout << "Usage: ./learnparams <.ctbn file> " << endl; exit(0); } ifstream fin(argv[1]); if (!fin.good()) { cout << "bad CTBN file" << endl; exit(1); } //load CTBN model from the file Markov ctbn; ctbn.Load(fin); ifstream fdata(argv[2]); if (!fdata.good()) { cout << "bad datafile" << endl; exit(1); } int n; fdata >> n; //load data vector data; for(int i=0; i w(n, 1.0); //Calculate the sufficient statistics of the data SS *ss = ctbn.SuffStats(data, w); //randomly choose parameters ctbn.Scramble(); //learn the parameters from complete data ctbn.Maximize(ss); ctbn.Save(cout); if(ss!=NULL) delete ss; return 0; } ctbnrle.1.1.1/demo/sampleprocess.cc0000664000175000017500000000315112107032410017467 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ // this file reads in a model file and outputs a trajectory sampled from it // from time 0 to time as given by the first argument #include "process.h" #include #include "params.h" #include "ensurectbn.h" using namespace std; using namespace ctbn; int main(int argc, char **argv) { InitParams(argc, argv); if (argc < 2) { cerr << "Not enough arguments! ./sampleprocess ending_time" << endl; exit(1); } addnan(cout); addnan(cin); double endt = atof(argv[1]); if (endt<0.0) { cerr << "time must be non-negative" << endl; exit(1); } Process *p = Process::LoadPtr(cin); if (p==NULL) { cerr << "stdin is not a saved process pointer" << endl; exit(1); } Trajectory tr; tr.SetBeginTime(0.0); tr.SetEndTime(endt); p->Sample(tr); tr.Save(cout); cout << endl; return 0; } ctbnrle.1.1.1/demo/Makefile.bak0000664000175000017500000040336712107035500016514 0ustar csheltoncsheltonSRC := $(wildcard *.cc) LIBOBJ = ../src/ctbn.a ../glpk/glpk-4.47/src/.libs/libglpk.a LPOBJ = ../src/linearprogram.o EXES = clique_example makechain makedrug learnparams gibbssampquery sampleprocess traj2ascii using_graphic structurelearn sem makeqinput emlearn gen_fulldata gen_partialdata isquery twonode exactquery DATA = queryinput.data drug.ctbn drug.ctbndyn CXX = g++ OFLAG = -O3 #OFLAG = -ggdb CXXFLAGS += $(OFLAG) -std=c++03 -pedantic -Wall -Wextra -Wno-long-long -Wno-unused-parameter -Wno-reorder -Wno-unused-variable CXXFLAGS += -I../hdr -I../tst -I../glpk/glpk-4.47/include -I../glpk/glpk-4.47/src -L../glpk/glpk-4.47/src/.libs CXXFLAGS += -DUSE_PTHREADS LDFLAGS = -lglpk -lpthread DEPEND = makedepend -Y -- $(CXXFLAGS) -- all: $(EXES) $(DATA) depend: $(SRC) $(DEPEND) $(SRC) queryinput.data: makeqinput (rm -f makequeryinput.data; ./makeqinput > queryinput.data) drug.ctbn: makedrug (rm -f drug.ctbn; ./makedrug > drug.ctbn) drug.ctbndyn: makedrug (rm -f drug.ctbndyn; ./makedrug -Dsavedyn 1 > drug.ctbndyn) using_graphic: using_graphic.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o using_graphic using_graphic.o $(LIBOBJ) clique_example: clique_example.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o clique_example clique_example.o $(LIBOBJ) makechain: makechain.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o makechain makechain.o $(LIBOBJ) maketraj: maketraj.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o maketraj maketraj.o $(LIBOBJ) twonode: twonode.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o twonode twonode.o $(LIBOBJ) makedrug: makedrug.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o makedrug makedrug.o $(LIBOBJ) learnparams: learnparams.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o learnparams learnparams.o $(LIBOBJ) emlearn: emlearn.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o emlearn emlearn.o $(LIBOBJ) -lpthread gibbssampquery: gibbssampquery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o gibbssampquery gibbssampquery.o $(LIBOBJ) exactquery: exactquery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o exactquery exactquery.o $(LIBOBJ) isquery: isquery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o isquery isquery.o $(LIBOBJ) sampleprocess: sampleprocess.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o sampleprocess sampleprocess.o $(LIBOBJ) traj2ascii: traj2ascii.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o traj2ascii traj2ascii.o $(LIBOBJ) makeqinput: makeqinput.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o makeqinput makeqinput.o $(LIBOBJ) structurelearn: structurelearn.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o structurelearn structurelearn.o $(LIBOBJ) gen_fulldata: gen_fulldata.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o gen_fulldata gen_fulldata.o $(LIBOBJ) gen_partialdata: gen_partialdata.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o gen_partialdata gen_partialdata.o $(LIBOBJ) sem: sem.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o sem sem.o $(LIBOBJ) clean: rm -rf *.o *~ $(EXES) # DO NOT DELETE sampleprocess.o: ../hdr/process.h ../hdr/dynamics.h ../hdr/context.h sampleprocess.o: ../hdr/streamserial.h ../hdr/serial/serial.h sampleprocess.o: ../hdr/nullptr03.h ../hdr/streamserial-old.h sampleprocess.o: ../hdr/defines.h ../hdr/streamextra.h ../hdr/trajectory.h sampleprocess.o: ../hdr/rvsimple.h ../hdr/matrix.h ../hdr/matrix-eigen.h sampleprocess.o: ../hdr/Eigen/Core sampleprocess.o: ../hdr/Eigen/src/Core/util/DisableStupidWarnings.h sampleprocess.o: ../hdr/Eigen/src/Core/util/Macros.h sampleprocess.o: ../hdr/Eigen/src/Core/util/MKL_support.h sampleprocess.o: ../hdr/Eigen/src/Core/util/Constants.h sampleprocess.o: ../hdr/Eigen/src/Core/util/ForwardDeclarations.h sampleprocess.o: ../hdr/Eigen/src/Core/util/Meta.h sampleprocess.o: ../hdr/Eigen/src/Core/util/XprHelper.h sampleprocess.o: ../hdr/Eigen/src/Core/util/StaticAssert.h sampleprocess.o: ../hdr/Eigen/src/Core/util/Memory.h sampleprocess.o: ../hdr/Eigen/src/Core/NumTraits.h sampleprocess.o: ../hdr/Eigen/src/Core/MathFunctions.h sampleprocess.o: ../hdr/Eigen/src/Core/GenericPacketMath.h sampleprocess.o: ../hdr/Eigen/src/Core/arch/Default/Settings.h sampleprocess.o: ../hdr/Eigen/src/Core/Functors.h sampleprocess.o: ../hdr/Eigen/src/Core/DenseCoeffsBase.h sampleprocess.o: ../hdr/Eigen/src/Core/DenseBase.h sampleprocess.o: ../hdr/Eigen/src/plugins/BlockMethods.h sampleprocess.o: ../hdr/Eigen/src/Core/MatrixBase.h sampleprocess.o: ../hdr/Eigen/src/plugins/CommonCwiseUnaryOps.h sampleprocess.o: ../hdr/Eigen/src/plugins/CommonCwiseBinaryOps.h sampleprocess.o: ../hdr/Eigen/src/plugins/MatrixCwiseUnaryOps.h sampleprocess.o: ../hdr/Eigen/src/plugins/MatrixCwiseBinaryOps.h sampleprocess.o: ../hdr/matrix-eigenext.h ../hdr/Eigen/src/Core/EigenBase.h sampleprocess.o: ../hdr/Eigen/src/Core/Assign.h sampleprocess.o: ../hdr/Eigen/src/Core/util/BlasUtil.h sampleprocess.o: ../hdr/Eigen/src/Core/DenseStorage.h sampleprocess.o: ../hdr/Eigen/src/Core/NestByValue.h sampleprocess.o: ../hdr/Eigen/src/Core/ForceAlignedAccess.h sampleprocess.o: ../hdr/Eigen/src/Core/ReturnByValue.h sampleprocess.o: ../hdr/Eigen/src/Core/NoAlias.h sampleprocess.o: ../hdr/Eigen/src/Core/PlainObjectBase.h sampleprocess.o: ../hdr/Eigen/src/Core/Matrix.h ../hdr/Eigen/src/Core/Array.h sampleprocess.o: ../hdr/Eigen/src/Core/CwiseBinaryOp.h sampleprocess.o: ../hdr/Eigen/src/Core/CwiseUnaryOp.h sampleprocess.o: ../hdr/Eigen/src/Core/CwiseNullaryOp.h sampleprocess.o: ../hdr/Eigen/src/Core/CwiseUnaryView.h sampleprocess.o: ../hdr/Eigen/src/Core/SelfCwiseBinaryOp.h sampleprocess.o: ../hdr/Eigen/src/Core/Dot.h sampleprocess.o: ../hdr/Eigen/src/Core/StableNorm.h sampleprocess.o: ../hdr/Eigen/src/Core/MapBase.h sampleprocess.o: ../hdr/Eigen/src/Core/Stride.h ../hdr/Eigen/src/Core/Map.h sampleprocess.o: ../hdr/Eigen/src/Core/Block.h sampleprocess.o: ../hdr/Eigen/src/Core/VectorBlock.h sampleprocess.o: ../hdr/Eigen/src/Core/Transpose.h sampleprocess.o: ../hdr/Eigen/src/Core/DiagonalMatrix.h sampleprocess.o: ../hdr/Eigen/src/Core/Diagonal.h sampleprocess.o: ../hdr/Eigen/src/Core/DiagonalProduct.h sampleprocess.o: ../hdr/Eigen/src/Core/PermutationMatrix.h 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gen_fulldata.o: ../hdr/Eigen/src/Core/Swap.h gen_fulldata.o: ../hdr/Eigen/src/Core/CommaInitializer.h gen_fulldata.o: ../hdr/Eigen/src/Core/Flagged.h gen_fulldata.o: ../hdr/Eigen/src/Core/ProductBase.h gen_fulldata.o: ../hdr/Eigen/src/Core/GeneralProduct.h gen_fulldata.o: ../hdr/Eigen/src/Core/TriangularMatrix.h gen_fulldata.o: ../hdr/Eigen/src/Core/SelfAdjointView.h gen_fulldata.o: ../hdr/Eigen/src/Core/SolveTriangular.h gen_fulldata.o: ../hdr/Eigen/src/Core/products/Parallelizer.h gen_fulldata.o: ../hdr/Eigen/src/Core/products/CoeffBasedProduct.h gen_fulldata.o: ../hdr/Eigen/src/Core/products/GeneralBlockPanelKernel.h gen_fulldata.o: ../hdr/Eigen/src/Core/products/GeneralMatrixVector.h gen_fulldata.o: ../hdr/Eigen/src/Core/products/GeneralMatrixMatrix.h gen_fulldata.o: ../hdr/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h gen_fulldata.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixVector.h gen_fulldata.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixMatrix.h 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gibbssampquery.o: ../hdr/Eigen/src/Core/MathFunctions.h gibbssampquery.o: ../hdr/Eigen/src/Core/GenericPacketMath.h gibbssampquery.o: ../hdr/Eigen/src/Core/arch/Default/Settings.h gibbssampquery.o: ../hdr/Eigen/src/Core/Functors.h gibbssampquery.o: ../hdr/Eigen/src/Core/DenseCoeffsBase.h gibbssampquery.o: ../hdr/Eigen/src/Core/DenseBase.h gibbssampquery.o: ../hdr/Eigen/src/plugins/BlockMethods.h gibbssampquery.o: ../hdr/Eigen/src/Core/MatrixBase.h gibbssampquery.o: ../hdr/Eigen/src/plugins/CommonCwiseUnaryOps.h gibbssampquery.o: ../hdr/Eigen/src/plugins/CommonCwiseBinaryOps.h gibbssampquery.o: ../hdr/Eigen/src/plugins/MatrixCwiseUnaryOps.h gibbssampquery.o: ../hdr/Eigen/src/plugins/MatrixCwiseBinaryOps.h gibbssampquery.o: ../hdr/matrix-eigenext.h ../hdr/Eigen/src/Core/EigenBase.h gibbssampquery.o: ../hdr/Eigen/src/Core/Assign.h gibbssampquery.o: ../hdr/Eigen/src/Core/util/BlasUtil.h gibbssampquery.o: ../hdr/Eigen/src/Core/DenseStorage.h gibbssampquery.o: 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../hdr/suffstatsquery.h ../hdr/em.h ../hdr/inference.h makeqinput.o: ../hdr/process.h ../hdr/utils.h ../hdr/markov.h makeqinput.o: ../hdr/extramath.h makechain.o: ../hdr/markovdyn.h ../hdr/markovsimple.h ../hdr/dynsimple.h makechain.o: ../hdr/random.h ../hdr/matrix.h ../hdr/matrix-eigen.h makechain.o: ../hdr/streamserial.h ../hdr/serial/serial.h ../hdr/nullptr03.h makechain.o: ../hdr/streamserial-old.h ../hdr/defines.h ../hdr/streamextra.h makechain.o: ../hdr/Eigen/Core makechain.o: ../hdr/Eigen/src/Core/util/DisableStupidWarnings.h makechain.o: ../hdr/Eigen/src/Core/util/Macros.h makechain.o: ../hdr/Eigen/src/Core/util/MKL_support.h makechain.o: ../hdr/Eigen/src/Core/util/Constants.h makechain.o: ../hdr/Eigen/src/Core/util/ForwardDeclarations.h makechain.o: ../hdr/Eigen/src/Core/util/Meta.h makechain.o: ../hdr/Eigen/src/Core/util/XprHelper.h makechain.o: ../hdr/Eigen/src/Core/util/StaticAssert.h makechain.o: ../hdr/Eigen/src/Core/util/Memory.h makechain.o: 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makechain.o: ../hdr/Eigen/src/Core/PermutationMatrix.h makechain.o: ../hdr/Eigen/src/Core/Transpositions.h makechain.o: ../hdr/Eigen/src/Core/Redux.h ../hdr/Eigen/src/Core/Visitor.h makechain.o: ../hdr/Eigen/src/Core/Fuzzy.h ../hdr/Eigen/src/Core/IO.h makechain.o: ../hdr/Eigen/src/Core/Swap.h makechain.o: ../hdr/Eigen/src/Core/CommaInitializer.h makechain.o: ../hdr/Eigen/src/Core/Flagged.h makechain.o: ../hdr/Eigen/src/Core/ProductBase.h makechain.o: ../hdr/Eigen/src/Core/GeneralProduct.h makechain.o: ../hdr/Eigen/src/Core/TriangularMatrix.h makechain.o: ../hdr/Eigen/src/Core/SelfAdjointView.h makechain.o: ../hdr/Eigen/src/Core/SolveTriangular.h makechain.o: ../hdr/Eigen/src/Core/products/Parallelizer.h makechain.o: ../hdr/Eigen/src/Core/products/CoeffBasedProduct.h makechain.o: ../hdr/Eigen/src/Core/products/GeneralBlockPanelKernel.h makechain.o: ../hdr/Eigen/src/Core/products/GeneralMatrixVector.h makechain.o: ../hdr/Eigen/src/Core/products/GeneralMatrixMatrix.h makechain.o: 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makechain.o: ../hdr/Eigen/SparseCholesky ../hdr/Eigen/src/misc/Solve.h makechain.o: ../hdr/Eigen/src/misc/SparseSolve.h makechain.o: ../hdr/Eigen/src/SparseCholesky/SimplicialCholesky.h makechain.o: ../hdr/Eigen/IterativeLinearSolvers makechain.o: ../hdr/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h makechain.o: ../hdr/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h makechain.o: ../hdr/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h makechain.o: ../hdr/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h makechain.o: ../hdr/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h makechain.o: ../hdr/matrix-stream.h ../hdr/ss.h ../hdr/rvsimple.h ../hdr/rv.h makechain.o: ../hdr/context.h ../hdr/trajectory.h ../hdr/dyncomp.h makechain.o: ../hdr/dynamics.h ../hdr/markov.h ../hdr/process.h makechain.o: ../hdr/structure.h ../hdr/ctbndyn.h ../hdr/suffstatsquery.h makechain.o: ../hdr/extramath.h ../hdr/multirv.h ../hdr/rvcomp.h makechain.o: ../hdr/multisimple.h ../hdr/params.h ../hdr/bn.h gen_partialdata.o: ../hdr/markov.h ../hdr/process.h ../hdr/dynamics.h gen_partialdata.o: ../hdr/context.h ../hdr/streamserial.h gen_partialdata.o: ../hdr/serial/serial.h ../hdr/nullptr03.h gen_partialdata.o: ../hdr/streamserial-old.h ../hdr/defines.h gen_partialdata.o: ../hdr/streamextra.h ../hdr/trajectory.h ../hdr/rvsimple.h gen_partialdata.o: ../hdr/matrix.h ../hdr/matrix-eigen.h ../hdr/Eigen/Core gen_partialdata.o: ../hdr/Eigen/src/Core/util/DisableStupidWarnings.h gen_partialdata.o: ../hdr/Eigen/src/Core/util/Macros.h gen_partialdata.o: ../hdr/Eigen/src/Core/util/MKL_support.h gen_partialdata.o: ../hdr/Eigen/src/Core/util/Constants.h gen_partialdata.o: ../hdr/Eigen/src/Core/util/ForwardDeclarations.h gen_partialdata.o: ../hdr/Eigen/src/Core/util/Meta.h gen_partialdata.o: ../hdr/Eigen/src/Core/util/XprHelper.h gen_partialdata.o: ../hdr/Eigen/src/Core/util/StaticAssert.h gen_partialdata.o: ../hdr/Eigen/src/Core/util/Memory.h gen_partialdata.o: ../hdr/Eigen/src/Core/NumTraits.h gen_partialdata.o: ../hdr/Eigen/src/Core/MathFunctions.h gen_partialdata.o: ../hdr/Eigen/src/Core/GenericPacketMath.h gen_partialdata.o: ../hdr/Eigen/src/Core/arch/Default/Settings.h gen_partialdata.o: ../hdr/Eigen/src/Core/Functors.h gen_partialdata.o: ../hdr/Eigen/src/Core/DenseCoeffsBase.h gen_partialdata.o: ../hdr/Eigen/src/Core/DenseBase.h gen_partialdata.o: ../hdr/Eigen/src/plugins/BlockMethods.h gen_partialdata.o: ../hdr/Eigen/src/Core/MatrixBase.h gen_partialdata.o: ../hdr/Eigen/src/plugins/CommonCwiseUnaryOps.h gen_partialdata.o: ../hdr/Eigen/src/plugins/CommonCwiseBinaryOps.h gen_partialdata.o: ../hdr/Eigen/src/plugins/MatrixCwiseUnaryOps.h gen_partialdata.o: ../hdr/Eigen/src/plugins/MatrixCwiseBinaryOps.h gen_partialdata.o: ../hdr/matrix-eigenext.h ../hdr/Eigen/src/Core/EigenBase.h gen_partialdata.o: ../hdr/Eigen/src/Core/Assign.h gen_partialdata.o: ../hdr/Eigen/src/Core/util/BlasUtil.h gen_partialdata.o: ../hdr/Eigen/src/Core/DenseStorage.h gen_partialdata.o: 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gen_partialdata.o: ../hdr/Eigen/src/Core/Transpose.h gen_partialdata.o: ../hdr/Eigen/src/Core/DiagonalMatrix.h gen_partialdata.o: ../hdr/Eigen/src/Core/Diagonal.h gen_partialdata.o: ../hdr/Eigen/src/Core/DiagonalProduct.h gen_partialdata.o: ../hdr/Eigen/src/Core/PermutationMatrix.h gen_partialdata.o: ../hdr/Eigen/src/Core/Transpositions.h gen_partialdata.o: ../hdr/Eigen/src/Core/Redux.h gen_partialdata.o: ../hdr/Eigen/src/Core/Visitor.h gen_partialdata.o: ../hdr/Eigen/src/Core/Fuzzy.h ../hdr/Eigen/src/Core/IO.h gen_partialdata.o: ../hdr/Eigen/src/Core/Swap.h gen_partialdata.o: ../hdr/Eigen/src/Core/CommaInitializer.h gen_partialdata.o: ../hdr/Eigen/src/Core/Flagged.h gen_partialdata.o: ../hdr/Eigen/src/Core/ProductBase.h gen_partialdata.o: ../hdr/Eigen/src/Core/GeneralProduct.h gen_partialdata.o: ../hdr/Eigen/src/Core/TriangularMatrix.h gen_partialdata.o: ../hdr/Eigen/src/Core/SelfAdjointView.h gen_partialdata.o: ../hdr/Eigen/src/Core/SolveTriangular.h gen_partialdata.o: 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../hdr/Eigen/src/SparseCore/SparseFuzzy.h gen_partialdata.o: ../hdr/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h gen_partialdata.o: ../hdr/Eigen/src/SparseCore/SparseSparseProductWithPruning.h gen_partialdata.o: ../hdr/Eigen/src/SparseCore/SparseProduct.h gen_partialdata.o: ../hdr/Eigen/src/SparseCore/SparseDenseProduct.h gen_partialdata.o: ../hdr/Eigen/src/SparseCore/SparseDiagonalProduct.h gen_partialdata.o: ../hdr/Eigen/src/SparseCore/SparseTriangularView.h gen_partialdata.o: ../hdr/Eigen/src/SparseCore/SparseSelfAdjointView.h gen_partialdata.o: ../hdr/Eigen/src/SparseCore/TriangularSolver.h gen_partialdata.o: ../hdr/Eigen/src/SparseCore/SparseView.h gen_partialdata.o: ../hdr/Eigen/OrderingMethods gen_partialdata.o: ../hdr/Eigen/src/OrderingMethods/Amd.h gen_partialdata.o: ../hdr/Eigen/SparseCholesky ../hdr/Eigen/src/misc/Solve.h gen_partialdata.o: ../hdr/Eigen/src/misc/SparseSolve.h gen_partialdata.o: ../hdr/Eigen/src/SparseCholesky/SimplicialCholesky.h gen_partialdata.o: ../hdr/Eigen/IterativeLinearSolvers gen_partialdata.o: ../hdr/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h gen_partialdata.o: ../hdr/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h gen_partialdata.o: ../hdr/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h gen_partialdata.o: ../hdr/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h gen_partialdata.o: ../hdr/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h gen_partialdata.o: ../hdr/matrix-stream.h ../hdr/random.h ../hdr/rv.h gen_partialdata.o: ../hdr/ss.h ../hdr/structure.h ../hdr/ctbndyn.h gen_partialdata.o: ../hdr/suffstatsquery.h ../hdr/extramath.h ../hdr/params.h gen_partialdata.o: ../hdr/utils.h ../hdr/ensurectbn.h ../hdr/ctbn.h gen_partialdata.o: ../hdr/markovdyn.h ../hdr/markovsimple.h gen_partialdata.o: ../hdr/dynsimple.h ../hdr/dyncomp.h ../hdr/bn.h gen_partialdata.o: ../hdr/multirv.h ../hdr/rvcomp.h ../hdr/multisimple.h ctbnrle.1.1.1/demo/isquery.cc0000664000175000017500000000575512107032410016324 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "samplinginf.h" #include "importancesampler.h" #include "expmethod.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include #include "ensurectbn.h" using namespace std; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using impotrance sampling // Usage: // no inputs to run: ./importancequery >output_file int main(int argc, char **argv) { // load the input model, evidence and query answers in "queryinput.data" const char *input_file = "queryinput.data"; addnan(cout); InitParams(argc, argv); ifstream fin(input_file); // load model Markov m; m.Load(fin); const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics()); const BN *bn = dynamic_cast(m.GetStartDist()); Context context = ctbndyn->Domain() + ctbndyn->CondDomain(); //load evidence Trajectory evid; evid.Load(fin); // load true query answers double expect_q1, expect_q2; fin >> expect_q1 >> expect_q2; fin.close(); // construct the queries Instantiation x(context, -1); int querynode = ParamInt("QueryNode", 1); int querytimeval = ParamInt("QueryTimeVal", 1); Context c = ctbndyn->Node(querynode)->Domain(); x.SetVal(querynode, querytimeval); // query the amount of time node 1 spends on state 1 QueryTime query1(x); // query the number of times node 1 transition from state 0 to state 1 QueryTransition query2(c, 0, 1); // set up the approximate inference engine using importance sampler SamplingInf iinf; // set the number of samples int numofsamples = 1000; Sampler *sampler; ExpMethod method; sampler = new ImportanceSampler(&m, &evid, &method); iinf.SetSampler(sampler); iinf.SetProcess(&m); iinf.SetNumSample(numofsamples); // output the results cout << "the expected query answer is:" << endl; cout << expect_q1 << " " << expect_q2 << endl; cout << "the approximate inference answer using importance sampler to the query is:" << endl; cout << iinf.CalcQuery(query1) << " " << iinf.CalcQuery(query2) << endl; return 0; } ctbnrle.1.1.1/demo/gen_fulldata.cc0000664000175000017500000000437412107032410017244 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ //This code generates n fully observed trajectories from //a given ctbn. // //Usage: gen_fulldata <.ctbnfile> /* Command line parameters: * * Set the begin time of the trajectory * -DBeginTime (default: 0.0) * * Set the end time of the trajectory * -DEndTime (default: 5.0) * * Set the number of trajectories * -DNumTraj (default: 1) * */ #include "markov.h" #include "trajectory.h" #include "params.h" #include #include "ensurectbn.h" using namespace std; using namespace ctbn; int main(int argc, char**argv) { if (argc<3) { cout << "Usage: ./gen_fulldata <.ctbn file> " << endl; exit(0); } InitParams(argc, argv); double begintime = ParamDouble("BeginTime" , 0.0); double endtime = ParamDouble("EndTime", 5.0); if (endtime <= begintime) { cout << "invalid length of trajectory" << endl; exit(0); } int n = ParamInt("NumTraj", 1); ifstream fin(argv[1]); if (!fin.good()) { cout << "bad CTBN file" << endl; exit(1); } //load CTBN model from the file Markov ctbn; ctbn.Load(fin); ofstream fdata(argv[2]); if (!fdata.good()) { cout << "bad output file" << endl; exit(1); } fdata << n << endl; //generate data for(int i=0; i queryinput.data DEPENDS makeqinput ) add_custom_command(OUTPUT drug.ctbn COMMAND makedrug > drug.ctbn DEPENDS makedrug ) add_custom_command(OUTPUT drug.ctbndyn COMMAND makedrug -Dsavedyn 1 > drug.ctbndyn DEPENDS makedrug ) add_custom_target(datafiles ALL DEPENDS drug.ctbn queryinput.data drug.ctbndyn) foreach(demo_executable ${DEMO_EXECUTABLES}) add_executable(${demo_executable} ${demo_executable}.cc) target_link_libraries(${demo_executable} ${CTBN_LIBRARIES}) endforeach() ctbnrle.1.1.1/demo/using_graphic.cc0000664000175000017500000001103012107032410017424 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include #include "graphic.h" #include "markov.h" #include "params.h" #include "utils.h" #include "ensurectbn.h" using namespace std; using namespace ctbn; /* * This is code for generating the illustration of CTBN and trajectory * It takes in the MARKOV file (.ctbn) to extract the information. * */ /* Command line parameters: * * Pass in CTBN file * -DFile (default: drug.ctbn) * * Set the window x coordinate size in trajectory * -Dtraj_win_size (default: 800) * * * Set the font on trajectory graph * -Dtraj_font (default: 15) * * * Set the font on ctbn graph * -Dctbn_font (default: 16) * * * Set font on trajectory graph ruler * -Dtraj_ruler (default: 5) */ int main(int argc, char **argv) { //to initialize or use the params.h class InitParams(argc, argv); //to see if the file opens or not string File; //Read in the -DFile File = ParamStr("File", "drug.ctbn"); ifstream ls(File.c_str()); if(!ls.is_open()) return 0; //building the Markov to use in graphic program Markov ctbn; ctbn.Load(ls); //several different file to be written //dot extention for graphviz and eps extention for post-script //do not use temp.dot as the file name ofstream file("TrajRank.eps"); ofstream file2("CTBNRank.dot"); ofstream file3("TrajWName.eps"); ofstream file4("TrajWoName.eps"); ofstream file5("CTBNMatrix.dot"); //creating the Graphic class name test Graphic test; //setting the font for the trajectory graph test.SetFontTrajectory(16, 10); //to change the size of page in X use the below function //test.SetSizeTrajectory(const int &); //setting the font size for CTBN graphic test.SetFontCTBN(20); //string_ is the vector of name which will replace the number system //automatically assigned when graphic.cc has envoked. vector string_; string_.push_back("Eating"); string_.push_back("Full Stomach"); string_.push_back("Hungry"); string_.push_back("Uptake"); string_.push_back("Concentration"); string_.push_back("Barometer"); string_.push_back("Joint pain"); string_.push_back("Drowsy"); //changing the name of the CTBN and trajectory graphics test.Changes(string_); // use DrawBoth for specific trajectory need to be draw // DrawBoth take in // const Markov &, Trajectory & // std::ofstream &,std::string &, std::ofstream & //drawing the CTBN graph with rank system intergrated //it need to know what the file2 name is string filename = "CTBNRank.dot"; test.DrawCTBNRank(ctbn,file2, filename, file); //use the DrawCTBN if CTBN without matrix is needed //drawing the CTBN with matrixs for each node test.DrawCTBNMatrix(ctbn, file5); /* / Generate the trajectory using the CTBNDyn */ //to create the Trajectory::Index const CTBNDyn * B = dynamic_cast(ctbn.GetDynamics()); Context context = Context(); for (int i = 0; i < B->NumofNodes(); ++i) context = context + B->Node(i)->Domain(); Trajectory tr; //generating the trajectory starting from 0 to 3 second tr.SetBeginTime(0); tr.SetEndTime(3); ctbn.Sample(tr); //just save the trajectory which is created for later use ofstream os("using_graphic.traj"); //remove the part of the Information frmo the trajectory for(int i = 0; i < B->NumofNodes(); i ++) { RemoveInformation(tr,context, i, 1.0, 1.5); } tr.Save(os); //to draw the Index into trajectory graph test.DrawTrajectory(tr, context ,file3); //clear the changed name test.ClearName(); //draw the trajectory with name unchanged test.DrawTrajectory(tr,context ,file4); //example on DrawBoth //test.DrawBoth(ctbn,tr, file2, filename, file); //end of the file cout << "done" << endl; file.close(); file2.close(); file3.close(); file4.close(); file5.close(); return 0; } ctbnrle.1.1.1/demo/twonode.cc0000664000175000017500000000575512107032410016302 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "ctbn.h" #include "markovdyn.h" #include "bn.h" #include "multirv.h" #include using namespace std; using namespace ctbn; int main(int argc, char **argv) { Context nullc; // set of no variables Context var0,var1; // two variable sets, each with a single variable: var0.AddVar(0,2); // add variable 0 with 2 states var1.AddVar(1,2); // add variable 1 with 2 states // info for variable 0: MultiRV init0(var0,nullc); // the initial distribution, no parents vectr p0(2); // the initial distribution's parameters p0[0] = 0.5; // this variable's initial distribution is uniform p0[1] = 0.5; init0[0].SetDist(p0); // set the initial distribution ([0] b/c it has // no parents and so there is only 1 distribution) MarkovDyn dyn0(var0,var1); // the CIM has "var0" as its variables // with "var1" variables as parents // when var1=0, Q = [-1 1 ; 2 -2] dyn0(0)->Intensity()[0][0] = -1; dyn0(0)->Intensity()[0][1] = 1; dyn0(0)->Intensity()[1][0] = 2; dyn0(0)->Intensity()[1][1] = -2; // when var1=1, Q = [-10 10 ; 20 -20] dyn0(1)->Intensity()[0][0] = -10; dyn0(1)->Intensity()[0][1] = 10; dyn0(1)->Intensity()[1][0] = 20; dyn0(1)->Intensity()[1][1] = -20; // info for variable 1: MultiRV init1(var1,var0); // the initial distribution, 0 as parent vectr p1(2); p1[0] = 0.9; // when var0=0, var1 tends to start in state 0 p1[1] = 0.1; init1[0].SetDist(p1); // set the distribution p1[0] = 0.1; // when var0=1, var1 tends to start in state 1 p1[1] = 0.9; init1[1].SetDist(p1); // set the distribution MarkovDyn dyn1(var1,var0); // the CIM has "var0" as its variables // with "var1" variables as parents // when var0=0, Q = [-5 5; 6 -6] dyn1(0)->Intensity()[0][0] = -5; dyn1(0)->Intensity()[0][1] = 5; dyn1(0)->Intensity()[1][0] = 6; dyn1(0)->Intensity()[1][1] = -6; // when var0=1, Q = [-7 7; 8 -8] dyn1(1)->Intensity()[0][0] = -7; dyn1(1)->Intensity()[0][1] = 7; dyn1(1)->Intensity()[1][0] = 8; dyn1(1)->Intensity()[1][1] = -8; // now place into CTBN: CTBNDyn ctbndyn; ctbndyn.AddNode(dyn0.Clone()); ctbndyn.AddNode(dyn1.Clone()); BN bn; bn.AddNode(init0.Clone()); bn.AddNode(init1.Clone()); CTBN ctbn(bn.Clone(),ctbndyn.Clone()); ctbn.Save(cout); } ctbnrle.1.1.1/demo/gibbssampquery.cc0000664000175000017500000000575512107032410017660 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "samplinginf.h" #include "gibbssampler.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include #include "ensurectbn.h" using namespace std; using namespace ctbn; // this file reads in the input model & evidence & true query results from "queryinput.data" // it calculate the queries using gibbs sampling // Usage: // no inputs to run: ./gibbsquery >output_file int main(int argc, char **argv) { // load the input model, evidence and query answers in "queryinput.data" const char *input_file = "queryinput.data"; addnan(cout); InitParams(argc, argv); ifstream fin(input_file); // load model Markov m; m.Load(fin); const CTBNDyn *ctbndyn = dynamic_cast(m.GetDynamics()); const BN *bn = dynamic_cast(m.GetStartDist()); Context context = ctbndyn->Domain() + ctbndyn->CondDomain(); //load evidence Trajectory evid; evid.Load(fin); // load true query answers double expect_q1, expect_q2; fin >> expect_q1 >> expect_q2; fin.close(); // construct the queries Instantiation x(context, -1); int querynode = ParamInt("QueryNode", 1); int querytimeval = ParamInt("QueryTimeVal", 1); Context c = ctbndyn->Node(querynode)->Domain(); x.SetVal(querynode, querytimeval); // query the amount of time node 1 spends on state 1 QueryTime query1(x); // query the number of times node 1 transition from state 0 to state 1 QueryTransition query2(c, 0, 1); // set up the approximate inference engine using gibbs sampler SamplingInf ginf; // number of burn in iterations int numofburniniter = 1000; // number of samples int numofsamples = 1000; Sampler *sampler; sampler = new GibbsSampler(&m, &evid, numofburniniter); ginf.SetSampler(sampler); ginf.SetProcess(&m); ginf.SetNumSample(numofsamples); // output the results cout << "the expected query answer is:" << endl; cout << expect_q1 << " " << expect_q2 << endl; cout << "the approximate inference answer using gibbs sampler to the query is:" << endl; cout << ginf.CalcQuery(query1) << " " << ginf.CalcQuery(query2) << endl; return 0; } ctbnrle.1.1.1/demo/emlearn.cc0000664000175000017500000000465312107032410016242 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ //This code shows how to learn the paramters of a CTBN //given partially observed data using EM algorithm. Data //are n trajectories saved in datafile. They can be generated //using gen_partialdata. Exact inference method is used in EM. #include "markov.h" #include "trajectory.h" #include "params.h" #include "em.h" #include "exactmarkovinf.h" #include #include "ensurectbn.h" using namespace std; using namespace ctbn; int main(int argc, char**argv) { InitParams(argc, argv); //load CTBN model from the file Markov *model; string modelstr = ParamStr("ModelFile","-"); if (modelstr != "-") { ifstream fin(modelstr.c_str()); if (!fin.good()) { cerr << "bad CTBN file" << endl; exit(1); } model = Markov::LoadPtr(fin); } else { model = Markov::LoadPtr(cin); } string datastr = ParamStr("InputData","-"); ifstream dataf; if (datastr != "-") { dataf.open(datastr.c_str()); if (!dataf.good()) { cerr << "bad datafile" << endl; exit(1); } } istream &fdata = datastr =="-" ? cin : dataf; int n; fdata >> n; //load data vector data; for(int i=0; iScramble(); EM(data, model, inf); string outstr = ParamStr("OutputFile","-"); if (outstr != "-") { ofstream outf(outstr.c_str()); if (!outf.good()) { cerr << "bad datafile" << endl; exit(1); } model->Save(outf); } else { model->Save(cout); } delete inf; return 0; } ctbnrle.1.1.1/demo/Makefile0000664000175000017500000040342212251374045015762 0ustar csheltoncsheltonSRC := $(wildcard *.cc) LIBOBJ = ../src/ctbn.a ../glpk/glpk-4.47/src/.libs/libglpk.a LPOBJ = ../src/linearprogram.o EXES = clique_example makechain makedrug learnparams gibbssampquery sampleprocess traj2ascii using_graphic structurelearn sem makeqinput emlearn gen_fulldata gen_partialdata isquery twonode exactquery DATA = queryinput.data drug.ctbn drug.ctbndyn CXX = g++ OFLAG = -O3 #OFLAG = -ggdb CXXFLAGS += $(OFLAG) -std=c++03 -pedantic -Wall -Wextra -Wno-unused-local-typedefs -Wno-long-long -Wno-unused-parameter -Wno-reorder -Wno-unused-variable CXXFLAGS += -I../hdr -I../tst -I../glpk/glpk-4.47/include -I../glpk/glpk-4.47/src -L../glpk/glpk-4.47/src/.libs CXXFLAGS += -DUSE_PTHREADS LDFLAGS = -lglpk -lpthread DEPEND = makedepend -Y -- $(CXXFLAGS) -- all: $(EXES) $(DATA) depend: $(SRC) $(DEPEND) $(SRC) queryinput.data: makeqinput (rm -f makequeryinput.data; ./makeqinput > queryinput.data) drug.ctbn: makedrug (rm -f drug.ctbn; ./makedrug > drug.ctbn) drug.ctbndyn: makedrug (rm -f drug.ctbndyn; ./makedrug -Dsavedyn 1 > drug.ctbndyn) using_graphic: using_graphic.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o using_graphic using_graphic.o $(LIBOBJ) clique_example: clique_example.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o clique_example clique_example.o $(LIBOBJ) makechain: makechain.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o makechain makechain.o $(LIBOBJ) maketraj: maketraj.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o maketraj maketraj.o $(LIBOBJ) twonode: twonode.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o twonode twonode.o $(LIBOBJ) makedrug: makedrug.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o makedrug makedrug.o $(LIBOBJ) learnparams: learnparams.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o learnparams learnparams.o $(LIBOBJ) emlearn: emlearn.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o emlearn emlearn.o $(LIBOBJ) -lpthread gibbssampquery: gibbssampquery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o gibbssampquery gibbssampquery.o $(LIBOBJ) exactquery: exactquery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o exactquery exactquery.o $(LIBOBJ) isquery: isquery.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o isquery isquery.o $(LIBOBJ) sampleprocess: sampleprocess.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o sampleprocess sampleprocess.o $(LIBOBJ) traj2ascii: traj2ascii.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o traj2ascii traj2ascii.o $(LIBOBJ) makeqinput: makeqinput.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o makeqinput makeqinput.o $(LIBOBJ) structurelearn: structurelearn.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o structurelearn structurelearn.o $(LIBOBJ) gen_fulldata: gen_fulldata.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o gen_fulldata gen_fulldata.o $(LIBOBJ) gen_partialdata: gen_partialdata.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o gen_partialdata gen_partialdata.o $(LIBOBJ) sem: sem.o $(LIBOBJ) $(CXX) $(CXXFLAGS) $(LDFLAGS) -o sem sem.o $(LIBOBJ) clean: rm -rf *.o *~ $(EXES) # DO NOT DELETE sampleprocess.o: ../hdr/process.h ../hdr/dynamics.h ../hdr/context.h sampleprocess.o: ../hdr/streamserial.h ../hdr/serial/serial.h sampleprocess.o: ../hdr/nullptr03.h ../hdr/streamserial-old.h sampleprocess.o: ../hdr/defines.h ../hdr/streamextra.h ../hdr/trajectory.h sampleprocess.o: ../hdr/rvsimple.h ../hdr/matrix.h ../hdr/matrix-eigen.h sampleprocess.o: ../hdr/Eigen/Core sampleprocess.o: ../hdr/Eigen/src/Core/util/DisableStupidWarnings.h sampleprocess.o: ../hdr/Eigen/src/Core/util/Macros.h sampleprocess.o: ../hdr/Eigen/src/Core/util/MKL_support.h sampleprocess.o: ../hdr/Eigen/src/Core/util/Constants.h sampleprocess.o: ../hdr/Eigen/src/Core/util/ForwardDeclarations.h sampleprocess.o: ../hdr/Eigen/src/Core/util/Meta.h sampleprocess.o: ../hdr/Eigen/src/Core/util/XprHelper.h sampleprocess.o: ../hdr/Eigen/src/Core/util/StaticAssert.h sampleprocess.o: ../hdr/Eigen/src/Core/util/Memory.h sampleprocess.o: ../hdr/Eigen/src/Core/NumTraits.h sampleprocess.o: ../hdr/Eigen/src/Core/MathFunctions.h sampleprocess.o: ../hdr/Eigen/src/Core/GenericPacketMath.h sampleprocess.o: ../hdr/Eigen/src/Core/arch/Default/Settings.h sampleprocess.o: ../hdr/Eigen/src/Core/Functors.h sampleprocess.o: ../hdr/Eigen/src/Core/DenseCoeffsBase.h sampleprocess.o: ../hdr/Eigen/src/Core/DenseBase.h sampleprocess.o: ../hdr/Eigen/src/plugins/BlockMethods.h sampleprocess.o: ../hdr/Eigen/src/Core/MatrixBase.h sampleprocess.o: ../hdr/Eigen/src/plugins/CommonCwiseUnaryOps.h sampleprocess.o: ../hdr/Eigen/src/plugins/CommonCwiseBinaryOps.h sampleprocess.o: ../hdr/Eigen/src/plugins/MatrixCwiseUnaryOps.h sampleprocess.o: ../hdr/Eigen/src/plugins/MatrixCwiseBinaryOps.h sampleprocess.o: ../hdr/matrix-eigenext.h ../hdr/Eigen/src/Core/EigenBase.h sampleprocess.o: ../hdr/Eigen/src/Core/Assign.h sampleprocess.o: ../hdr/Eigen/src/Core/util/BlasUtil.h sampleprocess.o: ../hdr/Eigen/src/Core/DenseStorage.h sampleprocess.o: ../hdr/Eigen/src/Core/NestByValue.h sampleprocess.o: ../hdr/Eigen/src/Core/ForceAlignedAccess.h sampleprocess.o: ../hdr/Eigen/src/Core/ReturnByValue.h sampleprocess.o: ../hdr/Eigen/src/Core/NoAlias.h sampleprocess.o: ../hdr/Eigen/src/Core/PlainObjectBase.h sampleprocess.o: ../hdr/Eigen/src/Core/Matrix.h ../hdr/Eigen/src/Core/Array.h sampleprocess.o: ../hdr/Eigen/src/Core/CwiseBinaryOp.h sampleprocess.o: ../hdr/Eigen/src/Core/CwiseUnaryOp.h sampleprocess.o: ../hdr/Eigen/src/Core/CwiseNullaryOp.h sampleprocess.o: ../hdr/Eigen/src/Core/CwiseUnaryView.h sampleprocess.o: ../hdr/Eigen/src/Core/SelfCwiseBinaryOp.h sampleprocess.o: ../hdr/Eigen/src/Core/Dot.h sampleprocess.o: ../hdr/Eigen/src/Core/StableNorm.h sampleprocess.o: ../hdr/Eigen/src/Core/MapBase.h sampleprocess.o: ../hdr/Eigen/src/Core/Stride.h ../hdr/Eigen/src/Core/Map.h sampleprocess.o: ../hdr/Eigen/src/Core/Block.h sampleprocess.o: ../hdr/Eigen/src/Core/VectorBlock.h sampleprocess.o: ../hdr/Eigen/src/Core/Transpose.h sampleprocess.o: ../hdr/Eigen/src/Core/DiagonalMatrix.h sampleprocess.o: ../hdr/Eigen/src/Core/Diagonal.h sampleprocess.o: ../hdr/Eigen/src/Core/DiagonalProduct.h sampleprocess.o: ../hdr/Eigen/src/Core/PermutationMatrix.h sampleprocess.o: ../hdr/Eigen/src/Core/Transpositions.h sampleprocess.o: ../hdr/Eigen/src/Core/Redux.h sampleprocess.o: ../hdr/Eigen/src/Core/Visitor.h sampleprocess.o: ../hdr/Eigen/src/Core/Fuzzy.h ../hdr/Eigen/src/Core/IO.h sampleprocess.o: ../hdr/Eigen/src/Core/Swap.h sampleprocess.o: ../hdr/Eigen/src/Core/CommaInitializer.h sampleprocess.o: ../hdr/Eigen/src/Core/Flagged.h sampleprocess.o: ../hdr/Eigen/src/Core/ProductBase.h sampleprocess.o: ../hdr/Eigen/src/Core/GeneralProduct.h sampleprocess.o: ../hdr/Eigen/src/Core/TriangularMatrix.h sampleprocess.o: ../hdr/Eigen/src/Core/SelfAdjointView.h sampleprocess.o: ../hdr/Eigen/src/Core/SolveTriangular.h sampleprocess.o: ../hdr/Eigen/src/Core/products/Parallelizer.h sampleprocess.o: ../hdr/Eigen/src/Core/products/CoeffBasedProduct.h sampleprocess.o: ../hdr/Eigen/src/Core/products/GeneralBlockPanelKernel.h sampleprocess.o: ../hdr/Eigen/src/Core/products/GeneralMatrixVector.h sampleprocess.o: ../hdr/Eigen/src/Core/products/GeneralMatrixMatrix.h sampleprocess.o: ../hdr/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h sampleprocess.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixVector.h sampleprocess.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixMatrix.h sampleprocess.o: ../hdr/Eigen/src/Core/products/SelfadjointProduct.h sampleprocess.o: ../hdr/Eigen/src/Core/products/SelfadjointRank2Update.h sampleprocess.o: ../hdr/Eigen/src/Core/products/TriangularMatrixVector.h sampleprocess.o: ../hdr/Eigen/src/Core/products/TriangularMatrixMatrix.h sampleprocess.o: ../hdr/Eigen/src/Core/products/TriangularSolverMatrix.h sampleprocess.o: 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sampleprocess.o: ../hdr/Eigen/src/SparseCore/SparseSparseProductWithPruning.h sampleprocess.o: ../hdr/Eigen/src/SparseCore/SparseProduct.h sampleprocess.o: ../hdr/Eigen/src/SparseCore/SparseDenseProduct.h sampleprocess.o: ../hdr/Eigen/src/SparseCore/SparseDiagonalProduct.h sampleprocess.o: ../hdr/Eigen/src/SparseCore/SparseTriangularView.h sampleprocess.o: ../hdr/Eigen/src/SparseCore/SparseSelfAdjointView.h sampleprocess.o: ../hdr/Eigen/src/SparseCore/TriangularSolver.h sampleprocess.o: ../hdr/Eigen/src/SparseCore/SparseView.h sampleprocess.o: ../hdr/Eigen/OrderingMethods sampleprocess.o: ../hdr/Eigen/src/OrderingMethods/Amd.h sampleprocess.o: ../hdr/Eigen/SparseCholesky ../hdr/Eigen/src/misc/Solve.h sampleprocess.o: ../hdr/Eigen/src/misc/SparseSolve.h sampleprocess.o: ../hdr/Eigen/src/SparseCholesky/SimplicialCholesky.h sampleprocess.o: ../hdr/Eigen/IterativeLinearSolvers sampleprocess.o: ../hdr/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h sampleprocess.o: 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gen_fulldata.o: ../hdr/Eigen/src/SparseCore/SparseBlock.h gen_fulldata.o: ../hdr/Eigen/src/SparseCore/SparseTranspose.h gen_fulldata.o: ../hdr/Eigen/src/SparseCore/SparseCwiseUnaryOp.h gen_fulldata.o: ../hdr/Eigen/src/SparseCore/SparseCwiseBinaryOp.h gen_fulldata.o: ../hdr/Eigen/src/SparseCore/SparseDot.h gen_fulldata.o: ../hdr/Eigen/src/SparseCore/SparsePermutation.h gen_fulldata.o: ../hdr/Eigen/src/SparseCore/SparseAssign.h gen_fulldata.o: ../hdr/Eigen/src/SparseCore/SparseRedux.h gen_fulldata.o: ../hdr/Eigen/src/SparseCore/SparseFuzzy.h gen_fulldata.o: ../hdr/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h gen_fulldata.o: ../hdr/Eigen/src/SparseCore/SparseSparseProductWithPruning.h gen_fulldata.o: ../hdr/Eigen/src/SparseCore/SparseProduct.h gen_fulldata.o: ../hdr/Eigen/src/SparseCore/SparseDenseProduct.h gen_fulldata.o: ../hdr/Eigen/src/SparseCore/SparseDiagonalProduct.h gen_fulldata.o: ../hdr/Eigen/src/SparseCore/SparseTriangularView.h gen_fulldata.o: 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../hdr/structure.h ../hdr/ctbndyn.h gen_fulldata.o: ../hdr/suffstatsquery.h ../hdr/extramath.h ../hdr/params.h gen_fulldata.o: ../hdr/ensurectbn.h ../hdr/ctbn.h ../hdr/markovdyn.h gen_fulldata.o: ../hdr/markovsimple.h ../hdr/dynsimple.h ../hdr/dyncomp.h gen_fulldata.o: ../hdr/bn.h ../hdr/multirv.h ../hdr/rvcomp.h gen_fulldata.o: ../hdr/multisimple.h gibbssampquery.o: ../hdr/markovdyn.h ../hdr/markovsimple.h ../hdr/dynsimple.h gibbssampquery.o: ../hdr/random.h ../hdr/matrix.h ../hdr/matrix-eigen.h gibbssampquery.o: ../hdr/streamserial.h ../hdr/serial/serial.h gibbssampquery.o: ../hdr/nullptr03.h ../hdr/streamserial-old.h gibbssampquery.o: ../hdr/defines.h ../hdr/streamextra.h ../hdr/Eigen/Core gibbssampquery.o: ../hdr/Eigen/src/Core/util/DisableStupidWarnings.h gibbssampquery.o: ../hdr/Eigen/src/Core/util/Macros.h gibbssampquery.o: ../hdr/Eigen/src/Core/util/MKL_support.h gibbssampquery.o: ../hdr/Eigen/src/Core/util/Constants.h gibbssampquery.o: 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gibbssampquery.o: ../hdr/Eigen/src/Core/StableNorm.h gibbssampquery.o: ../hdr/Eigen/src/Core/MapBase.h gibbssampquery.o: ../hdr/Eigen/src/Core/Stride.h ../hdr/Eigen/src/Core/Map.h gibbssampquery.o: ../hdr/Eigen/src/Core/Block.h gibbssampquery.o: ../hdr/Eigen/src/Core/VectorBlock.h gibbssampquery.o: ../hdr/Eigen/src/Core/Transpose.h gibbssampquery.o: ../hdr/Eigen/src/Core/DiagonalMatrix.h gibbssampquery.o: ../hdr/Eigen/src/Core/Diagonal.h gibbssampquery.o: ../hdr/Eigen/src/Core/DiagonalProduct.h gibbssampquery.o: ../hdr/Eigen/src/Core/PermutationMatrix.h gibbssampquery.o: ../hdr/Eigen/src/Core/Transpositions.h gibbssampquery.o: ../hdr/Eigen/src/Core/Redux.h gibbssampquery.o: ../hdr/Eigen/src/Core/Visitor.h gibbssampquery.o: ../hdr/Eigen/src/Core/Fuzzy.h ../hdr/Eigen/src/Core/IO.h gibbssampquery.o: ../hdr/Eigen/src/Core/Swap.h gibbssampquery.o: ../hdr/Eigen/src/Core/CommaInitializer.h gibbssampquery.o: ../hdr/Eigen/src/Core/Flagged.h gibbssampquery.o: ../hdr/Eigen/src/Core/ProductBase.h gibbssampquery.o: ../hdr/Eigen/src/Core/GeneralProduct.h gibbssampquery.o: ../hdr/Eigen/src/Core/TriangularMatrix.h gibbssampquery.o: ../hdr/Eigen/src/Core/SelfAdjointView.h gibbssampquery.o: ../hdr/Eigen/src/Core/SolveTriangular.h gibbssampquery.o: ../hdr/Eigen/src/Core/products/Parallelizer.h gibbssampquery.o: ../hdr/Eigen/src/Core/products/CoeffBasedProduct.h gibbssampquery.o: ../hdr/Eigen/src/Core/products/GeneralBlockPanelKernel.h gibbssampquery.o: ../hdr/Eigen/src/Core/products/GeneralMatrixVector.h gibbssampquery.o: ../hdr/Eigen/src/Core/products/GeneralMatrixMatrix.h gibbssampquery.o: ../hdr/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h gibbssampquery.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixVector.h gibbssampquery.o: ../hdr/Eigen/src/Core/products/SelfadjointMatrixMatrix.h gibbssampquery.o: ../hdr/Eigen/src/Core/products/SelfadjointProduct.h gibbssampquery.o: ../hdr/Eigen/src/Core/products/SelfadjointRank2Update.h gibbssampquery.o: 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<\Context> <\RV> 0.3 0.7 0 <\vectr> <\MultiZSimple> <\vector> <\RVCondSimpleComp.MultiZSimple> <\RVComp> <\MultiRV> <\map> <\Context> <\map> <\Context> <\RV> 0.1 0.9 <\vectr> <\MultiZSimple> <\vector> <\RVCondSimpleComp.MultiZSimple> <\RVComp> <\MultiRV> <\map> <\Context> <\map> <\Context> <\RV> 0.1 0.9 <\vectr> <\MultiZSimple> <\vector> <\RVCondSimpleComp.MultiZSimple> <\RVComp> <\MultiRV> <\vector> <\BN> <\map> <\Context> <\map> <\Context> <\Dynamics> <\map> <\Context> <\map> <\Context> <\Dynamics> -0.01 0.01 10 -10 <\matrix> <\MarkovSimple> -2 2 1 -1 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -0.02 0.01 0.01 0.3 -0.31 0.01 0.01 1 -1.01 <\matrix> <\MarkovSimple> -6.01 6 0.01 0.01 -3.01 3 0.01 0.01 -0.02 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -2 2 0.01 -0.01 <\matrix> <\MarkovSimple> -0.01 0.01 0.01 -0.01 <\matrix> <\MarkovSimple> -0.01 0.01 10 -10 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -0 0 0.5 -0.5 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -0.02 0.01 0.01 0.25 -0.26 0.01 0.01 0.5 -0.51 <\matrix> <\MarkovSimple> -0.02 0.01 0.01 0.25 -0.26 0.01 0.01 0.5 -0.51 <\matrix> <\MarkovSimple> -0.02 0.01 0.01 0.25 -0.26 0.01 0.01 0.5 -0.51 <\matrix> <\MarkovSimple> -2.01 2 0.01 0.01 -1.01 1 0.01 0.01 -0.02 <\matrix> <\MarkovSimple> -1.01 1 0.01 0.01 -2.01 2 0.01 0.01 -0.02 <\matrix> <\MarkovSimple> -0.51 0.5 0.01 0.01 -4.01 4 0.01 0.01 -0.02 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -0.21 0.2 0.01 0.05 -0.1 0.05 0.01 0.2 -0.21 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -6 6 0.1 -0.1 <\matrix> <\MarkovSimple> -1 1 0.1 -0.1 <\matrix> <\MarkovSimple> -6 6 0.1 -0.1 <\matrix> <\MarkovSimple> -1 1 0.3 -0.3 <\matrix> <\MarkovSimple> -0.3 0.3 0.3 -0.3 <\matrix> <\MarkovSimple> -1 1 0.3 -0.3 <\matrix> <\MarkovSimple> -0.01 0.01 2 -2 <\matrix> <\MarkovSimple> -0.01 0.01 2 -2 <\matrix> <\MarkovSimple> -0.01 0.01 2 -2 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -0.17 0.17 0.5 -0.5 <\matrix> <\MarkovSimple> -0.33 0.33 0.33 -0.33 <\matrix> <\MarkovSimple> -1 1 0.1 -0.1 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\vector> <\CTBNDyn> <\Markov> 0 10 3 0 2 0.5 1 0.5 -1 1 1 2.5 2 6 1 5 1 ctbnrle.1.1.1/demo/README.txt0000664000175000017500000001130312107032410015774 0ustar csheltoncsheltonThe excutables in this folder give examples of how to use the CTBN-RLE libraries. CTBN models are usually loaded from files in the excutables. The following is a quick description of the file formats and their extensions File Ext Description .ctbn A CTBN model saved by method Save() .ctbnptr A CTBN model saved by method SavePtr() .ctbndyn A CTBNDyn model saved by method Save() .ctbndynptr A pointer of CTBNDyn model saved by method SavePtr() .traj A single trajectory saved by method Save() The example files drug.ctbn, drug.ctnbptr, and queryinput.data are created by the Makefile. This is because, for this version of the code, the file formats depend on the RTTI information supplied by the compiler, which differs across compilers. Demo executables ================ Constructing a network ---------------------- twonode: use as "twonode" Creates a simple two-node network: The initial distribution BN has the structure 0->1 and the dynamics have the structure 0<->1. The parameters are arbitrarily chosen. Good to read to understand how to build a CTBN. makedrug: use as "makedrug [options] > " Creates the drug network as used in Nodelman et al. 2002 and saves it to stdout. See makedrug.cc for details on optional parameters. makechain: use as "makechain [option] > " Creates a chain structured network and saves it to stdout. See makechain.cc for details on optional parameters. Generating trajectories ------------------------- sampleprocess: use as "sampleprocess < " Loads a process from stdin and outputs (to stdout) a sample from 0 until time endtime. gen_fulldata: use as "gen_fulldata <.ctbn file> [option]" Generates complete trajectories from a given CTBN and saves the trajectories in a file. The trajectories can be used as fully observed data for learning CTBN parameters. See gen_fulldata.cc for details on optional paramters. gen_partialdata: use as "gen_partialdata [option] < .ctbnfile > outfile" Generates incomplete trajectories from a given CTBN and save the trajectories in a file. The trajectories can be used as partially observed data for learning CTBN parameters using EM algorithm. See gen_partialdata.cc for details on optional patameters. Queries ------- exactquery: use as "exactquery" Load in model, evidence, true query answers from the file "queryinput.data" and compute queries using exact Markov inference and compare to the true answers. See exactquery.cc for details. importancequery: use as "importancequery" Load in model, evidence, true query answers from the file "queryinput.data" Compute queries using approximate inference via importance sampler and compare to the true answers. See importancequery.cc for details. gibbsquery: use as "gibbsquery" Load in model, evidence, true query answers from the file "queryinput.data" and compute queries using approximate inference via gibbs sampler and compare to the true answers. See gibbsquery.cc for details. Note there are other inference methods also available. See the tst directory for other examples. Parameter learning ------------------ learnparams: use as "learnparams <.ctbn file> " Demonstates parameter learning of a given CTBN with complete data. The algorithm was described in Nodelman et al., 2003. The data can be generated using gen_fulldata. emlearn: use as "emlearn <.ctbn file> " Demonstate parameter learning of a given CTBN with incomplete data. The algorithm was described in Nodelman et al., 2005. The data can be generated using gen_partialdata. Structure learning ------------------ structurelearn: use as "structurelearn" Demonstrates structure learning on the drug network used in Nodelman et al. 2002. Reports on the difference in number of parameters for the learned BN and Hamming distance for the learned CTBN. See structurelearn.cc for details on optional parameters. sem: use as "sem" Demonstrates structural EM on the drug network used in Nodelman et al. 2002. Reports on the difference in number of parameters for the learned BN and Hamming distance for the learned CTBN. See sem.cc for details on optional parameters. Constructing a clique tree -------------------------- clique_example: use as "clique_example <.ctbn file>" Generates a clique tree from a CTBN. Visualization ------------- traj2ascii: use as "traj2ascii < .trajfile" Takes a trajectory (say, produced from sampleprocess) and tries to make a nice "ASCII" picture of the trajectory. using_graphic: use as "using_graphic" Produces a .dot file for graphviz from a CTBN Draws a trajectory as a postscript file. see using_graphic.cc for details on optional parameters. ctbnrle.1.1.1/demo/drug.ctbn0000664000175000017500000003157412107036451016134 0ustar csheltoncshelton <\map> <\Context> <\map> <\Context> <\RV> <\map> <\Context> <\map> <\Context> <\RV> 0.1 0.9 <\vectr> <\MultiZSimple> <\vector> <\RVCondSimpleComp.MultiZSimple> <\RVComp> <\MultiRV> <\map> <\Context> <\map> <\Context> <\RV> 0.7 0.2 0.1 <\vectr> <\MultiZSimple> <\vector> <\RVCondSimpleComp.MultiZSimple> <\RVComp> <\MultiRV> <\map> <\Context> <\map> <\Context> <\RV> 0.6 0.4 <\vectr> <\MultiZSimple> <\vector> <\RVCondSimpleComp.MultiZSimple> <\RVComp> <\MultiRV> <\map> <\Context> <\map> <\Context> <\RV> 0.45 0.55 <\vectr> <\MultiZSimple> <\vector> <\RVCondSimpleComp.MultiZSimple> <\RVComp> <\MultiRV> <\map> <\Context> <\map> <\Context> <\RV> 0.7 0.2 0.1 <\vectr> <\MultiZSimple> <\vector> <\RVCondSimpleComp.MultiZSimple> <\RVComp> <\MultiRV> <\map> <\Context> <\map> <\Context> <\RV> 0.3 0.7 0 <\vectr> <\MultiZSimple> <\vector> <\RVCondSimpleComp.MultiZSimple> <\RVComp> <\MultiRV> <\map> <\Context> <\map> <\Context> <\RV> 0.1 0.9 <\vectr> <\MultiZSimple> <\vector> <\RVCondSimpleComp.MultiZSimple> <\RVComp> <\MultiRV> <\map> <\Context> <\map> <\Context> <\RV> 0.1 0.9 <\vectr> <\MultiZSimple> <\vector> <\RVCondSimpleComp.MultiZSimple> <\RVComp> <\MultiRV> <\vector> <\BN> <\map> <\Context> <\map> <\Context> <\Dynamics> <\map> <\Context> <\map> <\Context> <\Dynamics> -0.01 0.01 10 -10 <\matrix> <\MarkovSimple> -2 2 1 -1 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -0.02 0.01 0.01 0.3 -0.31 0.01 0.01 1 -1.01 <\matrix> <\MarkovSimple> -6.01 6 0.01 0.01 -3.01 3 0.01 0.01 -0.02 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -2 2 0.01 -0.01 <\matrix> <\MarkovSimple> -0.01 0.01 0.01 -0.01 <\matrix> <\MarkovSimple> -0.01 0.01 10 -10 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -0 0 0.5 -0.5 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -0.02 0.01 0.01 0.25 -0.26 0.01 0.01 0.5 -0.51 <\matrix> <\MarkovSimple> -0.02 0.01 0.01 0.25 -0.26 0.01 0.01 0.5 -0.51 <\matrix> <\MarkovSimple> -0.02 0.01 0.01 0.25 -0.26 0.01 0.01 0.5 -0.51 <\matrix> <\MarkovSimple> -2.01 2 0.01 0.01 -1.01 1 0.01 0.01 -0.02 <\matrix> <\MarkovSimple> -1.01 1 0.01 0.01 -2.01 2 0.01 0.01 -0.02 <\matrix> <\MarkovSimple> -0.51 0.5 0.01 0.01 -4.01 4 0.01 0.01 -0.02 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -0.21 0.2 0.01 0.05 -0.1 0.05 0.01 0.2 -0.21 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -6 6 0.1 -0.1 <\matrix> <\MarkovSimple> -1 1 0.1 -0.1 <\matrix> <\MarkovSimple> -6 6 0.1 -0.1 <\matrix> <\MarkovSimple> -1 1 0.3 -0.3 <\matrix> <\MarkovSimple> -0.3 0.3 0.3 -0.3 <\matrix> <\MarkovSimple> -1 1 0.3 -0.3 <\matrix> <\MarkovSimple> -0.01 0.01 2 -2 <\matrix> <\MarkovSimple> -0.01 0.01 2 -2 <\matrix> <\MarkovSimple> -0.01 0.01 2 -2 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -0.17 0.17 0.5 -0.5 <\matrix> <\MarkovSimple> -0.33 0.33 0.33 -0.33 <\matrix> <\MarkovSimple> -1 1 0.1 -0.1 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\vector> <\CTBNDyn> <\Markov> 0 10 3 0 2 0.5 1 0.5 -1 1 1 2.5 2 6 1 5 1 ctbnrle.1.1.1/demo/traj2ascii.cc0000664000175000017500000000232012107032410016637 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ // this file reads in a trajectory from stdin and "draws" an // ASCII representation of it on stdout #include "trajectory.h" #include #include "params.h" #include "streamextra.h" using namespace std; using namespace ctbn; int main(int argc, char **argv) { InitParams(argc, argv); addnan(cout); addnan(cin); Trajectory tr; tr.Load(cin); tr.Draw(cout); cout << endl; return 0; } ctbnrle.1.1.1/demo/sem.cc0000664000175000017500000004266712107032410015412 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ // This code generates the drug effect network as first seen in // Continuous Time Bayesian Networks by Nodelman et al., 2002. // To demonstrate structure search on incomplete data, trajectories // are sampled from the network and evidence is removed. SEM is then // used (with an underlying inference method) to learn the structure // and parameters of the model. /* Command line parameters: * * Set the number of trajectories * -Dnumtraj (default: 1000) * * Set the length of each trajectory * -Dtlen (default: 5.0) * * Set the alpha prior (prior belief for number of transitions) * -Dalpha (default: 1.0) * * Set the tau prior (prior belief for amount of time spent) * -Dtau (default: 1.0) * * Set the amount of loss in the trajectory data * -Dloss (default: 0.25) */ #include "markovdyn.h" #include "markov.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "trajectory.h" #include "samplinginf.h" #include "expmethod.h" #include "importancesampler.h" #include "params.h" #include "ctbndyn.h" #include "bn.h" #include "em.h" #include "ctbn.h" #include "nonexpsuffstatsquery.h" #include "famscore.h" #include "grapheditsearch.h" #include "utils.h" #include #include using namespace std; using namespace ctbn; int main(int argc, char **argv) { InitParams(argc, argv); cout << setprecision(4); //******************************************************* // Create contexts for each variable Context nullc; Context eatc, fullc, hungryc, uptakec, concentrationc, barometerc, painc, drowsyc; eatc.AddVar(0,2); fullc.AddVar(1,3); hungryc.AddVar(2,2); uptakec.AddVar(3,2); concentrationc.AddVar(4,3); barometerc.AddVar(5,3); painc.AddVar(6,2); drowsyc.AddVar(7,2); // Define the connections between variables. For a variable with multiple // parents, the context used is the union of the two parent contexts MarkovDyn eat(eatc, hungryc); MarkovDyn full(fullc, eatc); MarkovDyn hungry(hungryc, fullc); MarkovDyn uptake(uptakec, nullc); Context full_and_uptakec(fullc, uptakec); MarkovDyn concentration(concentrationc, full_and_uptakec); MarkovDyn barometer(barometerc, nullc); MarkovDyn pain(painc, Context(barometerc, concentrationc)); MarkovDyn drowsy(drowsyc, concentrationc); // Generate starting distributions for each variable MultiRV eatrv(eatc, nullc); MultiRV fullrv(fullc, eatc); MultiRV hungryrv(hungryc, eatc); MultiRV uptakerv(uptakec, nullc); MultiRV concentrationrv(concentrationc, uptakec+hungryc); MultiRV barometerrv(barometerc, nullc); MultiRV painrv(painc, barometerc); MultiRV drowsyrv(drowsyc, concentrationc+painc); // The following fills the conditional intensity matrices for each // variable and the initial distributions (multinomial distributions). //------eat-------- eat(0)->Intensity()[0][0] = -0.01; eat(0)->Intensity()[0][1] = 0.01; eat(0)->Intensity()[1][0] = 10.0; eat(0)->Intensity()[1][1] = -10.0; eat(1)->Intensity()[0][0] = -2.0; eat(1)->Intensity()[0][1] = 2.0; eat(1)->Intensity()[1][0] = 1.0; eat(1)->Intensity()[1][1] = -1.0; vectr peat(2, 0.0); peat[0] = 0.3; peat[1] = 0.7; eatrv[0].SetDist(peat); //------full-------- full(0)->Intensity()[0][0] = -0.02; full(0)->Intensity()[0][1] = 0.01; full(0)->Intensity()[0][2] = 0.01; full(0)->Intensity()[1][0] = 0.3; full(0)->Intensity()[1][1] = -0.31; full(0)->Intensity()[1][2] = 0.01; full(0)->Intensity()[2][0] = 0.01; full(0)->Intensity()[2][1] = 1.0; full(0)->Intensity()[2][2] = -1.01; full(1)->Intensity()[0][0] = -6.01; full(1)->Intensity()[0][1] = 6.0; full(1)->Intensity()[0][2] = 0.01; full(1)->Intensity()[1][0] = 0.01; full(1)->Intensity()[1][1] = -3.01; full(1)->Intensity()[1][2] = 3.0; full(1)->Intensity()[2][0] = 0.01; full(1)->Intensity()[2][1] = 0.01; full(1)->Intensity()[2][2] = -0.02; vectr pfull(3, 0.0); pfull[0] = 0.5; pfull[1] = 0.25; pfull[2] = 0.25; fullrv[0].SetDist(pfull); pfull[0] = 0.2; pfull[1] = 0.25; pfull[2] = 0.55; fullrv[1].SetDist(pfull); //------hungry-------- hungry(0)->Intensity()[0][0] = -2.0; hungry(0)->Intensity()[0][1] = 2.0; hungry(0)->Intensity()[1][0] = 0.01; hungry(0)->Intensity()[1][1] = -0.01; hungry(1)->Intensity()[0][0] = -0.01; hungry(1)->Intensity()[0][1] = 0.01; hungry(1)->Intensity()[1][0] = 0.01; hungry(1)->Intensity()[1][1] = -0.01; hungry(2)->Intensity()[0][0] = -0.01; hungry(2)->Intensity()[0][1] = 0.01; hungry(2)->Intensity()[1][0] = 10.0; hungry(2)->Intensity()[1][1] = -10.0; vectr phungry(2, 0.0); phungry[0] = 0.35; phungry[1] = 0.65; hungryrv[0].SetDist(phungry); phungry[0] = 0.45; phungry[1] = 0.55; hungryrv[1].SetDist(phungry); //------uptake-------- uptake(0)->Intensity()[0][0] = -0.0; uptake(0)->Intensity()[0][1] = 0.0; uptake(0)->Intensity()[1][0] = 0.5; uptake(0)->Intensity()[1][1] = -0.5; vectr puptake(2, 0.0); puptake[1] = 1; uptakerv[0].SetDist(puptake); // For this variable there is more than one parent. // The CIM is filled by creating an Instantiation object that represents // the current intensity matrix we are entering values for. The // MarkovDyn objects can be then indexed with Instantiation objects. //------concentration------ Instantiation i(Context(fullc, uptakec)); // Set uptake to state 0; full to state 0 i.SetVal(3,0); i.SetVal(1,0); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; // Set uptake to state 1; full to state 0 // (We repeat this process for each parent instantiation.) i.SetVal(3,1); i.SetVal(1,0); concentration(i)->Intensity()[0][0] = -2.01; concentration(i)->Intensity()[0][1] = 2.0; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -1.01; concentration(i)->Intensity()[1][2] = 1.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; i.SetVal(3,0); i.SetVal(1,1); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; i.SetVal(3,1); i.SetVal(1,1); concentration(i)->Intensity()[0][0] = -1.01; concentration(i)->Intensity()[0][1] = 1.0; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -2.01; concentration(i)->Intensity()[1][2] = 2.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; i.SetVal(3,0); i.SetVal(1,2); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; i.SetVal(3,1); i.SetVal(1,2); concentration(i)->Intensity()[0][0] = -0.51; concentration(i)->Intensity()[0][1] = 0.5; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -4.01; concentration(i)->Intensity()[1][2] = 4.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; vectr pconcentration(3, 0.0); pconcentration[0] = 0.1; pconcentration[1] = 0.3; pconcentration[2] = 0.6; concentrationrv[0].SetDist(pconcentration); pconcentration[0] = 0.1; pconcentration[1] = 0.4; pconcentration[2] = 0.5; concentrationrv[1].SetDist(pconcentration); pconcentration[0] = 0.4; pconcentration[1] = 0.2; pconcentration[2] = 0.4; concentrationrv[2].SetDist(pconcentration); pconcentration[0] = 0.5; pconcentration[1] = 0.4; pconcentration[2] = 0.1; concentrationrv[3].SetDist(pconcentration); //------barometer------ barometer(0)->Intensity()[0][0] = -0.21; barometer(0)->Intensity()[0][1] = 0.2; barometer(0)->Intensity()[0][2] = 0.01; barometer(0)->Intensity()[1][0] = 0.05; barometer(0)->Intensity()[1][1] = -0.1; barometer(0)->Intensity()[1][2] = 0.05; barometer(0)->Intensity()[2][0] = 0.01; barometer(0)->Intensity()[2][1] = 0.2; barometer(0)->Intensity()[2][2] = -0.21; vectr pbarometer(3, 0.0); pbarometer[1] = 1; barometerrv[0].SetDist(pbarometer); //------pain------ Instantiation j(Context(barometerc, concentrationc)); j.SetVal(4,0); j.SetVal(5,0); pain(j)->Intensity()[0][0] = -6.0; pain(j)->Intensity()[0][1] = 6.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(4,1); j.SetVal(5,0); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(4,2); j.SetVal(5,0); pain(j)->Intensity()[0][0] = -6.0; pain(j)->Intensity()[0][1] = 6.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(4,0); j.SetVal(5,1); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(4,1); j.SetVal(5,1); pain(j)->Intensity()[0][0] = -0.3; pain(j)->Intensity()[0][1] = 0.3; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(4,2); j.SetVal(5,1); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(4,0); j.SetVal(5,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; j.SetVal(4,1); j.SetVal(5,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; j.SetVal(4,2); j.SetVal(5,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; vectr ppain(2, 0.0); ppain[0] = 0.1; ppain[1] = 0.9; painrv[0].SetDist(ppain); ppain[0] = 0.3; ppain[1] = 0.7; painrv[1].SetDist(ppain); ppain[0] = 0.9; ppain[1] = 0.1; painrv[2].SetDist(ppain); //------drowsy------ drowsy(0)->Intensity()[0][0] = -0.17; drowsy(0)->Intensity()[0][1] = 0.17; drowsy(0)->Intensity()[1][0] = 0.5; drowsy(0)->Intensity()[1][1] = -0.5; drowsy(1)->Intensity()[0][0] = -0.33; drowsy(1)->Intensity()[0][1] = 0.33; drowsy(1)->Intensity()[1][0] = 0.33; drowsy(1)->Intensity()[1][1] = -0.33; drowsy(2)->Intensity()[0][0] = -1.0; drowsy(2)->Intensity()[0][1] = 1.0; drowsy(2)->Intensity()[1][0] = 0.1; drowsy(2)->Intensity()[1][1] = -0.1; vectr pdrowsy(2, 0.0); pdrowsy[0] = 0.8; pdrowsy[1] = 0.2; drowsyrv[0].SetDist(pdrowsy); pdrowsy[0] = 0.7; pdrowsy[1] = 0.3; drowsyrv[1].SetDist(pdrowsy); pdrowsy[0] = 0.3; pdrowsy[1] = 0.7; drowsyrv[2].SetDist(pdrowsy); pdrowsy[0] = 0.4; pdrowsy[1] = 0.6; drowsyrv[3].SetDist(pdrowsy); pdrowsy[0] = 0.2; pdrowsy[1] = 0.8; drowsyrv[4].SetDist(pdrowsy); pdrowsy[0] = 0.8; pdrowsy[1] = 0.2; drowsyrv[5].SetDist(pdrowsy); // The dynamics of a CTBN are defined by the dynamics of each conditional // Markov process. Here it is the MarkovDyn objects created above. CTBNDyn ctbndyn; ctbndyn.AddNode(eat.Clone()); ctbndyn.AddNode(full.Clone()); ctbndyn.AddNode(hungry.Clone()); ctbndyn.AddNode(uptake.Clone()); ctbndyn.AddNode(concentration.Clone()); ctbndyn.AddNode(barometer.Clone()); ctbndyn.AddNode(pain.Clone()); ctbndyn.AddNode(drowsy.Clone()); // The initial distribution of a CTBN is defined by the underlying // Bayesian network containing a multinomial distribution for each // variable. BN bn; bn.AddNode(eatrv.Clone()); bn.AddNode(fullrv.Clone()); bn.AddNode(hungryrv.Clone()); bn.AddNode(uptakerv.Clone()); bn.AddNode(concentrationrv.Clone()); bn.AddNode(barometerrv.Clone()); bn.AddNode(painrv.Clone()); bn.AddNode(drowsyrv.Clone()); // Construct the final object that represents the CTBN. Markov m(bn.Clone(), ctbndyn.Clone()); //Building independent structure MultiRV eatrvi(eatc, nullc); MultiRV fullrvi(fullc, nullc); MultiRV hungryrvi(hungryc, nullc); MultiRV uptakervi(uptakec, nullc); MultiRV concentrationrvi(concentrationc, nullc); MultiRV barometerrvi(barometerc, nullc); MultiRV painrvi(painc, nullc); MultiRV drowsyrvi(drowsyc, nullc); BN bnindep; bnindep.AddNode(eatrvi.Clone()); bnindep.AddNode(fullrvi.Clone()); bnindep.AddNode(hungryrvi.Clone()); bnindep.AddNode(uptakervi.Clone()); bnindep.AddNode(concentrationrvi.Clone()); bnindep.AddNode(barometerrvi.Clone()); bnindep.AddNode(painrvi.Clone()); bnindep.AddNode(drowsyrvi.Clone()); MarkovDyn eatindep(eatc, nullc); MarkovDyn fullindep(fullc, nullc); MarkovDyn hungryindep(hungryc, nullc); MarkovDyn uptakeindep(uptakec, nullc); MarkovDyn concentrationindep(concentrationc, nullc); MarkovDyn barometerindep(barometerc, nullc); MarkovDyn painindep(painc, nullc); MarkovDyn drowsyindep(drowsyc, nullc); CTBNDyn ctbndynindep; ctbndynindep.AddNode(eatindep.Clone()); ctbndynindep.AddNode(fullindep.Clone()); ctbndynindep.AddNode(hungryindep.Clone()); ctbndynindep.AddNode(uptakeindep.Clone()); ctbndynindep.AddNode(concentrationindep.Clone()); ctbndynindep.AddNode(barometerindep.Clone()); ctbndynindep.AddNode(painindep.Clone()); ctbndynindep.AddNode(drowsyindep.Clone()); Markov mindep(bnindep.Clone(), ctbndynindep.Clone()); cout << "Demo of Structural EM" << endl << "================================="<< endl; // Load the actual structure into a structure object Structure sABN; Structure sACTBN; bn.GetStructure(sABN); ctbndyn.GetStructure(sACTBN); cout << "Actual Structure (BN):\n"; sABN.Print(cout); cout << endl << endl; cout << "Actual Structure (CTBN):\n"; sABN.Print(cout); cout << endl << endl; // Set the number and length of trajectories to sample int numTrajs = ParamInt("numtraj",500); double tlen = ParamDouble("tlen",5.0); double loss = ParamDouble("loss",0.25); double factor = loss/0.25; const Context& con = ctbndyn.Domain(); int numNodes = ctbndyn.NumofNodes(); // Sample the trajectories and remove evidence randomly // in 0.25 time step segments. Also removes starting evidence // from half of the variables at random from each trajectory. cout << "Sampling trajectories...\n\n"; vector trVec; for(int k=0; k(sem_ctbn)->Scramble(); // Structure classes to load the learned structures into Structure sLBN; Structure sLCTBN; // Use importance sampling as the inference method for SEM ExpMethod method; SamplingInf inf; Sampler *sampler; sampler = new ImportanceSampler(sem_ctbn, &trVec[0], &method); inf.SetSampler(sampler); cout << "Running SEM...\n"; // Pass in data, starting process, and inference method to run SEM SEM(trVec,sem_ctbn,&inf); Markov *learned = dynamic_cast (sem_ctbn); // Get the learned Bayesian network const BN *learnedBN = dynamic_cast ( learned->GetStartDist()); learnedBN->GetStructure(sLBN); cout << "Learned structure (BN):\n"; sLBN.Print(cout); cout << endl; // Compute difference in number of parameters to measure accuracy int paramd = abs(sABN.GetNumParams(bn.Domain()) - sLBN.GetNumParams(learnedBN->Domain())); cout << "Param Distance: " << paramd << endl << endl; // Get the learned CTBN const CTBNDyn* learnedCTBN = dynamic_cast ( learned->GetDynamics()); learnedCTBN->GetStructure(sLCTBN); cout << "Learned structure (CTBN):\n"; sLCTBN.Print(cout); cout << endl; // Compute Hamming distance to measure accuracy int hamming = sACTBN.HammingDist(sLCTBN); cout << "Hamming Distance: " << hamming << endl << endl; } ctbnrle.1.1.1/demo/makeqinput.cc0000664000175000017500000000762112107032410016773 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "markovdyn.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include "em.h" #include "utils.h" #include #include "markov.h" using namespace std; using namespace ctbn; // this file is used to create an example CTBN model and a partial trajectory sampled from the model // the model, the evidence, and the true results of queries will be saved to a file "queryinput.data" // "queryinput.data" will be feeded as an input to: // exactquery.cc, importancequery.cc and gibbsquery.cc // the above three .cc file will do the queries, and compare the results with the true answers in "queryinput.data" // the queries will be specified in these three files // In case you want to change the example model, you can modify this file and rewrite the output to "queryinput.data" // and then do the exactquery.cc/importancequery.cc/gibbsquery.cc // Usage: // no inputs to run: ./makequeryinput >output_file int main (int argc, char *argv) { randomizer.Reset(1); // build up the context Context X,Y,Z; Context Null; X.AddVar(0,2); Y.AddVar(1,2); Z.AddVar(2,3); // build up the dynamics MarkovDyn PX(X,Null); // process of X MarkovDyn PY(Y,X); // process of Y given X MarkovDyn PZ(Z,Null); PX(0)->Intensity()[0][0] = -1; PX(0)->Intensity()[0][1] = 1; PX(0)->Intensity()[1][0] = 2; PX(0)->Intensity()[1][1] = -2; PY(0)->Intensity()[0][0] = -3; PY(0)->Intensity()[0][1] = 3; PY(0)->Intensity()[1][0] = 4; PY(0)->Intensity()[1][1] = -4; PY(1)->Intensity()[0][0] = -5; PY(1)->Intensity()[0][1] = 5; PY(1)->Intensity()[1][0] = 6; PY(1)->Intensity()[1][1] = -6; PZ(0)->Intensity()[0][0] = -6; PZ(0)->Intensity()[0][1] = 3; PZ(0)->Intensity()[0][2] = 3; PZ(0)->Intensity()[1][1] = -12; PZ(0)->Intensity()[1][0] = 6; PZ(0)->Intensity()[1][2] = 6; PZ(0)->Intensity()[2][2] = -18; PZ(0)->Intensity()[2][0] = 9; PZ(0)->Intensity()[2][1] = 9; // build up the start distribution MultiRV P0X(X,Null); MultiRV P0Y(Y,X); MultiRV P0Z(Z,Null); vectr p0(2,0.0); p0[0] = 0.5; p0[1] = 0.5; P0X[0].SetDist(p0); p0[0] = 0.8; p0[1] = 0.2; P0Y[0].SetDist(p0); p0[0] = 0.3; p0[1] = 0.7; P0Y[1].SetDist(p0); vectr pz(3,0.0); pz[0] = 0.2; pz[1] = 0.3; pz[2] = 0.5; P0Z[0].SetDist(pz); // set up CTBN: CTBNDyn ctbndyn; ctbndyn.AddNode(PX.Clone()); ctbndyn.AddNode(PY.Clone()); ctbndyn.AddNode(PZ.Clone()); BN bn; bn.AddNode(P0X.Clone()); bn.AddNode(P0Y.Clone()); bn.AddNode(P0Z.Clone()); Markov ctbn(bn.Clone(),ctbndyn.Clone()); Context context = ctbndyn.Domain() + ctbndyn.CondDomain(); // sample a partial trajectory as the input evidence double begintime = ParamDouble("BeginTime", 0.0); double endtime = ParamDouble("EndTime", 20.0); Trajectory evid; evid.SetBeginTime(begintime); evid.SetEndTime(endtime); ctbn.Sample(evid); RemoveNodesInformation(evid, context, ctbndyn.NumofNodes(), ctbndyn.NumofNodes()*4 , 0.2); // output the model ctbn.Save(cout); cout << endl; evid.Save(cout); cout << endl; // output the expected query results (exact): cout << 8.1538 << " " << 42.5293 << endl; return 0; } ctbnrle.1.1.1/demo/makechain.cc0000664000175000017500000000755512107032410016543 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ // Usage: makechain [options] > // // This code creates a chain structured network and saves // it into a file. // // The intensities of leaving each state for the head node // are the same, which can be set using the parameter "headrate". // When leaving the current state, the head node has equal // probability to jump to other states. // // For the other nodes, each node stays in its current state // if it matches its parents and otherwise transitions // to its parent’s state with a high probability (set using // parameter "chaserate".) /* Command line parameters: * * Set the number of nodes * -Dnumofnodes (default: 3) * * Set the number of states for each node * -Dnumofstates (default: 2) * * Set the intensity of leaving each state for the head node. * -Dheadrate (default: 1.0) * * Set the intensity of transitioning to the state that is the same * as the current state of the parent for nodes other than the head node * -Dchaserate (default: 1.0) * * Set the intensity of transitioning to other states * -Drandrate (default: 0.1) */ #include "markovdyn.h" #include "markov.h" #include "streamextra.h" #include "context.h" #include #include "multirv.h" #include "trajectory.h" #include "params.h" #include "bn.h" #include "ctbndyn.h" #include using namespace std; using namespace ctbn; int main(int argc, char **argv) { InitParams(argc, argv); cout << setprecision(4); // Set various parameters including the number of nodes, states per node, // initial distribution values, and Q matrix values. string paramval; int nnode = ParamInt("numofnodes", 3); int nstate = ParamInt("numofstates", 2); double headrate = ParamDouble("headrate", 1.0); double chaserate = ParamDouble("chaserate", 1.0); double randrate = ParamDouble("randrate", 0.1 ); // Generate the head node Context nullc; Context headc; headc.AddVar(0, nstate); MarkovDyn Phead(headc, nullc); MultiRV P0head(headc, nullc); vectr p0(nstate, 0.0); for (int i=0; iIntensity()[i][j] = (i==j ? -headrate : headrate/(nstate-1)); p0[i] = (i==0 ? 1.0 : 0.0); } P0head[0].SetDist(p0); CTBNDyn ctbndyn; ctbndyn.AddNode(Phead.Clone()); BN bn; bn.AddNode(P0head.Clone()); // Generate the rest of the nodes for (int i=1; iIntensity()[k][l] = (j==k ? ((k==l) ? -randrate*(nstate-1) : randrate) : ((k==l) ? -chaserate-randrate*(nstate-2) : (l==j ? chaserate : randrate))); ctbndyn.AddNode(node.Clone()); Context initp; initp.AddVar(i, nstate); MultiRV P0node(initp, nullc); P0node[0].SetDist(p0); bn.AddNode(P0node.Clone()); } // Create the final object to represent the chain network Markov m(bn.Clone(), ctbndyn.Clone()); m.Save(cout); } ctbnrle.1.1.1/demo/structurelearn.cc0000664000175000017500000004070012107032410017672 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ // This code generates the drug effect network as first seen in // Continuous Time Bayesian Networks by Nodelman et al., 2002. // To demonstrate structure search on complete data, trajectories // are sampled from the network and used to learn the structure. /* Command line parameters: * * Set the number of trajectories * -Dnumtraj (default: 1000) * * Set the length of each trajectory * -Dtlen (default: 5.0) * * Set the alpha prior (prior belief for number of transitions) * -Dalpha (defualt: 1.0) * * Set the tau prior (prior belief for amount of time spent) * -Dtau (defualt: 1.0) */ #include "markovdyn.h" #include "markov.h" #include "streamextra.h" #include "context.h" #include "multirv.h" #include "trajectory.h" #include "params.h" #include "ctbndyn.h" #include "bn.h" #include "em.h" #include "ctbn.h" #include "nonexpsuffstatsquery.h" #include "famscore.h" #include "grapheditsearch.h" #include #include using namespace std; using namespace ctbn; int main(int argc, char **argv) { InitParams(argc, argv); cout << setprecision(4); //******************************************************* // Create contexts for each variable Context nullc; Context eatc, fullc, hungryc, uptakec, concentrationc, barometerc, painc, drowsyc; eatc.AddVar(0, 2); fullc.AddVar(1,3); hungryc.AddVar(2,2); uptakec.AddVar(3, 2); concentrationc.AddVar(4,3); barometerc.AddVar(5,3); painc.AddVar(6, 2); drowsyc.AddVar(7,2); // Define the connections between variables. For a variable with multiple // parents, the context used is the union of the two parent contexts MarkovDyn eat(eatc, hungryc); MarkovDyn full(fullc, eatc); MarkovDyn hungry(hungryc, fullc); MarkovDyn uptake(uptakec, nullc); Context full_and_uptakec(fullc, uptakec); MarkovDyn concentration(concentrationc, full_and_uptakec); MarkovDyn barometer(barometerc, nullc); MarkovDyn pain(painc, Context(barometerc, concentrationc)); MarkovDyn drowsy(drowsyc, concentrationc); // Generate starting distributions for each variable MultiRV eatrv(eatc, nullc); MultiRV fullrv(fullc, eatc); MultiRV hungryrv(hungryc, eatc); MultiRV uptakerv(uptakec, nullc); MultiRV concentrationrv(concentrationc, uptakec+hungryc); MultiRV barometerrv(barometerc, nullc); MultiRV painrv(painc, barometerc); MultiRV drowsyrv(drowsyc, concentrationc+painc); // The following fills the conditional intensity matrices for each // variable and the initial distributions (multinomial distributions). //------eat-------- eat(0)->Intensity()[0][0] = -0.01; eat(0)->Intensity()[0][1] = 0.01; eat(0)->Intensity()[1][0] = 10.0; eat(0)->Intensity()[1][1] = -10.0; eat(1)->Intensity()[0][0] = -2.0; eat(1)->Intensity()[0][1] = 2.0; eat(1)->Intensity()[1][0] = 1.0; eat(1)->Intensity()[1][1] = -1.0; vectr peat(2, 0.0); peat[0] = 0.3; peat[1] = 0.7; eatrv[0].SetDist(peat); //------full-------- full(0)->Intensity()[0][0] = -0.02; full(0)->Intensity()[0][1] = 0.01; full(0)->Intensity()[0][2] = 0.01; full(0)->Intensity()[1][0] = 0.3; full(0)->Intensity()[1][1] = -0.31; full(0)->Intensity()[1][2] = 0.01; full(0)->Intensity()[2][0] = 0.01; full(0)->Intensity()[2][1] = 1.0; full(0)->Intensity()[2][2] = -1.01; full(1)->Intensity()[0][0] = -6.01; full(1)->Intensity()[0][1] = 6.0; full(1)->Intensity()[0][2] = 0.01; full(1)->Intensity()[1][0] = 0.01; full(1)->Intensity()[1][1] = -3.01; full(1)->Intensity()[1][2] = 3.0; full(1)->Intensity()[2][0] = 0.01; full(1)->Intensity()[2][1] = 0.01; full(1)->Intensity()[2][2] = -0.02; vectr pfull(3, 0.0); pfull[0] = 0.5; pfull[1] = 0.25; pfull[2] = 0.25; fullrv[0].SetDist(pfull); pfull[0] = 0.2; pfull[1] = 0.25; pfull[2] = 0.55; fullrv[1].SetDist(pfull); //------hungry-------- hungry(0)->Intensity()[0][0] = -2.0; hungry(0)->Intensity()[0][1] = 2.0; hungry(0)->Intensity()[1][0] = 0.01; hungry(0)->Intensity()[1][1] = -0.01; hungry(1)->Intensity()[0][0] = -0.01; hungry(1)->Intensity()[0][1] = 0.01; hungry(1)->Intensity()[1][0] = 0.01; hungry(1)->Intensity()[1][1] = -0.01; hungry(2)->Intensity()[0][0] = -0.01; hungry(2)->Intensity()[0][1] = 0.01; hungry(2)->Intensity()[1][0] = 10.0; hungry(2)->Intensity()[1][1] = -10.0; vectr phungry(2, 0.0); phungry[0] = 0.35; phungry[1] = 0.65; hungryrv[0].SetDist(phungry); phungry[0] = 0.45; phungry[1] = 0.55; hungryrv[1].SetDist(phungry); //------uptake-------- uptake(0)->Intensity()[0][0] = -0.0; uptake(0)->Intensity()[0][1] = 0.0; uptake(0)->Intensity()[1][0] = 0.5; uptake(0)->Intensity()[1][1] = -0.5; vectr puptake(2, 0.0); puptake[1] = 1; uptakerv[0].SetDist(puptake); // For this variable there is more than one parent. // The CIM is filled by creating an Instantiation object that represents // the current intensity matrix we are entering values for. The // MarkovDyn objects can be then indexed with Instantiation objects. //------concentration------ Instantiation i(Context(fullc, uptakec)); // Set uptake to state 0; full to state 0 i.SetVal(3,0); i.SetVal(1,0); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; // Set uptake to state 1; full to state 0 // (We repeat this process for each parent instantiation.) i.SetVal(3,1); i.SetVal(1,0); concentration(i)->Intensity()[0][0] = -2.01; concentration(i)->Intensity()[0][1] = 2.0; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -1.01; concentration(i)->Intensity()[1][2] = 1.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; i.SetVal(3,0); i.SetVal(1,1); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; i.SetVal(3,1); i.SetVal(1,1); concentration(i)->Intensity()[0][0] = -1.01; concentration(i)->Intensity()[0][1] = 1.0; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -2.01; concentration(i)->Intensity()[1][2] = 2.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; i.SetVal(3,0); i.SetVal(1,2); concentration(i)->Intensity()[0][0] = -0.02; concentration(i)->Intensity()[0][1] = 0.01; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.25; concentration(i)->Intensity()[1][1] = -0.26; concentration(i)->Intensity()[1][2] = 0.01; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.5; concentration(i)->Intensity()[2][2] = -0.51; i.SetVal(3,1); i.SetVal(1,2); concentration(i)->Intensity()[0][0] = -0.51; concentration(i)->Intensity()[0][1] = 0.5; concentration(i)->Intensity()[0][2] = 0.01; concentration(i)->Intensity()[1][0] = 0.01; concentration(i)->Intensity()[1][1] = -4.01; concentration(i)->Intensity()[1][2] = 4.0; concentration(i)->Intensity()[2][0] = 0.01; concentration(i)->Intensity()[2][1] = 0.01; concentration(i)->Intensity()[2][2] = -0.02; vectr pconcentration(3, 0.0); pconcentration[0] = 0.1; pconcentration[1] = 0.3; pconcentration[2] = 0.6; concentrationrv[0].SetDist(pconcentration); pconcentration[0] = 0.1; pconcentration[1] = 0.4; pconcentration[2] = 0.5; concentrationrv[1].SetDist(pconcentration); pconcentration[0] = 0.4; pconcentration[1] = 0.2; pconcentration[2] = 0.4; concentrationrv[2].SetDist(pconcentration); pconcentration[0] = 0.5; pconcentration[1] = 0.4; pconcentration[2] = 0.1; concentrationrv[3].SetDist(pconcentration); //------barometer------ barometer(0)->Intensity()[0][0] = -0.21; barometer(0)->Intensity()[0][1] = 0.2; barometer(0)->Intensity()[0][2] = 0.01; barometer(0)->Intensity()[1][0] = 0.05; barometer(0)->Intensity()[1][1] = -0.1; barometer(0)->Intensity()[1][2] = 0.05; barometer(0)->Intensity()[2][0] = 0.01; barometer(0)->Intensity()[2][1] = 0.2; barometer(0)->Intensity()[2][2] = -0.21; vectr pbarometer(3, 0.0); pbarometer[1] = 1; barometerrv[0].SetDist(pbarometer); //------pain------ Instantiation j(Context(barometerc, concentrationc)); j.SetVal(4,0); j.SetVal(5,0); pain(j)->Intensity()[0][0] = -6.0; pain(j)->Intensity()[0][1] = 6.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(4,1); j.SetVal(5,0); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(4,2); j.SetVal(5,0); pain(j)->Intensity()[0][0] = -6.0; pain(j)->Intensity()[0][1] = 6.0; pain(j)->Intensity()[1][0] = 0.1; pain(j)->Intensity()[1][1] = -0.1; j.SetVal(4,0); j.SetVal(5,1); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(4,1); j.SetVal(5,1); pain(j)->Intensity()[0][0] = -0.3; pain(j)->Intensity()[0][1] = 0.3; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(4,2); j.SetVal(5,1); pain(j)->Intensity()[0][0] = -1.0; pain(j)->Intensity()[0][1] = 1.0; pain(j)->Intensity()[1][0] = 0.3; pain(j)->Intensity()[1][1] = -0.3; j.SetVal(4,0); j.SetVal(5,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; j.SetVal(4,1); j.SetVal(5,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; j.SetVal(4,2); j.SetVal(5,2); pain(j)->Intensity()[0][0] = -0.01; pain(j)->Intensity()[0][1] = 0.01; pain(j)->Intensity()[1][0] = 2.0; pain(j)->Intensity()[1][1] = -2.0; vectr ppain(2, 0.0); ppain[0] = 0.1; ppain[1] = 0.9; painrv[0].SetDist(ppain); ppain[0] = 0.3; ppain[1] = 0.7; painrv[1].SetDist(ppain); ppain[0] = 0.9; ppain[1] = 0.1; painrv[2].SetDist(ppain); //------drowsy------ drowsy(0)->Intensity()[0][0] = -0.17; drowsy(0)->Intensity()[0][1] = 0.17; drowsy(0)->Intensity()[1][0] = 0.5; drowsy(0)->Intensity()[1][1] = -0.5; drowsy(1)->Intensity()[0][0] = -0.33; drowsy(1)->Intensity()[0][1] = 0.33; drowsy(1)->Intensity()[1][0] = 0.33; drowsy(1)->Intensity()[1][1] = -0.33; drowsy(2)->Intensity()[0][0] = -1.0; drowsy(2)->Intensity()[0][1] = 1.0; drowsy(2)->Intensity()[1][0] = 0.1; drowsy(2)->Intensity()[1][1] = -0.1; vectr pdrowsy(2, 0.0); pdrowsy[0] = 0.8; pdrowsy[1] = 0.2; drowsyrv[0].SetDist(pdrowsy); pdrowsy[0] = 0.7; pdrowsy[1] = 0.3; drowsyrv[1].SetDist(pdrowsy); pdrowsy[0] = 0.3; pdrowsy[1] = 0.7; drowsyrv[2].SetDist(pdrowsy); pdrowsy[0] = 0.4; pdrowsy[1] = 0.6; drowsyrv[3].SetDist(pdrowsy); pdrowsy[0] = 0.2; pdrowsy[1] = 0.8; drowsyrv[4].SetDist(pdrowsy); pdrowsy[0] = 0.8; pdrowsy[1] = 0.2; drowsyrv[5].SetDist(pdrowsy); // The dynamics of a CTBN are defined by the dynamics of each conditional // Markov process. Here it is the MarkovDyn objects created above. CTBNDyn ctbndyn; ctbndyn.AddNode(eat.Clone()); ctbndyn.AddNode(full.Clone()); ctbndyn.AddNode(hungry.Clone()); ctbndyn.AddNode(uptake.Clone()); ctbndyn.AddNode(concentration.Clone()); ctbndyn.AddNode(barometer.Clone()); ctbndyn.AddNode(pain.Clone()); ctbndyn.AddNode(drowsy.Clone()); // The initial distribution of a CTBN is defined by the underlying // Bayesian network containing a multinomial distribution for each // variable. BN bn; bn.AddNode(eatrv.Clone()); bn.AddNode(fullrv.Clone()); bn.AddNode(hungryrv.Clone()); bn.AddNode(uptakerv.Clone()); bn.AddNode(concentrationrv.Clone()); bn.AddNode(barometerrv.Clone()); bn.AddNode(painrv.Clone()); bn.AddNode(drowsyrv.Clone()); // Construct the final object that represents the CTBN. Markov m(bn.Clone(), ctbndyn.Clone()); cout << "Demo of learning a CTBN structure" << endl << "================================="<< endl; // Load the actual structure into a structure object Structure sA; ctbndyn.GetStructure(sA); cout << "Actual Structure:\n"; sA.Print(cout); cout << endl << endl; // Set the number and length of trajectories to sample int numTrajs = ParamInt("numtraj",1000); double tlen = ParamDouble("tlen",5.0); // Sample the trajectories cout << "Sampling trajectories...\n\n"; vector trVec; for(int i=0; iSetData(&trVec); ssQuery->SetDynamics(&ctbndyn); // Create the scoring object by specifying priors and the // sufficient statistics object to use for scoring double alphaPrior = ParamDouble("alpha",1.0); double tauPrior = ParamDouble("tau",1.0); CTBNFamScore* cfs = new CTBNFamScore(alphaPrior,tauPrior,ssQuery); // Create the search object by providing the context to search // over and the scoring function to use GraphEditSearch ges(ctbndyn.Domain(),cfs); cout << "Learning the structure...\n\n"; // Calling LearnStructure() returns a structure object with the // learned structure Structure s = ges.LearnStructure(); cout << "Learned Structure:\n"; s.Print(cout); cout << endl; // Compared the learned structure against the actual structure // with Hamming distance cout << "Hamming Distance: " << s.HammingDist(sA) << endl; delete ssQuery; delete cfs; cout << endl << endl; cout << "Demo of learning a BN structure" << endl << "================================="<< endl; // Load the actual structure into a structure object Structure sABN; bn.GetStructure(sABN); cout << "Actual Structure:\n"; sABN.Print(cout); cout << endl << endl; // Sample the trajectories (point evidence for BNs) cout << "Sampling...\n\n"; vector trVecBN; for(int i=0; iSetData(&trVecBN); ssQueryBN->SetInitRV(&bn); // Create the scoring object by specifying priors and the // sufficient statistics object to use for scoring BNFamScore* bfs = new BNFamScore(alphaPrior,ssQueryBN); // Create the search object by providing the context to search // over and the scoring function to use // We need to specify true as the 3rd parameter here to restrict // to structures without cycles GraphEditSearch gesBN(bn.Domain(),bfs,true); cout << "Learning the structure...\n\n"; // Calling LearnStructure() returns a structure object with the // learned structure Structure sBN = gesBN.LearnStructure(); cout << "Learned Structure:\n"; sBN.Print(cout); cout << endl; // Compared the learned structure against the actual structure // by checking the difference between the number of parameters cout << "Difference in number of parameters: " << abs(sABN.GetNumParams(bn.Domain()) - sBN.GetNumParams(bn.Domain())) << endl; delete ssQueryBN; delete bfs; } ctbnrle.1.1.1/demo/queryinput.data0000664000175000017500000002620612107036451017377 0ustar csheltoncshelton <\map> <\Context> <\map> <\Context> <\RV> <\map> <\Context> <\map> <\Context> <\RV> 0.5 0.5 <\vectr> <\MultiZSimple> <\vector> <\RVCondSimpleComp.MultiZSimple> <\RVComp> <\MultiRV> <\map> <\Context> <\map> <\Context> <\RV> 0.8 0.2 <\vectr> <\MultiZSimple> 0.3 0.7 <\vectr> <\MultiZSimple> <\vector> <\RVCondSimpleComp.MultiZSimple> <\RVComp> <\MultiRV> <\map> <\Context> <\map> <\Context> <\RV> 0.2 0.3 0.5 <\vectr> <\MultiZSimple> <\vector> <\RVCondSimpleComp.MultiZSimple> <\RVComp> <\MultiRV> <\vector> <\BN> <\map> <\Context> <\map> <\Context> <\Dynamics> <\map> <\Context> <\map> <\Context> <\Dynamics> -1 1 2 -2 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -3 3 4 -4 <\matrix> <\MarkovSimple> -5 5 6 -6 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\map> <\Context> <\map> <\Context> <\Dynamics> -6 3 3 6 -12 6 9 9 -18 <\matrix> <\MarkovSimple> <\vector> <\DynComp> <\MarkovDyn> <\vector> <\CTBNDyn> <\Markov> <\map> <\pair> <\map> <\pair> <\map> <\pair> <\map> <\Trajectory> 8.1538 42.5293 ctbnrle.1.1.1/demo/clique_example.cc0000664000175000017500000000451112107032410017605 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include #include #include "clique.h" #include "markov.h" using namespace std; using namespace ctbn; /* * Pass in ctbn file * */ int main(int argc, char **argv) { if(argc < 2) { cout << " you need to include a ctbn file" << endl; return 0; } //to initialize the CTBN ifstream ls(argv[1]); Markov ctbn(ls); //initializing the CliqueTree class CliqueTree check; //build the clique tree check.BuildClique(ctbn); cout << "Outputting the clique tree" << endl << endl; //print out the clique tree in shell check.CliquePrint(cout); //recieve the clique that been created by vector of vector test2 = check.ReturnCliques(); cout << "received item" << endl; //this will output clique::cliques //conn_context is whatthis contextes is connected to //contextes is contexts for(unsigned int i = 0; i < test2.size(); i++) { cout << "clique " << i << ": " << endl; cout << " variable ids(size): "; vector varlist = test2[i].vars.VarList(); for (unsigned int j=0;j. */ //This code generates n partially observed trajectories from //a given ctbn by sampling trajectories and randomly remove //certain fraction of information from the trajectories. // //Usage: gen_partialdata <.ctbnfile> /* Command line parameters: * * Set input model file * -DModelFile (default: - => stdin) * * Set output trajectories file * -DOutputFile (default: - => stdout) * * Set the begin time of the trajectory * -DBeginTime (default: 0.0) * * Set the end time of the trajectory * -DEndTime (default: 5.0) * * Set the number of trajectories * -DNumTraj (default: 1) * * Set the fraction of information removed in each iteration * -DRemoveFrac (default: 0.1) * * Set the number of iterations * -DRemoveIt (default 1) */ #include "markov.h" #include "trajectory.h" #include "params.h" #include "utils.h" #include #include "ensurectbn.h" using namespace std; using namespace ctbn; int main(int argc, char**argv) { InitParams(argc, argv); double begintime = ParamDouble("BeginTime" , 0.0); double endtime = ParamDouble("EndTime", 5.0); int n = ParamInt("NumTraj", 1); double frac = ParamDouble("RemoveFrac", 0.1); int nit = ParamInt("RemoveIt", 1); if (endtime <= begintime) { cout << "invalid length of trajectory" << endl; exit(0); } //load CTBN model from the file string instr = ParamStr("ModelFile","-"); Markov *model; if (instr != "-") { ifstream fin(instr.c_str()); if (!fin.good()) { cout << "bad CTBN file" << endl; exit(1); } model = Markov::LoadPtr(fin); } else { model = Markov::LoadPtr(cin); } string outstr = ParamStr("OutputFile","-"); ofstream outf; if (outstr != "-") { outf.open(outstr.c_str()); if (!outf.good()) { cout << "bad output file" << endl; exit(1); } } ostream &fdata = (outstr=="-" ? cout : outf); fdata.precision(10); fdata << n << endl; //generate partially observed data Context context = model->GetDynamics()->Domain(); int numvar = context.NumVars(); for(int i=0; iSample(tr); //randomly remove some information of the trajectory RemoveNodesInformation(tr, context, numvar, nit*numvar, frac); tr.Save(fdata); fdata << endl; } return 0; } ctbnrle.1.1.1/hdr/0000775000175000017500000000000012251375735014155 5ustar csheltoncsheltonctbnrle.1.1.1/hdr/ctbndyn.h0000664000175000017500000001640612107032422015755 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_CTBNDYN_H #define CTBNRLE_CTBNDYN_H #include "dynamics.h" #include "structure.h" #include "suffstatsquery.h" namespace ctbn { class CTBNDyn; class Structure; // These are the sufficient statistics for a CTBN Dynamics // (that is everything in a CTBN, except the initial distribution) // It consists of a vector (for each node in the graph) of its // sufficient statistics. class CTBNDynSS : public SS { friend class CTBNDyn; friend class MeanFieldInf; SOBJCLASSDECL(CTBNDynSS) public: CTBNDynSS(std::istream &is); CTBNDynSS(const CTBNDynSS &ctbnss); CTBNDynSS &operator=(const CTBNDynSS &ctbnss); virtual CTBNDynSS *Clone() const; virtual ~CTBNDynSS(); virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; virtual void Scale(double w); virtual void AddSS(const SS* nss, double w=1.0); virtual double NodeSS(int id, int val1, int val2, const Instantiation &cond) const; const SS* GetNodeSS(int id) const {return dynss[id];} CTBNDynSS(const CTBNDyn *d=NULL); private: void Compact() const; mutable std::vector dynss; mutable SS *jss; const CTBNDyn *dyn; SERIAL_START_V(CTBNDynSS) SERIAL_SUPER(SS) SERIAL_VAR(std::vector,dynss) SERIAL_END public: void serial_presave() const; void serial_preload(); }; // This is a CTBN Dynamics (that is, everything except the // initial distribution). All methods work just as described in // dynamics.h class CTBNDyn : public Dynamics { friend class CTBNDynSS; SOBJCLASSDECL(CTBNDyn) public: CTBNDyn(); CTBNDyn(std::istream &is); CTBNDyn(const CTBNDyn &ctbn); CTBNDyn(const Structure &s, const Context& vars); CTBNDyn &operator=(const CTBNDyn &ctbn); virtual CTBNDyn *Clone() const; virtual ~CTBNDyn(); virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; virtual void Normalize(); virtual void Restrict(const Instantiation &x); virtual void Mult(const Dynamics *x); virtual RVCondSimple *Cond(double t0, double t1, const Instantiation &x) const; virtual RVCondSimple *Cond(double t, const Instantiation &from, const Instantiation &to, bool transition=true) const; virtual CTBNDynSS *BlankSS() const; virtual void AddSS(const Instantiation &x, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddTransSS(const Instantiation &x1, const Instantiation &x2, double t, SS *ss, double w=1.0) const; virtual void AddExpSS(const RV *alpha, const RV *beta, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddExpTransSS(const RV *x1, const RV *x2, const Context &changevar, double t, SS *ss, double w=1.0) const; virtual void AddExpSS(const Instantiation &condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddExpSS(int condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddExpTransSS(const Instantiation &condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w=1.0) const; virtual void AddExpTransSS(int condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w=1.0) const; virtual void AddSS(const SS *toadd, const Dynamics *dyn, SS *ss, double w=1.0) const; virtual void SampleNextEvent(const Instantiation &i, double t, double &nextt, Instantiation &nexti, Random &rand=randomizer) const; virtual void SampleTrajectory(Trajectory &tr, double t, Random &rand=randomizer) const; virtual void Maximize(const SS *ss); virtual double LLH(const SS *ss) const; // These methods are new to a CTBNDyn: // CTBNDyn now owns the pointer (call ->Clone() to give it a copy) void AddNode(Dynamics *d); //Places the structure of the graph in s void GetStructure(Structure &s) const; // returns the number of nodes in the graph int NumofNodes() const {return nodes.size();} // returns the ith node const Dynamics *Node(int i) const {return nodes[i];} // returns the node for the variable with ID i const Dynamics *NodeByVar(int i) const; // randomizes the parameters of the network virtual void Scramble(double a=1.0, double b=1.0, double alpha=1.0, double degree=1.0, Random &rand=randomizer); // returns a joint intensity matrix for the entire // CTBN (not recommended if your CTBN is large!) // the rows (and columns) are ordered as by the Inc() method // for Instantiation (that is, construct an instantiation // from the context of this CTBNDyn and increment it to iterate // through the assignments for the rows). matrix JointMatrix() const; // returns a dynamics that represents the entire process // (flattened) -- again not recommended for large CTBNs const Dynamics *GetJoint() const {return Joint();} // returns the Bayesian structure score for the CTBN (given // the sufficient statistics) with the BDe prior with numTrans // and amtTime as the two meta-parameters virtual double GetScore(double numTrans, double amtTime, const SS* ss) const; // fills in the parameters from a different CTBNDyn. Used // in structural EM to initial the parameters after the // structure has been changed void FillParams(const CTBNDyn &src, bool useRandom=false); // returns the sufficient statistics just for variable ID i const SS *NodeSSByVar(int i, SS* ss) const; // Get a list of children by varid. The children list are also varids std::vector GetChildrenByVar(int varid) const; protected: // cannot delete returned value const Dynamics *Joint() const; std::vector nodes; mutable Dynamics *joint; // cache of joint RV // generates the items below // call before use -- will do nothing if they already exist void PrepNode2IdMaps() const; // clears them (call if nodes changes) void ClearNode2IdMaps() const; // cache of mapping from index into "nodes" to the variable id represented mutable std::vector node2var; // cache of mapping from variable id to the index into "nodes" mutable std::map var2node; // cache of the indexes into "nodes" of the children for a particular // variable id (Note this one is slightly odd as it maps a variable id into // a list of node-indexes (not back into varids) mutable std::map > children; mutable std::vector assocss; SERIAL_START_V(CTBNDyn) SERIAL_SUPER(Dynamics) SERIAL_VAR(std::vector,nodes) SERIAL_END public: void serial_preload(); }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/params.h0000664000175000017500000000705012107032422015572 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_PARAMS_H #define CTBNRLE_PARAMS_H #include namespace ctbn { // This file creates a method for setting global parameters in a variety // of ways and preventing the values from needing to be passed all around // if they are more-or-less "assumed." // To make this work, the function "InitParams" (below) should be the // first thing called by the main function. This will initialize the // parameters (argc and argv should be the corresponding arguments to // the main function). void InitParams(int argc, char **argv); // Then, whenever the program needs a parameter, it can call one of the // three functions immediately following. The first argument is the // name of the parameter. The second value is the value that should be // returned in case the parameter was not specified. std::string ParamStr(const std::string &name, const std::string &dflt=""); int ParamInt(const std::string &name, int dflt=0); double ParamDouble(const std::string &name, double dflt=0.0); // How this works: // InitParams sets up a global variable (see top of .cc file) who scope // is hidden from all except the functions above. // It reads parameters from two places: parameter files and the command // line. Parameter files take the form of one parameter per line. // Lines beginning with "#" or ";" are ignored. Whitespace at the // beginning of a line is ignored. The first token on the line // (delimited by whitespace) is the name of the parameter. The remainder // of the line (after the separating whitespace) is the parameter value. // // So, the file // #----------- // parameter1 3.0 // par two words // #do not use // q 4 // #----------- // defines three parameters: // "parameter1" => 3.0 // "par" => "two words" // "q" => 4 // // InitParams reads the parameters in in the following order with more // recent values overriding older values: // 1. Parameter file "params" (in the current directory) // 2. Parameter file ".params" (where is the executed name of // the program) // 3. Command line // // On the command line, -D defines paramname to have // value value. The argument "-param " reads in file filename. // And the argument "-listparams" will cause the program to read in all // of the parameters, list them all to stdout, and then quit // with error code 0. // This sets a parameter. Use with caution as it slightly destroys // the value of *user* set parameters. Best probably if set before // the call to InitParams so that user values can override this value // (but this value overrides the default given by the associated "Param*" // call) void SetParam(const std::string &name, const std::string &val); } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/dyncomp.tcc0000664000175000017500000001335212107032422016304 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "dyncomp.h" #include "rvcomp.h" namespace ctbn { using namespace std; SOBJCLASSDEFTEMPL1(DynComp,DS) template DynComp::DynComp(const Context &var, const Context &cvar) : Dynamics(var,cvar), impl(cvar.Size(),DS(var.Size())) { } template DynComp::DynComp(std::istream &is) : Dynamics(is), impl(CondDomain().Size(),DS(Domain().Size())) { int n = impl.size(); for(int i=0;i DynComp::~DynComp() { } template void DynComp::LoadOld(std::istream &is) { Dynamics::LoadOld(is); int n = CondDomain().Size(); impl.resize(n); for(int i=0;i void DynComp::SaveOld(std::ostream &os) const { Dynamics::SaveOld(os); int n = impl.size(); for(int i=0;i DynComp *DynComp::Clone() const { return new DynComp(*this); } template void DynComp::Normalize() { int n = impl.size(); for(int i=0;i void DynComp::Restrict(const Instantiation &x) { if (impl.size()==1) { vector ids; Domain().ConsistentIndexes(ids,x); impl[0].Restrict(ids); } else { vector condi; CondDomain().ConsistentIndexes(condi,x); int ci=0; int n = impl.size(); for(int i=0;i ids; Domain().ConsistentIndexes(ids,x); impl[i].Restrict(ids); ++ci; } else { vector ids; impl[i].Restrict(ids); } } } } template void DynComp::Mult(const Dynamics *x) { const DynComp *xx = dynamic_cast *>(x); assert(xx!=NULL); // otherwise, I'm not sure what to do // update domains (and save old ones): Context oldv(Domain()); Context oldcv(CondDomain()); Dynamics::Mult(x); vector newimpl(CondDomain().Size()); Instantiation v(Domain()+CondDomain(),0); Context notc(v,oldcv,Context::DIFFERENCE); do { int ci = CondDomain().Index(v); int myci = oldcv.Index(v); vector > myind(oldv.Size()); do { int myi = oldv.Index(v); int i = Domain().Index(v); myind[myi].push_back(i); } while(v.Inc(notc)); impl[myci].Expand(myind,Domain().Size()); newimpl[ci].Mult(&impl[myci]); } while(v.Inc(oldcv)); v.SetIndex(0); notc = v-xx->CondDomain(); do { int ci = CondDomain().Index(v); int xci = xx->CondDomain().Index(v); vector > xind(xx->Domain().Size()); do { int xi = xx->Domain().Index(v); int i = Domain().Index(v); xind[xi].push_back(i); } while(v.Inc(notc)); DS ds(xx->impl[xci]); ds.Expand(xind,Domain().Size()); newimpl[ci].Mult(&ds); } while(v.Inc(xx->CondDomain())); impl = newimpl; } template RV *DynComp::Cond(double t0, double t1) const { // difficult due to same variable names on "both sides" // need a method for remapping variable names... assert(0); } template RV *DynComp::CondRestrict(double t0, double t1, const Context &c) const { // difficult due to same variable names on "both sides" // need a method for remapping variable names... assert(0); } template DynCompSS *DynComp::BlankSS() const { int nc = impl.size(); DynCompSS *ret = new DynCompSS(nc); for(int i=0;iss[i] = impl[i].BlankSS(); return ret; } template void DynComp::AddSS(const Instantiation &x, double t0, double t1, SS *ss, double w) const { DynCompSS *dcss = dynamic_cast(ss); int condi = CondIndex(x); int i = Domain().Index(x); impl[condi].AddSS(i,t0,t1,dcss->ss[condi],w); } template void DynComp::AddTransSS(const Instantiation &x1, const Instantiation &x2, double t, SS *ss, double w) const { DynCompSS *dcss = dynamic_cast(ss); // assume conditioning variables are the same... int condi = CondIndex(x1); int i1 = Domain().Index(x1), i2 = Domain().Index(x2); impl[condi].AddTransSS(i1,i2,t,dcss->ss[condi],w); } template void DynComp::AddExpSS(const RV *alpha, const RV *beta, double t0, double t1, SS *ss, double w) const { // need to know how to convert alpha into a RVSimple object // probably best way is to make DynComp have two template // arguments (the second being the class C for RVComp) // to do later... assert(0); } template void DynComp::AddExpTransSS(const RV *x1, const RV *x2, double t0, SS *ss, double w) const { // see above assert(0); } template void DynComp::SampleNextEvent(const Instantiation &i, double t, double &nextt, Instantiation &nexti, Random rand) const { int condi = CondIndex(i); int ii = Domain().Index(i); int nextii; impl[condi].SampleNextEvent(ii,t,nextt,nextii,rand); nexti.SetIndex(nextii); } template void DynComp::Maximize(const SS *ss) { const DynCompSS *dcss = dynamic_cast(ss); int nc = impl.size(); for(int i=0;iss[i]); } } ctbnrle.1.1.1/hdr/rk.h0000664000175000017500000000637112107032422014730 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_RK_H #define CTBNRLE_RK_H #include "matrix.h" #include "defines.h" #include "meanfieldinf.h" namespace ctbn { // eps is the desired tolerance of the answer // (EPS below is the default value) which can be overridden // by the parameter (see params.h) RKEPS // (the default of -1.0 below forces the code to check for // the default parameter) #define RKEPS 1e-8 // h is a "recommended starting step size" // If h<0, then it uses 0.1/max(|diag(Q)|) as h // computes a * exp(Qt), via integration with desired error of eps // (h, if non-negative, gives a starting estimate of step size) // a is replaced with the result // the return value is the log of the factor by which a should // be multiplied for the true result (the returned result is normalized // to sum to 1) double vexpmt(vectr &a, const matrix &Q, double t, double eps=-1.0, double h=-1.0, vector *timesteps=NULL); // same as above, but calculates exp(Qt) * b double expmtv(vectr &b, const matrix &Q, double t, double eps=-1.0, double h=-1.0); // calculates \int_u=0^t a*exp(Q*u)*e_i e_j*exp(Q*(t-u))*b du // [this is a matrix indexed by (i,j)] // the transpose of this calculation is returned in c // (it is natural to calculate the transpose... the callee can // transpose it back) // Again, the returned value is the log of the factor by which the // result should be multiplied to find the "true result" double suffstat(const vectr &a, const vectr &b, matrix &c, const matrix &Q, double t, double eps=-1.0, double h=-1.0); // And here are two versions for 2-by-2 matrices. They are // often faster. Not currently used, but here for reference double vexpmt2(vectr &a, const matrix &Q, double t); double expmtv2(vectr &b, const matrix &Q, double t); double suffstat2(const vectr &a, const vectr &b, matrix &c, const matrix &Q, double t); void mfBackward(MeanFieldInf *inf, int varid, double t1, double t0, double eps=-1.0, double h=-1.0); void mfForward(MeanFieldInf *inf, int varid, double t0, double t1, double eps=-1.0, double h=-1.0); double mfEnergy(MeanFieldInf *inf, int varid, double t0, double t1, double eps=-1.0, double h=-1.0); double mfSuffStat(MeanFieldInf *inf, const Instantiation &x, double t0, double t1, double eps=-1.0, double h=-1.0); double mfTransSuffStat(MeanFieldInf *inf, int varid, const Instantiation &x1, const Instantiation &x2, double t0, double t1, double eps=-1.0, double h=-1.0); } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/matrix-eigenext.h0000664000175000017500000000314412107032422017421 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ // This file is added into the "middle" of the class definition of // MatrixBase from the Eigen package (done by the #def and #inc in // matrix-eigen.h -- see Eigen matrix package struct valid_visitor { bool valid; inline void init(const Scalar &, Index i, Index j) { valid = true; } inline void operator() (const Scalar &value, Index i, Index j) { if (!isfinite(value)) valid = false; } }; inline bool isvalid() const { valid_visitor vv; this->visit(vv); return vv.valid; } inline Scalar min() const { return this->minCoeff(); } inline Scalar max() const { return this->maxCoeff(); } inline Scalar absmin() const { return this->array().abs().minCoeff(); } inline Scalar absmax() const { return this->array().abs().maxCoeff(); } inline Scalar norm2() const { return squaredNorm(); } ctbnrle.1.1.1/hdr/random.h0000664000175000017500000000645312107032422015575 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_RANDOM_H #define CTBNRLE_RANDOM_H #include #include #include "matrix.h" namespace ctbn { // Class for generating (pseduo-)random numbers. Keeps the state of the // generator so that different instantiations can be used for different // parts of the code and be predictable (their pseudo-random draws don't // affect each other) // Class also includes method for drawing from non uniform distributions class Random { public: typedef unsigned long uint32; // just needs to be at least 32 bits Random (uint32=-1); void Reset(uint32 in_seed=-1); // returns [0.0, 1.0) double RandReal(); // same, but only uses 32 bits of randomness (instead of 64) double RandRealFast(); // returns [0, range-1] int RandInt (int range); // returns ~N(0,1) double SampleNormal (void); // returns ~Beta(a,b) where a and b are positive reals double SampleBeta (double a, double b); // returns ~Gamma(a,b) where a and b are positive reals double SampleGamma (double a, double b); /// samples from the Dirichlet joint distribution over thetas // theta ~ theta[0]^(alpha[0]-1) * ... * theta[n]^(alpha[n]-1) void SampleDirichlet(double const *alpha, int n, double *theta); // samples from Exponential Dist for intensity a = 1/mean double SampleExp (double a); //sample from Exponential dist for intensity q, given value is // less than t double SampleTruncateExp(double q, double t); // samples from a set of additive weights (for multi) int SampleAddWeights (const std::vector& additive_weights); // samples from a weighted distribution int SampleMultinomial (const std::vector& weights, double sum=1.0); int SampleMultinomial (const double *weights, int n, double sum=1.0); int SampleMultinomial (const vectr &weights, double sum=1.0); //sample n samples a time void SampleMultinomial(const std::vector &weights, std::vector &samples, int n); private: double DblGammaLessThanOne(double dblaphla); double DblGammaGreaterThanOne(double dblaphla); uint32 Rand32(); void generateMore(); private: // starting seed number uint32 seed; // state of the generator: uint32 MT[624]; int loc; static const double Conv32Double, Conv64Double; /* SERIAL_START(Random) SERIAL_VAR(uint32,seed) TODO: add fixed-length array to serialization code SERIAL_VAR_ARRAY(uint32,MT,624) SERIAL_VAR(int,loc) SERIAL_END */ }; extern Random randomizer; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/streamextra.h0000664000175000017500000002457712107032422016663 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_STREAMEXTRA_H #define CTBNRLE_STREAMEXTRA_H #include #include #include #include "defines.h" /* This header file does two separated (but related) things: 1. It defines a way to save and load via streams (ie serialize) maps and vectors from STL. Because of scoping issues it has to be defined in the std namespace (which is unfortunate) 2. It defines a function addnan which can be called on any input or output character stream. It makes sure that stream can read or write "NaN" and infinite values properly. */ #include #include #include #include #include #include // first declaration of some helper methods to push maps and vectors // automatically through operator<< and operator>> // It would be *great* if this could be taken out of the std namespace! // Unfortunately, with current C++ compilers (March 2010), that is not // possible. namespace std { template ostream &operator<<(ostream &os, const pair &p) { return os << p.first << os.fill() << p.second; } template istream &operator>>(istream &is, pair &p) { return is >> p.first >> p.second; } // basically the same as istream_iterator, but with a limit // on the number of items that can be read. template, typename Dist = ptrdiff_t> class istream_lim_iterator : public iterator { private: int togo; basic_istream *s; T t; public: istream_lim_iterator() { togo = -1; s = NULL; } istream_lim_iterator(basic_istream &is, int limit) : togo(limit), s(&is) { read(); } istream_lim_iterator& operator++() { read(); return *this; } const istream_lim_iterator operator++(int) { istream_lim_iterator tmp = *this; (*this)++; return tmp; } const T operator*() const { return t; } const T* operator->() const { return &(operator*()); } bool isequal(const istream_lim_iterator &ili) const { return (togo==-1 && ili.togo==-1) || (togo!=-1 && ili.togo!=-1); } private: void read() { if (togo>=0) { if (togo>0 && s && s->good()) { *s >> t; togo--; if (!s || !s->good()) togo = -1; } else togo=-1; } } }; template inline bool operator==( const istream_lim_iterator &x, const istream_lim_iterator &y) { return x.isequal(y); } template inline bool operator!=( const istream_lim_iterator &x, const istream_lim_iterator &y) { return !x.isequal(y); } // And now we have the definitions of the shift operators: template ostream &operator<<(ostream &os, const map &m) { os << m.size() << os.fill(); copy(m.begin(),m.end(),ostream_iterator >(os," ")); return os; } template istream &operator>>(istream &is, map &m) { m.clear(); int s; is >> s; m.insert(istream_lim_iterator >(is,s), istream_lim_iterator >()); return is; } template ostream &operator<<(ostream &os, const vector &v) { os << v.size() << os.fill(); copy(v.begin(),v.end(),ostream_iterator(os," ")); return os; } template istream &operator>>(istream &is, vector &v) { int s; is >> s; v.assign(istream_lim_iterator(is,s), istream_lim_iterator()); return is; } } // Here is some code to define a new locale so that floating point // numbers like -inf and Nan can be read and written. // You can forget about all of it. What you need to do is call // this function (addnan) on any input or output stream before // using it. It will make sure that stream reads or writes "inf" // and "NaN" values properly void addnan(std::basic_ios &str); // This should work, but someone didn't add num_put_byname // (there is time_put_byname however!) //template //class nannum_put : public std::num_put_byname { // so instead we do... template > class nannum_put : public std::num_put { public: virtual ~nannum_put() throw() {}; explicit nannum_put(const char *locname) //: std::num_put_byname(locname) {} : std::num_put() {} protected: virtual OutputIterator do_put(OutputIterator itt, std::ios_base &iob, charT fill, double d) const { if (!isfinite(d)) { itt = writeit(itt,d); } else { //itt = std::num_put_byname:: // do_put(itt,iob,fill,d); itt = std::num_put:: do_put(itt,iob,fill,d); } return itt; } virtual OutputIterator do_put(OutputIterator itt, std::ios_base &iob, charT fill, long double d) const { if (!isfinite(d)) { itt = writeit(itt,d); } else { //itt = std::num_put_byname:: // do_put(itt,iob,fill,d); itt = std::num_put:: do_put(itt,iob,fill,d); } return itt; } virtual OutputIterator do_put(OutputIterator itt, std::ios_base &iob, charT fill, float f) const { if (!isfinite(f)) { itt = writeit(itt,f); } else { //itt = std::num_put_byname:: // do_put(itt,iob,fill,f); itt = std::num_put:: do_put(itt,iob,fill,f); } return itt; } private: template static OutputIterator writeit(OutputIterator itt, FP f) { static const char *minus = "-"; // commented-out code works for g++'s STL, but // not for others. Uncommented-out code is fine // more generally if (isinf(f)) { if (f<0) //itt = std::__write(itt,"-",1); std::copy(minus,minus+1,itt); std::copy(infstr, infstr +std::char_traits::length(infstr), itt); /* itt = std::__write(itt, infstr, std:: char_traits:: length(infstr)); */ } else std::copy(nanstr, nanstr +std::char_traits::length(nanstr), itt); /* itt = std::__write(itt, nanstr, std:: char_traits:: length(nanstr)); */ return itt; } }; // This should work, but someone didn't add num_get_byname // (there is time_get_byname however!) //template //class nannum_get : public std::num_put_byname { // so instead we do... template > class nannum_get : public std::num_get { public: virtual ~nannum_get() throw() {}; typedef std::basic_string strtype; explicit nannum_get(const char *locname) //: std::num_get_byname(locname) {} : std::num_get() {} protected: virtual InputIterator do_get(InputIterator beg, InputIterator end, std::ios_base &iob, std::ios_base::iostate &iost, double &d) const { bool negate = false; if (*beg == '-') { negate=true; ++beg; } if (*beg == '+') { negate=false; ++beg; } if (*beg==*infstr) { if (checkstr(beg,end,infstr,iost)) d = negate ? -INFINITY : INFINITY; return beg; } else if (*beg==*nanstr) { if (checkstr(beg,end,nanstr,iost)) d = nan(""); return beg; } else { InputIterator ret = std::num_get:: do_get(beg,end,iob,iost,d); if (negate) d = -d; return ret; } } virtual InputIterator do_get(InputIterator beg, InputIterator end, std::ios_base &iob, std::ios_base::iostate &iost, long double &d) const { bool negate = false; if (*beg == '-') { negate = true; ++beg; } if (*beg == '+') { negate = false; ++beg; } if (*beg==*infstr) { if (checkstr(beg,end,infstr,iost)) d = negate ? -INFINITY : INFINITY; return beg; } else if (*beg==*nanstr) { if (checkstr(beg,end,nanstr,iost)) d = nanl(""); return beg; } else { InputIterator ret = std::num_get:: do_get(beg,end,iob,iost,d); if (negate) d = -d; return ret; } } virtual InputIterator do_get(InputIterator beg, InputIterator end, std::ios_base &iob, std::ios_base::iostate &iost, float &f) const { bool negate = false; if (*beg == '-') { negate = true; ++beg; } if (*beg == '+') { negate = false; ++beg; } if (*beg==*infstr) { if (checkstr(beg,end,infstr,iost)) f = negate ? -INFINITY : INFINITY; return beg; } else if (*beg==*nanstr) { if (checkstr(beg,end,nanstr,iost)) f = nanf(""); return beg; } else { InputIterator ret = std::num_get:: do_get(beg,end,iob,iost,f); if (negate) f = -f; return ret; } } private: static bool checkstr(InputIterator &beg, InputIterator end, charT *str, std::ios_base::iostate &iost) { while(*str && beg!=end) { if (*beg==*str) { ++str; ++beg; } else { iost |= std::ios_base::failbit; return false; } } return *str==0; } }; // End of stuff for addnan #endif ctbnrle.1.1.1/hdr/factoredmatrix.h0000664000175000017500000001325612107032422017330 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_FACTORED_MATRIX_H #define CTBNRLE_FACTORED_MATRIX_H #include "ctbndyn.h" #include "factoredvector.h" // cshelton: added #include "expmcache.h" #include "rk.h" #include namespace ctbn { #define RKEPS 1e-8 class FactoredMatrix :public RVCondSimple { friend class FactoredVector; friend class TOPETree; public: FactoredMatrix(); FactoredMatrix(Dynamics *dyn); FactoredMatrix(const FactoredMatrix &m); ~FactoredMatrix(); RVCondSimple *Clone() const; double FindMinVal(Dynamics *node) const; double FindMinRate() const; void Cond(double t0, double t1, const Instantiation &x); void Cond(double t0, const Instantiation &from, const Instantiation &to, bool transition=true); void CondMultByVec(FactoredVector &v, bool transpose, double &maxval) const; void MultByVec(FactoredVector &v, bool transpose, double &maxval) const; void TransMultByVec(FactoredVector &v, bool transpose) const; void TransMult(RVSimple *&x, bool transpose) const; void Mult(RVSimple *&x) const; void Mult(RVSimple *&x, bool transpose) const; double FindAvgRate(FactoredVector &v, const Dynamics *node, int from, int to, const std::vector &cind, bool transpose, double &maxval) const; void RMult(RVSimple *&x) const; double Unifvexpmt(FactoredVector &v, bool conditioned, bool transition) const; //tope // cshelton: added void initRKcache(double maxtime, double eps=1e-12) const; double RKvexpmtA(FactoredVector & v, double time, bool conditioned, bool transpose, vector< vector > *timesteps=NULL) const; void BMultByVec(FactoredVector &v, int varid, int bno, bool transpose) const; matrix *MakeAB(Dynamics *bnode); // void SetL(int terms_in_taylor_expansion); void SetTheta(double theta); void Load(std::istream &is); void Save(std::ostream &os) const; // returns the probability of an individual conditional event double Prob(int ind, int cind, bool log=false) const; // Makes sum to 1 for each conditioning value void Normalize(); // returns an object representing the conditioning // on that value RVSimple *Condition(int ind) const; // Sets to zero all indexes that are not listed in ind and cind // That is, for conditions mentioned in cind, the measure is // restricted to the indexes in ind. For conditions not in cind, // the measure is made to be zero everywhere. void Restrict(const std::vector &ind, const std::vector &cind); // Marginalizes, reindexes, or expands depending on the // argument // n = # new values nc = # new conditioning values // ind[] is a mapping that needs to be applied to old // conditioning index j and then added to i // (see Reindex for RVSimple above to see how a mapping is // applied) // (Really, this is probably too confusing to be "simple.") typedef std::map, std::vector > > RemapT; void Reindex(int n, int nc, const RemapT &ind); // Multiplies by another measure (point-wise) void MultBy(const RVCondSimple *x); // return suitable sufficient statistics object SS *BlankSS() const; // add independent draw of (cind,ind) (conditioning value, value) to ss void AddSS(int cind, int ind, SS *ss, double w=1.0) const; // add average of independent draws to ss void AddExpSS(int cind, SS *ss, double w=1.0) const; void AddExpSS(int cind, const RVSimple *rvs, SS *ss, double w) const; void AddSS(const SS *toadd, const RVCondSimple* rv, const std::vector > &mapping, int mycondi, int rvccondi, SS *ss, double w=1.0) const; // returns a sample int Sample(int cx, Random &rand = randomizer) const; // sets parameters to ML estimate void Maximize(const SS *ss); // Returns a new object that can be multiplied into this // object (as per Mult and RMult) RVSimple * Convert(const RVSimple *rvs) const; // see above class for description void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer); //by Yu double LLH(const SS *ss) const; double GetScore(double numTrans, const SS *ss)const; /////// private: void Uniformize(Dynamics *node); double minrate; double t; bool trans; int taylor_expansion_term_count; double theta_value; //holds nodes by varids std::vector nodes; //for tope std::vector bnodes; std::vector amatrices; // cshelton: added mutable double cachemaxt; mutable std::vector rkcache; // //consistent parent indices of the variables that are not in the evidence std::map > condpars; std::map > children_in_evidence; ///consistent parent indices of the variables that are in the evidence std::map > condpars_evid; std::map evid; std::map > trans_evid; //for from to conditioning std::map notrans_evid; //for evidence that went from uninst to inst (when there is no transition) }; } #endif ctbnrle.1.1.1/hdr/nonexpsuffstatsquery.h0000664000175000017500000000243412107032422020650 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_NONEXPSUFFSTATSQUERY_H #define CTBNRLE_NONEXPSUFFSTATSQUERY_H // A class for performing queries for sufficient statistics where we have // complete trajectories. #include "suffstatsquery.h" #include "trajectory.h" #include namespace ctbn { class NonExpSuffStatsQuery : public SuffStatsQuery { public: NonExpSuffStatsQuery(); virtual ~NonExpSuffStatsQuery(); virtual SS* GetP0SS(); virtual SS* GetDSS(); }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/bn.h0000664000175000017500000001203012107032422014700 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_BN_H #define CTBNRLE_BN_H #include "rv.h" #include "streamserial.h" #include "structure.h" namespace ctbn { class BN; class RVSimple; // sufficient statistics object for a Bayesian network class BNSS : public SS { friend class BN; SOBJCLASSDECL(BNSS) public: BNSS(std::istream &is); BNSS(const BNSS &bnss); BNSS &operator=(const BNSS &bnss); virtual BNSS *Clone() const; virtual ~BNSS(); virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; virtual void Scale(double w); virtual void AddSS(const SS* nss, double w=1.0); BNSS(); private: std::vector rvss; SERIAL_START_V(BNSS) SERIAL_SUPER(SS) SERIAL_VAR(std::vector,rvss) SERIAL_END public: void serial_preload(); }; // BN is a class to represent a Bayesian Network // See RV for description of most of the methods class BN : public RV { SOBJCLASSDECL(BN) friend class FBInf; public: BN(); BN(const BN &bn); // load from stream BN(std::istream &is); // build with structure and variables given BN(const Structure &s, const Context &vars); BN &operator=(const BN &bn); virtual BN *Clone() const; virtual ~BN(); // next set: standard RV methods: virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; virtual void Normalize(); virtual double Prob(const Instantiation &x, bool log=false) const; virtual void Restrict(const Instantiation &x); virtual void Project(const Context &c, const Context &cc); virtual void MultBy(const RV *x); virtual BNSS *BlankSS() const; virtual void AddSS(const Instantiation &x, SS *ss, double w=1.0) const; virtual void AddExpSS(const Instantiation &x, SS *ss, double w=1.0) const; virtual void AddExpSS(const Instantiation &x, const RVSimple *rvs, SS *ss, double w=1.0) const; virtual void AddSS(const SS* toadd, const RV *rv, SS *ss, double w=1.0) const {} virtual void Sample(Instantiation &x, Random &rand=randomizer) const; virtual void Maximize(const SS *ss); virtual RVSimple * MakeSimple(Instantiation const & x, bool normalize = true) const; virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer); virtual double LLH(const SS *ss) const; virtual double GetScore(double numTrans, const SS* ss) const; // these are specific to a BN: // the BN now owns the pointer rv (call with rv->Clone()) if the // caller doesn't want to give up the variable void AddNode(RV *rv); // implements importance sampling and returns the weight of the // sample double ImportanceSample(Instantiation &x, bool log=false, Random &rand=randomizer) const; // get parameters from a BN that might not have the same // structure (say to initialize the E step inside SEM) void FillParams(const BN &src); // i is the index of the node (might not be variable "i") const RV *Node(int i) const {return nodes[i]; } // i is the "name" of a variable const RV *NodeByVar(int i) const; int NumofNodes() const { return nodes.size(); } // Get a list of children by varid; The children list are also varids std::vector GetChildrenByVar(int varid) const; void GetStructure(Structure &s) const; protected: void SetSampleOrder() const; std::vector nodes; mutable std::vector sampleorder; // as a cache // generates the items below // call before use -- will do nothing if they already exist void PrepNode2IdMaps() const; // clears them (call if nodes changes) void ClearNode2IdMaps() const; // returns joint distribution restricted to x RV *Joint(const Instantiation &x) const; RVSimple *MakeSimpleSparse(const Instantiation &x, bool normalize=true) const; // cache of mapping from index into "nodes" to the variable id represented mutable std::vector node2var; // cache of mapping from variable id to the index into "nodes" mutable std::map var2node; // cache of the indexes into "nodes" of the children for a particular // variable id (Note this one is slightly odd as it maps a variable id into // a list of node-indexes (not back into varids) mutable std::map > children; SERIAL_START_V(BN) SERIAL_SUPER(RV) SERIAL_VAR(std::vector,nodes) SERIAL_END public: void serial_preload(); }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/notyetimplementederror.h0000664000175000017500000000254512107032422021133 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef NOTYETIMPLEMENTEDERROR_H #define NOTYETIMPLEMENTEDERROR_H #include #include namespace ctbn { using std::string; using std::logic_error; class not_yet_implemented_error : logic_error { public: not_yet_implemented_error( string const & the_class_name, string const & the_method_name) : logic_error(string("The function ") + the_class_name + (the_class_name == "" ? "" : "::") + the_method_name + " has not yet been implemented.") {}; }; } #endif ctbnrle.1.1.1/hdr/process.h0000664000175000017500000000325412107032422015767 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_PROCESS_H #define CTBNRLE_PROCESS_H #include "dynamics.h" #include "rv.h" namespace ctbn { // Process is a ABC for any general dynamic process class Process : public StreamObj { //SOBJCLASSDECL(Process) public: virtual ~Process(); // virtual copy constructor virtual Process *Clone() const = 0; // the amalgamtion operation virtual void Mult(const Process *p) = 0; // returns a suitable sufficient statistics object virtual SS *BlankSS() const = 0; // sets parameters from ML estimate virtual void Maximize(const SS *ss) = 0; // returns log-likelihood from sufficient statistics virtual double LLH(const SS *ss) const = 0; // sample a trajectory of events from the process virtual void Sample(Trajectory &tr, Random &rand=randomizer) const = 0; SERIAL_START_V(Process) SERIAL_END }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/dynamics.h0000664000175000017500000001565012107032422016123 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_DYNAMICS_H #define CTBNRLE_DYNAMICS_H #include "context.h" #include "trajectory.h" #include "rvsimple.h" #include "rv.h" namespace ctbn { // Dynamics is a continuous-time semi-Markov process except for the // starting distribution // The domain is explicit class Dynamics : public StreamObj { public: // var is the set of variables (state space of the process) // cvar is the set of variables on which the process is // conditioned. Let cvar be a null context (just create a // context without any arguments to the constructor) if this // process is not dependent on any others. Dynamics(const Context &var=Context(), const Context &cvar=Context()); // load from file.. Dynamics(std::istream &is); // destructor virtual ~Dynamics(); // load and save... virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; // virtual copy constructor virtual Dynamics *Clone() const = 0; // Returns the state space const Context &Domain() const { return v; } // Returns the conditioning variables const Context &CondDomain() const {return cv; } // Cause the dynamics to be "closed" (no probability of leaving // the system) virtual void Normalize() = 0; // Add evidence that the system stays within the states // defined in x virtual void Restrict(const Instantiation &x) = 0; // the version here just sets up the contexts (v and cv) virtual void Mult(const Dynamics *x); // The next two return a conditional random variable that // describes the distribution at one time point conditioned on // a previous time point. They are returned as RVCondSimple // because the domains are implied // the implied domain for RVCondSimple is the Context // of this dynamic process // x gives the values for the conditional context // as well as any subsystem restrictions for the process's context virtual RVCondSimple *Cond(double t0, double t1, const Instantiation &x) const = 0; // same, but instantaneous intensity of transitioning // if transition==false, this doesn't correspond to a // transition, but rather just a change of support virtual RVCondSimple *Cond(double t, const Instantiation &from, const Instantiation &to, bool transition=true) const = 0; // Return a blank sufficient statistics object suitable for // this dynamics virtual SS *BlankSS() const = 0; // Adds sufficient statistics for being in state x from t0 // to t0+deltat by adjusting ss. Addition can be weighted by w virtual void AddSS(const Instantiation &x, double t0, double deltat, SS *ss, double w=1.0) const = 0; // Same, but adds ss for a transition from x1 to x2 at time t virtual void AddTransSS(const Instantiation &x1, const Instantiation &x2, double t, SS *ss, double w=1.0) const = 0; // Adds expected sufficient statistics for the interval t0 to // to+deltat if alpha is the distribution at t0 given evidence // before t0 and beta is the distribution at t0+deltat given // evidence after t0+deltat. virtual void AddExpSS(const RV *alpha, const RV *beta, double t0, double deltat, SS *ss, double w=1.0) const = 0; // Same as above, but for a transition at t. x1 is the // distribution just before the transition (conditioned on evidence // before t) and x2 is the same just after t (conditioned on // evidence after t) // changevar is the variable that changed value virtual void AddExpTransSS(const RV *x1, const RV *x2, const Context &changevar, double t, SS *ss, double w=1.0) const = 0; // These are the same as above, but where the context is implied // and the conditioning variables (cvar) takes on a known value (not // a distribution over them) -- either its index or a full // instantiation is supplied... virtual void AddExpSS(const Instantiation &condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w=1.0) const = 0; virtual void AddExpSS(int condi, const RVSimple * const alpha, const RVSimple * const beta, double t0, double deltat, SS * const ss, double w = 1.0) const = 0; virtual void AddExpTransSS(const Instantiation &condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w=1.0) const = 0; virtual void AddExpTransSS(int condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w=1.0) const = 0; // This adds the sufficient statistics of a different process // to this process's sufficient statistics virtual void AddSS(const SS *toadd, const Dynamics *dyn, SS *ss, double w=1.0) const = 0; // i is the assignment at t. nextt becomes the next time when // a transition occurs. nexti is the next assignment // assumes that the context of nexti equals Domain virtual void SampleNextEvent(const Instantiation &i, double t, double &nextt, Instantiation &nexti, Random &rand=randomizer) const = 0; // completes the trajectory, starting at t and going until // tr.EndTime() // the method implemented here repeatedly calls SampleNextEvent // override it if you can do something more efficient virtual void SampleTrajectory(Trajectory &tr, double t, Random &rand=randomizer) const; // perform ML by adjusting the parameters, using the sufficient // statistics in ss (which you may assume are of the correct type) virtual void Maximize(const SS *ss) = 0; // Randomize the parameters virtual void Scramble(double a=1.0, double b=1.0, double alpha=1.0, double degree=1.0, Random &rand=randomizer) = 0; // return the log-likelihood from the sufficient statistics ss virtual double LLH(const SS *ss) const = 0; // returnn the sufficient statistics from the set of trajectories // tr, each weighted by the weight w (w is *not* in log space) SS* SuffStats(const std::vector &tr, const std::vector &w) const; // returns a structure search score based on ss // numTrans and amtTime are the Bayesian prior parameters // for a BDe prior virtual double GetScore(double numTrans, double amtTime, const SS *ss) const = 0; protected: Context v,cv; SERIAL_START_V(Dynamics) SERIAL_VAR(Context,v) SERIAL_VAR(Context,cv) SERIAL_END }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/brutestructuresearch.h0000664000175000017500000000267012107032422020602 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_BRUTESTRUCTURESEARCH_H #define CTBNRLE_BRUTESTRUCTURESEARCH_H #include "structuresearch.h" namespace ctbn { // Class to perform exhaustive "brute-force" structure search. class BruteStructureSearch : public StructureSearch { public: // The FamScore pointer is not owned by this class. It is owned by // the caller of the constructor. BruteStructureSearch(const Context &c, FamScore* fs, int mp = 1); inline virtual ~BruteStructureSearch() throw() {} virtual Structure LearnStructure(); void SetMaxParents(int mp) {maxParents = mp;} protected: int maxParents; }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/uniformizedfactoredinf.h0000664000175000017500000000534512107032422021054 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_UNIFORMIZEDFACTOREDINF_H #define CTBNRLE_UNIFORMIZEDFACTOREDINF_H #include "markov.h" #include "ctbndyn.h" #include "dyncomp.h" #include "bn.h" #include "rvcomp.h" #include "inference.h" #include "fbinf.h" #include "factoredvector.h" #include "factoredmatrix.h" #include namespace ctbn { // This class performs approximate forward inference (filtering) only // (currently) by using the uniformized approximation of Celikkaya & Shelton // (2010) class UniformizedFactoredInf : public FBInf { public: UniformizedFactoredInf(); virtual ~UniformizedFactoredInf() throw(); virtual UniformizedFactoredInf *Clone() const; void SetL(int l){ lval = l; } void SetTheta(double th) { thval = th; } private: RVSimple * Convert(RVCondSimple *&rvcond, RVSimple *&rv); void Restrict(FactoredVector * & rv, Instantiation const & x) const; int lval; double thval; public: virtual void Restrict(RVSimple *, Instantiation const & variable_assignment) const; virtual RVSimple * MakeSimple(RV * prior, Instantiation const &, bool normalize = true) const; virtual bool IsAlphaElementValid(RVSimple * const alpha_sub_x); virtual FactoredMatrix * IntervalPropagator(Dynamics *, double, double, Instantiation const &) const; virtual FactoredMatrix * PointTransitionPropagator(Dynamics *, double, Instantiation const &, Instantiation const &) const; virtual FactoredMatrix * PointChangeEvidencePropagator(Dynamics *, double, Instantiation const &, Instantiation const &) const; void AddExpectedSufficientStatistics(Dynamics const *, unsigned int, SS *, double) const { throw "Not yet implemented."; } void AddExpectedTransitionSufficientStatistics(Dynamics const *, unsigned int, SS *, double) const { throw "Not yet implemented."; } void AddExpectedInitialSufficientStatistics(Trajectory const *, RVSimple *, const RV *, SS *, double) const { throw "Not yet implemented."; } }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/grapheditsearch.h0000664000175000017500000000451312107032422017445 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_GRAPHEDITSEARCH_H #define CTBNRLE_GRAPHEDITSEARCH_H #include "structuresearch.h" #include "markovdyn.h" #include "searchqueue.h" #include #include namespace ctbn { // Performs the graph edit search algorithm (a greedy search) // The user has the option to disallow cycles (generally for BNs) class GraphEditSearch : public StructureSearch { public: // The FamScore pointer is owned by the caller of the constructor // but must remain valid for the lifetime of the GraphEditSearch object GraphEditSearch(const Context &c, FamScore* fs, bool noCyc=false); inline virtual ~GraphEditSearch() throw() { delete sQ; } virtual Structure LearnStructure(); virtual void SetStructure(const Structure& s); protected: // Called by constructor to setup the nodes used to score // and determine initial scores. virtual void Initialize(); // Enqueues the operators performed by the variable of nodeid i virtual void EnqueueOps(int i); // Checks if adding nodeid j to the parent set of i produces a cycle virtual bool ProducesCycle(int i, int j); // Checks if adding nodeid j to the parent set of i // and removing nodeidi from the parents set of j produces a cycle virtual bool ProducesCycleRev(int i, int j); // Keeps the current scores std::vector scores; // Priority queue designed for this algorithm // This class owns this pointer and creates the object for it upon // construction. SearchQueue* sQ; bool noCycles; bool learnCalled; }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/dyncomp.h0000664000175000017500000001234112107032422015757 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_DYNCOMP_H #define CTBNRLE_DYNCOMP_H #include "streamextra.h" #include "streamserial.h" #include "dynamics.h" #include "dynsimple.h" namespace ctbn { class DynComp; // sufficient statistics for a DynComp class DynCompSS : public SS { SOBJCLASSDECL(DynCompSS) friend class DynComp; public: DynCompSS(int n=0); DynCompSS(const DynCompSS &dcss); DynCompSS(std::istream &is); DynCompSS &operator=(const DynCompSS &dcss); virtual ~DynCompSS(); virtual DynCompSS *Clone() const; virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; virtual void Scale(double w); virtual void AddSS(const SS* nss, double w=1.0); virtual double Element(std::vector index, int s1, int s2) const; private: std::vector ss; SERIAL_START_V(DynCompSS) SERIAL_SUPER(SS) SERIAL_VAR(std::vector,ss) SERIAL_END }; // a class that creates a Dynamics object out of a DynSimple class // see dynamics.h for a description of the methods // see dynsimple.h for a description of DynSimple // This class is essentially just a set of DynSimple objects (one // for each possible value of the conditioning context), with // wrappers to map from instantiations to the raw indexes used by // DynSimple class DynComp : public Dynamics { SOBJCLASSDECL(DynComp) public: // the base argument will only be used for Clone() // it is still owned by the caller DynComp(const Context &var=Context(), const Context &cvar=Context(), const DynSimple *base=NULL); DynComp(std::istream &is); DynComp(const DynComp &dc); DynComp &operator=(const DynComp &dc); virtual ~DynComp(); virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; virtual DynComp *Clone() const; virtual void Normalize(); virtual void Restrict(const Instantiation &x); virtual void Mult(const Dynamics *x); virtual RVCondSimple *Cond(double t0, double t1, const Instantiation &x) const; virtual RVCondSimple *Cond(double t, const Instantiation &from, const Instantiation &to, bool transition=true) const; virtual DynCompSS *BlankSS() const; virtual void AddSS(const Instantiation &x, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddTransSS(const Instantiation &x1, const Instantiation &x2, double t, SS *ss, double w=1.0) const; virtual void AddExpSS(const RV *alpha, const RV *beta, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddExpTransSS(const RV *x1, const RV *x2, const Context &changevar, double t, SS *ss, double w=1.0) const; virtual void AddExpSS(const Instantiation &condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddExpSS(int condi, const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w=1.0) const; virtual void AddExpTransSS(const Instantiation &condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w=1.0) const; virtual void AddExpTransSS(int condi, const RVSimple *x1, const RVSimple *x2, const Context &changevar, double t, SS *ss, double w=1.0) const; virtual void AddSS(const SS *toadd, const Dynamics *dyn, SS *ss, double w=1.0) const; virtual void SampleNextEvent(const Instantiation &i, double t, double &nextt, Instantiation &nexti, Random &rand=randomizer) const; virtual void Maximize(const SS *ss); inline DynSimple *operator[](const Instantiation &i) { return impl[CondDomain().Index(i)]; } inline const DynSimple *operator[](const Instantiation &i) const { return impl[CondDomain().Index(i)]; } virtual void Scramble(double a=1.0, double b=1.0, double alpha=1.0, double degree=1.0, Random &rand=randomizer); virtual double LLH(const SS *ss) const; virtual double GetScore(double numTrans, double amtTime, const SS* ss) const; // It is questionable as to whether this breaks the abstraction... // note that the pointer returned is still owned by this object inline DynSimple *operator[](int i) { return impl[i]; } inline const DynSimple *operator[](int i) const { return impl[i]; } protected: inline int CondIndex(const Instantiation &x) const { if (impl.size()<2) return 0; else return CondDomain().Index(x); } std::vector impl; SERIAL_START_V(DynComp) SERIAL_SUPER(Dynamics) SERIAL_VAR(std::vector,impl) SERIAL_END }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/clique.h0000664000175000017500000001116312107032422015571 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_CLIQUE_H_ #define CTBNRLE_CLIQUE_H_ #include "bn.h" #include "dynamics.h" #include "context.h" #include "ctbndyn.h" #include "markov.h" #include "markovdyn.h" #include "random.h" #include #include #include #include #include #include namespace ctbn { // Class to build a clique tree from a set of factors, // either defined from a BN, a CTBNDyn, or both (Markov process) class CliqueTree: public StreamObj{ public: CliqueTree(); //build the clique tree for a markov (both CTBNDyn and BN factors) void BuildClique(const Markov &process, Random &rand = randomizer); //build the clique tree for a CTBNDyn // caller retains ownership of dyn void BuildClique(const CTBNDyn *dyn, Random &rand = randomizer); //build the clique tree for a BN // caller retains ownership of bn void BuildClique(const BN *bn, Random &rand = randomizer); // build the clique truee for Contexts void BuildClique(const std::vector &, Random & rand = randomizer); //print the clique tree void CliquePrint(std::ostream &os); //save the current state virtual void SaveOld(std::ostream &os) const; //loading the value into the class so it will have all the data virtual void LoadOld(std::istream &is); //to use for testing: //if true is passed in random number generator is ignored //and factors are processed in a deterministic order void IgnoreRand(bool ignore) { unit_test = ignore; } //change the number of random restarts void SetNumRestarts(int num) { numrandrestarts = num; } // stores a node of the clique tree class Node { public: Context vars; // variables that are part of this factor std::vector adj; // index of adjacent nodes in tree }; //return the clique tree std::vector ReturnCliques() const; virtual ~CliqueTree(); private: //all the private functions with pointers own their pointers class CliqueNode { friend class CliqueTree; public: CliqueNode(); void AddVar(const int &); void print (std::ostream &); ~CliqueNode(); private: std::set connected; std::vector next; CliqueNode * prev; SERIAL_START(CliqueNode) SERIAL_VAR(std::set,connected) SERIAL_VAR(std::vector,next) SERIAL_END public: void serial_preload(); void serial_postload(); }; void CliqueTrees(Random &rand); void CliqueTrees(int); void CliqueNodePrint(CliqueNode *, int, std::ostream &); static void Destroy(CliqueNode *); void ReturnClique(CliqueNode *, int, std::vector &) const; void InsertCliqueNode(CliqueNode *, int, std::vector &) const; Context Clique2Context(CliqueNode *) const; void CliqueSave(std::ostream &os, CliqueNode * print, int size) const; void Eliminate(); bool EliminateDups(CliqueNode *); void EliminatePrev(CliqueNode *); static void RemoveElem(std::vector &, int); void prints(std::set , std::ostream &) const; double MaxSize() const; double MaxSize(CliqueNode *) const; void Dequeue(std::vector remove, CliqueNode * newitem); void BuildIt(Random &); void AddFromBN(const BN *); void AddFromCTBNDyn(const CTBNDyn *); void AddFromContext(const std::vector &); std::vector data; std::vector save_data; std::vector clique_built; // these pointers at the bottom are owned by this class CliqueNode * Head_compare; int numrandrestarts; CliqueNode * Head; bool unit_test; Context vars; /* int VarSize(int varid) const; void AddVarSize(int varid, int size); // maps varid -> size std::map context_size; */ SERIAL_START(CliqueTree) SERIAL_VAR(Context,vars) SERIAL_VAR(int,numrandrestarts) SERIAL_VAR(bool,unit_test) SERIAL_VAR(std::vector,data) SERIAL_END public: void serial_preload(); }; } // end of ctbn namespace #endif /* CLIQUE_H_ */ ctbnrle.1.1.1/hdr/rvsimple.h0000664000175000017500000004020712107032422016151 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_RVSIMPLE_H #define CTBNRLE_RVSIMPLE_H #include "serial/serial.h" #include "matrix.h" #include "random.h" #include "rv.h" #include "ss.h" #include #include #include namespace ctbn { // RVSimple is the ABC (Abstract Base Class) for measures. // It can be thought of as a random variable that is not conditional // and whose domain is implied (or just indexed by integers from 0 to n-1) class RVSimple : public StreamObj { public: RVSimple(); RVSimple(std::istream &is); virtual ~RVSimple(); virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; // virtual copy constructor virtual RVSimple *Clone() const = 0; virtual std::ostream & Print(std::ostream & out, RV * the_initial_distribution_of_the_markov_process) const; virtual std::ostream & PrintJoint(std::ostream & out, RV * the_initial_distribution_of_the_markov_process) const; virtual std::ostream & PrintMarginal(std::ostream & out, RV * the_initial_distribution_of_the_markov_process) const; // Makes sum to 1 -- returns sum prior to normalization virtual double Normalize() = 0; // returns the probability of an individual event (index) virtual double Prob(int ind, bool log=false) const = 0; // Returns the sum (or log thereof) virtual double Sum(bool log=false) const = 0; // Sets to zero all indexes that are not listed in ind // Restrict followed by Normalize is the same as // conditioning the RV to the set of ind virtual void Restrict(const std::vector &ind) = 0; // Marginalizes, reindexes, or expands depending on the argument // ind is a list, for each new index, of the list of old indexes // which should be summed virtual void Reindex(const std::vector > &ind) = 0; // Multiplies by another measure (point-wise) virtual void MultBy(const RVSimple *x) = 0; // Adds in another measure virtual void Add(const RVSimple *x) = 0; virtual void Add(const RVSimple *x, double w) = 0; // Multiplies measure by constant virtual void Mult(double x) = 0; // return suitable sufficient statistics object virtual SS *BlankSS() const = 0; // add independent draw of x to ss virtual void AddSS(int x, SS *ss, double w=1.0) const = 0; // add average of independent draws of x to ss virtual void AddExpSS(SS *ss, double w=1.0) const = 0; virtual void AddSS(const SS *toadd, const RVSimple* rvs, const std::vector > &mapping, SS *ss, double w=1.0) const = 0; // returns a sample virtual int Sample(Random &rand = randomizer) const = 0; // sets parameters to ML estimate virtual void Maximize(const SS *ss) = 0; // set measure to be 1 everywhere it has support virtual void MakeUniform() = 0; virtual void GetDist(vectr &d, double &logfactor) const = 0; virtual void SetDist(const vectr &d, double logfactor=0.0) = 0; // Draws parameters from Dirichlet distribution // with all alphas the same (equal to alpha) // If degree equals 1, all parameters are replaced // If degree is between 0 and 1, the old parameters are // mixed with the new one (linear combination, degree controls // the mixing). virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer) = 0; // Returns the log-likelihood of a set of sufficient statistics virtual double LLH(const SS *ss) const = 0; virtual double GetScore(double numTrans, const SS* ss) const; SERIAL_START_V(RVSimple) SERIAL_END }; // This is the same, but with conditioning // That is, it represents a conditional measure (indexed by 0..n-1) // conditioned on an integer from 0..m-1 class RVCondSimple : public StreamObj { public: RVCondSimple(); RVCondSimple(std::istream &is); virtual ~RVCondSimple(); virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; // virtual copy constructor virtual RVCondSimple *Clone() const = 0; virtual std::ostream & PrintCond(std::ostream & out) { throw "Not yet implemented."; } // returns the probability of an individual conditional event virtual double Prob(int ind, int cind, bool log=false) const = 0; // Makes sum to 1 for each conditioning value virtual void Normalize() = 0; // returns an object representing the conditioning // on that value virtual RVSimple *Condition(int ind) const = 0; // Sets to zero all indexes that are not listed in ind and cind // That is, for conditions mentioned in cind, the measure is // restricted to the indexes in ind. For conditions not in cind, // the measure is made to be zero everywhere. virtual void Restrict(const std::vector &ind, const std::vector &cind) = 0; // Marginalizes, reindexes, or expands depending on the // argument // n = # new values nc = # new conditioning values // ind[] is a mapping that needs to be applied to old // conditioning index j and then added to i // (see Reindex for RVSimple above to see how a mapping is // applied) // (Really, this is probably too confusing to be "simple.") typedef std::map, std::vector > > RemapT; virtual void Reindex(int n, int nc, const RemapT &ind) = 0; // Multiplies by another measure (point-wise) virtual void MultBy(const RVCondSimple *x) = 0; // return suitable sufficient statistics object virtual SS *BlankSS() const = 0; // add independent draw of (cind,ind) (conditioning value, value) to ss virtual void AddSS(int cind, int ind, SS *ss, double w=1.0) const = 0; // add average of independent draws to ss virtual void AddExpSS(int cind, SS *ss, double w=1.0) const = 0; virtual void AddExpSS(int cind, const RVSimple *rvs, SS *ss, double w) const = 0; virtual void AddSS(const SS *toadd, const RVCondSimple* rv, const std::vector > &mapping, int mycondi, int rvccondi, SS *ss, double w=1.0) const = 0; // returns a sample virtual int Sample(int cx, Random &rand = randomizer) const = 0; // sets parameters to ML estimate virtual void Maximize(const SS *ss) = 0; // assumes that x is over the conditional range // multiplies x by *this and then sums out over the conditional // range to produce a distribution over the domain // x is changed to the result // Think of *this as p(x|y) and the input as p(y) // This function changes the input to be the resulting // margin p(x) virtual void Mult(RVSimple *&x) const = 0; // this is the reverse (not a generally used operation except // in the forward-backward algorithm) // it assumes x is over the range of the non-conditioning variable // and returns a measure over the conditioning variables virtual void RMult(RVSimple *&x) const = 0; // Returns a new object that can be multiplied into this // object (as per Mult and RMult) virtual RVSimple *Convert(const RVSimple *rvs) const = 0; // see above class for description virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer) = 0; //by Yu virtual double LLH(const SS *ss) const = 0; virtual double GetScore(double numTrans, const SS *ss) const = 0; SERIAL_START_V(RVCondSimple) SERIAL_END }; // This is a template. It takes in a subclass of RVSimple and // creates a RVCondSimple from it (by making one copy for each // conditioning value) template class RVCondSimpleComp : public RVCondSimple { SOBJCLASSDECL(RVCondSimpleComp) public: RVCondSimpleComp(int n=0, int nc=0); RVCondSimpleComp(std::istream &is); virtual ~RVCondSimpleComp(); virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; virtual RVCondSimpleComp *Clone() const; virtual double Prob(int ind, int cind, bool log=false) const; virtual void Normalize(); virtual RVS *Condition(int ind) const; virtual void Restrict(const std::vector &ind, const std::vector &cind); virtual void Reindex(int n, int nc, const RVCondSimple::RemapT &ind); virtual void MultBy(const RVCondSimple *x); virtual SS *BlankSS() const; virtual void AddSS(int cind, int ind, SS *ss, double w=1.0) const; virtual void AddExpSS(int cind, SS *ss, double w=1.0) const; virtual void AddExpSS(int cind, const RVSimple *rvs, SS *ss, double w) const; virtual void AddSS(const SS *toadd, const RVCondSimple* rv, const std::vector > &mapping, int mycondi, int rvccondi, SS* ss, double w=1.0) const; virtual int Sample(int cx, Random &rand = randomizer) const; virtual void Maximize(const SS *ss); virtual void Mult(RVSimple *&x) const; virtual void RMult(RVSimple *&x) const; virtual RVSimple *Convert(const RVSimple *rvs) const; RVS &operator[](int i) { return impl[i]; } const RVS &operator[](int i) const { return impl[i]; } virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer); //by Yu virtual double LLH(const SS *ss) const; virtual double GetScore(double numTrans, const SS* ss) const; private: std::vector impl; SERIAL_START1_V(RVCondSimpleComp,RVS) SERIAL_SUPER(RVCondSimple) SERIAL_VAR(std::vector,impl) SERIAL_END }; // This is the corresponding sufficient statistics for RVCondSimpleComp // (for any template instantiation) class RVCSCompSS : public SS { SOBJCLASSDECL(RVCSCompSS) public: RVCSCompSS(int nc=0); RVCSCompSS(const RVCSCompSS &x); RVCSCompSS(std::istream &is); virtual ~RVCSCompSS(); virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; virtual RVCSCompSS *Clone() const; RVCSCompSS &operator=(const RVCSCompSS &x); virtual void Scale(double w); virtual void AddSS(const SS* nss, double w=1.0); // can't make the general set of RVCondSimpleComp<*> friends // for some reason in g++ //private: std::vector ss; SERIAL_START_V(RVCSCompSS) SERIAL_SUPER(SS) SERIAL_VAR(std::vector,ss) SERIAL_END }; //#include "rvsimple.tcc" #include SOBJCLASSDEFTEMPL1(RVCondSimpleComp,RVS) template RVCondSimpleComp::RVCondSimpleComp(int n, int nc) : RVCondSimple(), impl(nc,RVS(n)){ } template RVCondSimpleComp::RVCondSimpleComp(std::istream &is) : RVCondSimple(is) { int nc; is >> nc; impl.resize(nc); for(int i=0;i RVCondSimpleComp::~RVCondSimpleComp() {} template void RVCondSimpleComp::LoadOld(std::istream &is) { RVCondSimple::LoadOld(is); int nc; is >> nc; impl.resize(nc,RVS(0)); for(int i=0;i void RVCondSimpleComp::SaveOld(std::ostream &os) const { RVCondSimple::SaveOld(os); int nc = impl.size(); os << os.fill() << nc; for(int i=0;i RVCondSimpleComp *RVCondSimpleComp::Clone() const { return new RVCondSimpleComp(*this); } template double RVCondSimpleComp::Prob(int ind, int cind, bool log) const { return impl[cind].Prob(ind,log); } template void RVCondSimpleComp::Normalize() { int nc = impl.size(); for(int i=0;i RVS *RVCondSimpleComp::Condition(int ind) const { return impl[ind].Clone(); } template void RVCondSimpleComp::Restrict(const std::vector &ind, const std::vector &cind) { int nc = impl.size(); std::vector blank; unsigned int k=0; for(int i=0;i void RVCondSimpleComp::Reindex(int n, int nc, const RVCondSimple::RemapT &ind) { std::vector newimpl(nc,RVS(n)); for (RVCondSimple::RemapT::const_iterator i=ind.begin();i!=ind.end();++i) { int newci = i->first.first; int oldci = i->first.second; RVS cpy(impl[oldci]); cpy.Reindex(i->second); newimpl[newci].Add(&cpy); } impl = newimpl; } template void RVCondSimpleComp::MultBy(const RVCondSimple *x) { const RVCondSimpleComp *xx = dynamic_cast *>(x); int nc = impl.size(); for(int i=0;iimpl[i])); } template SS *RVCondSimpleComp::BlankSS() const { int nc = impl.size(); RVCSCompSS *ret = new RVCSCompSS(nc); for(int i=0;iss[i] = impl[i].BlankSS(); return ret; } template void RVCondSimpleComp::AddSS(int cind, int ind, SS *ss, double w) const { RVCSCompSS *rss = dynamic_cast(ss); impl[cind].AddSS(ind,rss->ss[cind],w); } template void RVCondSimpleComp::AddExpSS(int cind, SS *ss, double w) const { RVCSCompSS *rss = dynamic_cast(ss); impl[cind].AddExpSS(rss->ss[cind],w); } template void RVCondSimpleComp::AddExpSS(int cind, const RVSimple *rvs, SS *ss, double w) const { RVCSCompSS *rss = dynamic_cast(ss); rvs->AddExpSS(rss->ss[cind],w); } template void RVCondSimpleComp::AddSS(const SS *toadd, const RVCondSimple *rv, const std::vector > &mapping, int mycondi, int rvccondi, SS *ss, double w) const { const RVCondSimpleComp *rvcsc = dynamic_cast *>(rv); const RVCSCompSS* toaddrvcss = dynamic_cast(toadd); RVCSCompSS* rvcss = dynamic_cast(ss); impl[mycondi].AddSS(toaddrvcss->ss[rvccondi], &(rvcsc->impl[rvccondi]), mapping,rvcss->ss[mycondi]); } template int RVCondSimpleComp::Sample(int cx, Random &rand) const { return impl[cx].Sample(rand); } template void RVCondSimpleComp::Maximize(const SS *ss) { const RVCSCompSS *sss = dynamic_cast(ss); int nc = impl.size(); for(int i=0;iss[i]); } template void RVCondSimpleComp::Mult(RVSimple *&x) const { int nc = impl.size(); RVSimple *ret = impl[0].Clone(); ret->Mult(x->Prob(0)); for(int i=1;iAdd(&(impl[i]),x->Prob(i)); delete x; x = ret; } template void RVCondSimpleComp::RMult(RVSimple *&x) const { assert(0); // not easily doable (and probably not important) } template RVSimple *RVCondSimpleComp::Convert(const RVSimple *rvs) const { // can't really multiply anyway... so we'll just call Clone return rvs->Clone(); } template void RVCondSimpleComp::Scramble(double alpha, double degree, Random &rand) { int nc = impl.size(); for(int i=0; i double RVCondSimpleComp::LLH(const SS *ss) const { double llh = 0.0; const RVCSCompSS *sss = dynamic_cast(ss); int nc = impl.size(); for(int i=0; iss[i]); return llh; } template double RVCondSimpleComp::GetScore(double numTrans, const SS* ss) const { double localScore(0); const RVCSCompSS *rvss = dynamic_cast (ss); int nc = impl.size(); for(int i = 0; i < nc; i++) localScore += impl[i].GetScore(numTrans/nc, rvss->ss[i]); return localScore; } } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/gibbsbase.h0000664000175000017500000001220712107032422016230 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_GIBBSBASE_H #define CTBNRLE_GIBBSBASE_H #include #include #include "sampler.h" namespace ctbn { class matrix; class MultiSimple; class RV; class MarkovDyn; // abstract base class for Gibbs sampling. Takes care of pulling each variable // out in turn, splitting trajectory into regions where variable is free, etc. // handles burnin, etc. // Sample usage: class GibbsBase : public Sampler { public: // the caller of the function owns the process and evidence pointers // but they must remain valid for the life of the GibbsBase object GibbsBase(const Process *process, const Trajectory *evidence, int burnin); virtual ~GibbsBase() { if (init_traj) delete init_traj; } // the caller of the function owns the process pointer // but it must remain valid for the life of the GibbsSampler object virtual void SetProcess(const Process *process) { p = process; Initialize(); } // the caller of the function owns the evidence pointer // but it must remain valid for the life of the GibbsSampler object virtual void SetTrajectory(const Trajectory *evidence) { Sampler::SetTrajectory(evidence); burntin=false; } virtual void SampleTrajectories(std::vector &traj, std::vector &w, int numsamples, Random &rand = randomizer); protected: // specific public interfaces for GibbsSampler // burn in, do numBurninIter steps of markov transitions void BurnIn(Random &rand = randomizer) const; // one step in the markov chain state transition; void Next(int num_iter = 1, Random &rand = randomizer) const; // get the current trajectory const Trajectory &Get() const { return tr; } // get the start point - the initial trajectory const Trajectory &GetInitTraj() const { return *init_traj; } // set the initial trajectory void SetInitTraj(const Trajectory &t); // delete the initial trajetcory void ClearInitTraj(); int numBurninIter; // # of burn in iterations Context context; std::vector own_var_list; const RV *p0; const MarkovDyn *md; // mutable so that we can access parents[v] // etc. easily in const member fuctions. // each variable's parents and children mutable std::map parents, children; // each variable's markovdyn mutable std::map mds; // each variable's Markov Blanket mutable std::map markovblanket; // initial trajectory mutable Trajectory *init_traj; // the current sample (and previous one for MCMC samplers) mutable Trajectory tr,oldtr; mutable bool burntin; // inherited functions from base class, currently do nothing double SampleInitial(Instantiation &inst, Random &rand=randomizer){return 0.0;}; double SampleDyn(Trajectory &traj, Random &rand=randomizer){return 0.0;}; // precompute context, p0, dynamics, parents/children and markovdyn // for each variable; void Initialize(); // Find the {tau} splitting points (timing, full instantiation) for // transitioned variables who are in v's Markov blanket static void GetTau(const Trajectory &t, double begin_time, double end_time, int v, const Context &varset, std::vector &tau, std::vector &inst); // Resample the entire trajectory of v given all the other variables' // full trajectory. void SampleVariable(int v, Random &rand = randomizer) const; // Sample initial trajectory that agrees with evidence *evid. void SampleInitialTrajectory(Random &rand = randomizer) const; protected: // find evidence on v, and split into "free" intervals for v // there is no v's evidence inside each [t0, tT] interval void SplitFreeIntervals(int v, std::vector &x0, std::vector &xT, std::vector &t0, std::vector &tT, const Instantiation &inst0, Random &rand = randomizer) const; // sampler v's trajectory for interval [t0, tT]; virtual void SampleVariableInterval(int v, int x0, int xT, double t0, double tT, Random &rand = randomizer) const = 0; // returns the diagonal transition matrix for v when Markov Blanket // changes from oldinst to newinst // returns false iff no variable in child set changed // NOTE: assumes that ret is the right size // ret is just the diagonal of the T Matrix (which is a diag matrix) bool GetTMatrix(int v, const Instantiation &oldinst, const Instantiation &newinst, vectr &ret) const; }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/gibbssampler.h0000664000175000017500000000555112107032422016765 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_GIBBSSAMPLER_H #define CTBNRLE_GIBBSSAMPLER_H #include #include #include "sampler.h" #include "gibbsbase.h" namespace ctbn { class matrix; class MultiSimple; class RV; class MarkovDyn; // GibbsSampler is an implementation of the paper: // Tal El-Hay, Nir Friedman, and Raz Kupferman (2008). "Gibbs Sampling in // Factorized Continuous-Time Markov Processes." // This class will take in an evidence trajectory, a markov process, // the number of samples needed as parameters. // It will first burn in for several iterations (number of burnin_iter is // specified in construction function), then generate a set of sampled // trajectories. It is an approximate inference method used for learning // and inference on CTBNs. // Sample usage: // // Markov ctbn; // Trajectory evidence; // GibbsSampler sampler(&ctbn, &evidence); // Trajectory sample_traj; // sampler.SampleTrajectory(sample_traj); class GibbsSampler : public GibbsBase { public: // the caller of the function owns the process and evidence pointers // but they must remain valid for the life of the GibbsSampler object GibbsSampler(const Process *process, const Trajectory *evidence, int burnin); virtual ~GibbsSampler(); // inherited interface functions from base class virtual Sampler *Clone() const { return new GibbsSampler(p, evid, numBurninIter); } protected: virtual void SampleVariableInterval(int v, int x0, int xT, double t0, double tT, Random &rand = randomizer) const; private: // equivalent to Qss; matrix GetRMatrix(int v, const Instantiation &inst) const; // search the exact time points between t0 and t1 using binary search; double SearchPoint(double ksi, int x0, const matrix &R, double log_p_future0, double log_p_past, MultiSimple p_future, double log_z, double t0, double t1, double epsilon = 1e-6) const; // sample the initial bn conditioned on evindence "partial" Instantiation SampleConditionalRv(const Instantiation &partial, double t, int v, Random &rand = randomizer) const; }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/suffstatsquery.h0000664000175000017500000000330512107032422017416 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_SUFFSTATSQUERY_H #define CTBNRLE_SUFFSTATSQUERY_H // A class to perform queries for sufficient statistics // The object is to be passed into a function as an empty object. // The member variables are set by the function using this class. #include "ss.h" #include "rv.h" #include "dynamics.h" #include "trajectory.h" #include namespace ctbn { class SuffStatsQuery { public: SuffStatsQuery(); virtual ~SuffStatsQuery(); virtual SS* GetP0SS() = 0; virtual SS* GetDSS() = 0; // The caller of these functions own the pointers. void SetInitRV(const RV* p0) {this->p0 = p0;} void SetDynamics(const Dynamics* d) {this->d = d;} virtual void SetData(const std::vector* data) {tr = data;} protected: // This class does not own any of these pointers. const RV* p0; const Dynamics* d; const std::vector* tr; }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/samplequeue.h0000664000175000017500000000470212107032422016636 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_SAMPLEQUEUE_H #define CTBNRLE_SAMPLEQUEUE_H namespace ctbn { // a priority queue just for sampling quickly // It keeps track of a set of event associated with a set of variables // will return the next event (earliest event) and can remove an event // associated with a particular variable even if that event is not next // Different from STL's pqueue in that we need to be able // to remove an event from the middle of the queue without having // to search for it. Also, we know the total number of events to be stored // (this could be done by storing a structure with a copy operator that // kept track of its position. However, this is simpler and faster.) // Note: the "var" stored in an event is assume to be one of a // set of consecutive integers starting at 0. Therefore, you may need // a map to map your varid onto this value. class SampleQueue { public: // a class to store an event // pretty much just a structure class Event { public: Event(int v,int val, double t) { var = v; value = val; time = t; } Event() { } int var; int value; double time; }; // create a queue with a maximum of maxn variables SampleQueue(int maxn); ~SampleQueue(); // place the earliest event in e // return false if there are no events // Note: this event is NOT removed from the queue bool Head(Event &e); // add an event to the queue void Add(const Event &e); // remove the event for variable var from the queue void Remove(int var); private: int Heapify(int i, double tempk); Event *heap; int *places; int n; }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/serial/0000775000175000017500000000000012251373541015425 5ustar csheltoncsheltonctbnrle.1.1.1/hdr/serial/serial.tcc0000664000175000017500000007713612107032422017403 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "serial.h" #if 0 #ifndef CTBNRLE_SERIAL_TCC #define CTBNRLE_SERIAL_TCC #include "serial.h" #include "nullptr03.h" #include #include #include #include #include #include #include #include #include #include #include namespace SERIALNAMESPACE { using ctbn::nullptr03; char *TName(const char *cname, int n, ...); void Indent(std::ostream &os, int indent); void *AllocByName(const std::string &name); typedef void *(*createfntype)(void); const char *AddAlloc(const char *name, createfntype fn); // a simple list: struct ListEnd {}; template struct List {}; // pretty standard enable_if template (see Boost, for example) template struct Type_If { typedef T type; }; template struct Type_If { }; #define SERIAL_DECVAL(vname,expr) \ enum { vname = (expr) }; //static int const vname = (expr); // This one has appeared in one form or another all over the // internet template struct TypeProp { template struct type_check; template static char (& chksave(type_check*))[1]; template static char (& chksave(...))[2]; SERIAL_DECVAL(HasSave,sizeof(chksave(0)) == 1) template static char (& chkid(type_check*))[1]; template static char (& chkid(...))[2]; SERIAL_DECVAL(HasIDname,sizeof(chkid(0)) == 1) template static char (& chkshift(type_check*))[1]; template static char (& chkshift(...))[2]; SERIAL_DECVAL(HasShift,sizeof(chkshift(0)) == 1) template static char (& chkdef(type_check*))[1]; template static char (& chkdef(...))[2]; SERIAL_DECVAL(HasDefault,sizeof(chkdef(0)) == 1) template static char (& chkv(type_check*))[1]; template static char (& chkv(...))[2]; SERIAL_DECVAL(HasV,sizeof(chkv(0)) == 1) template static char (& chkpreload(type_check*))[1]; template static char (& chkpreload(...))[2]; SERIAL_DECVAL(HasPreLoad,sizeof(chkpreload(0)) == 1) template static char (& chkpostload(type_check*))[1]; template static char (& chkpostload(...))[2]; SERIAL_DECVAL(HasPostLoad,sizeof(chkpostload(0)) == 1) template static char (& chkpresave(type_check*))[1]; template static char (& chkpresave(...))[2]; SERIAL_DECVAL(HasPreSave,sizeof(chkpresave(0)) == 1) template static char (& chkpostsave(type_check*))[1]; template static char (& chkpostsave(...))[2]; SERIAL_DECVAL(HasPostSave,sizeof(chkpostsave(0)) == 1) //enum { BlankList = SameType::value }; }; template struct HasDefCon { struct small { char x[1]; }; struct large { char x[2]; }; template static small chk(U (*)[1]); template static large chk(...); //SERIAL_DECVAL(value,sizeof(HasDefCon::template chk(0))==sizeof(small)) SERIAL_DECVAL(value,sizeof(chk(0))==sizeof(small)) }; template struct IsEmpty { SERIAL_DECVAL(value,false) }; template struct ListEmpty { SERIAL_DECVAL(value,false) }; template<> struct ListEmpty { SERIAL_DECVAL(value,true) }; template struct ListEmpty > { SERIAL_DECVAL(value,IsEmpty::value && ListEmpty::value) }; template struct IsEmpty::value,void>::type> { SERIAL_DECVAL(value,true) }; template struct creator { template inline static typename Type_If::value,RT *>::type create() { return new AT(); } template inline static typename Type_If::value,RT *>::type create() { return nullptr03; } }; struct XMLTagInfo { std::string name; std::map attr; bool isstart,isend; }; template struct SaveItem; template struct LoadList; void ReadTag(std::istream &is,XMLTagInfo &info); void ReadEndTag(std::istream &is,const char *ename); void ReadStr(std::istream &is, std::string &ret,const char *endchar); void WriteStr(std::ostream &os, const std::string &s,bool escape=true); // a reverse iterator on a list for saving template struct SaveItt { template inline static void exec(O o,std::ostream &os,int indent) { } }; template struct SaveItt, typename Type_If::value,void>::type> { template inline static void exec(O o,std::ostream &os,int indent) { SaveItt::exec(o,os,indent); SaveItem::apply(o,os,indent); } }; template struct SaveItt, typename Type_If::value,void>::type> { template inline static void exec(O o,std::ostream &os,int indent) { SaveItt::exec(o,os,indent); } }; template<> struct SaveItt { template inline static void exec(O o,std::ostream &os, int indent) { } }; template struct LoadOne { template inline static bool exec(O o,std::istream &, const XMLTagInfo &) { return false; } }; template<> struct LoadOne { template inline static bool exec(O o,std::istream &, const XMLTagInfo &) { return false; } }; template struct LoadOne > { template inline static bool exec(O o,std::istream &is, const XMLTagInfo &info) { std::map::const_iterator ni =info.attr.find("name"); if (ni!=info.attr.end() && ni->second == H::getname(o)) { LoadWrapper(H::getvalue(o),info,is); return true; } else return LoadOne::exec(o,is,info); } }; template struct AllLoaded { template inline static bool exec(O o,const std::set &nset) { return true; } }; template<> struct AllLoaded { template inline static bool exec(O o,const std::set &nset) { return true; } }; template struct AllLoaded, typename Type_If::HasDefault,void>::type > { template inline static bool exec(O o,const std::set &nset) { if (nset.find(H::getname(o)) == nset.end()) H::setdefault(H::getvalue(o)); return AllLoaded::exec(o,nset); } }; template struct AllLoaded, typename Type_If::HasDefault && IsEmpty::value,void>::type > { template inline static bool exec(O o,const std::set &nset) { return AllLoaded::exec(o,nset); } }; template struct AllLoaded, typename Type_If::HasDefault && !IsEmpty::value,void>::type > { template inline static bool exec(O o,const std::set &nset) { if (nset.find(H::getname(o)) == nset.end()) return false; else return AllLoaded::exec(o,nset); } }; template struct IsShiftable { SERIAL_DECVAL(atall,false) SERIAL_DECVAL(quickly,false) }; template struct IsShiftable { SERIAL_DECVAL(atall,IsShiftable::atall) SERIAL_DECVAL(quickly,IsShiftable::quickly) }; template struct IsShiftable::HasShift,void>::type> { SERIAL_DECVAL(atall,true) SERIAL_DECVAL(quickly,T::SERIAL_ISSHORT) }; #define SERIAL__BASETYPE(tname,strname) \ template<> \ struct IsShiftable { \ SERIAL_DECVAL(atall,true) \ SERIAL_DECVAL(quickly,true) \ }; \ template<> \ struct TypeInfo { \ inline static const char *namestr() { return #strname; } \ inline static void writeotherattr(std::ostream &, const tname &) { } \ inline static bool isshort(const tname &) { return true; } \ inline static bool isinline(const tname &) { return false; } \ inline static void save(const tname &t,std::ostream &os, int indent) { \ os << t; \ } \ inline static void load(tname &t, const XMLTagInfo &info, std::istream &is) {\ std::map::const_iterator vi \ =info.attr.find("value"); \ if (vi!=info.attr.end()) { \ std::istringstream ss(vi->second); \ ss.copyfmt(is); \ ss >> t; \ if (info.isend) return; \ } else { \ is >> t; \ } \ ReadEndTag(is,namestr()); \ } \ }; template struct TypeInfo { enum { x = sizeof(T::__Attempting_to_serialize_type_without_serialization_information) }; inline static const char *namestr() { throw streamexception("Streaming Error: type info asked about unknown class"); } inline static void writeotherattr(std::ostream &, const T &) { throw streamexception("Streaming Error: type info asked about unknown class"); } inline static bool isshort(const T &) { throw streamexception("Streaming Error: type info asked about unknown class"); } inline static bool isinline(const T &) { throw streamexception("Streaming Error: type info asked about unknown class"); } inline static void save(const T &t,std::ostream &os, int indent) { throw streamexception("Streaming Error: save called for unknown class"); } inline static void load(T &t, const XMLTagInfo &info, std::istream &is) { throw streamexception("Streaming Error: load called for unknown class"); } }; template struct TypeInfo { inline static const char *namestr() { return TypeInfo::namestr(); } inline static void writeotherattr(std::ostream &os, const T &t) { TypeInfo::writeotherattr(os,t); } inline static void writeotherattr(std::ostream &os, const T &t, const char *s1, const char *s2) { TypeInfo::otherattr(os,t,s1,s2); } inline static bool isshort(const T &t) { return TypeInfo::isshort(t); } inline static bool isinline(const T &t) { return TypeInfo::isinline(t); } inline static void save(const T &t,std::ostream &os, int indent) { TypeInfo::save(t,os,indent); } inline static void load(const T &t, const XMLTagInfo &info, std::istream &is) { throw streamexception("Streaming Error: load called for constant type"); } }; template inline void SaveWrapper(const T &v,const char *vname, std::ostream &os,int indent) { Indent(os,indent); os << "<" << TypeInfo::namestr(); TypeInfo::writeotherattr(os,v); if (vname && vname[0]!=0) os << " name=\"" << vname << "\""; if (TypeInfo::isshort(v)) { os << " value=\""; TypeInfo::save(v,os,indent); os << "\" \\>" << std::endl; } else if (TypeInfo::isinline(v)) { os << " \\>" << std::endl; } else { os << ">"; TypeInfo::save(v,os,indent); os << "<\\" << TypeInfo::namestr() << ">" << std::endl; } } template inline typename Type_If::HasV,void>::type SaveWrapper(T * const &v, const char *vname, std::ostream &os, int indent) { if (v == nullptr03) { Indent(os,indent); os << "<" << TypeInfo::namestr(); if (vname && vname[0]!=0) os << " name=\"" << vname << "\""; os << " isnull=\"1\" \\>" << std::endl; } else v->SaveV(os,vname,indent); } template inline typename Type_If::HasV,void>::type SaveWrapper(T * const &v, const char *vname, std::ostream &os, int indent) { if (v == nullptr03) { Indent(os,indent); os << "<" << TypeInfo::namestr(); if (vname && vname[0]!=0) os << " name=\"" << vname << "\""; os << " isnull=\"1\" \\>" << std::endl; } else SaveWrapper(*v,vname,os,indent); } template inline void SaveWrapper(const T &v,const char *vname, std::ostream &os,int indent, const char *e1, const char *e2) { Indent(os,indent); os << "<" << TypeInfo::namestr(); TypeInfo::writeotherattr(os,v,e1,e2); if (vname && vname[0]!=0) os << " name=\"" << vname << "\""; if (TypeInfo::isinline(v)) { os << " \\>" << std::endl; } else { os << ">"; TypeInfo::save(v,os,indent,e1,e2); os << "<\\" << TypeInfo::namestr() << ">" << std::endl; } } template struct CheckTag { inline static void check(const XMLTagInfo &info) { if (!info.isstart) throw streamexception(std::string("Stream Input Format Error: expected start tag for ")+TypeInfo::namestr()+", received end tag for "+info.name); if (info.name != TypeInfo::namestr()) throw streamexception(std::string("Stream Input Format Error: expected start tag for ")+TypeInfo::namestr()+", received start tag for "+info.name); } }; template inline void LoadWrapper(T &v, const XMLTagInfo &info, std::istream &is) { CheckTag::check(info); TypeInfo::load(v,info,is); } template inline typename Type_If::HasV,void>::type LoadWrapper(T *&v, const XMLTagInfo &info, std::istream &is) { if (!info.isstart) throw streamexception(std::string("Stream Input Format Error: expected start tag, received end tag for ")+info.name); std::map::const_iterator vi =info.attr.find("isnull"); if (vi!=info.attr.end() && vi->second=="1") { v = nullptr03; if (!info.isend) { XMLTagInfo einfo; ReadTag(is,einfo); if (einfo.name!=info.name || !info.isend || info.isstart) throw streamexception(std::string("Stream Input Format Error: null pointer object for type ")+TypeInfo::namestr()+" and name "+info.name+" has non-empty contents"); } return; } T *vv = T::serial__valloc::allocbyname(info.name); if (vv == nullptr03) throw streamexception(std::string("Stream Input Format Error: expected start tag for subtype of ")+TypeInfo::namestr()+", received start tag for type "+info.name+" which is either unknown or not a subtype"); v = vv; v->serial__loadwrapv(is,info); } template inline typename Type_If::HasV,void>::type LoadWrapper(T *&v, const XMLTagInfo &info, std::istream &is) { std::map::const_iterator vi =info.attr.find("isnull"); if (vi!=info.attr.end() && vi->second=="1") { v = nullptr03; if (!info.isend) { XMLTagInfo einfo; ReadTag(is,einfo); if (einfo.name!=info.name || !info.isend || info.isstart) throw streamexception(std::string("Stream Input Format Error: null pointer object for type ")+TypeInfo::namestr()+" and name "+info.name+" has non-empty contents"); } return; } v = new T(); LoadWrapper(*v,info,is); } template inline void LoadWrapper(T &v, std::istream &is) { XMLTagInfo info; ReadTag(is,info); LoadWrapper(v,info,is); } template inline typename Type_If::HasPreLoad && !TypeProp::HasPostLoad,void>::type SerialLoadWrap(T &t, std::istream &is) { t.serial__Load(is); } template inline typename Type_If::HasPreLoad && !TypeProp::HasPostLoad,void>::type SerialLoadWrap(T &t, std::istream &is) { t.serial_preload(); t.serial__Load(is); } template inline typename Type_If::HasPreLoad && TypeProp::HasPostLoad,void>::type SerialLoadWrap(T &t, std::istream &is) { t.serial__Load(is); t.serial_postload(); } template inline typename Type_If::HasPreLoad && TypeProp::HasPostLoad,void>::type SerialLoadWrap(T &t, std::istream &is) { t.serial_preload(); t.serial__Load(is); t.serial_postload(); } template inline typename Type_If::HasPreSave && !TypeProp::HasPostSave,void>::type SerialSaveWrap(const T &t, std::ostream &os, int indent) { t.serial__Save(os,indent); } template inline typename Type_If::HasPreSave && !TypeProp::HasPostSave,void>::type SerialSaveWrap(const T &t, std::ostream &os, int indent) { t.serial_presave(); t.serial__Save(os,indent); } template inline typename Type_If::HasPreSave && TypeProp::HasPostSave,void>::type SerialSaveWrap(const T &t, std::ostream &os, int indent) { t.serial__Save(os,indent); t.serial_postsave(); } template inline typename Type_If::HasPreSave && TypeProp::HasPostSave,void>::type SerialSaveWrap(const T &t, std::ostream &os, int indent) { t.serial_presave(); t.serial__Save(os,indent); t.serial_postsave(); } template struct TypeInfo::HasIDname,void>::type> { inline static const char *namestr() { return T::serial_IDname(); } inline static void writeotherattr(std::ostream &,const T &) { } inline static bool isshort(const T &) { return false; } inline static bool isinline(const T &) { return false; } inline static void save(const T &t,std::ostream &os,int indent) { os << std::endl; SerialSaveWrap(t,os,indent+1); Indent(os,indent); } inline static void load(T &t,const XMLTagInfo &info, std::istream &is) { SerialLoadWrap(t,is); } }; template struct TypeInfo::HasShift,void>::type> { inline static const char *namestr() { return T::serial__shiftname(); } inline static void writeotherattr(std::ostream &, const T &) {} inline static bool isshort(const T &) { return T::SERIAL_ISSHORT; } inline static bool isinline(const T &) { return false; } inline static void save(const T &t,std::ostream &os,int indent) { os << t; } inline static void load(T &t, const XMLTagInfo &info, std::istream &is) { std::map::const_iterator vi =info.attr.find("value"); if (vi!=info.attr.end()) { std::istringstream ss(vi->second); ss.copyfmt(is); ss >> t; if (info.isend) return; } else { is >> t; } ReadEndTag(is,namestr()); } }; template<> struct TypeInfo { inline static const char *namestr() { return "string"; } inline static void writeotherattr(std::ostream &, const std::string &) { } inline static bool isshort(const std::string &s) { return s.length()<20; } inline static bool isinline(const std::string &s) { return false; } inline static void save(const std::string &s, std::ostream &os, int indent) { WriteStr(os,s,isshort(s)); } inline static void load(std::string &s, const XMLTagInfo &info, std::istream &is) { std::map::const_iterator vi =info.attr.find("value"); if (vi!=info.attr.end()) { std::istringstream ss(vi->second); ss.copyfmt(is); ReadStr(ss,s,""); if (info.isend) return; } else ReadStr(is,s,"<"); ReadEndTag(is,namestr()); } }; template struct TypeInfo, typename Type_If::quickly || !IsShiftable::quickly,void>::type> { inline static const char *namestr() { return "pair"; } inline static void writeotherattr(std::ostream &, const std::pair &, const char * fn = nullptr03, const char * sn = nullptr03) { } inline static bool isshort(const std::pair &) { return false; } inline static bool isinline(const std::pair &) { return false; } inline static void save(const std::pair &p, std::ostream &os,int indent, const char *firstname= "first", const char *secondname="second") { os << std::endl; SaveWrapper(p.first,firstname,os,indent+1); SaveWrapper(p.second,secondname,os,indent+1); Indent(os,indent); } class PName { public: PName(std::pair &pa,const char *n1, const char *n2) : p(pa), s1(n1), s2(n2) {} std::pair &p; const char *s1,*s2; }; struct Get1 { inline static T1 &getvalue(PName &o) { return o.p.first; } inline static const char *getname(PName &o) { return o.s1; } }; struct Get2 { inline static T2 &getvalue(PName &o) { return o.p.second; } inline static const char *getname(PName &o) { return o.s2; } }; typedef List > PList; inline static void load(std::pair &p, const XMLTagInfo &info, std::istream &is, const char *firstname = "first", const char *secondname = "second") { PName pn(p,firstname,secondname); LoadList::exec(pn,is,"pair"); \ } }; template struct TypeInfo, typename Type_If::quickly && IsShiftable::quickly,void>::type> { inline static const char *namestr() { static std::string ret = std::string("pair.") +TypeInfo::namestr() +"."+TypeInfo::namestr(); return ret.c_str(); } inline static void writeotherattr(std::ostream &os, const std::pair &p, const char *firstname="first", const char *secondname="second") { os << " " << firstname << "=\"" << p.first << "\" " << secondname << "=\"" << p.second << "\""; } inline static bool isshort(const std::pair &) { return false; } inline static bool isinline(const std::pair &) { return true; } inline static void save(const std::pair &p, std::ostream &os,int indent, const char *firstname= "first", const char *secondname="second") { } inline static void load(std::pair &p, const XMLTagInfo &info, std::istream &is, const char *firstname = "first", const char *secondname = "second") { std::map::const_iterator vi =info.attr.find(firstname); if (vi!=info.attr.end()) { std::istringstream ss(vi->second); ss.copyfmt(is); ss >> p.first; } else throw streamexception(std::string("Stream Input Format Error: missing attribute ")+firstname+" in tag "+info.name); vi = info.attr.find(secondname); if (vi!=info.attr.end()) { std::istringstream ss(vi->second); ss.copyfmt(is); ss >> p.second; } else throw streamexception(std::string("Stream Input Format Error: missing attribute ")+secondname+" in tag "+info.name); if (!info.isend) { XMLTagInfo einfo; ReadTag(is,einfo); if (einfo.name!=info.name || !info.isend || info.isstart) throw streamexception(std::string("Stream Input Format Error: tag ")+info.name+" has non-empty contents"); } } }; template struct TypeInfo, typename Type_If::atall,void>::type> { inline static const char *namestr() { return "vector"; } inline static void writeotherattr(std::ostream &os, const std::vector &v) { os << " nelem=" << v.size(); } inline static bool isshort(const std::vector &) { return false; } inline static bool isinline(const std::vector &) { return false; } inline static void save(const std::vector &v, std::ostream &os,int indent) { os << std::endl; int c=0; for(typename std::vector::const_iterator i=v.begin(); i!=v.end();++i,++c) { SaveWrapper(*i,"",os,indent+1); } Indent(os,indent); } inline static void load(std::vector &v, const XMLTagInfo &info, std::istream &is) { std::map::const_iterator ni = info.attr.find("nelem"); int n = 0; if (ni != info.attr.end()) v.resize(n = atoi(ni->second.c_str())); else v.resize(0); XMLTagInfo eleminfo; int i=0; while(1) { ReadTag(is,eleminfo); if (eleminfo.isend && !eleminfo.isstart) { if (eleminfo.name == namestr()) { if (i!=n) v.resize(i); return; } throw streamexception(std::string("Stream Input Format Error: expected end tag for ")+namestr()+", received end tag for "+eleminfo.name); } if (i>=n) v.resize(n=i+1); LoadWrapper(v[i++],eleminfo,is); } } }; template struct TypeInfo, typename Type_If::atall,void>::type> { inline static const char *namestr() { static char *ret = TName("vector",1,TypeInfo::namestr()); return ret; } inline static void writeotherattr(std::ostream &os, const std::vector &v) { os << " nelem=" << v.size(); } inline static bool isshort(const std::vector &) { return false; } inline static bool isinline(const std::vector &) { return false; } inline static void save(const std::vector &v, std::ostream &os, int indent) { for(typename std::vector::const_iterator i=v.begin();i!=v.end();++i) os << *i << ' '; } inline static void load(std::vector &v, const XMLTagInfo &info, std::istream &is) { std::map::const_iterator ni = info.attr.find("nelem"); if (ni == info.attr.end()) throw streamexception("Stream Input Format Error: ivector needs nelem attribute"); int n = atoi(ni->second.c_str()); v.resize(n); for(int i=0;i> v[i]; ReadEndTag(is,namestr()); } }; template struct TypeInfo, typename Type_If::atall,void>::type> { inline static const char *namestr() { return "set"; } inline static void writeotherattr(std::ostream &,const std::set &) { } inline static bool isshort(const std::set &) { return false; } inline static bool isinline(const std::set &) { return false; } inline static void save(const std::set &s, std::ostream &os,int indent) { os << std::endl; int c=0; for(typename std::set::const_iterator i=s.begin(); i!=s.end();++i,++c) { SaveWrapper(*i,"",os,indent+1); } Indent(os,indent); } inline static void load(std::set &s, const XMLTagInfo &info, std::istream &is) { s.clear(); XMLTagInfo eleminfo; int i=0; while(1) { ReadTag(is,eleminfo); if (eleminfo.isend && !eleminfo.isstart) { if (eleminfo.name == namestr()) return; throw streamexception(std::string("Stream Input Format Error: expected end tag for ")+namestr()+", received end tag for "+eleminfo.name); } T temp; LoadWrapper(temp,eleminfo,is); s.insert(temp); } } }; template struct TypeInfo, typename Type_If::atall,void>::type> { inline static const char *namestr() { static char *ret = TName("set",1,TypeInfo::namestr()); return ret; } inline static void writeotherattr(std::ostream &os, const std::set &v) { os << " nelem=" << v.size(); } inline static bool isshort(const std::set &) { return false; } inline static bool isinline(const std::set &) { return false; } inline static void save(const std::set &s, std::ostream &os, int indent) { for(typename std::set::const_iterator i=s.begin();i!=s.end();++i) os << *i << ' '; } inline static void load(std::set &s, const XMLTagInfo &info, std::istream &is) { std::map::const_iterator ni = info.attr.find("nelem"); if (ni == info.attr.end()) throw streamexception("Stream Input Format Error: ivector needs nelem attribute"); int n = atoi(ni->second.c_str()); s.clear(); for(int i=0;i> temp; s.insert(temp); } ReadEndTag(is,namestr()); } }; template struct TypeInfo, void> { inline static const char *namestr() { return "map"; } inline static void writeotherattr(std::ostream &,const std::map &) { } inline static bool isshort(const std::map &) { return false; } inline static bool isinline(const std::map &) { return false; } inline static void save(const std::map m, std::ostream &os,int indent) { os << std::endl; int c=0; for(typename std::map::const_iterator i=m.begin(); i!=m.end();++i,++c) { SaveWrapper(*i,"",os,indent+1,"key","value"); } Indent(os,indent); } inline static void load(std::map &m, const XMLTagInfo &info, std::istream &is) { m.clear(); XMLTagInfo eleminfo; int i=0; while(1) { ReadTag(is,eleminfo); if (eleminfo.isend && !eleminfo.isstart) { if (eleminfo.name == namestr()) return; throw streamexception(std::string("Stream Input Format Error: expected end tag for ")+namestr()+", received end tag for "+eleminfo.name); } std::pair elem; // not as efficient as I would like TypeInfo >::load(elem,eleminfo,is,"key","value"); m.insert(elem); // copying elements here (C++0x might help) } } }; SERIAL__BASETYPE(bool,bool) SERIAL__BASETYPE(char,char) SERIAL__BASETYPE(unsigned char,u_char) SERIAL__BASETYPE(short,short) SERIAL__BASETYPE(unsigned short,u_short) SERIAL__BASETYPE(int,int) SERIAL__BASETYPE(unsigned int,u_int) SERIAL__BASETYPE(long,long) SERIAL__BASETYPE(unsigned long,u_long) SERIAL__BASETYPE(long long,l_long) SERIAL__BASETYPE(float,float) SERIAL__BASETYPE(double,double) SERIAL__BASETYPE(long double,l_double) // how to save an item where G is a "getter" for value,type,name template struct SaveItem { template inline static void apply(T o,std::ostream &os,int indent) { SaveWrapper(G::getvalue(o),G::getname(o),os,indent); } }; template struct LoadItem { template inline static bool apply(T o,std::istream &is, const XMLTagInfo &info) { std::map::const_iterator ni =info.attr.find("name"); if (ni!=info.attr.end() || ni->second != G::getname(o)) return false; LoadWrapper(G::getvalue(o),info,is); return true; } }; template struct LoadList { template inline static void exec(O o, std::istream &is, const char *cname) { XMLTagInfo info; std::set loadedmem; while(1) { ReadTag(is,info); if (info.isend && !info.isstart) { if (info.name != cname) throw streamexception(std::string("Stream Input Format Error: expected end tag for ")+cname+", received end tag for "+info.name); if (!AllLoaded::exec(o,loadedmem)) throw streamexception(std::string("Stream Input Format Error: not all fields present for ")+cname); return; } if (LoadOne::exec(o,is,info)) loadedmem.insert(info.attr["name"]); else throw streamexception(std::string("Extra field ")+info.attr["name"]+" of type "+info.name+" in object "+cname); } } }; } #endif #endif ctbnrle.1.1.1/hdr/serial/serial.txt0000664000175000017500000003027712107032422017444 0ustar csheltoncsheltonDocumentation for serial{.h,.tcc,.cc}: [Note, there is a boost library that has similar functionality. This was developed before that boost library was included in boost. Future version of the code may use the boost version instead.] This code (set of marcos, and the templates and functions they invoke) will automatically set up serialization code for a class. The serialization input/output is in an (almost) XML format. In particular it will supply a Save and Load method for each class (which take a std::ostream and std::istream respectively and have return type void). It will also declare a static member function LoadPtr that takes no arguments and returns a (newly allocated) pointer to the type. If the type is "virtual" (see below), then that pointer could be to any derived type. If the type is "virtual" it will also declare a virtual method SaveV like Save that will save the true type of the object (no slicing). Additionally, for classes declared to this code as "virtual," any pointer to the class will be loaded as its true class (determined at runtime) -- either the type of the pointer or a subclass thereof. If "virtual" is not used, then no virtual methods are created. If it is used, they are created and you need to make sure your destructor is virtual. There are two requirements on the class in order to use it with this code: 1. There must be a constructor that takes no arguments (a default constructor) 2. All member data or super-classes that you wish to serialize must satistfy one of the following: A. use this code for serialization B. be a basic data type (int, double, char, etc.) C. be an STL class of a type T that meets this requirement (currently pair, set, map, or vector) D. be an STL string E. be a pointer to a datatype that used this code for serialization Note that this code will not correctly serialize data structures in which two pointers point to the same object. It assumes that all pointers point to unique locations. USAGE: The basic usage for a simple class is illustrated as below. class MyClass { SERIAL_START(MyClass) SERIAL_VAR(int,x) SERIAL_VAR(double,y) SERIAL_VAR(std::string,s) SERIAL_END public: MyClass(); // make sure to have a default constructor //... rest of interface ... private: int x; // all members mentioned above for serialization double y; // must be declared std::string s; // (they are "private" here, but that is // not required. Order doesn't matter }; Somewhere in the class declaration, include the macro "SERIAL_START" with the argument of the class name. Then, list the member variables to be serialized with "SERIAL_VAR" which takes two arguments: the member type and the member name. Finally, finish the serialization declarations with "SERIAL_END" (which takes no arguments. The SERIAL_END macro leaves the code in the "public:" state. However, to be safe, you should always place a "public:" "private:" or "protected:" right after it. The order of the member list in the "SERIAL_" section will dictate the order in which they are written to the output. However, input can be organized in any order. The members themselves can be declared in any order. More advanced usage cases are as follows. - If you would like to specify a default value for a member variable or super class (see below), use "SERIAL_VAR_DEFAULT" instead of SERIAL_VAR (or SERIAL_SUPER_DEFAULT instead of SERIAL_SUPER). It takes an extra argument: the value to assign to the member or super class if the value is not given on the input stream. An example: class ClassA { SERIAL_START(ClassA) SERIAL_VAR_DEFAULT(int,a,5) // if a is not mentioned on // an input stream, set it to 5 SERIAL_END public: ClassA(); // be sure to have a default constructor // ... rest of declaration private: int a; }; - If you would like to specify a different name under which the member should be saved, you can use the SERIAL_VAR_N and SERIAL_VAR_DEFAULT_N macros which take one additional argument (at the end) that is a string with the name. An example: class SaveRename { SERIAL_START(SaveRename) SERIAL_VAR_N(int,a,"important integer") SERIAL_VAR_DEFAULT_N(double,d,3.14,"double member") SERIAL_END public: SaveRename(); // be sure to have a default constructor // ... rest of declaration private: int a; double d; }; Be careful! You must make sure that all members of the same class have different names! - If the class has a super-class that needs to be serialized, all such super classes should be declared using SERIAL_SUPER which takes a single argument: the type of the superclass An example: class ClassA { SERIAL_START(ClassA) SERIAL_VAR(int,a) SERIAL_END public: ClassA(); // be sure to have a default constructor // ... rest of declaration private: int a; }; class ClassB : public ClassA { SERIAL_START(ClassB) SERIAL_SUPER(ClassA) SERIAL_VAR(float,f) SERIAL_END public: ClassB(); // be sure to have a default constructor // ... rest of declaration private: float f; }; Multiple inheritance is okay (but see below): use SERIAL_SUPER multiple times. There are also "_N" versions of these macros (see point above) - To declare a class to be "virtual" and able to load and save pointers to it or any derived type with the types being determined at *runtime*, all such classes should use SERIAL_START_V instead of SERIAL_START An example (modifying the example above only slightly): class ClassA { SERIAL_START_V(ClassA) SERIAL_VAR(int,a) SERIAL_END public: ClassA(); // be sure to have a default constructor ~ClassA(); // be sure to have a virtual destructor // ... rest of declaration private: int a; }; class ClassB : public ClassA { SERIAL_START_V(ClassB) SERIAL_SUPER(ClassA) SERIAL_VAR(float,f) SERIAL_END public: ClassB(); // be sure to have a default constructor ~ClassB(); // be sure to have a virtual destructor // ... rest of declaration private: float f; }; class ClassC : public ClassA { SERIAL_START_V(ClassC) SERIAL_SUPER(ClassA) SERIAL_VAR(double,d) SERIAL_END public: ClassC(); // be sure to have a default constructor ~ClassC(); // be sure to have a virtual destructor // ... rest of declaration private: double d; }; class OtherClass { SERIAL_START(OtherClass) SERIAL_VAR(ClassA *,p) SERIAL_END public: OtherClass(); // be sure to have a default constructor // ... rest of declaration private: ClassA *p; }; In this example, the member p in OtherClass might point to a ClassA, ClassB, or ClassC. Because ClassA, ClassB, and ClassC used SERIAL_START_V (instead of SERIAL_START), this pointer will be correctly saved and loaded, no matter which class it actually points to. If you have multiple inheritance, only one of the superclasses may be declared as "virtual" in this fashion. As an example, if ClassC also derived from ClassX, then only one of ClassA and ClassX could be declared with "SERIAL_START_V" (the other would have to be just "SERIAL_START"), but both could be included with "SERIAL_SUPER" in ClassC. - To declare a template class, use the macro SERIAL_STARTn instead of SERIAL_START (where n is the number of arguments to the template). This macro takes extra arguments, one for each template parameter. An example: template class MyTempl { SERIAL_START1(MyTempl,T) SERIAL_VAR(int,x) SERIAL_VAR(T,y) SERIAL_END public: MyTempl(); // be sure to have a default constructor // ... rest of declaration private: int x; T y; }; If you wish to also make this class "virtual," use SERIAL_STARTn_V instead (for example, SERIAL_START1_V above). - To declare a member variable or a superclass for a type that contains a comma (like a template with multiple arguments), use a typedef to give the type a new name. Some examples: class YetAnotherClass { typedef std::map mymap; SERIAL_START(YetAnotherClass) SERIAL_VAR(mymap,m) SERIAL_END public: YetAnotherClass(); // be sure to have a default constructor // ... rest of declaration private: mymap m; }; template class TemplateMap { typedef std::map mymap; SERIAL_START2(TemplateMap,I,T) SERIAL_VAR(mymap,m) SERIAL_END public: TemplateMap(); // be sure to have a default constructor // ... rest of declaration private: mymap m; }; class CMap : public TemplateMap { typedef TemplateMap Base; SERIAL_START(CMap) SERIAL_SUPER(Base) SERIAL_END public: CMap(); // be sure to have a default constructor // ... rest of declaration }; - If there is some "set-up" or "clean-up" code that needs to be run before or after saving or loading (say to build caches or to compact the structure), the class can declare the following public methods, which will be run at the appropriate times: void serial_preload(); void serial_postload(); void serial_presave() const; void serial_postsave() const; - To declare that you type will do its own serialization using the shift operators (<< and >>), simply use "SERIAL_USESHIFT" with the class name. An example: class Simple { SERIAL_USESHIFT(Simple) public: Simple(); // still need a default constructor // ... rest of declaration // need to make sure that somewhere the following // functions are declared: // std::istream &operator>>(std::istream &,Simple &); // std::ostream &operator>>(std::ostream &,const Simple &); private: // whatever members are necessary -- make sure all // are saved/loaded by shift functions above // in ASCII format without using the 5 special characters // of XML {",',&,<,>} }; Note that the operator<< and operator>> should *not* output characters quote("), single-quote('), ampersand(&), less-than(<), or greater-than(>). If the output is very small, and you would like it be be placed in a "value" attribute (instead of between tags), then use the macro "SERIAL_USESHIFT_SHORT" instead of "SERIAL_USESHIFT" The two operators must read and write exactly the same characters (not leaving any extras on the stream or consuming extras from the stream) For classes like this, it is not possible to make them "virtual." There are template versions (named SERIAL_USESHIFTn and SERIAL_USESHIFTn_SHORT -- where n is the number of template args) A few final points: The loading methods (Load) can throw a streamexception (see below). All extra classes and functions are placed in their own namespace. The name defaults to serial (see above). However, if you define the compile symbol SERIALNAMESPACE, you can change the namespace. Inside of the class, extra types and methods are added. These all begin with serial_ or SERIAL_. This header defines compiler symbols (like macros) that all begin with SERIAL_. If you do not directly use a class in your code (that is create an object of its type at some point), the class will not be available for "virtual" loading. To do this, include somewhere in your code (a .cpp file is better than a .h file, as this creates a single pointer, and you only need one of them) the macro ENSURECLASS(HiddenClass) which will cause the class HiddenClass to be registered. There are templated version of this too (ENSURECLASSn), but the extra arguments are not template formal parameter, but template actual parameters. As an example: template class TClass : public ParentClass { SERIAL_START1_V(TClass,T) SERIAL_SUPER(ParentClass) //... normal macros for each member variable SERIAL_END public: //... default constructor, etc //... and your class declarations/definitions }; ENSURECLASS(TClass,int) ENSURECLASS(TClass,double) Will make sure that TClass and TClass can be loaded even if they are never used in the code and you are only trying to load a pointer to ParentClass (which might point to a TClass or TClass). ctbnrle.1.1.1/hdr/serial/serial.h0000664000175000017500000015570312251373407017071 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_SERIAL_H #define CTBNRLE_SERIAL_H #include "nullptr03.h" /* I think the following remark is based upon a misunderstanding * of "exec" within the template metaprogramming below. // need prototype for exec() #ifdef _MSC_VER # include #else # include #endif */ #include #include #ifndef SERIALNAMESPACE #define SERIALNAMESPACE serial #endif namespace SERIALNAMESPACE { using ctbn::nullptr03; // can be thrown by the "Load" method class streamexception : public std::exception { public: streamexception(std::string txt) throw() : expl(txt) { } streamexception(const streamexception &e) throw() { expl = e.expl; } streamexception &operator=(const streamexception &e) throw() { if (this != &e) expl = e.expl; return *this; } virtual ~streamexception() throw() { } virtual const char *what() const throw() { return expl.c_str(); } private: std::string expl; }; } /* Here starts "helper" macros: */ #define SERIAL_GETTERCOMMON(expr,cexpr,vtype,vname) \ typedef vtype valtype; \ static inline vtype &getvalue(SERIAL_BASETYPE *o) \ { return expr; } \ static inline const char *getname(SERIAL_BASETYPE *o) \ { return vname; } \ \ static inline vtype const &getvalue(const SERIAL_BASETYPE *o) \ { return cexpr; } \ static inline const char *getname(const SERIAL_BASETYPE *o) \ { return vname; } \ #define SERIAL_GETTERSTRUCT(expr,cexpr,vtype,vname) \ struct { SERIAL_GETTERCOMMON(expr,cexpr,vtype,vname) } #define SERIAL_GETTERSTRUCT_DEFAULT(expr,cexpr,vtype,vname,dval) \ struct { \ SERIAL_GETTERCOMMON(expr,cexpr,vtype,vname) \ static inline void setdefault(vtype &v) { v = dval; } \ } #define SERIAL_NOTV_COMMON \ friend struct SERIALNAMESPACE::IsEmpty; \ private: \ template \ struct serial__vallocT { \ const static bool valid = !SERIAL__B; \ typedef SERIAL_BASETYPE rettype; \ typedef SERIAL_BASETYPE *(*createfntype)(void); \ typedef std::map allocmaptype; \ inline static SERIAL_BASETYPE *\ allocbyname(const std::string &name) { \ return nullptr03; } \ inline static allocmaptype &alloctable() { \ static allocmaptype table; \ return table; \ } \ inline static const char * \ addalloc(const char *name, createfntype fn) \ { return name; } \ }; \ #define SERIAL_V_COMMON(myname) \ friend struct SERIALNAMESPACE::IsEmpty; \ private: \ template \ struct serial__vallocT { \ const static bool valid = SERIAL__B; \ typedef SERIAL_BASETYPE rettype; \ typedef SERIAL_BASETYPE *(*createfntype)(void); \ typedef std::map allocmaptype; \ inline static SERIAL_BASETYPE * \ allocbyname(const std::string &name) { \ typename allocmaptype::iterator i = alloctable().find(name); \ if (i==alloctable().end()) return nullptr03; \ else return i->second(); \ } \ inline static allocmaptype &alloctable() { \ static allocmaptype table; \ return table; \ } \ inline static const char * \ addalloc(const char *name, createfntype fn) { \ alloctable()[std::string(name)] = fn; \ return name; \ } \ }; \ public: \ virtual void SaveV(std::ostream &os, const char *name="", int indent=0) const { \ Save(os,name,indent); \ } \ inline static SERIAL_BASETYPE *LoadV(std::istream &is) { \ SERIAL_BASETYPE *ret; SERIALNAMESPACE::LoadWrapper(ret,is); \ return ret; \ } \ virtual void serial__loadwrapv(std::istream &is, \ const SERIALNAMESPACE::XMLTagInfo &info) { \ SERIALNAMESPACE::LoadWrapper(*this,info,is); \ } #define SERIAL_SUPER_COMMON(cname) \ serial__class##cname##lst; \ template \ struct serial__vallocT::type,SERIAL__B> { \ const static bool valid = SERIAL__B; \ typedef cname::serial__valloc::rettype rettype; \ typedef cname::serial__valloc::createfntype createfntype; \ inline static SERIAL_BASETYPE *allocbyname(const std::string &name) { \ cname::serial__valloc::rettype *cret = cname::serial__valloc::allocbyname(name); \ if (cret == nullptr03) return nullptr03; \ SERIAL_BASETYPE *ret = dynamic_cast(cret); \ if (ret == nullptr03) return nullptr03; \ return ret; \ } \ inline static const char *addalloc(const char *name, createfntype fn) \ { return cname::serial__valloc::addalloc(name,fn); } \ }; \ #define SERIAL_NAME0(cname) \ #cname #define SERIAL_NAME1(cname,tname1) \ SERIALNAMESPACE::TName(#cname,1, \ SERIALNAMESPACE::TypeInfo::namestr()) #define SERIAL_NAME2(cname,tname1,tname2) \ SERIALNAMESPACE::TName(#cname,2, \ SERIALNAMESPACE::TypeInfo::namestr(), \ SERIALNAMESPACE::TypeInfo::namestr()) #define SERIAL_NAME3(cname,tname1,tname2,tname3) \ SERIALNAMESPACE::TName(#cname,3, \ SERIALNAMESPACE::TypeInfo::namestr(), \ SERIALNAMESPACE::TypeInfo::namestr(), \ SERIALNAMESPACE::TypeInfo::namestr()) #define SERIAL_NAME4(cname,tname1,tname2,tname3,tname4) \ SERIALNAMESPACE::TName(#cname,4, \ SERIALNAMESPACE::TypeInfo::namestr(), \ SERIALNAMESPACE::TypeInfo::namestr(), \ SERIALNAMESPACE::TypeInfo::namestr(), \ SERIALNAMESPACE::TypeInfo::namestr()) #define SERIAL_ID0(cname) \ static const char *serial_IDname() { \ static const char *ret = SERIAL_NAME0(cname); \ return ret; \ } \ #define SERIAL_ID1(cname,tname1) \ static const char *serial_IDname() { \ static const char *ret = SERIAL_NAME1(cname,tname1); \ return ret; \ } #define SERIAL_ID2(cname,tname1,tname2) \ static const char *serial_IDname() { \ static const char *ret = SERIAL_NAME2(cname,tname1,tname2); \ return ret; \ } #define SERIAL_ID3(cname,tname1,tname2,tname3) \ static const char *serial_IDname() { \ static const char *ret = SERIAL_NAME3(cname,tname1,tname2,tname3); \ return ret; \ } #define SERIAL_ID4(cname,tname1,tname2,tname3,tname4) \ static const char *serial_IDname() { \ static const char *ret = \ SERIAL_NAME4(cname,tname1,tname2,tname3,tname4); \ return ret; \ } #define SERIAL_SNAME0(cname) \ static const char *serial__shiftname() { \ static const char *ret = SERIAL_NAME0(cname); \ return ret; \ } \ #define SERIAL_SNAME1(cname,tname1) \ static const char *serial__shiftname() { \ static const char *ret = SERIAL_NAME1(cname,tname1); \ return ret; \ } #define SERIAL_SNAME2(cname,tname1,tname2) \ static const char *serial__shiftname() { \ static const char *ret = SERIAL_NAME2(cname,tname1,tname2); \ return ret; \ } #define SERIAL_SNAME3(cname,tname1,tname2,tname3) \ static const char *serial__shiftname() { \ static const char *ret = SERIAL_NAME3(cname,tname1,tname2,tname3); \ return ret; \ } #define SERIAL_SNAME4(cname,tname1,tname2,tname3,tname4) \ static const char *serial__shiftname() { \ static const char *ret = \ SERIAL_NAME4(cname,tname1,tname2,tname3,tname4); \ return ret; \ } #define SERIAL_ID0_V(cname) \ static const char *serial_IDname() { \ static const char *ret = serial__valloc::addalloc( \ SERIAL_NAME0(cname), \ SERIALNAMESPACE::creator::create); \ return ret; \ } \ #define SERIAL_ID1_V(cname,tname1) \ static const char *serial_IDname() { \ static const char *ret = serial__valloc::addalloc( \ SERIAL_NAME1(cname,tname1), \ SERIALNAMESPACE::template creator::template create); \ return ret; \ } #define SERIAL_ID2_V(cname,tname1,tname2) \ static const char *serial_IDname() { \ static const char *ret = serial__valloc::addalloc( \ SERIAL_NAME2(cname,tname1,tname2), \ SERIALNAMESPACE::template creator::template create); \ return ret; \ } #define SERIAL_ID3_V(cname,tname1,tname2,tname3) \ static const char *serial_IDname() { \ static const char *ret = serial__valloc::addalloc( \ SERIAL_NAME3(cname,tname1,tname2,tname3), \ SERIALNAMESPACE::template creator::template create); \ return ret; \ } #define SERIAL_ID4_V(cname,tname1,tname2,tname3,tname4) \ static const char *serial_IDname() { \ static const char *ret = serial__valloc::addalloc( \ SERIAL_NAME4(cname,tname1,tname2,tname3,tname4), \ SERIALNAMESPACE::creator::create); \ return ret; \ } /* Here starts "USESHIFT" macros: */ #define SERIAL_USESHIFT(cname) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_NOTV_COMMON \ public: \ SERIAL_SNAME0(cname) \ enum { SERIAL_ISSHORT = false }; //inline static bool serial__isshort() { return false; } #define SERIAL_USESHIFT_SHORT(cname) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_NOTV_COMMON \ public: \ SERIAL_SNAME0(cname) \ enum { SERIAL_ISSHORT = true }; //inline static bool serial__isshort() { return true; } #define SERIAL_USESHIFT1(cname,tname1) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_NOTV_COMMON \ public: \ SERIAL_SNAME1(cname,tname1) \ inline static bool serial__isshort() { return false; } #define SERIAL_USESHIFT1_SHORT(cname) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_NOTV_COMMON \ public: \ SERIAL_SNAME1(cname,tname1) \ inline static bool serial__isshort() { return true; } #define SERIAL_USESHIFT2(cname,tname1,tname2) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_NOTV_COMMON \ public: \ SERIAL_SNAME2(cname,tname1,tname2) \ inline static bool serial__isshort() { return false; } #define SERIAL_USESHIFT2_SHORT(cname,tname1,tname2) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_NOTV_COMMON \ public: \ SERIAL_SNAME2(cname,tname1,tname2) \ inline static bool serial__isshort() { return true; } #define SERIAL_USESHIFT3(cname) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_NOTV_COMMON \ public: \ SERIAL_SNAME3(cname,tname1,tname2,tname3) \ inline static bool serial__isshort() { return false; } #define SERIAL_USESHIFT3_SHORT(cname,tname1,tname2,tname3) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_NOTV_COMMON \ public: \ SERIAL_SNAME3(cname,tname1,tname2,tname3) \ inline static bool serial__isshort() { return true; } #define SERIAL_USESHIFT4(cname,tname1,tname2,tname3,tname4) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_NOTV_COMMON \ public: \ SERIAL_SNAME4(cname,tname1,tname2,tname3,tname4) \ inline static bool serial__isshort() { return false; } #define SERIAL_USESHIFT4_SHORT(cname) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_NOTV_COMMON \ public: \ SERIAL_SNAME4(cname,tname1,tname2,tname3,tname4) \ inline static bool serial__isshort() { return true; } /* Here starts "START" macros: */ #define SERIAL_START(cname) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_NOTV_COMMON \ public: \ SERIAL_ID0(cname) \ private: \ typedef SERIALNAMESPACE::ListEnd #define SERIAL_START1(cname,tname) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_NOTV_COMMON \ public: \ SERIAL_ID1(cname,tname) \ private: \ typedef SERIALNAMESPACE::ListEnd #define SERIAL_START2(cname,tname1,tname2) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_NOTV_COMMON \ public: \ SERIAL_ID2(cname,tname1,tname2) \ private: \ typedef SERIALNAMESPACE::ListEnd #define SERIAL_START3(cname,tname1,tname2,tname3) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_NOTV_COMMON \ public: \ SERIAL_ID3(cname,tname1,tname2,tname3) \ private: \ typedef SERIALNAMESPACE::ListEnd #define SERIAL_START4(cname,tname1,tname2,tname3,tname4) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_NOTV_COMMON \ public: \ SERIAL_ID4(cname,tname1,tname2,tname3,tname4) \ private: \ typedef SERIALNAMESPACE::ListEnd /* Here starts "START_V" macros: */ #define SERIAL_START_V(cname) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_V_COMMON(cname) \ public: \ SERIAL_ID0_V(cname); \ private: \ typedef SERIALNAMESPACE::ListEnd #define SERIAL_START1_V(cname,tname) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_V_COMMON(cname) \ public: \ SERIAL_ID1_V(cname,tname); \ private: \ typedef SERIALNAMESPACE::ListEnd #define SERIAL_START2_V(cname,tname1,tname2) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_V_COMMON(cname) \ public: \ SERIAL_ID2_V(cname,tname1,tname2); \ private: \ typedef SERIALNAMESPACE::ListEnd #define SERIAL_START3_V(cname,tname1,tname2,tname3) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_V_COMMON(cname) \ public: \ SERIAL_ID3_V(cname,tname1,tname2,tname3); \ private: \ typedef SERIALNAMESPACE::ListEnd #define SERIAL_START4_V(cname,tname1,tname2,tname3,tname4) \ private: \ typedef cname SERIAL_BASETYPE; \ SERIAL_V_COMMON(cname) \ public: \ SERIAL_ID4_V(cname,tname1,tname2,tname3,tname4); \ private: \ typedef SERIALNAMESPACE::ListEnd /* Here starts "VAR" macros */ #define SERIAL_VAR(vtype,vname) \ serial__##vname##lst; \ typedef SERIAL_GETTERSTRUCT((o->vname),(o->vname),vtype,#vname) \ serial__get##vname; \ typedef SERIALNAMESPACE::List #define SERIAL_VAR_DEFAULT(vtype,vname,val) \ serial__##vname##lst; \ typedef SERIAL_GETTERSTRUCT_DEFAULT((o->vname),(o->vname),vtype,#vname,val) \ serial__get##vname; \ typedef SERIALNAMESPACE::List #define SERIAL_VAR_N(vtype,vname,sname) \ serial__##vname##lst; \ typedef SERIAL_GETTERSTRUCT((o->vname),(o->vname),vtype,sname) \ serial__get##vname; \ typedef SERIALNAMESPACE::List #define SERIAL_VAR_DEFAULT_N(vtype,vname,val,sname) \ serial__##vname##lst; \ typedef SERIAL_GETTERSTRUCT_DEFAULT((o->vname),(o->vname),vtype,sname,val) \ serial__get##vname; \ typedef SERIALNAMESPACE::List /* Here starts "SUPER" macros */ #define SERIAL_SUPER(cname) \ SERIAL_SUPER_COMMON(cname) \ typedef SERIAL_GETTERSTRUCT((*(static_cast(o))),*(static_cast(o)),cname,#cname) \ serial__getclass##cname; \ typedef SERIALNAMESPACE::List #define SERIAL_SUPER_DEFAULT(cname,val) \ SERIAL_SUPER_COMMON(cname) \ typedef SERIAL_GETTERSTRUCT_DEFAULT((*(static_cast(o))), \ *(static_cast(o)),cname,#cname,val) \ serial__getclass##cname; \ typedef SERIALNAMESPACE::List #define SERIAL_SUPER_N(cname,sname) \ SERIAL_SUPER_COMMON(cname) \ typedef SERIAL_GETTERSTRUCT((*(static_cast(o))),*(static_cast(o)),cname,sname) \ serial__getclass##cname; \ typedef SERIALNAMESPACE::List #define SERIAL_SUPER_DEFAULT_N(cname,val,sname) \ SERIAL_SUPER_COMMON(cname) \ typedef SERIAL_GETTERSTRUCT_DEFAULT((*(static_cast(o))), \ *(static_cast(o)),cname,sname,val) \ serial__getclass##cname; \ typedef SERIALNAMESPACE::List /* Here starts "ENSURECLASS" macros */ #define ENSURECLASS(cname) \ namespace { static const char *namefor__##cname=cname::serial_IDname(); } #define ENSURECLASS1(cname,tname1) \ namespace { static const char *namefor__##cname##tname1 = \ cname::serial_IDname(); } #define ENSURECLASS2(cname,tname1,tname2) \ namespace { static const char *namefor__##cname##tname1##tname2 = \ cname::serial_IDname(); } #define ENSURECLASS3(cname,tname1,tname2,tname3) \ namespace { \ static const char *namefor__##cname##tname1##tname2##tname3 = \ cname::serial_IDname(); \ } #define ENSURECLASS4(cname,tname1,tname2,tname3,tname4) \ namespace { \ static const char *namefor__##cname##tname1##tname2##tname3##tname4 \ = cname::serial_IDname(); \ } //#include "serial.tcc" #include #include #include #include #include #include #include #include #include #include #include namespace SERIALNAMESPACE { using ctbn::nullptr03; char *TName(const char *cname, int n, ...); void Indent(std::ostream &os, int indent); void *AllocByName(const std::string &name); typedef void *(*createfntype)(void); const char *AddAlloc(const char *name, createfntype fn); // a simple list: struct ListEnd {}; template struct List {}; // pretty standard enable_if template (see Boost, for example) template struct Type_If { typedef T type; }; template struct Type_If { }; #define SERIAL_DECVAL(vname,expr) \ enum { vname = (expr) }; //static int const vname = (expr); // This one has appeared in one form or another all over the // internet template struct TypeProp { template struct type_check; template static char (& chksave(type_check*))[1]; template static char (& chksave(...))[2]; SERIAL_DECVAL(HasSave,sizeof(chksave(0)) == 1) template static char (& chkid(type_check*))[1]; template static char (& chkid(...))[2]; SERIAL_DECVAL(HasIDname,sizeof(chkid(0)) == 1) template static char (& chkshift(type_check*))[1]; template static char (& chkshift(...))[2]; SERIAL_DECVAL(HasShift,sizeof(chkshift(0)) == 1) template static char (& chkdef(type_check*))[1]; template static char (& chkdef(...))[2]; SERIAL_DECVAL(HasDefault,sizeof(chkdef(0)) == 1) template static char (& chkv(type_check*))[1]; template static char (& chkv(...))[2]; SERIAL_DECVAL(HasV,sizeof(chkv(0)) == 1) template static char (& chkpreload(type_check*))[1]; template static char (& chkpreload(...))[2]; SERIAL_DECVAL(HasPreLoad,sizeof(chkpreload(0)) == 1) template static char (& chkpostload(type_check*))[1]; template static char (& chkpostload(...))[2]; SERIAL_DECVAL(HasPostLoad,sizeof(chkpostload(0)) == 1) template static char (& chkpresave(type_check*))[1]; template static char (& chkpresave(...))[2]; SERIAL_DECVAL(HasPreSave,sizeof(chkpresave(0)) == 1) template static char (& chkpostsave(type_check*))[1]; template static char (& chkpostsave(...))[2]; SERIAL_DECVAL(HasPostSave,sizeof(chkpostsave(0)) == 1) //enum { BlankList = SameType::value }; }; template struct HasDefCon { struct small { char x[1]; }; struct large { char x[2]; }; template static small chk(U (*)[1]); template static large chk(...); //SERIAL_DECVAL(value,sizeof(HasDefCon::template chk(0))==sizeof(small)) SERIAL_DECVAL(value,sizeof(chk(0))==sizeof(small)) }; template struct IsEmpty { SERIAL_DECVAL(value,false) }; template struct ListEmpty { SERIAL_DECVAL(value,false) }; template<> struct ListEmpty { SERIAL_DECVAL(value,true) }; template struct ListEmpty > { SERIAL_DECVAL(value,IsEmpty::value && ListEmpty::value) }; template struct IsEmpty::value,void>::type> { SERIAL_DECVAL(value,true) }; template struct creator { template inline static typename Type_If::value,RT *>::type create() { return new AT(); } template inline static typename Type_If::value,RT *>::type create() { return nullptr03; } }; struct XMLTagInfo { std::string name; std::map attr; bool isstart,isend; }; template struct SaveItem; template struct LoadList; void ReadTag(std::istream &is,XMLTagInfo &info); void ReadEndTag(std::istream &is,const char *ename); void ReadStr(std::istream &is, std::string &ret,const char *endchar); void WriteStr(std::ostream &os, const std::string &s,bool escape=true); // a reverse iterator on a list for saving template struct SaveItt { template inline static void exec(O o,std::ostream &os,int indent) { } }; template struct SaveItt, typename Type_If::value,void>::type> { template inline static void exec(O o,std::ostream &os,int indent) { SaveItt::exec(o,os,indent); SaveItem::apply(o,os,indent); } }; template struct SaveItt, typename Type_If::value,void>::type> { template inline static void exec(O o,std::ostream &os,int indent) { SaveItt::exec(o,os,indent); } }; template<> struct SaveItt { template inline static void exec(O o,std::ostream &os, int indent) { } }; template struct LoadOne { template inline static bool exec(O o,std::istream &, const XMLTagInfo &) { return false; } }; template<> struct LoadOne { template inline static bool exec(O o,std::istream &, const XMLTagInfo &) { return false; } }; template struct LoadOne > { template inline static bool exec(O o,std::istream &is, const XMLTagInfo &info) { std::map::const_iterator ni =info.attr.find("name"); if (ni!=info.attr.end() && ni->second == H::getname(o)) { LoadWrapper(H::getvalue(o),info,is); return true; } else return LoadOne::exec(o,is,info); } }; template struct AllLoaded { template inline static bool exec(O o,const std::set &nset) { return true; } }; template<> struct AllLoaded { template inline static bool exec(O o,const std::set &nset) { return true; } }; template struct AllLoaded, typename Type_If::HasDefault,void>::type > { template inline static bool exec(O o,const std::set &nset) { if (nset.find(H::getname(o)) == nset.end()) H::setdefault(H::getvalue(o)); return AllLoaded::exec(o,nset); } }; template struct AllLoaded, typename Type_If::HasDefault && IsEmpty::value,void>::type > { template inline static bool exec(O o,const std::set &nset) { return AllLoaded::exec(o,nset); } }; template struct AllLoaded, typename Type_If::HasDefault && !IsEmpty::value,void>::type > { template inline static bool exec(O o,const std::set &nset) { if (nset.find(H::getname(o)) == nset.end()) return false; else return AllLoaded::exec(o,nset); } }; template struct IsShiftable { SERIAL_DECVAL(atall,false) SERIAL_DECVAL(quickly,false) }; template struct IsShiftable { SERIAL_DECVAL(atall,IsShiftable::atall) SERIAL_DECVAL(quickly,IsShiftable::quickly) }; template struct IsShiftable::HasShift,void>::type> { SERIAL_DECVAL(atall,true) SERIAL_DECVAL(quickly,T::SERIAL_ISSHORT) }; #define SERIAL__BASETYPE(tname,strname) \ template<> \ struct IsShiftable { \ SERIAL_DECVAL(atall,true) \ SERIAL_DECVAL(quickly,true) \ }; \ template<> \ struct TypeInfo { \ inline static const char *namestr() { return #strname; } \ inline static void writeotherattr(std::ostream &, const tname &) { } \ inline static bool isshort(const tname &) { return true; } \ inline static bool isinline(const tname &) { return false; } \ inline static void save(const tname &t,std::ostream &os, int indent) { \ os << t; \ } \ inline static void load(tname &t, const XMLTagInfo &info, std::istream &is) {\ std::map::const_iterator vi \ =info.attr.find("value"); \ if (vi!=info.attr.end()) { \ std::istringstream ss(vi->second); \ ss.copyfmt(is); \ ss >> t; \ if (info.isend) return; \ } else { \ is >> t; \ } \ ReadEndTag(is,namestr()); \ } \ }; template struct TypeInfo { enum { x = sizeof(T::__Attempting_to_serialize_type_without_serialization_information) }; inline static const char *namestr() { throw streamexception("Streaming Error: type info asked about unknown class"); } inline static void writeotherattr(std::ostream &, const T &) { throw streamexception("Streaming Error: type info asked about unknown class"); } inline static bool isshort(const T &) { throw streamexception("Streaming Error: type info asked about unknown class"); } inline static bool isinline(const T &) { throw streamexception("Streaming Error: type info asked about unknown class"); } inline static void save(const T &t,std::ostream &os, int indent) { throw streamexception("Streaming Error: save called for unknown class"); } inline static void load(T &t, const XMLTagInfo &info, std::istream &is) { throw streamexception("Streaming Error: load called for unknown class"); } }; // These were moved upward to avoid ambiguity. SERIAL__BASETYPE(bool,bool) SERIAL__BASETYPE(char,char) SERIAL__BASETYPE(unsigned char,u_char) SERIAL__BASETYPE(short,short) SERIAL__BASETYPE(unsigned short,u_short) SERIAL__BASETYPE(int,int) SERIAL__BASETYPE(unsigned int,u_int) SERIAL__BASETYPE(long,long) SERIAL__BASETYPE(unsigned long,u_long) SERIAL__BASETYPE(long long,l_long) SERIAL__BASETYPE(float,float) SERIAL__BASETYPE(double,double) SERIAL__BASETYPE(long double,l_double) template struct TypeInfo { inline static const char *namestr() { return TypeInfo::namestr(); } inline static void writeotherattr(std::ostream &os, const T &t) { TypeInfo::writeotherattr(os,t); } inline static void writeotherattr(std::ostream &os, const T &t, const char *s1, const char *s2) { TypeInfo::otherattr(os,t,s1,s2); } inline static bool isshort(const T &t) { return TypeInfo::isshort(t); } inline static bool isinline(const T &t) { return TypeInfo::isinline(t); } inline static void save(const T &t,std::ostream &os, int indent) { TypeInfo::save(t,os,indent); } inline static void load(const T &t, const XMLTagInfo &info, std::istream &is) { throw streamexception("Streaming Error: load called for constant type"); } }; template inline void SaveWrapper(const T &v,const char *vname, std::ostream &os,int indent) { Indent(os,indent); os << "<" << TypeInfo::namestr(); TypeInfo::writeotherattr(os,v); if (vname && vname[0]!=0) os << " name=\"" << vname << "\""; if (TypeInfo::isshort(v)) { os << " value=\""; TypeInfo::save(v,os,indent); os << "\" \\>" << std::endl; } else if (TypeInfo::isinline(v)) { os << " \\>" << std::endl; } else { os << ">"; TypeInfo::save(v,os,indent); os << "<\\" << TypeInfo::namestr() << ">" << std::endl; } } template inline typename Type_If::HasV,void>::type SaveWrapper(T * const &v, const char *vname, std::ostream &os, int indent) { if (v == nullptr03) { Indent(os,indent); os << "<" << TypeInfo::namestr(); if (vname && vname[0]!=0) os << " name=\"" << vname << "\""; os << " isnull=\"1\" \\>" << std::endl; } else v->SaveV(os,vname,indent); } template inline typename Type_If::HasV,void>::type SaveWrapper(T * const &v, const char *vname, std::ostream &os, int indent) { if (v == nullptr03) { Indent(os,indent); os << "<" << TypeInfo::namestr(); if (vname && vname[0]!=0) os << " name=\"" << vname << "\""; os << " isnull=\"1\" \\>" << std::endl; } else SaveWrapper(*v,vname,os,indent); } template inline void SaveWrapper(const T &v,const char *vname, std::ostream &os,int indent, const char *e1, const char *e2) { Indent(os,indent); os << "<" << TypeInfo::namestr(); TypeInfo::writeotherattr(os,v,e1,e2); if (vname && vname[0]!=0) os << " name=\"" << vname << "\""; if (TypeInfo::isinline(v)) { os << " \\>" << std::endl; } else { os << ">"; TypeInfo::save(v,os,indent,e1,e2); os << "<\\" << TypeInfo::namestr() << ">" << std::endl; } } template struct CheckTag { inline static void check(const XMLTagInfo &info) { if (!info.isstart) throw streamexception(std::string("Stream Input Format Error: expected start tag for ")+TypeInfo::namestr()+", received end tag for "+info.name); if (info.name != TypeInfo::namestr()) throw streamexception(std::string("Stream Input Format Error: expected start tag for ")+TypeInfo::namestr()+", received start tag for "+info.name); } }; template inline void LoadWrapper(T &v, const XMLTagInfo &info, std::istream &is) { CheckTag::check(info); TypeInfo::load(v,info,is); } template inline typename Type_If::HasV,void>::type LoadWrapper(T *&v, const XMLTagInfo &info, std::istream &is) { if (!info.isstart) throw streamexception(std::string("Stream Input Format Error: expected start tag, received end tag for ")+info.name); std::map::const_iterator vi =info.attr.find("isnull"); if (vi!=info.attr.end() && vi->second=="1") { v = nullptr03; if (!info.isend) { XMLTagInfo einfo; ReadTag(is,einfo); if (einfo.name!=info.name || !info.isend || info.isstart) throw streamexception(std::string("Stream Input Format Error: null pointer object for type ")+TypeInfo::namestr()+" and name "+info.name+" has non-empty contents"); } return; } T *vv = T::serial__valloc::allocbyname(info.name); if (vv == nullptr03) throw streamexception(std::string("Stream Input Format Error: expected start tag for subtype of ")+TypeInfo::namestr()+", received start tag for type "+info.name+" which is either unknown or not a subtype"); v = vv; v->serial__loadwrapv(is,info); } template inline typename Type_If::HasV,void>::type LoadWrapper(T *&v, const XMLTagInfo &info, std::istream &is) { std::map::const_iterator vi =info.attr.find("isnull"); if (vi!=info.attr.end() && vi->second=="1") { v = nullptr03; if (!info.isend) { XMLTagInfo einfo; ReadTag(is,einfo); if (einfo.name!=info.name || !info.isend || info.isstart) throw streamexception(std::string("Stream Input Format Error: null pointer object for type ")+TypeInfo::namestr()+" and name "+info.name+" has non-empty contents"); } return; } v = new T(); LoadWrapper(*v,info,is); } template inline void LoadWrapper(T &v, std::istream &is) { XMLTagInfo info; ReadTag(is,info); LoadWrapper(v,info,is); } template inline typename Type_If::HasPreLoad && !TypeProp::HasPostLoad,void>::type SerialLoadWrap(T &t, std::istream &is) { t.serial__Load(is); } template inline typename Type_If::HasPreLoad && !TypeProp::HasPostLoad,void>::type SerialLoadWrap(T &t, std::istream &is) { t.serial_preload(); t.serial__Load(is); } template inline typename Type_If::HasPreLoad && TypeProp::HasPostLoad,void>::type SerialLoadWrap(T &t, std::istream &is) { t.serial__Load(is); t.serial_postload(); } template inline typename Type_If::HasPreLoad && TypeProp::HasPostLoad,void>::type SerialLoadWrap(T &t, std::istream &is) { t.serial_preload(); t.serial__Load(is); t.serial_postload(); } template inline typename Type_If::HasPreSave && !TypeProp::HasPostSave,void>::type SerialSaveWrap(const T &t, std::ostream &os, int indent) { t.serial__Save(os,indent); } template inline typename Type_If::HasPreSave && !TypeProp::HasPostSave,void>::type SerialSaveWrap(const T &t, std::ostream &os, int indent) { t.serial_presave(); t.serial__Save(os,indent); } template inline typename Type_If::HasPreSave && TypeProp::HasPostSave,void>::type SerialSaveWrap(const T &t, std::ostream &os, int indent) { t.serial__Save(os,indent); t.serial_postsave(); } template inline typename Type_If::HasPreSave && TypeProp::HasPostSave,void>::type SerialSaveWrap(const T &t, std::ostream &os, int indent) { t.serial_presave(); t.serial__Save(os,indent); t.serial_postsave(); } template<> struct TypeInfo { inline static const char *namestr() { return "string"; } inline static void writeotherattr(std::ostream &, const std::string &) { } inline static bool isshort(const std::string &s) { return s.length()<20; } inline static bool isinline(const std::string &s) { return false; } inline static void save(const std::string &s, std::ostream &os, int indent) { WriteStr(os,s,isshort(s)); } inline static void load(std::string &s, const XMLTagInfo &info, std::istream &is) { std::map::const_iterator vi =info.attr.find("value"); if (vi!=info.attr.end()) { std::istringstream ss(vi->second); ss.copyfmt(is); ReadStr(ss,s,""); if (info.isend) return; } else ReadStr(is,s,"<"); ReadEndTag(is,namestr()); } }; template struct TypeInfo::HasIDname,void>::type> { inline static const char *namestr() { return T::serial_IDname(); } inline static void writeotherattr(std::ostream &,const T &) { } inline static bool isshort(const T &) { return false; } inline static bool isinline(const T &) { return false; } inline static void save(const T &t,std::ostream &os,int indent) { os << std::endl; SerialSaveWrap(t,os,indent+1); Indent(os,indent); } inline static void load(T &t,const XMLTagInfo &info, std::istream &is) { SerialLoadWrap(t,is); } }; template struct TypeInfo::HasShift,void>::type> { inline static const char *namestr() { return T::serial__shiftname(); } inline static void writeotherattr(std::ostream &, const T &) {} inline static bool isshort(const T &) { return T::SERIAL_ISSHORT; } inline static bool isinline(const T &) { return false; } inline static void save(const T &t,std::ostream &os,int indent) { os << t; } inline static void load(T &t, const XMLTagInfo &info, std::istream &is) { std::map::const_iterator vi =info.attr.find("value"); if (vi!=info.attr.end()) { std::istringstream ss(vi->second); ss.copyfmt(is); ss >> t; if (info.isend) return; } else { is >> t; } ReadEndTag(is,namestr()); } }; template struct TypeInfo, typename Type_If::quickly || !IsShiftable::quickly,void>::type> { inline static const char *namestr() { return "pair"; } inline static void writeotherattr(std::ostream &, const std::pair &, const char * fn = nullptr03, const char * sn = nullptr03) { } inline static bool isshort(const std::pair &) { return false; } inline static bool isinline(const std::pair &) { return false; } inline static void save(const std::pair &p, std::ostream &os,int indent, const char *firstname= "first", const char *secondname="second") { os << std::endl; SaveWrapper(p.first,firstname,os,indent+1); SaveWrapper(p.second,secondname,os,indent+1); Indent(os,indent); } class PName { public: PName(std::pair &pa,const char *n1, const char *n2) : p(pa), s1(n1), s2(n2) {} std::pair &p; const char *s1,*s2; }; struct Get1 { inline static T1 &getvalue(PName &o) { return o.p.first; } inline static const char *getname(PName &o) { return o.s1; } }; struct Get2 { inline static T2 &getvalue(PName &o) { return o.p.second; } inline static const char *getname(PName &o) { return o.s2; } }; typedef List > PList; inline static void load(std::pair &p, const XMLTagInfo &info, std::istream &is, const char *firstname = "first", const char *secondname = "second") { PName pn(p,firstname,secondname); LoadList::exec(pn,is,"pair"); \ } }; template struct TypeInfo, typename Type_If::quickly && IsShiftable::quickly,void>::type> { inline static const char *namestr() { static std::string ret = std::string("pair.") +TypeInfo::namestr() +"."+TypeInfo::namestr(); return ret.c_str(); } inline static void writeotherattr(std::ostream &os, const std::pair &p, const char *firstname="first", const char *secondname="second") { os << " " << firstname << "=\"" << p.first << "\" " << secondname << "=\"" << p.second << "\""; } inline static bool isshort(const std::pair &) { return false; } inline static bool isinline(const std::pair &) { return true; } inline static void save(const std::pair &p, std::ostream &os,int indent, const char *firstname= "first", const char *secondname="second") { } inline static void load(std::pair &p, const XMLTagInfo &info, std::istream &is, const char *firstname = "first", const char *secondname = "second") { std::map::const_iterator vi =info.attr.find(firstname); if (vi!=info.attr.end()) { std::istringstream ss(vi->second); ss.copyfmt(is); ss >> p.first; } else throw streamexception(std::string("Stream Input Format Error: missing attribute ")+firstname+" in tag "+info.name); vi = info.attr.find(secondname); if (vi!=info.attr.end()) { std::istringstream ss(vi->second); ss.copyfmt(is); ss >> p.second; } else throw streamexception(std::string("Stream Input Format Error: missing attribute ")+secondname+" in tag "+info.name); if (!info.isend) { XMLTagInfo einfo; ReadTag(is,einfo); if (einfo.name!=info.name || !info.isend || info.isstart) throw streamexception(std::string("Stream Input Format Error: tag ")+info.name+" has non-empty contents"); } } }; template struct TypeInfo, typename Type_If::atall,void>::type> { inline static const char *namestr() { return "vector"; } inline static void writeotherattr(std::ostream &os, const std::vector &v) { os << " nelem=" << v.size(); } inline static bool isshort(const std::vector &) { return false; } inline static bool isinline(const std::vector &) { return false; } inline static void save(const std::vector &v, std::ostream &os,int indent) { os << std::endl; int c=0; for(typename std::vector::const_iterator i=v.begin(); i!=v.end();++i,++c) { SaveWrapper(*i,"",os,indent+1); } Indent(os,indent); } inline static void load(std::vector &v, const XMLTagInfo &info, std::istream &is) { std::map::const_iterator ni = info.attr.find("nelem"); int n = 0; if (ni != info.attr.end()) v.resize(n = atoi(ni->second.c_str())); else v.resize(0); XMLTagInfo eleminfo; int i=0; while(1) { ReadTag(is,eleminfo); if (eleminfo.isend && !eleminfo.isstart) { if (eleminfo.name == namestr()) { if (i!=n) v.resize(i); return; } throw streamexception(std::string("Stream Input Format Error: expected end tag for ")+namestr()+", received end tag for "+eleminfo.name); } if (i>=n) v.resize(n=i+1); LoadWrapper(v[i++],eleminfo,is); } } }; template struct TypeInfo, typename Type_If::atall,void>::type> { inline static const char *namestr() { static char *ret = TName("vector",1,TypeInfo::namestr()); return ret; } inline static void writeotherattr(std::ostream &os, const std::vector &v) { os << " nelem=" << v.size(); } inline static bool isshort(const std::vector &) { return false; } inline static bool isinline(const std::vector &) { return false; } inline static void save(const std::vector &v, std::ostream &os, int indent) { for(typename std::vector::const_iterator i=v.begin();i!=v.end();++i) os << *i << ' '; } inline static void load(std::vector &v, const XMLTagInfo &info, std::istream &is) { std::map::const_iterator ni = info.attr.find("nelem"); if (ni == info.attr.end()) throw streamexception("Stream Input Format Error: ivector needs nelem attribute"); int n = atoi(ni->second.c_str()); v.resize(n); for(int i=0;i> v[i]; ReadEndTag(is,namestr()); } }; template struct TypeInfo, typename Type_If::atall,void>::type> { inline static const char *namestr() { return "set"; } inline static void writeotherattr(std::ostream &,const std::set &) { } inline static bool isshort(const std::set &) { return false; } inline static bool isinline(const std::set &) { return false; } inline static void save(const std::set &s, std::ostream &os,int indent) { os << std::endl; int c=0; for(typename std::set::const_iterator i=s.begin(); i!=s.end();++i,++c) { SaveWrapper(*i,"",os,indent+1); } Indent(os,indent); } inline static void load(std::set &s, const XMLTagInfo &info, std::istream &is) { s.clear(); XMLTagInfo eleminfo; int i=0; while(1) { ReadTag(is,eleminfo); if (eleminfo.isend && !eleminfo.isstart) { if (eleminfo.name == namestr()) return; throw streamexception(std::string("Stream Input Format Error: expected end tag for ")+namestr()+", received end tag for "+eleminfo.name); } T temp; LoadWrapper(temp,eleminfo,is); s.insert(temp); } } }; template struct TypeInfo, typename Type_If::atall,void>::type> { inline static const char *namestr() { static char *ret = TName("set",1,TypeInfo::namestr()); return ret; } inline static void writeotherattr(std::ostream &os, const std::set &v) { os << " nelem=" << v.size(); } inline static bool isshort(const std::set &) { return false; } inline static bool isinline(const std::set &) { return false; } inline static void save(const std::set &s, std::ostream &os, int indent) { for(typename std::set::const_iterator i=s.begin();i!=s.end();++i) os << *i << ' '; } inline static void load(std::set &s, const XMLTagInfo &info, std::istream &is) { std::map::const_iterator ni = info.attr.find("nelem"); if (ni == info.attr.end()) throw streamexception("Stream Input Format Error: ivector needs nelem attribute"); int n = atoi(ni->second.c_str()); s.clear(); for(int i=0;i> temp; s.insert(temp); } ReadEndTag(is,namestr()); } }; template struct TypeInfo, void> { inline static const char *namestr() { return "map"; } inline static void writeotherattr(std::ostream &,const std::map &) { } inline static bool isshort(const std::map &) { return false; } inline static bool isinline(const std::map &) { return false; } inline static void save(const std::map m, std::ostream &os,int indent) { os << std::endl; int c=0; for(typename std::map::const_iterator i=m.begin(); i!=m.end();++i,++c) { SaveWrapper(*i,"",os,indent+1,"key","value"); } Indent(os,indent); } inline static void load(std::map &m, const XMLTagInfo &info, std::istream &is) { m.clear(); XMLTagInfo eleminfo; int i=0; while(1) { ReadTag(is,eleminfo); if (eleminfo.isend && !eleminfo.isstart) { if (eleminfo.name == namestr()) return; throw streamexception(std::string("Stream Input Format Error: expected end tag for ")+namestr()+", received end tag for "+eleminfo.name); } std::pair elem; // not as efficient as I would like TypeInfo >::load(elem,eleminfo,is,"key","value"); m.insert(elem); // copying elements here (C++0x might help) } } }; // how to save an item where G is a "getter" for value,type,name template struct SaveItem { template inline static void apply(T o,std::ostream &os,int indent) { SaveWrapper(G::getvalue(o),G::getname(o),os,indent); } }; template struct LoadItem { template inline static bool apply(T o,std::istream &is, const XMLTagInfo &info) { std::map::const_iterator ni =info.attr.find("name"); if (ni!=info.attr.end() || ni->second != G::getname(o)) return false; LoadWrapper(G::getvalue(o),info,is); return true; } }; template struct LoadList { template inline static void exec(O o, std::istream &is, const char *cname) { XMLTagInfo info; std::set loadedmem; while(1) { ReadTag(is,info); if (info.isend && !info.isstart) { if (info.name != cname) throw streamexception(std::string("Stream Input Format Error: expected end tag for ")+cname+", received end tag for "+info.name); if (!AllLoaded::exec(o,loadedmem)) throw streamexception(std::string("Stream Input Format Error: not all fields present for ")+cname); return; } if (LoadOne::exec(o,is,info)) loadedmem.insert(info.attr["name"]); else throw streamexception(std::string("Extra field ")+info.attr["name"]+" of type "+info.name+" in object "+cname); } } }; /* Here starts "END" macros (only 1, actually) */ #define SERIAL_END \ serial__tmp__alllist; \ public: \ typedef serial__tmp__alllist serial__alllist; \ typedef serial__vallocT serial__valloc; \ inline void serial__Save(std::ostream &os, int indent=0) const { \ SERIALNAMESPACE::SaveItt::exec(this,os,indent); } \ inline void Save(std::ostream &os, const char *name="", \ int indent=0) const { \ SERIALNAMESPACE::SaveWrapper(*this,name,os,indent); \ } \ inline void serial__Load(std::istream &is) { \ SERIALNAMESPACE::LoadList::exec \ (this,is,serial_IDname()); \ } \ inline void Load(std::istream &is) { \ SERIALNAMESPACE::LoadWrapper(*this,is); \ } \ inline static SERIAL_BASETYPE *LoadPtr(std::istream &is) { \ SERIAL_BASETYPE *ret; \ SERIALNAMESPACE::LoadWrapper(ret,is); \ return ret; \ } } #endif ctbnrle.1.1.1/hdr/rvsimple.tcc0000664000175000017500000001435712107032422016502 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "rvsimple.h" #if 0 #ifndef CTBNRLE_RVSIMPLE_TCC #define CTBNRLE_RVSIMPLE_TCC // implementation of RVCondSimpleComp template #include "rvsimple.h" #include SOBJCLASSDEFTEMPL1(RVCondSimpleComp,RVS) template RVCondSimpleComp::RVCondSimpleComp(int n, int nc) : RVCondSimple(), impl(nc,RVS(n)){ } template RVCondSimpleComp::RVCondSimpleComp(std::istream &is) : RVCondSimple(is) { int nc; is >> nc; impl.resize(nc); for(int i=0;i RVCondSimpleComp::~RVCondSimpleComp() {} template void RVCondSimpleComp::LoadOld(std::istream &is) { RVCondSimple::LoadOld(is); int nc; is >> nc; impl.resize(nc,RVS(0)); for(int i=0;i void RVCondSimpleComp::SaveOld(std::ostream &os) const { RVCondSimple::SaveOld(os); int nc = impl.size(); os << os.fill() << nc; for(int i=0;i RVCondSimpleComp *RVCondSimpleComp::Clone() const { return new RVCondSimpleComp(*this); } template double RVCondSimpleComp::Prob(int ind, int cind, bool log) const { return impl[cind].Prob(ind,log); } template void RVCondSimpleComp::Normalize() { int nc = impl.size(); for(int i=0;i RVS *RVCondSimpleComp::Condition(int ind) const { return impl[ind].Clone(); } template void RVCondSimpleComp::Restrict(const std::vector &ind, const std::vector &cind) { int nc = impl.size(); std::vector blank; unsigned int k=0; for(int i=0;i void RVCondSimpleComp::Reindex(int n, int nc, const RVCondSimple::RemapT &ind) { std::vector newimpl(nc,RVS(n)); for (RVCondSimple::RemapT::const_iterator i=ind.begin();i!=ind.end();++i) { int newci = i->first.first; int oldci = i->first.second; RVS cpy(impl[oldci]); cpy.Reindex(i->second); newimpl[newci].Add(&cpy); } impl = newimpl; } template void RVCondSimpleComp::MultBy(const RVCondSimple *x) { const RVCondSimpleComp *xx = dynamic_cast *>(x); int nc = impl.size(); for(int i=0;iimpl[i])); } template SS *RVCondSimpleComp::BlankSS() const { int nc = impl.size(); RVCSCompSS *ret = new RVCSCompSS(nc); for(int i=0;iss[i] = impl[i].BlankSS(); return ret; } template void RVCondSimpleComp::AddSS(int cind, int ind, SS *ss, double w) const { RVCSCompSS *rss = dynamic_cast(ss); impl[cind].AddSS(ind,rss->ss[cind],w); } template void RVCondSimpleComp::AddExpSS(int cind, SS *ss, double w) const { RVCSCompSS *rss = dynamic_cast(ss); impl[cind].AddExpSS(rss->ss[cind],w); } template void RVCondSimpleComp::AddExpSS(int cind, const RVSimple *rvs, SS *ss, double w) const { RVCSCompSS *rss = dynamic_cast(ss); rvs->AddExpSS(rss->ss[cind],w); } template void RVCondSimpleComp::AddSS(const SS *toadd, const RVCondSimple *rv, const std::vector > &mapping, int mycondi, int rvccondi, SS *ss, double w) const { const RVCondSimpleComp *rvcsc = dynamic_cast *>(rv); const RVCSCompSS* toaddrvcss = dynamic_cast(toadd); RVCSCompSS* rvcss = dynamic_cast(ss); impl[mycondi].AddSS(toaddrvcss->ss[rvccondi], &(rvcsc->impl[rvccondi]), mapping,rvcss->ss[mycondi]); } template int RVCondSimpleComp::Sample(int cx, Random &rand) const { return impl[cx].Sample(rand); } template void RVCondSimpleComp::Maximize(const SS *ss) { const RVCSCompSS *sss = dynamic_cast(ss); int nc = impl.size(); for(int i=0;iss[i]); } template void RVCondSimpleComp::Mult(RVSimple *&x) const { int nc = impl.size(); RVSimple *ret = impl[0].Clone(); ret->Mult(x->Prob(0)); for(int i=1;iAdd(&(impl[i]),x->Prob(i)); delete x; x = ret; } template void RVCondSimpleComp::RMult(RVSimple *&x) const { assert(0); // not easily doable (and probably not important) } template RVSimple *RVCondSimpleComp::Convert(const RVSimple *rvs) const { // can't really multiply anyway... so we'll just call Clone return rvs->Clone(); } template void RVCondSimpleComp::Scramble(double alpha, double degree, Random &rand) { int nc = impl.size(); for(int i=0; i double RVCondSimpleComp::LLH(const SS *ss) const { double llh = 0.0; const RVCSCompSS *sss = dynamic_cast(ss); int nc = impl.size(); for(int i=0; iss[i]); return llh; } template double RVCondSimpleComp::GetScore(double numTrans, const SS* ss) const { double localScore(0); const RVCSCompSS *rvss = dynamic_cast (ss); int nc = impl.size(); for(int i = 0; i < nc; i++) localScore += impl[i].GetScore(numTrans/nc, rvss->ss[i]); return localScore; } #endif #endif ctbnrle.1.1.1/hdr/samplinginf.h0000664000175000017500000001175712107032422016627 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_SAMPLINGINF_H #define CTBNRLE_SAMPLINGINF_H #include "inference.h" #include "sampler.h" #include "trajectory.h" #include "context.h" #include "markov.h" #include "trajectory.h" #include "ss.h" #include namespace ctbn { class SamplingInfAbs : public Inference { public: SamplingInfAbs(); virtual ~SamplingInfAbs(); virtual SamplingInfAbs *Clone() const=0; //The caller of the function owns the pointers // (but they must remain valid for the lifetime of the object) virtual void SetProcess(const Process *pr); virtual void SetTrajectory(const Trajectory *tr); virtual void SetSampler(const Sampler *s); void SetNumSample(int n); void SetTimeLimit(double t, int batchsize=10); virtual double Filter(const Instantiation &x, double t, bool log=false) = 0; virtual double Smooth(const Instantiation &x, double t, bool log=false) = 0; virtual double Prob(double t, bool log=false) = 0; //the caller of the function own the pointer virtual void AddExpSuffStats(SS *ss, double w=1.0) = 0; //The caller of the function own the pointers virtual void AddExpSuffStats(const Dynamics *dyn, SS *ss, double w=1.0) = 0; //The caller of the function own the pointers virtual void AddExpSuffStats(const RV *p0, SS *ss, double w=1.0) = 0; //double LLH(const std::vector &tr, // const std::vector &w) const; double CalcQuery(QueryCalculator &calc) = 0; protected: virtual void Sample(); virtual void AddSamples(int nsamp, bool all) = 0; virtual void Reset() = 0; static double NormalizeSet(std::vector &w); const Markov *p; //the class does not own this pointer Sampler *sampler; //SamplingInfAbs own this pointer int numofsamples; double timelimit; int bsize; // # of traj to grab at a time when time limited }; // See inference.h for a description of the methods class SamplingInf : public SamplingInfAbs { public: SamplingInf(); virtual ~SamplingInf(); virtual SamplingInf *Clone() const; virtual double Filter(const Instantiation &x, double t, bool log=false); virtual double Smooth(const Instantiation &x, double t, bool log=false); virtual double Prob(double t, bool log=false); //the caller of the function own the pointer virtual void AddExpSuffStats(SS *ss, double w=1.0); //The caller of the function own the pointers virtual void AddExpSuffStats(const Dynamics *dyn, SS *ss, double w=1.0); //The caller of the function own the pointers virtual void AddExpSuffStats(const RV *p0, SS *ss, double w=1.0); //double LLH(const std::vector &tr, // const std::vector &w) const; double CalcQuery(QueryCalculator &calc); protected: virtual void Sample(); virtual void AddSamples(int nsamp, bool all); virtual void Reset(); std::vector traj; std::vector weights; }; // Same as above, but the queries to be asked are known in // advance, so the samples need not be stored. class SamplingPreInf : public SamplingInfAbs { public: // these pointers need to stay valid for the life of // this object. Further, references to the exact same queries must be // used in CalcQuery. SamplingPreInf(const std::vector &queries, int workingsetsize=1000); virtual ~SamplingPreInf(); virtual SamplingPreInf *Clone() const; virtual double Filter(const Instantiation &x, double t, bool log=false); virtual double Smooth(const Instantiation &x, double t, bool log=false); virtual double Prob(double t, bool log=false); //the caller of the function own the pointer virtual void AddExpSuffStats(SS *ss, double w=1.0); //The caller of the function own the pointers virtual void AddExpSuffStats(const Dynamics *dyn, SS *ss, double w=1.0); //The caller of the function own the pointers virtual void AddExpSuffStats(const RV *p0, SS *ss, double w=1.0); //double LLH(const std::vector &tr, // const std::vector &w) const; double CalcQuery(QueryCalculator &calc); protected: virtual void AddSamples(int nsamp, bool all); virtual void Reset(); bool sampled; double maxlnw,ttlw; int setsize; std::vector qs; std::vector ans; }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/em.h0000664000175000017500000000300012107032422014677 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_EM_H #define CTBNRLE_EM_H #include "inference.h" #include "process.h" #include "trajectory.h" #include "structure.h" // All of the pointers in these functions are owned by the caller namespace ctbn { // Perform Expectation-Maximization with the data data // on the Process dist with inference method inf void EM(const std::vector &data, Process *dist, Inference *inf); // Perform Structural Expectation-Maximization // In this case, dist may need to be changed in which case the original // one will be "deleted" (freed) and replaced with a new one void SEM(const std::vector &data, Process *&dist, Inference *inf); } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/streamserial.h0000664000175000017500000000173212107032422017003 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_STREAMSERIAL_H #define CTBNRLE_STREAMSERIAL_H #include "serial/serial.h" #include "streamserial-old.h" #include "streamextra.h" #endif ctbnrle.1.1.1/hdr/varsample.h0000664000175000017500000000455212107032422016305 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_VARSAMPLE_H #define CTBNRLE_VARSAMPLE_H #include "dynamics.h" #include "context.h" #include "trajectory.h" namespace ctbn { //VarSample is abstract class to define sample method //for Markov process, including how to sample the transition //time, how to sample the next state and the corresponding //sampling weights. //*weights are all in log scale!* class VarSample{ public: VarSample(); virtual ~VarSample(); //sample the next transition time for virtual double SampleTime(const matrix &Q, int id, int val, const Instantiation &currinst, double currt, const Trajectory *evid, double te, int e, Random &rand=randomizer) const = 0; //sample the next state //and update the corresponding weight contribution virtual int SampleTransition(const matrix &Q, int id, int val, const Instantiation &inst, double currt, const Trajectory *evid, double te, int e, double &weight, Random &rand=randomizer) const = 0; //calculate the weight contribution of sampling time virtual double TimeWeight(const matrix &Q, const Instantiation &currinst, int id, int transitionid, int val, double currt, double deltat, const Trajectory *evid, double te, int e) const = 0; //calculate the weight contribution of sampling next state /* virtual double TransitionWeight(const matrix &Q, int transitionid, int val, const Instantiation &currinst, const Instantiation &nextinst, double currt, double deltat, const Trajectory *evid, double te, int e, bool logscale = 1) const = 0; */ }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/utils.h0000664000175000017500000000430412107032422015446 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_UTILS_H #define CTBNRLE_UTILS_H #include #include #include #include // A set of useful utility functions namespace ctbn { class Trajectory; class Context; bool IsTrajSame(const Trajectory &tr1, const Trajectory &tr2, const Context &c); bool IsTrajValid(const Trajectory &tr, const Context &c); void RemoveInformation(Trajectory &tr, const Context &c, int var, double start, double end); void RemoveInformation(Trajectory &tr, const Context &c, int nvar, int nit, double frac); void RemoveNodesInformation(Trajectory &tr, const Context &c, int nvar, int nit, double frac); void SelectNodesInformation(Trajectory &tr, const Context &c, int nvar, double thresh, double frac); double SelectTime(std::priority_queue &select, double ratio, double begint, double endt, double thresh ); double getcputime(void); void Normalize(std::vector &logw); double mean(const std::vector &v); double relative_bias(const std::vector &v, double vtrue); double relative_std(const std::vector &v, double vtrue); bool LoadNetworkTraj(std::istream &in, std::vector &results); bool SplitPortTraj(const std::vector &whole, std::vector > &splitted); std::vector PartitionTraj(const Trajectory &whole); Trajectory LoadTraj(std::ifstream &in); } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/defines.h0000664000175000017500000000632712107032422015732 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_DEFINES_H #define CTBNRLE_DEFINES_H /* This file describes changes that need to be made for compilation * on different platforms. */ #include #include #include #include #include #include typedef long long int longlong; #if defined __linux__ || __CYGWIN__ // Linux/Cygwin section... #define TIME_STRUCT longlong #define GET_TIME GetLinuxTime() TIME_STRUCT GET_TIME; #elif defined _WIN32 // Windows section... #include #ifndef NOMINMAX #define NOMINMAX #endif #define WIN32_LEAN_AND_MEAN #include #ifdef UNION #undef UNION #endif #ifdef INTERSECTION #undef INTERSECTION #endif #ifdef DIFFERENCE #undef DIFFERENCE #endif #define TIME_STRUCT longlong #define GET_TIME GetWindowsTime() /* #define TIME_STRUCT longlong #define GET_TIME GetTime() */ TIME_STRUCT GET_TIME; #else // Mac OS section... // #define TIME_STRUCT hrtime_t // #define GET_TIME gethrtime() // for mac os x #include #define GET_TIME mach_absolute_time() #endif #if defined(_WIN32) // more Windows section... # ifndef finite # define finite(x) (_finite (x)) # endif # ifndef isfinite # define isfinite(x) (_finite (x)) # endif # ifndef isnan # define isnan(d) (_isnan(d)) # endif # ifndef __func__ # define __func__ __FUNCTION__ # endif # ifndef isinf //# define isinf(x) (!_finite(x) && !_isnan(x)) # inline bool isinf(double x) { return !_finite(x) && !_isnan(x); } # endif # ifndef isinfinite # inline bool isinfinite(double x) { return isinf(x); } # endif # ifndef INFINITY # define INFINITY (std::numeric_limits::infinity()) # endif extern const unsigned long win32nan[2]; double nan(const char *); float nanf(const char *); long double nanl(const char *); #else // non-Windows solution to isfinite being C99 and not C++ // should be changed when C++0x is adopted # include # ifdef __INTEL_COMPILER # include inline bool isinfinite(double x) { return isinf(x); } inline bool isfinite(double x) { return !isinf(x) && !isnan(x); } inline bool finite(double x) { return !isinf(x); } # else inline bool isinfinite(double x) { return std::isinf(x); } inline bool isfinite(double x) { return !std::isinf(x) && !std::isnan(x); } # endif #endif #endif // _DEFINES_H ctbnrle.1.1.1/hdr/contfunction.tcc0000664000175000017500000001620012107032422017337 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #include "contfunction.h" #include #include #include namespace ctbn { using namespace std; template ContFunction::ContFunction() : start(0), end(0), empty(true) { } template ContFunction::~ContFunction() { Clear(); } /* template ContFunction::ContFunction(const ContFunction &rhs) : start(rhs.start), end(rhs.end), empty(rhs.empty) { values.clear(); typename map::const_iterator it = rhs.values.begin(); for(; it != rhs.values.end(); ++it) { AddVal(it->first,(it->second)); } } */ template VALUE ContFunction::GetVal(double t) const { if(empty) { cerr << "no values initialized" << endl; exit(0); } if(start == end) if(fabs(t-start) < 1e-9) return values.begin()->second; if(fabs(t-start) < 1e-9) return values.begin()->second; if(fabs(t-end) < 1e-9) { typename map::const_iterator it = values.end(); it--; return it->second; } if(t < start || t > end) { cerr << "t=" << t << endl; cerr << "start=" << start << endl; cerr << "end=" << end << endl; cerr << "time out of bounds" << endl; cin.get(); exit(0); } /* typename map::const_iterator it = values.find(t); if(it != values.end()) return it->second; it = values.begin(); while(it != values.end() && t > it->first) ++it; */ typename map::const_iterator it = values.lower_bound(t); typename map::const_iterator ite = it; --it; //VALUE *vs = new VALUE(*(it->second)); //VALUE *ve = new VALUE(*(ite->second)); double ts = it->first; double te = ite->first; double c = (t-ts) / (te-ts); return ite->second*c + it->second*(1-c);//*ve - *vs; //ret *= c; //ret += *vs; //delete vs, //delete ve; //return ret; } template VALUE ContFunction::GetDeriv(double t) { if(empty) { cerr << "no values initialized" << endl; exit(0); } else if(start == end || fabs(t-start) < 1e-9 || fabs(t-end) < 1e-9) { cout << "Not differentiable." << endl; exit(0); } else if(t < start || t > end) { cerr << "t=" << t << endl; cerr << "start=" << start << endl; cerr << "end=" << end << endl; cerr << "time out of bounds" << endl; cin.get(); exit(0); } /* typename map::const_iterator it = values.find(t); if(it != values.end()) { cout << "Not differentiable." << endl; exit(0); } it = values.begin(); while(it != values.end() && t > it->first) ++it; */ typename map::const_iterator it = values.lower_bound(t); typename map::const_iterator ite = it; --it; return (ite->second - it->second)/(ite->first-it->first); /* VALUE *vs = new VALUE(*(it->second)); VALUE *ve = new VALUE(*(ite->second)); double ts = it->first; double te = ite->first; VALUE ret = *ve - *vs; ret /= te - ts; delete vs, delete ve; return ret; */ } template VALUE ContFunction::GetIntercept(double t) { if(empty) { cerr << "no values initialized" << endl; exit(0); } else if(start == end || fabs(t-start) < 1e-9 || fabs(t-end) < 1e-9) { cout << "No intercept." << endl; exit(0); } else if(t < start || t > end) { cerr << "t=" << t << endl; cerr << "start=" << start << endl; cerr << "end=" << end << endl; cerr << "time out of bounds" << endl; cin.get(); exit(0); } typename map::const_iterator it = values.lower_bound(t); typename map::const_iterator ite = it; --it; double den = ite->first - it->first; return it->second * (ite->first/den) + ite->second * (-it->first/den); /* VALUE m = GetDeriv(t); typename map::const_iterator it = values.find(t); if(it != values.end()) { cout << "No intercept." << endl; exit(0); } it = values.begin(); while(it != values.end() && t > it->first) ++it; it--; VALUE *vs = new VALUE(*(it->second)); double ts = it->first; m *= ts; VALUE ret = *vs - m; delete vs; return ret; */ } template vector ContFunction::GetCutPoints() const { vector ret; typename map::const_iterator it = values.begin(); for(; it != values.end(); ++it) ret.push_back(it->first); return ret; } template void ContFunction::Print() const { cout << "start: " << start << endl; cout << "end: " << end << endl; typename map::const_iterator it = values.begin(); for(; it != values.end(); ++it) cout << it->first << "\t" << it->second << endl; cout << endl; } template void ContFunction::AddVal(double t, const VALUE& val) { if(values.find(t) == values.end()) { values.insert(make_pair(t,val)); if(empty) { start = t; end = t; empty = false; } if(t < start) start = t; if(t > end) end = t; } } template void ContFunction::Combine(const ContFunction &fun) { typename map::const_iterator it = fun.values.begin(); for(; it!=fun.values.end(); it++) AddVal(it->first,it->second); } template void ContFunction::EraseRange(double t0, double t1) { //typename map::iterator it = values.begin(); //while(it->first < t0) it++; typename map::iterator it = values.lower_bound(t0); //typename map::iterator itend = it; //while(itend->first < t1 && itend != values.end()) itend++; typename map::iterator itend = values.lower_bound(t1+1e-12); //while(itend != values.end() && abs(itend->first - t1) < 1e-12) itend++; values.erase(it,itend); /* vector times; for(;it != itend; it++) times.push_back(it->first); for(unsigned int i=0; i void ContFunction::EraseVal(double t) { values.erase(t); /* typename map::iterator it = values.find(t); if(it != values.end()) { //delete it->second; values.erase(it); } */ } template void ContFunction::Replace(const ContFunction &fun) { EraseRange(fun.GetStart(),fun.GetEnd()); Combine(fun); } template void ContFunction::Clear() { /* typename map::iterator it = values.begin(); for(; it != values.end(); ++it) { if(it->second) { delete it->second; it->second = NULL; } } */ values.clear(); start = 0; end = 0; empty = true; } } // end of ctbn namespace ctbnrle.1.1.1/hdr/dynsimple.h0000664000175000017500000001206712107032422016317 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_DYNSIMPLE_H #define CTBNRLE_DYNSIMPLE_H #include "random.h" #include "ss.h" #include "rvsimple.h" #include #include namespace ctbn { // DynSimple is the ABC for continuous-time semi-Markov dynamics // Just like RVSimple, its domain is implied // So this is an abstract representation of a semi-Markov process // dynamics over the state space 0..n-1 with no conditioning class DynSimple : public StreamObj { public: DynSimple(); DynSimple(std::istream &is); virtual ~DynSimple(); virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; // virtual copy constructor virtual DynSimple *Clone() const = 0; // virtual blank constructor virtual DynSimple *MakeNew(int nstates) const = 0; // Conditions the system being closed virtual void Normalize() = 0; // Sets to zero all probability of leaving the set of ind virtual void Restrict(const std::vector &ind) = 0; // Changes the indexing. // newn is the new number of states // (let n be the old number of states) // ind is a list of size n // each element of ind is a list of size s (s is a constant) // each of these sublists is a set of states that were // indistinguishable from the original dynamics point of view virtual void Expand(const std::vector > &ind, int newn) = 0; // Returns the conditional distribution of going from t0 to t1 virtual RVCondSimple *Cond(double t0, double t1) const = 0; // same, but with the dynamics constrainted to the subsystem // described by ind virtual RVCondSimple *CondRestrict(double t0, double t1, const std::vector &ind) const = 0; // Returns the density of instantaneous transitioning virtual RVCondSimple *Cond(double t) const = 0; // Same, but constrained (see same method in Dynamics) virtual RVCondSimple *CondRestrict(double t, const std::vector &fromind, const std::vector &toind, bool transition=true) const = 0; // returns a suitable sufficient statistics object virtual SS *BlankSS() const = 0; // the amalgamation operation... assumes that indices // already agree... virtual void Mult(const DynSimple *x) = 0; // add interval to ss virtual void AddSS(int x, double t0, double t1, SS *ss, double w=1.0) const = 0; // add transition to ss virtual void AddTransSS(int x1, int x2, double t, SS *ss, double w=1.0) const = 0; // adds expected ss (assumes that alpha and beta have the // same domain and that domain is the one to which the data // restricts transitions) virtual void AddExpSS(const RVSimple *alpha, const RVSimple *beta, double t0, double deltat, SS *ss, double w=1.0) const = 0; // adds a transition from x1 to x2 virtual void AddExpTransSS(const RVSimple *x1, const RVSimple *x2, double t, SS *ss, double w=1.0) const = 0; // Add the sufficient statistics in "toadd" to those in "ss" // However, using "mapping" to map state-indexes for toadd to // those to ss. "ss" should be for "this" while "toadd" should be // for "dyn." Currently dyn needs to be of type MarkovSimple. // mapping[i] is a list of the states from toadd/dyn that correspond // to state i for ss/this. // [The code might also clarify -- see .cc file.] virtual void AddSS(const SS *toadd, const DynSimple *dyn, const std::vector > &mapping, SS *ss, double w=1.0) const = 0; // sample the next event - time: newt, state index: newind virtual void SampleNextEvent(int ind, double t, double &newt, int &newind, Random &rand=randomizer) const = 0; // sets parameters from ML estimate virtual void Maximize(const SS *ss) = 0; // Randomize the parameters virtual void Scramble(double a=1.0, double b=1.0, double alpha=1.0, double degree=1.0, Random &rand=randomizer) = 0; // calculate the log-likelihood of the sufficient SS virtual double LLH(const SS *ss) const = 0; // Returns the structure search score where parentCard is // the number of parents this node has (this object represents // the process for just one parent instantiation, presumably) // numTrans and amtTime are the BDe prior parameters virtual double GetScore(int parentCard, double numTrans, double amtTime, const SS* ss) const = 0; SERIAL_START_V(DynSimple) SERIAL_END }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/expsuffstatsquery.h0000664000175000017500000000417612107032422020142 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_EXPSUFFSTATSQUERY_H #define CTBNRLE_EXPSUFFSTATSQUERY_H // A class for performing queries for sufficient statistics where we have // incomplete trajectories. #include "suffstatsquery.h" #include "trajectory.h" #include "process.h" #include "inference.h" #include namespace ctbn { class ExpSuffStatsQuery : public SuffStatsQuery { public: // Expected sufficient statistics need a process for its expectation, // along with an inference method. The pointer of the inference // object is owned by the caller. ExpSuffStatsQuery(Inference* i, bool cloneInf = false); virtual ~ExpSuffStatsQuery(); // In the event of a high number of queries with the same process // and data, caching the inference objects eliminates the need to // set the same trajectories multiple times. void InitInfCache(); // changes the process (associated with the Inference object // passed into the constructor) from which the expectaction is to // be taken // (SetInitRV and SetDynamics from SuffStatsQuery change the // process who sufficient statistics are taken) void SetProcess(const Process* p); // Calculate and return sufficient statistics. virtual SS* GetP0SS(); virtual SS* GetDSS(); protected: Inference* inf; std::vector infCache; bool useInfCache; }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/ensurectbn.h0000664000175000017500000000230012107032422016450 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef ENSURECTBN_H #define ENSURECTBN_H #include "ctbn.h" #include "bn.h" #include "markov.h" #include "multirv.h" #include "ctbn.h" namespace ctbn { ENSURECLASS(CTBN) ENSURECLASS(BN) ENSURECLASS(Markov) ENSURECLASS(CTBNDyn) ENSURECLASS(MultiRV) ENSURECLASS(MarkovDyn) ENSURECLASS(MarkovSimple) ENSURECLASS(MarkovSimpleToggle) ENSURECLASS1(RVCondSimpleComp,MultiZSimple) } #endif ctbnrle.1.1.1/hdr/exactmarkovinf.h0000664000175000017500000000670312107032422017334 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_EXACTMARKOVINF_H #define CTBNRLE_EXACTMARKOVINF_H #include "fbinf.h" #include "markov.h" #include "ctbndyn.h" #include "dyncomp.h" #include "bn.h" #include "rvcomp.h" #include "multisimple.h" #include "inference.h" #include namespace ctbn { // This class performs exact inference by using the conditional distributions // supplied by the process's (assumed to be a Markov process) dynamics's // method "Cond." For compact structures (like a CTBN) this will probably // require generation of the full "flattened" dynamics which is exponentially // (in the number of variables) large. However, for some processes (like a // fully factored process), it might be possible to return a conditional // object that can represent this more compactly. // // The dynamics's "cond" methods should return RVCondSimple objects that // are compatible with the RVSimple object's "MakeSimple" method (in that // the RVCondSimple can accept the output of the MakeSimple method). class ExactMarkovInf : public FBInf { public: ExactMarkovInf(); virtual ~ExactMarkovInf() throw(); virtual ExactMarkovInf * Clone() const; virtual void Restrict(RVSimple *, Instantiation const & variable_assignment) const; virtual RVSimple * MakeSimple(RV * prior, Instantiation const &, bool normalize) const; virtual bool IsAlphaElementValid(RVSimple * const alpha_sub_x); virtual RVCondSimple * IntervalPropagator(Dynamics * const the_markov_process_dynamics, double start_time_point, double past_end_time_point, Instantiation const & current_evidence) const; virtual RVCondSimple * PointTransitionPropagator(Dynamics * const the_markov_process_dynamics, double time_point, Instantiation const & prior_evidence, Instantiation const & current_evidence) const; virtual RVCondSimple * PointChangeEvidencePropagator(Dynamics * const the_markov_process_dynamics, double time_point, Instantiation const & prior_evidence, Instantiation const & current_evidence) const; virtual void AddExpectedSufficientStatistics(Dynamics const * const dynamics, unsigned int const trajectory_index, SS * const sufficient_statistics, double const weight = 1.0) const; virtual void AddExpectedTransitionSufficientStatistics(Dynamics const * const dynamics, unsigned int const trajectory_index, SS * const sufficient_statistics, double const weight = 1.0) const; virtual void AddExpectedInitialSufficientStatistics(Trajectory const * const trajectory, RVSimple * initial_prob_dist, const RV * ssrv, SS * const sufficient_statistics, double const weight = 1.0) const; private: SparseMultiZSimple * dynamic_cast_to_SparseMultiZSimple(RVSimple * const ptr); }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/multisimple.h0000664000175000017500000002233712107032422016660 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_MULTISIMPLE_H #define CTBNRLE_MULTISIMPLE_H #include "rvsimple.h" #include "matrix.h" namespace ctbn { // Four different versions of a RVSimple (see rvsimple.h) // Each one stores the multinomial distribution differently // However, all assume a general multinomial form (no parameter tieing) class MultiSimple; class MultiLogSimple; class MultiZSimple; class SparseMultiZSimple; // This is the Sufficient Statistics for any of the RVSimple // subclasses in this file class MultiSimpleSS : public SS { SOBJCLASSDECL(MultiSimpleSS) public: friend class MultiSimple; friend class MultiLogSimple; friend class MultiZSimple; friend class SparseMultiZSimple; friend class RVSimple; public: MultiSimpleSS(int n=1); MultiSimpleSS(std::istream &is); MultiSimpleSS(const MultiSimpleSS &msss); virtual ~MultiSimpleSS(); virtual MultiSimpleSS *Clone() const; virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; virtual void Scale(double w); virtual void AddSS(const SS* nss, double w=1.0); private: vectr c; // vector of counts SERIAL_START_V(MultiSimpleSS) SERIAL_SUPER(SS) SERIAL_VAR(vectr,c) SERIAL_END }; // This class stores the probabilties directly (as theta, below) class MultiSimple : public RVSimple { SOBJCLASSDECL(MultiSimple) public: MultiSimple(int n=1); MultiSimple(std::istream &is); virtual ~MultiSimple(); virtual MultiSimple *Clone() const; virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; virtual double Normalize(); virtual double Prob(int ind, bool log=false) const; virtual double Sum(bool log=false) const; virtual void Restrict(const std::vector &ind); virtual void Reindex(const std::vector > &ind); virtual void MultBy(const RVSimple *x); virtual void Add(const RVSimple *x); virtual void Add(const RVSimple *x, double w); virtual void Mult(double x); virtual MultiSimpleSS *BlankSS() const; virtual void AddSS(int x, SS *ss, double w=1.0) const; virtual void AddExpSS(SS *ss, double w=1.0) const; virtual void AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w=1.0) const; virtual int Sample(Random &rand = randomizer) const; virtual void Maximize(const SS *ss); virtual void GetDist(vectr &d, double &logfactor) const; virtual void SetDist(const vectr &d, double logfactor=0.0); virtual void MakeUniform(); virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand = randomizer); virtual double LLH(const SS *ss) const; protected: vectr theta; // parameters of the multinomial distribution SERIAL_START_V(MultiSimple) SERIAL_SUPER(RVSimple) SERIAL_VAR(vectr,theta) SERIAL_END }; // same as above, but log of parameters is stored class MultiLogSimple : public RVSimple { SOBJCLASSDECL(MultiLogSimple) public: MultiLogSimple(int n=1); MultiLogSimple(std::istream &is); virtual ~MultiLogSimple(); virtual MultiLogSimple *Clone() const; virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; virtual double Normalize(); virtual double Prob(int ind, bool log=false) const; virtual double Sum(bool log=false) const; virtual void Restrict(const std::vector &ind); virtual void Reindex(const std::vector > &ind); virtual void MultBy(const RVSimple *x); virtual void Add(const RVSimple *x); virtual void Add(const RVSimple *x, double w); virtual void Mult(double x); virtual MultiSimpleSS *BlankSS() const; virtual void AddSS(int x, SS *ss, double w=1.0) const; virtual void AddExpSS(SS *ss, double w=1.0) const; virtual void AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w=1.0) const; virtual int Sample(Random &rand = randomizer) const; virtual void Maximize(const SS *ss); virtual void GetDist(vectr &d, double &logfactor) const; virtual void SetDist(const vectr &d, double logfactor=0.0); virtual void MakeUniform(); virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand = randomizer); virtual double LLH(const SS *ss) const; protected: vectr logtheta; // parameters of the multinomial distribution SERIAL_START_V(MultiLogSimple) SERIAL_SUPER(RVSimple) SERIAL_VAR(vectr,logtheta) SERIAL_END }; // Same as MultiSimple, but normalization constant is kept separately // (in log-space) class MultiZSimple : public RVSimple { SOBJCLASSDECL(MultiZSimple) public: MultiZSimple(int n=1); MultiZSimple(const vectr &dist, double logfactor=0.0); MultiZSimple(std::istream &is); virtual ~MultiZSimple(); virtual MultiZSimple *Clone() const; virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; virtual double Normalize(); virtual double Prob(int ind, bool log=false) const; virtual double Sum(bool log=false) const; virtual void Restrict(const std::vector &ind); virtual void Reindex(const std::vector > &ind); virtual void MultBy(const RVSimple *x); virtual void Add(const RVSimple *x); virtual void Add(const RVSimple *x, double w); virtual void Mult(double x); virtual MultiSimpleSS *BlankSS() const; virtual void AddSS(int x, SS *ss, double w=1.0) const; virtual void AddExpSS(SS *ss, double w=1.0) const; virtual void AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w=1.0) const; virtual int Sample(Random &rand = randomizer) const; virtual void Maximize(const SS *ss); virtual void GetDist(vectr &d, double &logfactor) const; virtual void SetDist(const vectr &d, double logfactor=0.0); virtual void MakeUniform(); virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand = randomizer); virtual double LLH(const SS *ss) const; protected: void SetZ(); vectr theta; // parameters of the multinomial distribution double logz; // the normalizing factor: true theta = exp(logz)*theta SERIAL_START_V(MultiZSimple) SERIAL_SUPER(RVSimple) SERIAL_VAR(double,logz) SERIAL_VAR(vectr,theta) SERIAL_END }; // Same as MultiZSimple, but only a sparse number of non-zero elements // are kept class SparseMultiZSimple : public MultiZSimple { SOBJCLASSDECL(SparseMultiZSimple) public: SparseMultiZSimple(int n=1); // indexes need to be in sorted order SparseMultiZSimple(const vectr &dist, const std::vector &indexes, double logfactor=0.0); SparseMultiZSimple(std::istream &is); virtual ~SparseMultiZSimple(); virtual SparseMultiZSimple *Clone() const; virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; // These work as before: using MultiZSimple::Normalize; using MultiZSimple::Sum; using MultiZSimple::Mult; using MultiZSimple::MultBy; // this one is a bit dangerous // as it assumes the represented indices are the same using MultiZSimple::Add; // as is this one using MultiZSimple::BlankSS; //using MultiZSimple::AddSS; using MultiZSimple::MakeUniform; using MultiZSimple::Scramble; // note that this means that // scramble will not "unzero" zero elements (unlike Maximize) // These are modified: virtual double Prob(int ind, bool log=false) const; virtual void Restrict(const std::vector &ind); virtual void Reindex(const std::vector > &ind); virtual void AddSS(int x, SS *ss, double w=1.0) const; virtual void AddExpSS(SS *ss, double w=1.0) const; virtual void AddSS(const SS *toadd, const RVSimple *rvs, const std::vector > &mapping, SS *ss, double w=1.0) const; virtual int Sample(Random &rand = randomizer) const; virtual void Maximize(const SS *ss); virtual void GetDist(vectr &d, double &logfactor) const; virtual void SetDist(const vectr &d, double logfactor=0.0); //Busra virtual void GetSparseDist(vectr &d, vectr &ind, double &logfactor) const; virtual void SetSparseDist(const vectr &d, const vectr &ind, double logfactor=0.0); // virtual void SetSparseDist(const vectr &d, double logfactor=0.0); virtual void GetSparseDist(vectr &d, double &logfactor) const; virtual void SetSparseIndexes(const std::vector &ind); virtual double LLH(const SS *ss) const; inline const std::vector &Indexes() const { return ix; } inline double LogFactor() const { return logz; } inline const vectr &Dist() const { return theta; } protected: std::vector ix; SERIAL_START_V(SparseMultiZSimple) SERIAL_SUPER(MultiZSimple) SERIAL_VAR(std::vector,ix) SERIAL_END }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/rv.h0000664000175000017500000001103112107032422014730 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_RV_H #define CTBNRLE_RV_H #include "context.h" #include "trajectory.h" #include "random.h" #include "streamserial.h" namespace ctbn { class RVSimple; class SS; // RV is a random variable. It can be conditional and joint // It keeps track of its context class RV : public StreamObj { public: // var = varaibles, cvar = conditioning variables RV(const Context &var, const Context &cvar); RV(std::istream &is); virtual ~RV(); virtual void LoadOld(std::istream &is); virtual void SaveOld(std::ostream &os) const; // virtual copy constructor virtual RV *Clone() const = 0; const Context &Domain() const { return v; } const Context &CondDomain() const { return cv; } // makes sum to 1 for each conditional instantiation virtual void Normalize() = 0; // returns the probability (or log thereof) virtual double Prob(const Instantiation &x, bool log=false) const = 0; // Sets to zero all probabilities that are not consistent with v // (followed by a normalize, this is the same as conditioning // except that it doesn't change the domain of the variable) // (if again followed by a Project, then you have conditioning) virtual void Restrict(const Instantiation &x) = 0; // Marginalizes to c // ids not mentioned in c are marginalized out // can also be used to transfer ids to/from conditioning virtual void Project(const Context &c, const Context &cc) { v = c; cv = cc; } // Multiplies by another measure (point-wise) // Should expand context to include union virtual void MultBy(const RV *x) = 0; // returns a suitable sufficient statistics object virtual SS *BlankSS() const =0; // adds the instantiation to ss virtual void AddSS(const Instantiation &x, SS *ss, double w=1.0) const = 0; // adds the expected sufficient statistic to ss // (ie x can be incomplete -- but not in the conditioning variables virtual void AddExpSS(const Instantiation &x, SS *ss, double w=1.0) const = 0; // adds the distribution to the sufficient statistics // (x only gives the conditional context. rvs gives a distribution // over the domain of this variable) virtual void AddExpSS(const Instantiation &x, const RVSimple *rvs, SS *ss, double w=1.0) const = 0; //This adds the sufficient statistics of a different random variable //to this random variable's sufficient statistics virtual void AddSS(const SS* toadd, const RV *rv, SS *ss, double w=1.0) const = 0; // Completes the instantiation v with a conditional sample virtual void Sample(Instantiation &x, Random &rand = randomizer) const = 0; // sets parameters to ML estimate virtual void Maximize(const SS *ss) = 0; // returns an RVSimple object (owned by caller) that represents // the distribution conditioned on x (note that x must include // a complete assignment to all variables in the CondDomain() // context) without explicit reference to the domain // (normalize indicates whether the RV should be renormalized // -- in the case where x has assignments in Domain()) virtual RVSimple * MakeSimple(Instantiation const & x, bool normalize = true) const = 0; virtual void Scramble(double alpha=1.0, double degree=1.0, Random &rand=randomizer) = 0; // computes the log-likelihood from the sufficient statistics passed in virtual double LLH(const SS *ss) const = 0; // scores this variable (and the implied parent assignment given // by the conditional context) by the Bayesian structure score // with a uniform BDe prior with numTrans strength. virtual double GetScore(double numTrans, const SS *aa) const = 0; SS* SuffStats(const std::vector &tr, const std::vector &w) const; protected: Context v,cv; SERIAL_START_V(RV) SERIAL_VAR(Context,cv) SERIAL_VAR(Context,v) SERIAL_END }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/meanfieldinf.h0000664000175000017500000001732412107032422016735 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_MEANFIELDINF_H_ #define CTBNRLE_MEANFIELDINF_H_ #include "inference.h" #include "contfunction.h" #include "markov.h" #include namespace ctbn { // MeanFieldInf is an implementation of the paper: // Ido Cohn, Tal El-Hay, Nir Friedman, and Raz Kupferman (2009). "Mean Field // Variational Approximation for Continuous-Time Bayesian Networks." // This class takes in an evidence trajectory and a markov process. // It assumes that the process is defined with a BN as its initial // distribution and a CTBN for its dynamics. This also performs the mean // field method for the underlying BN after each backward integration before // integrating forward. // Sample usage: // // Markov ctbn; // Trajectory evidence; // MeanFieldInf inf; // inf.SetProcess(&ctbn); // inf.SetTrajectory(&evidence); // inf.SetEPS(desiredEPS); // (optional, default is 1e-5) // After the process and trajectory are set, the various inference functions // (Smooth, AddExpSuffStats, CalcQuery) may be called. For the first time any // one of them are called, the optimization procedure is also called and the // results are cached. Subsequent calls will then used these cached results // unless the process or evidence has changed. class MeanFieldInf : public Inference { public: MeanFieldInf(); virtual ~MeanFieldInf(); virtual MeanFieldInf *Clone() const; // See inference.h for ownership of p and tr in the following // functions virtual void SetProcess(const Process *p); virtual void SetTrajectory(const Trajectory *tr); virtual double Filter(const Instantiation &x, double t, bool log=false); virtual double Smooth(const Instantiation &x, double t, bool log=false); virtual double Prob(double t, bool log=false); virtual void AddExpSuffStats(SS *ss, double w=1.0); virtual void AddExpSuffStats(const Dynamics *dyn, SS *ss, double w=1.0); virtual void AddExpSuffStats(const RV *p0, SS *ss, double w=1.0); double CalcQuery(QueryCalculator &calc); // Sets the EPS for Runge-Kutta calls. Calls Reset(). virtual void SetEPS(double neweps); // Computes the gradient of the rho at t and the current rho value // Result is returned in rhoGrad virtual void CalcRhoGrad(int varid, double t, const vectr &rhoVal, vectr &rhoGrad); // Computes the gradient of the mu at t and the current mu value // Result is returned in muGrad virtual void CalcMuGrad(int varid, double t, const matrix &gammaVal, vectr &muGrad); // Computes gamma at t based on the current mu and rho values // Result is returned in gamma virtual void CalcGamma(int varid, double t, const vectr &muVal, const vectr &rhoVal, matrix &gamma); // Computes the integrand of the suffstats integral at t for // transitions based on the gammas and mus virtual double PointTransSuffStat(int varid, const Instantiation &x1, const Instantiation &x2, double t); // Calculates the integrand of the energy for variable varid at // time t virtual double CalcPointCompEnergy(int varid, double t); // These allow the external RK function to access and // update the temp functions as it integrates virtual void GetRhoTempVal(double t, vectr &rhoVal); virtual void GetMuTempVal(double t, vectr &muVal); virtual void GetGammaTempVal(double t, matrix &gammaVal); virtual void AddRhoTempVal(double t, const vectr &rhoVal); virtual void AddMuTempVal(double t, const vectr &muVal); virtual void AddGammaTempVal(double t, const matrix &gammaVal); // Print out the marginals and joints void PrintMu() const; void PrintGamma() const; // Print out the times that where the state is hidden void PrintHidden() const; int GetNumStates(int varid) const { const CTBNDyn *cdyn = dynamic_cast(p->GetDynamics()); int ret = cdyn->NodeByVar(varid)->Domain().Size(); return ret; } private: // Create a factored CTBN by randomly choosing one of // the intensity matrices for each variable virtual void TransToFactored(); // Performs forward-backward passes on each of the variables using // the factored process created by TransToFactored() to initialize // the mus and gammas virtual void Initialize(); // Performs the backward-forward ODE passes until convergence // (or specified KMAX steps[default is 30]) virtual void Optimize(); // Calculates the expected Q matrix based on the current states // of the system. // Specifying jvarid and xj makes this Calculate the conditional // expected rates virtual void CalcExpectQ(int varid, double t, matrix &expectQ, int jvarid = -1, int xj = -1, bool log = true); virtual void CalcPsi(int varid, double t, vectr &psi); // Calculates the energy for the specified varid double CalcCompEnergy(int varid); // Calculates the energy for the entire system double CalcEnergy(); // Resets the inference object to a state before any // initialization/optimization void Reset(); // Returns the state pair containing the states at the // instants before and after time t for a variable std::pair GetTrans(int varid, double t) const; // Given a variable, returns all the observed transition times of // its children mapped to the varid of its children, respectively std::map GetChildTransTimes(int varid, double start, double end) const; // Returns all of the observed transition time of a variable std::vector GetObservedTransTimes(int varid, double start, double end) const; // Perform mean-field for BNs at the start of the trajectory void CalcMuStart(int varid, const vectr &rho0, vectr &mu); // Calculates the probability of the system being in instantiation x // based on the marginals double GetProb(const Instantiation &x, double t, bool log=false); // Add a variable to maps storing the marginals, joints, and hidden // times void AddVar(int varid); // Adds values to the maps storing the marginals, joints, and hidden // times void AddMuVal(int varid, double t, const vectr &vals); void AddGammaVal(int varid, double t, const matrix &vals); void AddHiddenInterval(int varid, double start, double end); // Not owned by this class const Markov *p; const Trajectory *tr; // Owned by this class Markov *factored; // The solved ODEs are stored here std::map > mus; std::map > gammas; // Determines where backward-forward integration is needed typedef std::pair double_pair; std::map > hiddenIntervals; // Cache the transition times, used to keep the energy calculation // more stable by not integrating over a transition in the system, // where the function is not differentiable std::map > transTimes; // Used during the integration process ContFunction rhoTemp; ContFunction muTemp; ContFunction gammaTemp; // Flags to mark whether initialize/optimize have been executed bool initialized; bool optimized; double eps; }; } // end of ctbn namespace #endif ctbnrle.1.1.1/hdr/expmcache.h0000664000175000017500000000263212107032422016245 0ustar csheltoncshelton/* Continuous Time Bayesian Network Reasoning and Learning Engine * Copyright (C) 2009 The Regents of the University of California * * see docs/AUTHORS for contributor list * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see . */ #ifndef CTBNRLE_EXPMCACHE_H #define CTBNRLE_EXPMCACHE_H #include "contfunction.h" #include "matrix.h" #include namespace ctbn { class ExpMCache { public: ExpMCache(const matrix &Q, double maxt, double eps=1e-6); void vexpmt(vectr &a, double t) const; // not yet implemented: //double expmtv(vectr &b, double t) const; private: /* VERSION 1: // one for each dimension of vectr // element i is the result of e_i exp(Qt) // where e_i is a vector of all 0s except a 1 in position i std::vector > expm; */ /* VERSION 2: */ ContFunction expm; }; } #endif ctbnrle.1.1.1/hdr/Eigen/0000775000175000017500000000000012107032422015163 5ustar csheltoncsheltonctbnrle.1.1.1/hdr/Eigen/src/0000775000175000017500000000000012107032422015752 5ustar csheltoncsheltonctbnrle.1.1.1/hdr/Eigen/src/Cholesky/0000775000175000017500000000000012107032422017533 5ustar csheltoncsheltonctbnrle.1.1.1/hdr/Eigen/src/Cholesky/LDLT.h0000775000175000017500000005117112107032422020453 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2011 Gael Guennebaud // Copyright (C) 2009 Keir Mierle // Copyright (C) 2009 Benoit Jacob // Copyright (C) 2011 Timothy E. Holy // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_LDLT_H #define EIGEN_LDLT_H namespace Eigen { namespace internal { template struct LDLT_Traits; } /** \ingroup Cholesky_Module * * \class LDLT * * \brief Robust Cholesky decomposition of a matrix with pivoting * * \param MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition * \param UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper. * The other triangular part won't be read. * * Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite * matrix \f$ A \f$ such that \f$ A = P^TLDL^*P \f$, where P is a permutation matrix, L * is lower triangular with a unit diagonal and D is a diagonal matrix. * * The decomposition uses pivoting to ensure stability, so that L will have * zeros in the bottom right rank(A) - n submatrix. Avoiding the square root * on D also stabilizes the computation. * * Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky * decomposition to determine whether a system of equations has a solution. * * \sa MatrixBase::ldlt(), class LLT */ template class LDLT { public: typedef _MatrixType MatrixType; enum { RowsAtCompileTime = MatrixType::RowsAtCompileTime, ColsAtCompileTime = MatrixType::ColsAtCompileTime, Options = MatrixType::Options & ~RowMajorBit, // these are the options for the TmpMatrixType, we need a ColMajor matrix here! MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime, UpLo = _UpLo }; typedef typename MatrixType::Scalar Scalar; typedef typename NumTraits::Real RealScalar; typedef typename MatrixType::Index Index; typedef Matrix TmpMatrixType; typedef Transpositions TranspositionType; typedef PermutationMatrix PermutationType; typedef internal::LDLT_Traits Traits; /** \brief Default Constructor. * * The default constructor is useful in cases in which the user intends to * perform decompositions via LDLT::compute(const MatrixType&). */ LDLT() : m_matrix(), m_transpositions(), m_isInitialized(false) {} /** \brief Default Constructor with memory preallocation * * Like the default constructor but with preallocation of the internal data * according to the specified problem \a size. * \sa LDLT() */ LDLT(Index size) : m_matrix(size, size), m_transpositions(size), m_temporary(size), m_isInitialized(false) {} /** \brief Constructor with decomposition * * This calculates the decomposition for the input \a matrix. * \sa LDLT(Index size) */ LDLT(const MatrixType& matrix) : m_matrix(matrix.rows(), matrix.cols()), m_transpositions(matrix.rows()), m_temporary(matrix.rows()), m_isInitialized(false) { compute(matrix); } /** Clear any existing decomposition * \sa rankUpdate(w,sigma) */ void setZero() { m_isInitialized = false; } /** \returns a view of the upper triangular matrix U */ inline typename Traits::MatrixU matrixU() const { eigen_assert(m_isInitialized && "LDLT is not initialized."); return Traits::getU(m_matrix); } /** \returns a view of the lower triangular matrix L */ inline typename Traits::MatrixL matrixL() const { eigen_assert(m_isInitialized && "LDLT is not initialized."); return Traits::getL(m_matrix); } /** \returns the permutation matrix P as a transposition sequence. */ inline const TranspositionType& transpositionsP() const { eigen_assert(m_isInitialized && "LDLT is not initialized."); return m_transpositions; } /** \returns the coefficients of the diagonal matrix D */ inline Diagonal vectorD() const { eigen_assert(m_isInitialized && "LDLT is not initialized."); return m_matrix.diagonal(); } /** \returns true if the matrix is positive (semidefinite) */ inline bool isPositive() const { eigen_assert(m_isInitialized && "LDLT is not initialized."); return m_sign == 1; } #ifdef EIGEN2_SUPPORT inline bool isPositiveDefinite() const { return isPositive(); } #endif /** \returns true if the matrix is negative (semidefinite) */ inline bool isNegative(void) const { eigen_assert(m_isInitialized && "LDLT is not initialized."); return m_sign == -1; } /** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A. * * This function also supports in-place solves using the syntax x = decompositionObject.solve(x) . * * \note_about_checking_solutions * * More precisely, this method solves \f$ A x = b \f$ using the decomposition \f$ A = P^T L D L^* P \f$ * by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$, * \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then * \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the * least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function * computes the least-square solution of \f$ A x = b \f$ is \f$ A \f$ is singular. * * \sa MatrixBase::ldlt() */ template inline const internal::solve_retval solve(const MatrixBase& b) const { eigen_assert(m_isInitialized && "LDLT is not initialized."); eigen_assert(m_matrix.rows()==b.rows() && "LDLT::solve(): invalid number of rows of the right hand side matrix b"); return internal::solve_retval(*this, b.derived()); } #ifdef EIGEN2_SUPPORT template bool solve(const MatrixBase& b, ResultType *result) const { *result = this->solve(b); return true; } #endif template bool solveInPlace(MatrixBase &bAndX) const; LDLT& compute(const MatrixType& matrix); template LDLT& rankUpdate(const MatrixBase& w,RealScalar alpha=1); /** \returns the internal LDLT decomposition matrix * * TODO: document the storage layout */ inline const MatrixType& matrixLDLT() const { eigen_assert(m_isInitialized && "LDLT is not initialized."); return m_matrix; } MatrixType reconstructedMatrix() const; inline Index rows() const { return m_matrix.rows(); } inline Index cols() const { return m_matrix.cols(); } /** \brief Reports whether previous computation was successful. * * \returns \c Success if computation was succesful, * \c NumericalIssue if the matrix.appears to be negative. */ ComputationInfo info() const { eigen_assert(m_isInitialized && "LDLT is not initialized."); return Success; } protected: /** \internal * Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U. * The strict upper part is used during the decomposition, the strict lower * part correspond to the coefficients of L (its diagonal is equal to 1 and * is not stored), and the diagonal entries correspond to D. */ MatrixType m_matrix; TranspositionType m_transpositions; TmpMatrixType m_temporary; int m_sign; bool m_isInitialized; }; namespace internal { template struct ldlt_inplace; template<> struct ldlt_inplace { template static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, int* sign=0) { typedef typename MatrixType::Scalar Scalar; typedef typename MatrixType::RealScalar RealScalar; typedef typename MatrixType::Index Index; eigen_assert(mat.rows()==mat.cols()); const Index size = mat.rows(); if (size <= 1) { transpositions.setIdentity(); if(sign) *sign = real(mat.coeff(0,0))>0 ? 1:-1; return true; } RealScalar cutoff(0), biggest_in_corner; for (Index k = 0; k < size; ++k) { // Find largest diagonal element Index index_of_biggest_in_corner; biggest_in_corner = mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner); index_of_biggest_in_corner += k; if(k == 0) { // The biggest overall is the point of reference to which further diagonals // are compared; if any diagonal is negligible compared // to the largest overall, the algorithm bails. cutoff = abs(NumTraits::epsilon() * biggest_in_corner); if(sign) *sign = real(mat.diagonal().coeff(index_of_biggest_in_corner)) > 0 ? 1 : -1; } // Finish early if the matrix is not full rank. if(biggest_in_corner < cutoff) { for(Index i = k; i < size; i++) transpositions.coeffRef(i) = i; break; } transpositions.coeffRef(k) = index_of_biggest_in_corner; if(k != index_of_biggest_in_corner) { // apply the transposition while taking care to consider only // the lower triangular part Index s = size-index_of_biggest_in_corner-1; // trailing size after the biggest element mat.row(k).head(k).swap(mat.row(index_of_biggest_in_corner).head(k)); mat.col(k).tail(s).swap(mat.col(index_of_biggest_in_corner).tail(s)); std::swap(mat.coeffRef(k,k),mat.coeffRef(index_of_biggest_in_corner,index_of_biggest_in_corner)); for(int i=k+1;i::IsComplex) mat.coeffRef(index_of_biggest_in_corner,k) = conj(mat.coeff(index_of_biggest_in_corner,k)); } // partition the matrix: // A00 | - | - // lu = A10 | A11 | - // A20 | A21 | A22 Index rs = size - k - 1; Block A21(mat,k+1,k,rs,1); Block A10(mat,k,0,1,k); Block A20(mat,k+1,0,rs,k); if(k>0) { temp.head(k) = mat.diagonal().head(k).asDiagonal() * A10.adjoint(); mat.coeffRef(k,k) -= (A10 * temp.head(k)).value(); if(rs>0) A21.noalias() -= A20 * temp.head(k); } if((rs>0) && (abs(mat.coeffRef(k,k)) > cutoff)) A21 /= mat.coeffRef(k,k); } return true; } // Reference for the algorithm: Davis and Hager, "Multiple Rank // Modifications of a Sparse Cholesky Factorization" (Algorithm 1) // Trivial rearrangements of their computations (Timothy E. Holy) // allow their algorithm to work for rank-1 updates even if the // original matrix is not of full rank. // Here only rank-1 updates are implemented, to reduce the // requirement for intermediate storage and improve accuracy template static bool updateInPlace(MatrixType& mat, MatrixBase& w, typename MatrixType::RealScalar sigma=1) { using internal::isfinite; typedef typename MatrixType::Scalar Scalar; typedef typename MatrixType::RealScalar RealScalar; typedef typename MatrixType::Index Index; const Index size = mat.rows(); eigen_assert(mat.cols() == size && w.size()==size); RealScalar alpha = 1; // Apply the update for (Index j = 0; j < size; j++) { // Check for termination due to an original decomposition of low-rank if (!isfinite(alpha)) break; // Update the diagonal terms RealScalar dj = real(mat.coeff(j,j)); Scalar wj = w.coeff(j); RealScalar swj2 = sigma*abs2(wj); RealScalar gamma = dj*alpha + swj2; mat.coeffRef(j,j) += swj2/alpha; alpha += swj2/dj; // Update the terms of L Index rs = size-j-1; w.tail(rs) -= wj * mat.col(j).tail(rs); if(gamma != 0) mat.col(j).tail(rs) += (sigma*conj(wj)/gamma)*w.tail(rs); } return true; } template static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, typename MatrixType::RealScalar sigma=1) { // Apply the permutation to the input w tmp = transpositions * w; return ldlt_inplace::updateInPlace(mat,tmp,sigma); } }; template<> struct ldlt_inplace { template static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, int* sign=0) { Transpose matt(mat); return ldlt_inplace::unblocked(matt, transpositions, temp, sign); } template static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, typename MatrixType::RealScalar sigma=1) { Transpose matt(mat); return ldlt_inplace::update(matt, transpositions, tmp, w.conjugate(), sigma); } }; template struct LDLT_Traits { typedef const TriangularView MatrixL; typedef const TriangularView MatrixU; static inline MatrixL getL(const MatrixType& m) { return m; } static inline MatrixU getU(const MatrixType& m) { return m.adjoint(); } }; template struct LDLT_Traits { typedef const TriangularView MatrixL; typedef const TriangularView MatrixU; static inline MatrixL getL(const MatrixType& m) { return m.adjoint(); } static inline MatrixU getU(const MatrixType& m) { return m; } }; } // end namespace internal /** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix */ template LDLT& LDLT::compute(const MatrixType& a) { eigen_assert(a.rows()==a.cols()); const Index size = a.rows(); m_matrix = a; m_transpositions.resize(size); m_isInitialized = false; m_temporary.resize(size); internal::ldlt_inplace::unblocked(m_matrix, m_transpositions, m_temporary, &m_sign); m_isInitialized = true; return *this; } /** Update the LDLT decomposition: given A = L D L^T, efficiently compute the decomposition of A + sigma w w^T. * \param w a vector to be incorporated into the decomposition. * \param sigma a scalar, +1 for updates and -1 for "downdates," which correspond to removing previously-added column vectors. Optional; default value is +1. * \sa setZero() */ template template LDLT& LDLT::rankUpdate(const MatrixBase& w,typename NumTraits::Real sigma) { const Index size = w.rows(); if (m_isInitialized) { eigen_assert(m_matrix.rows()==size); } else { m_matrix.resize(size,size); m_matrix.setZero(); m_transpositions.resize(size); for (Index i = 0; i < size; i++) m_transpositions.coeffRef(i) = i; m_temporary.resize(size); m_sign = sigma; m_isInitialized = true; } internal::ldlt_inplace::update(m_matrix, m_transpositions, m_temporary, w, sigma); return *this; } namespace internal { template struct solve_retval, Rhs> : solve_retval_base, Rhs> { typedef LDLT<_MatrixType,_UpLo> LDLTType; EIGEN_MAKE_SOLVE_HELPERS(LDLTType,Rhs) template void evalTo(Dest& dst) const { eigen_assert(rhs().rows() == dec().matrixLDLT().rows()); // dst = P b dst = dec().transpositionsP() * rhs(); // dst = L^-1 (P b) dec().matrixL().solveInPlace(dst); // dst = D^-1 (L^-1 P b) // more precisely, use pseudo-inverse of D (see bug 241) using std::abs; using std::max; typedef typename LDLTType::MatrixType MatrixType; typedef typename LDLTType::Scalar Scalar; typedef typename LDLTType::RealScalar RealScalar; const Diagonal vectorD = dec().vectorD(); RealScalar tolerance = (max)(vectorD.array().abs().maxCoeff() * NumTraits::epsilon(), RealScalar(1) / NumTraits::highest()); // motivated by LAPACK's xGELSS for (Index i = 0; i < vectorD.size(); ++i) { if(abs(vectorD(i)) > tolerance) dst.row(i) /= vectorD(i); else dst.row(i).setZero(); } // dst = L^-T (D^-1 L^-1 P b) dec().matrixU().solveInPlace(dst); // dst = P^-1 (L^-T D^-1 L^-1 P b) = A^-1 b dst = dec().transpositionsP().transpose() * dst; } }; } /** \internal use x = ldlt_object.solve(x); * * This is the \em in-place version of solve(). * * \param bAndX represents both the right-hand side matrix b and result x. * * \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD. * * This version avoids a copy when the right hand side matrix b is not * needed anymore. * * \sa LDLT::solve(), MatrixBase::ldlt() */ template template bool LDLT::solveInPlace(MatrixBase &bAndX) const { eigen_assert(m_isInitialized && "LDLT is not initialized."); const Index size = m_matrix.rows(); eigen_assert(size == bAndX.rows()); bAndX = this->solve(bAndX); return true; } /** \returns the matrix represented by the decomposition, * i.e., it returns the product: P^T L D L^* P. * This function is provided for debug purpose. */ template MatrixType LDLT::reconstructedMatrix() const { eigen_assert(m_isInitialized && "LDLT is not initialized."); const Index size = m_matrix.rows(); MatrixType res(size,size); // P res.setIdentity(); res = transpositionsP() * res; // L^* P res = matrixU() * res; // D(L^*P) res = vectorD().asDiagonal() * res; // L(DL^*P) res = matrixL() * res; // P^T (LDL^*P) res = transpositionsP().transpose() * res; return res; } /** \cholesky_module * \returns the Cholesky decomposition with full pivoting without square root of \c *this */ template inline const LDLT::PlainObject, UpLo> SelfAdjointView::ldlt() const { return LDLT(m_matrix); } /** \cholesky_module * \returns the Cholesky decomposition with full pivoting without square root of \c *this */ template inline const LDLT::PlainObject> MatrixBase::ldlt() const { return LDLT(derived()); } } // end namespace Eigen #endif // EIGEN_LDLT_H ctbnrle.1.1.1/hdr/Eigen/src/Cholesky/LLT_MKL.h0000775000175000017500000000766112107032422021057 0ustar csheltoncshelton/* Copyright (c) 2011, Intel Corporation. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Intel Corporation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ******************************************************************************** * Content : Eigen bindings to Intel(R) MKL * LLt decomposition based on LAPACKE_?potrf function. ******************************************************************************** */ #ifndef EIGEN_LLT_MKL_H #define EIGEN_LLT_MKL_H #include "Eigen/src/Core/util/MKL_support.h" #include namespace Eigen { namespace internal { template struct mkl_llt; #define EIGEN_MKL_LLT(EIGTYPE, MKLTYPE, MKLPREFIX) \ template<> struct mkl_llt \ { \ template \ static inline typename MatrixType::Index potrf(MatrixType& m, char uplo) \ { \ lapack_int matrix_order; \ lapack_int size, lda, info, StorageOrder; \ EIGTYPE* a; \ eigen_assert(m.rows()==m.cols()); \ /* Set up parameters for ?potrf */ \ size = m.rows(); \ StorageOrder = MatrixType::Flags&RowMajorBit?RowMajor:ColMajor; \ matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \ a = &(m.coeffRef(0,0)); \ lda = m.outerStride(); \ \ info = LAPACKE_##MKLPREFIX##potrf( matrix_order, uplo, size, (MKLTYPE*)a, lda ); \ info = (info==0) ? Success : NumericalIssue; \ return info; \ } \ }; \ template<> struct llt_inplace \ { \ template \ static typename MatrixType::Index blocked(MatrixType& m) \ { \ return mkl_llt::potrf(m, 'L'); \ } \ template \ static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \ { return Eigen::internal::llt_rank_update_lower(mat, vec, sigma); } \ }; \ template<> struct llt_inplace \ { \ template \ static typename MatrixType::Index blocked(MatrixType& m) \ { \ return mkl_llt::potrf(m, 'U'); \ } \ template \ static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \ { \ Transpose matt(mat); \ return llt_inplace::rankUpdate(matt, vec.conjugate(), sigma); \ } \ }; EIGEN_MKL_LLT(double, double, d) EIGEN_MKL_LLT(float, float, s) EIGEN_MKL_LLT(dcomplex, MKL_Complex16, z) EIGEN_MKL_LLT(scomplex, MKL_Complex8, c) } // end namespace internal } // end namespace Eigen #endif // EIGEN_LLT_MKL_H ctbnrle.1.1.1/hdr/Eigen/src/Cholesky/CMakeLists.txt0000775000175000017500000000023112107032422022272 0ustar csheltoncsheltonFILE(GLOB Eigen_Cholesky_SRCS "*.h") INSTALL(FILES ${Eigen_Cholesky_SRCS} DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Cholesky COMPONENT Devel ) ctbnrle.1.1.1/hdr/Eigen/src/Cholesky/LLT.h0000775000175000017500000004055212107032422020350 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_LLT_H #define EIGEN_LLT_H namespace Eigen { namespace internal{ template struct LLT_Traits; } /** \ingroup Cholesky_Module * * \class LLT * * \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features * * \param MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition * \param UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper. * The other triangular part won't be read. * * This class performs a LL^T Cholesky decomposition of a symmetric, positive definite * matrix A such that A = LL^* = U^*U, where L is lower triangular. * * While the Cholesky decomposition is particularly useful to solve selfadjoint problems like D^*D x = b, * for that purpose, we recommend the Cholesky decomposition without square root which is more stable * and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other * situations like generalised eigen problems with hermitian matrices. * * Remember that Cholesky decompositions are not rank-revealing. This LLT decomposition is only stable on positive definite matrices, * use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine whether a system of equations * has a solution. * * Example: \include LLT_example.cpp * Output: \verbinclude LLT_example.out * * \sa MatrixBase::llt(), class LDLT */ /* HEY THIS DOX IS DISABLED BECAUSE THERE's A BUG EITHER HERE OR IN LDLT ABOUT THAT (OR BOTH) * Note that during the decomposition, only the upper triangular part of A is considered. Therefore, * the strict lower part does not have to store correct values. */ template class LLT { public: typedef _MatrixType MatrixType; enum { RowsAtCompileTime = MatrixType::RowsAtCompileTime, ColsAtCompileTime = MatrixType::ColsAtCompileTime, Options = MatrixType::Options, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime }; typedef typename MatrixType::Scalar Scalar; typedef typename NumTraits::Real RealScalar; typedef typename MatrixType::Index Index; enum { PacketSize = internal::packet_traits::size, AlignmentMask = int(PacketSize)-1, UpLo = _UpLo }; typedef internal::LLT_Traits Traits; /** * \brief Default Constructor. * * The default constructor is useful in cases in which the user intends to * perform decompositions via LLT::compute(const MatrixType&). */ LLT() : m_matrix(), m_isInitialized(false) {} /** \brief Default Constructor with memory preallocation * * Like the default constructor but with preallocation of the internal data * according to the specified problem \a size. * \sa LLT() */ LLT(Index size) : m_matrix(size, size), m_isInitialized(false) {} LLT(const MatrixType& matrix) : m_matrix(matrix.rows(), matrix.cols()), m_isInitialized(false) { compute(matrix); } /** \returns a view of the upper triangular matrix U */ inline typename Traits::MatrixU matrixU() const { eigen_assert(m_isInitialized && "LLT is not initialized."); return Traits::getU(m_matrix); } /** \returns a view of the lower triangular matrix L */ inline typename Traits::MatrixL matrixL() const { eigen_assert(m_isInitialized && "LLT is not initialized."); return Traits::getL(m_matrix); } /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. * * Since this LLT class assumes anyway that the matrix A is invertible, the solution * theoretically exists and is unique regardless of b. * * Example: \include LLT_solve.cpp * Output: \verbinclude LLT_solve.out * * \sa solveInPlace(), MatrixBase::llt() */ template inline const internal::solve_retval solve(const MatrixBase& b) const { eigen_assert(m_isInitialized && "LLT is not initialized."); eigen_assert(m_matrix.rows()==b.rows() && "LLT::solve(): invalid number of rows of the right hand side matrix b"); return internal::solve_retval(*this, b.derived()); } #ifdef EIGEN2_SUPPORT template bool solve(const MatrixBase& b, ResultType *result) const { *result = this->solve(b); return true; } bool isPositiveDefinite() const { return true; } #endif template void solveInPlace(MatrixBase &bAndX) const; LLT& compute(const MatrixType& matrix); /** \returns the LLT decomposition matrix * * TODO: document the storage layout */ inline const MatrixType& matrixLLT() const { eigen_assert(m_isInitialized && "LLT is not initialized."); return m_matrix; } MatrixType reconstructedMatrix() const; /** \brief Reports whether previous computation was successful. * * \returns \c Success if computation was succesful, * \c NumericalIssue if the matrix.appears to be negative. */ ComputationInfo info() const { eigen_assert(m_isInitialized && "LLT is not initialized."); return m_info; } inline Index rows() const { return m_matrix.rows(); } inline Index cols() const { return m_matrix.cols(); } template LLT rankUpdate(const VectorType& vec, const RealScalar& sigma = 1); protected: /** \internal * Used to compute and store L * The strict upper part is not used and even not initialized. */ MatrixType m_matrix; bool m_isInitialized; ComputationInfo m_info; }; namespace internal { template struct llt_inplace; template static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) { typedef typename MatrixType::Scalar Scalar; typedef typename MatrixType::RealScalar RealScalar; typedef typename MatrixType::Index Index; typedef typename MatrixType::ColXpr ColXpr; typedef typename internal::remove_all::type ColXprCleaned; typedef typename ColXprCleaned::SegmentReturnType ColXprSegment; typedef Matrix TempVectorType; typedef typename TempVectorType::SegmentReturnType TempVecSegment; int n = mat.cols(); eigen_assert(mat.rows()==n && vec.size()==n); TempVectorType temp; if(sigma>0) { // This version is based on Givens rotations. // It is faster than the other one below, but only works for updates, // i.e., for sigma > 0 temp = sqrt(sigma) * vec; for(int i=0; i g; g.makeGivens(mat(i,i), -temp(i), &mat(i,i)); int rs = n-i-1; if(rs>0) { ColXprSegment x(mat.col(i).tail(rs)); TempVecSegment y(temp.tail(rs)); apply_rotation_in_the_plane(x, y, g); } } } else { temp = vec; RealScalar beta = 1; for(int j=0; j struct llt_inplace { typedef typename NumTraits::Real RealScalar; template static typename MatrixType::Index unblocked(MatrixType& mat) { typedef typename MatrixType::Index Index; eigen_assert(mat.rows()==mat.cols()); const Index size = mat.rows(); for(Index k = 0; k < size; ++k) { Index rs = size-k-1; // remaining size Block A21(mat,k+1,k,rs,1); Block A10(mat,k,0,1,k); Block A20(mat,k+1,0,rs,k); RealScalar x = real(mat.coeff(k,k)); if (k>0) x -= A10.squaredNorm(); if (x<=RealScalar(0)) return k; mat.coeffRef(k,k) = x = sqrt(x); if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint(); if (rs>0) A21 *= RealScalar(1)/x; } return -1; } template static typename MatrixType::Index blocked(MatrixType& m) { typedef typename MatrixType::Index Index; eigen_assert(m.rows()==m.cols()); Index size = m.rows(); if(size<32) return unblocked(m); Index blockSize = size/8; blockSize = (blockSize/16)*16; blockSize = (std::min)((std::max)(blockSize,Index(8)), Index(128)); for (Index k=0; k A11(m,k, k, bs,bs); Block A21(m,k+bs,k, rs,bs); Block A22(m,k+bs,k+bs,rs,rs); Index ret; if((ret=unblocked(A11))>=0) return k+ret; if(rs>0) A11.adjoint().template triangularView().template solveInPlace(A21); if(rs>0) A22.template selfadjointView().rankUpdate(A21,-1); // bottleneck } return -1; } template static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) { return Eigen::internal::llt_rank_update_lower(mat, vec, sigma); } }; template struct llt_inplace { typedef typename NumTraits::Real RealScalar; template static EIGEN_STRONG_INLINE typename MatrixType::Index unblocked(MatrixType& mat) { Transpose matt(mat); return llt_inplace::unblocked(matt); } template static EIGEN_STRONG_INLINE typename MatrixType::Index blocked(MatrixType& mat) { Transpose matt(mat); return llt_inplace::blocked(matt); } template static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) { Transpose matt(mat); return llt_inplace::rankUpdate(matt, vec.conjugate(), sigma); } }; template struct LLT_Traits { typedef const TriangularView MatrixL; typedef const TriangularView MatrixU; static inline MatrixL getL(const MatrixType& m) { return m; } static inline MatrixU getU(const MatrixType& m) { return m.adjoint(); } static bool inplace_decomposition(MatrixType& m) { return llt_inplace::blocked(m)==-1; } }; template struct LLT_Traits { typedef const TriangularView MatrixL; typedef const TriangularView MatrixU; static inline MatrixL getL(const MatrixType& m) { return m.adjoint(); } static inline MatrixU getU(const MatrixType& m) { return m; } static bool inplace_decomposition(MatrixType& m) { return llt_inplace::blocked(m)==-1; } }; } // end namespace internal /** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix * * \returns a reference to *this * * Example: \include TutorialLinAlgComputeTwice.cpp * Output: \verbinclude TutorialLinAlgComputeTwice.out */ template LLT& LLT::compute(const MatrixType& a) { eigen_assert(a.rows()==a.cols()); const Index size = a.rows(); m_matrix.resize(size, size); m_matrix = a; m_isInitialized = true; bool ok = Traits::inplace_decomposition(m_matrix); m_info = ok ? Success : NumericalIssue; return *this; } /** Performs a rank one update (or dowdate) of the current decomposition. * If A = LL^* before the rank one update, * then after it we have LL^* = A + sigma * v v^* where \a v must be a vector * of same dimension. */ template template LLT<_MatrixType,_UpLo> LLT<_MatrixType,_UpLo>::rankUpdate(const VectorType& v, const RealScalar& sigma) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType); eigen_assert(v.size()==m_matrix.cols()); eigen_assert(m_isInitialized); if(internal::llt_inplace::rankUpdate(m_matrix,v,sigma)>=0) m_info = NumericalIssue; else m_info = Success; return *this; } namespace internal { template struct solve_retval, Rhs> : solve_retval_base, Rhs> { typedef LLT<_MatrixType,UpLo> LLTType; EIGEN_MAKE_SOLVE_HELPERS(LLTType,Rhs) template void evalTo(Dest& dst) const { dst = rhs(); dec().solveInPlace(dst); } }; } /** \internal use x = llt_object.solve(x); * * This is the \em in-place version of solve(). * * \param bAndX represents both the right-hand side matrix b and result x. * * \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD. * * This version avoids a copy when the right hand side matrix b is not * needed anymore. * * \sa LLT::solve(), MatrixBase::llt() */ template template void LLT::solveInPlace(MatrixBase &bAndX) const { eigen_assert(m_isInitialized && "LLT is not initialized."); eigen_assert(m_matrix.rows()==bAndX.rows()); matrixL().solveInPlace(bAndX); matrixU().solveInPlace(bAndX); } /** \returns the matrix represented by the decomposition, * i.e., it returns the product: L L^*. * This function is provided for debug purpose. */ template MatrixType LLT::reconstructedMatrix() const { eigen_assert(m_isInitialized && "LLT is not initialized."); return matrixL() * matrixL().adjoint().toDenseMatrix(); } /** \cholesky_module * \returns the LLT decomposition of \c *this */ template inline const LLT::PlainObject> MatrixBase::llt() const { return LLT(derived()); } /** \cholesky_module * \returns the LLT decomposition of \c *this */ template inline const LLT::PlainObject, UpLo> SelfAdjointView::llt() const { return LLT(m_matrix); } } // end namespace Eigen #endif // EIGEN_LLT_H ctbnrle.1.1.1/hdr/Eigen/src/LU/0000775000175000017500000000000012107032422016272 5ustar csheltoncsheltonctbnrle.1.1.1/hdr/Eigen/src/LU/FullPivLU.h0000775000175000017500000007001112107032422020267 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2006-2009 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_LU_H #define EIGEN_LU_H namespace Eigen { /** \ingroup LU_Module * * \class FullPivLU * * \brief LU decomposition of a matrix with complete pivoting, and related features * * \param MatrixType the type of the matrix of which we are computing the LU decomposition * * This class represents a LU decomposition of any matrix, with complete pivoting: the matrix A * is decomposed as A = PLUQ where L is unit-lower-triangular, U is upper-triangular, and P and Q * are permutation matrices. This is a rank-revealing LU decomposition. The eigenvalues (diagonal * coefficients) of U are sorted in such a way that any zeros are at the end. * * This decomposition provides the generic approach to solving systems of linear equations, computing * the rank, invertibility, inverse, kernel, and determinant. * * This LU decomposition is very stable and well tested with large matrices. However there are use cases where the SVD * decomposition is inherently more stable and/or flexible. For example, when computing the kernel of a matrix, * working with the SVD allows to select the smallest singular values of the matrix, something that * the LU decomposition doesn't see. * * The data of the LU decomposition can be directly accessed through the methods matrixLU(), * permutationP(), permutationQ(). * * As an exemple, here is how the original matrix can be retrieved: * \include class_FullPivLU.cpp * Output: \verbinclude class_FullPivLU.out * * \sa MatrixBase::fullPivLu(), MatrixBase::determinant(), MatrixBase::inverse() */ template class FullPivLU { public: typedef _MatrixType MatrixType; enum { RowsAtCompileTime = MatrixType::RowsAtCompileTime, ColsAtCompileTime = MatrixType::ColsAtCompileTime, Options = MatrixType::Options, MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime }; typedef typename MatrixType::Scalar Scalar; typedef typename NumTraits::Real RealScalar; typedef typename internal::traits::StorageKind StorageKind; typedef typename MatrixType::Index Index; typedef typename internal::plain_row_type::type IntRowVectorType; typedef typename internal::plain_col_type::type IntColVectorType; typedef PermutationMatrix PermutationQType; typedef PermutationMatrix PermutationPType; /** * \brief Default Constructor. * * The default constructor is useful in cases in which the user intends to * perform decompositions via LU::compute(const MatrixType&). */ FullPivLU(); /** \brief Default Constructor with memory preallocation * * Like the default constructor but with preallocation of the internal data * according to the specified problem \a size. * \sa FullPivLU() */ FullPivLU(Index rows, Index cols); /** Constructor. * * \param matrix the matrix of which to compute the LU decomposition. * It is required to be nonzero. */ FullPivLU(const MatrixType& matrix); /** Computes the LU decomposition of the given matrix. * * \param matrix the matrix of which to compute the LU decomposition. * It is required to be nonzero. * * \returns a reference to *this */ FullPivLU& compute(const MatrixType& matrix); /** \returns the LU decomposition matrix: the upper-triangular part is U, the * unit-lower-triangular part is L (at least for square matrices; in the non-square * case, special care is needed, see the documentation of class FullPivLU). * * \sa matrixL(), matrixU() */ inline const MatrixType& matrixLU() const { eigen_assert(m_isInitialized && "LU is not initialized."); return m_lu; } /** \returns the number of nonzero pivots in the LU decomposition. * Here nonzero is meant in the exact sense, not in a fuzzy sense. * So that notion isn't really intrinsically interesting, but it is * still useful when implementing algorithms. * * \sa rank() */ inline Index nonzeroPivots() const { eigen_assert(m_isInitialized && "LU is not initialized."); return m_nonzero_pivots; } /** \returns the absolute value of the biggest pivot, i.e. the biggest * diagonal coefficient of U. */ RealScalar maxPivot() const { return m_maxpivot; } /** \returns the permutation matrix P * * \sa permutationQ() */ inline const PermutationPType& permutationP() const { eigen_assert(m_isInitialized && "LU is not initialized."); return m_p; } /** \returns the permutation matrix Q * * \sa permutationP() */ inline const PermutationQType& permutationQ() const { eigen_assert(m_isInitialized && "LU is not initialized."); return m_q; } /** \returns the kernel of the matrix, also called its null-space. The columns of the returned matrix * will form a basis of the kernel. * * \note If the kernel has dimension zero, then the returned matrix is a column-vector filled with zeros. * * \note This method has to determine which pivots should be considered nonzero. * For that, it uses the threshold value that you can control by calling * setThreshold(const RealScalar&). * * Example: \include FullPivLU_kernel.cpp * Output: \verbinclude FullPivLU_kernel.out * * \sa image() */ inline const internal::kernel_retval kernel() const { eigen_assert(m_isInitialized && "LU is not initialized."); return internal::kernel_retval(*this); } /** \returns the image of the matrix, also called its column-space. The columns of the returned matrix * will form a basis of the kernel. * * \param originalMatrix the original matrix, of which *this is the LU decomposition. * The reason why it is needed to pass it here, is that this allows * a large optimization, as otherwise this method would need to reconstruct it * from the LU decomposition. * * \note If the image has dimension zero, then the returned matrix is a column-vector filled with zeros. * * \note This method has to determine which pivots should be considered nonzero. * For that, it uses the threshold value that you can control by calling * setThreshold(const RealScalar&). * * Example: \include FullPivLU_image.cpp * Output: \verbinclude FullPivLU_image.out * * \sa kernel() */ inline const internal::image_retval image(const MatrixType& originalMatrix) const { eigen_assert(m_isInitialized && "LU is not initialized."); return internal::image_retval(*this, originalMatrix); } /** \return a solution x to the equation Ax=b, where A is the matrix of which * *this is the LU decomposition. * * \param b the right-hand-side of the equation to solve. Can be a vector or a matrix, * the only requirement in order for the equation to make sense is that * b.rows()==A.rows(), where A is the matrix of which *this is the LU decomposition. * * \returns a solution. * * \note_about_checking_solutions * * \note_about_arbitrary_choice_of_solution * \note_about_using_kernel_to_study_multiple_solutions * * Example: \include FullPivLU_solve.cpp * Output: \verbinclude FullPivLU_solve.out * * \sa TriangularView::solve(), kernel(), inverse() */ template inline const internal::solve_retval solve(const MatrixBase& b) const { eigen_assert(m_isInitialized && "LU is not initialized."); return internal::solve_retval(*this, b.derived()); } /** \returns the determinant of the matrix of which * *this is the LU decomposition. It has only linear complexity * (that is, O(n) where n is the dimension of the square matrix) * as the LU decomposition has already been computed. * * \note This is only for square matrices. * * \note For fixed-size matrices of size up to 4, MatrixBase::determinant() offers * optimized paths. * * \warning a determinant can be very big or small, so for matrices * of large enough dimension, there is a risk of overflow/underflow. * * \sa MatrixBase::determinant() */ typename internal::traits::Scalar determinant() const; /** Allows to prescribe a threshold to be used by certain methods, such as rank(), * who need to determine when pivots are to be considered nonzero. This is not used for the * LU decomposition itself. * * When it needs to get the threshold value, Eigen calls threshold(). By default, this * uses a formula to automatically determine a reasonable threshold. * Once you have called the present method setThreshold(const RealScalar&), * your value is used instead. * * \param threshold The new value to use as the threshold. * * A pivot will be considered nonzero if its absolute value is strictly greater than * \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$ * where maxpivot is the biggest pivot. * * If you want to come back to the default behavior, call setThreshold(Default_t) */ FullPivLU& setThreshold(const RealScalar& threshold) { m_usePrescribedThreshold = true; m_prescribedThreshold = threshold; return *this; } /** Allows to come back to the default behavior, letting Eigen use its default formula for * determining the threshold. * * You should pass the special object Eigen::Default as parameter here. * \code lu.setThreshold(Eigen::Default); \endcode * * See the documentation of setThreshold(const RealScalar&). */ FullPivLU& setThreshold(Default_t) { m_usePrescribedThreshold = false; return *this; } /** Returns the threshold that will be used by certain methods such as rank(). * * See the documentation of setThreshold(const RealScalar&). */ RealScalar threshold() const { eigen_assert(m_isInitialized || m_usePrescribedThreshold); return m_usePrescribedThreshold ? m_prescribedThreshold // this formula comes from experimenting (see "LU precision tuning" thread on the list) // and turns out to be identical to Higham's formula used already in LDLt. : NumTraits::epsilon() * m_lu.diagonalSize(); } /** \returns the rank of the matrix of which *this is the LU decomposition. * * \note This method has to determine which pivots should be considered nonzero. * For that, it uses the threshold value that you can control by calling * setThreshold(const RealScalar&). */ inline Index rank() const { eigen_assert(m_isInitialized && "LU is not initialized."); RealScalar premultiplied_threshold = internal::abs(m_maxpivot) * threshold(); Index result = 0; for(Index i = 0; i < m_nonzero_pivots; ++i) result += (internal::abs(m_lu.coeff(i,i)) > premultiplied_threshold); return result; } /** \returns the dimension of the kernel of the matrix of which *this is the LU decomposition. * * \note This method has to determine which pivots should be considered nonzero. * For that, it uses the threshold value that you can control by calling * setThreshold(const RealScalar&). */ inline Index dimensionOfKernel() const { eigen_assert(m_isInitialized && "LU is not initialized."); return cols() - rank(); } /** \returns true if the matrix of which *this is the LU decomposition represents an injective * linear map, i.e. has trivial kernel; false otherwise. * * \note This method has to determine which pivots should be considered nonzero. * For that, it uses the threshold value that you can control by calling * setThreshold(const RealScalar&). */ inline bool isInjective() const { eigen_assert(m_isInitialized && "LU is not initialized."); return rank() == cols(); } /** \returns true if the matrix of which *this is the LU decomposition represents a surjective * linear map; false otherwise. * * \note This method has to determine which pivots should be considered nonzero. * For that, it uses the threshold value that you can control by calling * setThreshold(const RealScalar&). */ inline bool isSurjective() const { eigen_assert(m_isInitialized && "LU is not initialized."); return rank() == rows(); } /** \returns true if the matrix of which *this is the LU decomposition is invertible. * * \note This method has to determine which pivots should be considered nonzero. * For that, it uses the threshold value that you can control by calling * setThreshold(const RealScalar&). */ inline bool isInvertible() const { eigen_assert(m_isInitialized && "LU is not initialized."); return isInjective() && (m_lu.rows() == m_lu.cols()); } /** \returns the inverse of the matrix of which *this is the LU decomposition. * * \note If this matrix is not invertible, the returned matrix has undefined coefficients. * Use isInvertible() to first determine whether this matrix is invertible. * * \sa MatrixBase::inverse() */ inline const internal::solve_retval inverse() const { eigen_assert(m_isInitialized && "LU is not initialized."); eigen_assert(m_lu.rows() == m_lu.cols() && "You can't take the inverse of a non-square matrix!"); return internal::solve_retval (*this, MatrixType::Identity(m_lu.rows(), m_lu.cols())); } MatrixType reconstructedMatrix() const; inline Index rows() const { return m_lu.rows(); } inline Index cols() const { return m_lu.cols(); } protected: MatrixType m_lu; PermutationPType m_p; PermutationQType m_q; IntColVectorType m_rowsTranspositions; IntRowVectorType m_colsTranspositions; Index m_det_pq, m_nonzero_pivots; RealScalar m_maxpivot, m_prescribedThreshold; bool m_isInitialized, m_usePrescribedThreshold; }; template FullPivLU::FullPivLU() : m_isInitialized(false), m_usePrescribedThreshold(false) { } template FullPivLU::FullPivLU(Index rows, Index cols) : m_lu(rows, cols), m_p(rows), m_q(cols), m_rowsTranspositions(rows), m_colsTranspositions(cols), m_isInitialized(false), m_usePrescribedThreshold(false) { } template FullPivLU::FullPivLU(const MatrixType& matrix) : m_lu(matrix.rows(), matrix.cols()), m_p(matrix.rows()), m_q(matrix.cols()), m_rowsTranspositions(matrix.rows()), m_colsTranspositions(matrix.cols()), m_isInitialized(false), m_usePrescribedThreshold(false) { compute(matrix); } template FullPivLU& FullPivLU::compute(const MatrixType& matrix) { m_isInitialized = true; m_lu = matrix; const Index size = matrix.diagonalSize(); const Index rows = matrix.rows(); const Index cols = matrix.cols(); // will store the transpositions, before we accumulate them at the end. // can't accumulate on-the-fly because that will be done in reverse order for the rows. m_rowsTranspositions.resize(matrix.rows()); m_colsTranspositions.resize(matrix.cols()); Index number_of_transpositions = 0; // number of NONTRIVIAL transpositions, i.e. m_rowsTranspositions[i]!=i m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case) m_maxpivot = RealScalar(0); for(Index k = 0; k < size; ++k) { // First, we need to find the pivot. // biggest coefficient in the remaining bottom-right corner (starting at row k, col k) Index row_of_biggest_in_corner, col_of_biggest_in_corner; RealScalar biggest_in_corner; biggest_in_corner = m_lu.bottomRightCorner(rows-k, cols-k) .cwiseAbs() .maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner); row_of_biggest_in_corner += k; // correct the values! since they were computed in the corner, col_of_biggest_in_corner += k; // need to add k to them. if(biggest_in_corner==RealScalar(0)) { // before exiting, make sure to initialize the still uninitialized transpositions // in a sane state without destroying what we already have. m_nonzero_pivots = k; for(Index i = k; i < size; ++i) { m_rowsTranspositions.coeffRef(i) = i; m_colsTranspositions.coeffRef(i) = i; } break; } if(biggest_in_corner > m_maxpivot) m_maxpivot = biggest_in_corner; // Now that we've found the pivot, we need to apply the row/col swaps to // bring it to the location (k,k). m_rowsTranspositions.coeffRef(k) = row_of_biggest_in_corner; m_colsTranspositions.coeffRef(k) = col_of_biggest_in_corner; if(k != row_of_biggest_in_corner) { m_lu.row(k).swap(m_lu.row(row_of_biggest_in_corner)); ++number_of_transpositions; } if(k != col_of_biggest_in_corner) { m_lu.col(k).swap(m_lu.col(col_of_biggest_in_corner)); ++number_of_transpositions; } // Now that the pivot is at the right location, we update the remaining // bottom-right corner by Gaussian elimination. if(k= 0; --k) m_p.applyTranspositionOnTheRight(k, m_rowsTranspositions.coeff(k)); m_q.setIdentity(cols); for(Index k = 0; k < size; ++k) m_q.applyTranspositionOnTheRight(k, m_colsTranspositions.coeff(k)); m_det_pq = (number_of_transpositions%2) ? -1 : 1; return *this; } template typename internal::traits::Scalar FullPivLU::determinant() const { eigen_assert(m_isInitialized && "LU is not initialized."); eigen_assert(m_lu.rows() == m_lu.cols() && "You can't take the determinant of a non-square matrix!"); return Scalar(m_det_pq) * Scalar(m_lu.diagonal().prod()); } /** \returns the matrix represented by the decomposition, * i.e., it returns the product: P^{-1} L U Q^{-1}. * This function is provided for debug purpose. */ template MatrixType FullPivLU::reconstructedMatrix() const { eigen_assert(m_isInitialized && "LU is not initialized."); const Index smalldim = (std::min)(m_lu.rows(), m_lu.cols()); // LU MatrixType res(m_lu.rows(),m_lu.cols()); // FIXME the .toDenseMatrix() should not be needed... res = m_lu.leftCols(smalldim) .template triangularView().toDenseMatrix() * m_lu.topRows(smalldim) .template triangularView().toDenseMatrix(); // P^{-1}(LU) res = m_p.inverse() * res; // (P^{-1}LU)Q^{-1} res = res * m_q.inverse(); return res; } /********* Implementation of kernel() **************************************************/ namespace internal { template struct kernel_retval > : kernel_retval_base > { EIGEN_MAKE_KERNEL_HELPERS(FullPivLU<_MatrixType>) enum { MaxSmallDimAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED( MatrixType::MaxColsAtCompileTime, MatrixType::MaxRowsAtCompileTime) }; template void evalTo(Dest& dst) const { const Index cols = dec().matrixLU().cols(), dimker = cols - rank(); if(dimker == 0) { // The Kernel is just {0}, so it doesn't have a basis properly speaking, but let's // avoid crashing/asserting as that depends on floating point calculations. Let's // just return a single column vector filled with zeros. dst.setZero(); return; } /* Let us use the following lemma: * * Lemma: If the matrix A has the LU decomposition PAQ = LU, * then Ker A = Q(Ker U). * * Proof: trivial: just keep in mind that P, Q, L are invertible. */ /* Thus, all we need to do is to compute Ker U, and then apply Q. * * U is upper triangular, with eigenvalues sorted so that any zeros appear at the end. * Thus, the diagonal of U ends with exactly * dimKer zero's. Let us use that to construct dimKer linearly * independent vectors in Ker U. */ Matrix pivots(rank()); RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold(); Index p = 0; for(Index i = 0; i < dec().nonzeroPivots(); ++i) if(abs(dec().matrixLU().coeff(i,i)) > premultiplied_threshold) pivots.coeffRef(p++) = i; eigen_internal_assert(p == rank()); // we construct a temporaty trapezoid matrix m, by taking the U matrix and // permuting the rows and cols to bring the nonnegligible pivots to the top of // the main diagonal. We need that to be able to apply our triangular solvers. // FIXME when we get triangularView-for-rectangular-matrices, this can be simplified Matrix m(dec().matrixLU().block(0, 0, rank(), cols)); for(Index i = 0; i < rank(); ++i) { if(i) m.row(i).head(i).setZero(); m.row(i).tail(cols-i) = dec().matrixLU().row(pivots.coeff(i)).tail(cols-i); } m.block(0, 0, rank(), rank()); m.block(0, 0, rank(), rank()).template triangularView().setZero(); for(Index i = 0; i < rank(); ++i) m.col(i).swap(m.col(pivots.coeff(i))); // ok, we have our trapezoid matrix, we can apply the triangular solver. // notice that the math behind this suggests that we should apply this to the // negative of the RHS, but for performance we just put the negative sign elsewhere, see below. m.topLeftCorner(rank(), rank()) .template triangularView().solveInPlace( m.topRightCorner(rank(), dimker) ); // now we must undo the column permutation that we had applied! for(Index i = rank()-1; i >= 0; --i) m.col(i).swap(m.col(pivots.coeff(i))); // see the negative sign in the next line, that's what we were talking about above. for(Index i = 0; i < rank(); ++i) dst.row(dec().permutationQ().indices().coeff(i)) = -m.row(i).tail(dimker); for(Index i = rank(); i < cols; ++i) dst.row(dec().permutationQ().indices().coeff(i)).setZero(); for(Index k = 0; k < dimker; ++k) dst.coeffRef(dec().permutationQ().indices().coeff(rank()+k), k) = Scalar(1); } }; /***** Implementation of image() *****************************************************/ template struct image_retval > : image_retval_base > { EIGEN_MAKE_IMAGE_HELPERS(FullPivLU<_MatrixType>) enum { MaxSmallDimAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED( MatrixType::MaxColsAtCompileTime, MatrixType::MaxRowsAtCompileTime) }; template void evalTo(Dest& dst) const { if(rank() == 0) { // The Image is just {0}, so it doesn't have a basis properly speaking, but let's // avoid crashing/asserting as that depends on floating point calculations. Let's // just return a single column vector filled with zeros. dst.setZero(); return; } Matrix pivots(rank()); RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold(); Index p = 0; for(Index i = 0; i < dec().nonzeroPivots(); ++i) if(abs(dec().matrixLU().coeff(i,i)) > premultiplied_threshold) pivots.coeffRef(p++) = i; eigen_internal_assert(p == rank()); for(Index i = 0; i < rank(); ++i) dst.col(i) = originalMatrix().col(dec().permutationQ().indices().coeff(pivots.coeff(i))); } }; /***** Implementation of solve() *****************************************************/ template struct solve_retval, Rhs> : solve_retval_base, Rhs> { EIGEN_MAKE_SOLVE_HELPERS(FullPivLU<_MatrixType>,Rhs) template void evalTo(Dest& dst) const { /* The decomposition PAQ = LU can be rewritten as A = P^{-1} L U Q^{-1}. * So we proceed as follows: * Step 1: compute c = P * rhs. * Step 2: replace c by the solution x to Lx = c. Exists because L is invertible. * Step 3: replace c by the solution x to Ux = c. May or may not exist. * Step 4: result = Q * c; */ const Index rows = dec().rows(), cols = dec().cols(), nonzero_pivots = dec().nonzeroPivots(); eigen_assert(rhs().rows() == rows); const Index smalldim = (std::min)(rows, cols); if(nonzero_pivots == 0) { dst.setZero(); return; } typename Rhs::PlainObject c(rhs().rows(), rhs().cols()); // Step 1 c = dec().permutationP() * rhs(); // Step 2 dec().matrixLU() .topLeftCorner(smalldim,smalldim) .template triangularView() .solveInPlace(c.topRows(smalldim)); if(rows>cols) { c.bottomRows(rows-cols) -= dec().matrixLU().bottomRows(rows-cols) * c.topRows(cols); } // Step 3 dec().matrixLU() .topLeftCorner(nonzero_pivots, nonzero_pivots) .template triangularView() .solveInPlace(c.topRows(nonzero_pivots)); // Step 4 for(Index i = 0; i < nonzero_pivots; ++i) dst.row(dec().permutationQ().indices().coeff(i)) = c.row(i); for(Index i = nonzero_pivots; i < dec().matrixLU().cols(); ++i) dst.row(dec().permutationQ().indices().coeff(i)).setZero(); } }; } // end namespace internal /******* MatrixBase methods *****************************************************************/ /** \lu_module * * \return the full-pivoting LU decomposition of \c *this. * * \sa class FullPivLU */ template inline const FullPivLU::PlainObject> MatrixBase::fullPivLu() const { return FullPivLU(eval()); } } // end namespace Eigen #endif // EIGEN_LU_H ctbnrle.1.1.1/hdr/Eigen/src/LU/CMakeLists.txt0000775000175000017500000000024012107032422021031 0ustar csheltoncsheltonFILE(GLOB Eigen_LU_SRCS "*.h") INSTALL(FILES ${Eigen_LU_SRCS} DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/LU COMPONENT Devel ) ADD_SUBDIRECTORY(arch) ctbnrle.1.1.1/hdr/Eigen/src/LU/PartialPivLU_MKL.h0000775000175000017500000000662712107032422021500 0ustar csheltoncshelton/* Copyright (c) 2011, Intel Corporation. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Intel Corporation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ******************************************************************************** * Content : Eigen bindings to Intel(R) MKL * LU decomposition with partial pivoting based on LAPACKE_?getrf function. ******************************************************************************** */ #ifndef EIGEN_PARTIALLU_LAPACK_H #define EIGEN_PARTIALLU_LAPACK_H #include "Eigen/src/Core/util/MKL_support.h" namespace Eigen { namespace internal { /** \internal Specialization for the data types supported by MKL */ #define EIGEN_MKL_LU_PARTPIV(EIGTYPE, MKLTYPE, MKLPREFIX) \ template \ struct partial_lu_impl \ { \ /* \internal performs the LU decomposition in-place of the matrix represented */ \ static lapack_int blocked_lu(lapack_int rows, lapack_int cols, EIGTYPE* lu_data, lapack_int luStride, lapack_int* row_transpositions, lapack_int& nb_transpositions, lapack_int maxBlockSize=256) \ { \ EIGEN_UNUSED_VARIABLE(maxBlockSize);\ lapack_int matrix_order, first_zero_pivot; \ lapack_int m, n, lda, *ipiv, info; \ EIGTYPE* a; \ /* Set up parameters for ?getrf */ \ matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \ lda = luStride; \ a = lu_data; \ ipiv = row_transpositions; \ m = rows; \ n = cols; \ nb_transpositions = 0; \ \ info = LAPACKE_##MKLPREFIX##getrf( matrix_order, m, n, (MKLTYPE*)a, lda, ipiv ); \ \ for(int i=0;i= 0); \ /* something should be done with nb_transpositions */ \ \ first_zero_pivot = info; \ return first_zero_pivot; \ } \ }; EIGEN_MKL_LU_PARTPIV(double, double, d) EIGEN_MKL_LU_PARTPIV(float, float, s) EIGEN_MKL_LU_PARTPIV(dcomplex, MKL_Complex16, z) EIGEN_MKL_LU_PARTPIV(scomplex, MKL_Complex8, c) } // end namespace internal } // end namespace Eigen #endif // EIGEN_PARTIALLU_LAPACK_H ctbnrle.1.1.1/hdr/Eigen/src/LU/PartialPivLU.h0000775000175000017500000004436112107032422020772 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2006-2009 Benoit Jacob // Copyright (C) 2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_PARTIALLU_H #define EIGEN_PARTIALLU_H namespace Eigen { /** \ingroup LU_Module * * \class PartialPivLU * * \brief LU decomposition of a matrix with partial pivoting, and related features * * \param MatrixType the type of the matrix of which we are computing the LU decomposition * * This class represents a LU decomposition of a \b square \b invertible matrix, with partial pivoting: the matrix A * is decomposed as A = PLU where L is unit-lower-triangular, U is upper-triangular, and P * is a permutation matrix. * * Typically, partial pivoting LU decomposition is only considered numerically stable for square invertible * matrices. Thus LAPACK's dgesv and dgesvx require the matrix to be square and invertible. The present class * does the same. It will assert that the matrix is square, but it won't (actually it can't) check that the * matrix is invertible: it is your task to check that you only use this decomposition on invertible matrices. * * The guaranteed safe alternative, working for all matrices, is the full pivoting LU decomposition, provided * by class FullPivLU. * * This is \b not a rank-revealing LU decomposition. Many features are intentionally absent from this class, * such as rank computation. If you need these features, use class FullPivLU. * * This LU decomposition is suitable to invert invertible matrices. It is what MatrixBase::inverse() uses * in the general case. * On the other hand, it is \b not suitable to determine whether a given matrix is invertible. * * The data of the LU decomposition can be directly accessed through the methods matrixLU(), permutationP(). * * \sa MatrixBase::partialPivLu(), MatrixBase::determinant(), MatrixBase::inverse(), MatrixBase::computeInverse(), class FullPivLU */ template class PartialPivLU { public: typedef _MatrixType MatrixType; enum { RowsAtCompileTime = MatrixType::RowsAtCompileTime, ColsAtCompileTime = MatrixType::ColsAtCompileTime, Options = MatrixType::Options, MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime }; typedef typename MatrixType::Scalar Scalar; typedef typename NumTraits::Real RealScalar; typedef typename internal::traits::StorageKind StorageKind; typedef typename MatrixType::Index Index; typedef PermutationMatrix PermutationType; typedef Transpositions TranspositionType; /** * \brief Default Constructor. * * The default constructor is useful in cases in which the user intends to * perform decompositions via PartialPivLU::compute(const MatrixType&). */ PartialPivLU(); /** \brief Default Constructor with memory preallocation * * Like the default constructor but with preallocation of the internal data * according to the specified problem \a size. * \sa PartialPivLU() */ PartialPivLU(Index size); /** Constructor. * * \param matrix the matrix of which to compute the LU decomposition. * * \warning The matrix should have full rank (e.g. if it's square, it should be invertible). * If you need to deal with non-full rank, use class FullPivLU instead. */ PartialPivLU(const MatrixType& matrix); PartialPivLU& compute(const MatrixType& matrix); /** \returns the LU decomposition matrix: the upper-triangular part is U, the * unit-lower-triangular part is L (at least for square matrices; in the non-square * case, special care is needed, see the documentation of class FullPivLU). * * \sa matrixL(), matrixU() */ inline const MatrixType& matrixLU() const { eigen_assert(m_isInitialized && "PartialPivLU is not initialized."); return m_lu; } /** \returns the permutation matrix P. */ inline const PermutationType& permutationP() const { eigen_assert(m_isInitialized && "PartialPivLU is not initialized."); return m_p; } /** This method returns the solution x to the equation Ax=b, where A is the matrix of which * *this is the LU decomposition. * * \param b the right-hand-side of the equation to solve. Can be a vector or a matrix, * the only requirement in order for the equation to make sense is that * b.rows()==A.rows(), where A is the matrix of which *this is the LU decomposition. * * \returns the solution. * * Example: \include PartialPivLU_solve.cpp * Output: \verbinclude PartialPivLU_solve.out * * Since this PartialPivLU class assumes anyway that the matrix A is invertible, the solution * theoretically exists and is unique regardless of b. * * \sa TriangularView::solve(), inverse(), computeInverse() */ template inline const internal::solve_retval solve(const MatrixBase& b) const { eigen_assert(m_isInitialized && "PartialPivLU is not initialized."); return internal::solve_retval(*this, b.derived()); } /** \returns the inverse of the matrix of which *this is the LU decomposition. * * \warning The matrix being decomposed here is assumed to be invertible. If you need to check for * invertibility, use class FullPivLU instead. * * \sa MatrixBase::inverse(), LU::inverse() */ inline const internal::solve_retval inverse() const { eigen_assert(m_isInitialized && "PartialPivLU is not initialized."); return internal::solve_retval (*this, MatrixType::Identity(m_lu.rows(), m_lu.cols())); } /** \returns the determinant of the matrix of which * *this is the LU decomposition. It has only linear complexity * (that is, O(n) where n is the dimension of the square matrix) * as the LU decomposition has already been computed. * * \note For fixed-size matrices of size up to 4, MatrixBase::determinant() offers * optimized paths. * * \warning a determinant can be very big or small, so for matrices * of large enough dimension, there is a risk of overflow/underflow. * * \sa MatrixBase::determinant() */ typename internal::traits::Scalar determinant() const; MatrixType reconstructedMatrix() const; inline Index rows() const { return m_lu.rows(); } inline Index cols() const { return m_lu.cols(); } protected: MatrixType m_lu; PermutationType m_p; TranspositionType m_rowsTranspositions; Index m_det_p; bool m_isInitialized; }; template PartialPivLU::PartialPivLU() : m_lu(), m_p(), m_rowsTranspositions(), m_det_p(0), m_isInitialized(false) { } template PartialPivLU::PartialPivLU(Index size) : m_lu(size, size), m_p(size), m_rowsTranspositions(size), m_det_p(0), m_isInitialized(false) { } template PartialPivLU::PartialPivLU(const MatrixType& matrix) : m_lu(matrix.rows(), matrix.rows()), m_p(matrix.rows()), m_rowsTranspositions(matrix.rows()), m_det_p(0), m_isInitialized(false) { compute(matrix); } namespace internal { /** \internal This is the blocked version of fullpivlu_unblocked() */ template struct partial_lu_impl { // FIXME add a stride to Map, so that the following mapping becomes easier, // another option would be to create an expression being able to automatically // warp any Map, Matrix, and Block expressions as a unique type, but since that's exactly // a Map + stride, why not adding a stride to Map, and convenient ctors from a Matrix, // and Block. typedef Map > MapLU; typedef Block MatrixType; typedef Block BlockType; typedef typename MatrixType::RealScalar RealScalar; typedef typename MatrixType::Index Index; /** \internal performs the LU decomposition in-place of the matrix \a lu * using an unblocked algorithm. * * In addition, this function returns the row transpositions in the * vector \a row_transpositions which must have a size equal to the number * of columns of the matrix \a lu, and an integer \a nb_transpositions * which returns the actual number of transpositions. * * \returns The index of the first pivot which is exactly zero if any, or a negative number otherwise. */ static Index unblocked_lu(MatrixType& lu, PivIndex* row_transpositions, PivIndex& nb_transpositions) { const Index rows = lu.rows(); const Index cols = lu.cols(); const Index size = (std::min)(rows,cols); nb_transpositions = 0; int first_zero_pivot = -1; for(Index k = 0; k < size; ++k) { Index rrows = rows-k-1; Index rcols = cols-k-1; Index row_of_biggest_in_col; RealScalar biggest_in_corner = lu.col(k).tail(rows-k).cwiseAbs().maxCoeff(&row_of_biggest_in_col); row_of_biggest_in_col += k; row_transpositions[k] = row_of_biggest_in_col; if(biggest_in_corner != RealScalar(0)) { if(k != row_of_biggest_in_col) { lu.row(k).swap(lu.row(row_of_biggest_in_col)); ++nb_transpositions; } // FIXME shall we introduce a safe quotient expression in cas 1/lu.coeff(k,k) // overflow but not the actual quotient? lu.col(k).tail(rrows) /= lu.coeff(k,k); } else if(first_zero_pivot==-1) { // the pivot is exactly zero, we record the index of the first pivot which is exactly 0, // and continue the factorization such we still have A = PLU first_zero_pivot = k; } if(k > > */ static Index blocked_lu(Index rows, Index cols, Scalar* lu_data, Index luStride, PivIndex* row_transpositions, PivIndex& nb_transpositions, Index maxBlockSize=256) { MapLU lu1(lu_data,StorageOrder==RowMajor?rows:luStride,StorageOrder==RowMajor?luStride:cols); MatrixType lu(lu1,0,0,rows,cols); const Index size = (std::min)(rows,cols); // if the matrix is too small, no blocking: if(size<=16) { return unblocked_lu(lu, row_transpositions, nb_transpositions); } // automatically adjust the number of subdivisions to the size // of the matrix so that there is enough sub blocks: Index blockSize; { blockSize = size/8; blockSize = (blockSize/16)*16; blockSize = (std::min)((std::max)(blockSize,Index(8)), maxBlockSize); } nb_transpositions = 0; int first_zero_pivot = -1; for(Index k = 0; k < size; k+=blockSize) { Index bs = (std::min)(size-k,blockSize); // actual size of the block Index trows = rows - k - bs; // trailing rows Index tsize = size - k - bs; // trailing size // partition the matrix: // A00 | A01 | A02 // lu = A_0 | A_1 | A_2 = A10 | A11 | A12 // A20 | A21 | A22 BlockType A_0(lu,0,0,rows,k); BlockType A_2(lu,0,k+bs,rows,tsize); BlockType A11(lu,k,k,bs,bs); BlockType A12(lu,k,k+bs,bs,tsize); BlockType A21(lu,k+bs,k,trows,bs); BlockType A22(lu,k+bs,k+bs,trows,tsize); PivIndex nb_transpositions_in_panel; // recursively call the blocked LU algorithm on [A11^T A21^T]^T // with a very small blocking size: Index ret = blocked_lu(trows+bs, bs, &lu.coeffRef(k,k), luStride, row_transpositions+k, nb_transpositions_in_panel, 16); if(ret>=0 && first_zero_pivot==-1) first_zero_pivot = k+ret; nb_transpositions += nb_transpositions_in_panel; // update permutations and apply them to A_0 for(Index i=k; i().solveInPlace(A12); A22.noalias() -= A21 * A12; } } return first_zero_pivot; } }; /** \internal performs the LU decomposition with partial pivoting in-place. */ template void partial_lu_inplace(MatrixType& lu, TranspositionType& row_transpositions, typename TranspositionType::Index& nb_transpositions) { eigen_assert(lu.cols() == row_transpositions.size()); eigen_assert((&row_transpositions.coeffRef(1)-&row_transpositions.coeffRef(0)) == 1); partial_lu_impl ::blocked_lu(lu.rows(), lu.cols(), &lu.coeffRef(0,0), lu.outerStride(), &row_transpositions.coeffRef(0), nb_transpositions); } } // end namespace internal template PartialPivLU& PartialPivLU::compute(const MatrixType& matrix) { m_lu = matrix; eigen_assert(matrix.rows() == matrix.cols() && "PartialPivLU is only for square (and moreover invertible) matrices"); const Index size = matrix.rows(); m_rowsTranspositions.resize(size); typename TranspositionType::Index nb_transpositions; internal::partial_lu_inplace(m_lu, m_rowsTranspositions, nb_transpositions); m_det_p = (nb_transpositions%2) ? -1 : 1; m_p = m_rowsTranspositions; m_isInitialized = true; return *this; } template typename internal::traits::Scalar PartialPivLU::determinant() const { eigen_assert(m_isInitialized && "PartialPivLU is not initialized."); return Scalar(m_det_p) * m_lu.diagonal().prod(); } /** \returns the matrix represented by the decomposition, * i.e., it returns the product: P^{-1} L U. * This function is provided for debug purpose. */ template MatrixType PartialPivLU::reconstructedMatrix() const { eigen_assert(m_isInitialized && "LU is not initialized."); // LU MatrixType res = m_lu.template triangularView().toDenseMatrix() * m_lu.template triangularView(); // P^{-1}(LU) res = m_p.inverse() * res; return res; } /***** Implementation of solve() *****************************************************/ namespace internal { template struct solve_retval, Rhs> : solve_retval_base, Rhs> { EIGEN_MAKE_SOLVE_HELPERS(PartialPivLU<_MatrixType>,Rhs) template void evalTo(Dest& dst) const { /* The decomposition PA = LU can be rewritten as A = P^{-1} L U. * So we proceed as follows: * Step 1: compute c = Pb. * Step 2: replace c by the solution x to Lx = c. * Step 3: replace c by the solution x to Ux = c. */ eigen_assert(rhs().rows() == dec().matrixLU().rows()); // Step 1 dst = dec().permutationP() * rhs(); // Step 2 dec().matrixLU().template triangularView().solveInPlace(dst); // Step 3 dec().matrixLU().template triangularView().solveInPlace(dst); } }; } // end namespace internal /******** MatrixBase methods *******/ /** \lu_module * * \return the partial-pivoting LU decomposition of \c *this. * * \sa class PartialPivLU */ template inline const PartialPivLU::PlainObject> MatrixBase::partialPivLu() const { return PartialPivLU(eval()); } #if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS /** \lu_module * * Synonym of partialPivLu(). * * \return the partial-pivoting LU decomposition of \c *this. * * \sa class PartialPivLU */ template inline const PartialPivLU::PlainObject> MatrixBase::lu() const { return PartialPivLU(eval()); } #endif } // end namespace Eigen #endif // EIGEN_PARTIALLU_H ctbnrle.1.1.1/hdr/Eigen/src/LU/Determinant.h0000775000175000017500000000734212107032422020726 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_DETERMINANT_H #define EIGEN_DETERMINANT_H namespace Eigen { namespace internal { template inline const typename Derived::Scalar bruteforce_det3_helper (const MatrixBase& matrix, int a, int b, int c) { return matrix.coeff(0,a) * (matrix.coeff(1,b) * matrix.coeff(2,c) - matrix.coeff(1,c) * matrix.coeff(2,b)); } template const typename Derived::Scalar bruteforce_det4_helper (const MatrixBase& matrix, int j, int k, int m, int n) { return (matrix.coeff(j,0) * matrix.coeff(k,1) - matrix.coeff(k,0) * matrix.coeff(j,1)) * (matrix.coeff(m,2) * matrix.coeff(n,3) - matrix.coeff(n,2) * matrix.coeff(m,3)); } template struct determinant_impl { static inline typename traits::Scalar run(const Derived& m) { if(Derived::ColsAtCompileTime==Dynamic && m.rows()==0) return typename traits::Scalar(1); return m.partialPivLu().determinant(); } }; template struct determinant_impl { static inline typename traits::Scalar run(const Derived& m) { return m.coeff(0,0); } }; template struct determinant_impl { static inline typename traits::Scalar run(const Derived& m) { return m.coeff(0,0) * m.coeff(1,1) - m.coeff(1,0) * m.coeff(0,1); } }; template struct determinant_impl { static inline typename traits::Scalar run(const Derived& m) { return bruteforce_det3_helper(m,0,1,2) - bruteforce_det3_helper(m,1,0,2) + bruteforce_det3_helper(m,2,0,1); } }; template struct determinant_impl { static typename traits::Scalar run(const Derived& m) { // trick by Martin Costabel to compute 4x4 det with only 30 muls return bruteforce_det4_helper(m,0,1,2,3) - bruteforce_det4_helper(m,0,2,1,3) + bruteforce_det4_helper(m,0,3,1,2) + bruteforce_det4_helper(m,1,2,0,3) - bruteforce_det4_helper(m,1,3,0,2) + bruteforce_det4_helper(m,2,3,0,1); } }; } // end namespace internal /** \lu_module * * \returns the determinant of this matrix */ template inline typename internal::traits::Scalar MatrixBase::determinant() const { assert(rows() == cols()); typedef typename internal::nested::type Nested; return internal::determinant_impl::type>::run(derived()); } } // end namespace Eigen #endif // EIGEN_DETERMINANT_H ctbnrle.1.1.1/hdr/Eigen/src/LU/Inverse.h0000775000175000017500000003543212107032422020070 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2010 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_INVERSE_H #define EIGEN_INVERSE_H namespace Eigen { namespace internal { /********************************** *** General case implementation *** **********************************/ template struct compute_inverse { static inline void run(const MatrixType& matrix, ResultType& result) { result = matrix.partialPivLu().inverse(); } }; template struct compute_inverse_and_det_with_check { /* nothing! general case not supported. */ }; /**************************** *** Size 1 implementation *** ****************************/ template struct compute_inverse { static inline void run(const MatrixType& matrix, ResultType& result) { typedef typename MatrixType::Scalar Scalar; result.coeffRef(0,0) = Scalar(1) / matrix.coeff(0,0); } }; template struct compute_inverse_and_det_with_check { static inline void run( const MatrixType& matrix, const typename MatrixType::RealScalar& absDeterminantThreshold, ResultType& result, typename ResultType::Scalar& determinant, bool& invertible ) { determinant = matrix.coeff(0,0); invertible = abs(determinant) > absDeterminantThreshold; if(invertible) result.coeffRef(0,0) = typename ResultType::Scalar(1) / determinant; } }; /**************************** *** Size 2 implementation *** ****************************/ template inline void compute_inverse_size2_helper( const MatrixType& matrix, const typename ResultType::Scalar& invdet, ResultType& result) { result.coeffRef(0,0) = matrix.coeff(1,1) * invdet; result.coeffRef(1,0) = -matrix.coeff(1,0) * invdet; result.coeffRef(0,1) = -matrix.coeff(0,1) * invdet; result.coeffRef(1,1) = matrix.coeff(0,0) * invdet; } template struct compute_inverse { static inline void run(const MatrixType& matrix, ResultType& result) { typedef typename ResultType::Scalar Scalar; const Scalar invdet = typename MatrixType::Scalar(1) / matrix.determinant(); compute_inverse_size2_helper(matrix, invdet, result); } }; template struct compute_inverse_and_det_with_check { static inline void run( const MatrixType& matrix, const typename MatrixType::RealScalar& absDeterminantThreshold, ResultType& inverse, typename ResultType::Scalar& determinant, bool& invertible ) { typedef typename ResultType::Scalar Scalar; determinant = matrix.determinant(); invertible = abs(determinant) > absDeterminantThreshold; if(!invertible) return; const Scalar invdet = Scalar(1) / determinant; compute_inverse_size2_helper(matrix, invdet, inverse); } }; /**************************** *** Size 3 implementation *** ****************************/ template inline typename MatrixType::Scalar cofactor_3x3(const MatrixType& m) { enum { i1 = (i+1) % 3, i2 = (i+2) % 3, j1 = (j+1) % 3, j2 = (j+2) % 3 }; return m.coeff(i1, j1) * m.coeff(i2, j2) - m.coeff(i1, j2) * m.coeff(i2, j1); } template inline void compute_inverse_size3_helper( const MatrixType& matrix, const typename ResultType::Scalar& invdet, const Matrix& cofactors_col0, ResultType& result) { result.row(0) = cofactors_col0 * invdet; result.coeffRef(1,0) = cofactor_3x3(matrix) * invdet; result.coeffRef(1,1) = cofactor_3x3(matrix) * invdet; result.coeffRef(1,2) = cofactor_3x3(matrix) * invdet; result.coeffRef(2,0) = cofactor_3x3(matrix) * invdet; result.coeffRef(2,1) = cofactor_3x3(matrix) * invdet; result.coeffRef(2,2) = cofactor_3x3(matrix) * invdet; } template struct compute_inverse { static inline void run(const MatrixType& matrix, ResultType& result) { typedef typename ResultType::Scalar Scalar; Matrix cofactors_col0; cofactors_col0.coeffRef(0) = cofactor_3x3(matrix); cofactors_col0.coeffRef(1) = cofactor_3x3(matrix); cofactors_col0.coeffRef(2) = cofactor_3x3(matrix); const Scalar det = (cofactors_col0.cwiseProduct(matrix.col(0))).sum(); const Scalar invdet = Scalar(1) / det; compute_inverse_size3_helper(matrix, invdet, cofactors_col0, result); } }; template struct compute_inverse_and_det_with_check { static inline void run( const MatrixType& matrix, const typename MatrixType::RealScalar& absDeterminantThreshold, ResultType& inverse, typename ResultType::Scalar& determinant, bool& invertible ) { typedef typename ResultType::Scalar Scalar; Matrix cofactors_col0; cofactors_col0.coeffRef(0) = cofactor_3x3(matrix); cofactors_col0.coeffRef(1) = cofactor_3x3(matrix); cofactors_col0.coeffRef(2) = cofactor_3x3(matrix); determinant = (cofactors_col0.cwiseProduct(matrix.col(0))).sum(); invertible = abs(determinant) > absDeterminantThreshold; if(!invertible) return; const Scalar invdet = Scalar(1) / determinant; compute_inverse_size3_helper(matrix, invdet, cofactors_col0, inverse); } }; /**************************** *** Size 4 implementation *** ****************************/ template inline const typename Derived::Scalar general_det3_helper (const MatrixBase& matrix, int i1, int i2, int i3, int j1, int j2, int j3) { return matrix.coeff(i1,j1) * (matrix.coeff(i2,j2) * matrix.coeff(i3,j3) - matrix.coeff(i2,j3) * matrix.coeff(i3,j2)); } template inline typename MatrixType::Scalar cofactor_4x4(const MatrixType& matrix) { enum { i1 = (i+1) % 4, i2 = (i+2) % 4, i3 = (i+3) % 4, j1 = (j+1) % 4, j2 = (j+2) % 4, j3 = (j+3) % 4 }; return general_det3_helper(matrix, i1, i2, i3, j1, j2, j3) + general_det3_helper(matrix, i2, i3, i1, j1, j2, j3) + general_det3_helper(matrix, i3, i1, i2, j1, j2, j3); } template struct compute_inverse_size4 { static void run(const MatrixType& matrix, ResultType& result) { result.coeffRef(0,0) = cofactor_4x4(matrix); result.coeffRef(1,0) = -cofactor_4x4(matrix); result.coeffRef(2,0) = cofactor_4x4(matrix); result.coeffRef(3,0) = -cofactor_4x4(matrix); result.coeffRef(0,2) = cofactor_4x4(matrix); result.coeffRef(1,2) = -cofactor_4x4(matrix); result.coeffRef(2,2) = cofactor_4x4(matrix); result.coeffRef(3,2) = -cofactor_4x4(matrix); result.coeffRef(0,1) = -cofactor_4x4(matrix); result.coeffRef(1,1) = cofactor_4x4(matrix); result.coeffRef(2,1) = -cofactor_4x4(matrix); result.coeffRef(3,1) = cofactor_4x4(matrix); result.coeffRef(0,3) = -cofactor_4x4(matrix); result.coeffRef(1,3) = cofactor_4x4(matrix); result.coeffRef(2,3) = -cofactor_4x4(matrix); result.coeffRef(3,3) = cofactor_4x4(matrix); result /= (matrix.col(0).cwiseProduct(result.row(0).transpose())).sum(); } }; template struct compute_inverse : compute_inverse_size4 { }; template struct compute_inverse_and_det_with_check { static inline void run( const MatrixType& matrix, const typename MatrixType::RealScalar& absDeterminantThreshold, ResultType& inverse, typename ResultType::Scalar& determinant, bool& invertible ) { determinant = matrix.determinant(); invertible = abs(determinant) > absDeterminantThreshold; if(invertible) compute_inverse::run(matrix, inverse); } }; /************************* *** MatrixBase methods *** *************************/ template struct traits > { typedef typename MatrixType::PlainObject ReturnType; }; template struct inverse_impl : public ReturnByValue > { typedef typename MatrixType::Index Index; typedef typename internal::eval::type MatrixTypeNested; typedef typename remove_all::type MatrixTypeNestedCleaned; MatrixTypeNested m_matrix; inverse_impl(const MatrixType& matrix) : m_matrix(matrix) {} inline Index rows() const { return m_matrix.rows(); } inline Index cols() const { return m_matrix.cols(); } template inline void evalTo(Dest& dst) const { const int Size = EIGEN_PLAIN_ENUM_MIN(MatrixType::ColsAtCompileTime,Dest::ColsAtCompileTime); EIGEN_ONLY_USED_FOR_DEBUG(Size); eigen_assert(( (Size<=1) || (Size>4) || (extract_data(m_matrix)!=extract_data(dst))) && "Aliasing problem detected in inverse(), you need to do inverse().eval() here."); compute_inverse::run(m_matrix, dst); } }; } // end namespace internal /** \lu_module * * \returns the matrix inverse of this matrix. * * For small fixed sizes up to 4x4, this method uses cofactors. * In the general case, this method uses class PartialPivLU. * * \note This matrix must be invertible, otherwise the result is undefined. If you need an * invertibility check, do the following: * \li for fixed sizes up to 4x4, use computeInverseAndDetWithCheck(). * \li for the general case, use class FullPivLU. * * Example: \include MatrixBase_inverse.cpp * Output: \verbinclude MatrixBase_inverse.out * * \sa computeInverseAndDetWithCheck() */ template inline const internal::inverse_impl MatrixBase::inverse() const { EIGEN_STATIC_ASSERT(!NumTraits::IsInteger,THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES) eigen_assert(rows() == cols()); return internal::inverse_impl(derived()); } /** \lu_module * * Computation of matrix inverse and determinant, with invertibility check. * * This is only for fixed-size square matrices of size up to 4x4. * * \param inverse Reference to the matrix in which to store the inverse. * \param determinant Reference to the variable in which to store the inverse. * \param invertible Reference to the bool variable in which to store whether the matrix is invertible. * \param absDeterminantThreshold Optional parameter controlling the invertibility check. * The matrix will be declared invertible if the absolute value of its * determinant is greater than this threshold. * * Example: \include MatrixBase_computeInverseAndDetWithCheck.cpp * Output: \verbinclude MatrixBase_computeInverseAndDetWithCheck.out * * \sa inverse(), computeInverseWithCheck() */ template template inline void MatrixBase::computeInverseAndDetWithCheck( ResultType& inverse, typename ResultType::Scalar& determinant, bool& invertible, const RealScalar& absDeterminantThreshold ) const { // i'd love to put some static assertions there, but SFINAE means that they have no effect... eigen_assert(rows() == cols()); // for 2x2, it's worth giving a chance to avoid evaluating. // for larger sizes, evaluating has negligible cost and limits code size. typedef typename internal::conditional< RowsAtCompileTime == 2, typename internal::remove_all::type>::type, PlainObject >::type MatrixType; internal::compute_inverse_and_det_with_check::run (derived(), absDeterminantThreshold, inverse, determinant, invertible); } /** \lu_module * * Computation of matrix inverse, with invertibility check. * * This is only for fixed-size square matrices of size up to 4x4. * * \param inverse Reference to the matrix in which to store the inverse. * \param invertible Reference to the bool variable in which to store whether the matrix is invertible. * \param absDeterminantThreshold Optional parameter controlling the invertibility check. * The matrix will be declared invertible if the absolute value of its * determinant is greater than this threshold. * * Example: \include MatrixBase_computeInverseWithCheck.cpp * Output: \verbinclude MatrixBase_computeInverseWithCheck.out * * \sa inverse(), computeInverseAndDetWithCheck() */ template template inline void MatrixBase::computeInverseWithCheck( ResultType& inverse, bool& invertible, const RealScalar& absDeterminantThreshold ) const { RealScalar determinant; // i'd love to put some static assertions there, but SFINAE means that they have no effect... eigen_assert(rows() == cols()); computeInverseAndDetWithCheck(inverse,determinant,invertible,absDeterminantThreshold); } } // end namespace Eigen #endif // EIGEN_INVERSE_H ctbnrle.1.1.1/hdr/Eigen/src/LU/arch/0000775000175000017500000000000012107032422017207 5ustar csheltoncsheltonctbnrle.1.1.1/hdr/Eigen/src/LU/arch/Inverse_SSE.h0000775000175000017500000003304012107032422021510 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2001 Intel Corporation // Copyright (C) 2010 Gael Guennebaud // Copyright (C) 2009 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . // The SSE code for the 4x4 float and double matrix inverse in this file // comes from the following Intel's library: // http://software.intel.com/en-us/articles/optimized-matrix-library-for-use-with-the-intel-pentiumr-4-processors-sse2-instructions/ // // Here is the respective copyright and license statement: // // Copyright (c) 2001 Intel Corporation. // // Permition is granted to use, copy, distribute and prepare derivative works // of this library for any purpose and without fee, provided, that the above // copyright notice and this statement appear in all copies. // Intel makes no representations about the suitability of this software for // any purpose, and specifically disclaims all warranties. // See LEGAL.TXT for all the legal information. #ifndef EIGEN_INVERSE_SSE_H #define EIGEN_INVERSE_SSE_H namespace Eigen { namespace internal { template struct compute_inverse_size4 { enum { MatrixAlignment = bool(MatrixType::Flags&AlignedBit), ResultAlignment = bool(ResultType::Flags&AlignedBit), StorageOrdersMatch = (MatrixType::Flags&RowMajorBit) == (ResultType::Flags&RowMajorBit) }; static void run(const MatrixType& matrix, ResultType& result) { EIGEN_ALIGN16 const unsigned int _Sign_PNNP[4] = { 0x00000000, 0x80000000, 0x80000000, 0x00000000 }; // Load the full matrix into registers __m128 _L1 = matrix.template packet( 0); __m128 _L2 = matrix.template packet( 4); __m128 _L3 = matrix.template packet( 8); __m128 _L4 = matrix.template packet(12); // The inverse is calculated using "Divide and Conquer" technique. The // original matrix is divide into four 2x2 sub-matrices. Since each // register holds four matrix element, the smaller matrices are // represented as a registers. Hence we get a better locality of the // calculations. __m128 A, B, C, D; // the four sub-matrices if(!StorageOrdersMatch) { A = _mm_unpacklo_ps(_L1, _L2); B = _mm_unpacklo_ps(_L3, _L4); C = _mm_unpackhi_ps(_L1, _L2); D = _mm_unpackhi_ps(_L3, _L4); } else { A = _mm_movelh_ps(_L1, _L2); B = _mm_movehl_ps(_L2, _L1); C = _mm_movelh_ps(_L3, _L4); D = _mm_movehl_ps(_L4, _L3); } __m128 iA, iB, iC, iD, // partial inverse of the sub-matrices DC, AB; __m128 dA, dB, dC, dD; // determinant of the sub-matrices __m128 det, d, d1, d2; __m128 rd; // reciprocal of the determinant // AB = A# * B AB = _mm_mul_ps(_mm_shuffle_ps(A,A,0x0F), B); AB = _mm_sub_ps(AB,_mm_mul_ps(_mm_shuffle_ps(A,A,0xA5), _mm_shuffle_ps(B,B,0x4E))); // DC = D# * C DC = _mm_mul_ps(_mm_shuffle_ps(D,D,0x0F), C); DC = _mm_sub_ps(DC,_mm_mul_ps(_mm_shuffle_ps(D,D,0xA5), _mm_shuffle_ps(C,C,0x4E))); // dA = |A| dA = _mm_mul_ps(_mm_shuffle_ps(A, A, 0x5F),A); dA = _mm_sub_ss(dA, _mm_movehl_ps(dA,dA)); // dB = |B| dB = _mm_mul_ps(_mm_shuffle_ps(B, B, 0x5F),B); dB = _mm_sub_ss(dB, _mm_movehl_ps(dB,dB)); // dC = |C| dC = _mm_mul_ps(_mm_shuffle_ps(C, C, 0x5F),C); dC = _mm_sub_ss(dC, _mm_movehl_ps(dC,dC)); // dD = |D| dD = _mm_mul_ps(_mm_shuffle_ps(D, D, 0x5F),D); dD = _mm_sub_ss(dD, _mm_movehl_ps(dD,dD)); // d = trace(AB*DC) = trace(A#*B*D#*C) d = _mm_mul_ps(_mm_shuffle_ps(DC,DC,0xD8),AB); // iD = C*A#*B iD = _mm_mul_ps(_mm_shuffle_ps(C,C,0xA0), _mm_movelh_ps(AB,AB)); iD = _mm_add_ps(iD,_mm_mul_ps(_mm_shuffle_ps(C,C,0xF5), _mm_movehl_ps(AB,AB))); // iA = B*D#*C iA = _mm_mul_ps(_mm_shuffle_ps(B,B,0xA0), _mm_movelh_ps(DC,DC)); iA = _mm_add_ps(iA,_mm_mul_ps(_mm_shuffle_ps(B,B,0xF5), _mm_movehl_ps(DC,DC))); // d = trace(AB*DC) = trace(A#*B*D#*C) [continue] d = _mm_add_ps(d, _mm_movehl_ps(d, d)); d = _mm_add_ss(d, _mm_shuffle_ps(d, d, 1)); d1 = _mm_mul_ss(dA,dD); d2 = _mm_mul_ss(dB,dC); // iD = D*|A| - C*A#*B iD = _mm_sub_ps(_mm_mul_ps(D,_mm_shuffle_ps(dA,dA,0)), iD); // iA = A*|D| - B*D#*C; iA = _mm_sub_ps(_mm_mul_ps(A,_mm_shuffle_ps(dD,dD,0)), iA); // det = |A|*|D| + |B|*|C| - trace(A#*B*D#*C) det = _mm_sub_ss(_mm_add_ss(d1,d2),d); rd = _mm_div_ss(_mm_set_ss(1.0f), det); // #ifdef ZERO_SINGULAR // rd = _mm_and_ps(_mm_cmpneq_ss(det,_mm_setzero_ps()), rd); // #endif // iB = D * (A#B)# = D*B#*A iB = _mm_mul_ps(D, _mm_shuffle_ps(AB,AB,0x33)); iB = _mm_sub_ps(iB, _mm_mul_ps(_mm_shuffle_ps(D,D,0xB1), _mm_shuffle_ps(AB,AB,0x66))); // iC = A * (D#C)# = A*C#*D iC = _mm_mul_ps(A, _mm_shuffle_ps(DC,DC,0x33)); iC = _mm_sub_ps(iC, _mm_mul_ps(_mm_shuffle_ps(A,A,0xB1), _mm_shuffle_ps(DC,DC,0x66))); rd = _mm_shuffle_ps(rd,rd,0); rd = _mm_xor_ps(rd, _mm_load_ps((float*)_Sign_PNNP)); // iB = C*|B| - D*B#*A iB = _mm_sub_ps(_mm_mul_ps(C,_mm_shuffle_ps(dB,dB,0)), iB); // iC = B*|C| - A*C#*D; iC = _mm_sub_ps(_mm_mul_ps(B,_mm_shuffle_ps(dC,dC,0)), iC); // iX = iX / det iA = _mm_mul_ps(rd,iA); iB = _mm_mul_ps(rd,iB); iC = _mm_mul_ps(rd,iC); iD = _mm_mul_ps(rd,iD); result.template writePacket( 0, _mm_shuffle_ps(iA,iB,0x77)); result.template writePacket( 4, _mm_shuffle_ps(iA,iB,0x22)); result.template writePacket( 8, _mm_shuffle_ps(iC,iD,0x77)); result.template writePacket(12, _mm_shuffle_ps(iC,iD,0x22)); } }; template struct compute_inverse_size4 { enum { MatrixAlignment = bool(MatrixType::Flags&AlignedBit), ResultAlignment = bool(ResultType::Flags&AlignedBit), StorageOrdersMatch = (MatrixType::Flags&RowMajorBit) == (ResultType::Flags&RowMajorBit) }; static void run(const MatrixType& matrix, ResultType& result) { const __m128d _Sign_NP = _mm_castsi128_pd(_mm_set_epi32(0x0,0x0,0x80000000,0x0)); const __m128d _Sign_PN = _mm_castsi128_pd(_mm_set_epi32(0x80000000,0x0,0x0,0x0)); // The inverse is calculated using "Divide and Conquer" technique. The // original matrix is divide into four 2x2 sub-matrices. Since each // register of the matrix holds two element, the smaller matrices are // consisted of two registers. Hence we get a better locality of the // calculations. // the four sub-matrices __m128d A1, A2, B1, B2, C1, C2, D1, D2; if(StorageOrdersMatch) { A1 = matrix.template packet( 0); B1 = matrix.template packet( 2); A2 = matrix.template packet( 4); B2 = matrix.template packet( 6); C1 = matrix.template packet( 8); D1 = matrix.template packet(10); C2 = matrix.template packet(12); D2 = matrix.template packet(14); } else { __m128d tmp; A1 = matrix.template packet( 0); C1 = matrix.template packet( 2); A2 = matrix.template packet( 4); C2 = matrix.template packet( 6); tmp = A1; A1 = _mm_unpacklo_pd(A1,A2); A2 = _mm_unpackhi_pd(tmp,A2); tmp = C1; C1 = _mm_unpacklo_pd(C1,C2); C2 = _mm_unpackhi_pd(tmp,C2); B1 = matrix.template packet( 8); D1 = matrix.template packet(10); B2 = matrix.template packet(12); D2 = matrix.template packet(14); tmp = B1; B1 = _mm_unpacklo_pd(B1,B2); B2 = _mm_unpackhi_pd(tmp,B2); tmp = D1; D1 = _mm_unpacklo_pd(D1,D2); D2 = _mm_unpackhi_pd(tmp,D2); } __m128d iA1, iA2, iB1, iB2, iC1, iC2, iD1, iD2, // partial invese of the sub-matrices DC1, DC2, AB1, AB2; __m128d dA, dB, dC, dD; // determinant of the sub-matrices __m128d det, d1, d2, rd; // dA = |A| dA = _mm_shuffle_pd(A2, A2, 1); dA = _mm_mul_pd(A1, dA); dA = _mm_sub_sd(dA, _mm_shuffle_pd(dA,dA,3)); // dB = |B| dB = _mm_shuffle_pd(B2, B2, 1); dB = _mm_mul_pd(B1, dB); dB = _mm_sub_sd(dB, _mm_shuffle_pd(dB,dB,3)); // AB = A# * B AB1 = _mm_mul_pd(B1, _mm_shuffle_pd(A2,A2,3)); AB2 = _mm_mul_pd(B2, _mm_shuffle_pd(A1,A1,0)); AB1 = _mm_sub_pd(AB1, _mm_mul_pd(B2, _mm_shuffle_pd(A1,A1,3))); AB2 = _mm_sub_pd(AB2, _mm_mul_pd(B1, _mm_shuffle_pd(A2,A2,0))); // dC = |C| dC = _mm_shuffle_pd(C2, C2, 1); dC = _mm_mul_pd(C1, dC); dC = _mm_sub_sd(dC, _mm_shuffle_pd(dC,dC,3)); // dD = |D| dD = _mm_shuffle_pd(D2, D2, 1); dD = _mm_mul_pd(D1, dD); dD = _mm_sub_sd(dD, _mm_shuffle_pd(dD,dD,3)); // DC = D# * C DC1 = _mm_mul_pd(C1, _mm_shuffle_pd(D2,D2,3)); DC2 = _mm_mul_pd(C2, _mm_shuffle_pd(D1,D1,0)); DC1 = _mm_sub_pd(DC1, _mm_mul_pd(C2, _mm_shuffle_pd(D1,D1,3))); DC2 = _mm_sub_pd(DC2, _mm_mul_pd(C1, _mm_shuffle_pd(D2,D2,0))); // rd = trace(AB*DC) = trace(A#*B*D#*C) d1 = _mm_mul_pd(AB1, _mm_shuffle_pd(DC1, DC2, 0)); d2 = _mm_mul_pd(AB2, _mm_shuffle_pd(DC1, DC2, 3)); rd = _mm_add_pd(d1, d2); rd = _mm_add_sd(rd, _mm_shuffle_pd(rd, rd,3)); // iD = C*A#*B iD1 = _mm_mul_pd(AB1, _mm_shuffle_pd(C1,C1,0)); iD2 = _mm_mul_pd(AB1, _mm_shuffle_pd(C2,C2,0)); iD1 = _mm_add_pd(iD1, _mm_mul_pd(AB2, _mm_shuffle_pd(C1,C1,3))); iD2 = _mm_add_pd(iD2, _mm_mul_pd(AB2, _mm_shuffle_pd(C2,C2,3))); // iA = B*D#*C iA1 = _mm_mul_pd(DC1, _mm_shuffle_pd(B1,B1,0)); iA2 = _mm_mul_pd(DC1, _mm_shuffle_pd(B2,B2,0)); iA1 = _mm_add_pd(iA1, _mm_mul_pd(DC2, _mm_shuffle_pd(B1,B1,3))); iA2 = _mm_add_pd(iA2, _mm_mul_pd(DC2, _mm_shuffle_pd(B2,B2,3))); // iD = D*|A| - C*A#*B dA = _mm_shuffle_pd(dA,dA,0); iD1 = _mm_sub_pd(_mm_mul_pd(D1, dA), iD1); iD2 = _mm_sub_pd(_mm_mul_pd(D2, dA), iD2); // iA = A*|D| - B*D#*C; dD = _mm_shuffle_pd(dD,dD,0); iA1 = _mm_sub_pd(_mm_mul_pd(A1, dD), iA1); iA2 = _mm_sub_pd(_mm_mul_pd(A2, dD), iA2); d1 = _mm_mul_sd(dA, dD); d2 = _mm_mul_sd(dB, dC); // iB = D * (A#B)# = D*B#*A iB1 = _mm_mul_pd(D1, _mm_shuffle_pd(AB2,AB1,1)); iB2 = _mm_mul_pd(D2, _mm_shuffle_pd(AB2,AB1,1)); iB1 = _mm_sub_pd(iB1, _mm_mul_pd(_mm_shuffle_pd(D1,D1,1), _mm_shuffle_pd(AB2,AB1,2))); iB2 = _mm_sub_pd(iB2, _mm_mul_pd(_mm_shuffle_pd(D2,D2,1), _mm_shuffle_pd(AB2,AB1,2))); // det = |A|*|D| + |B|*|C| - trace(A#*B*D#*C) det = _mm_add_sd(d1, d2); det = _mm_sub_sd(det, rd); // iC = A * (D#C)# = A*C#*D iC1 = _mm_mul_pd(A1, _mm_shuffle_pd(DC2,DC1,1)); iC2 = _mm_mul_pd(A2, _mm_shuffle_pd(DC2,DC1,1)); iC1 = _mm_sub_pd(iC1, _mm_mul_pd(_mm_shuffle_pd(A1,A1,1), _mm_shuffle_pd(DC2,DC1,2))); iC2 = _mm_sub_pd(iC2, _mm_mul_pd(_mm_shuffle_pd(A2,A2,1), _mm_shuffle_pd(DC2,DC1,2))); rd = _mm_div_sd(_mm_set_sd(1.0), det); // #ifdef ZERO_SINGULAR // rd = _mm_and_pd(_mm_cmpneq_sd(det,_mm_setzero_pd()), rd); // #endif rd = _mm_shuffle_pd(rd,rd,0); // iB = C*|B| - D*B#*A dB = _mm_shuffle_pd(dB,dB,0); iB1 = _mm_sub_pd(_mm_mul_pd(C1, dB), iB1); iB2 = _mm_sub_pd(_mm_mul_pd(C2, dB), iB2); d1 = _mm_xor_pd(rd, _Sign_PN); d2 = _mm_xor_pd(rd, _Sign_NP); // iC = B*|C| - A*C#*D; dC = _mm_shuffle_pd(dC,dC,0); iC1 = _mm_sub_pd(_mm_mul_pd(B1, dC), iC1); iC2 = _mm_sub_pd(_mm_mul_pd(B2, dC), iC2); result.template writePacket( 0, _mm_mul_pd(_mm_shuffle_pd(iA2, iA1, 3), d1)); // iA# / det result.template writePacket( 4, _mm_mul_pd(_mm_shuffle_pd(iA2, iA1, 0), d2)); result.template writePacket( 2, _mm_mul_pd(_mm_shuffle_pd(iB2, iB1, 3), d1)); // iB# / det result.template writePacket( 6, _mm_mul_pd(_mm_shuffle_pd(iB2, iB1, 0), d2)); result.template writePacket( 8, _mm_mul_pd(_mm_shuffle_pd(iC2, iC1, 3), d1)); // iC# / det result.template writePacket(12, _mm_mul_pd(_mm_shuffle_pd(iC2, iC1, 0), d2)); result.template writePacket(10, _mm_mul_pd(_mm_shuffle_pd(iD2, iD1, 3), d1)); // iD# / det result.template writePacket(14, _mm_mul_pd(_mm_shuffle_pd(iD2, iD1, 0), d2)); } }; } // end namespace internal } // end namespace Eigen #endif // EIGEN_INVERSE_SSE_H ctbnrle.1.1.1/hdr/Eigen/src/LU/arch/CMakeLists.txt0000775000175000017500000000022612107032422021752 0ustar csheltoncsheltonFILE(GLOB Eigen_LU_arch_SRCS "*.h") INSTALL(FILES ${Eigen_LU_arch_SRCS} DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/LU/arch COMPONENT Devel ) ctbnrle.1.1.1/hdr/Eigen/src/PardisoSupport/0000775000175000017500000000000012107032422020750 5ustar csheltoncsheltonctbnrle.1.1.1/hdr/Eigen/src/PardisoSupport/PardisoSupport.h0000775000175000017500000005201212107032422024122 0ustar csheltoncshelton/* Copyright (c) 2011, Intel Corporation. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Intel Corporation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ******************************************************************************** * Content : Eigen bindings to Intel(R) MKL PARDISO ******************************************************************************** */ #ifndef EIGEN_PARDISOSUPPORT_H #define EIGEN_PARDISOSUPPORT_H namespace Eigen { template class PardisoLU; template class PardisoLLT; template class PardisoLDLT; namespace internal { template struct pardiso_run_selector { static Index run( _MKL_DSS_HANDLE_t pt, Index maxfct, Index mnum, Index type, Index phase, Index n, void *a, Index *ia, Index *ja, Index *perm, Index nrhs, Index *iparm, Index msglvl, void *b, void *x) { Index error = 0; ::pardiso(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error); return error; } }; template<> struct pardiso_run_selector { typedef long long int Index; static Index run( _MKL_DSS_HANDLE_t pt, Index maxfct, Index mnum, Index type, Index phase, Index n, void *a, Index *ia, Index *ja, Index *perm, Index nrhs, Index *iparm, Index msglvl, void *b, void *x) { Index error = 0; ::pardiso_64(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error); return error; } }; template struct pardiso_traits; template struct pardiso_traits< PardisoLU<_MatrixType> > { typedef _MatrixType MatrixType; typedef typename _MatrixType::Scalar Scalar; typedef typename _MatrixType::RealScalar RealScalar; typedef typename _MatrixType::Index Index; }; template struct pardiso_traits< PardisoLLT<_MatrixType, Options> > { typedef _MatrixType MatrixType; typedef typename _MatrixType::Scalar Scalar; typedef typename _MatrixType::RealScalar RealScalar; typedef typename _MatrixType::Index Index; }; template struct pardiso_traits< PardisoLDLT<_MatrixType, Options> > { typedef _MatrixType MatrixType; typedef typename _MatrixType::Scalar Scalar; typedef typename _MatrixType::RealScalar RealScalar; typedef typename _MatrixType::Index Index; }; } template class PardisoImpl { typedef internal::pardiso_traits Traits; public: typedef typename Traits::MatrixType MatrixType; typedef typename Traits::Scalar Scalar; typedef typename Traits::RealScalar RealScalar; typedef typename Traits::Index Index; typedef SparseMatrix SparseMatrixType; typedef Matrix VectorType; typedef Matrix IntRowVectorType; typedef Matrix IntColVectorType; enum { ScalarIsComplex = NumTraits::IsComplex }; PardisoImpl() { eigen_assert((sizeof(Index) >= sizeof(_INTEGER_t) && sizeof(Index) <= 8) && "Non-supported index type"); m_iparm.setZero(); m_msglvl = 0; // No output m_initialized = false; } ~PardisoImpl() { pardisoRelease(); } inline Index cols() const { return m_size; } inline Index rows() const { return m_size; } /** \brief Reports whether previous computation was successful. * * \returns \c Success if computation was succesful, * \c NumericalIssue if the matrix appears to be negative. */ ComputationInfo info() const { eigen_assert(m_initialized && "Decomposition is not initialized."); return m_info; } /** \warning for advanced usage only. * \returns a reference to the parameter array controlling PARDISO. * See the PARDISO manual to know how to use it. */ Array& pardisoParameterArray() { return m_iparm; } /** Performs a symbolic decomposition on the sparcity of \a matrix. * * This function is particularly useful when solving for several problems having the same structure. * * \sa factorize() */ Derived& analyzePattern(const MatrixType& matrix); /** Performs a numeric decomposition of \a matrix * * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed. * * \sa analyzePattern() */ Derived& factorize(const MatrixType& matrix); Derived& compute(const MatrixType& matrix); /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. * * \sa compute() */ template inline const internal::solve_retval solve(const MatrixBase& b) const { eigen_assert(m_initialized && "Pardiso solver is not initialized."); eigen_assert(rows()==b.rows() && "PardisoImpl::solve(): invalid number of rows of the right hand side matrix b"); return internal::solve_retval(*this, b.derived()); } /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. * * \sa compute() */ template inline const internal::sparse_solve_retval solve(const SparseMatrixBase& b) const { eigen_assert(m_initialized && "Pardiso solver is not initialized."); eigen_assert(rows()==b.rows() && "PardisoImpl::solve(): invalid number of rows of the right hand side matrix b"); return internal::sparse_solve_retval(*this, b.derived()); } Derived& derived() { return *static_cast(this); } const Derived& derived() const { return *static_cast(this); } template bool _solve(const MatrixBase &b, MatrixBase& x) const; /** \internal */ template void _solve_sparse(const Rhs& b, SparseMatrix &dest) const { eigen_assert(m_size==b.rows()); // we process the sparse rhs per block of NbColsAtOnce columns temporarily stored into a dense matrix. static const int NbColsAtOnce = 4; int rhsCols = b.cols(); int size = b.rows(); // Pardiso cannot solve in-place, // so we need two temporaries Eigen::Matrix tmp_rhs(size,rhsCols); Eigen::Matrix tmp_res(size,rhsCols); for(int k=0; k(rhsCols-k, NbColsAtOnce); tmp_rhs.leftCols(actualCols) = b.middleCols(k,actualCols); tmp_res.leftCols(actualCols) = derived().solve(tmp_rhs.leftCols(actualCols)); dest.middleCols(k,actualCols) = tmp_res.leftCols(actualCols).sparseView(); } } protected: void pardisoRelease() { if(m_initialized) // Factorization ran at least once { internal::pardiso_run_selector::run(m_pt, 1, 1, m_type, -1, m_size, 0, 0, 0, m_perm.data(), 0, m_iparm.data(), m_msglvl, 0, 0); } } void pardisoInit(int type) { m_type = type; bool symmetric = abs(m_type) < 10; m_iparm[0] = 1; // No solver default m_iparm[1] = 3; // use Metis for the ordering m_iparm[2] = 1; // Numbers of processors, value of OMP_NUM_THREADS m_iparm[3] = 0; // No iterative-direct algorithm m_iparm[4] = 0; // No user fill-in reducing permutation m_iparm[5] = 0; // Write solution into x m_iparm[6] = 0; // Not in use m_iparm[7] = 2; // Max numbers of iterative refinement steps m_iparm[8] = 0; // Not in use m_iparm[9] = 13; // Perturb the pivot elements with 1E-13 m_iparm[10] = symmetric ? 0 : 1; // Use nonsymmetric permutation and scaling MPS m_iparm[11] = 0; // Not in use m_iparm[12] = symmetric ? 0 : 1; // Maximum weighted matching algorithm is switched-off (default for symmetric). // Try m_iparm[12] = 1 in case of inappropriate accuracy m_iparm[13] = 0; // Output: Number of perturbed pivots m_iparm[14] = 0; // Not in use m_iparm[15] = 0; // Not in use m_iparm[16] = 0; // Not in use m_iparm[17] = -1; // Output: Number of nonzeros in the factor LU m_iparm[18] = -1; // Output: Mflops for LU factorization m_iparm[19] = 0; // Output: Numbers of CG Iterations m_iparm[20] = 0; // 1x1 pivoting m_iparm[26] = 0; // No matrix checker m_iparm[27] = (sizeof(RealScalar) == 4) ? 1 : 0; m_iparm[34] = 1; // C indexing m_iparm[59] = 1; // Automatic switch between In-Core and Out-of-Core modes } protected: // cached data to reduce reallocation, etc. void manageErrorCode(Index error) { switch(error) { case 0: m_info = Success; break; case -4: case -7: m_info = NumericalIssue; break; default: m_info = InvalidInput; } } mutable SparseMatrixType m_matrix; ComputationInfo m_info; bool m_initialized, m_analysisIsOk, m_factorizationIsOk; Index m_type, m_msglvl; mutable void *m_pt[64]; mutable Array m_iparm; mutable IntColVectorType m_perm; Index m_size; private: PardisoImpl(PardisoImpl &) {} }; template Derived& PardisoImpl::compute(const MatrixType& a) { m_size = a.rows(); eigen_assert(a.rows() == a.cols()); pardisoRelease(); memset(m_pt, 0, sizeof(m_pt)); m_perm.setZero(m_size); derived().getMatrix(a); Index error; error = internal::pardiso_run_selector::run(m_pt, 1, 1, m_type, 12, m_size, m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL); manageErrorCode(error); m_analysisIsOk = true; m_factorizationIsOk = true; m_initialized = true; return derived(); } template Derived& PardisoImpl::analyzePattern(const MatrixType& a) { m_size = a.rows(); eigen_assert(m_size == a.cols()); pardisoRelease(); memset(m_pt, 0, sizeof(m_pt)); m_perm.setZero(m_size); derived().getMatrix(a); Index error; error = internal::pardiso_run_selector::run(m_pt, 1, 1, m_type, 11, m_size, m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL); manageErrorCode(error); m_analysisIsOk = true; m_factorizationIsOk = false; m_initialized = true; return derived(); } template Derived& PardisoImpl::factorize(const MatrixType& a) { eigen_assert(m_analysisIsOk && "You must first call analyzePattern()"); eigen_assert(m_size == a.rows() && m_size == a.cols()); derived().getMatrix(a); Index error; error = internal::pardiso_run_selector::run(m_pt, 1, 1, m_type, 22, m_size, m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL); manageErrorCode(error); m_factorizationIsOk = true; return derived(); } template template bool PardisoImpl::_solve(const MatrixBase &b, MatrixBase& x) const { if(m_iparm[0] == 0) // Factorization was not computed return false; //Index n = m_matrix.rows(); Index nrhs = Index(b.cols()); eigen_assert(m_size==b.rows()); eigen_assert(((MatrixBase::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major right hand sides are not supported"); eigen_assert(((MatrixBase::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major matrices of unknowns are not supported"); eigen_assert(((nrhs == 1) || b.outerStride() == b.rows())); // switch (transposed) { // case SvNoTrans : m_iparm[11] = 0 ; break; // case SvTranspose : m_iparm[11] = 2 ; break; // case SvAdjoint : m_iparm[11] = 1 ; break; // default: // //std::cerr << "Eigen: transposition option \"" << transposed << "\" not supported by the PARDISO backend\n"; // m_iparm[11] = 0; // } Scalar* rhs_ptr = const_cast(b.derived().data()); Matrix tmp; // Pardiso cannot solve in-place if(rhs_ptr == x.derived().data()) { tmp = b; rhs_ptr = tmp.data(); } Index error; error = internal::pardiso_run_selector::run(m_pt, 1, 1, m_type, 33, m_size, m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), m_perm.data(), nrhs, m_iparm.data(), m_msglvl, rhs_ptr, x.derived().data()); return error==0; } /** \ingroup PardisoSupport_Module * \class PardisoLU * \brief A sparse direct LU factorization and solver based on the PARDISO library * * This class allows to solve for A.X = B sparse linear problems via a direct LU factorization * using the Intel MKL PARDISO library. The sparse matrix A must be squared and invertible. * The vectors or matrices X and B can be either dense or sparse. * * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> * * \sa \ref TutorialSparseDirectSolvers */ template class PardisoLU : public PardisoImpl< PardisoLU > { protected: typedef PardisoImpl< PardisoLU > Base; typedef typename Base::Scalar Scalar; typedef typename Base::RealScalar RealScalar; using Base::pardisoInit; using Base::m_matrix; friend class PardisoImpl< PardisoLU >; public: using Base::compute; using Base::solve; PardisoLU() : Base() { pardisoInit(Base::ScalarIsComplex ? 13 : 11); } PardisoLU(const MatrixType& matrix) : Base() { pardisoInit(Base::ScalarIsComplex ? 13 : 11); compute(matrix); } protected: void getMatrix(const MatrixType& matrix) { m_matrix = matrix; } private: PardisoLU(PardisoLU& ) {} }; /** \ingroup PardisoSupport_Module * \class PardisoLLT * \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library * * This class allows to solve for A.X = B sparse linear problems via a LL^T Cholesky factorization * using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite. * The vectors or matrices X and B can be either dense or sparse. * * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> * \tparam UpLo can be any bitwise combination of Upper, Lower. The default is Upper, meaning only the upper triangular part has to be used. * Upper|Lower can be used to tell both triangular parts can be used as input. * * \sa \ref TutorialSparseDirectSolvers */ template class PardisoLLT : public PardisoImpl< PardisoLLT > { protected: typedef PardisoImpl< PardisoLLT > Base; typedef typename Base::Scalar Scalar; typedef typename Base::Index Index; typedef typename Base::RealScalar RealScalar; using Base::pardisoInit; using Base::m_matrix; friend class PardisoImpl< PardisoLLT >; public: enum { UpLo = _UpLo }; using Base::compute; using Base::solve; PardisoLLT() : Base() { pardisoInit(Base::ScalarIsComplex ? 4 : 2); } PardisoLLT(const MatrixType& matrix) : Base() { pardisoInit(Base::ScalarIsComplex ? 4 : 2); compute(matrix); } protected: void getMatrix(const MatrixType& matrix) { // PARDISO supports only upper, row-major matrices PermutationMatrix p_null; m_matrix.resize(matrix.rows(), matrix.cols()); m_matrix.template selfadjointView() = matrix.template selfadjointView().twistedBy(p_null); } private: PardisoLLT(PardisoLLT& ) {} }; /** \ingroup PardisoSupport_Module * \class PardisoLDLT * \brief A sparse direct Cholesky (LDLT) factorization and solver based on the PARDISO library * * This class allows to solve for A.X = B sparse linear problems via a LDL^T Cholesky factorization * using the Intel MKL PARDISO library. The sparse matrix A is assumed to be selfajoint and positive definite. * For complex matrices, A can also be symmetric only, see the \a Options template parameter. * The vectors or matrices X and B can be either dense or sparse. * * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> * \tparam Options can be any bitwise combination of Upper, Lower, and Symmetric. The default is Upper, meaning only the upper triangular part has to be used. * Symmetric can be used for symmetric, non-selfadjoint complex matrices, the default being to assume a selfadjoint matrix. * Upper|Lower can be used to tell both triangular parts can be used as input. * * \sa \ref TutorialSparseDirectSolvers */ template class PardisoLDLT : public PardisoImpl< PardisoLDLT > { protected: typedef PardisoImpl< PardisoLDLT > Base; typedef typename Base::Scalar Scalar; typedef typename Base::Index Index; typedef typename Base::RealScalar RealScalar; using Base::pardisoInit; using Base::m_matrix; friend class PardisoImpl< PardisoLDLT >; public: using Base::compute; using Base::solve; enum { UpLo = Options&(Upper|Lower) }; PardisoLDLT() : Base() { pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2); } PardisoLDLT(const MatrixType& matrix) : Base() { pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2); compute(matrix); } void getMatrix(const MatrixType& matrix) { // PARDISO supports only upper, row-major matrices PermutationMatrix p_null; m_matrix.resize(matrix.rows(), matrix.cols()); m_matrix.template selfadjointView() = matrix.template selfadjointView().twistedBy(p_null); } private: PardisoLDLT(PardisoLDLT& ) {} }; namespace internal { template struct solve_retval, Rhs> : solve_retval_base, Rhs> { typedef PardisoImpl<_Derived> Dec; EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) template void evalTo(Dest& dst) const { dec()._solve(rhs(),dst); } }; template struct sparse_solve_retval, Rhs> : sparse_solve_retval_base, Rhs> { typedef PardisoImpl Dec; EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs) template void evalTo(Dest& dst) const { dec().derived()._solve_sparse(rhs(),dst); } }; } // end namespace internal } // end namespace Eigen #endif // EIGEN_PARDISOSUPPORT_H ctbnrle.1.1.1/hdr/Eigen/src/PardisoSupport/CMakeLists.txt0000775000175000017500000000025412107032422023514 0ustar csheltoncsheltonFILE(GLOB Eigen_PardisoSupport_SRCS "*.h") INSTALL(FILES ${Eigen_PardisoSupport_SRCS} DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/PardisoSupport COMPONENT Devel ) ctbnrle.1.1.1/hdr/Eigen/src/Sparse/0000775000175000017500000000000012107032422017207 5ustar csheltoncsheltonctbnrle.1.1.1/hdr/Eigen/src/StlSupport/0000775000175000017500000000000012107032422020111 5ustar csheltoncsheltonctbnrle.1.1.1/hdr/Eigen/src/StlSupport/StdList.h0000775000175000017500000001225112107032422021654 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Hauke Heibel // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_STDLIST_H #define EIGEN_STDLIST_H #include "Eigen/src/StlSupport/details.h" // Define the explicit instantiation (e.g. necessary for the Intel compiler) #if defined(__INTEL_COMPILER) || defined(__GNUC__) #define EIGEN_EXPLICIT_STL_LIST_INSTANTIATION(...) template class std::list<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> >; #else #define EIGEN_EXPLICIT_STL_LIST_INSTANTIATION(...) #endif /** * This section contains a convenience MACRO which allows an easy specialization of * std::list such that for data types with alignment issues the correct allocator * is used automatically. */ #define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...) \ EIGEN_EXPLICIT_STL_LIST_INSTANTIATION(__VA_ARGS__) \ namespace std \ { \ template \ class list<__VA_ARGS__, _Ay> \ : public list<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \ { \ typedef list<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > list_base; \ public: \ typedef __VA_ARGS__ value_type; \ typedef typename list_base::allocator_type allocator_type; \ typedef typename list_base::size_type size_type; \ typedef typename list_base::iterator iterator; \ explicit list(const allocator_type& a = allocator_type()) : list_base(a) {} \ template \ list(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : list_base(first, last, a) {} \ list(const list& c) : list_base(c) {} \ explicit list(size_type num, const value_type& val = value_type()) : list_base(num, val) {} \ list(iterator start, iterator end) : list_base(start, end) {} \ list& operator=(const list& x) { \ list_base::operator=(x); \ return *this; \ } \ }; \ } // check whether we really need the std::vector specialization #if !(defined(_GLIBCXX_VECTOR) && (!EIGEN_GNUC_AT_LEAST(4,1))) /* Note that before gcc-4.1 we already have: std::list::resize(size_type,const T&). */ namespace std { #define EIGEN_STD_LIST_SPECIALIZATION_BODY \ public: \ typedef T value_type; \ typedef typename list_base::allocator_type allocator_type; \ typedef typename list_base::size_type size_type; \ typedef typename list_base::iterator iterator; \ typedef typename list_base::const_iterator const_iterator; \ explicit list(const allocator_type& a = allocator_type()) : list_base(a) {} \ template \ list(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) \ : list_base(first, last, a) {} \ list(const list& c) : list_base(c) {} \ explicit list(size_type num, const value_type& val = value_type()) : list_base(num, val) {} \ list(iterator start, iterator end) : list_base(start, end) {} \ list& operator=(const list& x) { \ list_base::operator=(x); \ return *this; \ } template class list > : public list > { typedef list > list_base; EIGEN_STD_LIST_SPECIALIZATION_BODY void resize(size_type new_size) { resize(new_size, T()); } void resize(size_type new_size, const value_type& x) { if (list_base::size() < new_size) list_base::insert(list_base::end(), new_size - list_base::size(), x); else while (new_size < list_base::size()) list_base::pop_back(); } #if defined(_LIST_) // workaround MSVC std::list implementation void push_back(const value_type& x) { list_base::push_back(x); } using list_base::insert; iterator insert(const_iterator position, const value_type& x) { return list_base::insert(position,x); } void insert(const_iterator position, size_type new_size, const value_type& x) { list_base::insert(position, new_size, x); } #endif }; } #endif // check whether specialization is actually required #endif // EIGEN_STDLIST_H ctbnrle.1.1.1/hdr/Eigen/src/StlSupport/CMakeLists.txt0000775000175000017500000000023712107032422022656 0ustar csheltoncsheltonFILE(GLOB Eigen_StlSupport_SRCS "*.h") INSTALL(FILES ${Eigen_StlSupport_SRCS} DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/StlSupport COMPONENT Devel ) ctbnrle.1.1.1/hdr/Eigen/src/StlSupport/StdDeque.h0000775000175000017500000001416312107032422022010 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Gael Guennebaud // Copyright (C) 2009 Hauke Heibel // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_STDDEQUE_H #define EIGEN_STDDEQUE_H #include "Eigen/src/StlSupport/details.h" // Define the explicit instantiation (e.g. necessary for the Intel compiler) #if defined(__INTEL_COMPILER) || defined(__GNUC__) #define EIGEN_EXPLICIT_STL_DEQUE_INSTANTIATION(...) template class std::deque<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> >; #else #define EIGEN_EXPLICIT_STL_DEQUE_INSTANTIATION(...) #endif /** * This section contains a convenience MACRO which allows an easy specialization of * std::deque such that for data types with alignment issues the correct allocator * is used automatically. */ #define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...) \ EIGEN_EXPLICIT_STL_DEQUE_INSTANTIATION(__VA_ARGS__) \ namespace std \ { \ template \ class deque<__VA_ARGS__, _Ay> \ : public deque<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \ { \ typedef deque<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > deque_base; \ public: \ typedef __VA_ARGS__ value_type; \ typedef typename deque_base::allocator_type allocator_type; \ typedef typename deque_base::size_type size_type; \ typedef typename deque_base::iterator iterator; \ explicit deque(const allocator_type& a = allocator_type()) : deque_base(a) {} \ template \ deque(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : deque_base(first, last, a) {} \ deque(const deque& c) : deque_base(c) {} \ explicit deque(size_type num, const value_type& val = value_type()) : deque_base(num, val) {} \ deque(iterator start, iterator end) : deque_base(start, end) {} \ deque& operator=(const deque& x) { \ deque_base::operator=(x); \ return *this; \ } \ }; \ } // check whether we really need the std::deque specialization #if !(defined(_GLIBCXX_DEQUE) && (!EIGEN_GNUC_AT_LEAST(4,1))) /* Note that before gcc-4.1 we already have: std::deque::resize(size_type,const T&). */ namespace std { #define EIGEN_STD_DEQUE_SPECIALIZATION_BODY \ public: \ typedef T value_type; \ typedef typename deque_base::allocator_type allocator_type; \ typedef typename deque_base::size_type size_type; \ typedef typename deque_base::iterator iterator; \ typedef typename deque_base::const_iterator const_iterator; \ explicit deque(const allocator_type& a = allocator_type()) : deque_base(a) {} \ template \ deque(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) \ : deque_base(first, last, a) {} \ deque(const deque& c) : deque_base(c) {} \ explicit deque(size_type num, const value_type& val = value_type()) : deque_base(num, val) {} \ deque(iterator start, iterator end) : deque_base(start, end) {} \ deque& operator=(const deque& x) { \ deque_base::operator=(x); \ return *this; \ } template class deque > : public deque > { typedef deque > deque_base; EIGEN_STD_DEQUE_SPECIALIZATION_BODY void resize(size_type new_size) { resize(new_size, T()); } #if defined(_DEQUE_) // workaround MSVC std::deque implementation void resize(size_type new_size, const value_type& x) { if (deque_base::size() < new_size) deque_base::_Insert_n(deque_base::end(), new_size - deque_base::size(), x); else if (new_size < deque_base::size()) deque_base::erase(deque_base::begin() + new_size, deque_base::end()); } void push_back(const value_type& x) { deque_base::push_back(x); } void push_front(const value_type& x) { deque_base::push_front(x); } using deque_base::insert; iterator insert(const_iterator position, const value_type& x) { return deque_base::insert(position,x); } void insert(const_iterator position, size_type new_size, const value_type& x) { deque_base::insert(position, new_size, x); } #elif defined(_GLIBCXX_DEQUE) && EIGEN_GNUC_AT_LEAST(4,2) // workaround GCC std::deque implementation void resize(size_type new_size, const value_type& x) { if (new_size < deque_base::size()) deque_base::_M_erase_at_end(this->_M_impl._M_start + new_size); else deque_base::insert(deque_base::end(), new_size - deque_base::size(), x); } #else // either GCC 4.1 or non-GCC // default implementation which should always work. void resize(size_type new_size, const value_type& x) { if (new_size < deque_base::size()) deque_base::erase(deque_base::begin() + new_size, deque_base::end()); else if (new_size > deque_base::size()) deque_base::insert(deque_base::end(), new_size - deque_base::size(), x); } #endif }; } #endif // check whether specialization is actually required #endif // EIGEN_STDDEQUE_H ctbnrle.1.1.1/hdr/Eigen/src/StlSupport/details.h0000775000175000017500000000670712107032422021724 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Gael Guennebaud // Copyright (C) 2009 Hauke Heibel // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_STL_DETAILS_H #define EIGEN_STL_DETAILS_H #ifndef EIGEN_ALIGNED_ALLOCATOR #define EIGEN_ALIGNED_ALLOCATOR Eigen::aligned_allocator #endif namespace Eigen { // This one is needed to prevent reimplementing the whole std::vector. template class aligned_allocator_indirection : public EIGEN_ALIGNED_ALLOCATOR { public: typedef size_t size_type; typedef ptrdiff_t difference_type; typedef T* pointer; typedef const T* const_pointer; typedef T& reference; typedef const T& const_reference; typedef T value_type; template struct rebind { typedef aligned_allocator_indirection other; }; aligned_allocator_indirection() {} aligned_allocator_indirection(const aligned_allocator_indirection& ) : EIGEN_ALIGNED_ALLOCATOR() {} aligned_allocator_indirection(const EIGEN_ALIGNED_ALLOCATOR& ) {} template aligned_allocator_indirection(const aligned_allocator_indirection& ) {} template aligned_allocator_indirection(const EIGEN_ALIGNED_ALLOCATOR& ) {} ~aligned_allocator_indirection() {} }; #ifdef _MSC_VER // sometimes, MSVC detects, at compile time, that the argument x // in std::vector::resize(size_t s,T x) won't be aligned and generate an error // even if this function is never called. Whence this little wrapper. #define EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T) \ typename Eigen::internal::conditional< \ Eigen::internal::is_arithmetic::value, \ T, \ Eigen::internal::workaround_msvc_stl_support \ >::type namespace internal { template struct workaround_msvc_stl_support : public T { inline workaround_msvc_stl_support() : T() {} inline workaround_msvc_stl_support(const T& other) : T(other) {} inline operator T& () { return *static_cast(this); } inline operator const T& () const { return *static_cast(this); } template inline T& operator=(const OtherT& other) { T::operator=(other); return *this; } inline workaround_msvc_stl_support& operator=(const workaround_msvc_stl_support& other) { T::operator=(other); return *this; } }; } #else #define EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T) T #endif } #endif // EIGEN_STL_DETAILS_H ctbnrle.1.1.1/hdr/Eigen/src/StlSupport/StdVector.h0000775000175000017500000001352612107032422022211 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Gael Guennebaud // Copyright (C) 2009 Hauke Heibel // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_STDVECTOR_H #define EIGEN_STDVECTOR_H #include "Eigen/src/StlSupport/details.h" /** * This section contains a convenience MACRO which allows an easy specialization of * std::vector such that for data types with alignment issues the correct allocator * is used automatically. */ #define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...) \ namespace std \ { \ template<> \ class vector<__VA_ARGS__, std::allocator<__VA_ARGS__> > \ : public vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \ { \ typedef vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > vector_base; \ public: \ typedef __VA_ARGS__ value_type; \ typedef vector_base::allocator_type allocator_type; \ typedef vector_base::size_type size_type; \ typedef vector_base::iterator iterator; \ explicit vector(const allocator_type& a = allocator_type()) : vector_base(a) {} \ template \ vector(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : vector_base(first, last, a) {} \ vector(const vector& c) : vector_base(c) {} \ explicit vector(size_type num, const value_type& val = value_type()) : vector_base(num, val) {} \ vector(iterator start, iterator end) : vector_base(start, end) {} \ vector& operator=(const vector& x) { \ vector_base::operator=(x); \ return *this; \ } \ }; \ } namespace std { #define EIGEN_STD_VECTOR_SPECIALIZATION_BODY \ public: \ typedef T value_type; \ typedef typename vector_base::allocator_type allocator_type; \ typedef typename vector_base::size_type size_type; \ typedef typename vector_base::iterator iterator; \ typedef typename vector_base::const_iterator const_iterator; \ explicit vector(const allocator_type& a = allocator_type()) : vector_base(a) {} \ template \ vector(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) \ : vector_base(first, last, a) {} \ vector(const vector& c) : vector_base(c) {} \ explicit vector(size_type num, const value_type& val = value_type()) : vector_base(num, val) {} \ vector(iterator start, iterator end) : vector_base(start, end) {} \ vector& operator=(const vector& x) { \ vector_base::operator=(x); \ return *this; \ } template class vector > : public vector > { typedef vector > vector_base; EIGEN_STD_VECTOR_SPECIALIZATION_BODY void resize(size_type new_size) { resize(new_size, T()); } #if defined(_VECTOR_) // workaround MSVC std::vector implementation void resize(size_type new_size, const value_type& x) { if (vector_base::size() < new_size) vector_base::_Insert_n(vector_base::end(), new_size - vector_base::size(), x); else if (new_size < vector_base::size()) vector_base::erase(vector_base::begin() + new_size, vector_base::end()); } void push_back(const value_type& x) { vector_base::push_back(x); } using vector_base::insert; iterator insert(const_iterator position, const value_type& x) { return vector_base::insert(position,x); } void insert(const_iterator position, size_type new_size, const value_type& x) { vector_base::insert(position, new_size, x); } #elif defined(_GLIBCXX_VECTOR) && (!(EIGEN_GNUC_AT_LEAST(4,1))) /* Note that before gcc-4.1 we already have: std::vector::resize(size_type,const T&). * However, this specialization is still needed to make the above EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION trick to work. */ void resize(size_type new_size, const value_type& x) { vector_base::resize(new_size,x); } #elif defined(_GLIBCXX_VECTOR) && EIGEN_GNUC_AT_LEAST(4,2) // workaround GCC std::vector implementation void resize(size_type new_size, const value_type& x) { if (new_size < vector_base::size()) vector_base::_M_erase_at_end(this->_M_impl._M_start + new_size); else vector_base::insert(vector_base::end(), new_size - vector_base::size(), x); } #else // either GCC 4.1 or non-GCC // default implementation which should always work. void resize(size_type new_size, const value_type& x) { if (new_size < vector_base::size()) vector_base::erase(vector_base::begin() + new_size, vector_base::end()); else if (new_size > vector_base::size()) vector_base::insert(vector_base::end(), new_size - vector_base::size(), x); } #endif }; } #endif // EIGEN_STDVECTOR_H ctbnrle.1.1.1/hdr/Eigen/src/CMakeLists.txt0000775000175000017500000000046412107032422020521 0ustar csheltoncsheltonfile(GLOB Eigen_src_subdirectories "*") escape_string_as_regex(ESCAPED_CMAKE_CURRENT_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}") foreach(f ${Eigen_src_subdirectories}) if(NOT f MATCHES "\\.txt" AND NOT f MATCHES "${ESCAPED_CMAKE_CURRENT_SOURCE_DIR}/[.].+" ) add_subdirectory(${f}) endif() endforeach() ctbnrle.1.1.1/hdr/Eigen/src/CholmodSupport/0000775000175000017500000000000012107032422020734 5ustar csheltoncsheltonctbnrle.1.1.1/hdr/Eigen/src/CholmodSupport/CMakeLists.txt0000775000175000017500000000025412107032422023500 0ustar csheltoncsheltonFILE(GLOB Eigen_CholmodSupport_SRCS "*.h") INSTALL(FILES ${Eigen_CholmodSupport_SRCS} DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/CholmodSupport COMPONENT Devel ) ctbnrle.1.1.1/hdr/Eigen/src/CholmodSupport/CholmodSupport.h0000775000175000017500000004717412107032422024107 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2010 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_CHOLMODSUPPORT_H #define EIGEN_CHOLMODSUPPORT_H namespace Eigen { namespace internal { template void cholmod_configure_matrix(CholmodType& mat) { if (internal::is_same::value) { mat.xtype = CHOLMOD_REAL; mat.dtype = CHOLMOD_SINGLE; } else if (internal::is_same::value) { mat.xtype = CHOLMOD_REAL; mat.dtype = CHOLMOD_DOUBLE; } else if (internal::is_same >::value) { mat.xtype = CHOLMOD_COMPLEX; mat.dtype = CHOLMOD_SINGLE; } else if (internal::is_same >::value) { mat.xtype = CHOLMOD_COMPLEX; mat.dtype = CHOLMOD_DOUBLE; } else { eigen_assert(false && "Scalar type not supported by CHOLMOD"); } } } // namespace internal /** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object. * Note that the data are shared. */ template cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat) { typedef SparseMatrix<_Scalar,_Options,_Index> MatrixType; cholmod_sparse res; res.nzmax = mat.nonZeros(); res.nrow = mat.rows();; res.ncol = mat.cols(); res.p = mat.outerIndexPtr(); res.i = mat.innerIndexPtr(); res.x = mat.valuePtr(); res.sorted = 1; if(mat.isCompressed()) { res.packed = 1; } else { res.packed = 0; res.nz = mat.innerNonZeroPtr(); } res.dtype = 0; res.stype = -1; if (internal::is_same<_Index,int>::value) { res.itype = CHOLMOD_INT; } else { eigen_assert(false && "Index type different than int is not supported yet"); } // setup res.xtype internal::cholmod_configure_matrix<_Scalar>(res); res.stype = 0; return res; } template const cholmod_sparse viewAsCholmod(const SparseMatrix<_Scalar,_Options,_Index>& mat) { cholmod_sparse res = viewAsCholmod(mat.const_cast_derived()); return res; } /** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix. * The data are not copied but shared. */ template cholmod_sparse viewAsCholmod(const SparseSelfAdjointView, UpLo>& mat) { cholmod_sparse res = viewAsCholmod(mat.matrix().const_cast_derived()); if(UpLo==Upper) res.stype = 1; if(UpLo==Lower) res.stype = -1; return res; } /** Returns a view of the Eigen \b dense matrix \a mat as Cholmod dense matrix. * The data are not copied but shared. */ template cholmod_dense viewAsCholmod(MatrixBase& mat) { EIGEN_STATIC_ASSERT((internal::traits::Flags&RowMajorBit)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES); typedef typename Derived::Scalar Scalar; cholmod_dense res; res.nrow = mat.rows(); res.ncol = mat.cols(); res.nzmax = res.nrow * res.ncol; res.d = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride(); res.x = mat.derived().data(); res.z = 0; internal::cholmod_configure_matrix(res); return res; } /** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix. * The data are not copied but shared. */ template MappedSparseMatrix viewAsEigen(cholmod_sparse& cm) { return MappedSparseMatrix (cm.nrow, cm.ncol, reinterpret_cast(cm.p)[cm.ncol], reinterpret_cast(cm.p), reinterpret_cast(cm.i),reinterpret_cast(cm.x) ); } enum CholmodMode { CholmodAuto, CholmodSimplicialLLt, CholmodSupernodalLLt, CholmodLDLt }; /** \ingroup CholmodSupport_Module * \class CholmodBase * \brief The base class for the direct Cholesky factorization of Cholmod * \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT */ template class CholmodBase : internal::noncopyable { public: typedef _MatrixType MatrixType; enum { UpLo = _UpLo }; typedef typename MatrixType::Scalar Scalar; typedef typename MatrixType::RealScalar RealScalar; typedef MatrixType CholMatrixType; typedef typename MatrixType::Index Index; public: CholmodBase() : m_cholmodFactor(0), m_info(Success), m_isInitialized(false) { cholmod_start(&m_cholmod); } CholmodBase(const MatrixType& matrix) : m_cholmodFactor(0), m_info(Success), m_isInitialized(false) { cholmod_start(&m_cholmod); compute(matrix); } ~CholmodBase() { if(m_cholmodFactor) cholmod_free_factor(&m_cholmodFactor, &m_cholmod); cholmod_finish(&m_cholmod); } inline Index cols() const { return m_cholmodFactor->n; } inline Index rows() const { return m_cholmodFactor->n; } Derived& derived() { return *static_cast(this); } const Derived& derived() const { return *static_cast(this); } /** \brief Reports whether previous computation was successful. * * \returns \c Success if computation was succesful, * \c NumericalIssue if the matrix.appears to be negative. */ ComputationInfo info() const { eigen_assert(m_isInitialized && "Decomposition is not initialized."); return m_info; } /** Computes the sparse Cholesky decomposition of \a matrix */ Derived& compute(const MatrixType& matrix) { analyzePattern(matrix); factorize(matrix); return derived(); } /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. * * \sa compute() */ template inline const internal::solve_retval solve(const MatrixBase& b) const { eigen_assert(m_isInitialized && "LLT is not initialized."); eigen_assert(rows()==b.rows() && "CholmodDecomposition::solve(): invalid number of rows of the right hand side matrix b"); return internal::solve_retval(*this, b.derived()); } /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. * * \sa compute() */ template inline const internal::sparse_solve_retval solve(const SparseMatrixBase& b) const { eigen_assert(m_isInitialized && "LLT is not initialized."); eigen_assert(rows()==b.rows() && "CholmodDecomposition::solve(): invalid number of rows of the right hand side matrix b"); return internal::sparse_solve_retval(*this, b.derived()); } /** Performs a symbolic decomposition on the sparcity of \a matrix. * * This function is particularly useful when solving for several problems having the same structure. * * \sa factorize() */ void analyzePattern(const MatrixType& matrix) { if(m_cholmodFactor) { cholmod_free_factor(&m_cholmodFactor, &m_cholmod); m_cholmodFactor = 0; } cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView()); m_cholmodFactor = cholmod_analyze(&A, &m_cholmod); this->m_isInitialized = true; this->m_info = Success; m_analysisIsOk = true; m_factorizationIsOk = false; } /** Performs a numeric decomposition of \a matrix * * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed. * * \sa analyzePattern() */ void factorize(const MatrixType& matrix) { eigen_assert(m_analysisIsOk && "You must first call analyzePattern()"); cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView()); cholmod_factorize(&A, m_cholmodFactor, &m_cholmod); this->m_info = Success; m_factorizationIsOk = true; } /** Returns a reference to the Cholmod's configuration structure to get a full control over the performed operations. * See the Cholmod user guide for details. */ cholmod_common& cholmod() { return m_cholmod; } #ifndef EIGEN_PARSED_BY_DOXYGEN /** \internal */ template void _solve(const MatrixBase &b, MatrixBase &dest) const { eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()"); const Index size = m_cholmodFactor->n; eigen_assert(size==b.rows()); // note: cd stands for Cholmod Dense cholmod_dense b_cd = viewAsCholmod(b.const_cast_derived()); cholmod_dense* x_cd = cholmod_solve(CHOLMOD_A, m_cholmodFactor, &b_cd, &m_cholmod); if(!x_cd) { this->m_info = NumericalIssue; } // TODO optimize this copy by swapping when possible (be carreful with alignment, etc.) dest = Matrix::Map(reinterpret_cast(x_cd->x),b.rows(),b.cols()); cholmod_free_dense(&x_cd, &m_cholmod); } /** \internal */ template void _solve(const SparseMatrix &b, SparseMatrix &dest) const { eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()"); const Index size = m_cholmodFactor->n; eigen_assert(size==b.rows()); // note: cs stands for Cholmod Sparse cholmod_sparse b_cs = viewAsCholmod(b); cholmod_sparse* x_cs = cholmod_spsolve(CHOLMOD_A, m_cholmodFactor, &b_cs, &m_cholmod); if(!x_cs) { this->m_info = NumericalIssue; } // TODO optimize this copy by swapping when possible (be carreful with alignment, etc.) dest = viewAsEigen(*x_cs); cholmod_free_sparse(&x_cs, &m_cholmod); } #endif // EIGEN_PARSED_BY_DOXYGEN template void dumpMemory(Stream& s) {} protected: mutable cholmod_common m_cholmod; cholmod_factor* m_cholmodFactor; mutable ComputationInfo m_info; bool m_isInitialized; int m_factorizationIsOk; int m_analysisIsOk; }; /** \ingroup CholmodSupport_Module * \class CholmodSimplicialLLT * \brief A simplicial direct Cholesky (LLT) factorization and solver based on Cholmod * * This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization * using the Cholmod library. * This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Thefore, it has little practical interest. * The sparse matrix A must be selfajoint and positive definite. The vectors or matrices * X and B can be either dense or sparse. * * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower * or Upper. Default is Lower. * * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed. * * \sa \ref TutorialSparseDirectSolvers, class CholmodSupernodalLLT, class SimplicialLLT */ template class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT<_MatrixType, _UpLo> > { typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT> Base; using Base::m_cholmod; public: typedef _MatrixType MatrixType; CholmodSimplicialLLT() : Base() { init(); } CholmodSimplicialLLT(const MatrixType& matrix) : Base() { init(); compute(matrix); } ~CholmodSimplicialLLT() {} protected: void init() { m_cholmod.final_asis = 0; m_cholmod.supernodal = CHOLMOD_SIMPLICIAL; m_cholmod.final_ll = 1; } }; /** \ingroup CholmodSupport_Module * \class CholmodSimplicialLDLT * \brief A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod * * This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization * using the Cholmod library. * This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Thefore, it has little practical interest. * The sparse matrix A must be selfajoint and positive definite. The vectors or matrices * X and B can be either dense or sparse. * * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower * or Upper. Default is Lower. * * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed. * * \sa \ref TutorialSparseDirectSolvers, class CholmodSupernodalLLT, class SimplicialLDLT */ template class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT<_MatrixType, _UpLo> > { typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT> Base; using Base::m_cholmod; public: typedef _MatrixType MatrixType; CholmodSimplicialLDLT() : Base() { init(); } CholmodSimplicialLDLT(const MatrixType& matrix) : Base() { init(); compute(matrix); } ~CholmodSimplicialLDLT() {} protected: void init() { m_cholmod.final_asis = 1; m_cholmod.supernodal = CHOLMOD_SIMPLICIAL; } }; /** \ingroup CholmodSupport_Module * \class CholmodSupernodalLLT * \brief A supernodal Cholesky (LLT) factorization and solver based on Cholmod * * This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization * using the Cholmod library. * This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM. * The sparse matrix A must be selfajoint and positive definite. The vectors or matrices * X and B can be either dense or sparse. * * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower * or Upper. Default is Lower. * * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed. * * \sa \ref TutorialSparseDirectSolvers */ template class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT<_MatrixType, _UpLo> > { typedef CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT> Base; using Base::m_cholmod; public: typedef _MatrixType MatrixType; CholmodSupernodalLLT() : Base() { init(); } CholmodSupernodalLLT(const MatrixType& matrix) : Base() { init(); compute(matrix); } ~CholmodSupernodalLLT() {} protected: void init() { m_cholmod.final_asis = 1; m_cholmod.supernodal = CHOLMOD_SUPERNODAL; } }; /** \ingroup CholmodSupport_Module * \class CholmodDecomposition * \brief A general Cholesky factorization and solver based on Cholmod * * This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization * using the Cholmod library. The sparse matrix A must be selfajoint and positive definite. The vectors or matrices * X and B can be either dense or sparse. * * This variant permits to change the underlying Cholesky method at runtime. * On the other hand, it does not provide access to the result of the factorization. * The default is to let Cholmod automatically choose between a simplicial and supernodal factorization. * * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower * or Upper. Default is Lower. * * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed. * * \sa \ref TutorialSparseDirectSolvers */ template class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecomposition<_MatrixType, _UpLo> > { typedef CholmodBase<_MatrixType, _UpLo, CholmodDecomposition> Base; using Base::m_cholmod; public: typedef _MatrixType MatrixType; CholmodDecomposition() : Base() { init(); } CholmodDecomposition(const MatrixType& matrix) : Base() { init(); compute(matrix); } ~CholmodDecomposition() {} void setMode(CholmodMode mode) { switch(mode) { case CholmodAuto: m_cholmod.final_asis = 1; m_cholmod.supernodal = CHOLMOD_AUTO; break; case CholmodSimplicialLLt: m_cholmod.final_asis = 0; m_cholmod.supernodal = CHOLMOD_SIMPLICIAL; m_cholmod.final_ll = 1; break; case CholmodSupernodalLLt: m_cholmod.final_asis = 1; m_cholmod.supernodal = CHOLMOD_SUPERNODAL; break; case CholmodLDLt: m_cholmod.final_asis = 1; m_cholmod.supernodal = CHOLMOD_SIMPLICIAL; break; default: break; } } protected: void init() { m_cholmod.final_asis = 1; m_cholmod.supernodal = CHOLMOD_AUTO; } }; namespace internal { template struct solve_retval, Rhs> : solve_retval_base, Rhs> { typedef CholmodBase<_MatrixType,_UpLo,Derived> Dec; EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) template void evalTo(Dest& dst) const { dec()._solve(rhs(),dst); } }; template struct sparse_solve_retval, Rhs> : sparse_solve_retval_base, Rhs> { typedef CholmodBase<_MatrixType,_UpLo,Derived> Dec; EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs) template void evalTo(Dest& dst) const { dec()._solve(rhs(),dst); } }; } // end namespace internal } // end namespace Eigen #endif // EIGEN_CHOLMODSUPPORT_H ctbnrle.1.1.1/hdr/Eigen/src/UmfPackSupport/0000775000175000017500000000000012107032422020675 5ustar csheltoncsheltonctbnrle.1.1.1/hdr/Eigen/src/UmfPackSupport/UmfPackSupport.h0000775000175000017500000003570412107032422024005 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2011 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_UMFPACKSUPPORT_H #define EIGEN_UMFPACKSUPPORT_H namespace Eigen { /* TODO extract L, extract U, compute det, etc... */ // generic double/complex wrapper functions: inline void umfpack_free_numeric(void **Numeric, double) { umfpack_di_free_numeric(Numeric); *Numeric = 0; } inline void umfpack_free_numeric(void **Numeric, std::complex) { umfpack_zi_free_numeric(Numeric); *Numeric = 0; } inline void umfpack_free_symbolic(void **Symbolic, double) { umfpack_di_free_symbolic(Symbolic); *Symbolic = 0; } inline void umfpack_free_symbolic(void **Symbolic, std::complex) { umfpack_zi_free_symbolic(Symbolic); *Symbolic = 0; } inline int umfpack_symbolic(int n_row,int n_col, const int Ap[], const int Ai[], const double Ax[], void **Symbolic, const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO]) { return umfpack_di_symbolic(n_row,n_col,Ap,Ai,Ax,Symbolic,Control,Info); } inline int umfpack_symbolic(int n_row,int n_col, const int Ap[], const int Ai[], const std::complex Ax[], void **Symbolic, const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO]) { return umfpack_zi_symbolic(n_row,n_col,Ap,Ai,&internal::real_ref(Ax[0]),0,Symbolic,Control,Info); } inline int umfpack_numeric( const int Ap[], const int Ai[], const double Ax[], void *Symbolic, void **Numeric, const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO]) { return umfpack_di_numeric(Ap,Ai,Ax,Symbolic,Numeric,Control,Info); } inline int umfpack_numeric( const int Ap[], const int Ai[], const std::complex Ax[], void *Symbolic, void **Numeric, const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO]) { return umfpack_zi_numeric(Ap,Ai,&internal::real_ref(Ax[0]),0,Symbolic,Numeric,Control,Info); } inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const double Ax[], double X[], const double B[], void *Numeric, const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) { return umfpack_di_solve(sys,Ap,Ai,Ax,X,B,Numeric,Control,Info); } inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const std::complex Ax[], std::complex X[], const std::complex B[], void *Numeric, const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) { return umfpack_zi_solve(sys,Ap,Ai,&internal::real_ref(Ax[0]),0,&internal::real_ref(X[0]),0,&internal::real_ref(B[0]),0,Numeric,Control,Info); } inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, double) { return umfpack_di_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric); } inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, std::complex) { return umfpack_zi_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric); } inline int umfpack_get_numeric(int Lp[], int Lj[], double Lx[], int Up[], int Ui[], double Ux[], int P[], int Q[], double Dx[], int *do_recip, double Rs[], void *Numeric) { return umfpack_di_get_numeric(Lp,Lj,Lx,Up,Ui,Ux,P,Q,Dx,do_recip,Rs,Numeric); } inline int umfpack_get_numeric(int Lp[], int Lj[], std::complex Lx[], int Up[], int Ui[], std::complex Ux[], int P[], int Q[], std::complex Dx[], int *do_recip, double Rs[], void *Numeric) { double& lx0_real = internal::real_ref(Lx[0]); double& ux0_real = internal::real_ref(Ux[0]); double& dx0_real = internal::real_ref(Dx[0]); return umfpack_zi_get_numeric(Lp,Lj,Lx?&lx0_real:0,0,Up,Ui,Ux?&ux0_real:0,0,P,Q, Dx?&dx0_real:0,0,do_recip,Rs,Numeric); } inline int umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO]) { return umfpack_di_get_determinant(Mx,Ex,NumericHandle,User_Info); } inline int umfpack_get_determinant(std::complex *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO]) { double& mx_real = internal::real_ref(*Mx); return umfpack_zi_get_determinant(&mx_real,0,Ex,NumericHandle,User_Info); } /** \ingroup UmfPackSupport_Module * \brief A sparse LU factorization and solver based on UmfPack * * This class allows to solve for A.X = B sparse linear problems via a LU factorization * using the UmfPack library. The sparse matrix A must be squared and full rank. * The vectors or matrices X and B can be either dense or sparse. * * \WARNING The input matrix A should be in a \b compressed and \b column-major form. * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix. * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> * * \sa \ref TutorialSparseDirectSolvers */ template class UmfPackLU : internal::noncopyable { public: typedef _MatrixType MatrixType; typedef typename MatrixType::Scalar Scalar; typedef typename MatrixType::RealScalar RealScalar; typedef typename MatrixType::Index Index; typedef Matrix Vector; typedef Matrix IntRowVectorType; typedef Matrix IntColVectorType; typedef SparseMatrix LUMatrixType; typedef SparseMatrix UmfpackMatrixType; public: UmfPackLU() { init(); } UmfPackLU(const MatrixType& matrix) { init(); compute(matrix); } ~UmfPackLU() { if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar()); if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar()); } inline Index rows() const { return m_copyMatrix.rows(); } inline Index cols() const { return m_copyMatrix.cols(); } /** \brief Reports whether previous computation was successful. * * \returns \c Success if computation was succesful, * \c NumericalIssue if the matrix.appears to be negative. */ ComputationInfo info() const { eigen_assert(m_isInitialized && "Decomposition is not initialized."); return m_info; } inline const LUMatrixType& matrixL() const { if (m_extractedDataAreDirty) extractData(); return m_l; } inline const LUMatrixType& matrixU() const { if (m_extractedDataAreDirty) extractData(); return m_u; } inline const IntColVectorType& permutationP() const { if (m_extractedDataAreDirty) extractData(); return m_p; } inline const IntRowVectorType& permutationQ() const { if (m_extractedDataAreDirty) extractData(); return m_q; } /** Computes the sparse Cholesky decomposition of \a matrix * Note that the matrix should be column-major, and in compressed format for best performance. * \sa SparseMatrix::makeCompressed(). */ void compute(const MatrixType& matrix) { analyzePattern(matrix); factorize(matrix); } /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. * * \sa compute() */ template inline const internal::solve_retval solve(const MatrixBase& b) const { eigen_assert(m_isInitialized && "UmfPackLU is not initialized."); eigen_assert(rows()==b.rows() && "UmfPackLU::solve(): invalid number of rows of the right hand side matrix b"); return internal::solve_retval(*this, b.derived()); } /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. * * \sa compute() */ // template // inline const internal::sparse_solve_retval solve(const SparseMatrixBase& b) const // { // eigen_assert(m_isInitialized && "UmfPAckLU is not initialized."); // eigen_assert(rows()==b.rows() // && "UmfPAckLU::solve(): invalid number of rows of the right hand side matrix b"); // return internal::sparse_solve_retval(*this, b.derived()); // } /** Performs a symbolic decomposition on the sparcity of \a matrix. * * This function is particularly useful when solving for several problems having the same structure. * * \sa factorize(), compute() */ void analyzePattern(const MatrixType& matrix) { if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar()); if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar()); grapInput(matrix); int errorCode = 0; errorCode = umfpack_symbolic(matrix.rows(), matrix.cols(), m_outerIndexPtr, m_innerIndexPtr, m_valuePtr, &m_symbolic, 0, 0); m_isInitialized = true; m_info = errorCode ? InvalidInput : Success; m_analysisIsOk = true; m_factorizationIsOk = false; } /** Performs a numeric decomposition of \a matrix * * The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed. * * \sa analyzePattern(), compute() */ void factorize(const MatrixType& matrix) { eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()"); if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar()); grapInput(matrix); int errorCode; errorCode = umfpack_numeric(m_outerIndexPtr, m_innerIndexPtr, m_valuePtr, m_symbolic, &m_numeric, 0, 0); m_info = errorCode ? NumericalIssue : Success; m_factorizationIsOk = true; } #ifndef EIGEN_PARSED_BY_DOXYGEN /** \internal */ template bool _solve(const MatrixBase &b, MatrixBase &x) const; #endif Scalar determinant() const; void extractData() const; protected: void init() { m_info = InvalidInput; m_isInitialized = false; m_numeric = 0; m_symbolic = 0; m_outerIndexPtr = 0; m_innerIndexPtr = 0; m_valuePtr = 0; } void grapInput(const MatrixType& mat) { m_copyMatrix.resize(mat.rows(), mat.cols()); if( ((MatrixType::Flags&RowMajorBit)==RowMajorBit) || sizeof(typename MatrixType::Index)!=sizeof(int) || !mat.isCompressed() ) { // non supported input -> copy m_copyMatrix = mat; m_outerIndexPtr = m_copyMatrix.outerIndexPtr(); m_innerIndexPtr = m_copyMatrix.innerIndexPtr(); m_valuePtr = m_copyMatrix.valuePtr(); } else { m_outerIndexPtr = mat.outerIndexPtr(); m_innerIndexPtr = mat.innerIndexPtr(); m_valuePtr = mat.valuePtr(); } } // cached data to reduce reallocation, etc. mutable LUMatrixType m_l; mutable LUMatrixType m_u; mutable IntColVectorType m_p; mutable IntRowVectorType m_q; UmfpackMatrixType m_copyMatrix; const Scalar* m_valuePtr; const int* m_outerIndexPtr; const int* m_innerIndexPtr; void* m_numeric; void* m_symbolic; mutable ComputationInfo m_info; bool m_isInitialized; int m_factorizationIsOk; int m_analysisIsOk; mutable bool m_extractedDataAreDirty; private: UmfPackLU(UmfPackLU& ) { } }; template void UmfPackLU::extractData() const { if (m_extractedDataAreDirty) { // get size of the data int lnz, unz, rows, cols, nz_udiag; umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar()); // allocate data m_l.resize(rows,(std::min)(rows,cols)); m_l.resizeNonZeros(lnz); m_u.resize((std::min)(rows,cols),cols); m_u.resizeNonZeros(unz); m_p.resize(rows); m_q.resize(cols); // extract umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(), m_u.outerIndexPtr(), m_u.innerIndexPtr(), m_u.valuePtr(), m_p.data(), m_q.data(), 0, 0, 0, m_numeric); m_extractedDataAreDirty = false; } } template typename UmfPackLU::Scalar UmfPackLU::determinant() const { Scalar det; umfpack_get_determinant(&det, 0, m_numeric, 0); return det; } template template bool UmfPackLU::_solve(const MatrixBase &b, MatrixBase &x) const { const int rhsCols = b.cols(); eigen_assert((BDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major rhs yet"); eigen_assert((XDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major result yet"); int errorCode; for (int j=0; j struct solve_retval, Rhs> : solve_retval_base, Rhs> { typedef UmfPackLU<_MatrixType> Dec; EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) template void evalTo(Dest& dst) const { dec()._solve(rhs(),dst); } }; template struct sparse_solve_retval, Rhs> : sparse_solve_retval_base, Rhs> { typedef UmfPackLU<_MatrixType> Dec; EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs) template void evalTo(Dest& dst) const { dec()._solve(rhs(),dst); } }; } // end namespace internal } // end namespace Eigen #endif // EIGEN_UMFPACKSUPPORT_H ctbnrle.1.1.1/hdr/Eigen/src/UmfPackSupport/CMakeLists.txt0000775000175000017500000000025412107032422023441 0ustar csheltoncsheltonFILE(GLOB Eigen_UmfPackSupport_SRCS "*.h") INSTALL(FILES ${Eigen_UmfPackSupport_SRCS} DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/UmfPackSupport COMPONENT Devel ) ctbnrle.1.1.1/hdr/Eigen/src/Jacobi/0000775000175000017500000000000012107032422017141 5ustar csheltoncsheltonctbnrle.1.1.1/hdr/Eigen/src/Jacobi/Jacobi.h0000775000175000017500000003525412107032422020515 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Benoit Jacob // Copyright (C) 2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_JACOBI_H #define EIGEN_JACOBI_H namespace Eigen { /** \ingroup Jacobi_Module * \jacobi_module * \class JacobiRotation * \brief Rotation given by a cosine-sine pair. * * This class represents a Jacobi or Givens rotation. * This is a 2D rotation in the plane \c J of angle \f$ \theta \f$ defined by * its cosine \c c and sine \c s as follow: * \f$ J = \left ( \begin{array}{cc} c & \overline s \\ -s & \overline c \end{array} \right ) \f$ * * You can apply the respective counter-clockwise rotation to a column vector \c v by * applying its adjoint on the left: \f$ v = J^* v \f$ that translates to the following Eigen code: * \code * v.applyOnTheLeft(J.adjoint()); * \endcode * * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight() */ template class JacobiRotation { public: typedef typename NumTraits::Real RealScalar; /** Default constructor without any initialization. */ JacobiRotation() {} /** Construct a planar rotation from a cosine-sine pair (\a c, \c s). */ JacobiRotation(const Scalar& c, const Scalar& s) : m_c(c), m_s(s) {} Scalar& c() { return m_c; } Scalar c() const { return m_c; } Scalar& s() { return m_s; } Scalar s() const { return m_s; } /** Concatenates two planar rotation */ JacobiRotation operator*(const JacobiRotation& other) { return JacobiRotation(m_c * other.m_c - internal::conj(m_s) * other.m_s, internal::conj(m_c * internal::conj(other.m_s) + internal::conj(m_s) * internal::conj(other.m_c))); } /** Returns the transposed transformation */ JacobiRotation transpose() const { return JacobiRotation(m_c, -internal::conj(m_s)); } /** Returns the adjoint transformation */ JacobiRotation adjoint() const { return JacobiRotation(internal::conj(m_c), -m_s); } template bool makeJacobi(const MatrixBase&, typename Derived::Index p, typename Derived::Index q); bool makeJacobi(RealScalar x, Scalar y, RealScalar z); void makeGivens(const Scalar& p, const Scalar& q, Scalar* z=0); protected: void makeGivens(const Scalar& p, const Scalar& q, Scalar* z, internal::true_type); void makeGivens(const Scalar& p, const Scalar& q, Scalar* z, internal::false_type); Scalar m_c, m_s; }; /** Makes \c *this as a Jacobi rotation \a J such that applying \a J on both the right and left sides of the selfadjoint 2x2 matrix * \f$ B = \left ( \begin{array}{cc} x & y \\ \overline y & z \end{array} \right )\f$ yields a diagonal matrix \f$ A = J^* B J \f$ * * \sa MatrixBase::makeJacobi(const MatrixBase&, Index, Index), MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight() */ template bool JacobiRotation::makeJacobi(RealScalar x, Scalar y, RealScalar z) { typedef typename NumTraits::Real RealScalar; if(y == Scalar(0)) { m_c = Scalar(1); m_s = Scalar(0); return false; } else { RealScalar tau = (x-z)/(RealScalar(2)*internal::abs(y)); RealScalar w = internal::sqrt(internal::abs2(tau) + RealScalar(1)); RealScalar t; if(tau>RealScalar(0)) { t = RealScalar(1) / (tau + w); } else { t = RealScalar(1) / (tau - w); } RealScalar sign_t = t > RealScalar(0) ? RealScalar(1) : RealScalar(-1); RealScalar n = RealScalar(1) / internal::sqrt(internal::abs2(t)+RealScalar(1)); m_s = - sign_t * (internal::conj(y) / internal::abs(y)) * internal::abs(t) * n; m_c = n; return true; } } /** Makes \c *this as a Jacobi rotation \c J such that applying \a J on both the right and left sides of the 2x2 selfadjoint matrix * \f$ B = \left ( \begin{array}{cc} \text{this}_{pp} & \text{this}_{pq} \\ (\text{this}_{pq})^* & \text{this}_{qq} \end{array} \right )\f$ yields * a diagonal matrix \f$ A = J^* B J \f$ * * Example: \include Jacobi_makeJacobi.cpp * Output: \verbinclude Jacobi_makeJacobi.out * * \sa JacobiRotation::makeJacobi(RealScalar, Scalar, RealScalar), MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight() */ template template inline bool JacobiRotation::makeJacobi(const MatrixBase& m, typename Derived::Index p, typename Derived::Index q) { return makeJacobi(internal::real(m.coeff(p,p)), m.coeff(p,q), internal::real(m.coeff(q,q))); } /** Makes \c *this as a Givens rotation \c G such that applying \f$ G^* \f$ to the left of the vector * \f$ V = \left ( \begin{array}{c} p \\ q \end{array} \right )\f$ yields: * \f$ G^* V = \left ( \begin{array}{c} r \\ 0 \end{array} \right )\f$. * * The value of \a z is returned if \a z is not null (the default is null). * Also note that G is built such that the cosine is always real. * * Example: \include Jacobi_makeGivens.cpp * Output: \verbinclude Jacobi_makeGivens.out * * This function implements the continuous Givens rotation generation algorithm * found in Anderson (2000), Discontinuous Plane Rotations and the Symmetric Eigenvalue Problem. * LAPACK Working Note 150, University of Tennessee, UT-CS-00-454, December 4, 2000. * * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight() */ template void JacobiRotation::makeGivens(const Scalar& p, const Scalar& q, Scalar* z) { makeGivens(p, q, z, typename internal::conditional::IsComplex, internal::true_type, internal::false_type>::type()); } // specialization for complexes template void JacobiRotation::makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::true_type) { if(q==Scalar(0)) { m_c = internal::real(p)<0 ? Scalar(-1) : Scalar(1); m_s = 0; if(r) *r = m_c * p; } else if(p==Scalar(0)) { m_c = 0; m_s = -q/internal::abs(q); if(r) *r = internal::abs(q); } else { RealScalar p1 = internal::norm1(p); RealScalar q1 = internal::norm1(q); if(p1>=q1) { Scalar ps = p / p1; RealScalar p2 = internal::abs2(ps); Scalar qs = q / p1; RealScalar q2 = internal::abs2(qs); RealScalar u = internal::sqrt(RealScalar(1) + q2/p2); if(internal::real(p) void JacobiRotation::makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::false_type) { if(q==Scalar(0)) { m_c = p internal::abs(q)) { Scalar t = q/p; Scalar u = internal::sqrt(Scalar(1) + internal::abs2(t)); if(p void apply_rotation_in_the_plane(VectorX& _x, VectorY& _y, const JacobiRotation& j); } /** \jacobi_module * Applies the rotation in the plane \a j to the rows \a p and \a q of \c *this, i.e., it computes B = J * B, * with \f$ B = \left ( \begin{array}{cc} \text{*this.row}(p) \\ \text{*this.row}(q) \end{array} \right ) \f$. * * \sa class JacobiRotation, MatrixBase::applyOnTheRight(), internal::apply_rotation_in_the_plane() */ template template inline void MatrixBase::applyOnTheLeft(Index p, Index q, const JacobiRotation& j) { RowXpr x(this->row(p)); RowXpr y(this->row(q)); internal::apply_rotation_in_the_plane(x, y, j); } /** \ingroup Jacobi_Module * Applies the rotation in the plane \a j to the columns \a p and \a q of \c *this, i.e., it computes B = B * J * with \f$ B = \left ( \begin{array}{cc} \text{*this.col}(p) & \text{*this.col}(q) \end{array} \right ) \f$. * * \sa class JacobiRotation, MatrixBase::applyOnTheLeft(), internal::apply_rotation_in_the_plane() */ template template inline void MatrixBase::applyOnTheRight(Index p, Index q, const JacobiRotation& j) { ColXpr x(this->col(p)); ColXpr y(this->col(q)); internal::apply_rotation_in_the_plane(x, y, j.transpose()); } namespace internal { template void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(VectorX& _x, VectorY& _y, const JacobiRotation& j) { typedef typename VectorX::Index Index; typedef typename VectorX::Scalar Scalar; enum { PacketSize = packet_traits::size }; typedef typename packet_traits::type Packet; eigen_assert(_x.size() == _y.size()); Index size = _x.size(); Index incrx = _x.innerStride(); Index incry = _y.innerStride(); Scalar* EIGEN_RESTRICT x = &_x.coeffRef(0); Scalar* EIGEN_RESTRICT y = &_y.coeffRef(0); /*** dynamic-size vectorized paths ***/ if(VectorX::SizeAtCompileTime == Dynamic && (VectorX::Flags & VectorY::Flags & PacketAccessBit) && ((incrx==1 && incry==1) || PacketSize == 1)) { // both vectors are sequentially stored in memory => vectorization enum { Peeling = 2 }; Index alignedStart = internal::first_aligned(y, size); Index alignedEnd = alignedStart + ((size-alignedStart)/PacketSize)*PacketSize; const Packet pc = pset1(j.c()); const Packet ps = pset1(j.s()); conj_helper::IsComplex,false> pcj; for(Index i=0; i(px); Packet yi = pload(py); pstore(px, padd(pmul(pc,xi),pcj.pmul(ps,yi))); pstore(py, psub(pcj.pmul(pc,yi),pmul(ps,xi))); px += PacketSize; py += PacketSize; } } else { Index peelingEnd = alignedStart + ((size-alignedStart)/(Peeling*PacketSize))*(Peeling*PacketSize); for(Index i=alignedStart; i(px); Packet xi1 = ploadu(px+PacketSize); Packet yi = pload (py); Packet yi1 = pload (py+PacketSize); pstoreu(px, padd(pmul(pc,xi),pcj.pmul(ps,yi))); pstoreu(px+PacketSize, padd(pmul(pc,xi1),pcj.pmul(ps,yi1))); pstore (py, psub(pcj.pmul(pc,yi),pmul(ps,xi))); pstore (py+PacketSize, psub(pcj.pmul(pc,yi1),pmul(ps,xi1))); px += Peeling*PacketSize; py += Peeling*PacketSize; } if(alignedEnd!=peelingEnd) { Packet xi = ploadu(x+peelingEnd); Packet yi = pload (y+peelingEnd); pstoreu(x+peelingEnd, padd(pmul(pc,xi),pcj.pmul(ps,yi))); pstore (y+peelingEnd, psub(pcj.pmul(pc,yi),pmul(ps,xi))); } } for(Index i=alignedEnd; i(j.c()); const Packet ps = pset1(j.s()); conj_helper::IsComplex,false> pcj; Scalar* EIGEN_RESTRICT px = x; Scalar* EIGEN_RESTRICT py = y; for(Index i=0; i(px); Packet yi = pload(py); pstore(px, padd(pmul(pc,xi),pcj.pmul(ps,yi))); pstore(py, psub(pcj.pmul(pc,yi),pmul(ps,xi))); px += PacketSize; py += PacketSize; } } /*** non-vectorized path ***/ else { for(Index i=0; i // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_SOLVETRIANGULAR_H #define EIGEN_SOLVETRIANGULAR_H namespace Eigen { namespace internal { // Forward declarations: // The following two routines are implemented in the products/TriangularSolver*.h files template struct triangular_solve_vector; template struct triangular_solve_matrix; // small helper struct extracting some traits on the underlying solver operation template class trsolve_traits { private: enum { RhsIsVectorAtCompileTime = (Side==OnTheLeft ? Rhs::ColsAtCompileTime : Rhs::RowsAtCompileTime)==1 }; public: enum { Unrolling = (RhsIsVectorAtCompileTime && Rhs::SizeAtCompileTime != Dynamic && Rhs::SizeAtCompileTime <= 8) ? CompleteUnrolling : NoUnrolling, RhsVectors = RhsIsVectorAtCompileTime ? 1 : Dynamic }; }; template::Unrolling, int RhsVectors = trsolve_traits::RhsVectors > struct triangular_solver_selector; template struct triangular_solver_selector { typedef typename Lhs::Scalar LhsScalar; typedef typename Rhs::Scalar RhsScalar; typedef blas_traits LhsProductTraits; typedef typename LhsProductTraits::ExtractType ActualLhsType; typedef Map, Aligned> MappedRhs; static void run(const Lhs& lhs, Rhs& rhs) { ActualLhsType actualLhs = LhsProductTraits::extract(lhs); // FIXME find a way to allow an inner stride if packet_traits::size==1 bool useRhsDirectly = Rhs::InnerStrideAtCompileTime==1 || rhs.innerStride()==1; ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhs,rhs.size(), (useRhsDirectly ? rhs.data() : 0)); if(!useRhsDirectly) MappedRhs(actualRhs,rhs.size()) = rhs; triangular_solve_vector ::run(actualLhs.cols(), actualLhs.data(), actualLhs.outerStride(), actualRhs); if(!useRhsDirectly) rhs = MappedRhs(actualRhs, rhs.size()); } }; // the rhs is a matrix template struct triangular_solver_selector { typedef typename Rhs::Scalar Scalar; typedef typename Rhs::Index Index; typedef blas_traits LhsProductTraits; typedef typename LhsProductTraits::DirectLinearAccessType ActualLhsType; static void run(const Lhs& lhs, Rhs& rhs) { typename internal::add_const_on_value_type::type actualLhs = LhsProductTraits::extract(lhs); triangular_solve_matrix ::run(lhs.rows(), Side==OnTheLeft? rhs.cols() : rhs.rows(), &actualLhs.coeffRef(0,0), actualLhs.outerStride(), &rhs.coeffRef(0,0), rhs.outerStride()); } }; /*************************************************************************** * meta-unrolling implementation ***************************************************************************/ template struct triangular_solver_unroller; template struct triangular_solver_unroller { enum { IsLower = ((Mode&Lower)==Lower), I = IsLower ? Index : Size - Index - 1, S = IsLower ? 0 : I+1 }; static void run(const Lhs& lhs, Rhs& rhs) { if (Index>0) rhs.coeffRef(I) -= lhs.row(I).template segment(S).transpose() .cwiseProduct(rhs.template segment(S)).sum(); if(!(Mode & UnitDiag)) rhs.coeffRef(I) /= lhs.coeff(I,I); triangular_solver_unroller::run(lhs,rhs); } }; template struct triangular_solver_unroller { static void run(const Lhs&, Rhs&) {} }; template struct triangular_solver_selector { static void run(const Lhs& lhs, Rhs& rhs) { triangular_solver_unroller::run(lhs,rhs); } }; template struct triangular_solver_selector { static void run(const Lhs& lhs, Rhs& rhs) { Transpose trLhs(lhs); Transpose trRhs(rhs); triangular_solver_unroller,Transpose, ((Mode&Upper)==Upper ? Lower : Upper) | (Mode&UnitDiag), 0,Rhs::SizeAtCompileTime>::run(trLhs,trRhs); } }; } // end namespace internal /*************************************************************************** * TriangularView methods ***************************************************************************/ /** "in-place" version of TriangularView::solve() where the result is written in \a other * * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here. * This function will const_cast it, so constness isn't honored here. * * See TriangularView:solve() for the details. */ template template void TriangularView::solveInPlace(const MatrixBase& _other) const { OtherDerived& other = _other.const_cast_derived(); eigen_assert( cols() == rows() && ((Side==OnTheLeft && cols() == other.rows()) || (Side==OnTheRight && cols() == other.cols())) ); eigen_assert((!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower))); enum { copy = internal::traits::Flags & RowMajorBit && OtherDerived::IsVectorAtCompileTime }; typedef typename internal::conditional::type, OtherDerived&>::type OtherCopy; OtherCopy otherCopy(other); internal::triangular_solver_selector::type, Side, Mode>::run(nestedExpression(), otherCopy); if (copy) other = otherCopy; } /** \returns the product of the inverse of \c *this with \a other, \a *this being triangular. * * This function computes the inverse-matrix matrix product inverse(\c *this) * \a other if * \a Side==OnTheLeft (the default), or the right-inverse-multiply \a other * inverse(\c *this) if * \a Side==OnTheRight. * * The matrix \c *this must be triangular and invertible (i.e., all the coefficients of the * diagonal must be non zero). It works as a forward (resp. backward) substitution if \c *this * is an upper (resp. lower) triangular matrix. * * Example: \include MatrixBase_marked.cpp * Output: \verbinclude MatrixBase_marked.out * * This function returns an expression of the inverse-multiply and can works in-place if it is assigned * to the same matrix or vector \a other. * * For users coming from BLAS, this function (and more specifically solveInPlace()) offer * all the operations supported by the \c *TRSV and \c *TRSM BLAS routines. * * \sa TriangularView::solveInPlace() */ template template const internal::triangular_solve_retval,Other> TriangularView::solve(const MatrixBase& other) const { return internal::triangular_solve_retval(*this, other.derived()); } namespace internal { template struct traits > { typedef typename internal::plain_matrix_type_column_major::type ReturnType; }; template struct triangular_solve_retval : public ReturnByValue > { typedef typename remove_all::type RhsNestedCleaned; typedef ReturnByValue Base; typedef typename Base::Index Index; triangular_solve_retval(const TriangularType& tri, const Rhs& rhs) : m_triangularMatrix(tri), m_rhs(rhs) {} inline Index rows() const { return m_rhs.rows(); } inline Index cols() const { return m_rhs.cols(); } template inline void evalTo(Dest& dst) const { if(!(is_same::value && extract_data(dst) == extract_data(m_rhs))) dst = m_rhs; m_triangularMatrix.template solveInPlace(dst); } protected: const TriangularType& m_triangularMatrix; typename Rhs::Nested m_rhs; }; } // namespace internal } // end namespace Eigen #endif // EIGEN_SOLVETRIANGULAR_H ctbnrle.1.1.1/hdr/Eigen/src/Core/ReturnByValue.h0000775000175000017500000000752212107032422021573 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009-2010 Gael Guennebaud // Copyright (C) 2009-2010 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_RETURNBYVALUE_H #define EIGEN_RETURNBYVALUE_H namespace Eigen { /** \class ReturnByValue * \ingroup Core_Module * */ namespace internal { template struct traits > : public traits::ReturnType> { enum { // We're disabling the DirectAccess because e.g. the constructor of // the Block-with-DirectAccess expression requires to have a coeffRef method. // Also, we don't want to have to implement the stride stuff. Flags = (traits::ReturnType>::Flags | EvalBeforeNestingBit) & ~DirectAccessBit }; }; /* The ReturnByValue object doesn't even have a coeff() method. * So the only way that nesting it in an expression can work, is by evaluating it into a plain matrix. * So internal::nested always gives the plain return matrix type. * * FIXME: I don't understand why we need this specialization: isn't this taken care of by the EvalBeforeNestingBit ?? */ template struct nested, n, PlainObject> { typedef typename traits::ReturnType type; }; } // end namespace internal template class ReturnByValue : public internal::dense_xpr_base< ReturnByValue >::type { public: typedef typename internal::traits::ReturnType ReturnType; typedef typename internal::dense_xpr_base::type Base; EIGEN_DENSE_PUBLIC_INTERFACE(ReturnByValue) template inline void evalTo(Dest& dst) const { static_cast(this)->evalTo(dst); } inline Index rows() const { return static_cast(this)->rows(); } inline Index cols() const { return static_cast(this)->cols(); } #ifndef EIGEN_PARSED_BY_DOXYGEN #define Unusable YOU_ARE_TRYING_TO_ACCESS_A_SINGLE_COEFFICIENT_IN_A_SPECIAL_EXPRESSION_WHERE_THAT_IS_NOT_ALLOWED_BECAUSE_THAT_WOULD_BE_INEFFICIENT class Unusable{ Unusable(const Unusable&) {} Unusable& operator=(const Unusable&) {return *this;} }; const Unusable& coeff(Index) const { return *reinterpret_cast(this); } const Unusable& coeff(Index,Index) const { return *reinterpret_cast(this); } Unusable& coeffRef(Index) { return *reinterpret_cast(this); } Unusable& coeffRef(Index,Index) { return *reinterpret_cast(this); } #endif }; template template Derived& DenseBase::operator=(const ReturnByValue& other) { other.evalTo(derived()); return derived(); } } // end namespace Eigen #endif // EIGEN_RETURNBYVALUE_H ctbnrle.1.1.1/hdr/Eigen/src/Core/Assign_MKL.h0000775000175000017500000002566412107032422020762 0ustar csheltoncshelton/* Copyright (c) 2011, Intel Corporation. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Intel Corporation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ******************************************************************************** * Content : Eigen bindings to Intel(R) MKL * MKL VML support for coefficient-wise unary Eigen expressions like a=b.sin() ******************************************************************************** */ #ifndef EIGEN_ASSIGN_VML_H #define EIGEN_ASSIGN_VML_H namespace Eigen { namespace internal { template struct vml_call { enum { IsSupported = 0 }; }; template class vml_assign_traits { private: enum { DstHasDirectAccess = Dst::Flags & DirectAccessBit, SrcHasDirectAccess = Src::Flags & DirectAccessBit, StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)), InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime) : int(Dst::Flags)&RowMajorBit ? int(Dst::ColsAtCompileTime) : int(Dst::RowsAtCompileTime), InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime) : int(Dst::Flags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime) : int(Dst::MaxRowsAtCompileTime), MaxSizeAtCompileTime = Dst::SizeAtCompileTime, MightEnableVml = vml_call::IsSupported && StorageOrdersAgree && DstHasDirectAccess && SrcHasDirectAccess && Src::InnerStrideAtCompileTime==1 && Dst::InnerStrideAtCompileTime==1, MightLinearize = MightEnableVml && (int(Dst::Flags) & int(Src::Flags) & LinearAccessBit), VmlSize = MightLinearize ? MaxSizeAtCompileTime : InnerMaxSize, LargeEnough = VmlSize==Dynamic || VmlSize>=EIGEN_MKL_VML_THRESHOLD, MayEnableVml = MightEnableVml && LargeEnough, MayLinearize = MayEnableVml && MightLinearize }; public: enum { Traversal = MayLinearize ? LinearVectorizedTraversal : MayEnableVml ? InnerVectorizedTraversal : DefaultTraversal }; }; template::Traversal > struct vml_assign_impl : assign_impl,Traversal,Unrolling,BuiltIn> { }; template struct vml_assign_impl { typedef typename Derived1::Scalar Scalar; typedef typename Derived1::Index Index; static inline void run(Derived1& dst, const CwiseUnaryOp& src) { // in case we want to (or have to) skip VML at runtime we can call: // assign_impl,Traversal,Unrolling,BuiltIn>::run(dst,src); const Index innerSize = dst.innerSize(); const Index outerSize = dst.outerSize(); for(Index outer = 0; outer < outerSize; ++outer) { const Scalar *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer,0)) : &(src.nestedExpression().coeffRef(0, outer)); Scalar *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer)); vml_call::run(src.functor(), innerSize, src_ptr, dst_ptr ); } } }; template struct vml_assign_impl { static inline void run(Derived1& dst, const CwiseUnaryOp& src) { // in case we want to (or have to) skip VML at runtime we can call: // assign_impl,Traversal,Unrolling,BuiltIn>::run(dst,src); vml_call::run(src.functor(), dst.size(), src.nestedExpression().data(), dst.data() ); } }; // Macroses #define EIGEN_MKL_VML_SPECIALIZE_ASSIGN(TRAVERSAL,UNROLLING) \ template \ struct assign_impl, TRAVERSAL, UNROLLING, Specialized> { \ static inline void run(Derived1 &dst, const Eigen::CwiseUnaryOp &src) { \ vml_assign_impl::run(dst, src); \ } \ }; EIGEN_MKL_VML_SPECIALIZE_ASSIGN(DefaultTraversal,NoUnrolling) EIGEN_MKL_VML_SPECIALIZE_ASSIGN(DefaultTraversal,CompleteUnrolling) EIGEN_MKL_VML_SPECIALIZE_ASSIGN(DefaultTraversal,InnerUnrolling) EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearTraversal,NoUnrolling) EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearTraversal,CompleteUnrolling) EIGEN_MKL_VML_SPECIALIZE_ASSIGN(InnerVectorizedTraversal,NoUnrolling) EIGEN_MKL_VML_SPECIALIZE_ASSIGN(InnerVectorizedTraversal,CompleteUnrolling) EIGEN_MKL_VML_SPECIALIZE_ASSIGN(InnerVectorizedTraversal,InnerUnrolling) EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearVectorizedTraversal,CompleteUnrolling) EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearVectorizedTraversal,NoUnrolling) EIGEN_MKL_VML_SPECIALIZE_ASSIGN(SliceVectorizedTraversal,NoUnrolling) #if !defined (EIGEN_FAST_MATH) || (EIGEN_FAST_MATH != 1) #define EIGEN_MKL_VML_MODE VML_HA #else #define EIGEN_MKL_VML_MODE VML_LA #endif #define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE) \ template<> struct vml_call< scalar_##EIGENOP##_op > { \ enum { IsSupported = 1 }; \ static inline void run( const scalar_##EIGENOP##_op& /*func*/, \ int size, const EIGENTYPE* src, EIGENTYPE* dst) { \ VMLOP(size, (const VMLTYPE*)src, (VMLTYPE*)dst); \ } \ }; #define EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE) \ template<> struct vml_call< scalar_##EIGENOP##_op > { \ enum { IsSupported = 1 }; \ static inline void run( const scalar_##EIGENOP##_op& /*func*/, \ int size, const EIGENTYPE* src, EIGENTYPE* dst) { \ MKL_INT64 vmlMode = EIGEN_MKL_VML_MODE; \ VMLOP(size, (const VMLTYPE*)src, (VMLTYPE*)dst, vmlMode); \ } \ }; #define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE) \ template<> struct vml_call< scalar_##EIGENOP##_op > { \ enum { IsSupported = 1 }; \ static inline void run( const scalar_##EIGENOP##_op& func, \ int size, const EIGENTYPE* src, EIGENTYPE* dst) { \ EIGENTYPE exponent = func.m_exponent; \ MKL_INT64 vmlMode = EIGEN_MKL_VML_MODE; \ VMLOP(&size, (const VMLTYPE*)src, (const VMLTYPE*)&exponent, \ (VMLTYPE*)dst, &vmlMode); \ } \ }; #define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP) \ EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vs##VMLOP, float, float) \ EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vd##VMLOP, double, double) #define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX(EIGENOP, VMLOP) \ EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vc##VMLOP, scomplex, MKL_Complex8) \ EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vz##VMLOP, dcomplex, MKL_Complex16) #define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP) \ EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP) \ EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX(EIGENOP, VMLOP) #define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL_LA(EIGENOP, VMLOP) \ EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vms##VMLOP, float, float) \ EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmd##VMLOP, double, double) #define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX_LA(EIGENOP, VMLOP) \ EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmc##VMLOP, scomplex, MKL_Complex8) \ EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmz##VMLOP, dcomplex, MKL_Complex16) #define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(EIGENOP, VMLOP) \ EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL_LA(EIGENOP, VMLOP) \ EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX_LA(EIGENOP, VMLOP) EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(sin, Sin) EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(asin, Asin) EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(cos, Cos) EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(acos, Acos) EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(tan, Tan) //EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs, Abs) EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(exp, Exp) EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(log, Ln) EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(sqrt, Sqrt) EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr) EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmspowx_, float, float) EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdpowx_, double, double) EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcpowx_, scomplex, MKL_Complex8) EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzpowx_, dcomplex, MKL_Complex16) } // end namespace internal } // end namespace Eigen #endif // EIGEN_ASSIGN_VML_H ctbnrle.1.1.1/hdr/Eigen/src/Core/VectorwiseOp.h0000775000175000017500000006176412107032422021465 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2010 Gael Guennebaud // Copyright (C) 2006-2008 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_PARTIAL_REDUX_H #define EIGEN_PARTIAL_REDUX_H namespace Eigen { /** \class PartialReduxExpr * \ingroup Core_Module * * \brief Generic expression of a partially reduxed matrix * * \tparam MatrixType the type of the matrix we are applying the redux operation * \tparam MemberOp type of the member functor * \tparam Direction indicates the direction of the redux (#Vertical or #Horizontal) * * This class represents an expression of a partial redux operator of a matrix. * It is the return type of some VectorwiseOp functions, * and most of the time this is the only way it is used. * * \sa class VectorwiseOp */ template< typename MatrixType, typename MemberOp, int Direction> class PartialReduxExpr; namespace internal { template struct traits > : traits { typedef typename MemberOp::result_type Scalar; typedef typename traits::StorageKind StorageKind; typedef typename traits::XprKind XprKind; typedef typename MatrixType::Scalar InputScalar; typedef typename nested::type MatrixTypeNested; typedef typename remove_all::type _MatrixTypeNested; enum { RowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::RowsAtCompileTime, ColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::ColsAtCompileTime, MaxRowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::MaxRowsAtCompileTime, MaxColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::MaxColsAtCompileTime, Flags0 = (unsigned int)_MatrixTypeNested::Flags & HereditaryBits, Flags = (Flags0 & ~RowMajorBit) | (RowsAtCompileTime == 1 ? RowMajorBit : 0), TraversalSize = Direction==Vertical ? RowsAtCompileTime : ColsAtCompileTime }; #if EIGEN_GNUC_AT_LEAST(3,4) typedef typename MemberOp::template Cost CostOpType; #else typedef typename MemberOp::template Cost CostOpType; #endif enum { CoeffReadCost = TraversalSize * traits<_MatrixTypeNested>::CoeffReadCost + int(CostOpType::value) }; }; } template< typename MatrixType, typename MemberOp, int Direction> class PartialReduxExpr : internal::no_assignment_operator, public internal::dense_xpr_base< PartialReduxExpr >::type { public: typedef typename internal::dense_xpr_base::type Base; EIGEN_DENSE_PUBLIC_INTERFACE(PartialReduxExpr) typedef typename internal::traits::MatrixTypeNested MatrixTypeNested; typedef typename internal::traits::_MatrixTypeNested _MatrixTypeNested; PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp()) : m_matrix(mat), m_functor(func) {} Index rows() const { return (Direction==Vertical ? 1 : m_matrix.rows()); } Index cols() const { return (Direction==Horizontal ? 1 : m_matrix.cols()); } EIGEN_STRONG_INLINE const Scalar coeff(Index i, Index j) const { if (Direction==Vertical) return m_functor(m_matrix.col(j)); else return m_functor(m_matrix.row(i)); } const Scalar coeff(Index index) const { if (Direction==Vertical) return m_functor(m_matrix.col(index)); else return m_functor(m_matrix.row(index)); } protected: MatrixTypeNested m_matrix; const MemberOp m_functor; }; #define EIGEN_MEMBER_FUNCTOR(MEMBER,COST) \ template \ struct member_##MEMBER { \ EIGEN_EMPTY_STRUCT_CTOR(member_##MEMBER) \ typedef ResultType result_type; \ template struct Cost \ { enum { value = COST }; }; \ template \ EIGEN_STRONG_INLINE ResultType operator()(const XprType& mat) const \ { return mat.MEMBER(); } \ } namespace internal { EIGEN_MEMBER_FUNCTOR(squaredNorm, Size * NumTraits::MulCost + (Size-1)*NumTraits::AddCost); EIGEN_MEMBER_FUNCTOR(norm, (Size+5) * NumTraits::MulCost + (Size-1)*NumTraits::AddCost); EIGEN_MEMBER_FUNCTOR(stableNorm, (Size+5) * NumTraits::MulCost + (Size-1)*NumTraits::AddCost); EIGEN_MEMBER_FUNCTOR(blueNorm, (Size+5) * NumTraits::MulCost + (Size-1)*NumTraits::AddCost); EIGEN_MEMBER_FUNCTOR(hypotNorm, (Size-1) * functor_traits >::Cost ); EIGEN_MEMBER_FUNCTOR(sum, (Size-1)*NumTraits::AddCost); EIGEN_MEMBER_FUNCTOR(mean, (Size-1)*NumTraits::AddCost + NumTraits::MulCost); EIGEN_MEMBER_FUNCTOR(minCoeff, (Size-1)*NumTraits::AddCost); EIGEN_MEMBER_FUNCTOR(maxCoeff, (Size-1)*NumTraits::AddCost); EIGEN_MEMBER_FUNCTOR(all, (Size-1)*NumTraits::AddCost); EIGEN_MEMBER_FUNCTOR(any, (Size-1)*NumTraits::AddCost); EIGEN_MEMBER_FUNCTOR(count, (Size-1)*NumTraits::AddCost); EIGEN_MEMBER_FUNCTOR(prod, (Size-1)*NumTraits::MulCost); template struct member_redux { typedef typename result_of< BinaryOp(Scalar) >::type result_type; template struct Cost { enum { value = (Size-1) * functor_traits::Cost }; }; member_redux(const BinaryOp func) : m_functor(func) {} template inline result_type operator()(const DenseBase& mat) const { return mat.redux(m_functor); } const BinaryOp m_functor; }; } /** \class VectorwiseOp * \ingroup Core_Module * * \brief Pseudo expression providing partial reduction operations * * \param ExpressionType the type of the object on which to do partial reductions * \param Direction indicates the direction of the redux (#Vertical or #Horizontal) * * This class represents a pseudo expression with partial reduction features. * It is the return type of DenseBase::colwise() and DenseBase::rowwise() * and most of the time this is the only way it is used. * * Example: \include MatrixBase_colwise.cpp * Output: \verbinclude MatrixBase_colwise.out * * \sa DenseBase::colwise(), DenseBase::rowwise(), class PartialReduxExpr */ template class VectorwiseOp { public: typedef typename ExpressionType::Scalar Scalar; typedef typename ExpressionType::RealScalar RealScalar; typedef typename ExpressionType::Index Index; typedef typename internal::conditional::ret, ExpressionType, ExpressionType&>::type ExpressionTypeNested; typedef typename internal::remove_all::type ExpressionTypeNestedCleaned; template class Functor, typename Scalar=typename internal::traits::Scalar> struct ReturnType { typedef PartialReduxExpr, Direction > Type; }; template struct ReduxReturnType { typedef PartialReduxExpr::Scalar>, Direction > Type; }; enum { IsVertical = (Direction==Vertical) ? 1 : 0, IsHorizontal = (Direction==Horizontal) ? 1 : 0 }; protected: /** \internal * \returns the i-th subvector according to the \c Direction */ typedef typename internal::conditional::type SubVector; SubVector subVector(Index i) { return SubVector(m_matrix.derived(),i); } /** \internal * \returns the number of subvectors in the direction \c Direction */ Index subVectors() const { return Direction==Vertical?m_matrix.cols():m_matrix.rows(); } template struct ExtendedType { typedef Replicate Type; }; /** \internal * Replicates a vector to match the size of \c *this */ template typename ExtendedType::Type extendedTo(const DenseBase& other) const { EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Vertical, OtherDerived::MaxColsAtCompileTime==1), YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED) EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Horizontal, OtherDerived::MaxRowsAtCompileTime==1), YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED) return typename ExtendedType::Type (other.derived(), Direction==Vertical ? 1 : m_matrix.rows(), Direction==Horizontal ? 1 : m_matrix.cols()); } public: inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {} /** \internal */ inline const ExpressionType& _expression() const { return m_matrix; } /** \returns a row or column vector expression of \c *this reduxed by \a func * * The template parameter \a BinaryOp is the type of the functor * of the custom redux operator. Note that func must be an associative operator. * * \sa class VectorwiseOp, DenseBase::colwise(), DenseBase::rowwise() */ template const typename ReduxReturnType::Type redux(const BinaryOp& func = BinaryOp()) const { return typename ReduxReturnType::Type(_expression(), func); } /** \returns a row (or column) vector expression of the smallest coefficient * of each column (or row) of the referenced expression. * * Example: \include PartialRedux_minCoeff.cpp * Output: \verbinclude PartialRedux_minCoeff.out * * \sa DenseBase::minCoeff() */ const typename ReturnType::Type minCoeff() const { return _expression(); } /** \returns a row (or column) vector expression of the largest coefficient * of each column (or row) of the referenced expression. * * Example: \include PartialRedux_maxCoeff.cpp * Output: \verbinclude PartialRedux_maxCoeff.out * * \sa DenseBase::maxCoeff() */ const typename ReturnType::Type maxCoeff() const { return _expression(); } /** \returns a row (or column) vector expression of the squared norm * of each column (or row) of the referenced expression. * * Example: \include PartialRedux_squaredNorm.cpp * Output: \verbinclude PartialRedux_squaredNorm.out * * \sa DenseBase::squaredNorm() */ const typename ReturnType::Type squaredNorm() const { return _expression(); } /** \returns a row (or column) vector expression of the norm * of each column (or row) of the referenced expression. * * Example: \include PartialRedux_norm.cpp * Output: \verbinclude PartialRedux_norm.out * * \sa DenseBase::norm() */ const typename ReturnType::Type norm() const { return _expression(); } /** \returns a row (or column) vector expression of the norm * of each column (or row) of the referenced expression, using * blue's algorithm. * * \sa DenseBase::blueNorm() */ const typename ReturnType::Type blueNorm() const { return _expression(); } /** \returns a row (or column) vector expression of the norm * of each column (or row) of the referenced expression, avoiding * underflow and overflow. * * \sa DenseBase::stableNorm() */ const typename ReturnType::Type stableNorm() const { return _expression(); } /** \returns a row (or column) vector expression of the norm * of each column (or row) of the referenced expression, avoiding * underflow and overflow using a concatenation of hypot() calls. * * \sa DenseBase::hypotNorm() */ const typename ReturnType::Type hypotNorm() const { return _expression(); } /** \returns a row (or column) vector expression of the sum * of each column (or row) of the referenced expression. * * Example: \include PartialRedux_sum.cpp * Output: \verbinclude PartialRedux_sum.out * * \sa DenseBase::sum() */ const typename ReturnType::Type sum() const { return _expression(); } /** \returns a row (or column) vector expression of the mean * of each column (or row) of the referenced expression. * * \sa DenseBase::mean() */ const typename ReturnType::Type mean() const { return _expression(); } /** \returns a row (or column) vector expression representing * whether \b all coefficients of each respective column (or row) are \c true. * * \sa DenseBase::all() */ const typename ReturnType::Type all() const { return _expression(); } /** \returns a row (or column) vector expression representing * whether \b at \b least one coefficient of each respective column (or row) is \c true. * * \sa DenseBase::any() */ const typename ReturnType::Type any() const { return _expression(); } /** \returns a row (or column) vector expression representing * the number of \c true coefficients of each respective column (or row). * * Example: \include PartialRedux_count.cpp * Output: \verbinclude PartialRedux_count.out * * \sa DenseBase::count() */ const PartialReduxExpr, Direction> count() const { return _expression(); } /** \returns a row (or column) vector expression of the product * of each column (or row) of the referenced expression. * * Example: \include PartialRedux_prod.cpp * Output: \verbinclude PartialRedux_prod.out * * \sa DenseBase::prod() */ const typename ReturnType::Type prod() const { return _expression(); } /** \returns a matrix expression * where each column (or row) are reversed. * * Example: \include Vectorwise_reverse.cpp * Output: \verbinclude Vectorwise_reverse.out * * \sa DenseBase::reverse() */ const Reverse reverse() const { return Reverse( _expression() ); } typedef Replicate ReplicateReturnType; const ReplicateReturnType replicate(Index factor) const; /** * \return an expression of the replication of each column (or row) of \c *this * * Example: \include DirectionWise_replicate.cpp * Output: \verbinclude DirectionWise_replicate.out * * \sa VectorwiseOp::replicate(Index), DenseBase::replicate(), class Replicate */ // NOTE implemented here because of sunstudio's compilation errors template const Replicate replicate(Index factor = Factor) const { return Replicate (_expression(),Direction==Vertical?factor:1,Direction==Horizontal?factor:1); } /////////// Artithmetic operators /////////// /** Copies the vector \a other to each subvector of \c *this */ template ExpressionType& operator=(const DenseBase& other) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) //eigen_assert((m_matrix.isNull()) == (other.isNull())); FIXME return const_cast(m_matrix = extendedTo(other.derived())); } /** Adds the vector \a other to each subvector of \c *this */ template ExpressionType& operator+=(const DenseBase& other) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) return const_cast(m_matrix += extendedTo(other.derived())); } /** Substracts the vector \a other to each subvector of \c *this */ template ExpressionType& operator-=(const DenseBase& other) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) return const_cast(m_matrix -= extendedTo(other.derived())); } /** Multiples each subvector of \c *this by the vector \a other */ template ExpressionType& operator*=(const DenseBase& other) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) m_matrix *= extendedTo(other.derived()); return const_cast(m_matrix); } /** Divides each subvector of \c *this by the vector \a other */ template ExpressionType& operator/=(const DenseBase& other) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) m_matrix /= extendedTo(other.derived()); return const_cast(m_matrix); } /** Returns the expression of the sum of the vector \a other to each subvector of \c *this */ template EIGEN_STRONG_INLINE CwiseBinaryOp, const ExpressionTypeNestedCleaned, const typename ExtendedType::Type> operator+(const DenseBase& other) const { EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) return m_matrix + extendedTo(other.derived()); } /** Returns the expression of the difference between each subvector of \c *this and the vector \a other */ template CwiseBinaryOp, const ExpressionTypeNestedCleaned, const typename ExtendedType::Type> operator-(const DenseBase& other) const { EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) return m_matrix - extendedTo(other.derived()); } /** Returns the expression where each subvector is the product of the vector \a other * by the corresponding subvector of \c *this */ template EIGEN_STRONG_INLINE CwiseBinaryOp, const ExpressionTypeNestedCleaned, const typename ExtendedType::Type> operator*(const DenseBase& other) const { EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) return m_matrix * extendedTo(other.derived()); } /** Returns the expression where each subvector is the quotient of the corresponding * subvector of \c *this by the vector \a other */ template CwiseBinaryOp, const ExpressionTypeNestedCleaned, const typename ExtendedType::Type> operator/(const DenseBase& other) const { EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) return m_matrix / extendedTo(other.derived()); } /////////// Geometry module /////////// #if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS Homogeneous homogeneous() const; #endif typedef typename ExpressionType::PlainObject CrossReturnType; template const CrossReturnType cross(const MatrixBase& other) const; enum { HNormalized_Size = Direction==Vertical ? internal::traits::RowsAtCompileTime : internal::traits::ColsAtCompileTime, HNormalized_SizeMinusOne = HNormalized_Size==Dynamic ? Dynamic : HNormalized_Size-1 }; typedef Block::RowsAtCompileTime), Direction==Horizontal ? int(HNormalized_SizeMinusOne) : int(internal::traits::ColsAtCompileTime)> HNormalized_Block; typedef Block::RowsAtCompileTime), Direction==Horizontal ? 1 : int(internal::traits::ColsAtCompileTime)> HNormalized_Factors; typedef CwiseBinaryOp::Scalar>, const HNormalized_Block, const Replicate > HNormalizedReturnType; const HNormalizedReturnType hnormalized() const; protected: ExpressionTypeNested m_matrix; }; /** \returns a VectorwiseOp wrapper of *this providing additional partial reduction operations * * Example: \include MatrixBase_colwise.cpp * Output: \verbinclude MatrixBase_colwise.out * * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting */ template inline const typename DenseBase::ConstColwiseReturnType DenseBase::colwise() const { return derived(); } /** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations * * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting */ template inline typename DenseBase::ColwiseReturnType DenseBase::colwise() { return derived(); } /** \returns a VectorwiseOp wrapper of *this providing additional partial reduction operations * * Example: \include MatrixBase_rowwise.cpp * Output: \verbinclude MatrixBase_rowwise.out * * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting */ template inline const typename DenseBase::ConstRowwiseReturnType DenseBase::rowwise() const { return derived(); } /** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations * * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting */ template inline typename DenseBase::RowwiseReturnType DenseBase::rowwise() { return derived(); } } // end namespace Eigen #endif // EIGEN_PARTIAL_REDUX_H ctbnrle.1.1.1/hdr/Eigen/src/Core/ForceAlignedAccess.h0000775000175000017500000001215012107032422022461 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009-2010 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_FORCEALIGNEDACCESS_H #define EIGEN_FORCEALIGNEDACCESS_H namespace Eigen { /** \class ForceAlignedAccess * \ingroup Core_Module * * \brief Enforce aligned packet loads and stores regardless of what is requested * * \param ExpressionType the type of the object of which we are forcing aligned packet access * * This class is the return type of MatrixBase::forceAlignedAccess() * and most of the time this is the only way it is used. * * \sa MatrixBase::forceAlignedAccess() */ namespace internal { template struct traits > : public traits {}; } template class ForceAlignedAccess : public internal::dense_xpr_base< ForceAlignedAccess >::type { public: typedef typename internal::dense_xpr_base::type Base; EIGEN_DENSE_PUBLIC_INTERFACE(ForceAlignedAccess) inline ForceAlignedAccess(const ExpressionType& matrix) : m_expression(matrix) {} inline Index rows() const { return m_expression.rows(); } inline Index cols() const { return m_expression.cols(); } inline Index outerStride() const { return m_expression.outerStride(); } inline Index innerStride() const { return m_expression.innerStride(); } inline const CoeffReturnType coeff(Index row, Index col) const { return m_expression.coeff(row, col); } inline Scalar& coeffRef(Index row, Index col) { return m_expression.const_cast_derived().coeffRef(row, col); } inline const CoeffReturnType coeff(Index index) const { return m_expression.coeff(index); } inline Scalar& coeffRef(Index index) { return m_expression.const_cast_derived().coeffRef(index); } template inline const PacketScalar packet(Index row, Index col) const { return m_expression.template packet(row, col); } template inline void writePacket(Index row, Index col, const PacketScalar& x) { m_expression.const_cast_derived().template writePacket(row, col, x); } template inline const PacketScalar packet(Index index) const { return m_expression.template packet(index); } template inline void writePacket(Index index, const PacketScalar& x) { m_expression.const_cast_derived().template writePacket(index, x); } operator const ExpressionType&() const { return m_expression; } protected: const ExpressionType& m_expression; private: ForceAlignedAccess& operator=(const ForceAlignedAccess&); }; /** \returns an expression of *this with forced aligned access * \sa forceAlignedAccessIf(),class ForceAlignedAccess */ template inline const ForceAlignedAccess MatrixBase::forceAlignedAccess() const { return ForceAlignedAccess(derived()); } /** \returns an expression of *this with forced aligned access * \sa forceAlignedAccessIf(), class ForceAlignedAccess */ template inline ForceAlignedAccess MatrixBase::forceAlignedAccess() { return ForceAlignedAccess(derived()); } /** \returns an expression of *this with forced aligned access if \a Enable is true. * \sa forceAlignedAccess(), class ForceAlignedAccess */ template template inline typename internal::add_const_on_value_type,Derived&>::type>::type MatrixBase::forceAlignedAccessIf() const { return derived(); } /** \returns an expression of *this with forced aligned access if \a Enable is true. * \sa forceAlignedAccess(), class ForceAlignedAccess */ template template inline typename internal::conditional,Derived&>::type MatrixBase::forceAlignedAccessIf() { return derived(); } } // end namespace Eigen #endif // EIGEN_FORCEALIGNEDACCESS_H ctbnrle.1.1.1/hdr/Eigen/src/Core/NestByValue.h0000775000175000017500000000760712107032422021231 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Gael Guennebaud // Copyright (C) 2006-2008 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_NESTBYVALUE_H #define EIGEN_NESTBYVALUE_H namespace Eigen { /** \class NestByValue * \ingroup Core_Module * * \brief Expression which must be nested by value * * \param ExpressionType the type of the object of which we are requiring nesting-by-value * * This class is the return type of MatrixBase::nestByValue() * and most of the time this is the only way it is used. * * \sa MatrixBase::nestByValue() */ namespace internal { template struct traits > : public traits {}; } template class NestByValue : public internal::dense_xpr_base< NestByValue >::type { public: typedef typename internal::dense_xpr_base::type Base; EIGEN_DENSE_PUBLIC_INTERFACE(NestByValue) inline NestByValue(const ExpressionType& matrix) : m_expression(matrix) {} inline Index rows() const { return m_expression.rows(); } inline Index cols() const { return m_expression.cols(); } inline Index outerStride() const { return m_expression.outerStride(); } inline Index innerStride() const { return m_expression.innerStride(); } inline const CoeffReturnType coeff(Index row, Index col) const { return m_expression.coeff(row, col); } inline Scalar& coeffRef(Index row, Index col) { return m_expression.const_cast_derived().coeffRef(row, col); } inline const CoeffReturnType coeff(Index index) const { return m_expression.coeff(index); } inline Scalar& coeffRef(Index index) { return m_expression.const_cast_derived().coeffRef(index); } template inline const PacketScalar packet(Index row, Index col) const { return m_expression.template packet(row, col); } template inline void writePacket(Index row, Index col, const PacketScalar& x) { m_expression.const_cast_derived().template writePacket(row, col, x); } template inline const PacketScalar packet(Index index) const { return m_expression.template packet(index); } template inline void writePacket(Index index, const PacketScalar& x) { m_expression.const_cast_derived().template writePacket(index, x); } operator const ExpressionType&() const { return m_expression; } protected: const ExpressionType m_expression; }; /** \returns an expression of the temporary version of *this. */ template inline const NestByValue DenseBase::nestByValue() const { return NestByValue(derived()); } } // end namespace Eigen #endif // EIGEN_NESTBYVALUE_H ctbnrle.1.1.1/hdr/Eigen/src/Core/Map.h0000775000175000017500000002122712107032422017537 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2007-2010 Benoit Jacob // Copyright (C) 2008 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_MAP_H #define EIGEN_MAP_H namespace Eigen { /** \class Map * \ingroup Core_Module * * \brief A matrix or vector expression mapping an existing array of data. * * \tparam PlainObjectType the equivalent matrix type of the mapped data * \tparam MapOptions specifies whether the pointer is \c #Aligned, or \c #Unaligned. * The default is \c #Unaligned. * \tparam StrideType optionally specifies strides. By default, Map assumes the memory layout * of an ordinary, contiguous array. This can be overridden by specifying strides. * The type passed here must be a specialization of the Stride template, see examples below. * * This class represents a matrix or vector expression mapping an existing array of data. * It can be used to let Eigen interface without any overhead with non-Eigen data structures, * such as plain C arrays or structures from other libraries. By default, it assumes that the * data is laid out contiguously in memory. You can however override this by explicitly specifying * inner and outer strides. * * Here's an example of simply mapping a contiguous array as a \ref TopicStorageOrders "column-major" matrix: * \include Map_simple.cpp * Output: \verbinclude Map_simple.out * * If you need to map non-contiguous arrays, you can do so by specifying strides: * * Here's an example of mapping an array as a vector, specifying an inner stride, that is, the pointer * increment between two consecutive coefficients. Here, we're specifying the inner stride as a compile-time * fixed value. * \include Map_inner_stride.cpp * Output: \verbinclude Map_inner_stride.out * * Here's an example of mapping an array while specifying an outer stride. Here, since we're mapping * as a column-major matrix, 'outer stride' means the pointer increment between two consecutive columns. * Here, we're specifying the outer stride as a runtime parameter. Note that here \c OuterStride<> is * a short version of \c OuterStride because the default template parameter of OuterStride * is \c Dynamic * \include Map_outer_stride.cpp * Output: \verbinclude Map_outer_stride.out * * For more details and for an example of specifying both an inner and an outer stride, see class Stride. * * \b Tip: to change the array of data mapped by a Map object, you can use the C++ * placement new syntax: * * Example: \include Map_placement_new.cpp * Output: \verbinclude Map_placement_new.out * * This class is the return type of PlainObjectBase::Map() but can also be used directly. * * \sa PlainObjectBase::Map(), \ref TopicStorageOrders */ namespace internal { template struct traits > : public traits { typedef traits TraitsBase; typedef typename PlainObjectType::Index Index; typedef typename PlainObjectType::Scalar Scalar; enum { InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0 ? int(PlainObjectType::InnerStrideAtCompileTime) : int(StrideType::InnerStrideAtCompileTime), OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0 ? int(PlainObjectType::OuterStrideAtCompileTime) : int(StrideType::OuterStrideAtCompileTime), HasNoInnerStride = InnerStrideAtCompileTime == 1, HasNoOuterStride = StrideType::OuterStrideAtCompileTime == 0, HasNoStride = HasNoInnerStride && HasNoOuterStride, IsAligned = bool(EIGEN_ALIGN) && ((int(MapOptions)&Aligned)==Aligned), IsDynamicSize = PlainObjectType::SizeAtCompileTime==Dynamic, KeepsPacketAccess = bool(HasNoInnerStride) && ( bool(IsDynamicSize) || HasNoOuterStride || ( OuterStrideAtCompileTime!=Dynamic && ((static_cast(sizeof(Scalar))*OuterStrideAtCompileTime)%16)==0 ) ), Flags0 = TraitsBase::Flags & (~NestByRefBit), Flags1 = IsAligned ? (int(Flags0) | AlignedBit) : (int(Flags0) & ~AlignedBit), Flags2 = (bool(HasNoStride) || bool(PlainObjectType::IsVectorAtCompileTime)) ? int(Flags1) : int(Flags1 & ~LinearAccessBit), Flags3 = is_lvalue::value ? int(Flags2) : (int(Flags2) & ~LvalueBit), Flags = KeepsPacketAccess ? int(Flags3) : (int(Flags3) & ~PacketAccessBit) }; private: enum { Options }; // Expressions don't have Options }; } template class Map : public MapBase > { public: typedef MapBase Base; EIGEN_DENSE_PUBLIC_INTERFACE(Map) typedef typename Base::PointerType PointerType; #if EIGEN2_SUPPORT_STAGE <= STAGE30_FULL_EIGEN3_API typedef const Scalar* PointerArgType; inline PointerType cast_to_pointer_type(PointerArgType ptr) { return const_cast(ptr); } #else typedef PointerType PointerArgType; inline PointerType cast_to_pointer_type(PointerArgType ptr) { return ptr; } #endif inline Index innerStride() const { return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1; } inline Index outerStride() const { return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer() : IsVectorAtCompileTime ? this->size() : int(Flags)&RowMajorBit ? this->cols() : this->rows(); } /** Constructor in the fixed-size case. * * \param data pointer to the array to map * \param stride optional Stride object, passing the strides. */ inline Map(PointerArgType data, const StrideType& stride = StrideType()) : Base(cast_to_pointer_type(data)), m_stride(stride) { PlainObjectType::Base::_check_template_params(); } /** Constructor in the dynamic-size vector case. * * \param data pointer to the array to map * \param size the size of the vector expression * \param stride optional Stride object, passing the strides. */ inline Map(PointerArgType data, Index size, const StrideType& stride = StrideType()) : Base(cast_to_pointer_type(data), size), m_stride(stride) { PlainObjectType::Base::_check_template_params(); } /** Constructor in the dynamic-size matrix case. * * \param data pointer to the array to map * \param rows the number of rows of the matrix expression * \param cols the number of columns of the matrix expression * \param stride optional Stride object, passing the strides. */ inline Map(PointerArgType data, Index rows, Index cols, const StrideType& stride = StrideType()) : Base(cast_to_pointer_type(data), rows, cols), m_stride(stride) { PlainObjectType::Base::_check_template_params(); } EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map) protected: StrideType m_stride; }; template inline Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> ::Array(const Scalar *data) { this->_set_noalias(Eigen::Map(data)); } template inline Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> ::Matrix(const Scalar *data) { this->_set_noalias(Eigen::Map(data)); } } // end namespace Eigen #endif // EIGEN_MAP_H ctbnrle.1.1.1/hdr/Eigen/src/Core/CwiseUnaryOp.h0000775000175000017500000001253512107032422021414 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2010 Gael Guennebaud // Copyright (C) 2006-2008 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_CWISE_UNARY_OP_H #define EIGEN_CWISE_UNARY_OP_H namespace Eigen { /** \class CwiseUnaryOp * \ingroup Core_Module * * \brief Generic expression where a coefficient-wise unary operator is applied to an expression * * \param UnaryOp template functor implementing the operator * \param XprType the type of the expression to which we are applying the unary operator * * This class represents an expression where a unary operator is applied to an expression. * It is the return type of all operations taking exactly 1 input expression, regardless of the * presence of other inputs such as scalars. For example, the operator* in the expression 3*matrix * is considered unary, because only the right-hand side is an expression, and its * return type is a specialization of CwiseUnaryOp. * * Most of the time, this is the only way that it is used, so you typically don't have to name * CwiseUnaryOp types explicitly. * * \sa MatrixBase::unaryExpr(const CustomUnaryOp &) const, class CwiseBinaryOp, class CwiseNullaryOp */ namespace internal { template struct traits > : traits { typedef typename result_of< UnaryOp(typename XprType::Scalar) >::type Scalar; typedef typename XprType::Nested XprTypeNested; typedef typename remove_reference::type _XprTypeNested; enum { Flags = _XprTypeNested::Flags & ( HereditaryBits | LinearAccessBit | AlignedBit | (functor_traits::PacketAccess ? PacketAccessBit : 0)), CoeffReadCost = _XprTypeNested::CoeffReadCost + functor_traits::Cost }; }; } template class CwiseUnaryOpImpl; template class CwiseUnaryOp : internal::no_assignment_operator, public CwiseUnaryOpImpl::StorageKind> { public: typedef typename CwiseUnaryOpImpl::StorageKind>::Base Base; EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp) inline CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp()) : m_xpr(xpr), m_functor(func) {} EIGEN_STRONG_INLINE Index rows() const { return m_xpr.rows(); } EIGEN_STRONG_INLINE Index cols() const { return m_xpr.cols(); } /** \returns the functor representing the unary operation */ const UnaryOp& functor() const { return m_functor; } /** \returns the nested expression */ const typename internal::remove_all::type& nestedExpression() const { return m_xpr; } /** \returns the nested expression */ typename internal::remove_all::type& nestedExpression() { return m_xpr.const_cast_derived(); } protected: typename XprType::Nested m_xpr; const UnaryOp m_functor; }; // This is the generic implementation for dense storage. // It can be used for any expression types implementing the dense concept. template class CwiseUnaryOpImpl : public internal::dense_xpr_base >::type { public: typedef CwiseUnaryOp Derived; typedef typename internal::dense_xpr_base >::type Base; EIGEN_DENSE_PUBLIC_INTERFACE(Derived) EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const { return derived().functor()(derived().nestedExpression().coeff(row, col)); } template EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const { return derived().functor().packetOp(derived().nestedExpression().template packet(row, col)); } EIGEN_STRONG_INLINE const Scalar coeff(Index index) const { return derived().functor()(derived().nestedExpression().coeff(index)); } template EIGEN_STRONG_INLINE PacketScalar packet(Index index) const { return derived().functor().packetOp(derived().nestedExpression().template packet(index)); } }; } // end namespace Eigen #endif // EIGEN_CWISE_UNARY_OP_H ctbnrle.1.1.1/hdr/Eigen/src/Core/CommaInitializer.h0000775000175000017500000001255012107032422022261 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Gael Guennebaud // Copyright (C) 2006-2008 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_COMMAINITIALIZER_H #define EIGEN_COMMAINITIALIZER_H namespace Eigen { /** \class CommaInitializer * \ingroup Core_Module * * \brief Helper class used by the comma initializer operator * * This class is internally used to implement the comma initializer feature. It is * the return type of MatrixBase::operator<<, and most of the time this is the only * way it is used. * * \sa \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished() */ template struct CommaInitializer { typedef typename XprType::Scalar Scalar; typedef typename XprType::Index Index; inline CommaInitializer(XprType& xpr, const Scalar& s) : m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1) { m_xpr.coeffRef(0,0) = s; } template inline CommaInitializer(XprType& xpr, const DenseBase& other) : m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows()) { m_xpr.block(0, 0, other.rows(), other.cols()) = other; } /* inserts a scalar value in the target matrix */ CommaInitializer& operator,(const Scalar& s) { if (m_col==m_xpr.cols()) { m_row+=m_currentBlockRows; m_col = 0; m_currentBlockRows = 1; eigen_assert(m_row CommaInitializer& operator,(const DenseBase& other) { if (m_col==m_xpr.cols()) { m_row+=m_currentBlockRows; m_col = 0; m_currentBlockRows = other.rows(); eigen_assert(m_row+m_currentBlockRows<=m_xpr.rows() && "Too many rows passed to comma initializer (operator<<)"); } eigen_assert(m_col (m_row, m_col) = other; else m_xpr.block(m_row, m_col, other.rows(), other.cols()) = other; m_col += other.cols(); return *this; } inline ~CommaInitializer() { eigen_assert((m_row+m_currentBlockRows) == m_xpr.rows() && m_col == m_xpr.cols() && "Too few coefficients passed to comma initializer (operator<<)"); } /** \returns the built matrix once all its coefficients have been set. * Calling finished is 100% optional. Its purpose is to write expressions * like this: * \code * quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished()); * \endcode */ inline XprType& finished() { return m_xpr; } XprType& m_xpr; // target expression Index m_row; // current row id Index m_col; // current col id Index m_currentBlockRows; // current block height }; /** \anchor MatrixBaseCommaInitRef * Convenient operator to set the coefficients of a matrix. * * The coefficients must be provided in a row major order and exactly match * the size of the matrix. Otherwise an assertion is raised. * * Example: \include MatrixBase_set.cpp * Output: \verbinclude MatrixBase_set.out * * \sa CommaInitializer::finished(), class CommaInitializer */ template inline CommaInitializer DenseBase::operator<< (const Scalar& s) { return CommaInitializer(*static_cast(this), s); } /** \sa operator<<(const Scalar&) */ template template inline CommaInitializer DenseBase::operator<<(const DenseBase& other) { return CommaInitializer(*static_cast(this), other); } } // end namespace Eigen #endif // EIGEN_COMMAINITIALIZER_H ctbnrle.1.1.1/hdr/Eigen/src/Core/VectorBlock.h0000775000175000017500000002424212107032422021237 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2010 Gael Guennebaud // Copyright (C) 2006-2008 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_VECTORBLOCK_H #define EIGEN_VECTORBLOCK_H namespace Eigen { /** \class VectorBlock * \ingroup Core_Module * * \brief Expression of a fixed-size or dynamic-size sub-vector * * \param VectorType the type of the object in which we are taking a sub-vector * \param Size size of the sub-vector we are taking at compile time (optional) * * This class represents an expression of either a fixed-size or dynamic-size sub-vector. * It is the return type of DenseBase::segment(Index,Index) and DenseBase::segment(Index) and * most of the time this is the only way it is used. * * However, if you want to directly maniputate sub-vector expressions, * for instance if you want to write a function returning such an expression, you * will need to use this class. * * Here is an example illustrating the dynamic case: * \include class_VectorBlock.cpp * Output: \verbinclude class_VectorBlock.out * * \note Even though this expression has dynamic size, in the case where \a VectorType * has fixed size, this expression inherits a fixed maximal size which means that evaluating * it does not cause a dynamic memory allocation. * * Here is an example illustrating the fixed-size case: * \include class_FixedVectorBlock.cpp * Output: \verbinclude class_FixedVectorBlock.out * * \sa class Block, DenseBase::segment(Index,Index,Index,Index), DenseBase::segment(Index,Index) */ namespace internal { template struct traits > : public traits::Flags & RowMajorBit ? 1 : Size, traits::Flags & RowMajorBit ? Size : 1> > { }; } template class VectorBlock : public Block::Flags & RowMajorBit ? 1 : Size, internal::traits::Flags & RowMajorBit ? Size : 1> { typedef Block::Flags & RowMajorBit ? 1 : Size, internal::traits::Flags & RowMajorBit ? Size : 1> Base; enum { IsColVector = !(internal::traits::Flags & RowMajorBit) }; public: EIGEN_DENSE_PUBLIC_INTERFACE(VectorBlock) using Base::operator=; /** Dynamic-size constructor */ inline VectorBlock(VectorType& vector, Index start, Index size) : Base(vector, IsColVector ? start : 0, IsColVector ? 0 : start, IsColVector ? size : 1, IsColVector ? 1 : size) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorBlock); } /** Fixed-size constructor */ inline VectorBlock(VectorType& vector, Index start) : Base(vector, IsColVector ? start : 0, IsColVector ? 0 : start) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorBlock); } }; /** \returns a dynamic-size expression of a segment (i.e. a vector block) in *this. * * \only_for_vectors * * \param start the first coefficient in the segment * \param size the number of coefficients in the segment * * Example: \include MatrixBase_segment_int_int.cpp * Output: \verbinclude MatrixBase_segment_int_int.out * * \note Even though the returned expression has dynamic size, in the case * when it is applied to a fixed-size vector, it inherits a fixed maximal size, * which means that evaluating it does not cause a dynamic memory allocation. * * \sa class Block, segment(Index) */ template inline typename DenseBase::SegmentReturnType DenseBase::segment(Index start, Index size) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return SegmentReturnType(derived(), start, size); } /** This is the const version of segment(Index,Index).*/ template inline typename DenseBase::ConstSegmentReturnType DenseBase::segment(Index start, Index size) const { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return ConstSegmentReturnType(derived(), start, size); } /** \returns a dynamic-size expression of the first coefficients of *this. * * \only_for_vectors * * \param size the number of coefficients in the block * * Example: \include MatrixBase_start_int.cpp * Output: \verbinclude MatrixBase_start_int.out * * \note Even though the returned expression has dynamic size, in the case * when it is applied to a fixed-size vector, it inherits a fixed maximal size, * which means that evaluating it does not cause a dynamic memory allocation. * * \sa class Block, block(Index,Index) */ template inline typename DenseBase::SegmentReturnType DenseBase::head(Index size) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return SegmentReturnType(derived(), 0, size); } /** This is the const version of head(Index).*/ template inline typename DenseBase::ConstSegmentReturnType DenseBase::head(Index size) const { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return ConstSegmentReturnType(derived(), 0, size); } /** \returns a dynamic-size expression of the last coefficients of *this. * * \only_for_vectors * * \param size the number of coefficients in the block * * Example: \include MatrixBase_end_int.cpp * Output: \verbinclude MatrixBase_end_int.out * * \note Even though the returned expression has dynamic size, in the case * when it is applied to a fixed-size vector, it inherits a fixed maximal size, * which means that evaluating it does not cause a dynamic memory allocation. * * \sa class Block, block(Index,Index) */ template inline typename DenseBase::SegmentReturnType DenseBase::tail(Index size) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return SegmentReturnType(derived(), this->size() - size, size); } /** This is the const version of tail(Index).*/ template inline typename DenseBase::ConstSegmentReturnType DenseBase::tail(Index size) const { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return ConstSegmentReturnType(derived(), this->size() - size, size); } /** \returns a fixed-size expression of a segment (i.e. a vector block) in \c *this * * \only_for_vectors * * The template parameter \a Size is the number of coefficients in the block * * \param start the index of the first element of the sub-vector * * Example: \include MatrixBase_template_int_segment.cpp * Output: \verbinclude MatrixBase_template_int_segment.out * * \sa class Block */ template template inline typename DenseBase::template FixedSegmentReturnType::Type DenseBase::segment(Index start) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return typename FixedSegmentReturnType::Type(derived(), start); } /** This is the const version of segment(Index).*/ template template inline typename DenseBase::template ConstFixedSegmentReturnType::Type DenseBase::segment(Index start) const { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return typename ConstFixedSegmentReturnType::Type(derived(), start); } /** \returns a fixed-size expression of the first coefficients of *this. * * \only_for_vectors * * The template parameter \a Size is the number of coefficients in the block * * Example: \include MatrixBase_template_int_start.cpp * Output: \verbinclude MatrixBase_template_int_start.out * * \sa class Block */ template template inline typename DenseBase::template FixedSegmentReturnType::Type DenseBase::head() { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return typename FixedSegmentReturnType::Type(derived(), 0); } /** This is the const version of head().*/ template template inline typename DenseBase::template ConstFixedSegmentReturnType::Type DenseBase::head() const { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return typename ConstFixedSegmentReturnType::Type(derived(), 0); } /** \returns a fixed-size expression of the last coefficients of *this. * * \only_for_vectors * * The template parameter \a Size is the number of coefficients in the block * * Example: \include MatrixBase_template_int_end.cpp * Output: \verbinclude MatrixBase_template_int_end.out * * \sa class Block */ template template inline typename DenseBase::template FixedSegmentReturnType::Type DenseBase::tail() { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return typename FixedSegmentReturnType::Type(derived(), size() - Size); } /** This is the const version of tail.*/ template template inline typename DenseBase::template ConstFixedSegmentReturnType::Type DenseBase::tail() const { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return typename ConstFixedSegmentReturnType::Type(derived(), size() - Size); } } // end namespace Eigen #endif // EIGEN_VECTORBLOCK_H ctbnrle.1.1.1/hdr/Eigen/src/Core/TriangularMatrix.h0000775000175000017500000007363212107032422022326 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Benoit Jacob // Copyright (C) 2008-2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_TRIANGULARMATRIX_H #define EIGEN_TRIANGULARMATRIX_H namespace Eigen { namespace internal { template struct triangular_solve_retval; } /** \internal * * \class TriangularBase * \ingroup Core_Module * * \brief Base class for triangular part in a matrix */ template class TriangularBase : public EigenBase { public: enum { Mode = internal::traits::Mode, CoeffReadCost = internal::traits::CoeffReadCost, RowsAtCompileTime = internal::traits::RowsAtCompileTime, ColsAtCompileTime = internal::traits::ColsAtCompileTime, MaxRowsAtCompileTime = internal::traits::MaxRowsAtCompileTime, MaxColsAtCompileTime = internal::traits::MaxColsAtCompileTime }; typedef typename internal::traits::Scalar Scalar; typedef typename internal::traits::StorageKind StorageKind; typedef typename internal::traits::Index Index; typedef typename internal::traits::DenseMatrixType DenseMatrixType; typedef DenseMatrixType DenseType; inline TriangularBase() { eigen_assert(!((Mode&UnitDiag) && (Mode&ZeroDiag))); } inline Index rows() const { return derived().rows(); } inline Index cols() const { return derived().cols(); } inline Index outerStride() const { return derived().outerStride(); } inline Index innerStride() const { return derived().innerStride(); } inline Scalar coeff(Index row, Index col) const { return derived().coeff(row,col); } inline Scalar& coeffRef(Index row, Index col) { return derived().coeffRef(row,col); } /** \see MatrixBase::copyCoeff(row,col) */ template EIGEN_STRONG_INLINE void copyCoeff(Index row, Index col, Other& other) { derived().coeffRef(row, col) = other.coeff(row, col); } inline Scalar operator()(Index row, Index col) const { check_coordinates(row, col); return coeff(row,col); } inline Scalar& operator()(Index row, Index col) { check_coordinates(row, col); return coeffRef(row,col); } #ifndef EIGEN_PARSED_BY_DOXYGEN inline const Derived& derived() const { return *static_cast(this); } inline Derived& derived() { return *static_cast(this); } #endif // not EIGEN_PARSED_BY_DOXYGEN template void evalTo(MatrixBase &other) const; template void evalToLazy(MatrixBase &other) const; DenseMatrixType toDenseMatrix() const { DenseMatrixType res(rows(), cols()); evalToLazy(res); return res; } protected: void check_coordinates(Index row, Index col) const { EIGEN_ONLY_USED_FOR_DEBUG(row); EIGEN_ONLY_USED_FOR_DEBUG(col); eigen_assert(col>=0 && col=0 && row=row) || (mode==Lower && col<=row) || ((mode==StrictlyUpper || mode==UnitUpper) && col>row) || ((mode==StrictlyLower || mode==UnitLower) && col struct traits > : traits { typedef typename nested::type MatrixTypeNested; typedef typename remove_reference::type MatrixTypeNestedNonRef; typedef typename remove_all::type MatrixTypeNestedCleaned; typedef MatrixType ExpressionType; typedef typename MatrixType::PlainObject DenseMatrixType; enum { Mode = _Mode, Flags = (MatrixTypeNestedCleaned::Flags & (HereditaryBits) & (~(PacketAccessBit | DirectAccessBit | LinearAccessBit))) | Mode, CoeffReadCost = MatrixTypeNestedCleaned::CoeffReadCost }; }; } template struct TriangularProduct; template class TriangularView : public TriangularBase > { public: typedef TriangularBase Base; typedef typename internal::traits::Scalar Scalar; typedef _MatrixType MatrixType; typedef typename internal::traits::DenseMatrixType DenseMatrixType; typedef DenseMatrixType PlainObject; protected: typedef typename internal::traits::MatrixTypeNested MatrixTypeNested; typedef typename internal::traits::MatrixTypeNestedNonRef MatrixTypeNestedNonRef; typedef typename internal::traits::MatrixTypeNestedCleaned MatrixTypeNestedCleaned; typedef typename internal::remove_all::type MatrixConjugateReturnType; public: using Base::evalToLazy; typedef typename internal::traits::StorageKind StorageKind; typedef typename internal::traits::Index Index; enum { Mode = _Mode, TransposeMode = (Mode & Upper ? Lower : 0) | (Mode & Lower ? Upper : 0) | (Mode & (UnitDiag)) | (Mode & (ZeroDiag)) }; inline TriangularView(const MatrixType& matrix) : m_matrix(matrix) {} inline Index rows() const { return m_matrix.rows(); } inline Index cols() const { return m_matrix.cols(); } inline Index outerStride() const { return m_matrix.outerStride(); } inline Index innerStride() const { return m_matrix.innerStride(); } /** \sa MatrixBase::operator+=() */ template TriangularView& operator+=(const DenseBase& other) { return *this = m_matrix + other.derived(); } /** \sa MatrixBase::operator-=() */ template TriangularView& operator-=(const DenseBase& other) { return *this = m_matrix - other.derived(); } /** \sa MatrixBase::operator*=() */ TriangularView& operator*=(const typename internal::traits::Scalar& other) { return *this = m_matrix * other; } /** \sa MatrixBase::operator/=() */ TriangularView& operator/=(const typename internal::traits::Scalar& other) { return *this = m_matrix / other; } /** \sa MatrixBase::fill() */ void fill(const Scalar& value) { setConstant(value); } /** \sa MatrixBase::setConstant() */ TriangularView& setConstant(const Scalar& value) { return *this = MatrixType::Constant(rows(), cols(), value); } /** \sa MatrixBase::setZero() */ TriangularView& setZero() { return setConstant(Scalar(0)); } /** \sa MatrixBase::setOnes() */ TriangularView& setOnes() { return setConstant(Scalar(1)); } /** \sa MatrixBase::coeff() * \warning the coordinates must fit into the referenced triangular part */ inline Scalar coeff(Index row, Index col) const { Base::check_coordinates_internal(row, col); return m_matrix.coeff(row, col); } /** \sa MatrixBase::coeffRef() * \warning the coordinates must fit into the referenced triangular part */ inline Scalar& coeffRef(Index row, Index col) { Base::check_coordinates_internal(row, col); return m_matrix.const_cast_derived().coeffRef(row, col); } const MatrixTypeNestedCleaned& nestedExpression() const { return m_matrix; } MatrixTypeNestedCleaned& nestedExpression() { return *const_cast(&m_matrix); } /** Assigns a triangular matrix to a triangular part of a dense matrix */ template TriangularView& operator=(const TriangularBase& other); template TriangularView& operator=(const MatrixBase& other); TriangularView& operator=(const TriangularView& other) { return *this = other.nestedExpression(); } template void lazyAssign(const TriangularBase& other); template void lazyAssign(const MatrixBase& other); /** \sa MatrixBase::conjugate() */ inline TriangularView conjugate() { return m_matrix.conjugate(); } /** \sa MatrixBase::conjugate() const */ inline const TriangularView conjugate() const { return m_matrix.conjugate(); } /** \sa MatrixBase::adjoint() const */ inline const TriangularView adjoint() const { return m_matrix.adjoint(); } /** \sa MatrixBase::transpose() */ inline TriangularView,TransposeMode> transpose() { EIGEN_STATIC_ASSERT_LVALUE(MatrixType) return m_matrix.const_cast_derived().transpose(); } /** \sa MatrixBase::transpose() const */ inline const TriangularView,TransposeMode> transpose() const { return m_matrix.transpose(); } /** Efficient triangular matrix times vector/matrix product */ template TriangularProduct operator*(const MatrixBase& rhs) const { return TriangularProduct (m_matrix, rhs.derived()); } /** Efficient vector/matrix times triangular matrix product */ template friend TriangularProduct operator*(const MatrixBase& lhs, const TriangularView& rhs) { return TriangularProduct (lhs.derived(),rhs.m_matrix); } #ifdef EIGEN2_SUPPORT template struct eigen2_product_return_type { typedef typename TriangularView::DenseMatrixType DenseMatrixType; typedef typename OtherDerived::PlainObject::DenseType OtherPlainObject; typedef typename ProductReturnType::Type ProdRetType; typedef typename ProdRetType::PlainObject type; }; template const typename eigen2_product_return_type::type operator*(const EigenBase& rhs) const { typename OtherDerived::PlainObject::DenseType rhsPlainObject; rhs.evalTo(rhsPlainObject); return this->toDenseMatrix() * rhsPlainObject; } template bool isApprox(const TriangularView& other, typename NumTraits::Real precision = NumTraits::dummy_precision()) const { return this->toDenseMatrix().isApprox(other.toDenseMatrix(), precision); } template bool isApprox(const MatrixBase& other, typename NumTraits::Real precision = NumTraits::dummy_precision()) const { return this->toDenseMatrix().isApprox(other, precision); } #endif // EIGEN2_SUPPORT template inline const internal::triangular_solve_retval solve(const MatrixBase& other) const; template void solveInPlace(const MatrixBase& other) const; template inline const internal::triangular_solve_retval solve(const MatrixBase& other) const { return solve(other); } template void solveInPlace(const MatrixBase& other) const { return solveInPlace(other); } const SelfAdjointView selfadjointView() const { EIGEN_STATIC_ASSERT((Mode&UnitDiag)==0,PROGRAMMING_ERROR); return SelfAdjointView(m_matrix); } SelfAdjointView selfadjointView() { EIGEN_STATIC_ASSERT((Mode&UnitDiag)==0,PROGRAMMING_ERROR); return SelfAdjointView(m_matrix); } template void swap(TriangularBase const & other) { TriangularView,Mode>(const_cast(m_matrix)).lazyAssign(other.derived()); } template void swap(MatrixBase const & other) { SwapWrapper swaper(const_cast(m_matrix)); TriangularView,Mode>(swaper).lazyAssign(other.derived()); } Scalar determinant() const { if (Mode & UnitDiag) return 1; else if (Mode & ZeroDiag) return 0; else return m_matrix.diagonal().prod(); } // TODO simplify the following: template EIGEN_STRONG_INLINE TriangularView& operator=(const ProductBase& other) { setZero(); return assignProduct(other,1); } template EIGEN_STRONG_INLINE TriangularView& operator+=(const ProductBase& other) { return assignProduct(other,1); } template EIGEN_STRONG_INLINE TriangularView& operator-=(const ProductBase& other) { return assignProduct(other,-1); } template EIGEN_STRONG_INLINE TriangularView& operator=(const ScaledProduct& other) { setZero(); return assignProduct(other,other.alpha()); } template EIGEN_STRONG_INLINE TriangularView& operator+=(const ScaledProduct& other) { return assignProduct(other,other.alpha()); } template EIGEN_STRONG_INLINE TriangularView& operator-=(const ScaledProduct& other) { return assignProduct(other,-other.alpha()); } protected: template EIGEN_STRONG_INLINE TriangularView& assignProduct(const ProductBase& prod, const Scalar& alpha); MatrixTypeNested m_matrix; }; /*************************************************************************** * Implementation of triangular evaluation/assignment ***************************************************************************/ namespace internal { template struct triangular_assignment_selector { enum { col = (UnrollCount-1) / Derived1::RowsAtCompileTime, row = (UnrollCount-1) % Derived1::RowsAtCompileTime }; typedef typename Derived1::Scalar Scalar; static inline void run(Derived1 &dst, const Derived2 &src) { triangular_assignment_selector::run(dst, src); eigen_assert( Mode == Upper || Mode == Lower || Mode == StrictlyUpper || Mode == StrictlyLower || Mode == UnitUpper || Mode == UnitLower); if((Mode == Upper && row <= col) || (Mode == Lower && row >= col) || (Mode == StrictlyUpper && row < col) || (Mode == StrictlyLower && row > col) || (Mode == UnitUpper && row < col) || (Mode == UnitLower && row > col)) dst.copyCoeff(row, col, src); else if(ClearOpposite) { if (Mode&UnitDiag && row==col) dst.coeffRef(row, col) = Scalar(1); else dst.coeffRef(row, col) = Scalar(0); } } }; // prevent buggy user code from causing an infinite recursion template struct triangular_assignment_selector { static inline void run(Derived1 &, const Derived2 &) {} }; template struct triangular_assignment_selector { typedef typename Derived1::Index Index; typedef typename Derived1::Scalar Scalar; static inline void run(Derived1 &dst, const Derived2 &src) { for(Index j = 0; j < dst.cols(); ++j) { Index maxi = (std::min)(j, dst.rows()-1); for(Index i = 0; i <= maxi; ++i) dst.copyCoeff(i, j, src); if (ClearOpposite) for(Index i = maxi+1; i < dst.rows(); ++i) dst.coeffRef(i, j) = Scalar(0); } } }; template struct triangular_assignment_selector { typedef typename Derived1::Index Index; static inline void run(Derived1 &dst, const Derived2 &src) { for(Index j = 0; j < dst.cols(); ++j) { for(Index i = j; i < dst.rows(); ++i) dst.copyCoeff(i, j, src); Index maxi = (std::min)(j, dst.rows()); if (ClearOpposite) for(Index i = 0; i < maxi; ++i) dst.coeffRef(i, j) = static_cast(0); } } }; template struct triangular_assignment_selector { typedef typename Derived1::Index Index; static inline void run(Derived1 &dst, const Derived2 &src) { for(Index j = 0; j < dst.cols(); ++j) { Index maxi = (std::min)(j, dst.rows()); for(Index i = 0; i < maxi; ++i) dst.copyCoeff(i, j, src); if (ClearOpposite) for(Index i = maxi; i < dst.rows(); ++i) dst.coeffRef(i, j) = 0; } } }; template struct triangular_assignment_selector { typedef typename Derived1::Index Index; static inline void run(Derived1 &dst, const Derived2 &src) { for(Index j = 0; j < dst.cols(); ++j) { for(Index i = j+1; i < dst.rows(); ++i) dst.copyCoeff(i, j, src); Index maxi = (std::min)(j, dst.rows()-1); if (ClearOpposite) for(Index i = 0; i <= maxi; ++i) dst.coeffRef(i, j) = static_cast(0); } } }; template struct triangular_assignment_selector { typedef typename Derived1::Index Index; static inline void run(Derived1 &dst, const Derived2 &src) { for(Index j = 0; j < dst.cols(); ++j) { Index maxi = (std::min)(j, dst.rows()); for(Index i = 0; i < maxi; ++i) dst.copyCoeff(i, j, src); if (ClearOpposite) { for(Index i = maxi+1; i < dst.rows(); ++i) dst.coeffRef(i, j) = 0; } } dst.diagonal().setOnes(); } }; template struct triangular_assignment_selector { typedef typename Derived1::Index Index; static inline void run(Derived1 &dst, const Derived2 &src) { for(Index j = 0; j < dst.cols(); ++j) { Index maxi = (std::min)(j, dst.rows()); for(Index i = maxi+1; i < dst.rows(); ++i) dst.copyCoeff(i, j, src); if (ClearOpposite) { for(Index i = 0; i < maxi; ++i) dst.coeffRef(i, j) = 0; } } dst.diagonal().setOnes(); } }; } // end namespace internal // FIXME should we keep that possibility template template inline TriangularView& TriangularView::operator=(const MatrixBase& other) { if(OtherDerived::Flags & EvalBeforeAssigningBit) { typename internal::plain_matrix_type::type other_evaluated(other.rows(), other.cols()); other_evaluated.template triangularView().lazyAssign(other.derived()); lazyAssign(other_evaluated); } else lazyAssign(other.derived()); return *this; } // FIXME should we keep that possibility template template void TriangularView::lazyAssign(const MatrixBase& other) { enum { unroll = MatrixType::SizeAtCompileTime != Dynamic && internal::traits::CoeffReadCost != Dynamic && MatrixType::SizeAtCompileTime*internal::traits::CoeffReadCost/2 <= EIGEN_UNROLLING_LIMIT }; eigen_assert(m_matrix.rows() == other.rows() && m_matrix.cols() == other.cols()); internal::triangular_assignment_selector ::run(m_matrix.const_cast_derived(), other.derived()); } template template inline TriangularView& TriangularView::operator=(const TriangularBase& other) { eigen_assert(Mode == int(OtherDerived::Mode)); if(internal::traits::Flags & EvalBeforeAssigningBit) { typename OtherDerived::DenseMatrixType other_evaluated(other.rows(), other.cols()); other_evaluated.template triangularView().lazyAssign(other.derived().nestedExpression()); lazyAssign(other_evaluated); } else lazyAssign(other.derived().nestedExpression()); return *this; } template template void TriangularView::lazyAssign(const TriangularBase& other) { enum { unroll = MatrixType::SizeAtCompileTime != Dynamic && internal::traits::CoeffReadCost != Dynamic && MatrixType::SizeAtCompileTime * internal::traits::CoeffReadCost / 2 <= EIGEN_UNROLLING_LIMIT }; eigen_assert(m_matrix.rows() == other.rows() && m_matrix.cols() == other.cols()); internal::triangular_assignment_selector ::run(m_matrix.const_cast_derived(), other.derived().nestedExpression()); } /*************************************************************************** * Implementation of TriangularBase methods ***************************************************************************/ /** Assigns a triangular or selfadjoint matrix to a dense matrix. * If the matrix is triangular, the opposite part is set to zero. */ template template void TriangularBase::evalTo(MatrixBase &other) const { if(internal::traits::Flags & EvalBeforeAssigningBit) { typename internal::plain_matrix_type::type other_evaluated(rows(), cols()); evalToLazy(other_evaluated); other.derived().swap(other_evaluated); } else evalToLazy(other.derived()); } /** Assigns a triangular or selfadjoint matrix to a dense matrix. * If the matrix is triangular, the opposite part is set to zero. */ template template void TriangularBase::evalToLazy(MatrixBase &other) const { enum { unroll = DenseDerived::SizeAtCompileTime != Dynamic && internal::traits::CoeffReadCost != Dynamic && DenseDerived::SizeAtCompileTime * internal::traits::CoeffReadCost / 2 <= EIGEN_UNROLLING_LIMIT }; other.derived().resize(this->rows(), this->cols()); internal::triangular_assignment_selector ::MatrixTypeNestedCleaned, Derived::Mode, unroll ? int(DenseDerived::SizeAtCompileTime) : Dynamic, true // clear the opposite triangular part >::run(other.derived(), derived().nestedExpression()); } /*************************************************************************** * Implementation of TriangularView methods ***************************************************************************/ /*************************************************************************** * Implementation of MatrixBase methods ***************************************************************************/ #ifdef EIGEN2_SUPPORT // implementation of part<>(), including the SelfAdjoint case. namespace internal { template struct eigen2_part_return_type { typedef TriangularView type; }; template struct eigen2_part_return_type { typedef SelfAdjointView type; }; } /** \deprecated use MatrixBase::triangularView() */ template template const typename internal::eigen2_part_return_type::type MatrixBase::part() const { return derived(); } /** \deprecated use MatrixBase::triangularView() */ template template typename internal::eigen2_part_return_type::type MatrixBase::part() { return derived(); } #endif /** * \returns an expression of a triangular view extracted from the current matrix * * The parameter \a Mode can have the following values: \c #Upper, \c #StrictlyUpper, \c #UnitUpper, * \c #Lower, \c #StrictlyLower, \c #UnitLower. * * Example: \include MatrixBase_extract.cpp * Output: \verbinclude MatrixBase_extract.out * * \sa class TriangularView */ template template typename MatrixBase::template TriangularViewReturnType::Type MatrixBase::triangularView() { return derived(); } /** This is the const version of MatrixBase::triangularView() */ template template typename MatrixBase::template ConstTriangularViewReturnType::Type MatrixBase::triangularView() const { return derived(); } /** \returns true if *this is approximately equal to an upper triangular matrix, * within the precision given by \a prec. * * \sa isLowerTriangular() */ template bool MatrixBase::isUpperTriangular(RealScalar prec) const { RealScalar maxAbsOnUpperPart = static_cast(-1); for(Index j = 0; j < cols(); ++j) { Index maxi = (std::min)(j, rows()-1); for(Index i = 0; i <= maxi; ++i) { RealScalar absValue = internal::abs(coeff(i,j)); if(absValue > maxAbsOnUpperPart) maxAbsOnUpperPart = absValue; } } RealScalar threshold = maxAbsOnUpperPart * prec; for(Index j = 0; j < cols(); ++j) for(Index i = j+1; i < rows(); ++i) if(internal::abs(coeff(i, j)) > threshold) return false; return true; } /** \returns true if *this is approximately equal to a lower triangular matrix, * within the precision given by \a prec. * * \sa isUpperTriangular() */ template bool MatrixBase::isLowerTriangular(RealScalar prec) const { RealScalar maxAbsOnLowerPart = static_cast(-1); for(Index j = 0; j < cols(); ++j) for(Index i = j; i < rows(); ++i) { RealScalar absValue = internal::abs(coeff(i,j)); if(absValue > maxAbsOnLowerPart) maxAbsOnLowerPart = absValue; } RealScalar threshold = maxAbsOnLowerPart * prec; for(Index j = 1; j < cols(); ++j) { Index maxi = (std::min)(j, rows()-1); for(Index i = 0; i < maxi; ++i) if(internal::abs(coeff(i, j)) > threshold) return false; } return true; } } // end namespace Eigen #endif // EIGEN_TRIANGULARMATRIX_H ctbnrle.1.1.1/hdr/Eigen/src/Core/SelfCwiseBinaryOp.h0000775000175000017500000001734712107032422022362 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009-2010 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_SELFCWISEBINARYOP_H #define EIGEN_SELFCWISEBINARYOP_H namespace Eigen { /** \class SelfCwiseBinaryOp * \ingroup Core_Module * * \internal * * \brief Internal helper class for optimizing operators like +=, -= * * This is a pseudo expression class re-implementing the copyCoeff/copyPacket * method to directly performs a +=/-= operations in an optimal way. In particular, * this allows to make sure that the input/output data are loaded only once using * aligned packet loads. * * \sa class SwapWrapper for a similar trick. */ namespace internal { template struct traits > : traits > { enum { // Note that it is still a good idea to preserve the DirectAccessBit // so that assign can correctly align the data. Flags = traits >::Flags | (Lhs::Flags&DirectAccessBit) | (Lhs::Flags&LvalueBit), OuterStrideAtCompileTime = Lhs::OuterStrideAtCompileTime, InnerStrideAtCompileTime = Lhs::InnerStrideAtCompileTime }; }; } template class SelfCwiseBinaryOp : public internal::dense_xpr_base< SelfCwiseBinaryOp >::type { public: typedef typename internal::dense_xpr_base::type Base; EIGEN_DENSE_PUBLIC_INTERFACE(SelfCwiseBinaryOp) typedef typename internal::packet_traits::type Packet; inline SelfCwiseBinaryOp(Lhs& xpr, const BinaryOp& func = BinaryOp()) : m_matrix(xpr), m_functor(func) {} inline Index rows() const { return m_matrix.rows(); } inline Index cols() const { return m_matrix.cols(); } inline Index outerStride() const { return m_matrix.outerStride(); } inline Index innerStride() const { return m_matrix.innerStride(); } inline const Scalar* data() const { return m_matrix.data(); } // note that this function is needed by assign to correctly align loads/stores // TODO make Assign use .data() inline Scalar& coeffRef(Index row, Index col) { EIGEN_STATIC_ASSERT_LVALUE(Lhs) return m_matrix.const_cast_derived().coeffRef(row, col); } inline const Scalar& coeffRef(Index row, Index col) const { return m_matrix.coeffRef(row, col); } // note that this function is needed by assign to correctly align loads/stores // TODO make Assign use .data() inline Scalar& coeffRef(Index index) { EIGEN_STATIC_ASSERT_LVALUE(Lhs) return m_matrix.const_cast_derived().coeffRef(index); } inline const Scalar& coeffRef(Index index) const { return m_matrix.const_cast_derived().coeffRef(index); } template void copyCoeff(Index row, Index col, const DenseBase& other) { OtherDerived& _other = other.const_cast_derived(); eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols()); Scalar& tmp = m_matrix.coeffRef(row,col); tmp = m_functor(tmp, _other.coeff(row,col)); } template void copyCoeff(Index index, const DenseBase& other) { OtherDerived& _other = other.const_cast_derived(); eigen_internal_assert(index >= 0 && index < m_matrix.size()); Scalar& tmp = m_matrix.coeffRef(index); tmp = m_functor(tmp, _other.coeff(index)); } template void copyPacket(Index row, Index col, const DenseBase& other) { OtherDerived& _other = other.const_cast_derived(); eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols()); m_matrix.template writePacket(row, col, m_functor.packetOp(m_matrix.template packet(row, col),_other.template packet(row, col)) ); } template void copyPacket(Index index, const DenseBase& other) { OtherDerived& _other = other.const_cast_derived(); eigen_internal_assert(index >= 0 && index < m_matrix.size()); m_matrix.template writePacket(index, m_functor.packetOp(m_matrix.template packet(index),_other.template packet(index)) ); } // reimplement lazyAssign to handle complex *= real // see CwiseBinaryOp ctor for details template EIGEN_STRONG_INLINE SelfCwiseBinaryOp& lazyAssign(const DenseBase& rhs) { EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs,RhsDerived) EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename RhsDerived::Scalar); #ifdef EIGEN_DEBUG_ASSIGN internal::assign_traits::debug(); #endif eigen_assert(rows() == rhs.rows() && cols() == rhs.cols()); internal::assign_impl::run(*this,rhs.derived()); #ifndef EIGEN_NO_DEBUG this->checkTransposeAliasing(rhs.derived()); #endif return *this; } // overloaded to honor evaluation of special matrices // maybe another solution would be to not use SelfCwiseBinaryOp // at first... SelfCwiseBinaryOp& operator=(const Rhs& _rhs) { typename internal::nested::type rhs(_rhs); return Base::operator=(rhs); } Lhs& expression() const { return m_matrix; } const BinaryOp& functor() const { return m_functor; } protected: Lhs& m_matrix; const BinaryOp& m_functor; private: SelfCwiseBinaryOp& operator=(const SelfCwiseBinaryOp&); }; template inline Derived& DenseBase::operator*=(const Scalar& other) { typedef typename Derived::PlainObject PlainObject; SelfCwiseBinaryOp, Derived, typename PlainObject::ConstantReturnType> tmp(derived()); tmp = PlainObject::Constant(rows(),cols(),other); return derived(); } template inline Derived& DenseBase::operator/=(const Scalar& other) { typedef typename internal::conditional::IsInteger, internal::scalar_quotient_op, internal::scalar_product_op >::type BinOp; typedef typename Derived::PlainObject PlainObject; SelfCwiseBinaryOp tmp(derived()); tmp = PlainObject::Constant(rows(),cols(), NumTraits::IsInteger ? other : Scalar(1)/other); return derived(); } } // end namespace Eigen #endif // EIGEN_SELFCWISEBINARYOP_H ctbnrle.1.1.1/hdr/Eigen/src/Core/Redux.h0000775000175000017500000003461412107032422020115 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Gael Guennebaud // Copyright (C) 2006-2008 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_REDUX_H #define EIGEN_REDUX_H namespace Eigen { namespace internal { // TODO // * implement other kind of vectorization // * factorize code /*************************************************************************** * Part 1 : the logic deciding a strategy for vectorization and unrolling ***************************************************************************/ template struct redux_traits { public: enum { PacketSize = packet_traits::size, InnerMaxSize = int(Derived::IsRowMajor) ? Derived::MaxColsAtCompileTime : Derived::MaxRowsAtCompileTime }; enum { MightVectorize = (int(Derived::Flags)&ActualPacketAccessBit) && (functor_traits::PacketAccess), MayLinearVectorize = MightVectorize && (int(Derived::Flags)&LinearAccessBit), MaySliceVectorize = MightVectorize && int(InnerMaxSize)>=3*PacketSize }; public: enum { Traversal = int(MayLinearVectorize) ? int(LinearVectorizedTraversal) : int(MaySliceVectorize) ? int(SliceVectorizedTraversal) : int(DefaultTraversal) }; public: enum { Cost = ( Derived::SizeAtCompileTime == Dynamic || Derived::CoeffReadCost == Dynamic || (Derived::SizeAtCompileTime!=1 && functor_traits::Cost == Dynamic) ) ? Dynamic : Derived::SizeAtCompileTime * Derived::CoeffReadCost + (Derived::SizeAtCompileTime-1) * functor_traits::Cost, UnrollingLimit = EIGEN_UNROLLING_LIMIT * (int(Traversal) == int(DefaultTraversal) ? 1 : int(PacketSize)) }; public: enum { Unrolling = Cost != Dynamic && Cost <= UnrollingLimit ? CompleteUnrolling : NoUnrolling }; }; /*************************************************************************** * Part 2 : unrollers ***************************************************************************/ /*** no vectorization ***/ template struct redux_novec_unroller { enum { HalfLength = Length/2 }; typedef typename Derived::Scalar Scalar; static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func& func) { return func(redux_novec_unroller::run(mat,func), redux_novec_unroller::run(mat,func)); } }; template struct redux_novec_unroller { enum { outer = Start / Derived::InnerSizeAtCompileTime, inner = Start % Derived::InnerSizeAtCompileTime }; typedef typename Derived::Scalar Scalar; static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func&) { return mat.coeffByOuterInner(outer, inner); } }; // This is actually dead code and will never be called. It is required // to prevent false warnings regarding failed inlining though // for 0 length run() will never be called at all. template struct redux_novec_unroller { typedef typename Derived::Scalar Scalar; static EIGEN_STRONG_INLINE Scalar run(const Derived&, const Func&) { return Scalar(); } }; /*** vectorization ***/ template struct redux_vec_unroller { enum { PacketSize = packet_traits::size, HalfLength = Length/2 }; typedef typename Derived::Scalar Scalar; typedef typename packet_traits::type PacketScalar; static EIGEN_STRONG_INLINE PacketScalar run(const Derived &mat, const Func& func) { return func.packetOp( redux_vec_unroller::run(mat,func), redux_vec_unroller::run(mat,func) ); } }; template struct redux_vec_unroller { enum { index = Start * packet_traits::size, outer = index / int(Derived::InnerSizeAtCompileTime), inner = index % int(Derived::InnerSizeAtCompileTime), alignment = (Derived::Flags & AlignedBit) ? Aligned : Unaligned }; typedef typename Derived::Scalar Scalar; typedef typename packet_traits::type PacketScalar; static EIGEN_STRONG_INLINE PacketScalar run(const Derived &mat, const Func&) { return mat.template packetByOuterInner(outer, inner); } }; /*************************************************************************** * Part 3 : implementation of all cases ***************************************************************************/ template::Traversal, int Unrolling = redux_traits::Unrolling > struct redux_impl; template struct redux_impl { typedef typename Derived::Scalar Scalar; typedef typename Derived::Index Index; static EIGEN_STRONG_INLINE Scalar run(const Derived& mat, const Func& func) { eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix"); Scalar res; res = mat.coeffByOuterInner(0, 0); for(Index i = 1; i < mat.innerSize(); ++i) res = func(res, mat.coeffByOuterInner(0, i)); for(Index i = 1; i < mat.outerSize(); ++i) for(Index j = 0; j < mat.innerSize(); ++j) res = func(res, mat.coeffByOuterInner(i, j)); return res; } }; template struct redux_impl : public redux_novec_unroller {}; template struct redux_impl { typedef typename Derived::Scalar Scalar; typedef typename packet_traits::type PacketScalar; typedef typename Derived::Index Index; static Scalar run(const Derived& mat, const Func& func) { const Index size = mat.size(); eigen_assert(size && "you are using an empty matrix"); const Index packetSize = packet_traits::size; const Index alignedStart = internal::first_aligned(mat); enum { alignment = bool(Derived::Flags & DirectAccessBit) || bool(Derived::Flags & AlignedBit) ? Aligned : Unaligned }; const Index alignedSize2 = ((size-alignedStart)/(2*packetSize))*(2*packetSize); const Index alignedSize = ((size-alignedStart)/(packetSize))*(packetSize); const Index alignedEnd2 = alignedStart + alignedSize2; const Index alignedEnd = alignedStart + alignedSize; Scalar res; if(alignedSize) { PacketScalar packet_res0 = mat.template packet(alignedStart); if(alignedSize>packetSize) // we have at least two packets to partly unroll the loop { PacketScalar packet_res1 = mat.template packet(alignedStart+packetSize); for(Index index = alignedStart + 2*packetSize; index < alignedEnd2; index += 2*packetSize) { packet_res0 = func.packetOp(packet_res0, mat.template packet(index)); packet_res1 = func.packetOp(packet_res1, mat.template packet(index+packetSize)); } packet_res0 = func.packetOp(packet_res0,packet_res1); if(alignedEnd>alignedEnd2) packet_res0 = func.packetOp(packet_res0, mat.template packet(alignedEnd2)); } res = func.predux(packet_res0); for(Index index = 0; index < alignedStart; ++index) res = func(res,mat.coeff(index)); for(Index index = alignedEnd; index < size; ++index) res = func(res,mat.coeff(index)); } else // too small to vectorize anything. // since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize. { res = mat.coeff(0); for(Index index = 1; index < size; ++index) res = func(res,mat.coeff(index)); } return res; } }; template struct redux_impl { typedef typename Derived::Scalar Scalar; typedef typename packet_traits::type PacketScalar; typedef typename Derived::Index Index; static Scalar run(const Derived& mat, const Func& func) { eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix"); const Index innerSize = mat.innerSize(); const Index outerSize = mat.outerSize(); enum { packetSize = packet_traits::size }; const Index packetedInnerSize = ((innerSize)/packetSize)*packetSize; Scalar res; if(packetedInnerSize) { PacketScalar packet_res = mat.template packet(0,0); for(Index j=0; j(j,i)); res = func.predux(packet_res); for(Index j=0; j::run(mat, func); } return res; } }; template struct redux_impl { typedef typename Derived::Scalar Scalar; typedef typename packet_traits::type PacketScalar; enum { PacketSize = packet_traits::size, Size = Derived::SizeAtCompileTime, VectorizedSize = (Size / PacketSize) * PacketSize }; static EIGEN_STRONG_INLINE Scalar run(const Derived& mat, const Func& func) { eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix"); Scalar res = func.predux(redux_vec_unroller::run(mat,func)); if (VectorizedSize != Size) res = func(res,redux_novec_unroller::run(mat,func)); return res; } }; } // end namespace internal /*************************************************************************** * Part 4 : public API ***************************************************************************/ /** \returns the result of a full redux operation on the whole matrix or vector using \a func * * The template parameter \a BinaryOp is the type of the functor \a func which must be * an associative operator. Both current STL and TR1 functor styles are handled. * * \sa DenseBase::sum(), DenseBase::minCoeff(), DenseBase::maxCoeff(), MatrixBase::colwise(), MatrixBase::rowwise() */ template template EIGEN_STRONG_INLINE typename internal::result_of::Scalar)>::type DenseBase::redux(const Func& func) const { typedef typename internal::remove_all::type ThisNested; return internal::redux_impl ::run(derived(), func); } /** \returns the minimum of all coefficients of *this */ template EIGEN_STRONG_INLINE typename internal::traits::Scalar DenseBase::minCoeff() const { return this->redux(Eigen::internal::scalar_min_op()); } /** \returns the maximum of all coefficients of *this */ template EIGEN_STRONG_INLINE typename internal::traits::Scalar DenseBase::maxCoeff() const { return this->redux(Eigen::internal::scalar_max_op()); } /** \returns the sum of all coefficients of *this * * \sa trace(), prod(), mean() */ template EIGEN_STRONG_INLINE typename internal::traits::Scalar DenseBase::sum() const { if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0)) return Scalar(0); return this->redux(Eigen::internal::scalar_sum_op()); } /** \returns the mean of all coefficients of *this * * \sa trace(), prod(), sum() */ template EIGEN_STRONG_INLINE typename internal::traits::Scalar DenseBase::mean() const { return Scalar(this->redux(Eigen::internal::scalar_sum_op())) / Scalar(this->size()); } /** \returns the product of all coefficients of *this * * Example: \include MatrixBase_prod.cpp * Output: \verbinclude MatrixBase_prod.out * * \sa sum(), mean(), trace() */ template EIGEN_STRONG_INLINE typename internal::traits::Scalar DenseBase::prod() const { if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0)) return Scalar(1); return this->redux(Eigen::internal::scalar_product_op()); } /** \returns the trace of \c *this, i.e. the sum of the coefficients on the main diagonal. * * \c *this can be any matrix, not necessarily square. * * \sa diagonal(), sum() */ template EIGEN_STRONG_INLINE typename internal::traits::Scalar MatrixBase::trace() const { return derived().diagonal().sum(); } } // end namespace Eigen #endif // EIGEN_REDUX_H ctbnrle.1.1.1/hdr/Eigen/src/Core/Reverse.h0000775000175000017500000002055712107032422020442 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2006-2008 Benoit Jacob // Copyright (C) 2009 Ricard Marxer // Copyright (C) 2009-2010 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_REVERSE_H #define EIGEN_REVERSE_H namespace Eigen { /** \class Reverse * \ingroup Core_Module * * \brief Expression of the reverse of a vector or matrix * * \param MatrixType the type of the object of which we are taking the reverse * * This class represents an expression of the reverse of a vector. * It is the return type of MatrixBase::reverse() and VectorwiseOp::reverse() * and most of the time this is the only way it is used. * * \sa MatrixBase::reverse(), VectorwiseOp::reverse() */ namespace internal { template struct traits > : traits { typedef typename MatrixType::Scalar Scalar; typedef typename traits::StorageKind StorageKind; typedef typename traits::XprKind XprKind; typedef typename nested::type MatrixTypeNested; typedef typename remove_reference::type _MatrixTypeNested; enum { RowsAtCompileTime = MatrixType::RowsAtCompileTime, ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime, // let's enable LinearAccess only with vectorization because of the product overhead LinearAccess = ( (Direction==BothDirections) && (int(_MatrixTypeNested::Flags)&PacketAccessBit) ) ? LinearAccessBit : 0, Flags = int(_MatrixTypeNested::Flags) & (HereditaryBits | LvalueBit | PacketAccessBit | LinearAccess), CoeffReadCost = _MatrixTypeNested::CoeffReadCost }; }; template struct reverse_packet_cond { static inline PacketScalar run(const PacketScalar& x) { return preverse(x); } }; template struct reverse_packet_cond { static inline PacketScalar run(const PacketScalar& x) { return x; } }; } // end namespace internal template class Reverse : public internal::dense_xpr_base< Reverse >::type { public: typedef typename internal::dense_xpr_base::type Base; EIGEN_DENSE_PUBLIC_INTERFACE(Reverse) using Base::IsRowMajor; // next line is necessary because otherwise const version of operator() // is hidden by non-const version defined in this file using Base::operator(); protected: enum { PacketSize = internal::packet_traits::size, IsColMajor = !IsRowMajor, ReverseRow = (Direction == Vertical) || (Direction == BothDirections), ReverseCol = (Direction == Horizontal) || (Direction == BothDirections), OffsetRow = ReverseRow && IsColMajor ? PacketSize : 1, OffsetCol = ReverseCol && IsRowMajor ? PacketSize : 1, ReversePacket = (Direction == BothDirections) || ((Direction == Vertical) && IsColMajor) || ((Direction == Horizontal) && IsRowMajor) }; typedef internal::reverse_packet_cond reverse_packet; public: inline Reverse(const MatrixType& matrix) : m_matrix(matrix) { } EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Reverse) inline Index rows() const { return m_matrix.rows(); } inline Index cols() const { return m_matrix.cols(); } inline Index innerStride() const { return -m_matrix.innerStride(); } inline Scalar& operator()(Index row, Index col) { eigen_assert(row >= 0 && row < rows() && col >= 0 && col < cols()); return coeffRef(row, col); } inline Scalar& coeffRef(Index row, Index col) { return m_matrix.const_cast_derived().coeffRef(ReverseRow ? m_matrix.rows() - row - 1 : row, ReverseCol ? m_matrix.cols() - col - 1 : col); } inline CoeffReturnType coeff(Index row, Index col) const { return m_matrix.coeff(ReverseRow ? m_matrix.rows() - row - 1 : row, ReverseCol ? m_matrix.cols() - col - 1 : col); } inline CoeffReturnType coeff(Index index) const { return m_matrix.coeff(m_matrix.size() - index - 1); } inline Scalar& coeffRef(Index index) { return m_matrix.const_cast_derived().coeffRef(m_matrix.size() - index - 1); } inline Scalar& operator()(Index index) { eigen_assert(index >= 0 && index < m_matrix.size()); return coeffRef(index); } template inline const PacketScalar packet(Index row, Index col) const { return reverse_packet::run(m_matrix.template packet( ReverseRow ? m_matrix.rows() - row - OffsetRow : row, ReverseCol ? m_matrix.cols() - col - OffsetCol : col)); } template inline void writePacket(Index row, Index col, const PacketScalar& x) { m_matrix.const_cast_derived().template writePacket( ReverseRow ? m_matrix.rows() - row - OffsetRow : row, ReverseCol ? m_matrix.cols() - col - OffsetCol : col, reverse_packet::run(x)); } template inline const PacketScalar packet(Index index) const { return internal::preverse(m_matrix.template packet( m_matrix.size() - index - PacketSize )); } template inline void writePacket(Index index, const PacketScalar& x) { m_matrix.const_cast_derived().template writePacket(m_matrix.size() - index - PacketSize, internal::preverse(x)); } const typename internal::remove_all::type& nestedExpression() const { return m_matrix; } protected: typename MatrixType::Nested m_matrix; }; /** \returns an expression of the reverse of *this. * * Example: \include MatrixBase_reverse.cpp * Output: \verbinclude MatrixBase_reverse.out * */ template inline typename DenseBase::ReverseReturnType DenseBase::reverse() { return derived(); } /** This is the const version of reverse(). */ template inline const typename DenseBase::ConstReverseReturnType DenseBase::reverse() const { return derived(); } /** This is the "in place" version of reverse: it reverses \c *this. * * In most cases it is probably better to simply use the reversed expression * of a matrix. However, when reversing the matrix data itself is really needed, * then this "in-place" version is probably the right choice because it provides * the following additional features: * - less error prone: doing the same operation with .reverse() requires special care: * \code m = m.reverse().eval(); \endcode * - this API allows to avoid creating a temporary (the current implementation creates a temporary, but that could be avoided using swap) * - it allows future optimizations (cache friendliness, etc.) * * \sa reverse() */ template inline void DenseBase::reverseInPlace() { derived() = derived().reverse().eval(); } } // end namespace Eigen #endif // EIGEN_REVERSE_H ctbnrle.1.1.1/hdr/Eigen/src/Core/MapBase.h0000775000175000017500000002133012107032422020325 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2007-2010 Benoit Jacob // Copyright (C) 2008 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_MAPBASE_H #define EIGEN_MAPBASE_H #define EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) \ EIGEN_STATIC_ASSERT((int(internal::traits::Flags) & LinearAccessBit) || Derived::IsVectorAtCompileTime, \ YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT) namespace Eigen { /** \class MapBase * \ingroup Core_Module * * \brief Base class for Map and Block expression with direct access * * \sa class Map, class Block */ template class MapBase : public internal::dense_xpr_base::type { public: typedef typename internal::dense_xpr_base::type Base; enum { RowsAtCompileTime = internal::traits::RowsAtCompileTime, ColsAtCompileTime = internal::traits::ColsAtCompileTime, SizeAtCompileTime = Base::SizeAtCompileTime }; typedef typename internal::traits::StorageKind StorageKind; typedef typename internal::traits::Index Index; typedef typename internal::traits::Scalar Scalar; typedef typename internal::packet_traits::type PacketScalar; typedef typename NumTraits::Real RealScalar; typedef typename internal::conditional< bool(internal::is_lvalue::value), Scalar *, const Scalar *>::type PointerType; using Base::derived; // using Base::RowsAtCompileTime; // using Base::ColsAtCompileTime; // using Base::SizeAtCompileTime; using Base::MaxRowsAtCompileTime; using Base::MaxColsAtCompileTime; using Base::MaxSizeAtCompileTime; using Base::IsVectorAtCompileTime; using Base::Flags; using Base::IsRowMajor; using Base::rows; using Base::cols; using Base::size; using Base::coeff; using Base::coeffRef; using Base::lazyAssign; using Base::eval; using Base::innerStride; using Base::outerStride; using Base::rowStride; using Base::colStride; // bug 217 - compile error on ICC 11.1 using Base::operator=; typedef typename Base::CoeffReturnType CoeffReturnType; inline Index rows() const { return m_rows.value(); } inline Index cols() const { return m_cols.value(); } /** Returns a pointer to the first coefficient of the matrix or vector. * * \note When addressing this data, make sure to honor the strides returned by innerStride() and outerStride(). * * \sa innerStride(), outerStride() */ inline const Scalar* data() const { return m_data; } inline const Scalar& coeff(Index row, Index col) const { return m_data[col * colStride() + row * rowStride()]; } inline const Scalar& coeff(Index index) const { EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) return m_data[index * innerStride()]; } inline const Scalar& coeffRef(Index row, Index col) const { return this->m_data[col * colStride() + row * rowStride()]; } inline const Scalar& coeffRef(Index index) const { EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) return this->m_data[index * innerStride()]; } template inline PacketScalar packet(Index row, Index col) const { return internal::ploadt (m_data + (col * colStride() + row * rowStride())); } template inline PacketScalar packet(Index index) const { EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) return internal::ploadt(m_data + index * innerStride()); } inline MapBase(PointerType data) : m_data(data), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime) { EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived) checkSanity(); } inline MapBase(PointerType data, Index size) : m_data(data), m_rows(RowsAtCompileTime == Dynamic ? size : Index(RowsAtCompileTime)), m_cols(ColsAtCompileTime == Dynamic ? size : Index(ColsAtCompileTime)) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) eigen_assert(size >= 0); eigen_assert(data == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == size); checkSanity(); } inline MapBase(PointerType data, Index rows, Index cols) : m_data(data), m_rows(rows), m_cols(cols) { eigen_assert( (data == 0) || ( rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols))); checkSanity(); } protected: void checkSanity() const { EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(internal::traits::Flags&PacketAccessBit, internal::inner_stride_at_compile_time::ret==1), PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1); eigen_assert(EIGEN_IMPLIES(internal::traits::Flags&AlignedBit, (size_t(m_data) % 16) == 0) && "data is not aligned"); } PointerType m_data; const internal::variable_if_dynamic m_rows; const internal::variable_if_dynamic m_cols; }; template class MapBase : public MapBase { public: typedef MapBase Base; typedef typename Base::Scalar Scalar; typedef typename Base::PacketScalar PacketScalar; typedef typename Base::Index Index; typedef typename Base::PointerType PointerType; using Base::derived; using Base::rows; using Base::cols; using Base::size; using Base::coeff; using Base::coeffRef; using Base::innerStride; using Base::outerStride; using Base::rowStride; using Base::colStride; typedef typename internal::conditional< internal::is_lvalue::value, Scalar, const Scalar >::type ScalarWithConstIfNotLvalue; inline const Scalar* data() const { return this->m_data; } inline ScalarWithConstIfNotLvalue* data() { return this->m_data; } // no const-cast here so non-const-correct code will give a compile error inline ScalarWithConstIfNotLvalue& coeffRef(Index row, Index col) { return this->m_data[col * colStride() + row * rowStride()]; } inline ScalarWithConstIfNotLvalue& coeffRef(Index index) { EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) return this->m_data[index * innerStride()]; } template inline void writePacket(Index row, Index col, const PacketScalar& x) { internal::pstoret (this->m_data + (col * colStride() + row * rowStride()), x); } template inline void writePacket(Index index, const PacketScalar& x) { EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) internal::pstoret (this->m_data + index * innerStride(), x); } explicit inline MapBase(PointerType data) : Base(data) {} inline MapBase(PointerType data, Index size) : Base(data, size) {} inline MapBase(PointerType data, Index rows, Index cols) : Base(data, rows, cols) {} Derived& operator=(const MapBase& other) { Base::Base::operator=(other); return derived(); } using Base::Base::operator=; }; } // end namespace Eigen #endif // EIGEN_MAPBASE_H ctbnrle.1.1.1/hdr/Eigen/src/Core/GlobalFunctions.h0000775000175000017500000001155012107032422022111 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2010 Gael Guennebaud // Copyright (C) 2010 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_GLOBAL_FUNCTIONS_H #define EIGEN_GLOBAL_FUNCTIONS_H #define EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(NAME,FUNCTOR) \ template \ inline const Eigen::CwiseUnaryOp, const Derived> \ NAME(const Eigen::ArrayBase& x) { \ return x.derived(); \ } #define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME,FUNCTOR) \ \ template \ struct NAME##_retval > \ { \ typedef const Eigen::CwiseUnaryOp, const Derived> type; \ }; \ template \ struct NAME##_impl > \ { \ static inline typename NAME##_retval >::type run(const Eigen::ArrayBase& x) \ { \ return x.derived(); \ } \ }; namespace std { EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(real,scalar_real_op) EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(imag,scalar_imag_op) EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(sin,scalar_sin_op) EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(cos,scalar_cos_op) EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(asin,scalar_asin_op) EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(acos,scalar_acos_op) EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(tan,scalar_tan_op) EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(exp,scalar_exp_op) EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(log,scalar_log_op) EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(abs,scalar_abs_op) EIGEN_ARRAY_DECLARE_GLOBAL_STD_UNARY(sqrt,scalar_sqrt_op) template inline const Eigen::CwiseUnaryOp, const Derived> pow(const Eigen::ArrayBase& x, const typename Derived::Scalar& exponent) { return x.derived().pow(exponent); } template inline const Eigen::CwiseBinaryOp, const Derived, const Derived> pow(const Eigen::ArrayBase& x, const Eigen::ArrayBase& exponents) { return Eigen::CwiseBinaryOp, const Derived, const Derived>( x.derived(), exponents.derived() ); } } namespace Eigen { /** * \brief Component-wise division of a scalar by array elements. **/ template inline const Eigen::CwiseUnaryOp, const Derived> operator/(typename Derived::Scalar s, const Eigen::ArrayBase& a) { return Eigen::CwiseUnaryOp, const Derived>( a.derived(), Eigen::internal::scalar_inverse_mult_op(s) ); } namespace internal { EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(real,scalar_real_op) EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(imag,scalar_imag_op) EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(sin,scalar_sin_op) EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(cos,scalar_cos_op) EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(asin,scalar_asin_op) EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(acos,scalar_acos_op) EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(tan,scalar_tan_op) EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(exp,scalar_exp_op) EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(log,scalar_log_op) EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs,scalar_abs_op) EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs2,scalar_abs2_op) EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(sqrt,scalar_sqrt_op) } } // TODO: cleanly disable those functions that are not supported on Array (internal::real_ref, internal::random, internal::isApprox...) #endif // EIGEN_GLOBAL_FUNCTIONS_H ctbnrle.1.1.1/hdr/Eigen/src/Core/Flagged.h0000775000175000017500000001154312107032422020353 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_FLAGGED_H #define EIGEN_FLAGGED_H namespace Eigen { /** \class Flagged * \ingroup Core_Module * * \brief Expression with modified flags * * \param ExpressionType the type of the object of which we are modifying the flags * \param Added the flags added to the expression * \param Removed the flags removed from the expression (has priority over Added). * * This class represents an expression whose flags have been modified. * It is the return type of MatrixBase::flagged() * and most of the time this is the only way it is used. * * \sa MatrixBase::flagged() */ namespace internal { template struct traits > : traits { enum { Flags = (ExpressionType::Flags | Added) & ~Removed }; }; } template class Flagged : public MatrixBase > { public: typedef MatrixBase Base; EIGEN_DENSE_PUBLIC_INTERFACE(Flagged) typedef typename internal::conditional::ret, ExpressionType, const ExpressionType&>::type ExpressionTypeNested; typedef typename ExpressionType::InnerIterator InnerIterator; inline Flagged(const ExpressionType& matrix) : m_matrix(matrix) {} inline Index rows() const { return m_matrix.rows(); } inline Index cols() const { return m_matrix.cols(); } inline Index outerStride() const { return m_matrix.outerStride(); } inline Index innerStride() const { return m_matrix.innerStride(); } inline CoeffReturnType coeff(Index row, Index col) const { return m_matrix.coeff(row, col); } inline CoeffReturnType coeff(Index index) const { return m_matrix.coeff(index); } inline const Scalar& coeffRef(Index row, Index col) const { return m_matrix.const_cast_derived().coeffRef(row, col); } inline const Scalar& coeffRef(Index index) const { return m_matrix.const_cast_derived().coeffRef(index); } inline Scalar& coeffRef(Index row, Index col) { return m_matrix.const_cast_derived().coeffRef(row, col); } inline Scalar& coeffRef(Index index) { return m_matrix.const_cast_derived().coeffRef(index); } template inline const PacketScalar packet(Index row, Index col) const { return m_matrix.template packet(row, col); } template inline void writePacket(Index row, Index col, const PacketScalar& x) { m_matrix.const_cast_derived().template writePacket(row, col, x); } template inline const PacketScalar packet(Index index) const { return m_matrix.template packet(index); } template inline void writePacket(Index index, const PacketScalar& x) { m_matrix.const_cast_derived().template writePacket(index, x); } const ExpressionType& _expression() const { return m_matrix; } template typename ExpressionType::PlainObject solveTriangular(const MatrixBase& other) const; template void solveTriangularInPlace(const MatrixBase& other) const; protected: ExpressionTypeNested m_matrix; }; /** \returns an expression of *this with added and removed flags * * This is mostly for internal use. * * \sa class Flagged */ template template inline const Flagged DenseBase::flagged() const { return derived(); } } // end namespace Eigen #endif // EIGEN_FLAGGED_H ctbnrle.1.1.1/hdr/Eigen/src/Core/DenseCoeffsBase.h0000775000175000017500000006734312107032422022012 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2006-2010 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_DENSECOEFFSBASE_H #define EIGEN_DENSECOEFFSBASE_H namespace Eigen { namespace internal { template struct add_const_on_value_type_if_arithmetic { typedef typename conditional::value, T, typename add_const_on_value_type::type>::type type; }; } /** \brief Base class providing read-only coefficient access to matrices and arrays. * \ingroup Core_Module * \tparam Derived Type of the derived class * \tparam #ReadOnlyAccessors Constant indicating read-only access * * This class defines the \c operator() \c const function and friends, which can be used to read specific * entries of a matrix or array. * * \sa DenseCoeffsBase, DenseCoeffsBase, * \ref TopicClassHierarchy */ template class DenseCoeffsBase : public EigenBase { public: typedef typename internal::traits::StorageKind StorageKind; typedef typename internal::traits::Index Index; typedef typename internal::traits::Scalar Scalar; typedef typename internal::packet_traits::type PacketScalar; // Explanation for this CoeffReturnType typedef. // - This is the return type of the coeff() method. // - The LvalueBit means exactly that we can offer a coeffRef() method, which means exactly that we can get references // to coeffs, which means exactly that we can have coeff() return a const reference (as opposed to returning a value). // - The is_artihmetic check is required since "const int", "const double", etc. will cause warnings on some systems // while the declaration of "const T", where T is a non arithmetic type does not. Always returning "const Scalar&" is // not possible, since the underlying expressions might not offer a valid address the reference could be referring to. typedef typename internal::conditional::Flags&LvalueBit), const Scalar&, typename internal::conditional::value, Scalar, const Scalar>::type >::type CoeffReturnType; typedef typename internal::add_const_on_value_type_if_arithmetic< typename internal::packet_traits::type >::type PacketReturnType; typedef EigenBase Base; using Base::rows; using Base::cols; using Base::size; using Base::derived; EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner) const { return int(Derived::RowsAtCompileTime) == 1 ? 0 : int(Derived::ColsAtCompileTime) == 1 ? inner : int(Derived::Flags)&RowMajorBit ? outer : inner; } EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner) const { return int(Derived::ColsAtCompileTime) == 1 ? 0 : int(Derived::RowsAtCompileTime) == 1 ? inner : int(Derived::Flags)&RowMajorBit ? inner : outer; } /** Short version: don't use this function, use * \link operator()(Index,Index) const \endlink instead. * * Long version: this function is similar to * \link operator()(Index,Index) const \endlink, but without the assertion. * Use this for limiting the performance cost of debugging code when doing * repeated coefficient access. Only use this when it is guaranteed that the * parameters \a row and \a col are in range. * * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this * function equivalent to \link operator()(Index,Index) const \endlink. * * \sa operator()(Index,Index) const, coeffRef(Index,Index), coeff(Index) const */ EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const { eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols()); return derived().coeff(row, col); } EIGEN_STRONG_INLINE CoeffReturnType coeffByOuterInner(Index outer, Index inner) const { return coeff(rowIndexByOuterInner(outer, inner), colIndexByOuterInner(outer, inner)); } /** \returns the coefficient at given the given row and column. * * \sa operator()(Index,Index), operator[](Index) */ EIGEN_STRONG_INLINE CoeffReturnType operator()(Index row, Index col) const { eigen_assert(row >= 0 && row < rows() && col >= 0 && col < cols()); return derived().coeff(row, col); } /** Short version: don't use this function, use * \link operator[](Index) const \endlink instead. * * Long version: this function is similar to * \link operator[](Index) const \endlink, but without the assertion. * Use this for limiting the performance cost of debugging code when doing * repeated coefficient access. Only use this when it is guaranteed that the * parameter \a index is in range. * * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this * function equivalent to \link operator[](Index) const \endlink. * * \sa operator[](Index) const, coeffRef(Index), coeff(Index,Index) const */ EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { eigen_internal_assert(index >= 0 && index < size()); return derived().coeff(index); } /** \returns the coefficient at given index. * * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit. * * \sa operator[](Index), operator()(Index,Index) const, x() const, y() const, * z() const, w() const */ EIGEN_STRONG_INLINE CoeffReturnType operator[](Index index) const { #ifndef EIGEN2_SUPPORT EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime, THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD) #endif eigen_assert(index >= 0 && index < size()); return derived().coeff(index); } /** \returns the coefficient at given index. * * This is synonymous to operator[](Index) const. * * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit. * * \sa operator[](Index), operator()(Index,Index) const, x() const, y() const, * z() const, w() const */ EIGEN_STRONG_INLINE CoeffReturnType operator()(Index index) const { eigen_assert(index >= 0 && index < size()); return derived().coeff(index); } /** equivalent to operator[](0). */ EIGEN_STRONG_INLINE CoeffReturnType x() const { return (*this)[0]; } /** equivalent to operator[](1). */ EIGEN_STRONG_INLINE CoeffReturnType y() const { return (*this)[1]; } /** equivalent to operator[](2). */ EIGEN_STRONG_INLINE CoeffReturnType z() const { return (*this)[2]; } /** equivalent to operator[](3). */ EIGEN_STRONG_INLINE CoeffReturnType w() const { return (*this)[3]; } /** \internal * \returns the packet of coefficients starting at the given row and column. It is your responsibility * to ensure that a packet really starts there. This method is only available on expressions having the * PacketAccessBit. * * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets * starting at an address which is a multiple of the packet size. */ template EIGEN_STRONG_INLINE PacketReturnType packet(Index row, Index col) const { eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols()); return derived().template packet(row,col); } /** \internal */ template EIGEN_STRONG_INLINE PacketReturnType packetByOuterInner(Index outer, Index inner) const { return packet(rowIndexByOuterInner(outer, inner), colIndexByOuterInner(outer, inner)); } /** \internal * \returns the packet of coefficients starting at the given index. It is your responsibility * to ensure that a packet really starts there. This method is only available on expressions having the * PacketAccessBit and the LinearAccessBit. * * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets * starting at an address which is a multiple of the packet size. */ template EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const { eigen_internal_assert(index >= 0 && index < size()); return derived().template packet(index); } protected: // explanation: DenseBase is doing "using ..." on the methods from DenseCoeffsBase. // But some methods are only available in the DirectAccess case. // So we add dummy methods here with these names, so that "using... " doesn't fail. // It's not private so that the child class DenseBase can access them, and it's not public // either since it's an implementation detail, so has to be protected. void coeffRef(); void coeffRefByOuterInner(); void writePacket(); void writePacketByOuterInner(); void copyCoeff(); void copyCoeffByOuterInner(); void copyPacket(); void copyPacketByOuterInner(); void stride(); void innerStride(); void outerStride(); void rowStride(); void colStride(); }; /** \brief Base class providing read/write coefficient access to matrices and arrays. * \ingroup Core_Module * \tparam Derived Type of the derived class * \tparam #WriteAccessors Constant indicating read/write access * * This class defines the non-const \c operator() function and friends, which can be used to write specific * entries of a matrix or array. This class inherits DenseCoeffsBase which * defines the const variant for reading specific entries. * * \sa DenseCoeffsBase, \ref TopicClassHierarchy */ template class DenseCoeffsBase : public DenseCoeffsBase { public: typedef DenseCoeffsBase Base; typedef typename internal::traits::StorageKind StorageKind; typedef typename internal::traits::Index Index; typedef typename internal::traits::Scalar Scalar; typedef typename internal::packet_traits::type PacketScalar; typedef typename NumTraits::Real RealScalar; using Base::coeff; using Base::rows; using Base::cols; using Base::size; using Base::derived; using Base::rowIndexByOuterInner; using Base::colIndexByOuterInner; using Base::operator[]; using Base::operator(); using Base::x; using Base::y; using Base::z; using Base::w; /** Short version: don't use this function, use * \link operator()(Index,Index) \endlink instead. * * Long version: this function is similar to * \link operator()(Index,Index) \endlink, but without the assertion. * Use this for limiting the performance cost of debugging code when doing * repeated coefficient access. Only use this when it is guaranteed that the * parameters \a row and \a col are in range. * * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this * function equivalent to \link operator()(Index,Index) \endlink. * * \sa operator()(Index,Index), coeff(Index, Index) const, coeffRef(Index) */ EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) { eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols()); return derived().coeffRef(row, col); } EIGEN_STRONG_INLINE Scalar& coeffRefByOuterInner(Index outer, Index inner) { return coeffRef(rowIndexByOuterInner(outer, inner), colIndexByOuterInner(outer, inner)); } /** \returns a reference to the coefficient at given the given row and column. * * \sa operator[](Index) */ EIGEN_STRONG_INLINE Scalar& operator()(Index row, Index col) { eigen_assert(row >= 0 && row < rows() && col >= 0 && col < cols()); return derived().coeffRef(row, col); } /** Short version: don't use this function, use * \link operator[](Index) \endlink instead. * * Long version: this function is similar to * \link operator[](Index) \endlink, but without the assertion. * Use this for limiting the performance cost of debugging code when doing * repeated coefficient access. Only use this when it is guaranteed that the * parameters \a row and \a col are in range. * * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this * function equivalent to \link operator[](Index) \endlink. * * \sa operator[](Index), coeff(Index) const, coeffRef(Index,Index) */ EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) { eigen_internal_assert(index >= 0 && index < size()); return derived().coeffRef(index); } /** \returns a reference to the coefficient at given index. * * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit. * * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w() */ EIGEN_STRONG_INLINE Scalar& operator[](Index index) { #ifndef EIGEN2_SUPPORT EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime, THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD) #endif eigen_assert(index >= 0 && index < size()); return derived().coeffRef(index); } /** \returns a reference to the coefficient at given index. * * This is synonymous to operator[](Index). * * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit. * * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w() */ EIGEN_STRONG_INLINE Scalar& operator()(Index index) { eigen_assert(index >= 0 && index < size()); return derived().coeffRef(index); } /** equivalent to operator[](0). */ EIGEN_STRONG_INLINE Scalar& x() { return (*this)[0]; } /** equivalent to operator[](1). */ EIGEN_STRONG_INLINE Scalar& y() { return (*this)[1]; } /** equivalent to operator[](2). */ EIGEN_STRONG_INLINE Scalar& z() { return (*this)[2]; } /** equivalent to operator[](3). */ EIGEN_STRONG_INLINE Scalar& w() { return (*this)[3]; } /** \internal * Stores the given packet of coefficients, at the given row and column of this expression. It is your responsibility * to ensure that a packet really starts there. This method is only available on expressions having the * PacketAccessBit. * * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets * starting at an address which is a multiple of the packet size. */ template EIGEN_STRONG_INLINE void writePacket (Index row, Index col, const typename internal::packet_traits::type& x) { eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols()); derived().template writePacket(row,col,x); } /** \internal */ template EIGEN_STRONG_INLINE void writePacketByOuterInner (Index outer, Index inner, const typename internal::packet_traits::type& x) { writePacket(rowIndexByOuterInner(outer, inner), colIndexByOuterInner(outer, inner), x); } /** \internal * Stores the given packet of coefficients, at the given index in this expression. It is your responsibility * to ensure that a packet really starts there. This method is only available on expressions having the * PacketAccessBit and the LinearAccessBit. * * The \a LoadMode parameter may have the value \a Aligned or \a Unaligned. Its effect is to select * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets * starting at an address which is a multiple of the packet size. */ template EIGEN_STRONG_INLINE void writePacket (Index index, const typename internal::packet_traits::type& x) { eigen_internal_assert(index >= 0 && index < size()); derived().template writePacket(index,x); } #ifndef EIGEN_PARSED_BY_DOXYGEN /** \internal Copies the coefficient at position (row,col) of other into *this. * * This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code * with usual assignments. * * Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox. */ template EIGEN_STRONG_INLINE void copyCoeff(Index row, Index col, const DenseBase& other) { eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols()); derived().coeffRef(row, col) = other.derived().coeff(row, col); } /** \internal Copies the coefficient at the given index of other into *this. * * This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code * with usual assignments. * * Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox. */ template EIGEN_STRONG_INLINE void copyCoeff(Index index, const DenseBase& other) { eigen_internal_assert(index >= 0 && index < size()); derived().coeffRef(index) = other.derived().coeff(index); } template EIGEN_STRONG_INLINE void copyCoeffByOuterInner(Index outer, Index inner, const DenseBase& other) { const Index row = rowIndexByOuterInner(outer,inner); const Index col = colIndexByOuterInner(outer,inner); // derived() is important here: copyCoeff() may be reimplemented in Derived! derived().copyCoeff(row, col, other); } /** \internal Copies the packet at position (row,col) of other into *this. * * This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code * with usual assignments. * * Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox. */ template EIGEN_STRONG_INLINE void copyPacket(Index row, Index col, const DenseBase& other) { eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols()); derived().template writePacket(row, col, other.derived().template packet(row, col)); } /** \internal Copies the packet at the given index of other into *this. * * This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code * with usual assignments. * * Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox. */ template EIGEN_STRONG_INLINE void copyPacket(Index index, const DenseBase& other) { eigen_internal_assert(index >= 0 && index < size()); derived().template writePacket(index, other.derived().template packet(index)); } /** \internal */ template EIGEN_STRONG_INLINE void copyPacketByOuterInner(Index outer, Index inner, const DenseBase& other) { const Index row = rowIndexByOuterInner(outer,inner); const Index col = colIndexByOuterInner(outer,inner); // derived() is important here: copyCoeff() may be reimplemented in Derived! derived().template copyPacket< OtherDerived, StoreMode, LoadMode>(row, col, other); } #endif }; /** \brief Base class providing direct read-only coefficient access to matrices and arrays. * \ingroup Core_Module * \tparam Derived Type of the derived class * \tparam #DirectAccessors Constant indicating direct access * * This class defines functions to work with strides which can be used to access entries directly. This class * inherits DenseCoeffsBase which defines functions to access entries read-only using * \c operator() . * * \sa \ref TopicClassHierarchy */ template class DenseCoeffsBase : public DenseCoeffsBase { public: typedef DenseCoeffsBase Base; typedef typename internal::traits::Index Index; typedef typename internal::traits::Scalar Scalar; typedef typename NumTraits::Real RealScalar; using Base::rows; using Base::cols; using Base::size; using Base::derived; /** \returns the pointer increment between two consecutive elements within a slice in the inner direction. * * \sa outerStride(), rowStride(), colStride() */ inline Index innerStride() const { return derived().innerStride(); } /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns * in a column-major matrix). * * \sa innerStride(), rowStride(), colStride() */ inline Index outerStride() const { return derived().outerStride(); } // FIXME shall we remove it ? inline Index stride() const { return Derived::IsVectorAtCompileTime ? innerStride() : outerStride(); } /** \returns the pointer increment between two consecutive rows. * * \sa innerStride(), outerStride(), colStride() */ inline Index rowStride() const { return Derived::IsRowMajor ? outerStride() : innerStride(); } /** \returns the pointer increment between two consecutive columns. * * \sa innerStride(), outerStride(), rowStride() */ inline Index colStride() const { return Derived::IsRowMajor ? innerStride() : outerStride(); } }; /** \brief Base class providing direct read/write coefficient access to matrices and arrays. * \ingroup Core_Module * \tparam Derived Type of the derived class * \tparam #DirectWriteAccessors Constant indicating direct access * * This class defines functions to work with strides which can be used to access entries directly. This class * inherits DenseCoeffsBase which defines functions to access entries read/write using * \c operator(). * * \sa \ref TopicClassHierarchy */ template class DenseCoeffsBase : public DenseCoeffsBase { public: typedef DenseCoeffsBase Base; typedef typename internal::traits::Index Index; typedef typename internal::traits::Scalar Scalar; typedef typename NumTraits::Real RealScalar; using Base::rows; using Base::cols; using Base::size; using Base::derived; /** \returns the pointer increment between two consecutive elements within a slice in the inner direction. * * \sa outerStride(), rowStride(), colStride() */ inline Index innerStride() const { return derived().innerStride(); } /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns * in a column-major matrix). * * \sa innerStride(), rowStride(), colStride() */ inline Index outerStride() const { return derived().outerStride(); } // FIXME shall we remove it ? inline Index stride() const { return Derived::IsVectorAtCompileTime ? innerStride() : outerStride(); } /** \returns the pointer increment between two consecutive rows. * * \sa innerStride(), outerStride(), colStride() */ inline Index rowStride() const { return Derived::IsRowMajor ? outerStride() : innerStride(); } /** \returns the pointer increment between two consecutive columns. * * \sa innerStride(), outerStride(), rowStride() */ inline Index colStride() const { return Derived::IsRowMajor ? innerStride() : outerStride(); } }; namespace internal { template struct first_aligned_impl { static inline typename Derived::Index run(const Derived&) { return 0; } }; template struct first_aligned_impl { static inline typename Derived::Index run(const Derived& m) { return internal::first_aligned(&m.const_cast_derived().coeffRef(0,0), m.size()); } }; /** \internal \returns the index of the first element of the array that is well aligned for vectorization. * * There is also the variant first_aligned(const Scalar*, Integer) defined in Memory.h. See it for more * documentation. */ template static inline typename Derived::Index first_aligned(const Derived& m) { return first_aligned_impl ::run(m); } template::ret> struct inner_stride_at_compile_time { enum { ret = traits::InnerStrideAtCompileTime }; }; template struct inner_stride_at_compile_time { enum { ret = 0 }; }; template::ret> struct outer_stride_at_compile_time { enum { ret = traits::OuterStrideAtCompileTime }; }; template struct outer_stride_at_compile_time { enum { ret = 0 }; }; } // end namespace internal } // end namespace Eigen #endif // EIGEN_DENSECOEFFSBASE_H ctbnrle.1.1.1/hdr/Eigen/src/Core/SelfAdjointView.h0000775000175000017500000002776212107032422022071 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_SELFADJOINTMATRIX_H #define EIGEN_SELFADJOINTMATRIX_H namespace Eigen { /** \class SelfAdjointView * \ingroup Core_Module * * * \brief Expression of a selfadjoint matrix from a triangular part of a dense matrix * * \param MatrixType the type of the dense matrix storing the coefficients * \param TriangularPart can be either \c #Lower or \c #Upper * * This class is an expression of a sefladjoint matrix from a triangular part of a matrix * with given dense storage of the coefficients. It is the return type of MatrixBase::selfadjointView() * and most of the time this is the only way that it is used. * * \sa class TriangularBase, MatrixBase::selfadjointView() */ namespace internal { template struct traits > : traits { typedef typename nested::type MatrixTypeNested; typedef typename remove_all::type MatrixTypeNestedCleaned; typedef MatrixType ExpressionType; typedef typename MatrixType::PlainObject DenseMatrixType; enum { Mode = UpLo | SelfAdjoint, Flags = MatrixTypeNestedCleaned::Flags & (HereditaryBits) & (~(PacketAccessBit | DirectAccessBit | LinearAccessBit)), // FIXME these flags should be preserved CoeffReadCost = MatrixTypeNestedCleaned::CoeffReadCost }; }; } template struct SelfadjointProductMatrix; // FIXME could also be called SelfAdjointWrapper to be consistent with DiagonalWrapper ?? template class SelfAdjointView : public TriangularBase > { public: typedef TriangularBase Base; typedef typename internal::traits::MatrixTypeNested MatrixTypeNested; typedef typename internal::traits::MatrixTypeNestedCleaned MatrixTypeNestedCleaned; /** \brief The type of coefficients in this matrix */ typedef typename internal::traits::Scalar Scalar; typedef typename MatrixType::Index Index; enum { Mode = internal::traits::Mode }; typedef typename MatrixType::PlainObject PlainObject; inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix) {} inline Index rows() const { return m_matrix.rows(); } inline Index cols() const { return m_matrix.cols(); } inline Index outerStride() const { return m_matrix.outerStride(); } inline Index innerStride() const { return m_matrix.innerStride(); } /** \sa MatrixBase::coeff() * \warning the coordinates must fit into the referenced triangular part */ inline Scalar coeff(Index row, Index col) const { Base::check_coordinates_internal(row, col); return m_matrix.coeff(row, col); } /** \sa MatrixBase::coeffRef() * \warning the coordinates must fit into the referenced triangular part */ inline Scalar& coeffRef(Index row, Index col) { Base::check_coordinates_internal(row, col); return m_matrix.const_cast_derived().coeffRef(row, col); } /** \internal */ const MatrixTypeNestedCleaned& _expression() const { return m_matrix; } const MatrixTypeNestedCleaned& nestedExpression() const { return m_matrix; } MatrixTypeNestedCleaned& nestedExpression() { return *const_cast(&m_matrix); } /** Efficient self-adjoint matrix times vector/matrix product */ template SelfadjointProductMatrix operator*(const MatrixBase& rhs) const { return SelfadjointProductMatrix (m_matrix, rhs.derived()); } /** Efficient vector/matrix times self-adjoint matrix product */ template friend SelfadjointProductMatrix operator*(const MatrixBase& lhs, const SelfAdjointView& rhs) { return SelfadjointProductMatrix (lhs.derived(),rhs.m_matrix); } /** Perform a symmetric rank 2 update of the selfadjoint matrix \c *this: * \f$ this = this + \alpha u v^* + conj(\alpha) v u^* \f$ * \returns a reference to \c *this * * The vectors \a u and \c v \b must be column vectors, however they can be * a adjoint expression without any overhead. Only the meaningful triangular * part of the matrix is updated, the rest is left unchanged. * * \sa rankUpdate(const MatrixBase&, Scalar) */ template SelfAdjointView& rankUpdate(const MatrixBase& u, const MatrixBase& v, Scalar alpha = Scalar(1)); /** Perform a symmetric rank K update of the selfadjoint matrix \c *this: * \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix. * * \returns a reference to \c *this * * Note that to perform \f$ this = this + \alpha ( u^* u ) \f$ you can simply * call this function with u.adjoint(). * * \sa rankUpdate(const MatrixBase&, const MatrixBase&, Scalar) */ template SelfAdjointView& rankUpdate(const MatrixBase& u, Scalar alpha = Scalar(1)); /////////// Cholesky module /////////// const LLT llt() const; const LDLT ldlt() const; /////////// Eigenvalue module /////////// /** Real part of #Scalar */ typedef typename NumTraits::Real RealScalar; /** Return type of eigenvalues() */ typedef Matrix::ColsAtCompileTime, 1> EigenvaluesReturnType; EigenvaluesReturnType eigenvalues() const; RealScalar operatorNorm() const; #ifdef EIGEN2_SUPPORT template SelfAdjointView& operator=(const MatrixBase& other) { enum { OtherPart = UpLo == Upper ? StrictlyLower : StrictlyUpper }; m_matrix.const_cast_derived().template triangularView() = other; m_matrix.const_cast_derived().template triangularView() = other.adjoint(); return *this; } template SelfAdjointView& operator=(const TriangularView& other) { enum { OtherPart = UpLo == Upper ? StrictlyLower : StrictlyUpper }; m_matrix.const_cast_derived().template triangularView() = other.toDenseMatrix(); m_matrix.const_cast_derived().template triangularView() = other.toDenseMatrix().adjoint(); return *this; } #endif protected: MatrixTypeNested m_matrix; }; // template // internal::selfadjoint_matrix_product_returntype > // operator*(const MatrixBase& lhs, const SelfAdjointView& rhs) // { // return internal::matrix_selfadjoint_product_returntype >(lhs.derived(),rhs); // } // selfadjoint to dense matrix namespace internal { template struct triangular_assignment_selector { enum { col = (UnrollCount-1) / Derived1::RowsAtCompileTime, row = (UnrollCount-1) % Derived1::RowsAtCompileTime }; static inline void run(Derived1 &dst, const Derived2 &src) { triangular_assignment_selector::run(dst, src); if(row == col) dst.coeffRef(row, col) = real(src.coeff(row, col)); else if(row < col) dst.coeffRef(col, row) = conj(dst.coeffRef(row, col) = src.coeff(row, col)); } }; template struct triangular_assignment_selector { static inline void run(Derived1 &, const Derived2 &) {} }; template struct triangular_assignment_selector { enum { col = (UnrollCount-1) / Derived1::RowsAtCompileTime, row = (UnrollCount-1) % Derived1::RowsAtCompileTime }; static inline void run(Derived1 &dst, const Derived2 &src) { triangular_assignment_selector::run(dst, src); if(row == col) dst.coeffRef(row, col) = real(src.coeff(row, col)); else if(row > col) dst.coeffRef(col, row) = conj(dst.coeffRef(row, col) = src.coeff(row, col)); } }; template struct triangular_assignment_selector { static inline void run(Derived1 &, const Derived2 &) {} }; template struct triangular_assignment_selector { typedef typename Derived1::Index Index; static inline void run(Derived1 &dst, const Derived2 &src) { for(Index j = 0; j < dst.cols(); ++j) { for(Index i = 0; i < j; ++i) { dst.copyCoeff(i, j, src); dst.coeffRef(j,i) = conj(dst.coeff(i,j)); } dst.copyCoeff(j, j, src); } } }; template struct triangular_assignment_selector { static inline void run(Derived1 &dst, const Derived2 &src) { typedef typename Derived1::Index Index; for(Index i = 0; i < dst.rows(); ++i) { for(Index j = 0; j < i; ++j) { dst.copyCoeff(i, j, src); dst.coeffRef(j,i) = conj(dst.coeff(i,j)); } dst.copyCoeff(i, i, src); } } }; } // end namespace internal /*************************************************************************** * Implementation of MatrixBase methods ***************************************************************************/ template template typename MatrixBase::template ConstSelfAdjointViewReturnType::Type MatrixBase::selfadjointView() const { return derived(); } template template typename MatrixBase::template SelfAdjointViewReturnType::Type MatrixBase::selfadjointView() { return derived(); } } // end namespace Eigen #endif // EIGEN_SELFADJOINTMATRIX_H ctbnrle.1.1.1/hdr/Eigen/src/Core/util/0000775000175000017500000000000012107032422017617 5ustar csheltoncsheltonctbnrle.1.1.1/hdr/Eigen/src/Core/util/DisableStupidWarnings.h0000775000175000017500000000405112107032422024240 0ustar csheltoncshelton#ifndef EIGEN_WARNINGS_DISABLED #define EIGEN_WARNINGS_DISABLED #ifdef _MSC_VER // 4100 - unreferenced formal parameter (occurred e.g. in aligned_allocator::destroy(pointer p)) // 4101 - unreferenced local variable // 4127 - conditional expression is constant // 4181 - qualifier applied to reference type ignored // 4211 - nonstandard extension used : redefined extern to static // 4244 - 'argument' : conversion from 'type1' to 'type2', possible loss of data // 4273 - QtAlignedMalloc, inconsistent DLL linkage // 4324 - structure was padded due to declspec(align()) // 4512 - assignment operator could not be generated // 4522 - 'class' : multiple assignment operators specified // 4700 - uninitialized local variable 'xyz' used // 4717 - 'function' : recursive on all control paths, function will cause runtime stack overflow #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS #pragma warning( push ) #endif #pragma warning( disable : 4100 4101 4127 4181 4211 4244 4273 4324 4512 4522 4700 4717 ) #elif defined __INTEL_COMPILER // 2196 - routine is both "inline" and "noinline" ("noinline" assumed) // ICC 12 generates this warning even without any inline keyword, when defining class methods 'inline' i.e. inside of class body // typedef that may be a reference type. // 279 - controlling expression is constant // ICC 12 generates this warning on assert(constant_expression_depending_on_template_params) and frankly this is a legitimate use case. #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS #pragma warning push #endif #pragma warning disable 2196 279 #elif defined __clang__ // -Wconstant-logical-operand - warning: use of logical && with constant operand; switch to bitwise & or remove constant // this is really a stupid warning as it warns on compile-time expressions involving enums #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS #pragma clang diagnostic push #endif #pragma clang diagnostic ignored "-Wconstant-logical-operand" #endif #endif // not EIGEN_WARNINGS_DISABLED ctbnrle.1.1.1/hdr/Eigen/src/Core/util/Macros.h0000775000175000017500000004250612107032422021226 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2010 Gael Guennebaud // Copyright (C) 2006-2008 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_MACROS_H #define EIGEN_MACROS_H #define EIGEN_WORLD_VERSION 3 #define EIGEN_MAJOR_VERSION 0 #define EIGEN_MINOR_VERSION 93 #define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \ (EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \ EIGEN_MINOR_VERSION>=z)))) #ifdef __GNUC__ #define EIGEN_GNUC_AT_LEAST(x,y) ((__GNUC__==x && __GNUC_MINOR__>=y) || __GNUC__>x) #else #define EIGEN_GNUC_AT_LEAST(x,y) 0 #endif #ifdef __GNUC__ #define EIGEN_GNUC_AT_MOST(x,y) ((__GNUC__==x && __GNUC_MINOR__<=y) || __GNUC__::Scalar Scalar; /*!< \brief Numeric type, e.g. float, double, int or std::complex. */ \ typedef typename Eigen::NumTraits::Real RealScalar; /*!< \brief The underlying numeric type for composed scalar types. \details In cases where Scalar is e.g. std::complex, T were corresponding to RealScalar. */ \ typedef typename Base::CoeffReturnType CoeffReturnType; /*!< \brief The return type for coefficient access. \details Depending on whether the object allows direct coefficient access (e.g. for a MatrixXd), this type is either 'const Scalar&' or simply 'Scalar' for objects that do not allow direct coefficient access. */ \ typedef typename Eigen::internal::nested::type Nested; \ typedef typename Eigen::internal::traits::StorageKind StorageKind; \ typedef typename Eigen::internal::traits::Index Index; \ enum { RowsAtCompileTime = Eigen::internal::traits::RowsAtCompileTime, \ ColsAtCompileTime = Eigen::internal::traits::ColsAtCompileTime, \ Flags = Eigen::internal::traits::Flags, \ CoeffReadCost = Eigen::internal::traits::CoeffReadCost, \ SizeAtCompileTime = Base::SizeAtCompileTime, \ MaxSizeAtCompileTime = Base::MaxSizeAtCompileTime, \ IsVectorAtCompileTime = Base::IsVectorAtCompileTime }; #define EIGEN_DENSE_PUBLIC_INTERFACE(Derived) \ typedef typename Eigen::internal::traits::Scalar Scalar; /*!< \brief Numeric type, e.g. float, double, int or std::complex. */ \ typedef typename Eigen::NumTraits::Real RealScalar; /*!< \brief The underlying numeric type for composed scalar types. \details In cases where Scalar is e.g. std::complex, T were corresponding to RealScalar. */ \ typedef typename Base::PacketScalar PacketScalar; \ typedef typename Base::CoeffReturnType CoeffReturnType; /*!< \brief The return type for coefficient access. \details Depending on whether the object allows direct coefficient access (e.g. for a MatrixXd), this type is either 'const Scalar&' or simply 'Scalar' for objects that do not allow direct coefficient access. */ \ typedef typename Eigen::internal::nested::type Nested; \ typedef typename Eigen::internal::traits::StorageKind StorageKind; \ typedef typename Eigen::internal::traits::Index Index; \ enum { RowsAtCompileTime = Eigen::internal::traits::RowsAtCompileTime, \ ColsAtCompileTime = Eigen::internal::traits::ColsAtCompileTime, \ MaxRowsAtCompileTime = Eigen::internal::traits::MaxRowsAtCompileTime, \ MaxColsAtCompileTime = Eigen::internal::traits::MaxColsAtCompileTime, \ Flags = Eigen::internal::traits::Flags, \ CoeffReadCost = Eigen::internal::traits::CoeffReadCost, \ SizeAtCompileTime = Base::SizeAtCompileTime, \ MaxSizeAtCompileTime = Base::MaxSizeAtCompileTime, \ IsVectorAtCompileTime = Base::IsVectorAtCompileTime }; \ using Base::derived; \ using Base::const_cast_derived; #define EIGEN_PLAIN_ENUM_MIN(a,b) (((int)a <= (int)b) ? (int)a : (int)b) #define EIGEN_PLAIN_ENUM_MAX(a,b) (((int)a >= (int)b) ? (int)a : (int)b) // EIGEN_SIZE_MIN_PREFER_DYNAMIC gives the min between compile-time sizes. 0 has absolute priority, followed by 1, // followed by Dynamic, followed by other finite values. The reason for giving Dynamic the priority over // finite values is that min(3, Dynamic) should be Dynamic, since that could be anything between 0 and 3. #define EIGEN_SIZE_MIN_PREFER_DYNAMIC(a,b) (((int)a == 0 || (int)b == 0) ? 0 \ : ((int)a == 1 || (int)b == 1) ? 1 \ : ((int)a == Dynamic || (int)b == Dynamic) ? Dynamic \ : ((int)a <= (int)b) ? (int)a : (int)b) // EIGEN_SIZE_MIN_PREFER_FIXED is a variant of EIGEN_SIZE_MIN_PREFER_DYNAMIC comparing MaxSizes. The difference is that finite values // now have priority over Dynamic, so that min(3, Dynamic) gives 3. Indeed, whatever the actual value is // (between 0 and 3), it is not more than 3. #define EIGEN_SIZE_MIN_PREFER_FIXED(a,b) (((int)a == 0 || (int)b == 0) ? 0 \ : ((int)a == 1 || (int)b == 1) ? 1 \ : ((int)a == Dynamic && (int)b == Dynamic) ? Dynamic \ : ((int)a == Dynamic) ? (int)b \ : ((int)b == Dynamic) ? (int)a \ : ((int)a <= (int)b) ? (int)a : (int)b) // see EIGEN_SIZE_MIN_PREFER_DYNAMIC. No need for a separate variant for MaxSizes here. #define EIGEN_SIZE_MAX(a,b) (((int)a == Dynamic || (int)b == Dynamic) ? Dynamic \ : ((int)a >= (int)b) ? (int)a : (int)b) #define EIGEN_LOGICAL_XOR(a,b) (((a) || (b)) && !((a) && (b))) #define EIGEN_IMPLIES(a,b) (!(a) || (b)) #define EIGEN_MAKE_CWISE_BINARY_OP(METHOD,FUNCTOR) \ template \ EIGEN_STRONG_INLINE const CwiseBinaryOp, const Derived, const OtherDerived> \ (METHOD)(const EIGEN_CURRENT_STORAGE_BASE_CLASS &other) const \ { \ return CwiseBinaryOp, const Derived, const OtherDerived>(derived(), other.derived()); \ } // the expression type of a cwise product #define EIGEN_CWISE_PRODUCT_RETURN_TYPE(LHS,RHS) \ CwiseBinaryOp< \ internal::scalar_product_op< \ typename internal::traits::Scalar, \ typename internal::traits::Scalar \ >, \ const LHS, \ const RHS \ > #endif // EIGEN_MACROS_H ctbnrle.1.1.1/hdr/Eigen/src/Core/util/Memory.h0000775000175000017500000011045712107032422021253 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2010 Gael Guennebaud // Copyright (C) 2008-2009 Benoit Jacob // Copyright (C) 2009 Kenneth Riddile // Copyright (C) 2010 Hauke Heibel // Copyright (C) 2010 Thomas Capricelli // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . /***************************************************************************** *** Platform checks for aligned malloc functions *** *****************************************************************************/ #ifndef EIGEN_MEMORY_H #define EIGEN_MEMORY_H // On 64-bit systems, glibc's malloc returns 16-byte-aligned pointers, see: // http://www.gnu.org/s/libc/manual/html_node/Aligned-Memory-Blocks.html // This is true at least since glibc 2.8. // This leaves the question how to detect 64-bit. According to this document, // http://gcc.fyxm.net/summit/2003/Porting%20to%2064%20bit.pdf // page 114, "[The] LP64 model [...] is used by all 64-bit UNIX ports" so it's indeed // quite safe, at least within the context of glibc, to equate 64-bit with LP64. #if defined(__GLIBC__) && ((__GLIBC__>=2 && __GLIBC_MINOR__ >= 8) || __GLIBC__>2) \ && defined(__LP64__) #define EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED 1 #else #define EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED 0 #endif // FreeBSD 6 seems to have 16-byte aligned malloc // See http://svn.freebsd.org/viewvc/base/stable/6/lib/libc/stdlib/malloc.c?view=markup // FreeBSD 7 seems to have 16-byte aligned malloc except on ARM and MIPS architectures // See http://svn.freebsd.org/viewvc/base/stable/7/lib/libc/stdlib/malloc.c?view=markup #if defined(__FreeBSD__) && !defined(__arm__) && !defined(__mips__) #define EIGEN_FREEBSD_MALLOC_ALREADY_ALIGNED 1 #else #define EIGEN_FREEBSD_MALLOC_ALREADY_ALIGNED 0 #endif #if defined(__APPLE__) \ || defined(_WIN64) \ || EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED \ || EIGEN_FREEBSD_MALLOC_ALREADY_ALIGNED #define EIGEN_MALLOC_ALREADY_ALIGNED 1 #else #define EIGEN_MALLOC_ALREADY_ALIGNED 0 #endif #if ((defined __QNXNTO__) || (defined _GNU_SOURCE) || ((defined _XOPEN_SOURCE) && (_XOPEN_SOURCE >= 600))) \ && (defined _POSIX_ADVISORY_INFO) && (_POSIX_ADVISORY_INFO > 0) #define EIGEN_HAS_POSIX_MEMALIGN 1 #else #define EIGEN_HAS_POSIX_MEMALIGN 0 #endif #ifdef EIGEN_VECTORIZE_SSE #define EIGEN_HAS_MM_MALLOC 1 #else #define EIGEN_HAS_MM_MALLOC 0 #endif namespace Eigen { namespace internal { inline void throw_std_bad_alloc() { #ifdef EIGEN_EXCEPTIONS throw std::bad_alloc(); #else std::size_t huge = -1; new int[huge]; #endif } /***************************************************************************** *** Implementation of handmade aligned functions *** *****************************************************************************/ /* ----- Hand made implementations of aligned malloc/free and realloc ----- */ /** \internal Like malloc, but the returned pointer is guaranteed to be 16-byte aligned. * Fast, but wastes 16 additional bytes of memory. Does not throw any exception. */ inline void* handmade_aligned_malloc(size_t size) { void *original = std::malloc(size+16); if (original == 0) return 0; void *aligned = reinterpret_cast((reinterpret_cast(original) & ~(size_t(15))) + 16); *(reinterpret_cast(aligned) - 1) = original; return aligned; } /** \internal Frees memory allocated with handmade_aligned_malloc */ inline void handmade_aligned_free(void *ptr) { if (ptr) std::free(*(reinterpret_cast(ptr) - 1)); } /** \internal * \brief Reallocates aligned memory. * Since we know that our handmade version is based on std::realloc * we can use std::realloc to implement efficient reallocation. */ inline void* handmade_aligned_realloc(void* ptr, size_t size, size_t = 0) { if (ptr == 0) return handmade_aligned_malloc(size); void *original = *(reinterpret_cast(ptr) - 1); original = std::realloc(original,size+16); if (original == 0) return 0; void *aligned = reinterpret_cast((reinterpret_cast(original) & ~(size_t(15))) + 16); *(reinterpret_cast(aligned) - 1) = original; return aligned; } /***************************************************************************** *** Implementation of generic aligned realloc (when no realloc can be used)*** *****************************************************************************/ void* aligned_malloc(size_t size); void aligned_free(void *ptr); /** \internal * \brief Reallocates aligned memory. * Allows reallocation with aligned ptr types. This implementation will * always create a new memory chunk and copy the old data. */ inline void* generic_aligned_realloc(void* ptr, size_t size, size_t old_size) { if (ptr==0) return aligned_malloc(size); if (size==0) { aligned_free(ptr); return 0; } void* newptr = aligned_malloc(size); if (newptr == 0) { #ifdef EIGEN_HAS_ERRNO errno = ENOMEM; // according to the standard #endif return 0; } if (ptr != 0) { std::memcpy(newptr, ptr, (std::min)(size,old_size)); aligned_free(ptr); } return newptr; } /***************************************************************************** *** Implementation of portable aligned versions of malloc/free/realloc *** *****************************************************************************/ #ifdef EIGEN_NO_MALLOC inline void check_that_malloc_is_allowed() { eigen_assert(false && "heap allocation is forbidden (EIGEN_NO_MALLOC is defined)"); } #elif defined EIGEN_RUNTIME_NO_MALLOC inline bool is_malloc_allowed_impl(bool update, bool new_value = false) { static bool value = true; if (update == 1) value = new_value; return value; } inline bool is_malloc_allowed() { return is_malloc_allowed_impl(false); } inline bool set_is_malloc_allowed(bool new_value) { return is_malloc_allowed_impl(true, new_value); } inline void check_that_malloc_is_allowed() { eigen_assert(is_malloc_allowed() && "heap allocation is forbidden (EIGEN_RUNTIME_NO_MALLOC is defined and g_is_malloc_allowed is false)"); } #else inline void check_that_malloc_is_allowed() {} #endif /** \internal Allocates \a size bytes. The returned pointer is guaranteed to have 16 bytes alignment. * On allocation error, the returned pointer is null, and std::bad_alloc is thrown. */ inline void* aligned_malloc(size_t size) { check_that_malloc_is_allowed(); void *result; #if !EIGEN_ALIGN result = std::malloc(size); #elif EIGEN_MALLOC_ALREADY_ALIGNED result = std::malloc(size); #elif EIGEN_HAS_POSIX_MEMALIGN if(posix_memalign(&result, 16, size)) result = 0; #elif EIGEN_HAS_MM_MALLOC result = _mm_malloc(size, 16); #elif (defined _MSC_VER) result = _aligned_malloc(size, 16); #else result = handmade_aligned_malloc(size); #endif if(!result && size) throw_std_bad_alloc(); return result; } /** \internal Frees memory allocated with aligned_malloc. */ inline void aligned_free(void *ptr) { #if !EIGEN_ALIGN std::free(ptr); #elif EIGEN_MALLOC_ALREADY_ALIGNED std::free(ptr); #elif EIGEN_HAS_POSIX_MEMALIGN std::free(ptr); #elif EIGEN_HAS_MM_MALLOC _mm_free(ptr); #elif defined(_MSC_VER) _aligned_free(ptr); #else handmade_aligned_free(ptr); #endif } /** * \internal * \brief Reallocates an aligned block of memory. * \throws std::bad_alloc on allocation failure **/ inline void* aligned_realloc(void *ptr, size_t new_size, size_t old_size) { EIGEN_UNUSED_VARIABLE(old_size); void *result; #if !EIGEN_ALIGN result = std::realloc(ptr,new_size); #elif EIGEN_MALLOC_ALREADY_ALIGNED result = std::realloc(ptr,new_size); #elif EIGEN_HAS_POSIX_MEMALIGN result = generic_aligned_realloc(ptr,new_size,old_size); #elif EIGEN_HAS_MM_MALLOC // The defined(_mm_free) is just here to verify that this MSVC version // implements _mm_malloc/_mm_free based on the corresponding _aligned_ // functions. This may not always be the case and we just try to be safe. #if defined(_MSC_VER) && defined(_mm_free) result = _aligned_realloc(ptr,new_size,16); #else result = generic_aligned_realloc(ptr,new_size,old_size); #endif #elif defined(_MSC_VER) result = _aligned_realloc(ptr,new_size,16); #else result = handmade_aligned_realloc(ptr,new_size,old_size); #endif if (!result && new_size) throw_std_bad_alloc(); return result; } /***************************************************************************** *** Implementation of conditionally aligned functions *** *****************************************************************************/ /** \internal Allocates \a size bytes. If Align is true, then the returned ptr is 16-byte-aligned. * On allocation error, the returned pointer is null, and a std::bad_alloc is thrown. */ template inline void* conditional_aligned_malloc(size_t size) { return aligned_malloc(size); } template<> inline void* conditional_aligned_malloc(size_t size) { check_that_malloc_is_allowed(); void *result = std::malloc(size); if(!result && size) throw_std_bad_alloc(); return result; } /** \internal Frees memory allocated with conditional_aligned_malloc */ template inline void conditional_aligned_free(void *ptr) { aligned_free(ptr); } template<> inline void conditional_aligned_free(void *ptr) { std::free(ptr); } template inline void* conditional_aligned_realloc(void* ptr, size_t new_size, size_t old_size) { return aligned_realloc(ptr, new_size, old_size); } template<> inline void* conditional_aligned_realloc(void* ptr, size_t new_size, size_t) { return std::realloc(ptr, new_size); } /***************************************************************************** *** Construction/destruction of array elements *** *****************************************************************************/ /** \internal Constructs the elements of an array. * The \a size parameter tells on how many objects to call the constructor of T. */ template inline T* construct_elements_of_array(T *ptr, size_t size) { for (size_t i=0; i < size; ++i) ::new (ptr + i) T; return ptr; } /** \internal Destructs the elements of an array. * The \a size parameters tells on how many objects to call the destructor of T. */ template inline void destruct_elements_of_array(T *ptr, size_t size) { // always destruct an array starting from the end. if(ptr) while(size) ptr[--size].~T(); } /***************************************************************************** *** Implementation of aligned new/delete-like functions *** *****************************************************************************/ template EIGEN_ALWAYS_INLINE void check_size_for_overflow(size_t size) { if(size > size_t(-1) / sizeof(T)) throw_std_bad_alloc(); } /** \internal Allocates \a size objects of type T. The returned pointer is guaranteed to have 16 bytes alignment. * On allocation error, the returned pointer is undefined, but a std::bad_alloc is thrown. * The default constructor of T is called. */ template inline T* aligned_new(size_t size) { check_size_for_overflow(size); T *result = reinterpret_cast(aligned_malloc(sizeof(T)*size)); return construct_elements_of_array(result, size); } template inline T* conditional_aligned_new(size_t size) { check_size_for_overflow(size); T *result = reinterpret_cast(conditional_aligned_malloc(sizeof(T)*size)); return construct_elements_of_array(result, size); } /** \internal Deletes objects constructed with aligned_new * The \a size parameters tells on how many objects to call the destructor of T. */ template inline void aligned_delete(T *ptr, size_t size) { destruct_elements_of_array(ptr, size); aligned_free(ptr); } /** \internal Deletes objects constructed with conditional_aligned_new * The \a size parameters tells on how many objects to call the destructor of T. */ template inline void conditional_aligned_delete(T *ptr, size_t size) { destruct_elements_of_array(ptr, size); conditional_aligned_free(ptr); } template inline T* conditional_aligned_realloc_new(T* pts, size_t new_size, size_t old_size) { check_size_for_overflow(new_size); check_size_for_overflow(old_size); if(new_size < old_size) destruct_elements_of_array(pts+new_size, old_size-new_size); T *result = reinterpret_cast(conditional_aligned_realloc(reinterpret_cast(pts), sizeof(T)*new_size, sizeof(T)*old_size)); if(new_size > old_size) construct_elements_of_array(result+old_size, new_size-old_size); return result; } template inline T* conditional_aligned_new_auto(size_t size) { check_size_for_overflow(size); T *result = reinterpret_cast(conditional_aligned_malloc(sizeof(T)*size)); if(NumTraits::RequireInitialization) construct_elements_of_array(result, size); return result; } template inline T* conditional_aligned_realloc_new_auto(T* pts, size_t new_size, size_t old_size) { check_size_for_overflow(new_size); check_size_for_overflow(old_size); if(NumTraits::RequireInitialization && (new_size < old_size)) destruct_elements_of_array(pts+new_size, old_size-new_size); T *result = reinterpret_cast(conditional_aligned_realloc(reinterpret_cast(pts), sizeof(T)*new_size, sizeof(T)*old_size)); if(NumTraits::RequireInitialization && (new_size > old_size)) construct_elements_of_array(result+old_size, new_size-old_size); return result; } template inline void conditional_aligned_delete_auto(T *ptr, size_t size) { if(NumTraits::RequireInitialization) destruct_elements_of_array(ptr, size); conditional_aligned_free(ptr); } /****************************************************************************/ /** \internal Returns the index of the first element of the array that is well aligned for vectorization. * * \param array the address of the start of the array * \param size the size of the array * * \note If no element of the array is well aligned, the size of the array is returned. Typically, * for example with SSE, "well aligned" means 16-byte-aligned. If vectorization is disabled or if the * packet size for the given scalar type is 1, then everything is considered well-aligned. * * \note If the scalar type is vectorizable, we rely on the following assumptions: sizeof(Scalar) is a * power of 2, the packet size in bytes is also a power of 2, and is a multiple of sizeof(Scalar). On the * other hand, we do not assume that the array address is a multiple of sizeof(Scalar), as that fails for * example with Scalar=double on certain 32-bit platforms, see bug #79. * * There is also the variant first_aligned(const MatrixBase&) defined in DenseCoeffsBase.h. */ template static inline Index first_aligned(const Scalar* array, Index size) { typedef typename packet_traits::type Packet; enum { PacketSize = packet_traits::size, PacketAlignedMask = PacketSize-1 }; if(PacketSize==1) { // Either there is no vectorization, or a packet consists of exactly 1 scalar so that all elements // of the array have the same alignment. return 0; } else if(size_t(array) & (sizeof(Scalar)-1)) { // There is vectorization for this scalar type, but the array is not aligned to the size of a single scalar. // Consequently, no element of the array is well aligned. return size; } else { return std::min( (PacketSize - (Index((size_t(array)/sizeof(Scalar))) & PacketAlignedMask)) & PacketAlignedMask, size); } } // std::copy is much slower than memcpy, so let's introduce a smart_copy which // use memcpy on trivial types, i.e., on types that does not require an initialization ctor. template struct smart_copy_helper; template void smart_copy(const T* start, const T* end, T* target) { smart_copy_helper::RequireInitialization>::run(start, end, target); } template struct smart_copy_helper { static inline void run(const T* start, const T* end, T* target) { memcpy(target, start, std::ptrdiff_t(end)-std::ptrdiff_t(start)); } }; template struct smart_copy_helper { static inline void run(const T* start, const T* end, T* target) { std::copy(start, end, target); } }; /***************************************************************************** *** Implementation of runtime stack allocation (falling back to malloc) *** *****************************************************************************/ // you can overwrite Eigen's default behavior regarding alloca by defining EIGEN_ALLOCA // to the appropriate stack allocation function #ifndef EIGEN_ALLOCA #if (defined __linux__) #define EIGEN_ALLOCA alloca #elif defined(_MSC_VER) #define EIGEN_ALLOCA _alloca #endif #endif // This helper class construct the allocated memory, and takes care of destructing and freeing the handled data // at destruction time. In practice this helper class is mainly useful to avoid memory leak in case of exceptions. template class aligned_stack_memory_handler { public: /* Creates a stack_memory_handler responsible for the buffer \a ptr of size \a size. * Note that \a ptr can be 0 regardless of the other parameters. * This constructor takes care of constructing/initializing the elements of the buffer if required by the scalar type T (see NumTraits::RequireInitialization). * In this case, the buffer elements will also be destructed when this handler will be destructed. * Finally, if \a dealloc is true, then the pointer \a ptr is freed. **/ aligned_stack_memory_handler(T* ptr, size_t size, bool dealloc) : m_ptr(ptr), m_size(size), m_deallocate(dealloc) { if(NumTraits::RequireInitialization && m_ptr) Eigen::internal::construct_elements_of_array(m_ptr, size); } ~aligned_stack_memory_handler() { if(NumTraits::RequireInitialization && m_ptr) Eigen::internal::destruct_elements_of_array(m_ptr, m_size); if(m_deallocate) Eigen::internal::aligned_free(m_ptr); } protected: T* m_ptr; size_t m_size; bool m_deallocate; }; } // end namespace internal /** \internal * Declares, allocates and construct an aligned buffer named NAME of SIZE elements of type TYPE on the stack * if SIZE is smaller than EIGEN_STACK_ALLOCATION_LIMIT, and if stack allocation is supported by the platform * (currently, this is Linux and Visual Studio only). Otherwise the memory is allocated on the heap. * The allocated buffer is automatically deleted when exiting the scope of this declaration. * If BUFFER is non null, then the declared variable is simply an alias for BUFFER, and no allocation/deletion occurs. * Here is an example: * \code * { * ei_declare_aligned_stack_constructed_variable(float,data,size,0); * // use data[0] to data[size-1] * } * \endcode * The underlying stack allocation function can controlled with the EIGEN_ALLOCA preprocessor token. */ #ifdef EIGEN_ALLOCA #ifdef __arm__ #define EIGEN_ALIGNED_ALLOCA(SIZE) reinterpret_cast((reinterpret_cast(EIGEN_ALLOCA(SIZE+16)) & ~(size_t(15))) + 16) #else #define EIGEN_ALIGNED_ALLOCA EIGEN_ALLOCA #endif #define ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) \ Eigen::internal::check_size_for_overflow(SIZE); \ TYPE* NAME = (BUFFER)!=0 ? (BUFFER) \ : reinterpret_cast( \ (sizeof(TYPE)*SIZE<=EIGEN_STACK_ALLOCATION_LIMIT) ? EIGEN_ALIGNED_ALLOCA(sizeof(TYPE)*SIZE) \ : Eigen::internal::aligned_malloc(sizeof(TYPE)*SIZE) ); \ Eigen::internal::aligned_stack_memory_handler EIGEN_CAT(NAME,_stack_memory_destructor)((BUFFER)==0 ? NAME : 0,SIZE,sizeof(TYPE)*SIZE>EIGEN_STACK_ALLOCATION_LIMIT) #else #define ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) \ Eigen::internal::check_size_for_overflow(SIZE); \ TYPE* NAME = (BUFFER)!=0 ? BUFFER : reinterpret_cast(Eigen::internal::aligned_malloc(sizeof(TYPE)*SIZE)); \ Eigen::internal::aligned_stack_memory_handler EIGEN_CAT(NAME,_stack_memory_destructor)((BUFFER)==0 ? NAME : 0,SIZE,true) #endif /***************************************************************************** *** Implementation of EIGEN_MAKE_ALIGNED_OPERATOR_NEW [_IF] *** *****************************************************************************/ #if EIGEN_ALIGN #ifdef EIGEN_EXCEPTIONS #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign) \ void* operator new(size_t size, const std::nothrow_t&) throw() { \ try { return Eigen::internal::conditional_aligned_malloc(size); } \ catch (...) { return 0; } \ return 0; \ } #else #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign) \ void* operator new(size_t size, const std::nothrow_t&) throw() { \ return Eigen::internal::conditional_aligned_malloc(size); \ } #endif #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign) \ void *operator new(size_t size) { \ return Eigen::internal::conditional_aligned_malloc(size); \ } \ void *operator new[](size_t size) { \ return Eigen::internal::conditional_aligned_malloc(size); \ } \ void operator delete(void * ptr) throw() { Eigen::internal::conditional_aligned_free(ptr); } \ void operator delete[](void * ptr) throw() { Eigen::internal::conditional_aligned_free(ptr); } \ /* in-place new and delete. since (at least afaik) there is no actual */ \ /* memory allocated we can safely let the default implementation handle */ \ /* this particular case. */ \ static void *operator new(size_t size, void *ptr) { return ::operator new(size,ptr); } \ void operator delete(void * memory, void *ptr) throw() { return ::operator delete(memory,ptr); } \ /* nothrow-new (returns zero instead of std::bad_alloc) */ \ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign) \ void operator delete(void *ptr, const std::nothrow_t&) throw() { \ Eigen::internal::conditional_aligned_free(ptr); \ } \ typedef void eigen_aligned_operator_new_marker_type; #else #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign) #endif #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(true) #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar,Size) \ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(bool(((Size)!=Eigen::Dynamic) && ((sizeof(Scalar)*(Size))%16==0))) /****************************************************************************/ /** \class aligned_allocator * \ingroup Core_Module * * \brief STL compatible allocator to use with with 16 byte aligned types * * Example: * \code * // Matrix4f requires 16 bytes alignment: * std::map< int, Matrix4f, std::less, * aligned_allocator > > my_map_mat4; * // Vector3f does not require 16 bytes alignment, no need to use Eigen's allocator: * std::map< int, Vector3f > my_map_vec3; * \endcode * * \sa \ref TopicStlContainers. */ template class aligned_allocator { public: typedef size_t size_type; typedef std::ptrdiff_t difference_type; typedef T* pointer; typedef const T* const_pointer; typedef T& reference; typedef const T& const_reference; typedef T value_type; template struct rebind { typedef aligned_allocator other; }; pointer address( reference value ) const { return &value; } const_pointer address( const_reference value ) const { return &value; } aligned_allocator() { } aligned_allocator( const aligned_allocator& ) { } template aligned_allocator( const aligned_allocator& ) { } ~aligned_allocator() { } size_type max_size() const { return (std::numeric_limits::max)(); } pointer allocate( size_type num, const void* hint = 0 ) { EIGEN_UNUSED_VARIABLE(hint); internal::check_size_for_overflow(num); return static_cast( internal::aligned_malloc( num * sizeof(T) ) ); } void construct( pointer p, const T& value ) { ::new( p ) T( value ); } // Support for c++11 #if (__cplusplus >= 201103L) template void construct(pointer p, Args&&... args) { ::new(p) T(std::forward(args)...); } #endif void destroy( pointer p ) { p->~T(); } void deallocate( pointer p, size_type /*num*/ ) { internal::aligned_free( p ); } bool operator!=(const aligned_allocator& ) const { return false; } bool operator==(const aligned_allocator& ) const { return true; } }; //---------- Cache sizes ---------- #if !defined(EIGEN_NO_CPUID) # if defined(__GNUC__) && ( defined(__i386__) || defined(__x86_64__) ) # if defined(__PIC__) && defined(__i386__) // Case for x86 with PIC # define EIGEN_CPUID(abcd,func,id) \ __asm__ __volatile__ ("xchgl %%ebx, %%esi;cpuid; xchgl %%ebx,%%esi": "=a" (abcd[0]), "=S" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "a" (func), "c" (id)); # else // Case for x86_64 or x86 w/o PIC # define EIGEN_CPUID(abcd,func,id) \ __asm__ __volatile__ ("cpuid": "=a" (abcd[0]), "=b" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "a" (func), "c" (id) ); # endif # elif defined(_MSC_VER) # if (_MSC_VER > 1500) # define EIGEN_CPUID(abcd,func,id) __cpuidex((int*)abcd,func,id) # endif # endif #endif namespace internal { #ifdef EIGEN_CPUID inline bool cpuid_is_vendor(int abcd[4], const char* vendor) { return abcd[1]==(reinterpret_cast(vendor))[0] && abcd[3]==(reinterpret_cast(vendor))[1] && abcd[2]==(reinterpret_cast(vendor))[2]; } inline void queryCacheSizes_intel_direct(int& l1, int& l2, int& l3) { int abcd[4]; l1 = l2 = l3 = 0; int cache_id = 0; int cache_type = 0; do { abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0; EIGEN_CPUID(abcd,0x4,cache_id); cache_type = (abcd[0] & 0x0F) >> 0; if(cache_type==1||cache_type==3) // data or unified cache { int cache_level = (abcd[0] & 0xE0) >> 5; // A[7:5] int ways = (abcd[1] & 0xFFC00000) >> 22; // B[31:22] int partitions = (abcd[1] & 0x003FF000) >> 12; // B[21:12] int line_size = (abcd[1] & 0x00000FFF) >> 0; // B[11:0] int sets = (abcd[2]); // C[31:0] int cache_size = (ways+1) * (partitions+1) * (line_size+1) * (sets+1); switch(cache_level) { case 1: l1 = cache_size; break; case 2: l2 = cache_size; break; case 3: l3 = cache_size; break; default: break; } } cache_id++; } while(cache_type>0 && cache_id<16); } inline void queryCacheSizes_intel_codes(int& l1, int& l2, int& l3) { int abcd[4]; abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0; l1 = l2 = l3 = 0; EIGEN_CPUID(abcd,0x00000002,0); unsigned char * bytes = reinterpret_cast(abcd)+2; bool check_for_p2_core2 = false; for(int i=0; i<14; ++i) { switch(bytes[i]) { case 0x0A: l1 = 8; break; // 0Ah data L1 cache, 8 KB, 2 ways, 32 byte lines case 0x0C: l1 = 16; break; // 0Ch data L1 cache, 16 KB, 4 ways, 32 byte lines case 0x0E: l1 = 24; break; // 0Eh data L1 cache, 24 KB, 6 ways, 64 byte lines case 0x10: l1 = 16; break; // 10h data L1 cache, 16 KB, 4 ways, 32 byte lines (IA-64) case 0x15: l1 = 16; break; // 15h code L1 cache, 16 KB, 4 ways, 32 byte lines (IA-64) case 0x2C: l1 = 32; break; // 2Ch data L1 cache, 32 KB, 8 ways, 64 byte lines case 0x30: l1 = 32; break; // 30h code L1 cache, 32 KB, 8 ways, 64 byte lines case 0x60: l1 = 16; break; // 60h data L1 cache, 16 KB, 8 ways, 64 byte lines, sectored case 0x66: l1 = 8; break; // 66h data L1 cache, 8 KB, 4 ways, 64 byte lines, sectored case 0x67: l1 = 16; break; // 67h data L1 cache, 16 KB, 4 ways, 64 byte lines, sectored case 0x68: l1 = 32; break; // 68h data L1 cache, 32 KB, 4 ways, 64 byte lines, sectored case 0x1A: l2 = 96; break; // code and data L2 cache, 96 KB, 6 ways, 64 byte lines (IA-64) case 0x22: l3 = 512; break; // code and data L3 cache, 512 KB, 4 ways (!), 64 byte lines, dual-sectored case 0x23: l3 = 1024; break; // code and data L3 cache, 1024 KB, 8 ways, 64 byte lines, dual-sectored case 0x25: l3 = 2048; break; // code and data L3 cache, 2048 KB, 8 ways, 64 byte lines, dual-sectored case 0x29: l3 = 4096; break; // code and data L3 cache, 4096 KB, 8 ways, 64 byte lines, dual-sectored case 0x39: l2 = 128; break; // code and data L2 cache, 128 KB, 4 ways, 64 byte lines, sectored case 0x3A: l2 = 192; break; // code and data L2 cache, 192 KB, 6 ways, 64 byte lines, sectored case 0x3B: l2 = 128; break; // code and data L2 cache, 128 KB, 2 ways, 64 byte lines, sectored case 0x3C: l2 = 256; break; // code and data L2 cache, 256 KB, 4 ways, 64 byte lines, sectored case 0x3D: l2 = 384; break; // code and data L2 cache, 384 KB, 6 ways, 64 byte lines, sectored case 0x3E: l2 = 512; break; // code and data L2 cache, 512 KB, 4 ways, 64 byte lines, sectored case 0x40: l2 = 0; break; // no integrated L2 cache (P6 core) or L3 cache (P4 core) case 0x41: l2 = 128; break; // code and data L2 cache, 128 KB, 4 ways, 32 byte lines case 0x42: l2 = 256; break; // code and data L2 cache, 256 KB, 4 ways, 32 byte lines case 0x43: l2 = 512; break; // code and data L2 cache, 512 KB, 4 ways, 32 byte lines case 0x44: l2 = 1024; break; // code and data L2 cache, 1024 KB, 4 ways, 32 byte lines case 0x45: l2 = 2048; break; // code and data L2 cache, 2048 KB, 4 ways, 32 byte lines case 0x46: l3 = 4096; break; // code and data L3 cache, 4096 KB, 4 ways, 64 byte lines case 0x47: l3 = 8192; break; // code and data L3 cache, 8192 KB, 8 ways, 64 byte lines case 0x48: l2 = 3072; break; // code and data L2 cache, 3072 KB, 12 ways, 64 byte lines case 0x49: if(l2!=0) l3 = 4096; else {check_for_p2_core2=true; l3 = l2 = 4096;} break;// code and data L3 cache, 4096 KB, 16 ways, 64 byte lines (P4) or L2 for core2 case 0x4A: l3 = 6144; break; // code and data L3 cache, 6144 KB, 12 ways, 64 byte lines case 0x4B: l3 = 8192; break; // code and data L3 cache, 8192 KB, 16 ways, 64 byte lines case 0x4C: l3 = 12288; break; // code and data L3 cache, 12288 KB, 12 ways, 64 byte lines case 0x4D: l3 = 16384; break; // code and data L3 cache, 16384 KB, 16 ways, 64 byte lines case 0x4E: l2 = 6144; break; // code and data L2 cache, 6144 KB, 24 ways, 64 byte lines case 0x78: l2 = 1024; break; // code and data L2 cache, 1024 KB, 4 ways, 64 byte lines case 0x79: l2 = 128; break; // code and data L2 cache, 128 KB, 8 ways, 64 byte lines, dual-sectored case 0x7A: l2 = 256; break; // code and data L2 cache, 256 KB, 8 ways, 64 byte lines, dual-sectored case 0x7B: l2 = 512; break; // code and data L2 cache, 512 KB, 8 ways, 64 byte lines, dual-sectored case 0x7C: l2 = 1024; break; // code and data L2 cache, 1024 KB, 8 ways, 64 byte lines, dual-sectored case 0x7D: l2 = 2048; break; // code and data L2 cache, 2048 KB, 8 ways, 64 byte lines case 0x7E: l2 = 256; break; // code and data L2 cache, 256 KB, 8 ways, 128 byte lines, sect. (IA-64) case 0x7F: l2 = 512; break; // code and data L2 cache, 512 KB, 2 ways, 64 byte lines case 0x80: l2 = 512; break; // code and data L2 cache, 512 KB, 8 ways, 64 byte lines case 0x81: l2 = 128; break; // code and data L2 cache, 128 KB, 8 ways, 32 byte lines case 0x82: l2 = 256; break; // code and data L2 cache, 256 KB, 8 ways, 32 byte lines case 0x83: l2 = 512; break; // code and data L2 cache, 512 KB, 8 ways, 32 byte lines case 0x84: l2 = 1024; break; // code and data L2 cache, 1024 KB, 8 ways, 32 byte lines case 0x85: l2 = 2048; break; // code and data L2 cache, 2048 KB, 8 ways, 32 byte lines case 0x86: l2 = 512; break; // code and data L2 cache, 512 KB, 4 ways, 64 byte lines case 0x87: l2 = 1024; break; // code and data L2 cache, 1024 KB, 8 ways, 64 byte lines case 0x88: l3 = 2048; break; // code and data L3 cache, 2048 KB, 4 ways, 64 byte lines (IA-64) case 0x89: l3 = 4096; break; // code and data L3 cache, 4096 KB, 4 ways, 64 byte lines (IA-64) case 0x8A: l3 = 8192; break; // code and data L3 cache, 8192 KB, 4 ways, 64 byte lines (IA-64) case 0x8D: l3 = 3072; break; // code and data L3 cache, 3072 KB, 12 ways, 128 byte lines (IA-64) default: break; } } if(check_for_p2_core2 && l2 == l3) l3 = 0; l1 *= 1024; l2 *= 1024; l3 *= 1024; } inline void queryCacheSizes_intel(int& l1, int& l2, int& l3, int max_std_funcs) { if(max_std_funcs>=4) queryCacheSizes_intel_direct(l1,l2,l3); else queryCacheSizes_intel_codes(l1,l2,l3); } inline void queryCacheSizes_amd(int& l1, int& l2, int& l3) { int abcd[4]; abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0; EIGEN_CPUID(abcd,0x80000005,0); l1 = (abcd[2] >> 24) * 1024; // C[31:24] = L1 size in KB abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0; EIGEN_CPUID(abcd,0x80000006,0); l2 = (abcd[2] >> 16) * 1024; // C[31;16] = l2 cache size in KB l3 = ((abcd[3] & 0xFFFC000) >> 18) * 512 * 1024; // D[31;18] = l3 cache size in 512KB } #endif /** \internal * Queries and returns the cache sizes in Bytes of the L1, L2, and L3 data caches respectively */ inline void queryCacheSizes(int& l1, int& l2, int& l3) { #ifdef EIGEN_CPUID int abcd[4]; // identify the CPU vendor EIGEN_CPUID(abcd,0x0,0); int max_std_funcs = abcd[1]; if(cpuid_is_vendor(abcd,"GenuineIntel")) queryCacheSizes_intel(l1,l2,l3,max_std_funcs); else if(cpuid_is_vendor(abcd,"AuthenticAMD") || cpuid_is_vendor(abcd,"AMDisbetter!")) queryCacheSizes_amd(l1,l2,l3); else // by default let's use Intel's API queryCacheSizes_intel(l1,l2,l3,max_std_funcs); // here is the list of other vendors: // ||cpuid_is_vendor(abcd,"VIA VIA VIA ") // ||cpuid_is_vendor(abcd,"CyrixInstead") // ||cpuid_is_vendor(abcd,"CentaurHauls") // ||cpuid_is_vendor(abcd,"GenuineTMx86") // ||cpuid_is_vendor(abcd,"TransmetaCPU") // ||cpuid_is_vendor(abcd,"RiseRiseRise") // ||cpuid_is_vendor(abcd,"Geode by NSC") // ||cpuid_is_vendor(abcd,"SiS SiS SiS ") // ||cpuid_is_vendor(abcd,"UMC UMC UMC ") // ||cpuid_is_vendor(abcd,"NexGenDriven") #else l1 = l2 = l3 = -1; #endif } /** \internal * \returns the size in Bytes of the L1 data cache */ inline int queryL1CacheSize() { int l1(-1), l2, l3; queryCacheSizes(l1,l2,l3); return l1; } /** \internal * \returns the size in Bytes of the L2 or L3 cache if this later is present */ inline int queryTopLevelCacheSize() { int l1, l2(-1), l3(-1); queryCacheSizes(l1,l2,l3); return (std::max)(l2,l3); } } // end namespace internal } // end namespace Eigen #endif // EIGEN_MEMORY_H ctbnrle.1.1.1/hdr/Eigen/src/Core/util/Meta.h0000775000175000017500000002436212107032422020670 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2009 Gael Guennebaud // Copyright (C) 2006-2008 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_META_H #define EIGEN_META_H namespace Eigen { namespace internal { /** \internal * \file Meta.h * This file contains generic metaprogramming classes which are not specifically related to Eigen. * \note In case you wonder, yes we're aware that Boost already provides all these features, * we however don't want to add a dependency to Boost. */ struct true_type { enum { value = 1 }; }; struct false_type { enum { value = 0 }; }; template struct conditional { typedef Then type; }; template struct conditional { typedef Else type; }; template struct is_same { enum { value = 0 }; }; template struct is_same { enum { value = 1 }; }; template struct remove_reference { typedef T type; }; template struct remove_reference { typedef T type; }; template struct remove_pointer { typedef T type; }; template struct remove_pointer { typedef T type; }; template struct remove_pointer { typedef T type; }; template struct remove_const { typedef T type; }; template struct remove_const { typedef T type; }; template struct remove_const { typedef T type[]; }; template struct remove_const { typedef T type[Size]; }; template struct remove_all { typedef T type; }; template struct remove_all { typedef typename remove_all::type type; }; template struct remove_all { typedef typename remove_all::type type; }; template struct remove_all { typedef typename remove_all::type type; }; template struct remove_all { typedef typename remove_all::type type; }; template struct remove_all { typedef typename remove_all::type type; }; template struct is_arithmetic { enum { value = false }; }; template<> struct is_arithmetic { enum { value = true }; }; template<> struct is_arithmetic { enum { value = true }; }; template<> struct is_arithmetic { enum { value = true }; }; template<> struct is_arithmetic { enum { value = true }; }; template<> struct is_arithmetic { enum { value = true }; }; template<> struct is_arithmetic { enum { value = true }; }; template<> struct is_arithmetic { enum { value = true }; }; template<> struct is_arithmetic { enum { value = true }; }; template<> struct is_arithmetic{ enum { value = true }; }; template<> struct is_arithmetic { enum { value = true }; }; template<> struct is_arithmetic { enum { value = true }; }; template<> struct is_arithmetic { enum { value = true }; }; template<> struct is_arithmetic { enum { value = true }; }; template struct add_const { typedef const T type; }; template struct add_const { typedef T& type; }; template struct is_const { enum { value = 0 }; }; template struct is_const { enum { value = 1 }; }; template struct add_const_on_value_type { typedef const T type; }; template struct add_const_on_value_type { typedef T const& type; }; template struct add_const_on_value_type { typedef T const* type; }; template struct add_const_on_value_type { typedef T const* const type; }; template struct add_const_on_value_type { typedef T const* const type; }; /** \internal Allows to enable/disable an overload * according to a compile time condition. */ template struct enable_if; template struct enable_if { typedef T type; }; /** \internal * A base class do disable default copy ctor and copy assignement operator. */ class noncopyable { noncopyable(const noncopyable&); const noncopyable& operator=(const noncopyable&); protected: noncopyable() {} ~noncopyable() {} }; /** \internal * Convenient struct to get the result type of a unary or binary functor. * * It supports both the current STL mechanism (using the result_type member) as well as * upcoming next STL generation (using a templated result member). * If none of these members is provided, then the type of the first argument is returned. FIXME, that behavior is a pretty bad hack. */ template struct result_of {}; struct has_none {int a[1];}; struct has_std_result_type {int a[2];}; struct has_tr1_result {int a[3];}; template struct unary_result_of_select {typedef ArgType type;}; template struct unary_result_of_select {typedef typename Func::result_type type;}; template struct unary_result_of_select {typedef typename Func::template result::type type;}; template struct result_of { template static has_std_result_type testFunctor(T const *, typename T::result_type const * = 0); template static has_tr1_result testFunctor(T const *, typename T::template result::type const * = 0); static has_none testFunctor(...); // note that the following indirection is needed for gcc-3.3 enum {FunctorType = sizeof(testFunctor(static_cast(0)))}; typedef typename unary_result_of_select::type type; }; template struct binary_result_of_select {typedef ArgType0 type;}; template struct binary_result_of_select {typedef typename Func::result_type type;}; template struct binary_result_of_select {typedef typename Func::template result::type type;}; template struct result_of { template static has_std_result_type testFunctor(T const *, typename T::result_type const * = 0); template static has_tr1_result testFunctor(T const *, typename T::template result::type const * = 0); static has_none testFunctor(...); // note that the following indirection is needed for gcc-3.3 enum {FunctorType = sizeof(testFunctor(static_cast(0)))}; typedef typename binary_result_of_select::type type; }; /** \internal In short, it computes int(sqrt(\a Y)) with \a Y an integer. * Usage example: \code meta_sqrt<1023>::ret \endcode */ template Y))) > // use ?: instead of || just to shut up a stupid gcc 4.3 warning class meta_sqrt { enum { MidX = (InfX+SupX)/2, TakeInf = MidX*MidX > Y ? 1 : 0, NewInf = int(TakeInf) ? InfX : int(MidX), NewSup = int(TakeInf) ? int(MidX) : SupX }; public: enum { ret = meta_sqrt::ret }; }; template class meta_sqrt { public: enum { ret = (SupX*SupX <= Y) ? SupX : InfX }; }; /** \internal determines whether the product of two numeric types is allowed and what the return type is */ template struct scalar_product_traits; template struct scalar_product_traits { //enum { Cost = NumTraits::MulCost }; typedef T ReturnType; }; template struct scalar_product_traits > { //enum { Cost = 2*NumTraits::MulCost }; typedef std::complex ReturnType; }; template struct scalar_product_traits, T> { //enum { Cost = 2*NumTraits::MulCost }; typedef std::complex ReturnType; }; // FIXME quick workaround around current limitation of result_of // template // struct result_of(ArgType0,ArgType1)> { // typedef typename scalar_product_traits::type, typename remove_all::type>::ReturnType type; // }; template struct is_diagonal { enum { ret = false }; }; template struct is_diagonal > { enum { ret = true }; }; template struct is_diagonal > { enum { ret = true }; }; template struct is_diagonal > { enum { ret = true }; }; } // end namespace internal } // end namespace Eigen #endif // EIGEN_META_H ctbnrle.1.1.1/hdr/Eigen/src/Core/util/XprHelper.h0000775000175000017500000004162212107032422021711 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Gael Guennebaud // Copyright (C) 2006-2008 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_XPRHELPER_H #define EIGEN_XPRHELPER_H // just a workaround because GCC seems to not really like empty structs // FIXME: gcc 4.3 generates bad code when strict-aliasing is enabled // so currently we simply disable this optimization for gcc 4.3 #if (defined __GNUG__) && !((__GNUC__==4) && (__GNUC_MINOR__==3)) #define EIGEN_EMPTY_STRUCT_CTOR(X) \ EIGEN_STRONG_INLINE X() {} \ EIGEN_STRONG_INLINE X(const X& ) {} #else #define EIGEN_EMPTY_STRUCT_CTOR(X) #endif namespace Eigen { typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE DenseIndex; namespace internal { //classes inheriting no_assignment_operator don't generate a default operator=. class no_assignment_operator { private: no_assignment_operator& operator=(const no_assignment_operator&); }; /** \internal return the index type with the largest number of bits */ template struct promote_index_type { typedef typename conditional<(sizeof(I1)::type type; }; /** \internal If the template parameter Value is Dynamic, this class is just a wrapper around a T variable that * can be accessed using value() and setValue(). * Otherwise, this class is an empty structure and value() just returns the template parameter Value. */ template class variable_if_dynamic { public: EIGEN_EMPTY_STRUCT_CTOR(variable_if_dynamic) explicit variable_if_dynamic(T v) { EIGEN_ONLY_USED_FOR_DEBUG(v); assert(v == T(Value)); } static T value() { return T(Value); } void setValue(T) {} }; template class variable_if_dynamic { T m_value; variable_if_dynamic() { assert(false); } public: explicit variable_if_dynamic(T value) : m_value(value) {} T value() const { return m_value; } void setValue(T value) { m_value = value; } }; template struct functor_traits { enum { Cost = 10, PacketAccess = false }; }; template struct packet_traits; template struct unpacket_traits { typedef T type; enum {size=1}; }; template class make_proper_matrix_type { enum { IsColVector = _Cols==1 && _Rows!=1, IsRowVector = _Rows==1 && _Cols!=1, Options = IsColVector ? (_Options | ColMajor) & ~RowMajor : IsRowVector ? (_Options | RowMajor) & ~ColMajor : _Options }; public: typedef Matrix<_Scalar, _Rows, _Cols, Options, _MaxRows, _MaxCols> type; }; template class compute_matrix_flags { enum { row_major_bit = Options&RowMajor ? RowMajorBit : 0, is_dynamic_size_storage = MaxRows==Dynamic || MaxCols==Dynamic, aligned_bit = ( ((Options&DontAlign)==0) && ( #if EIGEN_ALIGN_STATICALLY ((!is_dynamic_size_storage) && (((MaxCols*MaxRows*int(sizeof(Scalar))) % 16) == 0)) #else 0 #endif || #if EIGEN_ALIGN is_dynamic_size_storage #else 0 #endif ) ) ? AlignedBit : 0, packet_access_bit = packet_traits::Vectorizable && aligned_bit ? PacketAccessBit : 0 }; public: enum { ret = LinearAccessBit | LvalueBit | DirectAccessBit | NestByRefBit | packet_access_bit | row_major_bit | aligned_bit }; }; template struct size_at_compile_time { enum { ret = (_Rows==Dynamic || _Cols==Dynamic) ? Dynamic : _Rows * _Cols }; }; /* plain_matrix_type : the difference from eval is that plain_matrix_type is always a plain matrix type, * whereas eval is a const reference in the case of a matrix */ template::StorageKind> struct plain_matrix_type; template struct plain_matrix_type_dense; template struct plain_matrix_type { typedef typename plain_matrix_type_dense::XprKind>::type type; }; template struct plain_matrix_type_dense { typedef Matrix::Scalar, traits::RowsAtCompileTime, traits::ColsAtCompileTime, AutoAlign | (traits::Flags&RowMajorBit ? RowMajor : ColMajor), traits::MaxRowsAtCompileTime, traits::MaxColsAtCompileTime > type; }; template struct plain_matrix_type_dense { typedef Array::Scalar, traits::RowsAtCompileTime, traits::ColsAtCompileTime, AutoAlign | (traits::Flags&RowMajorBit ? RowMajor : ColMajor), traits::MaxRowsAtCompileTime, traits::MaxColsAtCompileTime > type; }; /* eval : the return type of eval(). For matrices, this is just a const reference * in order to avoid a useless copy */ template::StorageKind> struct eval; template struct eval { typedef typename plain_matrix_type::type type; // typedef typename T::PlainObject type; // typedef T::Matrix::Scalar, // traits::RowsAtCompileTime, // traits::ColsAtCompileTime, // AutoAlign | (traits::Flags&RowMajorBit ? RowMajor : ColMajor), // traits::MaxRowsAtCompileTime, // traits::MaxColsAtCompileTime // > type; }; // for matrices, no need to evaluate, just use a const reference to avoid a useless copy template struct eval, Dense> { typedef const Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>& type; }; template struct eval, Dense> { typedef const Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>& type; }; /* plain_matrix_type_column_major : same as plain_matrix_type but guaranteed to be column-major */ template struct plain_matrix_type_column_major { enum { Rows = traits::RowsAtCompileTime, Cols = traits::ColsAtCompileTime, MaxRows = traits::MaxRowsAtCompileTime, MaxCols = traits::MaxColsAtCompileTime }; typedef Matrix::Scalar, Rows, Cols, (MaxRows==1&&MaxCols!=1) ? RowMajor : ColMajor, MaxRows, MaxCols > type; }; /* plain_matrix_type_row_major : same as plain_matrix_type but guaranteed to be row-major */ template struct plain_matrix_type_row_major { enum { Rows = traits::RowsAtCompileTime, Cols = traits::ColsAtCompileTime, MaxRows = traits::MaxRowsAtCompileTime, MaxCols = traits::MaxColsAtCompileTime }; typedef Matrix::Scalar, Rows, Cols, (MaxCols==1&&MaxRows!=1) ? RowMajor : ColMajor, MaxRows, MaxCols > type; }; // we should be able to get rid of this one too template struct must_nest_by_value { enum { ret = false }; }; /** \internal The reference selector for template expressions. The idea is that we don't * need to use references for expressions since they are light weight proxy * objects which should generate no copying overhead. */ template struct ref_selector { typedef typename conditional< bool(traits::Flags & NestByRefBit), T const&, const T >::type type; }; /** \internal Adds the const qualifier on the value-type of T2 if and only if T1 is a const type */ template struct transfer_constness { typedef typename conditional< bool(internal::is_const::value), typename internal::add_const_on_value_type::type, T2 >::type type; }; /** \internal Determines how a given expression should be nested into another one. * For example, when you do a * (b+c), Eigen will determine how the expression b+c should be * nested into the bigger product expression. The choice is between nesting the expression b+c as-is, or * evaluating that expression b+c into a temporary variable d, and nest d so that the resulting expression is * a*d. Evaluating can be beneficial for example if every coefficient access in the resulting expression causes * many coefficient accesses in the nested expressions -- as is the case with matrix product for example. * * \param T the type of the expression being nested * \param n the number of coefficient accesses in the nested expression for each coefficient access in the bigger expression. * * Note that if no evaluation occur, then the constness of T is preserved. * * Example. Suppose that a, b, and c are of type Matrix3d. The user forms the expression a*(b+c). * b+c is an expression "sum of matrices", which we will denote by S. In order to determine how to nest it, * the Product expression uses: nested::ret, which turns out to be Matrix3d because the internal logic of * nested determined that in this case it was better to evaluate the expression b+c into a temporary. On the other hand, * since a is of type Matrix3d, the Product expression nests it as nested::ret, which turns out to be * const Matrix3d&, because the internal logic of nested determined that since a was already a matrix, there was no point * in copying it into another matrix. */ template::type> struct nested { enum { // for the purpose of this test, to keep it reasonably simple, we arbitrarily choose a value of Dynamic values. // the choice of 10000 makes it larger than any practical fixed value and even most dynamic values. // in extreme cases where these assumptions would be wrong, we would still at worst suffer performance issues // (poor choice of temporaries). // it's important that this value can still be squared without integer overflowing. DynamicAsInteger = 10000, ScalarReadCost = NumTraits::Scalar>::ReadCost, ScalarReadCostAsInteger = ScalarReadCost == Dynamic ? DynamicAsInteger : ScalarReadCost, CoeffReadCost = traits::CoeffReadCost, CoeffReadCostAsInteger = CoeffReadCost == Dynamic ? DynamicAsInteger : CoeffReadCost, NAsInteger = n == Dynamic ? int(DynamicAsInteger) : n, CostEvalAsInteger = (NAsInteger+1) * ScalarReadCostAsInteger + CoeffReadCostAsInteger, CostNoEvalAsInteger = NAsInteger * CoeffReadCostAsInteger }; typedef typename conditional< ( (int(traits::Flags) & EvalBeforeNestingBit) || int(CostEvalAsInteger) < int(CostNoEvalAsInteger) ), PlainObject, typename ref_selector::type >::type type; }; template T* const_cast_ptr(const T* ptr) { return const_cast(ptr); } template::XprKind> struct dense_xpr_base { /* dense_xpr_base should only ever be used on dense expressions, thus falling either into the MatrixXpr or into the ArrayXpr cases */ }; template struct dense_xpr_base { typedef MatrixBase type; }; template struct dense_xpr_base { typedef ArrayBase type; }; /** \internal Helper base class to add a scalar multiple operator * overloads for complex types */ template::value > struct special_scalar_op_base : public DenseCoeffsBase { // dummy operator* so that the // "using special_scalar_op_base::operator*" compiles void operator*() const; }; template struct special_scalar_op_base : public DenseCoeffsBase { const CwiseUnaryOp, Derived> operator*(const OtherScalar& scalar) const { return CwiseUnaryOp, Derived> (*static_cast(this), scalar_multiple2_op(scalar)); } inline friend const CwiseUnaryOp, Derived> operator*(const OtherScalar& scalar, const Derived& matrix) { return static_cast(matrix).operator*(scalar); } }; template struct cast_return_type { typedef typename XprType::Scalar CurrentScalarType; typedef typename remove_all::type _CastType; typedef typename _CastType::Scalar NewScalarType; typedef typename conditional::value, const XprType&,CastType>::type type; }; template struct promote_storage_type; template struct promote_storage_type { typedef A ret; }; /** \internal gives the plain matrix or array type to store a row/column/diagonal of a matrix type. * \param Scalar optional parameter allowing to pass a different scalar type than the one of the MatrixType. */ template struct plain_row_type { typedef Matrix MatrixRowType; typedef Array ArrayRowType; typedef typename conditional< is_same< typename traits::XprKind, MatrixXpr >::value, MatrixRowType, ArrayRowType >::type type; }; template struct plain_col_type { typedef Matrix MatrixColType; typedef Array ArrayColType; typedef typename conditional< is_same< typename traits::XprKind, MatrixXpr >::value, MatrixColType, ArrayColType >::type type; }; template struct plain_diag_type { enum { diag_size = EIGEN_SIZE_MIN_PREFER_DYNAMIC(ExpressionType::RowsAtCompileTime, ExpressionType::ColsAtCompileTime), max_diag_size = EIGEN_SIZE_MIN_PREFER_FIXED(ExpressionType::MaxRowsAtCompileTime, ExpressionType::MaxColsAtCompileTime) }; typedef Matrix MatrixDiagType; typedef Array ArrayDiagType; typedef typename conditional< is_same< typename traits::XprKind, MatrixXpr >::value, MatrixDiagType, ArrayDiagType >::type type; }; template struct is_lvalue { enum { value = !bool(is_const::value) && bool(traits::Flags & LvalueBit) }; }; } // end namespace internal } // end namespace Eigen #endif // EIGEN_XPRHELPER_H ctbnrle.1.1.1/hdr/Eigen/src/Core/util/StaticAssert.h0000775000175000017500000002334212107032422022410 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Gael Guennebaud // Copyright (C) 2008 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_STATIC_ASSERT_H #define EIGEN_STATIC_ASSERT_H /* Some notes on Eigen's static assertion mechanism: * * - in EIGEN_STATIC_ASSERT(CONDITION,MSG) the parameter CONDITION must be a compile time boolean * expression, and MSG an enum listed in struct internal::static_assertion * * - define EIGEN_NO_STATIC_ASSERT to disable them (and save compilation time) * in that case, the static assertion is converted to the following runtime assert: * eigen_assert(CONDITION && "MSG") * * - currently EIGEN_STATIC_ASSERT can only be used in function scope * */ #ifndef EIGEN_NO_STATIC_ASSERT #if defined(__GXX_EXPERIMENTAL_CXX0X__) || (defined(_MSC_VER) && (_MSC_VER >= 1600)) // if native static_assert is enabled, let's use it #define EIGEN_STATIC_ASSERT(X,MSG) static_assert(X,#MSG); #else // not CXX0X namespace Eigen { namespace internal { template struct static_assertion {}; template<> struct static_assertion { enum { YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX, YOU_MIXED_VECTORS_OF_DIFFERENT_SIZES, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES, THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE, THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE, THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE, YOU_MADE_A_PROGRAMMING_MISTAKE, EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT, EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE, YOU_CALLED_A_FIXED_SIZE_METHOD_ON_A_DYNAMIC_SIZE_MATRIX_OR_VECTOR, YOU_CALLED_A_DYNAMIC_SIZE_METHOD_ON_A_FIXED_SIZE_MATRIX_OR_VECTOR, UNALIGNED_LOAD_AND_STORE_OPERATIONS_UNIMPLEMENTED_ON_ALTIVEC, THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES, FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED, NUMERIC_TYPE_MUST_BE_REAL, COEFFICIENT_WRITE_ACCESS_TO_SELFADJOINT_NOT_SUPPORTED, WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED, THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE, INVALID_MATRIX_PRODUCT, INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS, INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION, YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES, THIS_METHOD_IS_ONLY_FOR_ROW_MAJOR_MATRICES, INVALID_MATRIX_TEMPLATE_PARAMETERS, INVALID_MATRIXBASE_TEMPLATE_PARAMETERS, BOTH_MATRICES_MUST_HAVE_THE_SAME_STORAGE_ORDER, THIS_METHOD_IS_ONLY_FOR_DIAGONAL_MATRIX, THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE, THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_WITH_DIRECT_MEMORY_ACCESS_SUCH_AS_MAP_OR_PLAIN_MATRICES, YOU_ALREADY_SPECIFIED_THIS_STRIDE, INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION, THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD, PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1, THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS, YOU_CANNOT_MIX_ARRAYS_AND_MATRICES, YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION, THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY, YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT, THIS_METHOD_IS_ONLY_FOR_1x1_EXPRESSIONS, THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL, THIS_METHOD_IS_ONLY_FOR_ARRAYS_NOT_MATRICES, YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED, YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED, THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE, THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH }; }; } // end namespace internal } // end namespace Eigen // Specialized implementation for MSVC to avoid "conditional // expression is constant" warnings. This implementation doesn't // appear to work under GCC, hence the multiple implementations. #ifdef _MSC_VER #define EIGEN_STATIC_ASSERT(CONDITION,MSG) \ {Eigen::internal::static_assertion::MSG;} #else #define EIGEN_STATIC_ASSERT(CONDITION,MSG) \ if (Eigen::internal::static_assertion::MSG) {} #endif #endif // not CXX0X #else // EIGEN_NO_STATIC_ASSERT #define EIGEN_STATIC_ASSERT(CONDITION,MSG) eigen_assert((CONDITION) && #MSG); #endif // EIGEN_NO_STATIC_ASSERT // static assertion failing if the type \a TYPE is not a vector type #define EIGEN_STATIC_ASSERT_VECTOR_ONLY(TYPE) \ EIGEN_STATIC_ASSERT(TYPE::IsVectorAtCompileTime, \ YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX) // static assertion failing if the type \a TYPE is not fixed-size #define EIGEN_STATIC_ASSERT_FIXED_SIZE(TYPE) \ EIGEN_STATIC_ASSERT(TYPE::SizeAtCompileTime!=Eigen::Dynamic, \ YOU_CALLED_A_FIXED_SIZE_METHOD_ON_A_DYNAMIC_SIZE_MATRIX_OR_VECTOR) // static assertion failing if the type \a TYPE is not dynamic-size #define EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(TYPE) \ EIGEN_STATIC_ASSERT(TYPE::SizeAtCompileTime==Eigen::Dynamic, \ YOU_CALLED_A_DYNAMIC_SIZE_METHOD_ON_A_FIXED_SIZE_MATRIX_OR_VECTOR) // static assertion failing if the type \a TYPE is not a vector type of the given size #define EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(TYPE, SIZE) \ EIGEN_STATIC_ASSERT(TYPE::IsVectorAtCompileTime && TYPE::SizeAtCompileTime==SIZE, \ THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE) // static assertion failing if the type \a TYPE is not a vector type of the given size #define EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(TYPE, ROWS, COLS) \ EIGEN_STATIC_ASSERT(TYPE::RowsAtCompileTime==ROWS && TYPE::ColsAtCompileTime==COLS, \ THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE) // static assertion failing if the two vector expression types are not compatible (same fixed-size or dynamic size) #define EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(TYPE0,TYPE1) \ EIGEN_STATIC_ASSERT( \ (int(TYPE0::SizeAtCompileTime)==Eigen::Dynamic \ || int(TYPE1::SizeAtCompileTime)==Eigen::Dynamic \ || int(TYPE0::SizeAtCompileTime)==int(TYPE1::SizeAtCompileTime)),\ YOU_MIXED_VECTORS_OF_DIFFERENT_SIZES) #define EIGEN_PREDICATE_SAME_MATRIX_SIZE(TYPE0,TYPE1) \ ( \ (int(TYPE0::SizeAtCompileTime)==0 && int(TYPE1::SizeAtCompileTime)==0) \ || (\ (int(TYPE0::RowsAtCompileTime)==Eigen::Dynamic \ || int(TYPE1::RowsAtCompileTime)==Eigen::Dynamic \ || int(TYPE0::RowsAtCompileTime)==int(TYPE1::RowsAtCompileTime)) \ && (int(TYPE0::ColsAtCompileTime)==Eigen::Dynamic \ || int(TYPE1::ColsAtCompileTime)==Eigen::Dynamic \ || int(TYPE0::ColsAtCompileTime)==int(TYPE1::ColsAtCompileTime))\ ) \ ) #ifdef EIGEN2_SUPPORT #define EIGEN_STATIC_ASSERT_NON_INTEGER(TYPE) \ eigen_assert(!NumTraits::IsInteger); #else #define EIGEN_STATIC_ASSERT_NON_INTEGER(TYPE) \ EIGEN_STATIC_ASSERT(!NumTraits::IsInteger, THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES) #endif // static assertion failing if it is guaranteed at compile-time that the two matrix expression types have different sizes #define EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(TYPE0,TYPE1) \ EIGEN_STATIC_ASSERT( \ EIGEN_PREDICATE_SAME_MATRIX_SIZE(TYPE0,TYPE1),\ YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES) #define EIGEN_STATIC_ASSERT_SIZE_1x1(TYPE) \ EIGEN_STATIC_ASSERT((TYPE::RowsAtCompileTime == 1 || TYPE::RowsAtCompileTime == Dynamic) && \ (TYPE::ColsAtCompileTime == 1 || TYPE::ColsAtCompileTime == Dynamic), \ THIS_METHOD_IS_ONLY_FOR_1x1_EXPRESSIONS) #define EIGEN_STATIC_ASSERT_LVALUE(Derived) \ EIGEN_STATIC_ASSERT(internal::is_lvalue::value, \ THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY) #define EIGEN_STATIC_ASSERT_ARRAYXPR(Derived) \ EIGEN_STATIC_ASSERT((internal::is_same::XprKind, ArrayXpr>::value), \ THIS_METHOD_IS_ONLY_FOR_ARRAYS_NOT_MATRICES) #define EIGEN_STATIC_ASSERT_SAME_XPR_KIND(Derived1, Derived2) \ EIGEN_STATIC_ASSERT((internal::is_same::XprKind, \ typename internal::traits::XprKind \ >::value), \ YOU_CANNOT_MIX_ARRAYS_AND_MATRICES) #endif // EIGEN_STATIC_ASSERT_H ctbnrle.1.1.1/hdr/Eigen/src/Core/util/CMakeLists.txt0000775000175000017500000000023512107032422022362 0ustar csheltoncsheltonFILE(GLOB Eigen_Core_util_SRCS "*.h") INSTALL(FILES ${Eigen_Core_util_SRCS} DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/util COMPONENT Devel ) ctbnrle.1.1.1/hdr/Eigen/src/Core/util/ForwardDeclarations.h0000775000175000017500000003410012107032422023726 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2007-2010 Benoit Jacob // Copyright (C) 2008-2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_FORWARDDECLARATIONS_H #define EIGEN_FORWARDDECLARATIONS_H namespace Eigen { namespace internal { template struct traits; // here we say once and for all that traits == traits // When constness must affect traits, it has to be constness on template parameters on which T itself depends. // For example, traits > != traits >, but // traits > == traits > template struct traits : traits {}; template struct has_direct_access { enum { ret = (traits::Flags & DirectAccessBit) ? 1 : 0 }; }; template struct accessors_level { enum { has_direct_access = (traits::Flags & DirectAccessBit) ? 1 : 0, has_write_access = (traits::Flags & LvalueBit) ? 1 : 0, value = has_direct_access ? (has_write_access ? DirectWriteAccessors : DirectAccessors) : (has_write_access ? WriteAccessors : ReadOnlyAccessors) }; }; } // end namespace internal template struct NumTraits; template struct EigenBase; template class DenseBase; template class PlainObjectBase; template::value > class DenseCoeffsBase; template class Matrix; template class MatrixBase; template class ArrayBase; template class Flagged; template class StorageBase > class NoAlias; template class NestByValue; template class ForceAlignedAccess; template class SwapWrapper; template::ret> class Block; template class VectorBlock; template class Transpose; template class Conjugate; template class CwiseNullaryOp; template class CwiseUnaryOp; template class CwiseUnaryView; template class CwiseBinaryOp; template class SelfCwiseBinaryOp; template class ProductBase; template class GeneralProduct; template class CoeffBasedProduct; template class DiagonalBase; template class DiagonalWrapper; template class DiagonalMatrix; template class DiagonalProduct; template class Diagonal; template class PermutationMatrix; template class Transpositions; template class PermutationBase; template class TranspositionsBase; template class PermutationWrapper; template class TranspositionsWrapper; template::has_write_access ? WriteAccessors : ReadOnlyAccessors > class MapBase; template class Stride; template > class Map; template class TriangularBase; template class TriangularView; template class SelfAdjointView; template class SparseView; template class WithFormat; template struct CommaInitializer; template class ReturnByValue; template class ArrayWrapper; template class MatrixWrapper; namespace internal { template struct solve_retval_base; template struct solve_retval; template struct kernel_retval_base; template struct kernel_retval; template struct image_retval_base; template struct image_retval; } // end namespace internal namespace internal { template class BandMatrix; } namespace internal { template struct product_type; } template::value> struct ProductReturnType; // this is a workaround for sun CC template struct LazyProductReturnType; namespace internal { // Provides scalar/packet-wise product and product with accumulation // with optional conjugation of the arguments. template struct conj_helper; template struct scalar_sum_op; template struct scalar_difference_op; template struct scalar_conj_product_op; template struct scalar_quotient_op; template struct scalar_opposite_op; template struct scalar_conjugate_op; template struct scalar_real_op; template struct scalar_imag_op; template struct scalar_abs_op; template struct scalar_abs2_op; template struct scalar_sqrt_op; template struct scalar_exp_op; template struct scalar_log_op; template struct scalar_cos_op; template struct scalar_sin_op; template struct scalar_acos_op; template struct scalar_asin_op; template struct scalar_tan_op; template struct scalar_pow_op; template struct scalar_inverse_op; template struct scalar_square_op; template struct scalar_cube_op; template struct scalar_cast_op; template struct scalar_multiple_op; template struct scalar_quotient1_op; template struct scalar_min_op; template struct scalar_max_op; template struct scalar_random_op; template struct scalar_add_op; template struct scalar_constant_op; template struct scalar_identity_op; template struct scalar_product_op; template struct scalar_multiple2_op; } // end namespace internal struct IOFormat; // Array module template class Array; template class Select; template class PartialReduxExpr; template class VectorwiseOp; template class Replicate; template class Reverse; template class FullPivLU; template class PartialPivLU; namespace internal { template struct inverse_impl; } template class HouseholderQR; template class ColPivHouseholderQR; template class FullPivHouseholderQR; template class JacobiSVD; template class LLT; template class LDLT; template class HouseholderSequence; template class JacobiRotation; // Geometry module: template class RotationBase; template class Cross; template class QuaternionBase; template class Rotation2D; template class AngleAxis; template class Translation; #ifdef EIGEN2_SUPPORT template class eigen2_RotationBase; template class eigen2_Cross; template class eigen2_Quaternion; template class eigen2_Rotation2D; template class eigen2_AngleAxis; template class eigen2_Transform; template class eigen2_ParametrizedLine; template class eigen2_Hyperplane; template class eigen2_Translation; template class eigen2_Scaling; #endif #if EIGEN2_SUPPORT_STAGE < STAGE20_RESOLVE_API_CONFLICTS template class Quaternion; template class Transform; template class ParametrizedLine; template class Hyperplane; template class Scaling; #endif #if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS template class Quaternion; template class Transform; template class ParametrizedLine; template class Hyperplane; template class UniformScaling; template class Homogeneous; #endif // MatrixFunctions module template struct MatrixExponentialReturnValue; template class MatrixFunctionReturnValue; template class MatrixSquareRootReturnValue; template class MatrixLogarithmReturnValue; namespace internal { template struct stem_function { typedef std::complex::Real> ComplexScalar; typedef ComplexScalar type(ComplexScalar, int); }; } #ifdef EIGEN2_SUPPORT template class Cwise; template class Minor; template class LU; template class QR; template class SVD; namespace internal { template struct eigen2_part_return_type; } #endif } // end namespace Eigen #endif // EIGEN_FORWARDDECLARATIONS_H ctbnrle.1.1.1/hdr/Eigen/src/Core/util/MKL_support.h0000775000175000017500000000722512107032422022220 0ustar csheltoncshelton/* Copyright (c) 2011, Intel Corporation. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Intel Corporation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ******************************************************************************** * Content : Eigen bindings to Intel(R) MKL * Include file with common MKL declarations ******************************************************************************** */ #ifndef EIGEN_MKL_SUPPORT_H #define EIGEN_MKL_SUPPORT_H #ifdef EIGEN_USE_MKL_ALL #ifndef EIGEN_USE_BLAS #define EIGEN_USE_BLAS #endif #ifndef EIGEN_USE_LAPACKE #define EIGEN_USE_LAPACKE #endif #ifndef EIGEN_USE_MKL_VML #define EIGEN_USE_MKL_VML #endif #endif #ifdef EIGEN_USE_LAPACKE_STRICT #define EIGEN_USE_LAPACKE #endif #if defined(EIGEN_USE_BLAS) || defined(EIGEN_USE_LAPACKE) || defined(EIGEN_USE_MKL_VML) #define EIGEN_USE_MKL #endif #if defined EIGEN_USE_MKL #include #include #define EIGEN_MKL_VML_THRESHOLD 128 namespace Eigen { typedef std::complex dcomplex; typedef std::complex scomplex; namespace internal { template static inline void assign_scalar_eig2mkl(MKLType& mklScalar, const EigenType& eigenScalar) { mklScalar=eigenScalar; } template static inline void assign_conj_scalar_eig2mkl(MKLType& mklScalar, const EigenType& eigenScalar) { mklScalar=eigenScalar; } template <> inline void assign_scalar_eig2mkl(MKL_Complex16& mklScalar, const dcomplex& eigenScalar) { mklScalar.real=eigenScalar.real(); mklScalar.imag=eigenScalar.imag(); } template <> inline void assign_scalar_eig2mkl(MKL_Complex8& mklScalar, const scomplex& eigenScalar) { mklScalar.real=eigenScalar.real(); mklScalar.imag=eigenScalar.imag(); } template <> inline void assign_conj_scalar_eig2mkl(MKL_Complex16& mklScalar, const dcomplex& eigenScalar) { mklScalar.real=eigenScalar.real(); mklScalar.imag=-eigenScalar.imag(); } template <> inline void assign_conj_scalar_eig2mkl(MKL_Complex8& mklScalar, const scomplex& eigenScalar) { mklScalar.real=eigenScalar.real(); mklScalar.imag=-eigenScalar.imag(); } } // end namespace internal } // end namespace Eigen #endif #endif // EIGEN_MKL_SUPPORT_H ctbnrle.1.1.1/hdr/Eigen/src/Core/util/ReenableStupidWarnings.h0000775000175000017500000000050512107032422024412 0ustar csheltoncshelton#ifdef EIGEN_WARNINGS_DISABLED #undef EIGEN_WARNINGS_DISABLED #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS #ifdef _MSC_VER #pragma warning( pop ) #elif defined __INTEL_COMPILER #pragma warning pop #elif defined __clang__ #pragma clang diagnostic pop #endif #endif #endif // EIGEN_WARNINGS_DISABLED ctbnrle.1.1.1/hdr/Eigen/src/Core/util/BlasUtil.h0000775000175000017500000002525112107032422021517 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009-2010 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_BLASUTIL_H #define EIGEN_BLASUTIL_H // This file contains many lightweight helper classes used to // implement and control fast level 2 and level 3 BLAS-like routines. namespace Eigen { namespace internal { // forward declarations template struct gebp_kernel; template struct gemm_pack_rhs; template struct gemm_pack_lhs; template< typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs, int ResStorageOrder> struct general_matrix_matrix_product; template struct general_matrix_vector_product; template struct conj_if; template<> struct conj_if { template inline T operator()(const T& x) { return conj(x); } template inline T pconj(const T& x) { return internal::pconj(x); } }; template<> struct conj_if { template inline const T& operator()(const T& x) { return x; } template inline const T& pconj(const T& x) { return x; } }; template struct conj_helper { EIGEN_STRONG_INLINE Scalar pmadd(const Scalar& x, const Scalar& y, const Scalar& c) const { return internal::pmadd(x,y,c); } EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const Scalar& y) const { return internal::pmul(x,y); } }; template struct conj_helper, std::complex, false,true> { typedef std::complex Scalar; EIGEN_STRONG_INLINE Scalar pmadd(const Scalar& x, const Scalar& y, const Scalar& c) const { return c + pmul(x,y); } EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const Scalar& y) const { return Scalar(real(x)*real(y) + imag(x)*imag(y), imag(x)*real(y) - real(x)*imag(y)); } }; template struct conj_helper, std::complex, true,false> { typedef std::complex Scalar; EIGEN_STRONG_INLINE Scalar pmadd(const Scalar& x, const Scalar& y, const Scalar& c) const { return c + pmul(x,y); } EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const Scalar& y) const { return Scalar(real(x)*real(y) + imag(x)*imag(y), real(x)*imag(y) - imag(x)*real(y)); } }; template struct conj_helper, std::complex, true,true> { typedef std::complex Scalar; EIGEN_STRONG_INLINE Scalar pmadd(const Scalar& x, const Scalar& y, const Scalar& c) const { return c + pmul(x,y); } EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const Scalar& y) const { return Scalar(real(x)*real(y) - imag(x)*imag(y), - real(x)*imag(y) - imag(x)*real(y)); } }; template struct conj_helper, RealScalar, Conj,false> { typedef std::complex Scalar; EIGEN_STRONG_INLINE Scalar pmadd(const Scalar& x, const RealScalar& y, const Scalar& c) const { return padd(c, pmul(x,y)); } EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const RealScalar& y) const { return conj_if()(x)*y; } }; template struct conj_helper, false,Conj> { typedef std::complex Scalar; EIGEN_STRONG_INLINE Scalar pmadd(const RealScalar& x, const Scalar& y, const Scalar& c) const { return padd(c, pmul(x,y)); } EIGEN_STRONG_INLINE Scalar pmul(const RealScalar& x, const Scalar& y) const { return x*conj_if()(y); } }; template struct get_factor { static EIGEN_STRONG_INLINE To run(const From& x) { return x; } }; template struct get_factor::Real> { static EIGEN_STRONG_INLINE typename NumTraits::Real run(const Scalar& x) { return real(x); } }; // Lightweight helper class to access matrix coefficients. // Yes, this is somehow redundant with Map<>, but this version is much much lighter, // and so I hope better compilation performance (time and code quality). template class blas_data_mapper { public: blas_data_mapper(Scalar* data, Index stride) : m_data(data), m_stride(stride) {} EIGEN_STRONG_INLINE Scalar& operator()(Index i, Index j) { return m_data[StorageOrder==RowMajor ? j + i*m_stride : i + j*m_stride]; } protected: Scalar* EIGEN_RESTRICT m_data; Index m_stride; }; // lightweight helper class to access matrix coefficients (const version) template class const_blas_data_mapper { public: const_blas_data_mapper(const Scalar* data, Index stride) : m_data(data), m_stride(stride) {} EIGEN_STRONG_INLINE const Scalar& operator()(Index i, Index j) const { return m_data[StorageOrder==RowMajor ? j + i*m_stride : i + j*m_stride]; } protected: const Scalar* EIGEN_RESTRICT m_data; Index m_stride; }; /* Helper class to analyze the factors of a Product expression. * In particular it allows to pop out operator-, scalar multiples, * and conjugate */ template struct blas_traits { typedef typename traits::Scalar Scalar; typedef const XprType& ExtractType; typedef XprType _ExtractType; enum { IsComplex = NumTraits::IsComplex, IsTransposed = false, NeedToConjugate = false, HasUsableDirectAccess = ( (int(XprType::Flags)&DirectAccessBit) && ( bool(XprType::IsVectorAtCompileTime) || int(inner_stride_at_compile_time::ret) == 1) ) ? 1 : 0 }; typedef typename conditional::type DirectLinearAccessType; static inline ExtractType extract(const XprType& x) { return x; } static inline const Scalar extractScalarFactor(const XprType&) { return Scalar(1); } }; // pop conjugate template struct blas_traits, NestedXpr> > : blas_traits { typedef blas_traits Base; typedef CwiseUnaryOp, NestedXpr> XprType; typedef typename Base::ExtractType ExtractType; enum { IsComplex = NumTraits::IsComplex, NeedToConjugate = Base::NeedToConjugate ? 0 : IsComplex }; static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); } static inline Scalar extractScalarFactor(const XprType& x) { return conj(Base::extractScalarFactor(x.nestedExpression())); } }; // pop scalar multiple template struct blas_traits, NestedXpr> > : blas_traits { typedef blas_traits Base; typedef CwiseUnaryOp, NestedXpr> XprType; typedef typename Base::ExtractType ExtractType; static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); } static inline Scalar extractScalarFactor(const XprType& x) { return x.functor().m_other * Base::extractScalarFactor(x.nestedExpression()); } }; // pop opposite template struct blas_traits, NestedXpr> > : blas_traits { typedef blas_traits Base; typedef CwiseUnaryOp, NestedXpr> XprType; typedef typename Base::ExtractType ExtractType; static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); } static inline Scalar extractScalarFactor(const XprType& x) { return - Base::extractScalarFactor(x.nestedExpression()); } }; // pop/push transpose template struct blas_traits > : blas_traits { typedef typename NestedXpr::Scalar Scalar; typedef blas_traits Base; typedef Transpose XprType; typedef Transpose ExtractType; // const to get rid of a compile error; anyway blas traits are only used on the RHS typedef Transpose _ExtractType; typedef typename conditional::type DirectLinearAccessType; enum { IsTransposed = Base::IsTransposed ? 0 : 1 }; static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); } static inline Scalar extractScalarFactor(const XprType& x) { return Base::extractScalarFactor(x.nestedExpression()); } }; template struct blas_traits : blas_traits {}; template::HasUsableDirectAccess> struct extract_data_selector { static const typename T::Scalar* run(const T& m) { return blas_traits::extract(m).data(); } }; template struct extract_data_selector { static typename T::Scalar* run(const T&) { return 0; } }; template const typename T::Scalar* extract_data(const T& m) { return extract_data_selector::run(m); } } // end namespace internal } // end namespace Eigen #endif // EIGEN_BLASUTIL_H ctbnrle.1.1.1/hdr/Eigen/src/Core/util/Constants.h0000775000175000017500000004221212107032422021750 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2009 Gael Guennebaud // Copyright (C) 2007-2009 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_CONSTANTS_H #define EIGEN_CONSTANTS_H namespace Eigen { /** This value means that a quantity is not known at compile-time, and that instead the value is * stored in some runtime variable. * * Changing the value of Dynamic breaks the ABI, as Dynamic is often used as a template parameter for Matrix. */ const int Dynamic = -1; /** This value means +Infinity; it is currently used only as the p parameter to MatrixBase::lpNorm(). * The value Infinity there means the L-infinity norm. */ const int Infinity = -1; /** \defgroup flags Flags * \ingroup Core_Module * * These are the possible bits which can be OR'ed to constitute the flags of a matrix or * expression. * * It is important to note that these flags are a purely compile-time notion. They are a compile-time property of * an expression type, implemented as enum's. They are not stored in memory at runtime, and they do not incur any * runtime overhead. * * \sa MatrixBase::Flags */ /** \ingroup flags * * for a matrix, this means that the storage order is row-major. * If this bit is not set, the storage order is column-major. * For an expression, this determines the storage order of * the matrix created by evaluation of that expression. * \sa \ref TopicStorageOrders */ const unsigned int RowMajorBit = 0x1; /** \ingroup flags * * means the expression should be evaluated by the calling expression */ const unsigned int EvalBeforeNestingBit = 0x2; /** \ingroup flags * * means the expression should be evaluated before any assignment */ const unsigned int EvalBeforeAssigningBit = 0x4; /** \ingroup flags * * Short version: means the expression might be vectorized * * Long version: means that the coefficients can be handled by packets * and start at a memory location whose alignment meets the requirements * of the present CPU architecture for optimized packet access. In the fixed-size * case, there is the additional condition that it be possible to access all the * coefficients by packets (this implies the requirement that the size be a multiple of 16 bytes, * and that any nontrivial strides don't break the alignment). In the dynamic-size case, * there is no such condition on the total size and strides, so it might not be possible to access * all coeffs by packets. * * \note This bit can be set regardless of whether vectorization is actually enabled. * To check for actual vectorizability, see \a ActualPacketAccessBit. */ const unsigned int PacketAccessBit = 0x8; #ifdef EIGEN_VECTORIZE /** \ingroup flags * * If vectorization is enabled (EIGEN_VECTORIZE is defined) this constant * is set to the value \a PacketAccessBit. * * If vectorization is not enabled (EIGEN_VECTORIZE is not defined) this constant * is set to the value 0. */ const unsigned int ActualPacketAccessBit = PacketAccessBit; #else const unsigned int ActualPacketAccessBit = 0x0; #endif /** \ingroup flags * * Short version: means the expression can be seen as 1D vector. * * Long version: means that one can access the coefficients * of this expression by coeff(int), and coeffRef(int) in the case of a lvalue expression. These * index-based access methods are guaranteed * to not have to do any runtime computation of a (row, col)-pair from the index, so that it * is guaranteed that whenever it is available, index-based access is at least as fast as * (row,col)-based access. Expressions for which that isn't possible don't have the LinearAccessBit. * * If both PacketAccessBit and LinearAccessBit are set, then the * packets of this expression can be accessed by packet(int), and writePacket(int) in the case of a * lvalue expression. * * Typically, all vector expressions have the LinearAccessBit, but there is one exception: * Product expressions don't have it, because it would be troublesome for vectorization, even when the * Product is a vector expression. Thus, vector Product expressions allow index-based coefficient access but * not index-based packet access, so they don't have the LinearAccessBit. */ const unsigned int LinearAccessBit = 0x10; /** \ingroup flags * * Means the expression has a coeffRef() method, i.e. is writable as its individual coefficients are directly addressable. * This rules out read-only expressions. * * Note that DirectAccessBit and LvalueBit are mutually orthogonal, as there are examples of expression having one but note * the other: * \li writable expressions that don't have a very simple memory layout as a strided array, have LvalueBit but not DirectAccessBit * \li Map-to-const expressions, for example Map, have DirectAccessBit but not LvalueBit * * Expressions having LvalueBit also have their coeff() method returning a const reference instead of returning a new value. */ const unsigned int LvalueBit = 0x20; /** \ingroup flags * * Means that the underlying array of coefficients can be directly accessed as a plain strided array. The memory layout * of the array of coefficients must be exactly the natural one suggested by rows(), cols(), * outerStride(), innerStride(), and the RowMajorBit. This rules out expressions such as Diagonal, whose coefficients, * though referencable, do not have such a regular memory layout. * * See the comment on LvalueBit for an explanation of how LvalueBit and DirectAccessBit are mutually orthogonal. */ const unsigned int DirectAccessBit = 0x40; /** \ingroup flags * * means the first coefficient packet is guaranteed to be aligned */ const unsigned int AlignedBit = 0x80; const unsigned int NestByRefBit = 0x100; // list of flags that are inherited by default const unsigned int HereditaryBits = RowMajorBit | EvalBeforeNestingBit | EvalBeforeAssigningBit; /** \defgroup enums Enumerations * \ingroup Core_Module * * Various enumerations used in %Eigen. Many of these are used as template parameters. */ /** \ingroup enums * Enum containing possible values for the \p Mode parameter of * MatrixBase::selfadjointView() and MatrixBase::triangularView(). */ enum { /** View matrix as a lower triangular matrix. */ Lower=0x1, /** View matrix as an upper triangular matrix. */ Upper=0x2, /** %Matrix has ones on the diagonal; to be used in combination with #Lower or #Upper. */ UnitDiag=0x4, /** %Matrix has zeros on the diagonal; to be used in combination with #Lower or #Upper. */ ZeroDiag=0x8, /** View matrix as a lower triangular matrix with ones on the diagonal. */ UnitLower=UnitDiag|Lower, /** View matrix as an upper triangular matrix with ones on the diagonal. */ UnitUpper=UnitDiag|Upper, /** View matrix as a lower triangular matrix with zeros on the diagonal. */ StrictlyLower=ZeroDiag|Lower, /** View matrix as an upper triangular matrix with zeros on the diagonal. */ StrictlyUpper=ZeroDiag|Upper, /** Used in BandMatrix and SelfAdjointView to indicate that the matrix is self-adjoint. */ SelfAdjoint=0x10, /** Used to support symmetric, non-selfadjoint, complex matrices. */ Symmetric=0x20 }; /** \ingroup enums * Enum for indicating whether an object is aligned or not. */ enum { /** Object is not correctly aligned for vectorization. */ Unaligned=0, /** Object is aligned for vectorization. */ Aligned=1 }; /** \ingroup enums * Enum used by DenseBase::corner() in Eigen2 compatibility mode. */ // FIXME after the corner() API change, this was not needed anymore, except by AlignedBox // TODO: find out what to do with that. Adapt the AlignedBox API ? enum CornerType { TopLeft, TopRight, BottomLeft, BottomRight }; /** \ingroup enums * Enum containing possible values for the \p Direction parameter of * Reverse, PartialReduxExpr and VectorwiseOp. */ enum DirectionType { /** For Reverse, all columns are reversed; * for PartialReduxExpr and VectorwiseOp, act on columns. */ Vertical, /** For Reverse, all rows are reversed; * for PartialReduxExpr and VectorwiseOp, act on rows. */ Horizontal, /** For Reverse, both rows and columns are reversed; * not used for PartialReduxExpr and VectorwiseOp. */ BothDirections }; /** \internal \ingroup enums * Enum to specify how to traverse the entries of a matrix. */ enum { /** \internal Default traversal, no vectorization, no index-based access */ DefaultTraversal, /** \internal No vectorization, use index-based access to have only one for loop instead of 2 nested loops */ LinearTraversal, /** \internal Equivalent to a slice vectorization for fixed-size matrices having good alignment * and good size */ InnerVectorizedTraversal, /** \internal Vectorization path using a single loop plus scalar loops for the * unaligned boundaries */ LinearVectorizedTraversal, /** \internal Generic vectorization path using one vectorized loop per row/column with some * scalar loops to handle the unaligned boundaries */ SliceVectorizedTraversal, /** \internal Special case to properly handle incompatible scalar types or other defecting cases*/ InvalidTraversal }; /** \internal \ingroup enums * Enum to specify whether to unroll loops when traversing over the entries of a matrix. */ enum { /** \internal Do not unroll loops. */ NoUnrolling, /** \internal Unroll only the inner loop, but not the outer loop. */ InnerUnrolling, /** \internal Unroll both the inner and the outer loop. If there is only one loop, * because linear traversal is used, then unroll that loop. */ CompleteUnrolling }; /** \internal \ingroup enums * Enum to specify whether to use the default (built-in) implementation or the specialization. */ enum { Specialized, BuiltIn }; /** \ingroup enums * Enum containing possible values for the \p _Options template parameter of * Matrix, Array and BandMatrix. */ enum { /** Storage order is column major (see \ref TopicStorageOrders). */ ColMajor = 0, /** Storage order is row major (see \ref TopicStorageOrders). */ RowMajor = 0x1, // it is only a coincidence that this is equal to RowMajorBit -- don't rely on that /** \internal Align the matrix itself if it is vectorizable fixed-size */ AutoAlign = 0, /** \internal Don't require alignment for the matrix itself (the array of coefficients, if dynamically allocated, may still be requested to be aligned) */ // FIXME --- clarify the situation DontAlign = 0x2 }; /** \ingroup enums * Enum for specifying whether to apply or solve on the left or right. */ enum { /** Apply transformation on the left. */ OnTheLeft = 1, /** Apply transformation on the right. */ OnTheRight = 2 }; /* the following used to be written as: * * struct NoChange_t {}; * namespace { * EIGEN_UNUSED NoChange_t NoChange; * } * * on the ground that it feels dangerous to disambiguate overloaded functions on enum/integer types. * However, this leads to "variable declared but never referenced" warnings on Intel Composer XE, * and we do not know how to get rid of them (bug 450). */ enum NoChange_t { NoChange }; enum Sequential_t { Sequential }; enum Default_t { Default }; /** \internal \ingroup enums * Used in AmbiVector. */ enum { IsDense = 0, IsSparse }; /** \ingroup enums * Used as template parameter in DenseCoeffBase and MapBase to indicate * which accessors should be provided. */ enum AccessorLevels { /** Read-only access via a member function. */ ReadOnlyAccessors, /** Read/write access via member functions. */ WriteAccessors, /** Direct read-only access to the coefficients. */ DirectAccessors, /** Direct read/write access to the coefficients. */ DirectWriteAccessors }; /** \ingroup enums * Enum with options to give to various decompositions. */ enum DecompositionOptions { /** \internal Not used (meant for LDLT?). */ Pivoting = 0x01, /** \internal Not used (meant for LDLT?). */ NoPivoting = 0x02, /** Used in JacobiSVD to indicate that the square matrix U is to be computed. */ ComputeFullU = 0x04, /** Used in JacobiSVD to indicate that the thin matrix U is to be computed. */ ComputeThinU = 0x08, /** Used in JacobiSVD to indicate that the square matrix V is to be computed. */ ComputeFullV = 0x10, /** Used in JacobiSVD to indicate that the thin matrix V is to be computed. */ ComputeThinV = 0x20, /** Used in SelfAdjointEigenSolver and GeneralizedSelfAdjointEigenSolver to specify * that only the eigenvalues are to be computed and not the eigenvectors. */ EigenvaluesOnly = 0x40, /** Used in SelfAdjointEigenSolver and GeneralizedSelfAdjointEigenSolver to specify * that both the eigenvalues and the eigenvectors are to be computed. */ ComputeEigenvectors = 0x80, /** \internal */ EigVecMask = EigenvaluesOnly | ComputeEigenvectors, /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should * solve the generalized eigenproblem \f$ Ax = \lambda B x \f$. */ Ax_lBx = 0x100, /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should * solve the generalized eigenproblem \f$ ABx = \lambda x \f$. */ ABx_lx = 0x200, /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should * solve the generalized eigenproblem \f$ BAx = \lambda x \f$. */ BAx_lx = 0x400, /** \internal */ GenEigMask = Ax_lBx | ABx_lx | BAx_lx }; /** \ingroup enums * Possible values for the \p QRPreconditioner template parameter of JacobiSVD. */ enum QRPreconditioners { /** Do not specify what is to be done if the SVD of a non-square matrix is asked for. */ NoQRPreconditioner, /** Use a QR decomposition without pivoting as the first step. */ HouseholderQRPreconditioner, /** Use a QR decomposition with column pivoting as the first step. */ ColPivHouseholderQRPreconditioner, /** Use a QR decomposition with full pivoting as the first step. */ FullPivHouseholderQRPreconditioner }; #ifdef Success #error The preprocessor symbol 'Success' is defined, possibly by the X11 header file X.h #endif /** \ingroup enums * Enum for reporting the status of a computation. */ enum ComputationInfo { /** Computation was successful. */ Success = 0, /** The provided data did not satisfy the prerequisites. */ NumericalIssue = 1, /** Iterative procedure did not converge. */ NoConvergence = 2, /** The inputs are invalid, or the algorithm has been improperly called. * When assertions are enabled, such errors trigger an assert. */ InvalidInput = 3 }; /** \ingroup enums * Enum used to specify how a particular transformation is stored in a matrix. * \sa Transform, Hyperplane::transform(). */ enum TransformTraits { /** Transformation is an isometry. */ Isometry = 0x1, /** Transformation is an affine transformation stored as a (Dim+1)^2 matrix whose last row is * assumed to be [0 ... 0 1]. */ Affine = 0x2, /** Transformation is an affine transformation stored as a (Dim) x (Dim+1) matrix. */ AffineCompact = 0x10 | Affine, /** Transformation is a general projective transformation stored as a (Dim+1)^2 matrix. */ Projective = 0x20 }; /** \internal \ingroup enums * Enum used to choose between implementation depending on the computer architecture. */ namespace Architecture { enum Type { Generic = 0x0, SSE = 0x1, AltiVec = 0x2, #if defined EIGEN_VECTORIZE_SSE Target = SSE #elif defined EIGEN_VECTORIZE_ALTIVEC Target = AltiVec #else Target = Generic #endif }; } /** \internal \ingroup enums * Enum used as template parameter in GeneralProduct. */ enum { CoeffBasedProductMode, LazyCoeffBasedProductMode, OuterProduct, InnerProduct, GemvProduct, GemmProduct }; /** \internal \ingroup enums * Enum used in experimental parallel implementation. */ enum Action {GetAction, SetAction}; /** The type used to identify a dense storage. */ struct Dense {}; /** The type used to identify a matrix expression */ struct MatrixXpr {}; /** The type used to identify an array expression */ struct ArrayXpr {}; } // end namespace Eigen #endif // EIGEN_CONSTANTS_H ctbnrle.1.1.1/hdr/Eigen/src/Core/PermutationMatrix.h0000775000175000017500000006003512107032422022516 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Benoit Jacob // Copyright (C) 2009-2011 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_PERMUTATIONMATRIX_H #define EIGEN_PERMUTATIONMATRIX_H namespace Eigen { template class PermutedImpl; /** \class PermutationBase * \ingroup Core_Module * * \brief Base class for permutations * * \param Derived the derived class * * This class is the base class for all expressions representing a permutation matrix, * internally stored as a vector of integers. * The convention followed here is that if \f$ \sigma \f$ is a permutation, the corresponding permutation matrix * \f$ P_\sigma \f$ is such that if \f$ (e_1,\ldots,e_p) \f$ is the canonical basis, we have: * \f[ P_\sigma(e_i) = e_{\sigma(i)}. \f] * This convention ensures that for any two permutations \f$ \sigma, \tau \f$, we have: * \f[ P_{\sigma\circ\tau} = P_\sigma P_\tau. \f] * * Permutation matrices are square and invertible. * * Notice that in addition to the member functions and operators listed here, there also are non-member * operator* to multiply any kind of permutation object with any kind of matrix expression (MatrixBase) * on either side. * * \sa class PermutationMatrix, class PermutationWrapper */ namespace internal { template struct permut_matrix_product_retval; template struct permut_sparsematrix_product_retval; enum PermPermProduct_t {PermPermProduct}; } // end namespace internal template class PermutationBase : public EigenBase { typedef internal::traits Traits; typedef EigenBase Base; public: #ifndef EIGEN_PARSED_BY_DOXYGEN typedef typename Traits::IndicesType IndicesType; enum { Flags = Traits::Flags, CoeffReadCost = Traits::CoeffReadCost, RowsAtCompileTime = Traits::RowsAtCompileTime, ColsAtCompileTime = Traits::ColsAtCompileTime, MaxRowsAtCompileTime = Traits::MaxRowsAtCompileTime, MaxColsAtCompileTime = Traits::MaxColsAtCompileTime }; typedef typename Traits::Scalar Scalar; typedef typename Traits::Index Index; typedef Matrix DenseMatrixType; typedef PermutationMatrix PlainPermutationType; using Base::derived; #endif /** Copies the other permutation into *this */ template Derived& operator=(const PermutationBase& other) { indices() = other.indices(); return derived(); } /** Assignment from the Transpositions \a tr */ template Derived& operator=(const TranspositionsBase& tr) { setIdentity(tr.size()); for(Index k=size()-1; k>=0; --k) applyTranspositionOnTheRight(k,tr.coeff(k)); return derived(); } #ifndef EIGEN_PARSED_BY_DOXYGEN /** This is a special case of the templated operator=. Its purpose is to * prevent a default operator= from hiding the templated operator=. */ Derived& operator=(const PermutationBase& other) { indices() = other.indices(); return derived(); } #endif /** \returns the number of rows */ inline Index rows() const { return indices().size(); } /** \returns the number of columns */ inline Index cols() const { return indices().size(); } /** \returns the size of a side of the respective square matrix, i.e., the number of indices */ inline Index size() const { return indices().size(); } #ifndef EIGEN_PARSED_BY_DOXYGEN template void evalTo(MatrixBase& other) const { other.setZero(); for (int i=0; i=0 && j>=0 && i=0 && j>=0 && i inverse() const { return derived(); } /** \returns the tranpose permutation matrix. * * \note \note_try_to_help_rvo */ inline Transpose transpose() const { return derived(); } /**** multiplication helpers to hopefully get RVO ****/ #ifndef EIGEN_PARSED_BY_DOXYGEN protected: template void assignTranspose(const PermutationBase& other) { for (int i=0; i void assignProduct(const Lhs& lhs, const Rhs& rhs) { eigen_assert(lhs.cols() == rhs.rows()); for (int i=0; i inline PlainPermutationType operator*(const PermutationBase& other) const { return PlainPermutationType(internal::PermPermProduct, derived(), other.derived()); } /** \returns the product of a permutation with another inverse permutation. * * \note \note_try_to_help_rvo */ template inline PlainPermutationType operator*(const Transpose >& other) const { return PlainPermutationType(internal::PermPermProduct, *this, other.eval()); } /** \returns the product of an inverse permutation with another permutation. * * \note \note_try_to_help_rvo */ template friend inline PlainPermutationType operator*(const Transpose >& other, const PermutationBase& perm) { return PlainPermutationType(internal::PermPermProduct, other.eval(), perm); } protected: }; /** \class PermutationMatrix * \ingroup Core_Module * * \brief Permutation matrix * * \param SizeAtCompileTime the number of rows/cols, or Dynamic * \param MaxSizeAtCompileTime the maximum number of rows/cols, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it. * \param IndexType the interger type of the indices * * This class represents a permutation matrix, internally stored as a vector of integers. * * \sa class PermutationBase, class PermutationWrapper, class DiagonalMatrix */ namespace internal { template struct traits > : traits > { typedef IndexType Index; typedef Matrix IndicesType; }; } template class PermutationMatrix : public PermutationBase > { typedef PermutationBase Base; typedef internal::traits Traits; public: #ifndef EIGEN_PARSED_BY_DOXYGEN typedef typename Traits::IndicesType IndicesType; #endif inline PermutationMatrix() {} /** Constructs an uninitialized permutation matrix of given size. */ inline PermutationMatrix(int size) : m_indices(size) {} /** Copy constructor. */ template inline PermutationMatrix(const PermutationBase& other) : m_indices(other.indices()) {} #ifndef EIGEN_PARSED_BY_DOXYGEN /** Standard copy constructor. Defined only to prevent a default copy constructor * from hiding the other templated constructor */ inline PermutationMatrix(const PermutationMatrix& other) : m_indices(other.indices()) {} #endif /** Generic constructor from expression of the indices. The indices * array has the meaning that the permutations sends each integer i to indices[i]. * * \warning It is your responsibility to check that the indices array that you passes actually * describes a permutation, i.e., each value between 0 and n-1 occurs exactly once, where n is the * array's size. */ template explicit inline PermutationMatrix(const MatrixBase& indices) : m_indices(indices) {} /** Convert the Transpositions \a tr to a permutation matrix */ template explicit PermutationMatrix(const TranspositionsBase& tr) : m_indices(tr.size()) { *this = tr; } /** Copies the other permutation into *this */ template PermutationMatrix& operator=(const PermutationBase& other) { m_indices = other.indices(); return *this; } /** Assignment from the Transpositions \a tr */ template PermutationMatrix& operator=(const TranspositionsBase& tr) { return Base::operator=(tr.derived()); } #ifndef EIGEN_PARSED_BY_DOXYGEN /** This is a special case of the templated operator=. Its purpose is to * prevent a default operator= from hiding the templated operator=. */ PermutationMatrix& operator=(const PermutationMatrix& other) { m_indices = other.m_indices; return *this; } #endif /** const version of indices(). */ const IndicesType& indices() const { return m_indices; } /** \returns a reference to the stored array representing the permutation. */ IndicesType& indices() { return m_indices; } /**** multiplication helpers to hopefully get RVO ****/ #ifndef EIGEN_PARSED_BY_DOXYGEN template PermutationMatrix(const Transpose >& other) : m_indices(other.nestedPermutation().size()) { for (int i=0; i PermutationMatrix(internal::PermPermProduct_t, const Lhs& lhs, const Rhs& rhs) : m_indices(lhs.indices().size()) { Base::assignProduct(lhs,rhs); } #endif protected: IndicesType m_indices; }; namespace internal { template struct traits,_PacketAccess> > : traits > { typedef IndexType Index; typedef Map, _PacketAccess> IndicesType; }; } template class Map,_PacketAccess> : public PermutationBase,_PacketAccess> > { typedef PermutationBase Base; typedef internal::traits Traits; public: #ifndef EIGEN_PARSED_BY_DOXYGEN typedef typename Traits::IndicesType IndicesType; typedef typename IndicesType::Scalar Index; #endif inline Map(const Index* indices) : m_indices(indices) {} inline Map(const Index* indices, Index size) : m_indices(indices,size) {} /** Copies the other permutation into *this */ template Map& operator=(const PermutationBase& other) { return Base::operator=(other.derived()); } /** Assignment from the Transpositions \a tr */ template Map& operator=(const TranspositionsBase& tr) { return Base::operator=(tr.derived()); } #ifndef EIGEN_PARSED_BY_DOXYGEN /** This is a special case of the templated operator=. Its purpose is to * prevent a default operator= from hiding the templated operator=. */ Map& operator=(const Map& other) { m_indices = other.m_indices; return *this; } #endif /** const version of indices(). */ const IndicesType& indices() const { return m_indices; } /** \returns a reference to the stored array representing the permutation. */ IndicesType& indices() { return m_indices; } protected: IndicesType m_indices; }; /** \class PermutationWrapper * \ingroup Core_Module * * \brief Class to view a vector of integers as a permutation matrix * * \param _IndicesType the type of the vector of integer (can be any compatible expression) * * This class allows to view any vector expression of integers as a permutation matrix. * * \sa class PermutationBase, class PermutationMatrix */ struct PermutationStorage {}; template class TranspositionsWrapper; namespace internal { template struct traits > { typedef PermutationStorage StorageKind; typedef typename _IndicesType::Scalar Scalar; typedef typename _IndicesType::Scalar Index; typedef _IndicesType IndicesType; enum { RowsAtCompileTime = _IndicesType::SizeAtCompileTime, ColsAtCompileTime = _IndicesType::SizeAtCompileTime, MaxRowsAtCompileTime = IndicesType::MaxRowsAtCompileTime, MaxColsAtCompileTime = IndicesType::MaxColsAtCompileTime, Flags = 0, CoeffReadCost = _IndicesType::CoeffReadCost }; }; } template class PermutationWrapper : public PermutationBase > { typedef PermutationBase Base; typedef internal::traits Traits; public: #ifndef EIGEN_PARSED_BY_DOXYGEN typedef typename Traits::IndicesType IndicesType; #endif inline PermutationWrapper(const IndicesType& indices) : m_indices(indices) {} /** const version of indices(). */ const typename internal::remove_all::type& indices() const { return m_indices; } protected: typename IndicesType::Nested m_indices; }; /** \returns the matrix with the permutation applied to the columns. */ template inline const internal::permut_matrix_product_retval operator*(const MatrixBase& matrix, const PermutationBase &permutation) { return internal::permut_matrix_product_retval (permutation.derived(), matrix.derived()); } /** \returns the matrix with the permutation applied to the rows. */ template inline const internal::permut_matrix_product_retval operator*(const PermutationBase &permutation, const MatrixBase& matrix) { return internal::permut_matrix_product_retval (permutation.derived(), matrix.derived()); } namespace internal { template struct traits > { typedef typename MatrixType::PlainObject ReturnType; }; template struct permut_matrix_product_retval : public ReturnByValue > { typedef typename remove_all::type MatrixTypeNestedCleaned; permut_matrix_product_retval(const PermutationType& perm, const MatrixType& matrix) : m_permutation(perm), m_matrix(matrix) {} inline int rows() const { return m_matrix.rows(); } inline int cols() const { return m_matrix.cols(); } template inline void evalTo(Dest& dst) const { const int n = Side==OnTheLeft ? rows() : cols(); if(is_same::value && extract_data(dst) == extract_data(m_matrix)) { // apply the permutation inplace Matrix mask(m_permutation.size()); mask.fill(false); int r = 0; while(r < m_permutation.size()) { // search for the next seed while(r=m_permutation.size()) break; // we got one, let's follow it until we are back to the seed int k0 = r++; int kPrev = k0; mask.coeffRef(k0) = true; for(int k=m_permutation.indices().coeff(k0); k!=k0; k=m_permutation.indices().coeff(k)) { Block(dst, k) .swap(Block (dst,((Side==OnTheLeft) ^ Transposed) ? k0 : kPrev)); mask.coeffRef(k) = true; kPrev = k; } } } else { for(int i = 0; i < n; ++i) { Block (dst, ((Side==OnTheLeft) ^ Transposed) ? m_permutation.indices().coeff(i) : i) = Block (m_matrix, ((Side==OnTheRight) ^ Transposed) ? m_permutation.indices().coeff(i) : i); } } } protected: const PermutationType& m_permutation; typename MatrixType::Nested m_matrix; }; /* Template partial specialization for transposed/inverse permutations */ template struct traits > > : traits {}; } // end namespace internal template class Transpose > : public EigenBase > > { typedef Derived PermutationType; typedef typename PermutationType::IndicesType IndicesType; typedef typename PermutationType::PlainPermutationType PlainPermutationType; public: #ifndef EIGEN_PARSED_BY_DOXYGEN typedef internal::traits Traits; typedef typename Derived::DenseMatrixType DenseMatrixType; enum { Flags = Traits::Flags, CoeffReadCost = Traits::CoeffReadCost, RowsAtCompileTime = Traits::RowsAtCompileTime, ColsAtCompileTime = Traits::ColsAtCompileTime, MaxRowsAtCompileTime = Traits::MaxRowsAtCompileTime, MaxColsAtCompileTime = Traits::MaxColsAtCompileTime }; typedef typename Traits::Scalar Scalar; #endif Transpose(const PermutationType& p) : m_permutation(p) {} inline int rows() const { return m_permutation.rows(); } inline int cols() const { return m_permutation.cols(); } #ifndef EIGEN_PARSED_BY_DOXYGEN template void evalTo(MatrixBase& other) const { other.setZero(); for (int i=0; i friend inline const internal::permut_matrix_product_retval operator*(const MatrixBase& matrix, const Transpose& trPerm) { return internal::permut_matrix_product_retval(trPerm.m_permutation, matrix.derived()); } /** \returns the matrix with the inverse permutation applied to the rows. */ template inline const internal::permut_matrix_product_retval operator*(const MatrixBase& matrix) const { return internal::permut_matrix_product_retval(m_permutation, matrix.derived()); } const PermutationType& nestedPermutation() const { return m_permutation; } protected: const PermutationType& m_permutation; }; template const PermutationWrapper MatrixBase::asPermutation() const { return derived(); } } // end namespace Eigen #endif // EIGEN_PERMUTATIONMATRIX_H ctbnrle.1.1.1/hdr/Eigen/src/Core/CMakeLists.txt0000775000175000017500000000032712107032422021407 0ustar csheltoncsheltonFILE(GLOB Eigen_Core_SRCS "*.h") INSTALL(FILES ${Eigen_Core_SRCS} DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core COMPONENT Devel ) ADD_SUBDIRECTORY(products) ADD_SUBDIRECTORY(util) ADD_SUBDIRECTORY(arch) ctbnrle.1.1.1/hdr/Eigen/src/Core/Assign.h0000775000175000017500000005610712107032422020253 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2007 Michael Olbrich // Copyright (C) 2006-2010 Benoit Jacob // Copyright (C) 2008 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_ASSIGN_H #define EIGEN_ASSIGN_H namespace Eigen { namespace internal { /*************************************************************************** * Part 1 : the logic deciding a strategy for traversal and unrolling * ***************************************************************************/ template struct assign_traits { public: enum { DstIsAligned = Derived::Flags & AlignedBit, DstHasDirectAccess = Derived::Flags & DirectAccessBit, SrcIsAligned = OtherDerived::Flags & AlignedBit, JointAlignment = bool(DstIsAligned) && bool(SrcIsAligned) ? Aligned : Unaligned }; private: enum { InnerSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::SizeAtCompileTime) : int(Derived::Flags)&RowMajorBit ? int(Derived::ColsAtCompileTime) : int(Derived::RowsAtCompileTime), InnerMaxSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::MaxSizeAtCompileTime) : int(Derived::Flags)&RowMajorBit ? int(Derived::MaxColsAtCompileTime) : int(Derived::MaxRowsAtCompileTime), MaxSizeAtCompileTime = Derived::SizeAtCompileTime, PacketSize = packet_traits::size }; enum { StorageOrdersAgree = (int(Derived::IsRowMajor) == int(OtherDerived::IsRowMajor)), MightVectorize = StorageOrdersAgree && (int(Derived::Flags) & int(OtherDerived::Flags) & ActualPacketAccessBit), MayInnerVectorize = MightVectorize && int(InnerSize)!=Dynamic && int(InnerSize)%int(PacketSize)==0 && int(DstIsAligned) && int(SrcIsAligned), MayLinearize = StorageOrdersAgree && (int(Derived::Flags) & int(OtherDerived::Flags) & LinearAccessBit), MayLinearVectorize = MightVectorize && MayLinearize && DstHasDirectAccess && (DstIsAligned || MaxSizeAtCompileTime == Dynamic), /* If the destination isn't aligned, we have to do runtime checks and we don't unroll, so it's only good for large enough sizes. */ MaySliceVectorize = MightVectorize && DstHasDirectAccess && (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=3*PacketSize) /* slice vectorization can be slow, so we only want it if the slices are big, which is indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block in a fixed-size matrix */ }; public: enum { Traversal = int(MayInnerVectorize) ? int(InnerVectorizedTraversal) : int(MayLinearVectorize) ? int(LinearVectorizedTraversal) : int(MaySliceVectorize) ? int(SliceVectorizedTraversal) : int(MayLinearize) ? int(LinearTraversal) : int(DefaultTraversal), Vectorized = int(Traversal) == InnerVectorizedTraversal || int(Traversal) == LinearVectorizedTraversal || int(Traversal) == SliceVectorizedTraversal }; private: enum { UnrollingLimit = EIGEN_UNROLLING_LIMIT * (Vectorized ? int(PacketSize) : 1), MayUnrollCompletely = int(Derived::SizeAtCompileTime) != Dynamic && int(OtherDerived::CoeffReadCost) != Dynamic && int(Derived::SizeAtCompileTime) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit), MayUnrollInner = int(InnerSize) != Dynamic && int(OtherDerived::CoeffReadCost) != Dynamic && int(InnerSize) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit) }; public: enum { Unrolling = (int(Traversal) == int(InnerVectorizedTraversal) || int(Traversal) == int(DefaultTraversal)) ? ( int(MayUnrollCompletely) ? int(CompleteUnrolling) : int(MayUnrollInner) ? int(InnerUnrolling) : int(NoUnrolling) ) : int(Traversal) == int(LinearVectorizedTraversal) ? ( bool(MayUnrollCompletely) && bool(DstIsAligned) ? int(CompleteUnrolling) : int(NoUnrolling) ) : int(Traversal) == int(LinearTraversal) ? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling) : int(NoUnrolling) ) : int(NoUnrolling) }; #ifdef EIGEN_DEBUG_ASSIGN static void debug() { EIGEN_DEBUG_VAR(DstIsAligned) EIGEN_DEBUG_VAR(SrcIsAligned) EIGEN_DEBUG_VAR(JointAlignment) EIGEN_DEBUG_VAR(InnerSize) EIGEN_DEBUG_VAR(InnerMaxSize) EIGEN_DEBUG_VAR(PacketSize) EIGEN_DEBUG_VAR(StorageOrdersAgree) EIGEN_DEBUG_VAR(MightVectorize) EIGEN_DEBUG_VAR(MayLinearize) EIGEN_DEBUG_VAR(MayInnerVectorize) EIGEN_DEBUG_VAR(MayLinearVectorize) EIGEN_DEBUG_VAR(MaySliceVectorize) EIGEN_DEBUG_VAR(Traversal) EIGEN_DEBUG_VAR(UnrollingLimit) EIGEN_DEBUG_VAR(MayUnrollCompletely) EIGEN_DEBUG_VAR(MayUnrollInner) EIGEN_DEBUG_VAR(Unrolling) } #endif }; /*************************************************************************** * Part 2 : meta-unrollers ***************************************************************************/ /************************ *** Default traversal *** ************************/ template struct assign_DefaultTraversal_CompleteUnrolling { enum { outer = Index / Derived1::InnerSizeAtCompileTime, inner = Index % Derived1::InnerSizeAtCompileTime }; static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) { dst.copyCoeffByOuterInner(outer, inner, src); assign_DefaultTraversal_CompleteUnrolling::run(dst, src); } }; template struct assign_DefaultTraversal_CompleteUnrolling { static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {} }; template struct assign_DefaultTraversal_InnerUnrolling { static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src, int outer) { dst.copyCoeffByOuterInner(outer, Index, src); assign_DefaultTraversal_InnerUnrolling::run(dst, src, outer); } }; template struct assign_DefaultTraversal_InnerUnrolling { static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, int) {} }; /*********************** *** Linear traversal *** ***********************/ template struct assign_LinearTraversal_CompleteUnrolling { static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) { dst.copyCoeff(Index, src); assign_LinearTraversal_CompleteUnrolling::run(dst, src); } }; template struct assign_LinearTraversal_CompleteUnrolling { static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {} }; /************************** *** Inner vectorization *** **************************/ template struct assign_innervec_CompleteUnrolling { enum { outer = Index / Derived1::InnerSizeAtCompileTime, inner = Index % Derived1::InnerSizeAtCompileTime, JointAlignment = assign_traits::JointAlignment }; static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) { dst.template copyPacketByOuterInner(outer, inner, src); assign_innervec_CompleteUnrolling::size, Stop>::run(dst, src); } }; template struct assign_innervec_CompleteUnrolling { static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {} }; template struct assign_innervec_InnerUnrolling { static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src, int outer) { dst.template copyPacketByOuterInner(outer, Index, src); assign_innervec_InnerUnrolling::size, Stop>::run(dst, src, outer); } }; template struct assign_innervec_InnerUnrolling { static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, int) {} }; /*************************************************************************** * Part 3 : implementation of all cases ***************************************************************************/ template::Traversal, int Unrolling = assign_traits::Unrolling, int Version = Specialized> struct assign_impl; /************************ *** Default traversal *** ************************/ template struct assign_impl { static inline void run(Derived1 &, const Derived2 &) { } }; template struct assign_impl { typedef typename Derived1::Index Index; static inline void run(Derived1 &dst, const Derived2 &src) { const Index innerSize = dst.innerSize(); const Index outerSize = dst.outerSize(); for(Index outer = 0; outer < outerSize; ++outer) for(Index inner = 0; inner < innerSize; ++inner) dst.copyCoeffByOuterInner(outer, inner, src); } }; template struct assign_impl { static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) { assign_DefaultTraversal_CompleteUnrolling ::run(dst, src); } }; template struct assign_impl { typedef typename Derived1::Index Index; static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) { const Index outerSize = dst.outerSize(); for(Index outer = 0; outer < outerSize; ++outer) assign_DefaultTraversal_InnerUnrolling ::run(dst, src, outer); } }; /*********************** *** Linear traversal *** ***********************/ template struct assign_impl { typedef typename Derived1::Index Index; static inline void run(Derived1 &dst, const Derived2 &src) { const Index size = dst.size(); for(Index i = 0; i < size; ++i) dst.copyCoeff(i, src); } }; template struct assign_impl { static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) { assign_LinearTraversal_CompleteUnrolling ::run(dst, src); } }; /************************** *** Inner vectorization *** **************************/ template struct assign_impl { typedef typename Derived1::Index Index; static inline void run(Derived1 &dst, const Derived2 &src) { const Index innerSize = dst.innerSize(); const Index outerSize = dst.outerSize(); const Index packetSize = packet_traits::size; for(Index outer = 0; outer < outerSize; ++outer) for(Index inner = 0; inner < innerSize; inner+=packetSize) dst.template copyPacketByOuterInner(outer, inner, src); } }; template struct assign_impl { static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) { assign_innervec_CompleteUnrolling ::run(dst, src); } }; template struct assign_impl { typedef typename Derived1::Index Index; static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) { const Index outerSize = dst.outerSize(); for(Index outer = 0; outer < outerSize; ++outer) assign_innervec_InnerUnrolling ::run(dst, src, outer); } }; /*************************** *** Linear vectorization *** ***************************/ template struct unaligned_assign_impl { template static EIGEN_STRONG_INLINE void run(const Derived&, OtherDerived&, typename Derived::Index, typename Derived::Index) {} }; template <> struct unaligned_assign_impl { // MSVC must not inline this functions. If it does, it fails to optimize the // packet access path. #ifdef _MSC_VER template static EIGEN_DONT_INLINE void run(const Derived& src, OtherDerived& dst, typename Derived::Index start, typename Derived::Index end) #else template static EIGEN_STRONG_INLINE void run(const Derived& src, OtherDerived& dst, typename Derived::Index start, typename Derived::Index end) #endif { for (typename Derived::Index index = start; index < end; ++index) dst.copyCoeff(index, src); } }; template struct assign_impl { typedef typename Derived1::Index Index; static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) { const Index size = dst.size(); typedef packet_traits PacketTraits; enum { packetSize = PacketTraits::size, dstAlignment = PacketTraits::AlignedOnScalar ? Aligned : int(assign_traits::DstIsAligned) , srcAlignment = assign_traits::JointAlignment }; const Index alignedStart = assign_traits::DstIsAligned ? 0 : internal::first_aligned(&dst.coeffRef(0), size); const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize; unaligned_assign_impl::DstIsAligned!=0>::run(src,dst,0,alignedStart); for(Index index = alignedStart; index < alignedEnd; index += packetSize) { dst.template copyPacket(index, src); } unaligned_assign_impl<>::run(src,dst,alignedEnd,size); } }; template struct assign_impl { typedef typename Derived1::Index Index; static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) { enum { size = Derived1::SizeAtCompileTime, packetSize = packet_traits::size, alignedSize = (size/packetSize)*packetSize }; assign_innervec_CompleteUnrolling::run(dst, src); assign_DefaultTraversal_CompleteUnrolling::run(dst, src); } }; /************************** *** Slice vectorization *** ***************************/ template struct assign_impl { typedef typename Derived1::Index Index; static inline void run(Derived1 &dst, const Derived2 &src) { typedef packet_traits PacketTraits; enum { packetSize = PacketTraits::size, alignable = PacketTraits::AlignedOnScalar, dstAlignment = alignable ? Aligned : int(assign_traits::DstIsAligned) , srcAlignment = assign_traits::JointAlignment }; const Index packetAlignedMask = packetSize - 1; const Index innerSize = dst.innerSize(); const Index outerSize = dst.outerSize(); const Index alignedStep = alignable ? (packetSize - dst.outerStride() % packetSize) & packetAlignedMask : 0; Index alignedStart = ((!alignable) || assign_traits::DstIsAligned) ? 0 : internal::first_aligned(&dst.coeffRef(0,0), innerSize); for(Index outer = 0; outer < outerSize; ++outer) { const Index alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask); // do the non-vectorizable part of the assignment for(Index inner = 0; inner(outer, inner, src); // do the non-vectorizable part of the assignment for(Index inner = alignedEnd; inner((alignedStart+alignedStep)%packetSize, innerSize); } } }; } // end namespace internal /*************************************************************************** * Part 4 : implementation of DenseBase methods ***************************************************************************/ template template EIGEN_STRONG_INLINE Derived& DenseBase ::lazyAssign(const DenseBase& other) { enum{ SameType = internal::is_same::value }; EIGEN_STATIC_ASSERT_LVALUE(Derived) EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived) EIGEN_STATIC_ASSERT(SameType,YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) #ifdef EIGEN_DEBUG_ASSIGN internal::assign_traits::debug(); #endif eigen_assert(rows() == other.rows() && cols() == other.cols()); internal::assign_impl::Traversal) : int(InvalidTraversal)>::run(derived(),other.derived()); #ifndef EIGEN_NO_DEBUG checkTransposeAliasing(other.derived()); #endif return derived(); } namespace internal { template struct assign_selector; template struct assign_selector { static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.derived()); } }; template struct assign_selector { static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.eval()); } }; template struct assign_selector { static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose()); } }; template struct assign_selector { static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose().eval()); } }; } // end namespace internal template template EIGEN_STRONG_INLINE Derived& DenseBase::operator=(const DenseBase& other) { return internal::assign_selector::run(derived(), other.derived()); } template EIGEN_STRONG_INLINE Derived& DenseBase::operator=(const DenseBase& other) { return internal::assign_selector::run(derived(), other.derived()); } template EIGEN_STRONG_INLINE Derived& MatrixBase::operator=(const MatrixBase& other) { return internal::assign_selector::run(derived(), other.derived()); } template template EIGEN_STRONG_INLINE Derived& MatrixBase::operator=(const DenseBase& other) { return internal::assign_selector::run(derived(), other.derived()); } template template EIGEN_STRONG_INLINE Derived& MatrixBase::operator=(const EigenBase& other) { other.derived().evalTo(derived()); return derived(); } template template EIGEN_STRONG_INLINE Derived& MatrixBase::operator=(const ReturnByValue& other) { other.evalTo(derived()); return derived(); } } // end namespace Eigen #endif // EIGEN_ASSIGN_H ctbnrle.1.1.1/hdr/Eigen/src/Core/Diagonal.h0000775000175000017500000002313612107032422020541 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2007-2009 Benoit Jacob // Copyright (C) 2009-2010 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_DIAGONAL_H #define EIGEN_DIAGONAL_H namespace Eigen { /** \class Diagonal * \ingroup Core_Module * * \brief Expression of a diagonal/subdiagonal/superdiagonal in a matrix * * \param MatrixType the type of the object in which we are taking a sub/main/super diagonal * \param DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal. * A positive value means a superdiagonal, a negative value means a subdiagonal. * You can also use Dynamic so the index can be set at runtime. * * The matrix is not required to be square. * * This class represents an expression of the main diagonal, or any sub/super diagonal * of a square matrix. It is the return type of MatrixBase::diagonal() and MatrixBase::diagonal(Index) and most of the * time this is the only way it is used. * * \sa MatrixBase::diagonal(), MatrixBase::diagonal(Index) */ namespace internal { template struct traits > : traits { typedef typename nested::type MatrixTypeNested; typedef typename remove_reference::type _MatrixTypeNested; typedef typename MatrixType::StorageKind StorageKind; enum { AbsDiagIndex = DiagIndex<0 ? -DiagIndex : DiagIndex, // only used if DiagIndex != Dynamic // FIXME these computations are broken in the case where the matrix is rectangular and DiagIndex!=0 RowsAtCompileTime = (int(DiagIndex) == Dynamic || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic : (EIGEN_SIZE_MIN_PREFER_DYNAMIC(MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime) - AbsDiagIndex), ColsAtCompileTime = 1, MaxRowsAtCompileTime = int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic : DiagIndex == Dynamic ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime) : (EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime) - AbsDiagIndex), MaxColsAtCompileTime = 1, MaskLvalueBit = is_lvalue::value ? LvalueBit : 0, Flags = (unsigned int)_MatrixTypeNested::Flags & (HereditaryBits | LinearAccessBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit, CoeffReadCost = _MatrixTypeNested::CoeffReadCost, MatrixTypeOuterStride = outer_stride_at_compile_time::ret, InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride+1, OuterStrideAtCompileTime = 0 }; }; } template class Diagonal : public internal::dense_xpr_base< Diagonal >::type { public: typedef typename internal::dense_xpr_base::type Base; EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal) inline Diagonal(MatrixType& matrix, Index index = DiagIndex) : m_matrix(matrix), m_index(index) {} EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal) inline Index rows() const { return m_index.value()<0 ? (std::min)(m_matrix.cols(),m_matrix.rows()+m_index.value()) : (std::min)(m_matrix.rows(),m_matrix.cols()-m_index.value()); } inline Index cols() const { return 1; } inline Index innerStride() const { return m_matrix.outerStride() + 1; } inline Index outerStride() const { return 0; } typedef typename internal::conditional< internal::is_lvalue::value, Scalar, const Scalar >::type ScalarWithConstIfNotLvalue; inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.const_cast_derived().coeffRef(rowOffset(), colOffset())); } inline const Scalar* data() const { return &(m_matrix.const_cast_derived().coeffRef(rowOffset(), colOffset())); } inline Scalar& coeffRef(Index row, Index) { EIGEN_STATIC_ASSERT_LVALUE(MatrixType) return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset()); } inline const Scalar& coeffRef(Index row, Index) const { return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset()); } inline CoeffReturnType coeff(Index row, Index) const { return m_matrix.coeff(row+rowOffset(), row+colOffset()); } inline Scalar& coeffRef(Index index) { EIGEN_STATIC_ASSERT_LVALUE(MatrixType) return m_matrix.const_cast_derived().coeffRef(index+rowOffset(), index+colOffset()); } inline const Scalar& coeffRef(Index index) const { return m_matrix.const_cast_derived().coeffRef(index+rowOffset(), index+colOffset()); } inline CoeffReturnType coeff(Index index) const { return m_matrix.coeff(index+rowOffset(), index+colOffset()); } const typename internal::remove_all::type& nestedExpression() const { return m_matrix; } int index() const { return m_index.value(); } protected: typename MatrixType::Nested m_matrix; const internal::variable_if_dynamic m_index; private: // some compilers may fail to optimize std::max etc in case of compile-time constants... EIGEN_STRONG_INLINE Index absDiagIndex() const { return m_index.value()>0 ? m_index.value() : -m_index.value(); } EIGEN_STRONG_INLINE Index rowOffset() const { return m_index.value()>0 ? 0 : -m_index.value(); } EIGEN_STRONG_INLINE Index colOffset() const { return m_index.value()>0 ? m_index.value() : 0; } // triger a compile time error is someone try to call packet template typename MatrixType::PacketReturnType packet(Index) const; template typename MatrixType::PacketReturnType packet(Index,Index) const; }; /** \returns an expression of the main diagonal of the matrix \c *this * * \c *this is not required to be square. * * Example: \include MatrixBase_diagonal.cpp * Output: \verbinclude MatrixBase_diagonal.out * * \sa class Diagonal */ template inline typename MatrixBase::DiagonalReturnType MatrixBase::diagonal() { return derived(); } /** This is the const version of diagonal(). */ template inline const typename MatrixBase::ConstDiagonalReturnType MatrixBase::diagonal() const { return ConstDiagonalReturnType(derived()); } /** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this * * \c *this is not required to be square. * * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0 * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal. * * Example: \include MatrixBase_diagonal_int.cpp * Output: \verbinclude MatrixBase_diagonal_int.out * * \sa MatrixBase::diagonal(), class Diagonal */ template inline typename MatrixBase::template DiagonalIndexReturnType::Type MatrixBase::diagonal(Index index) { return typename DiagonalIndexReturnType::Type(derived(), index); } /** This is the const version of diagonal(Index). */ template inline typename MatrixBase::template ConstDiagonalIndexReturnType::Type MatrixBase::diagonal(Index index) const { return typename ConstDiagonalIndexReturnType::Type(derived(), index); } /** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this * * \c *this is not required to be square. * * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0 * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal. * * Example: \include MatrixBase_diagonal_template_int.cpp * Output: \verbinclude MatrixBase_diagonal_template_int.out * * \sa MatrixBase::diagonal(), class Diagonal */ template template inline typename MatrixBase::template DiagonalIndexReturnType::Type MatrixBase::diagonal() { return derived(); } /** This is the const version of diagonal(). */ template template inline typename MatrixBase::template ConstDiagonalIndexReturnType::Type MatrixBase::diagonal() const { return derived(); } } // end namespace Eigen #endif // EIGEN_DIAGONAL_H ctbnrle.1.1.1/hdr/Eigen/src/Core/PlainObjectBase.h0000775000175000017500000010331512107032422022006 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2009 Gael Guennebaud // Copyright (C) 2006-2008 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_DENSESTORAGEBASE_H #define EIGEN_DENSESTORAGEBASE_H #ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO # define EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED for(int i=0;i EIGEN_ALWAYS_INLINE void check_rows_cols_for_overflow(Index rows, Index cols) { // http://hg.mozilla.org/mozilla-central/file/6c8a909977d3/xpcom/ds/CheckedInt.h#l242 // we assume Index is signed Index max_index = (size_t(1) << (8 * sizeof(Index) - 1)) - 1; // assume Index is signed bool error = (rows < 0 || cols < 0) ? true : (rows == 0 || cols == 0) ? false : (rows > max_index / cols); if (error) throw_std_bad_alloc(); } template struct conservative_resize_like_impl; template struct matrix_swap_impl; } // end namespace internal /** \class PlainObjectBase * \brief %Dense storage base class for matrices and arrays. * * This class can be extended with the help of the plugin mechanism described on the page * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_PLAINOBJECTBASE_PLUGIN. * * \sa \ref TopicClassHierarchy */ #ifdef EIGEN_PARSED_BY_DOXYGEN namespace internal { // this is a warkaround to doxygen not being able to understand the inheritence logic // when it is hidden by the dense_xpr_base helper struct. template struct dense_xpr_base_dispatcher_for_doxygen;// : public MatrixBase {}; /** This class is just a workaround for Doxygen and it does not not actually exist. */ template struct dense_xpr_base_dispatcher_for_doxygen > : public MatrixBase > {}; /** This class is just a workaround for Doxygen and it does not not actually exist. */ template struct dense_xpr_base_dispatcher_for_doxygen > : public ArrayBase > {}; } // namespace internal template class PlainObjectBase : public internal::dense_xpr_base_dispatcher_for_doxygen #else template class PlainObjectBase : public internal::dense_xpr_base::type #endif { public: enum { Options = internal::traits::Options }; typedef typename internal::dense_xpr_base::type Base; typedef typename internal::traits::StorageKind StorageKind; typedef typename internal::traits::Index Index; typedef typename internal::traits::Scalar Scalar; typedef typename internal::packet_traits::type PacketScalar; typedef typename NumTraits::Real RealScalar; typedef Derived DenseType; using Base::RowsAtCompileTime; using Base::ColsAtCompileTime; using Base::SizeAtCompileTime; using Base::MaxRowsAtCompileTime; using Base::MaxColsAtCompileTime; using Base::MaxSizeAtCompileTime; using Base::IsVectorAtCompileTime; using Base::Flags; template friend class Eigen::Map; friend class Eigen::Map; typedef Eigen::Map MapType; friend class Eigen::Map; typedef const Eigen::Map ConstMapType; friend class Eigen::Map; typedef Eigen::Map AlignedMapType; friend class Eigen::Map; typedef const Eigen::Map ConstAlignedMapType; template struct StridedMapType { typedef Eigen::Map type; }; template struct StridedConstMapType { typedef Eigen::Map type; }; template struct StridedAlignedMapType { typedef Eigen::Map type; }; template struct StridedConstAlignedMapType { typedef Eigen::Map type; }; protected: DenseStorage m_storage; public: enum { NeedsToAlign = SizeAtCompileTime != Dynamic && (internal::traits::Flags & AlignedBit) != 0 }; EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign) Base& base() { return *static_cast(this); } const Base& base() const { return *static_cast(this); } EIGEN_STRONG_INLINE Index rows() const { return m_storage.rows(); } EIGEN_STRONG_INLINE Index cols() const { return m_storage.cols(); } EIGEN_STRONG_INLINE const Scalar& coeff(Index row, Index col) const { if(Flags & RowMajorBit) return m_storage.data()[col + row * m_storage.cols()]; else // column-major return m_storage.data()[row + col * m_storage.rows()]; } EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const { return m_storage.data()[index]; } EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) { if(Flags & RowMajorBit) return m_storage.data()[col + row * m_storage.cols()]; else // column-major return m_storage.data()[row + col * m_storage.rows()]; } EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) { return m_storage.data()[index]; } EIGEN_STRONG_INLINE const Scalar& coeffRef(Index row, Index col) const { if(Flags & RowMajorBit) return m_storage.data()[col + row * m_storage.cols()]; else // column-major return m_storage.data()[row + col * m_storage.rows()]; } EIGEN_STRONG_INLINE const Scalar& coeffRef(Index index) const { return m_storage.data()[index]; } /** \internal */ template EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const { return internal::ploadt (m_storage.data() + (Flags & RowMajorBit ? col + row * m_storage.cols() : row + col * m_storage.rows())); } /** \internal */ template EIGEN_STRONG_INLINE PacketScalar packet(Index index) const { return internal::ploadt(m_storage.data() + index); } /** \internal */ template EIGEN_STRONG_INLINE void writePacket(Index row, Index col, const PacketScalar& x) { internal::pstoret (m_storage.data() + (Flags & RowMajorBit ? col + row * m_storage.cols() : row + col * m_storage.rows()), x); } /** \internal */ template EIGEN_STRONG_INLINE void writePacket(Index index, const PacketScalar& x) { internal::pstoret(m_storage.data() + index, x); } /** \returns a const pointer to the data array of this matrix */ EIGEN_STRONG_INLINE const Scalar *data() const { return m_storage.data(); } /** \returns a pointer to the data array of this matrix */ EIGEN_STRONG_INLINE Scalar *data() { return m_storage.data(); } /** Resizes \c *this to a \a rows x \a cols matrix. * * This method is intended for dynamic-size matrices, although it is legal to call it on any * matrix as long as fixed dimensions are left unchanged. If you only want to change the number * of rows and/or of columns, you can use resize(NoChange_t, Index), resize(Index, NoChange_t). * * If the current number of coefficients of \c *this exactly matches the * product \a rows * \a cols, then no memory allocation is performed and * the current values are left unchanged. In all other cases, including * shrinking, the data is reallocated and all previous values are lost. * * Example: \include Matrix_resize_int_int.cpp * Output: \verbinclude Matrix_resize_int_int.out * * \sa resize(Index) for vectors, resize(NoChange_t, Index), resize(Index, NoChange_t) */ EIGEN_STRONG_INLINE void resize(Index rows, Index cols) { #ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO internal::check_rows_cols_for_overflow(rows, cols); Index size = rows*cols; bool size_changed = size != this->size(); m_storage.resize(size, rows, cols); if(size_changed) EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED #else internal::check_rows_cols_for_overflow(rows, cols); m_storage.resize(rows*cols, rows, cols); #endif } /** Resizes \c *this to a vector of length \a size * * \only_for_vectors. This method does not work for * partially dynamic matrices when the static dimension is anything other * than 1. For example it will not work with Matrix. * * Example: \include Matrix_resize_int.cpp * Output: \verbinclude Matrix_resize_int.out * * \sa resize(Index,Index), resize(NoChange_t, Index), resize(Index, NoChange_t) */ inline void resize(Index size) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(PlainObjectBase) eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == size); #ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO bool size_changed = size != this->size(); #endif if(RowsAtCompileTime == 1) m_storage.resize(size, 1, size); else m_storage.resize(size, size, 1); #ifdef EIGEN_INITIALIZE_MATRICES_BY_ZERO if(size_changed) EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED #endif } /** Resizes the matrix, changing only the number of columns. For the parameter of type NoChange_t, just pass the special value \c NoChange * as in the example below. * * Example: \include Matrix_resize_NoChange_int.cpp * Output: \verbinclude Matrix_resize_NoChange_int.out * * \sa resize(Index,Index) */ inline void resize(NoChange_t, Index cols) { resize(rows(), cols); } /** Resizes the matrix, changing only the number of rows. For the parameter of type NoChange_t, just pass the special value \c NoChange * as in the example below. * * Example: \include Matrix_resize_int_NoChange.cpp * Output: \verbinclude Matrix_resize_int_NoChange.out * * \sa resize(Index,Index) */ inline void resize(Index rows, NoChange_t) { resize(rows, cols()); } /** Resizes \c *this to have the same dimensions as \a other. * Takes care of doing all the checking that's needed. * * Note that copying a row-vector into a vector (and conversely) is allowed. * The resizing, if any, is then done in the appropriate way so that row-vectors * remain row-vectors and vectors remain vectors. */ template EIGEN_STRONG_INLINE void resizeLike(const EigenBase& _other) { const OtherDerived& other = _other.derived(); internal::check_rows_cols_for_overflow(other.rows(), other.cols()); const Index othersize = other.rows()*other.cols(); if(RowsAtCompileTime == 1) { eigen_assert(other.rows() == 1 || other.cols() == 1); resize(1, othersize); } else if(ColsAtCompileTime == 1) { eigen_assert(other.rows() == 1 || other.cols() == 1); resize(othersize, 1); } else resize(other.rows(), other.cols()); } /** Resizes the matrix to \a rows x \a cols while leaving old values untouched. * * The method is intended for matrices of dynamic size. If you only want to change the number * of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or * conservativeResize(Index, NoChange_t). * * Matrices are resized relative to the top-left element. In case values need to be * appended to the matrix they will be uninitialized. */ EIGEN_STRONG_INLINE void conservativeResize(Index rows, Index cols) { internal::conservative_resize_like_impl::run(*this, rows, cols); } /** Resizes the matrix to \a rows x \a cols while leaving old values untouched. * * As opposed to conservativeResize(Index rows, Index cols), this version leaves * the number of columns unchanged. * * In case the matrix is growing, new rows will be uninitialized. */ EIGEN_STRONG_INLINE void conservativeResize(Index rows, NoChange_t) { // Note: see the comment in conservativeResize(Index,Index) conservativeResize(rows, cols()); } /** Resizes the matrix to \a rows x \a cols while leaving old values untouched. * * As opposed to conservativeResize(Index rows, Index cols), this version leaves * the number of rows unchanged. * * In case the matrix is growing, new columns will be uninitialized. */ EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index cols) { // Note: see the comment in conservativeResize(Index,Index) conservativeResize(rows(), cols); } /** Resizes the vector to \a size while retaining old values. * * \only_for_vectors. This method does not work for * partially dynamic matrices when the static dimension is anything other * than 1. For example it will not work with Matrix. * * When values are appended, they will be uninitialized. */ EIGEN_STRONG_INLINE void conservativeResize(Index size) { internal::conservative_resize_like_impl::run(*this, size); } /** Resizes the matrix to \a rows x \a cols of \c other, while leaving old values untouched. * * The method is intended for matrices of dynamic size. If you only want to change the number * of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or * conservativeResize(Index, NoChange_t). * * Matrices are resized relative to the top-left element. In case values need to be * appended to the matrix they will copied from \c other. */ template EIGEN_STRONG_INLINE void conservativeResizeLike(const DenseBase& other) { internal::conservative_resize_like_impl::run(*this, other); } /** This is a special case of the templated operator=. Its purpose is to * prevent a default operator= from hiding the templated operator=. */ EIGEN_STRONG_INLINE Derived& operator=(const PlainObjectBase& other) { return _set(other); } /** \sa MatrixBase::lazyAssign() */ template EIGEN_STRONG_INLINE Derived& lazyAssign(const DenseBase& other) { _resize_to_match(other); return Base::lazyAssign(other.derived()); } template EIGEN_STRONG_INLINE Derived& operator=(const ReturnByValue& func) { resize(func.rows(), func.cols()); return Base::operator=(func); } EIGEN_STRONG_INLINE explicit PlainObjectBase() : m_storage() { // _check_template_params(); // EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED } #ifndef EIGEN_PARSED_BY_DOXYGEN // FIXME is it still needed ? /** \internal */ PlainObjectBase(internal::constructor_without_unaligned_array_assert) : m_storage(internal::constructor_without_unaligned_array_assert()) { // _check_template_params(); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED } #endif EIGEN_STRONG_INLINE PlainObjectBase(Index size, Index rows, Index cols) : m_storage(size, rows, cols) { // _check_template_params(); // EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED } /** \copydoc MatrixBase::operator=(const EigenBase&) */ template EIGEN_STRONG_INLINE Derived& operator=(const EigenBase &other) { _resize_to_match(other); Base::operator=(other.derived()); return this->derived(); } /** \sa MatrixBase::operator=(const EigenBase&) */ template EIGEN_STRONG_INLINE PlainObjectBase(const EigenBase &other) : m_storage(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols()) { _check_template_params(); internal::check_rows_cols_for_overflow(other.derived().rows(), other.derived().cols()); Base::operator=(other.derived()); } /** \name Map * These are convenience functions returning Map objects. The Map() static functions return unaligned Map objects, * while the AlignedMap() functions return aligned Map objects and thus should be called only with 16-byte-aligned * \a data pointers. * * \see class Map */ //@{ static inline ConstMapType Map(const Scalar* data) { return ConstMapType(data); } static inline MapType Map(Scalar* data) { return MapType(data); } static inline ConstMapType Map(const Scalar* data, Index size) { return ConstMapType(data, size); } static inline MapType Map(Scalar* data, Index size) { return MapType(data, size); } static inline ConstMapType Map(const Scalar* data, Index rows, Index cols) { return ConstMapType(data, rows, cols); } static inline MapType Map(Scalar* data, Index rows, Index cols) { return MapType(data, rows, cols); } static inline ConstAlignedMapType MapAligned(const Scalar* data) { return ConstAlignedMapType(data); } static inline AlignedMapType MapAligned(Scalar* data) { return AlignedMapType(data); } static inline ConstAlignedMapType MapAligned(const Scalar* data, Index size) { return ConstAlignedMapType(data, size); } static inline AlignedMapType MapAligned(Scalar* data, Index size) { return AlignedMapType(data, size); } static inline ConstAlignedMapType MapAligned(const Scalar* data, Index rows, Index cols) { return ConstAlignedMapType(data, rows, cols); } static inline AlignedMapType MapAligned(Scalar* data, Index rows, Index cols) { return AlignedMapType(data, rows, cols); } template static inline typename StridedConstMapType >::type Map(const Scalar* data, const Stride& stride) { return typename StridedConstMapType >::type(data, stride); } template static inline typename StridedMapType >::type Map(Scalar* data, const Stride& stride) { return typename StridedMapType >::type(data, stride); } template static inline typename StridedConstMapType >::type Map(const Scalar* data, Index size, const Stride& stride) { return typename StridedConstMapType >::type(data, size, stride); } template static inline typename StridedMapType >::type Map(Scalar* data, Index size, const Stride& stride) { return typename StridedMapType >::type(data, size, stride); } template static inline typename StridedConstMapType >::type Map(const Scalar* data, Index rows, Index cols, const Stride& stride) { return typename StridedConstMapType >::type(data, rows, cols, stride); } template static inline typename StridedMapType >::type Map(Scalar* data, Index rows, Index cols, const Stride& stride) { return typename StridedMapType >::type(data, rows, cols, stride); } template static inline typename StridedConstAlignedMapType >::type MapAligned(const Scalar* data, const Stride& stride) { return typename StridedConstAlignedMapType >::type(data, stride); } template static inline typename StridedAlignedMapType >::type MapAligned(Scalar* data, const Stride& stride) { return typename StridedAlignedMapType >::type(data, stride); } template static inline typename StridedConstAlignedMapType >::type MapAligned(const Scalar* data, Index size, const Stride& stride) { return typename StridedConstAlignedMapType >::type(data, size, stride); } template static inline typename StridedAlignedMapType >::type MapAligned(Scalar* data, Index size, const Stride& stride) { return typename StridedAlignedMapType >::type(data, size, stride); } template static inline typename StridedConstAlignedMapType >::type MapAligned(const Scalar* data, Index rows, Index cols, const Stride& stride) { return typename StridedConstAlignedMapType >::type(data, rows, cols, stride); } template static inline typename StridedAlignedMapType >::type MapAligned(Scalar* data, Index rows, Index cols, const Stride& stride) { return typename StridedAlignedMapType >::type(data, rows, cols, stride); } //@} using Base::setConstant; Derived& setConstant(Index size, const Scalar& value); Derived& setConstant(Index rows, Index cols, const Scalar& value); using Base::setZero; Derived& setZero(Index size); Derived& setZero(Index rows, Index cols); using Base::setOnes; Derived& setOnes(Index size); Derived& setOnes(Index rows, Index cols); using Base::setRandom; Derived& setRandom(Index size); Derived& setRandom(Index rows, Index cols); #ifdef EIGEN_PLAINOBJECTBASE_PLUGIN #include EIGEN_PLAINOBJECTBASE_PLUGIN #endif protected: /** \internal Resizes *this in preparation for assigning \a other to it. * Takes care of doing all the checking that's needed. * * Note that copying a row-vector into a vector (and conversely) is allowed. * The resizing, if any, is then done in the appropriate way so that row-vectors * remain row-vectors and vectors remain vectors. */ template EIGEN_STRONG_INLINE void _resize_to_match(const EigenBase& other) { #ifdef EIGEN_NO_AUTOMATIC_RESIZING eigen_assert((this->size()==0 || (IsVectorAtCompileTime ? (this->size() == other.size()) : (rows() == other.rows() && cols() == other.cols()))) && "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined"); #else resizeLike(other); #endif } /** * \brief Copies the value of the expression \a other into \c *this with automatic resizing. * * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized), * it will be initialized. * * Note that copying a row-vector into a vector (and conversely) is allowed. * The resizing, if any, is then done in the appropriate way so that row-vectors * remain row-vectors and vectors remain vectors. * * \sa operator=(const MatrixBase&), _set_noalias() * * \internal */ template EIGEN_STRONG_INLINE Derived& _set(const DenseBase& other) { _set_selector(other.derived(), typename internal::conditional(int(OtherDerived::Flags) & EvalBeforeAssigningBit), internal::true_type, internal::false_type>::type()); return this->derived(); } template EIGEN_STRONG_INLINE void _set_selector(const OtherDerived& other, const internal::true_type&) { _set_noalias(other.eval()); } template EIGEN_STRONG_INLINE void _set_selector(const OtherDerived& other, const internal::false_type&) { _set_noalias(other); } /** \internal Like _set() but additionally makes the assumption that no aliasing effect can happen (which * is the case when creating a new matrix) so one can enforce lazy evaluation. * * \sa operator=(const MatrixBase&), _set() */ template EIGEN_STRONG_INLINE Derived& _set_noalias(const DenseBase& other) { // I don't think we need this resize call since the lazyAssign will anyways resize // and lazyAssign will be called by the assign selector. //_resize_to_match(other); // the 'false' below means to enforce lazy evaluation. We don't use lazyAssign() because // it wouldn't allow to copy a row-vector into a column-vector. return internal::assign_selector::run(this->derived(), other.derived()); } template EIGEN_STRONG_INLINE void _init2(Index rows, Index cols, typename internal::enable_if::type* = 0) { EIGEN_STATIC_ASSERT(bool(NumTraits::IsInteger) && bool(NumTraits::IsInteger), FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED) eigen_assert(rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)); internal::check_rows_cols_for_overflow(rows, cols); m_storage.resize(rows*cols,rows,cols); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED } template EIGEN_STRONG_INLINE void _init2(const Scalar& x, const Scalar& y, typename internal::enable_if::type* = 0) { EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2) m_storage.data()[0] = x; m_storage.data()[1] = y; } template friend struct internal::matrix_swap_impl; /** \internal generic implementation of swap for dense storage since for dynamic-sized matrices of same type it is enough to swap the * data pointers. */ template void _swap(DenseBase const & other) { enum { SwapPointers = internal::is_same::value && Base::SizeAtCompileTime==Dynamic }; internal::matrix_swap_impl::run(this->derived(), other.const_cast_derived()); } public: #ifndef EIGEN_PARSED_BY_DOXYGEN static EIGEN_STRONG_INLINE void _check_template_params() { EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, (Options&RowMajor)==RowMajor) && EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, (Options&RowMajor)==0) && ((RowsAtCompileTime == Dynamic) || (RowsAtCompileTime >= 0)) && ((ColsAtCompileTime == Dynamic) || (ColsAtCompileTime >= 0)) && ((MaxRowsAtCompileTime == Dynamic) || (MaxRowsAtCompileTime >= 0)) && ((MaxColsAtCompileTime == Dynamic) || (MaxColsAtCompileTime >= 0)) && (MaxRowsAtCompileTime == RowsAtCompileTime || RowsAtCompileTime==Dynamic) && (MaxColsAtCompileTime == ColsAtCompileTime || ColsAtCompileTime==Dynamic) && (Options & (DontAlign|RowMajor)) == Options), INVALID_MATRIX_TEMPLATE_PARAMETERS) } #endif private: enum { ThisConstantIsPrivateInPlainObjectBase }; }; template struct internal::conservative_resize_like_impl { typedef typename Derived::Index Index; static void run(DenseBase& _this, Index rows, Index cols) { if (_this.rows() == rows && _this.cols() == cols) return; EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived) if ( ( Derived::IsRowMajor && _this.cols() == cols) || // row-major and we change only the number of rows (!Derived::IsRowMajor && _this.rows() == rows) ) // column-major and we change only the number of columns { internal::check_rows_cols_for_overflow(rows, cols); _this.derived().m_storage.conservativeResize(rows*cols,rows,cols); } else { // The storage order does not allow us to use reallocation. typename Derived::PlainObject tmp(rows,cols); const Index common_rows = (std::min)(rows, _this.rows()); const Index common_cols = (std::min)(cols, _this.cols()); tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols); _this.derived().swap(tmp); } } static void run(DenseBase& _this, const DenseBase& other) { if (_this.rows() == other.rows() && _this.cols() == other.cols()) return; // Note: Here is space for improvement. Basically, for conservativeResize(Index,Index), // neither RowsAtCompileTime or ColsAtCompileTime must be Dynamic. If only one of the // dimensions is dynamic, one could use either conservativeResize(Index rows, NoChange_t) or // conservativeResize(NoChange_t, Index cols). For these methods new static asserts like // EIGEN_STATIC_ASSERT_DYNAMIC_ROWS and EIGEN_STATIC_ASSERT_DYNAMIC_COLS would be good. EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived) EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(OtherDerived) if ( ( Derived::IsRowMajor && _this.cols() == other.cols()) || // row-major and we change only the number of rows (!Derived::IsRowMajor && _this.rows() == other.rows()) ) // column-major and we change only the number of columns { const Index new_rows = other.rows() - _this.rows(); const Index new_cols = other.cols() - _this.cols(); _this.derived().m_storage.conservativeResize(other.size(),other.rows(),other.cols()); if (new_rows>0) _this.bottomRightCorner(new_rows, other.cols()) = other.bottomRows(new_rows); else if (new_cols>0) _this.bottomRightCorner(other.rows(), new_cols) = other.rightCols(new_cols); } else { // The storage order does not allow us to use reallocation. typename Derived::PlainObject tmp(other); const Index common_rows = (std::min)(tmp.rows(), _this.rows()); const Index common_cols = (std::min)(tmp.cols(), _this.cols()); tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols); _this.derived().swap(tmp); } } }; namespace internal { template struct conservative_resize_like_impl { typedef typename Derived::Index Index; static void run(DenseBase& _this, Index size) { const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : size; const Index new_cols = Derived::RowsAtCompileTime==1 ? size : 1; _this.derived().m_storage.conservativeResize(size,new_rows,new_cols); } static void run(DenseBase& _this, const DenseBase& other) { if (_this.rows() == other.rows() && _this.cols() == other.cols()) return; const Index num_new_elements = other.size() - _this.size(); const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : other.rows(); const Index new_cols = Derived::RowsAtCompileTime==1 ? other.cols() : 1; _this.derived().m_storage.conservativeResize(other.size(),new_rows,new_cols); if (num_new_elements > 0) _this.tail(num_new_elements) = other.tail(num_new_elements); } }; template struct matrix_swap_impl { static inline void run(MatrixTypeA& a, MatrixTypeB& b) { a.base().swap(b); } }; template struct matrix_swap_impl { static inline void run(MatrixTypeA& a, MatrixTypeB& b) { static_cast(a).m_storage.swap(static_cast(b).m_storage); } }; } // end namespace internal } // end namespace Eigen #endif // EIGEN_DENSESTORAGEBASE_H ctbnrle.1.1.1/hdr/Eigen/src/Core/DiagonalProduct.h0000775000175000017500000001400712107032422022077 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Gael Guennebaud // Copyright (C) 2007-2009 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_DIAGONALPRODUCT_H #define EIGEN_DIAGONALPRODUCT_H namespace Eigen { namespace internal { template struct traits > : traits { typedef typename scalar_product_traits::ReturnType Scalar; enum { RowsAtCompileTime = MatrixType::RowsAtCompileTime, ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime, _StorageOrder = MatrixType::Flags & RowMajorBit ? RowMajor : ColMajor, _PacketOnDiag = !((int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft) ||(int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)), _SameTypes = is_same::value, // FIXME currently we need same types, but in the future the next rule should be the one //_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagonalType::Flags)&PacketAccessBit))), _Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && _SameTypes && ((!_PacketOnDiag) || (bool(int(DiagonalType::Flags)&PacketAccessBit))), Flags = (HereditaryBits & (unsigned int)(MatrixType::Flags)) | (_Vectorizable ? PacketAccessBit : 0), CoeffReadCost = NumTraits::MulCost + MatrixType::CoeffReadCost + DiagonalType::DiagonalVectorType::CoeffReadCost }; }; } template class DiagonalProduct : internal::no_assignment_operator, public MatrixBase > { public: typedef MatrixBase Base; EIGEN_DENSE_PUBLIC_INTERFACE(DiagonalProduct) inline DiagonalProduct(const MatrixType& matrix, const DiagonalType& diagonal) : m_matrix(matrix), m_diagonal(diagonal) { eigen_assert(diagonal.diagonal().size() == (ProductOrder == OnTheLeft ? matrix.rows() : matrix.cols())); } inline Index rows() const { return m_matrix.rows(); } inline Index cols() const { return m_matrix.cols(); } const Scalar coeff(Index row, Index col) const { return m_diagonal.diagonal().coeff(ProductOrder == OnTheLeft ? row : col) * m_matrix.coeff(row, col); } template EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const { enum { StorageOrder = Flags & RowMajorBit ? RowMajor : ColMajor }; const Index indexInDiagonalVector = ProductOrder == OnTheLeft ? row : col; return packet_impl(row,col,indexInDiagonalVector,typename internal::conditional< ((int(StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft) ||(int(StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)), internal::true_type, internal::false_type>::type()); } protected: template EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::true_type) const { return internal::pmul(m_matrix.template packet(row, col), internal::pset1(m_diagonal.diagonal().coeff(id))); } template EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::false_type) const { enum { InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime, DiagonalVectorPacketLoadMode = (LoadMode == Aligned && ((InnerSize%16) == 0)) ? Aligned : Unaligned }; return internal::pmul(m_matrix.template packet(row, col), m_diagonal.diagonal().template packet(id)); } typename MatrixType::Nested m_matrix; typename DiagonalType::Nested m_diagonal; }; /** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal. */ template template inline const DiagonalProduct MatrixBase::operator*(const DiagonalBase &diagonal) const { return DiagonalProduct(derived(), diagonal.derived()); } /** \returns the diagonal matrix product of \c *this by the matrix \a matrix. */ template template inline const DiagonalProduct DiagonalBase::operator*(const MatrixBase &matrix) const { return DiagonalProduct(matrix.derived(), derived()); } } // end namespace Eigen #endif // EIGEN_DIAGONALPRODUCT_H ctbnrle.1.1.1/hdr/Eigen/src/Core/Matrix.h0000775000175000017500000004260212107032422020266 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2006-2010 Benoit Jacob // Copyright (C) 2008-2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_MATRIX_H #define EIGEN_MATRIX_H namespace Eigen { /** \class Matrix * \ingroup Core_Module * * \brief The matrix class, also used for vectors and row-vectors * * The %Matrix class is the work-horse for all \em dense (\ref dense "note") matrices and vectors within Eigen. * Vectors are matrices with one column, and row-vectors are matrices with one row. * * The %Matrix class encompasses \em both fixed-size and dynamic-size objects (\ref fixedsize "note"). * * The first three template parameters are required: * \tparam _Scalar \anchor matrix_tparam_scalar Numeric type, e.g. float, double, int or std::complex. * User defined sclar types are supported as well (see \ref user_defined_scalars "here"). * \tparam _Rows Number of rows, or \b Dynamic * \tparam _Cols Number of columns, or \b Dynamic * * The remaining template parameters are optional -- in most cases you don't have to worry about them. * \tparam _Options \anchor matrix_tparam_options A combination of either \b #RowMajor or \b #ColMajor, and of either * \b #AutoAlign or \b #DontAlign. * The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required * for vectorization. It defaults to aligning matrices except for fixed sizes that aren't a multiple of the packet size. * \tparam _MaxRows Maximum number of rows. Defaults to \a _Rows (\ref maxrows "note"). * \tparam _MaxCols Maximum number of columns. Defaults to \a _Cols (\ref maxrows "note"). * * Eigen provides a number of typedefs covering the usual cases. Here are some examples: * * \li \c Matrix2d is a 2x2 square matrix of doubles (\c Matrix) * \li \c Vector4f is a vector of 4 floats (\c Matrix) * \li \c RowVector3i is a row-vector of 3 ints (\c Matrix) * * \li \c MatrixXf is a dynamic-size matrix of floats (\c Matrix) * \li \c VectorXf is a dynamic-size vector of floats (\c Matrix) * * \li \c Matrix2Xf is a partially fixed-size (dynamic-size) matrix of floats (\c Matrix) * \li \c MatrixX3d is a partially dynamic-size (fixed-size) matrix of double (\c Matrix) * * See \link matrixtypedefs this page \endlink for a complete list of predefined \em %Matrix and \em Vector typedefs. * * You can access elements of vectors and matrices using normal subscripting: * * \code * Eigen::VectorXd v(10); * v[0] = 0.1; * v[1] = 0.2; * v(0) = 0.3; * v(1) = 0.4; * * Eigen::MatrixXi m(10, 10); * m(0, 1) = 1; * m(0, 2) = 2; * m(0, 3) = 3; * \endcode * * This class can be extended with the help of the plugin mechanism described on the page * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_MATRIX_PLUGIN. * * Some notes: * *
*
\anchor dense Dense versus sparse:
*
This %Matrix class handles dense, not sparse matrices and vectors. For sparse matrices and vectors, see the Sparse module. * * Dense matrices and vectors are plain usual arrays of coefficients. All the coefficients are stored, in an ordinary contiguous array. * This is unlike Sparse matrices and vectors where the coefficients are stored as a list of nonzero coefficients.
* *
\anchor fixedsize Fixed-size versus dynamic-size:
*
Fixed-size means that the numbers of rows and columns are known are compile-time. In this case, Eigen allocates the array * of coefficients as a fixed-size array, as a class member. This makes sense for very small matrices, typically up to 4x4, sometimes up * to 16x16. Larger matrices should be declared as dynamic-size even if one happens to know their size at compile-time. * * Dynamic-size means that the numbers of rows or columns are not necessarily known at compile-time. In this case they are runtime * variables, and the array of coefficients is allocated dynamically on the heap. * * Note that \em dense matrices, be they Fixed-size or Dynamic-size, do not expand dynamically in the sense of a std::map. * If you want this behavior, see the Sparse module.
* *
\anchor maxrows _MaxRows and _MaxCols:
*
In most cases, one just leaves these parameters to the default values. * These parameters mean the maximum size of rows and columns that the matrix may have. They are useful in cases * when the exact numbers of rows and columns are not known are compile-time, but it is known at compile-time that they cannot * exceed a certain value. This happens when taking dynamic-size blocks inside fixed-size matrices: in this case _MaxRows and _MaxCols * are the dimensions of the original matrix, while _Rows and _Cols are Dynamic.
*
* * \see MatrixBase for the majority of the API methods for matrices, \ref TopicClassHierarchy, * \ref TopicStorageOrders */ namespace internal { template struct traits > { typedef _Scalar Scalar; typedef Dense StorageKind; typedef DenseIndex Index; typedef MatrixXpr XprKind; enum { RowsAtCompileTime = _Rows, ColsAtCompileTime = _Cols, MaxRowsAtCompileTime = _MaxRows, MaxColsAtCompileTime = _MaxCols, Flags = compute_matrix_flags<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::ret, CoeffReadCost = NumTraits::ReadCost, Options = _Options, InnerStrideAtCompileTime = 1, OuterStrideAtCompileTime = (Options&RowMajor) ? ColsAtCompileTime : RowsAtCompileTime }; }; } template class Matrix : public PlainObjectBase > { public: /** \brief Base class typedef. * \sa PlainObjectBase */ typedef PlainObjectBase Base; enum { Options = _Options }; EIGEN_DENSE_PUBLIC_INTERFACE(Matrix) typedef typename Base::PlainObject PlainObject; using Base::base; using Base::coeffRef; /** * \brief Assigns matrices to each other. * * \note This is a special case of the templated operator=. Its purpose is * to prevent a default operator= from hiding the templated operator=. * * \callgraph */ EIGEN_STRONG_INLINE Matrix& operator=(const Matrix& other) { return Base::_set(other); } /** \internal * \brief Copies the value of the expression \a other into \c *this with automatic resizing. * * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized), * it will be initialized. * * Note that copying a row-vector into a vector (and conversely) is allowed. * The resizing, if any, is then done in the appropriate way so that row-vectors * remain row-vectors and vectors remain vectors. */ template EIGEN_STRONG_INLINE Matrix& operator=(const MatrixBase& other) { return Base::_set(other); } /* Here, doxygen failed to copy the brief information when using \copydoc */ /** * \brief Copies the generic expression \a other into *this. * \copydetails DenseBase::operator=(const EigenBase &other) */ template EIGEN_STRONG_INLINE Matrix& operator=(const EigenBase &other) { return Base::operator=(other); } template EIGEN_STRONG_INLINE Matrix& operator=(const ReturnByValue& func) { return Base::operator=(func); } /** \brief Default constructor. * * For fixed-size matrices, does nothing. * * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix * is called a null matrix. This constructor is the unique way to create null matrices: resizing * a matrix to 0 is not supported. * * \sa resize(Index,Index) */ EIGEN_STRONG_INLINE explicit Matrix() : Base() { Base::_check_template_params(); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED } // FIXME is it still needed Matrix(internal::constructor_without_unaligned_array_assert) : Base(internal::constructor_without_unaligned_array_assert()) { Base::_check_template_params(); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED } /** \brief Constructs a vector or row-vector with given dimension. \only_for_vectors * * Note that this is only useful for dynamic-size vectors. For fixed-size vectors, * it is redundant to pass the dimension here, so it makes more sense to use the default * constructor Matrix() instead. */ EIGEN_STRONG_INLINE explicit Matrix(Index dim) : Base(dim, RowsAtCompileTime == 1 ? 1 : dim, ColsAtCompileTime == 1 ? 1 : dim) { Base::_check_template_params(); EIGEN_STATIC_ASSERT_VECTOR_ONLY(Matrix) eigen_assert(dim >= 0); eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED } #ifndef EIGEN_PARSED_BY_DOXYGEN template EIGEN_STRONG_INLINE Matrix(const T0& x, const T1& y) { Base::_check_template_params(); Base::template _init2(x, y); } #else /** \brief Constructs an uninitialized matrix with \a rows rows and \a cols columns. * * This is useful for dynamic-size matrices. For fixed-size matrices, * it is redundant to pass these parameters, so one should use the default constructor * Matrix() instead. */ Matrix(Index rows, Index cols); /** \brief Constructs an initialized 2D vector with given coefficients */ Matrix(const Scalar& x, const Scalar& y); #endif /** \brief Constructs an initialized 3D vector with given coefficients */ EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z) { Base::_check_template_params(); EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 3) m_storage.data()[0] = x; m_storage.data()[1] = y; m_storage.data()[2] = z; } /** \brief Constructs an initialized 4D vector with given coefficients */ EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w) { Base::_check_template_params(); EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 4) m_storage.data()[0] = x; m_storage.data()[1] = y; m_storage.data()[2] = z; m_storage.data()[3] = w; } explicit Matrix(const Scalar *data); /** \brief Constructor copying the value of the expression \a other */ template EIGEN_STRONG_INLINE Matrix(const MatrixBase& other) : Base(other.rows() * other.cols(), other.rows(), other.cols()) { // This test resides here, to bring the error messages closer to the user. Normally, these checks // are performed deeply within the library, thus causing long and scary error traces. EIGEN_STATIC_ASSERT((internal::is_same::value), YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) Base::_check_template_params(); Base::_set_noalias(other); } /** \brief Copy constructor */ EIGEN_STRONG_INLINE Matrix(const Matrix& other) : Base(other.rows() * other.cols(), other.rows(), other.cols()) { Base::_check_template_params(); Base::_set_noalias(other); } /** \brief Copy constructor with in-place evaluation */ template EIGEN_STRONG_INLINE Matrix(const ReturnByValue& other) { Base::_check_template_params(); Base::resize(other.rows(), other.cols()); other.evalTo(*this); } /** \brief Copy constructor for generic expressions. * \sa MatrixBase::operator=(const EigenBase&) */ template EIGEN_STRONG_INLINE Matrix(const EigenBase &other) : Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols()) { Base::_check_template_params(); Base::resize(other.rows(), other.cols()); // FIXME/CHECK: isn't *this = other.derived() more efficient. it allows to // go for pure _set() implementations, right? *this = other; } /** \internal * \brief Override MatrixBase::swap() since for dynamic-sized matrices * of same type it is enough to swap the data pointers. */ template void swap(MatrixBase const & other) { this->_swap(other.derived()); } inline Index innerStride() const { return 1; } inline Index outerStride() const { return this->innerSize(); } /////////// Geometry module /////////// template explicit Matrix(const RotationBase& r); template Matrix& operator=(const RotationBase& r); #ifdef EIGEN2_SUPPORT template explicit Matrix(const eigen2_RotationBase& r); template Matrix& operator=(const eigen2_RotationBase& r); #endif // allow to extend Matrix outside Eigen #ifdef EIGEN_MATRIX_PLUGIN #include EIGEN_MATRIX_PLUGIN #endif protected: template friend struct internal::conservative_resize_like_impl; using Base::m_storage; }; /** \defgroup matrixtypedefs Global matrix typedefs * * \ingroup Core_Module * * Eigen defines several typedef shortcuts for most common matrix and vector types. * * The general patterns are the following: * * \c MatrixSizeType where \c Size can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size, * and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd * for complex double. * * For example, \c Matrix3d is a fixed-size 3x3 matrix type of doubles, and \c MatrixXf is a dynamic-size matrix of floats. * * There are also \c VectorSizeType and \c RowVectorSizeType which are self-explanatory. For example, \c Vector4cf is * a fixed-size vector of 4 complex floats. * * \sa class Matrix */ #define EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix) \ /** \ingroup matrixtypedefs */ \ typedef Matrix Matrix##SizeSuffix##TypeSuffix; \ /** \ingroup matrixtypedefs */ \ typedef Matrix Vector##SizeSuffix##TypeSuffix; \ /** \ingroup matrixtypedefs */ \ typedef Matrix RowVector##SizeSuffix##TypeSuffix; #define EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, Size) \ /** \ingroup matrixtypedefs */ \ typedef Matrix Matrix##Size##X##TypeSuffix; \ /** \ingroup matrixtypedefs */ \ typedef Matrix Matrix##X##Size##TypeSuffix; #define EIGEN_MAKE_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \ EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 2, 2) \ EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 3, 3) \ EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 4, 4) \ EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \ EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \ EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \ EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 4) EIGEN_MAKE_TYPEDEFS_ALL_SIZES(int, i) EIGEN_MAKE_TYPEDEFS_ALL_SIZES(float, f) EIGEN_MAKE_TYPEDEFS_ALL_SIZES(double, d) EIGEN_MAKE_TYPEDEFS_ALL_SIZES(std::complex, cf) EIGEN_MAKE_TYPEDEFS_ALL_SIZES(std::complex, cd) #undef EIGEN_MAKE_TYPEDEFS_ALL_SIZES #undef EIGEN_MAKE_TYPEDEFS #undef EIGEN_MAKE_FIXED_TYPEDEFS } // end namespace Eigen #endif // EIGEN_MATRIX_H ctbnrle.1.1.1/hdr/Eigen/src/Core/CwiseBinaryOp.h0000775000175000017500000002454312107032422021544 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2009 Gael Guennebaud // Copyright (C) 2006-2008 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_CWISE_BINARY_OP_H #define EIGEN_CWISE_BINARY_OP_H namespace Eigen { /** \class CwiseBinaryOp * \ingroup Core_Module * * \brief Generic expression where a coefficient-wise binary operator is applied to two expressions * * \param BinaryOp template functor implementing the operator * \param Lhs the type of the left-hand side * \param Rhs the type of the right-hand side * * This class represents an expression where a coefficient-wise binary operator is applied to two expressions. * It is the return type of binary operators, by which we mean only those binary operators where * both the left-hand side and the right-hand side are Eigen expressions. * For example, the return type of matrix1+matrix2 is a CwiseBinaryOp. * * Most of the time, this is the only way that it is used, so you typically don't have to name * CwiseBinaryOp types explicitly. * * \sa MatrixBase::binaryExpr(const MatrixBase &,const CustomBinaryOp &) const, class CwiseUnaryOp, class CwiseNullaryOp */ namespace internal { template struct traits > { // we must not inherit from traits since it has // the potential to cause problems with MSVC typedef typename remove_all::type Ancestor; typedef typename traits::XprKind XprKind; enum { RowsAtCompileTime = traits::RowsAtCompileTime, ColsAtCompileTime = traits::ColsAtCompileTime, MaxRowsAtCompileTime = traits::MaxRowsAtCompileTime, MaxColsAtCompileTime = traits::MaxColsAtCompileTime }; // even though we require Lhs and Rhs to have the same scalar type (see CwiseBinaryOp constructor), // we still want to handle the case when the result type is different. typedef typename result_of< BinaryOp( typename Lhs::Scalar, typename Rhs::Scalar ) >::type Scalar; typedef typename promote_storage_type::StorageKind, typename traits::StorageKind>::ret StorageKind; typedef typename promote_index_type::Index, typename traits::Index>::type Index; typedef typename Lhs::Nested LhsNested; typedef typename Rhs::Nested RhsNested; typedef typename remove_reference::type _LhsNested; typedef typename remove_reference::type _RhsNested; enum { LhsCoeffReadCost = _LhsNested::CoeffReadCost, RhsCoeffReadCost = _RhsNested::CoeffReadCost, LhsFlags = _LhsNested::Flags, RhsFlags = _RhsNested::Flags, SameType = is_same::value, StorageOrdersAgree = (int(Lhs::Flags)&RowMajorBit)==(int(Rhs::Flags)&RowMajorBit), Flags0 = (int(LhsFlags) | int(RhsFlags)) & ( HereditaryBits | (int(LhsFlags) & int(RhsFlags) & ( AlignedBit | (StorageOrdersAgree ? LinearAccessBit : 0) | (functor_traits::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0) ) ) ), Flags = (Flags0 & ~RowMajorBit) | (LhsFlags & RowMajorBit), CoeffReadCost = LhsCoeffReadCost + RhsCoeffReadCost + functor_traits::Cost }; }; } // end namespace internal // we require Lhs and Rhs to have the same scalar type. Currently there is no example of a binary functor // that would take two operands of different types. If there were such an example, then this check should be // moved to the BinaryOp functors, on a per-case basis. This would however require a change in the BinaryOp functors, as // currently they take only one typename Scalar template parameter. // It is tempting to always allow mixing different types but remember that this is often impossible in the vectorized paths. // So allowing mixing different types gives very unexpected errors when enabling vectorization, when the user tries to // add together a float matrix and a double matrix. #define EIGEN_CHECK_BINARY_COMPATIBILIY(BINOP,LHS,RHS) \ EIGEN_STATIC_ASSERT((internal::functor_allows_mixing_real_and_complex::ret \ ? int(internal::is_same::Real, typename NumTraits::Real>::value) \ : int(internal::is_same::value)), \ YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) template class CwiseBinaryOpImpl; template class CwiseBinaryOp : internal::no_assignment_operator, public CwiseBinaryOpImpl< BinaryOp, Lhs, Rhs, typename internal::promote_storage_type::StorageKind, typename internal::traits::StorageKind>::ret> { public: typedef typename CwiseBinaryOpImpl< BinaryOp, Lhs, Rhs, typename internal::promote_storage_type::StorageKind, typename internal::traits::StorageKind>::ret>::Base Base; EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseBinaryOp) typedef typename internal::nested::type LhsNested; typedef typename internal::nested::type RhsNested; typedef typename internal::remove_reference::type _LhsNested; typedef typename internal::remove_reference::type _RhsNested; EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& lhs, const Rhs& rhs, const BinaryOp& func = BinaryOp()) : m_lhs(lhs), m_rhs(rhs), m_functor(func) { EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename Rhs::Scalar); // require the sizes to match EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs) eigen_assert(lhs.rows() == rhs.rows() && lhs.cols() == rhs.cols()); } EIGEN_STRONG_INLINE Index rows() const { // return the fixed size type if available to enable compile time optimizations if (internal::traits::type>::RowsAtCompileTime==Dynamic) return m_rhs.rows(); else return m_lhs.rows(); } EIGEN_STRONG_INLINE Index cols() const { // return the fixed size type if available to enable compile time optimizations if (internal::traits::type>::ColsAtCompileTime==Dynamic) return m_rhs.cols(); else return m_lhs.cols(); } /** \returns the left hand side nested expression */ const _LhsNested& lhs() const { return m_lhs; } /** \returns the right hand side nested expression */ const _RhsNested& rhs() const { return m_rhs; } /** \returns the functor representing the binary operation */ const BinaryOp& functor() const { return m_functor; } protected: LhsNested m_lhs; RhsNested m_rhs; const BinaryOp m_functor; }; template class CwiseBinaryOpImpl : public internal::dense_xpr_base >::type { typedef CwiseBinaryOp Derived; public: typedef typename internal::dense_xpr_base >::type Base; EIGEN_DENSE_PUBLIC_INTERFACE( Derived ) EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const { return derived().functor()(derived().lhs().coeff(row, col), derived().rhs().coeff(row, col)); } template EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const { return derived().functor().packetOp(derived().lhs().template packet(row, col), derived().rhs().template packet(row, col)); } EIGEN_STRONG_INLINE const Scalar coeff(Index index) const { return derived().functor()(derived().lhs().coeff(index), derived().rhs().coeff(index)); } template EIGEN_STRONG_INLINE PacketScalar packet(Index index) const { return derived().functor().packetOp(derived().lhs().template packet(index), derived().rhs().template packet(index)); } }; /** replaces \c *this by \c *this - \a other. * * \returns a reference to \c *this */ template template EIGEN_STRONG_INLINE Derived & MatrixBase::operator-=(const MatrixBase &other) { SelfCwiseBinaryOp, Derived, OtherDerived> tmp(derived()); tmp = other.derived(); return derived(); } /** replaces \c *this by \c *this + \a other. * * \returns a reference to \c *this */ template template EIGEN_STRONG_INLINE Derived & MatrixBase::operator+=(const MatrixBase& other) { SelfCwiseBinaryOp, Derived, OtherDerived> tmp(derived()); tmp = other.derived(); return derived(); } } // end namespace Eigen #endif // EIGEN_CWISE_BINARY_OP_H ctbnrle.1.1.1/hdr/Eigen/src/Core/MatrixBase.h0000775000175000017500000005544512107032422021072 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2006-2009 Benoit Jacob // Copyright (C) 2008 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_MATRIXBASE_H #define EIGEN_MATRIXBASE_H namespace Eigen { /** \class MatrixBase * \ingroup Core_Module * * \brief Base class for all dense matrices, vectors, and expressions * * This class is the base that is inherited by all matrix, vector, and related expression * types. Most of the Eigen API is contained in this class, and its base classes. Other important * classes for the Eigen API are Matrix, and VectorwiseOp. * * Note that some methods are defined in other modules such as the \ref LU_Module LU module * for all functions related to matrix inversions. * * \tparam Derived is the derived type, e.g. a matrix type, or an expression, etc. * * When writing a function taking Eigen objects as argument, if you want your function * to take as argument any matrix, vector, or expression, just let it take a * MatrixBase argument. As an example, here is a function printFirstRow which, given * a matrix, vector, or expression \a x, prints the first row of \a x. * * \code template void printFirstRow(const Eigen::MatrixBase& x) { cout << x.row(0) << endl; } * \endcode * * This class can be extended with the help of the plugin mechanism described on the page * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_MATRIXBASE_PLUGIN. * * \sa \ref TopicClassHierarchy */ template class MatrixBase : public DenseBase { public: #ifndef EIGEN_PARSED_BY_DOXYGEN typedef MatrixBase StorageBaseType; typedef typename internal::traits::StorageKind StorageKind; typedef typename internal::traits::Index Index; typedef typename internal::traits::Scalar Scalar; typedef typename internal::packet_traits::type PacketScalar; typedef typename NumTraits::Real RealScalar; typedef DenseBase Base; using Base::RowsAtCompileTime; using Base::ColsAtCompileTime; using Base::SizeAtCompileTime; using Base::MaxRowsAtCompileTime; using Base::MaxColsAtCompileTime; using Base::MaxSizeAtCompileTime; using Base::IsVectorAtCompileTime; using Base::Flags; using Base::CoeffReadCost; using Base::derived; using Base::const_cast_derived; using Base::rows; using Base::cols; using Base::size; using Base::coeff; using Base::coeffRef; using Base::lazyAssign; using Base::eval; using Base::operator+=; using Base::operator-=; using Base::operator*=; using Base::operator/=; typedef typename Base::CoeffReturnType CoeffReturnType; typedef typename Base::ConstTransposeReturnType ConstTransposeReturnType; typedef typename Base::RowXpr RowXpr; typedef typename Base::ColXpr ColXpr; #endif // not EIGEN_PARSED_BY_DOXYGEN #ifndef EIGEN_PARSED_BY_DOXYGEN /** type of the equivalent square matrix */ typedef Matrix SquareMatrixType; #endif // not EIGEN_PARSED_BY_DOXYGEN /** \returns the size of the main diagonal, which is min(rows(),cols()). * \sa rows(), cols(), SizeAtCompileTime. */ inline Index diagonalSize() const { return (std::min)(rows(),cols()); } /** \brief The plain matrix type corresponding to this expression. * * This is not necessarily exactly the return type of eval(). In the case of plain matrices, * the return type of eval() is a const reference to a matrix, not a matrix! It is however guaranteed * that the return type of eval() is either PlainObject or const PlainObject&. */ typedef Matrix::Scalar, internal::traits::RowsAtCompileTime, internal::traits::ColsAtCompileTime, AutoAlign | (internal::traits::Flags&RowMajorBit ? RowMajor : ColMajor), internal::traits::MaxRowsAtCompileTime, internal::traits::MaxColsAtCompileTime > PlainObject; #ifndef EIGEN_PARSED_BY_DOXYGEN /** \internal Represents a matrix with all coefficients equal to one another*/ typedef CwiseNullaryOp,Derived> ConstantReturnType; /** \internal the return type of MatrixBase::adjoint() */ typedef typename internal::conditional::IsComplex, CwiseUnaryOp, ConstTransposeReturnType>, ConstTransposeReturnType >::type AdjointReturnType; /** \internal Return type of eigenvalues() */ typedef Matrix, internal::traits::ColsAtCompileTime, 1, ColMajor> EigenvaluesReturnType; /** \internal the return type of identity */ typedef CwiseNullaryOp,Derived> IdentityReturnType; /** \internal the return type of unit vectors */ typedef Block, SquareMatrixType>, internal::traits::RowsAtCompileTime, internal::traits::ColsAtCompileTime> BasisReturnType; #endif // not EIGEN_PARSED_BY_DOXYGEN #define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::MatrixBase # include "../plugins/CommonCwiseUnaryOps.h" # include "../plugins/CommonCwiseBinaryOps.h" # include "../plugins/MatrixCwiseUnaryOps.h" # include "../plugins/MatrixCwiseBinaryOps.h" # ifdef EIGEN_MATRIXBASE_PLUGIN # include EIGEN_MATRIXBASE_PLUGIN # endif #undef EIGEN_CURRENT_STORAGE_BASE_CLASS /** Special case of the template operator=, in order to prevent the compiler * from generating a default operator= (issue hit with g++ 4.1) */ Derived& operator=(const MatrixBase& other); // We cannot inherit here via Base::operator= since it is causing // trouble with MSVC. template Derived& operator=(const DenseBase& other); template Derived& operator=(const EigenBase& other); template Derived& operator=(const ReturnByValue& other); #ifndef EIGEN_PARSED_BY_DOXYGEN template Derived& lazyAssign(const ProductBase& other); #endif // not EIGEN_PARSED_BY_DOXYGEN template Derived& operator+=(const MatrixBase& other); template Derived& operator-=(const MatrixBase& other); template const typename ProductReturnType::Type operator*(const MatrixBase &other) const; template const typename LazyProductReturnType::Type lazyProduct(const MatrixBase &other) const; template Derived& operator*=(const EigenBase& other); template void applyOnTheLeft(const EigenBase& other); template void applyOnTheRight(const EigenBase& other); template const DiagonalProduct operator*(const DiagonalBase &diagonal) const; template typename internal::scalar_product_traits::Scalar,typename internal::traits::Scalar>::ReturnType dot(const MatrixBase& other) const; #ifdef EIGEN2_SUPPORT template Scalar eigen2_dot(const MatrixBase& other) const; #endif RealScalar squaredNorm() const; RealScalar norm() const; RealScalar stableNorm() const; RealScalar blueNorm() const; RealScalar hypotNorm() const; const PlainObject normalized() const; void normalize(); const AdjointReturnType adjoint() const; void adjointInPlace(); typedef Diagonal DiagonalReturnType; DiagonalReturnType diagonal(); typedef const Diagonal ConstDiagonalReturnType; const ConstDiagonalReturnType diagonal() const; template struct DiagonalIndexReturnType { typedef Diagonal Type; }; template struct ConstDiagonalIndexReturnType { typedef const Diagonal Type; }; template typename DiagonalIndexReturnType::Type diagonal(); template typename ConstDiagonalIndexReturnType::Type diagonal() const; // Note: The "MatrixBase::" prefixes are added to help MSVC9 to match these declarations with the later implementations. // On the other hand they confuse MSVC8... #if (defined _MSC_VER) && (_MSC_VER >= 1500) // 2008 or later typename MatrixBase::template DiagonalIndexReturnType::Type diagonal(Index index); typename MatrixBase::template ConstDiagonalIndexReturnType::Type diagonal(Index index) const; #else typename DiagonalIndexReturnType::Type diagonal(Index index); typename ConstDiagonalIndexReturnType::Type diagonal(Index index) const; #endif #ifdef EIGEN2_SUPPORT template typename internal::eigen2_part_return_type::type part(); template const typename internal::eigen2_part_return_type::type part() const; // huuuge hack. make Eigen2's matrix.part() work in eigen3. Problem: Diagonal is now a class template instead // of an integer constant. Solution: overload the part() method template wrt template parameters list. template class U> const DiagonalWrapper part() const { return diagonal().asDiagonal(); } #endif // EIGEN2_SUPPORT template struct TriangularViewReturnType { typedef TriangularView Type; }; template struct ConstTriangularViewReturnType { typedef const TriangularView Type; }; template typename TriangularViewReturnType::Type triangularView(); template typename ConstTriangularViewReturnType::Type triangularView() const; template struct SelfAdjointViewReturnType { typedef SelfAdjointView Type; }; template struct ConstSelfAdjointViewReturnType { typedef const SelfAdjointView Type; }; template typename SelfAdjointViewReturnType::Type selfadjointView(); template typename ConstSelfAdjointViewReturnType::Type selfadjointView() const; const SparseView sparseView(const Scalar& m_reference = Scalar(0), typename NumTraits::Real m_epsilon = NumTraits::dummy_precision()) const; static const IdentityReturnType Identity(); static const IdentityReturnType Identity(Index rows, Index cols); static const BasisReturnType Unit(Index size, Index i); static const BasisReturnType Unit(Index i); static const BasisReturnType UnitX(); static const BasisReturnType UnitY(); static const BasisReturnType UnitZ(); static const BasisReturnType UnitW(); const DiagonalWrapper asDiagonal() const; const PermutationWrapper asPermutation() const; Derived& setIdentity(); Derived& setIdentity(Index rows, Index cols); bool isIdentity(RealScalar prec = NumTraits::dummy_precision()) const; bool isDiagonal(RealScalar prec = NumTraits::dummy_precision()) const; bool isUpperTriangular(RealScalar prec = NumTraits::dummy_precision()) const; bool isLowerTriangular(RealScalar prec = NumTraits::dummy_precision()) const; template bool isOrthogonal(const MatrixBase& other, RealScalar prec = NumTraits::dummy_precision()) const; bool isUnitary(RealScalar prec = NumTraits::dummy_precision()) const; /** \returns true if each coefficients of \c *this and \a other are all exactly equal. * \warning When using floating point scalar values you probably should rather use a * fuzzy comparison such as isApprox() * \sa isApprox(), operator!= */ template inline bool operator==(const MatrixBase& other) const { return cwiseEqual(other).all(); } /** \returns true if at least one pair of coefficients of \c *this and \a other are not exactly equal to each other. * \warning When using floating point scalar values you probably should rather use a * fuzzy comparison such as isApprox() * \sa isApprox(), operator== */ template inline bool operator!=(const MatrixBase& other) const { return cwiseNotEqual(other).any(); } NoAlias noalias(); inline const ForceAlignedAccess forceAlignedAccess() const; inline ForceAlignedAccess forceAlignedAccess(); template inline typename internal::add_const_on_value_type,Derived&>::type>::type forceAlignedAccessIf() const; template inline typename internal::conditional,Derived&>::type forceAlignedAccessIf(); Scalar trace() const; /////////// Array module /////////// template RealScalar lpNorm() const; MatrixBase& matrix() { return *this; } const MatrixBase& matrix() const { return *this; } /** \returns an \link ArrayBase Array \endlink expression of this matrix * \sa ArrayBase::matrix() */ ArrayWrapper array() { return derived(); } const ArrayWrapper array() const { return derived(); } /////////// LU module /////////// const FullPivLU fullPivLu() const; const PartialPivLU partialPivLu() const; #if EIGEN2_SUPPORT_STAGE < STAGE20_RESOLVE_API_CONFLICTS const LU lu() const; #endif #ifdef EIGEN2_SUPPORT const LU eigen2_lu() const; #endif #if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS const PartialPivLU lu() const; #endif #ifdef EIGEN2_SUPPORT template void computeInverse(MatrixBase *result) const { *result = this->inverse(); } #endif const internal::inverse_impl inverse() const; template void computeInverseAndDetWithCheck( ResultType& inverse, typename ResultType::Scalar& determinant, bool& invertible, const RealScalar& absDeterminantThreshold = NumTraits::dummy_precision() ) const; template void computeInverseWithCheck( ResultType& inverse, bool& invertible, const RealScalar& absDeterminantThreshold = NumTraits::dummy_precision() ) const; Scalar determinant() const; /////////// Cholesky module /////////// const LLT llt() const; const LDLT ldlt() const; /////////// QR module /////////// const HouseholderQR householderQr() const; const ColPivHouseholderQR colPivHouseholderQr() const; const FullPivHouseholderQR fullPivHouseholderQr() const; #ifdef EIGEN2_SUPPORT const QR qr() const; #endif EigenvaluesReturnType eigenvalues() const; RealScalar operatorNorm() const; /////////// SVD module /////////// JacobiSVD jacobiSvd(unsigned int computationOptions = 0) const; #ifdef EIGEN2_SUPPORT SVD svd() const; #endif /////////// Geometry module /////////// #ifndef EIGEN_PARSED_BY_DOXYGEN /// \internal helper struct to form the return type of the cross product template struct cross_product_return_type { typedef typename internal::scalar_product_traits::Scalar,typename internal::traits::Scalar>::ReturnType Scalar; typedef Matrix type; }; #endif // EIGEN_PARSED_BY_DOXYGEN template typename cross_product_return_type::type cross(const MatrixBase& other) const; template PlainObject cross3(const MatrixBase& other) const; PlainObject unitOrthogonal(void) const; Matrix eulerAngles(Index a0, Index a1, Index a2) const; #if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS ScalarMultipleReturnType operator*(const UniformScaling& s) const; // put this as separate enum value to work around possible GCC 4.3 bug (?) enum { HomogeneousReturnTypeDirection = ColsAtCompileTime==1?Vertical:Horizontal }; typedef Homogeneous HomogeneousReturnType; HomogeneousReturnType homogeneous() const; #endif enum { SizeMinusOne = SizeAtCompileTime==Dynamic ? Dynamic : SizeAtCompileTime-1 }; typedef Block::ColsAtCompileTime==1 ? SizeMinusOne : 1, internal::traits::ColsAtCompileTime==1 ? 1 : SizeMinusOne> ConstStartMinusOne; typedef CwiseUnaryOp::Scalar>, const ConstStartMinusOne > HNormalizedReturnType; const HNormalizedReturnType hnormalized() const; ////////// Householder module /////////// void makeHouseholderInPlace(Scalar& tau, RealScalar& beta); template void makeHouseholder(EssentialPart& essential, Scalar& tau, RealScalar& beta) const; template void applyHouseholderOnTheLeft(const EssentialPart& essential, const Scalar& tau, Scalar* workspace); template void applyHouseholderOnTheRight(const EssentialPart& essential, const Scalar& tau, Scalar* workspace); ///////// Jacobi module ///////// template void applyOnTheLeft(Index p, Index q, const JacobiRotation& j); template void applyOnTheRight(Index p, Index q, const JacobiRotation& j); ///////// MatrixFunctions module ///////// typedef typename internal::stem_function::type StemFunction; const MatrixExponentialReturnValue exp() const; const MatrixFunctionReturnValue matrixFunction(StemFunction f) const; const MatrixFunctionReturnValue cosh() const; const MatrixFunctionReturnValue sinh() const; const MatrixFunctionReturnValue cos() const; const MatrixFunctionReturnValue sin() const; const MatrixSquareRootReturnValue sqrt() const; const MatrixLogarithmReturnValue log() const; #ifdef EIGEN2_SUPPORT template Derived& operator+=(const Flagged, 0, EvalBeforeAssigningBit>& other); template Derived& operator-=(const Flagged, 0, EvalBeforeAssigningBit>& other); /** \deprecated because .lazy() is deprecated * Overloaded for cache friendly product evaluation */ template Derived& lazyAssign(const Flagged& other) { return lazyAssign(other._expression()); } template const Flagged marked() const; const Flagged lazy() const; inline const Cwise cwise() const; inline Cwise cwise(); VectorBlock start(Index size); const VectorBlock start(Index size) const; VectorBlock end(Index size); const VectorBlock end(Index size) const; template VectorBlock start(); template const VectorBlock start() const; template VectorBlock end(); template const VectorBlock end() const; Minor minor(Index row, Index col); const Minor minor(Index row, Index col) const; #endif protected: MatrixBase() : Base() {} private: explicit MatrixBase(int); MatrixBase(int,int); template explicit MatrixBase(const MatrixBase&); protected: // mixing arrays and matrices is not legal template Derived& operator+=(const ArrayBase& ) {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;} // mixing arrays and matrices is not legal template Derived& operator-=(const ArrayBase& ) {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;} }; } // end namespace Eigen #endif // EIGEN_MATRIXBASE_H ctbnrle.1.1.1/hdr/Eigen/src/Core/Block.h0000775000175000017500000003512512107032422020056 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Gael Guennebaud // Copyright (C) 2006-2010 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_BLOCK_H #define EIGEN_BLOCK_H namespace Eigen { /** \class Block * \ingroup Core_Module * * \brief Expression of a fixed-size or dynamic-size block * * \param XprType the type of the expression in which we are taking a block * \param BlockRows the number of rows of the block we are taking at compile time (optional) * \param BlockCols the number of columns of the block we are taking at compile time (optional) * \param _DirectAccessStatus \internal used for partial specialization * * This class represents an expression of either a fixed-size or dynamic-size block. It is the return * type of DenseBase::block(Index,Index,Index,Index) and DenseBase::block(Index,Index) and * most of the time this is the only way it is used. * * However, if you want to directly maniputate block expressions, * for instance if you want to write a function returning such an expression, you * will need to use this class. * * Here is an example illustrating the dynamic case: * \include class_Block.cpp * Output: \verbinclude class_Block.out * * \note Even though this expression has dynamic size, in the case where \a XprType * has fixed size, this expression inherits a fixed maximal size which means that evaluating * it does not cause a dynamic memory allocation. * * Here is an example illustrating the fixed-size case: * \include class_FixedBlock.cpp * Output: \verbinclude class_FixedBlock.out * * \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock */ namespace internal { template struct traits > : traits { typedef typename traits::Scalar Scalar; typedef typename traits::StorageKind StorageKind; typedef typename traits::XprKind XprKind; typedef typename nested::type XprTypeNested; typedef typename remove_reference::type _XprTypeNested; enum{ MatrixRows = traits::RowsAtCompileTime, MatrixCols = traits::ColsAtCompileTime, RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows, ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols, MaxRowsAtCompileTime = BlockRows==0 ? 0 : RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime) : int(traits::MaxRowsAtCompileTime), MaxColsAtCompileTime = BlockCols==0 ? 0 : ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime) : int(traits::MaxColsAtCompileTime), XprTypeIsRowMajor = (int(traits::Flags)&RowMajorBit) != 0, IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1 : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0 : XprTypeIsRowMajor, HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor), InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime), InnerStrideAtCompileTime = HasSameStorageOrderAsXprType ? int(inner_stride_at_compile_time::ret) : int(outer_stride_at_compile_time::ret), OuterStrideAtCompileTime = HasSameStorageOrderAsXprType ? int(outer_stride_at_compile_time::ret) : int(inner_stride_at_compile_time::ret), MaskPacketAccessBit = (InnerSize == Dynamic || (InnerSize % packet_traits::size) == 0) && (InnerStrideAtCompileTime == 1) ? PacketAccessBit : 0, MaskAlignedBit = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic) && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % 16) == 0)) ? AlignedBit : 0, FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0, FlagsLvalueBit = is_lvalue::value ? LvalueBit : 0, FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0, Flags0 = traits::Flags & ( (HereditaryBits & ~RowMajorBit) | DirectAccessBit | MaskPacketAccessBit | MaskAlignedBit), Flags = Flags0 | FlagsLinearAccessBit | FlagsLvalueBit | FlagsRowMajorBit }; }; } template class Block : public internal::dense_xpr_base >::type { public: typedef typename internal::dense_xpr_base::type Base; EIGEN_DENSE_PUBLIC_INTERFACE(Block) class InnerIterator; /** Column or Row constructor */ inline Block(XprType& xpr, Index i) : m_xpr(xpr), // It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime, // and it is a column if and only if BlockRows==XprType::RowsAtCompileTime and BlockCols==1, // all other cases are invalid. // The case a 1x1 matrix seems ambiguous, but the result is the same anyway. m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0), m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0), m_blockRows(BlockRows==1 ? 1 : xpr.rows()), m_blockCols(BlockCols==1 ? 1 : xpr.cols()) { eigen_assert( (i>=0) && ( ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows() && startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols()); } /** Dynamic-size constructor */ inline Block(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols) : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol), m_blockRows(blockRows), m_blockCols(blockCols) { eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows) && (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols)); eigen_assert(startRow >= 0 && blockRows >= 0 && startRow + blockRows <= xpr.rows() && startCol >= 0 && blockCols >= 0 && startCol + blockCols <= xpr.cols()); } EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block) inline Index rows() const { return m_blockRows.value(); } inline Index cols() const { return m_blockCols.value(); } inline Scalar& coeffRef(Index row, Index col) { EIGEN_STATIC_ASSERT_LVALUE(XprType) return m_xpr.const_cast_derived() .coeffRef(row + m_startRow.value(), col + m_startCol.value()); } inline const Scalar& coeffRef(Index row, Index col) const { return m_xpr.derived() .coeffRef(row + m_startRow.value(), col + m_startCol.value()); } EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index row, Index col) const { return m_xpr.coeff(row + m_startRow.value(), col + m_startCol.value()); } inline Scalar& coeffRef(Index index) { EIGEN_STATIC_ASSERT_LVALUE(XprType) return m_xpr.const_cast_derived() .coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0)); } inline const Scalar& coeffRef(Index index) const { return m_xpr.const_cast_derived() .coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0)); } inline const CoeffReturnType coeff(Index index) const { return m_xpr .coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0)); } template inline PacketScalar packet(Index row, Index col) const { return m_xpr.template packet (row + m_startRow.value(), col + m_startCol.value()); } template inline void writePacket(Index row, Index col, const PacketScalar& x) { m_xpr.const_cast_derived().template writePacket (row + m_startRow.value(), col + m_startCol.value(), x); } template inline PacketScalar packet(Index index) const { return m_xpr.template packet (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0)); } template inline void writePacket(Index index, const PacketScalar& x) { m_xpr.const_cast_derived().template writePacket (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), x); } #ifdef EIGEN_PARSED_BY_DOXYGEN /** \sa MapBase::data() */ inline const Scalar* data() const; inline Index innerStride() const; inline Index outerStride() const; #endif const typename internal::remove_all::type& nestedExpression() const { return m_xpr; } Index startRow() const { return m_startRow.value(); } Index startCol() const { return m_startCol.value(); } protected: const typename XprType::Nested m_xpr; const internal::variable_if_dynamic m_startRow; const internal::variable_if_dynamic m_startCol; const internal::variable_if_dynamic m_blockRows; const internal::variable_if_dynamic m_blockCols; }; /** \internal */ template class Block : public MapBase > { public: typedef MapBase Base; EIGEN_DENSE_PUBLIC_INTERFACE(Block) EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block) /** Column or Row constructor */ inline Block(XprType& xpr, Index i) : Base(internal::const_cast_ptr(&xpr.coeffRef( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0, (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0)), BlockRows==1 ? 1 : xpr.rows(), BlockCols==1 ? 1 : xpr.cols()), m_xpr(xpr) { eigen_assert( (i>=0) && ( ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i= 0 && BlockRows >= 1 && startRow + BlockRows <= xpr.rows() && startCol >= 0 && BlockCols >= 1 && startCol + BlockCols <= xpr.cols()); init(); } /** Dynamic-size constructor */ inline Block(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols) : Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol)), blockRows, blockCols), m_xpr(xpr) { eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows) && (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols)); eigen_assert(startRow >= 0 && blockRows >= 0 && startRow + blockRows <= xpr.rows() && startCol >= 0 && blockCols >= 0 && startCol + blockCols <= xpr.cols()); init(); } const typename internal::remove_all::type& nestedExpression() const { return m_xpr; } /** \sa MapBase::innerStride() */ inline Index innerStride() const { return internal::traits::HasSameStorageOrderAsXprType ? m_xpr.innerStride() : m_xpr.outerStride(); } /** \sa MapBase::outerStride() */ inline Index outerStride() const { return m_outerStride; } #ifndef __SUNPRO_CC // FIXME sunstudio is not friendly with the above friend... // META-FIXME there is no 'friend' keyword around here. Is this obsolete? protected: #endif #ifndef EIGEN_PARSED_BY_DOXYGEN /** \internal used by allowAligned() */ inline Block(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols) : Base(data, blockRows, blockCols), m_xpr(xpr) { init(); } #endif protected: void init() { m_outerStride = internal::traits::HasSameStorageOrderAsXprType ? m_xpr.outerStride() : m_xpr.innerStride(); } typename XprType::Nested m_xpr; Index m_outerStride; }; } // end namespace Eigen #endif // EIGEN_BLOCK_H ctbnrle.1.1.1/hdr/Eigen/src/Core/Functors.h0000775000175000017500000011226512107032422020630 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2010 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_FUNCTORS_H #define EIGEN_FUNCTORS_H namespace Eigen { namespace internal { // associative functors: /** \internal * \brief Template functor to compute the sum of two scalars * * \sa class CwiseBinaryOp, MatrixBase::operator+, class VectorwiseOp, MatrixBase::sum() */ template struct scalar_sum_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_sum_op) EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a + b; } template EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const { return internal::padd(a,b); } template EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const { return internal::predux(a); } }; template struct functor_traits > { enum { Cost = NumTraits::AddCost, PacketAccess = packet_traits::HasAdd }; }; /** \internal * \brief Template functor to compute the product of two scalars * * \sa class CwiseBinaryOp, Cwise::operator*(), class VectorwiseOp, MatrixBase::redux() */ template struct scalar_product_op { enum { // TODO vectorize mixed product Vectorizable = is_same::value && packet_traits::HasMul && packet_traits::HasMul }; typedef typename scalar_product_traits::ReturnType result_type; EIGEN_EMPTY_STRUCT_CTOR(scalar_product_op) EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a * b; } template EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const { return internal::pmul(a,b); } template EIGEN_STRONG_INLINE const result_type predux(const Packet& a) const { return internal::predux_mul(a); } }; template struct functor_traits > { enum { Cost = (NumTraits::MulCost + NumTraits::MulCost)/2, // rough estimate! PacketAccess = scalar_product_op::Vectorizable }; }; /** \internal * \brief Template functor to compute the conjugate product of two scalars * * This is a short cut for conj(x) * y which is needed for optimization purpose; in Eigen2 support mode, this becomes x * conj(y) */ template struct scalar_conj_product_op { enum { Conj = NumTraits::IsComplex }; typedef typename scalar_product_traits::ReturnType result_type; EIGEN_EMPTY_STRUCT_CTOR(scalar_conj_product_op) EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return conj_helper().pmul(a,b); } template EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const { return conj_helper().pmul(a,b); } }; template struct functor_traits > { enum { Cost = NumTraits::MulCost, PacketAccess = internal::is_same::value && packet_traits::HasMul }; }; /** \internal * \brief Template functor to compute the min of two scalars * * \sa class CwiseBinaryOp, MatrixBase::cwiseMin, class VectorwiseOp, MatrixBase::minCoeff() */ template struct scalar_min_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_min_op) EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::min; return (min)(a, b); } template EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const { return internal::pmin(a,b); } template EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const { return internal::predux_min(a); } }; template struct functor_traits > { enum { Cost = NumTraits::AddCost, PacketAccess = packet_traits::HasMin }; }; /** \internal * \brief Template functor to compute the max of two scalars * * \sa class CwiseBinaryOp, MatrixBase::cwiseMax, class VectorwiseOp, MatrixBase::maxCoeff() */ template struct scalar_max_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_max_op) EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::max; return (max)(a, b); } template EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const { return internal::pmax(a,b); } template EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const { return internal::predux_max(a); } }; template struct functor_traits > { enum { Cost = NumTraits::AddCost, PacketAccess = packet_traits::HasMax }; }; /** \internal * \brief Template functor to compute the hypot of two scalars * * \sa MatrixBase::stableNorm(), class Redux */ template struct scalar_hypot_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_hypot_op) // typedef typename NumTraits::Real result_type; EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& _x, const Scalar& _y) const { using std::max; using std::min; Scalar p = (max)(_x, _y); Scalar q = (min)(_x, _y); Scalar qp = q/p; return p * sqrt(Scalar(1) + qp*qp); } }; template struct functor_traits > { enum { Cost = 5 * NumTraits::MulCost, PacketAccess=0 }; }; /** \internal * \brief Template functor to compute the pow of two scalars */ template struct scalar_binary_pow_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_binary_pow_op) inline Scalar operator() (const Scalar& a, const OtherScalar& b) const { return internal::pow(a, b); } }; template struct functor_traits > { enum { Cost = 5 * NumTraits::MulCost, PacketAccess = false }; }; // other binary functors: /** \internal * \brief Template functor to compute the difference of two scalars * * \sa class CwiseBinaryOp, MatrixBase::operator- */ template struct scalar_difference_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_difference_op) EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a - b; } template EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const { return internal::psub(a,b); } }; template struct functor_traits > { enum { Cost = NumTraits::AddCost, PacketAccess = packet_traits::HasSub }; }; /** \internal * \brief Template functor to compute the quotient of two scalars * * \sa class CwiseBinaryOp, Cwise::operator/() */ template struct scalar_quotient_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_quotient_op) EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a / b; } template EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const { return internal::pdiv(a,b); } }; template struct functor_traits > { enum { Cost = 2 * NumTraits::MulCost, PacketAccess = packet_traits::HasDiv }; }; /** \internal * \brief Template functor to compute the and of two booleans * * \sa class CwiseBinaryOp, ArrayBase::operator&& */ struct scalar_boolean_and_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_and_op) EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a && b; } }; template<> struct functor_traits { enum { Cost = NumTraits::AddCost, PacketAccess = false }; }; /** \internal * \brief Template functor to compute the or of two booleans * * \sa class CwiseBinaryOp, ArrayBase::operator|| */ struct scalar_boolean_or_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_or_op) EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a || b; } }; template<> struct functor_traits { enum { Cost = NumTraits::AddCost, PacketAccess = false }; }; // unary functors: /** \internal * \brief Template functor to compute the opposite of a scalar * * \sa class CwiseUnaryOp, MatrixBase::operator- */ template struct scalar_opposite_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_opposite_op) EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return -a; } template EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pnegate(a); } }; template struct functor_traits > { enum { Cost = NumTraits::AddCost, PacketAccess = packet_traits::HasNegate }; }; /** \internal * \brief Template functor to compute the absolute value of a scalar * * \sa class CwiseUnaryOp, Cwise::abs */ template struct scalar_abs_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_abs_op) typedef typename NumTraits::Real result_type; EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return abs(a); } template EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pabs(a); } }; template struct functor_traits > { enum { Cost = NumTraits::AddCost, PacketAccess = packet_traits::HasAbs }; }; /** \internal * \brief Template functor to compute the squared absolute value of a scalar * * \sa class CwiseUnaryOp, Cwise::abs2 */ template struct scalar_abs2_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_abs2_op) typedef typename NumTraits::Real result_type; EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return abs2(a); } template EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pmul(a,a); } }; template struct functor_traits > { enum { Cost = NumTraits::MulCost, PacketAccess = packet_traits::HasAbs2 }; }; /** \internal * \brief Template functor to compute the conjugate of a complex value * * \sa class CwiseUnaryOp, MatrixBase::conjugate() */ template struct scalar_conjugate_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_conjugate_op) EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return conj(a); } template EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pconj(a); } }; template struct functor_traits > { enum { Cost = NumTraits::IsComplex ? NumTraits::AddCost : 0, PacketAccess = packet_traits::HasConj }; }; /** \internal * \brief Template functor to cast a scalar to another type * * \sa class CwiseUnaryOp, MatrixBase::cast() */ template struct scalar_cast_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op) typedef NewType result_type; EIGEN_STRONG_INLINE const NewType operator() (const Scalar& a) const { return cast(a); } }; template struct functor_traits > { enum { Cost = is_same::value ? 0 : NumTraits::AddCost, PacketAccess = false }; }; /** \internal * \brief Template functor to extract the real part of a complex * * \sa class CwiseUnaryOp, MatrixBase::real() */ template struct scalar_real_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_real_op) typedef typename NumTraits::Real result_type; EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return real(a); } }; template struct functor_traits > { enum { Cost = 0, PacketAccess = false }; }; /** \internal * \brief Template functor to extract the imaginary part of a complex * * \sa class CwiseUnaryOp, MatrixBase::imag() */ template struct scalar_imag_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_op) typedef typename NumTraits::Real result_type; EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return imag(a); } }; template struct functor_traits > { enum { Cost = 0, PacketAccess = false }; }; /** \internal * \brief Template functor to extract the real part of a complex as a reference * * \sa class CwiseUnaryOp, MatrixBase::real() */ template struct scalar_real_ref_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_real_ref_op) typedef typename NumTraits::Real result_type; EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return real_ref(*const_cast(&a)); } }; template struct functor_traits > { enum { Cost = 0, PacketAccess = false }; }; /** \internal * \brief Template functor to extract the imaginary part of a complex as a reference * * \sa class CwiseUnaryOp, MatrixBase::imag() */ template struct scalar_imag_ref_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_ref_op) typedef typename NumTraits::Real result_type; EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return imag_ref(*const_cast(&a)); } }; template struct functor_traits > { enum { Cost = 0, PacketAccess = false }; }; /** \internal * * \brief Template functor to compute the exponential of a scalar * * \sa class CwiseUnaryOp, Cwise::exp() */ template struct scalar_exp_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_exp_op) inline const Scalar operator() (const Scalar& a) const { return exp(a); } typedef typename packet_traits::type Packet; inline Packet packetOp(const Packet& a) const { return internal::pexp(a); } }; template struct functor_traits > { enum { Cost = 5 * NumTraits::MulCost, PacketAccess = packet_traits::HasExp }; }; /** \internal * * \brief Template functor to compute the logarithm of a scalar * * \sa class CwiseUnaryOp, Cwise::log() */ template struct scalar_log_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_log_op) inline const Scalar operator() (const Scalar& a) const { return log(a); } typedef typename packet_traits::type Packet; inline Packet packetOp(const Packet& a) const { return internal::plog(a); } }; template struct functor_traits > { enum { Cost = 5 * NumTraits::MulCost, PacketAccess = packet_traits::HasLog }; }; /** \internal * \brief Template functor to multiply a scalar by a fixed other one * * \sa class CwiseUnaryOp, MatrixBase::operator*, MatrixBase::operator/ */ /* NOTE why doing the pset1() in packetOp *is* an optimization ? * indeed it seems better to declare m_other as a Packet and do the pset1() once * in the constructor. However, in practice: * - GCC does not like m_other as a Packet and generate a load every time it needs it * - on the other hand GCC is able to moves the pset1() away the loop :) * - simpler code ;) * (ICC and gcc 4.4 seems to perform well in both cases, the issue is visible with y = a*x + b*y) */ template struct scalar_multiple_op { typedef typename packet_traits::type Packet; // FIXME default copy constructors seems bugged with std::complex<> EIGEN_STRONG_INLINE scalar_multiple_op(const scalar_multiple_op& other) : m_other(other.m_other) { } EIGEN_STRONG_INLINE scalar_multiple_op(const Scalar& other) : m_other(other) { } EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a * m_other; } EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pmul(a, pset1(m_other)); } typename add_const_on_value_type::Nested>::type m_other; }; template struct functor_traits > { enum { Cost = NumTraits::MulCost, PacketAccess = packet_traits::HasMul }; }; template struct scalar_multiple2_op { typedef typename scalar_product_traits::ReturnType result_type; EIGEN_STRONG_INLINE scalar_multiple2_op(const scalar_multiple2_op& other) : m_other(other.m_other) { } EIGEN_STRONG_INLINE scalar_multiple2_op(const Scalar2& other) : m_other(other) { } EIGEN_STRONG_INLINE result_type operator() (const Scalar1& a) const { return a * m_other; } typename add_const_on_value_type::Nested>::type m_other; }; template struct functor_traits > { enum { Cost = NumTraits::MulCost, PacketAccess = false }; }; template struct scalar_quotient1_impl { typedef typename packet_traits::type Packet; // FIXME default copy constructors seems bugged with std::complex<> EIGEN_STRONG_INLINE scalar_quotient1_impl(const scalar_quotient1_impl& other) : m_other(other.m_other) { } EIGEN_STRONG_INLINE scalar_quotient1_impl(const Scalar& other) : m_other(static_cast(1) / other) {} EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a * m_other; } EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pmul(a, pset1(m_other)); } const Scalar m_other; }; template struct functor_traits > { enum { Cost = NumTraits::MulCost, PacketAccess = packet_traits::HasMul }; }; template struct scalar_quotient1_impl { // FIXME default copy constructors seems bugged with std::complex<> EIGEN_STRONG_INLINE scalar_quotient1_impl(const scalar_quotient1_impl& other) : m_other(other.m_other) { } EIGEN_STRONG_INLINE scalar_quotient1_impl(const Scalar& other) : m_other(other) {} EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a / m_other; } typename add_const_on_value_type::Nested>::type m_other; }; template struct functor_traits > { enum { Cost = 2 * NumTraits::MulCost, PacketAccess = false }; }; /** \internal * \brief Template functor to divide a scalar by a fixed other one * * This functor is used to implement the quotient of a matrix by * a scalar where the scalar type is not necessarily a floating point type. * * \sa class CwiseUnaryOp, MatrixBase::operator/ */ template struct scalar_quotient1_op : scalar_quotient1_impl::IsInteger > { EIGEN_STRONG_INLINE scalar_quotient1_op(const Scalar& other) : scalar_quotient1_impl::IsInteger >(other) {} }; template struct functor_traits > : functor_traits::IsInteger> > {}; // nullary functors template struct scalar_constant_op { typedef typename packet_traits::type Packet; EIGEN_STRONG_INLINE scalar_constant_op(const scalar_constant_op& other) : m_other(other.m_other) { } EIGEN_STRONG_INLINE scalar_constant_op(const Scalar& other) : m_other(other) { } template EIGEN_STRONG_INLINE const Scalar operator() (Index, Index = 0) const { return m_other; } template EIGEN_STRONG_INLINE const Packet packetOp(Index, Index = 0) const { return internal::pset1(m_other); } const Scalar m_other; }; template struct functor_traits > // FIXME replace this packet test by a safe one { enum { Cost = 1, PacketAccess = packet_traits::Vectorizable, IsRepeatable = true }; }; template struct scalar_identity_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_identity_op) template EIGEN_STRONG_INLINE const Scalar operator() (Index row, Index col) const { return row==col ? Scalar(1) : Scalar(0); } }; template struct functor_traits > { enum { Cost = NumTraits::AddCost, PacketAccess = false, IsRepeatable = true }; }; template struct linspaced_op_impl; // linear access for packet ops: // 1) initialization // base = [low, ..., low] + ([step, ..., step] * [-size, ..., 0]) // 2) each step // base += [size*step, ..., size*step] template struct linspaced_op_impl { typedef typename packet_traits::type Packet; linspaced_op_impl(Scalar low, Scalar step) : m_low(low), m_step(step), m_packetStep(pset1(packet_traits::size*step)), m_base(padd(pset1(low),pmul(pset1(step),plset(-packet_traits::size)))) {} template EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return m_low+i*m_step; } template EIGEN_STRONG_INLINE const Packet packetOp(Index) const { return m_base = padd(m_base,m_packetStep); } const Scalar m_low; const Scalar m_step; const Packet m_packetStep; mutable Packet m_base; }; // random access for packet ops: // 1) each step // [low, ..., low] + ( [step, ..., step] * ( [i, ..., i] + [0, ..., size] ) ) template struct linspaced_op_impl { typedef typename packet_traits::type Packet; linspaced_op_impl(Scalar low, Scalar step) : m_low(low), m_step(step), m_lowPacket(pset1(m_low)), m_stepPacket(pset1(m_step)), m_interPacket(plset(0)) {} template EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return m_low+i*m_step; } template EIGEN_STRONG_INLINE const Packet packetOp(Index i) const { return internal::padd(m_lowPacket, pmul(m_stepPacket, padd(pset1(i),m_interPacket))); } const Scalar m_low; const Scalar m_step; const Packet m_lowPacket; const Packet m_stepPacket; const Packet m_interPacket; }; // ----- Linspace functor ---------------------------------------------------------------- // Forward declaration (we default to random access which does not really give // us a speed gain when using packet access but it allows to use the functor in // nested expressions). template struct linspaced_op; template struct functor_traits< linspaced_op > { enum { Cost = 1, PacketAccess = packet_traits::HasSetLinear, IsRepeatable = true }; }; template struct linspaced_op { typedef typename packet_traits::type Packet; linspaced_op(Scalar low, Scalar high, int num_steps) : impl((num_steps==1 ? high : low), (num_steps==1 ? Scalar() : (high-low)/(num_steps-1))) {} template EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return impl(i); } // We need this function when assigning e.g. a RowVectorXd to a MatrixXd since // there row==0 and col is used for the actual iteration. template EIGEN_STRONG_INLINE const Scalar operator() (Index row, Index col) const { eigen_assert(col==0 || row==0); return impl(col + row); } template EIGEN_STRONG_INLINE const Packet packetOp(Index i) const { return impl.packetOp(i); } // We need this function when assigning e.g. a RowVectorXd to a MatrixXd since // there row==0 and col is used for the actual iteration. template EIGEN_STRONG_INLINE const Packet packetOp(Index row, Index col) const { eigen_assert(col==0 || row==0); return impl.packetOp(col + row); } // This proxy object handles the actual required temporaries, the different // implementations (random vs. sequential access) as well as the // correct piping to size 2/4 packet operations. const linspaced_op_impl impl; }; // all functors allow linear access, except scalar_identity_op. So we fix here a quick meta // to indicate whether a functor allows linear access, just always answering 'yes' except for // scalar_identity_op. // FIXME move this to functor_traits adding a functor_default template struct functor_has_linear_access { enum { ret = 1 }; }; template struct functor_has_linear_access > { enum { ret = 0 }; }; // in CwiseBinaryOp, we require the Lhs and Rhs to have the same scalar type, except for multiplication // where we only require them to have the same _real_ scalar type so one may multiply, say, float by complex. // FIXME move this to functor_traits adding a functor_default template struct functor_allows_mixing_real_and_complex { enum { ret = 0 }; }; template struct functor_allows_mixing_real_and_complex > { enum { ret = 1 }; }; template struct functor_allows_mixing_real_and_complex > { enum { ret = 1 }; }; /** \internal * \brief Template functor to add a scalar to a fixed other one * \sa class CwiseUnaryOp, Array::operator+ */ /* If you wonder why doing the pset1() in packetOp() is an optimization check scalar_multiple_op */ template struct scalar_add_op { typedef typename packet_traits::type Packet; // FIXME default copy constructors seems bugged with std::complex<> inline scalar_add_op(const scalar_add_op& other) : m_other(other.m_other) { } inline scalar_add_op(const Scalar& other) : m_other(other) { } inline Scalar operator() (const Scalar& a) const { return a + m_other; } inline const Packet packetOp(const Packet& a) const { return internal::padd(a, pset1(m_other)); } const Scalar m_other; }; template struct functor_traits > { enum { Cost = NumTraits::AddCost, PacketAccess = packet_traits::HasAdd }; }; /** \internal * \brief Template functor to compute the square root of a scalar * \sa class CwiseUnaryOp, Cwise::sqrt() */ template struct scalar_sqrt_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_sqrt_op) inline const Scalar operator() (const Scalar& a) const { return sqrt(a); } typedef typename packet_traits::type Packet; inline Packet packetOp(const Packet& a) const { return internal::psqrt(a); } }; template struct functor_traits > { enum { Cost = 5 * NumTraits::MulCost, PacketAccess = packet_traits::HasSqrt }; }; /** \internal * \brief Template functor to compute the cosine of a scalar * \sa class CwiseUnaryOp, ArrayBase::cos() */ template struct scalar_cos_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_cos_op) inline Scalar operator() (const Scalar& a) const { return cos(a); } typedef typename packet_traits::type Packet; inline Packet packetOp(const Packet& a) const { return internal::pcos(a); } }; template struct functor_traits > { enum { Cost = 5 * NumTraits::MulCost, PacketAccess = packet_traits::HasCos }; }; /** \internal * \brief Template functor to compute the sine of a scalar * \sa class CwiseUnaryOp, ArrayBase::sin() */ template struct scalar_sin_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_sin_op) inline const Scalar operator() (const Scalar& a) const { return sin(a); } typedef typename packet_traits::type Packet; inline Packet packetOp(const Packet& a) const { return internal::psin(a); } }; template struct functor_traits > { enum { Cost = 5 * NumTraits::MulCost, PacketAccess = packet_traits::HasSin }; }; /** \internal * \brief Template functor to compute the tan of a scalar * \sa class CwiseUnaryOp, ArrayBase::tan() */ template struct scalar_tan_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_tan_op) inline const Scalar operator() (const Scalar& a) const { return tan(a); } typedef typename packet_traits::type Packet; inline Packet packetOp(const Packet& a) const { return internal::ptan(a); } }; template struct functor_traits > { enum { Cost = 5 * NumTraits::MulCost, PacketAccess = packet_traits::HasTan }; }; /** \internal * \brief Template functor to compute the arc cosine of a scalar * \sa class CwiseUnaryOp, ArrayBase::acos() */ template struct scalar_acos_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_acos_op) inline const Scalar operator() (const Scalar& a) const { return acos(a); } typedef typename packet_traits::type Packet; inline Packet packetOp(const Packet& a) const { return internal::pacos(a); } }; template struct functor_traits > { enum { Cost = 5 * NumTraits::MulCost, PacketAccess = packet_traits::HasACos }; }; /** \internal * \brief Template functor to compute the arc sine of a scalar * \sa class CwiseUnaryOp, ArrayBase::asin() */ template struct scalar_asin_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_asin_op) inline const Scalar operator() (const Scalar& a) const { return asin(a); } typedef typename packet_traits::type Packet; inline Packet packetOp(const Packet& a) const { return internal::pasin(a); } }; template struct functor_traits > { enum { Cost = 5 * NumTraits::MulCost, PacketAccess = packet_traits::HasASin }; }; /** \internal * \brief Template functor to raise a scalar to a power * \sa class CwiseUnaryOp, Cwise::pow */ template struct scalar_pow_op { // FIXME default copy constructors seems bugged with std::complex<> inline scalar_pow_op(const scalar_pow_op& other) : m_exponent(other.m_exponent) { } inline scalar_pow_op(const Scalar& exponent) : m_exponent(exponent) {} inline Scalar operator() (const Scalar& a) const { return internal::pow(a, m_exponent); } const Scalar m_exponent; }; template struct functor_traits > { enum { Cost = 5 * NumTraits::MulCost, PacketAccess = false }; }; /** \internal * \brief Template functor to compute the quotient between a scalar and array entries. * \sa class CwiseUnaryOp, Cwise::inverse() */ template struct scalar_inverse_mult_op { scalar_inverse_mult_op(const Scalar& other) : m_other(other) {} inline Scalar operator() (const Scalar& a) const { return m_other / a; } template inline const Packet packetOp(const Packet& a) const { return internal::pdiv(pset1(m_other),a); } Scalar m_other; }; /** \internal * \brief Template functor to compute the inverse of a scalar * \sa class CwiseUnaryOp, Cwise::inverse() */ template struct scalar_inverse_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_inverse_op) inline Scalar operator() (const Scalar& a) const { return Scalar(1)/a; } template inline const Packet packetOp(const Packet& a) const { return internal::pdiv(pset1(Scalar(1)),a); } }; template struct functor_traits > { enum { Cost = NumTraits::MulCost, PacketAccess = packet_traits::HasDiv }; }; /** \internal * \brief Template functor to compute the square of a scalar * \sa class CwiseUnaryOp, Cwise::square() */ template struct scalar_square_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_square_op) inline Scalar operator() (const Scalar& a) const { return a*a; } template inline const Packet packetOp(const Packet& a) const { return internal::pmul(a,a); } }; template struct functor_traits > { enum { Cost = NumTraits::MulCost, PacketAccess = packet_traits::HasMul }; }; /** \internal * \brief Template functor to compute the cube of a scalar * \sa class CwiseUnaryOp, Cwise::cube() */ template struct scalar_cube_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_cube_op) inline Scalar operator() (const Scalar& a) const { return a*a*a; } template inline const Packet packetOp(const Packet& a) const { return internal::pmul(a,pmul(a,a)); } }; template struct functor_traits > { enum { Cost = 2*NumTraits::MulCost, PacketAccess = packet_traits::HasMul }; }; // default functor traits for STL functors: template struct functor_traits > { enum { Cost = NumTraits::MulCost, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = NumTraits::MulCost, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = NumTraits::AddCost, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = NumTraits::AddCost, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = NumTraits::AddCost, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = 1, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = 1, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = 1, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = 1, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = 1, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = 1, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = 1, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = 1, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = 1, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = functor_traits::Cost, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = functor_traits::Cost, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = 1 + functor_traits::Cost, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = 1 + functor_traits::Cost, PacketAccess = false }; }; #ifdef EIGEN_STDEXT_SUPPORT template struct functor_traits > { enum { Cost = 0, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = 0, PacketAccess = false }; }; template struct functor_traits > > { enum { Cost = 0, PacketAccess = false }; }; template struct functor_traits > > { enum { Cost = 0, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = functor_traits::Cost + functor_traits::Cost, PacketAccess = false }; }; template struct functor_traits > { enum { Cost = functor_traits::Cost + functor_traits::Cost + functor_traits::Cost, PacketAccess = false }; }; #endif // EIGEN_STDEXT_SUPPORT // allow to add new functors and specializations of functor_traits from outside Eigen. // this macro is really needed because functor_traits must be specialized after it is declared but before it is used... #ifdef EIGEN_FUNCTORS_PLUGIN #include EIGEN_FUNCTORS_PLUGIN #endif } // end namespace internal } // end namespace Eigen #endif // EIGEN_FUNCTORS_H ctbnrle.1.1.1/hdr/Eigen/src/Core/DenseStorage.h0000775000175000017500000003353312107032422021410 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Gael Guennebaud // Copyright (C) 2006-2009 Benoit Jacob // Copyright (C) 2010 Hauke Heibel // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_MATRIXSTORAGE_H #define EIGEN_MATRIXSTORAGE_H #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN EIGEN_DENSE_STORAGE_CTOR_PLUGIN; #else #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN #endif namespace Eigen { namespace internal { struct constructor_without_unaligned_array_assert {}; /** \internal * Static array. If the MatrixOrArrayOptions require auto-alignment, the array will be automatically aligned: * to 16 bytes boundary if the total size is a multiple of 16 bytes. */ template struct plain_array { T array[Size]; plain_array() {} plain_array(constructor_without_unaligned_array_assert) {} }; #ifdef EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) #else #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \ eigen_assert((reinterpret_cast(array) & sizemask) == 0 \ && "this assertion is explained here: " \ "http://eigen.tuxfamily.org/dox-devel/TopicUnalignedArrayAssert.html" \ " **** READ THIS WEB PAGE !!! ****"); #endif template struct plain_array { EIGEN_USER_ALIGN16 T array[Size]; plain_array() { EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(0xf) } plain_array(constructor_without_unaligned_array_assert) {} }; template struct plain_array { EIGEN_USER_ALIGN16 T array[1]; plain_array() {} plain_array(constructor_without_unaligned_array_assert) {} }; } // end namespace internal /** \internal * * \class DenseStorage * \ingroup Core_Module * * \brief Stores the data of a matrix * * This class stores the data of fixed-size, dynamic-size or mixed matrices * in a way as compact as possible. * * \sa Matrix */ template class DenseStorage; // purely fixed-size matrix template class DenseStorage { internal::plain_array m_data; public: inline explicit DenseStorage() {} inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(internal::constructor_without_unaligned_array_assert()) {} inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {} inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); } static inline DenseIndex rows(void) {return _Rows;} static inline DenseIndex cols(void) {return _Cols;} inline void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {} inline void resize(DenseIndex,DenseIndex,DenseIndex) {} inline const T *data() const { return m_data.array; } inline T *data() { return m_data.array; } }; // null matrix template class DenseStorage { public: inline explicit DenseStorage() {} inline DenseStorage(internal::constructor_without_unaligned_array_assert) {} inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {} inline void swap(DenseStorage& ) {} static inline DenseIndex rows(void) {return _Rows;} static inline DenseIndex cols(void) {return _Cols;} inline void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {} inline void resize(DenseIndex,DenseIndex,DenseIndex) {} inline const T *data() const { return 0; } inline T *data() { return 0; } }; // more specializations for null matrices; these are necessary to resolve ambiguities template class DenseStorage : public DenseStorage { }; template class DenseStorage : public DenseStorage { }; template class DenseStorage : public DenseStorage { }; // dynamic-size matrix with fixed-size storage template class DenseStorage { internal::plain_array m_data; DenseIndex m_rows; DenseIndex m_cols; public: inline explicit DenseStorage() : m_rows(0), m_cols(0) {} inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {} inline DenseStorage(DenseIndex, DenseIndex rows, DenseIndex cols) : m_rows(rows), m_cols(cols) {} inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); } inline DenseIndex rows(void) const {return m_rows;} inline DenseIndex cols(void) const {return m_cols;} inline void conservativeResize(DenseIndex, DenseIndex rows, DenseIndex cols) { m_rows = rows; m_cols = cols; } inline void resize(DenseIndex, DenseIndex rows, DenseIndex cols) { m_rows = rows; m_cols = cols; } inline const T *data() const { return m_data.array; } inline T *data() { return m_data.array; } }; // dynamic-size matrix with fixed-size storage and fixed width template class DenseStorage { internal::plain_array m_data; DenseIndex m_rows; public: inline explicit DenseStorage() : m_rows(0) {} inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {} inline DenseStorage(DenseIndex, DenseIndex rows, DenseIndex) : m_rows(rows) {} inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); } inline DenseIndex rows(void) const {return m_rows;} inline DenseIndex cols(void) const {return _Cols;} inline void conservativeResize(DenseIndex, DenseIndex rows, DenseIndex) { m_rows = rows; } inline void resize(DenseIndex, DenseIndex rows, DenseIndex) { m_rows = rows; } inline const T *data() const { return m_data.array; } inline T *data() { return m_data.array; } }; // dynamic-size matrix with fixed-size storage and fixed height template class DenseStorage { internal::plain_array m_data; DenseIndex m_cols; public: inline explicit DenseStorage() : m_cols(0) {} inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {} inline DenseStorage(DenseIndex, DenseIndex, DenseIndex cols) : m_cols(cols) {} inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); } inline DenseIndex rows(void) const {return _Rows;} inline DenseIndex cols(void) const {return m_cols;} inline void conservativeResize(DenseIndex, DenseIndex, DenseIndex cols) { m_cols = cols; } inline void resize(DenseIndex, DenseIndex, DenseIndex cols) { m_cols = cols; } inline const T *data() const { return m_data.array; } inline T *data() { return m_data.array; } }; // purely dynamic matrix. template class DenseStorage { T *m_data; DenseIndex m_rows; DenseIndex m_cols; public: inline explicit DenseStorage() : m_data(0), m_rows(0), m_cols(0) {} inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0), m_cols(0) {} inline DenseStorage(DenseIndex size, DenseIndex rows, DenseIndex cols) : m_data(internal::conditional_aligned_new_auto(size)), m_rows(rows), m_cols(cols) { EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN } inline ~DenseStorage() { internal::conditional_aligned_delete_auto(m_data, m_rows*m_cols); } inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); } inline DenseIndex rows(void) const {return m_rows;} inline DenseIndex cols(void) const {return m_cols;} inline void conservativeResize(DenseIndex size, DenseIndex rows, DenseIndex cols) { m_data = internal::conditional_aligned_realloc_new_auto(m_data, size, m_rows*m_cols); m_rows = rows; m_cols = cols; } void resize(DenseIndex size, DenseIndex rows, DenseIndex cols) { if(size != m_rows*m_cols) { internal::conditional_aligned_delete_auto(m_data, m_rows*m_cols); if (size) m_data = internal::conditional_aligned_new_auto(size); else m_data = 0; EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN } m_rows = rows; m_cols = cols; } inline const T *data() const { return m_data; } inline T *data() { return m_data; } }; // matrix with dynamic width and fixed height (so that matrix has dynamic size). template class DenseStorage { T *m_data; DenseIndex m_cols; public: inline explicit DenseStorage() : m_data(0), m_cols(0) {} inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {} inline DenseStorage(DenseIndex size, DenseIndex, DenseIndex cols) : m_data(internal::conditional_aligned_new_auto(size)), m_cols(cols) { EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN } inline ~DenseStorage() { internal::conditional_aligned_delete_auto(m_data, _Rows*m_cols); } inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); } static inline DenseIndex rows(void) {return _Rows;} inline DenseIndex cols(void) const {return m_cols;} inline void conservativeResize(DenseIndex size, DenseIndex, DenseIndex cols) { m_data = internal::conditional_aligned_realloc_new_auto(m_data, size, _Rows*m_cols); m_cols = cols; } EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex, DenseIndex cols) { if(size != _Rows*m_cols) { internal::conditional_aligned_delete_auto(m_data, _Rows*m_cols); if (size) m_data = internal::conditional_aligned_new_auto(size); else m_data = 0; EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN } m_cols = cols; } inline const T *data() const { return m_data; } inline T *data() { return m_data; } }; // matrix with dynamic height and fixed width (so that matrix has dynamic size). template class DenseStorage { T *m_data; DenseIndex m_rows; public: inline explicit DenseStorage() : m_data(0), m_rows(0) {} inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {} inline DenseStorage(DenseIndex size, DenseIndex rows, DenseIndex) : m_data(internal::conditional_aligned_new_auto(size)), m_rows(rows) { EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN } inline ~DenseStorage() { internal::conditional_aligned_delete_auto(m_data, _Cols*m_rows); } inline void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); } inline DenseIndex rows(void) const {return m_rows;} static inline DenseIndex cols(void) {return _Cols;} inline void conservativeResize(DenseIndex size, DenseIndex rows, DenseIndex) { m_data = internal::conditional_aligned_realloc_new_auto(m_data, size, m_rows*_Cols); m_rows = rows; } EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex rows, DenseIndex) { if(size != m_rows*_Cols) { internal::conditional_aligned_delete_auto(m_data, _Cols*m_rows); if (size) m_data = internal::conditional_aligned_new_auto(size); else m_data = 0; EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN } m_rows = rows; } inline const T *data() const { return m_data; } inline T *data() { return m_data; } }; } // end namespace Eigen #endif // EIGEN_MATRIX_H ctbnrle.1.1.1/hdr/Eigen/src/Core/IO.h0000775000175000017500000002013712107032422017330 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2006-2008 Benoit Jacob // Copyright (C) 2008 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_IO_H #define EIGEN_IO_H namespace Eigen { enum { DontAlignCols = 1 }; enum { StreamPrecision = -1, FullPrecision = -2 }; namespace internal { template std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt); } /** \class IOFormat * \ingroup Core_Module * * \brief Stores a set of parameters controlling the way matrices are printed * * List of available parameters: * - \b precision number of digits for floating point values, or one of the special constants \c StreamPrecision and \c FullPrecision. * The default is the special value \c StreamPrecision which means to use the * stream's own precision setting, as set for instance using \c cout.precision(3). The other special value * \c FullPrecision means that the number of digits will be computed to match the full precision of each floating-point * type. * - \b flags an OR-ed combination of flags, the default value is 0, the only currently available flag is \c DontAlignCols which * allows to disable the alignment of columns, resulting in faster code. * - \b coeffSeparator string printed between two coefficients of the same row * - \b rowSeparator string printed between two rows * - \b rowPrefix string printed at the beginning of each row * - \b rowSuffix string printed at the end of each row * - \b matPrefix string printed at the beginning of the matrix * - \b matSuffix string printed at the end of the matrix * * Example: \include IOFormat.cpp * Output: \verbinclude IOFormat.out * * \sa DenseBase::format(), class WithFormat */ struct IOFormat { /** Default contructor, see class IOFormat for the meaning of the parameters */ IOFormat(int _precision = StreamPrecision, int _flags = 0, const std::string& _coeffSeparator = " ", const std::string& _rowSeparator = "\n", const std::string& _rowPrefix="", const std::string& _rowSuffix="", const std::string& _matPrefix="", const std::string& _matSuffix="") : matPrefix(_matPrefix), matSuffix(_matSuffix), rowPrefix(_rowPrefix), rowSuffix(_rowSuffix), rowSeparator(_rowSeparator), coeffSeparator(_coeffSeparator), precision(_precision), flags(_flags) { rowSpacer = ""; int i = int(matSuffix.length())-1; while (i>=0 && matSuffix[i]!='\n') { rowSpacer += ' '; i--; } } std::string matPrefix, matSuffix; std::string rowPrefix, rowSuffix, rowSeparator, rowSpacer; std::string coeffSeparator; int precision; int flags; }; /** \class WithFormat * \ingroup Core_Module * * \brief Pseudo expression providing matrix output with given format * * \param ExpressionType the type of the object on which IO stream operations are performed * * This class represents an expression with stream operators controlled by a given IOFormat. * It is the return type of DenseBase::format() * and most of the time this is the only way it is used. * * See class IOFormat for some examples. * * \sa DenseBase::format(), class IOFormat */ template class WithFormat { public: WithFormat(const ExpressionType& matrix, const IOFormat& format) : m_matrix(matrix), m_format(format) {} friend std::ostream & operator << (std::ostream & s, const WithFormat& wf) { return internal::print_matrix(s, wf.m_matrix.eval(), wf.m_format); } protected: const typename ExpressionType::Nested m_matrix; IOFormat m_format; }; /** \returns a WithFormat proxy object allowing to print a matrix the with given * format \a fmt. * * See class IOFormat for some examples. * * \sa class IOFormat, class WithFormat */ template inline const WithFormat DenseBase::format(const IOFormat& fmt) const { return WithFormat(derived(), fmt); } namespace internal { template struct significant_decimals_default_impl { typedef typename NumTraits::Real RealScalar; static inline int run() { using std::ceil; return cast(ceil(-log(NumTraits::epsilon())/log(RealScalar(10)))); } }; template struct significant_decimals_default_impl { static inline int run() { return 0; } }; template struct significant_decimals_impl : significant_decimals_default_impl::IsInteger> {}; /** \internal * print the matrix \a _m to the output stream \a s using the output format \a fmt */ template std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt) { if(_m.size() == 0) { s << fmt.matPrefix << fmt.matSuffix; return s; } typename Derived::Nested m = _m; typedef typename Derived::Scalar Scalar; typedef typename Derived::Index Index; Index width = 0; std::streamsize explicit_precision; if(fmt.precision == StreamPrecision) { explicit_precision = 0; } else if(fmt.precision == FullPrecision) { if (NumTraits::IsInteger) { explicit_precision = 0; } else { explicit_precision = significant_decimals_impl::run(); } } else { explicit_precision = fmt.precision; } bool align_cols = !(fmt.flags & DontAlignCols); if(align_cols) { // compute the largest width for(Index j = 1; j < m.cols(); ++j) for(Index i = 0; i < m.rows(); ++i) { std::stringstream sstr; if(explicit_precision) sstr.precision(explicit_precision); sstr << m.coeff(i,j); width = std::max(width, Index(sstr.str().length())); } } std::streamsize old_precision = 0; if(explicit_precision) old_precision = s.precision(explicit_precision); s << fmt.matPrefix; for(Index i = 0; i < m.rows(); ++i) { if (i) s << fmt.rowSpacer; s << fmt.rowPrefix; if(width) s.width(width); s << m.coeff(i, 0); for(Index j = 1; j < m.cols(); ++j) { s << fmt.coeffSeparator; if (width) s.width(width); s << m.coeff(i, j); } s << fmt.rowSuffix; if( i < m.rows() - 1) s << fmt.rowSeparator; } s << fmt.matSuffix; if(explicit_precision) s.precision(old_precision); return s; } } // end namespace internal /** \relates DenseBase * * Outputs the matrix, to the given stream. * * If you wish to print the matrix with a format different than the default, use DenseBase::format(). * * It is also possible to change the default format by defining EIGEN_DEFAULT_IO_FORMAT before including Eigen headers. * If not defined, this will automatically be defined to Eigen::IOFormat(), that is the Eigen::IOFormat with default parameters. * * \sa DenseBase::format() */ template std::ostream & operator << (std::ostream & s, const DenseBase & m) { return internal::print_matrix(s, m.eval(), EIGEN_DEFAULT_IO_FORMAT); } } // end namespace Eigen #endif // EIGEN_IO_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/0000775000175000017500000000000012107032422020505 5ustar csheltoncsheltonctbnrle.1.1.1/hdr/Eigen/src/Core/products/GeneralMatrixMatrix_MKL.h0000775000175000017500000001105412107032422025314 0ustar csheltoncshelton/* Copyright (c) 2011, Intel Corporation. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Intel Corporation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ******************************************************************************** * Content : Eigen bindings to Intel(R) MKL * General matrix-matrix product functionality based on ?GEMM. ******************************************************************************** */ #ifndef EIGEN_GENERAL_MATRIX_MATRIX_MKL_H #define EIGEN_GENERAL_MATRIX_MATRIX_MKL_H namespace Eigen { namespace internal { /********************************************************************** * This file implements general matrix-matrix multiplication using BLAS * gemm function via partial specialization of * general_matrix_matrix_product::run(..) method for float, double, * std::complex and std::complex types **********************************************************************/ // gemm specialization #define GEMM_SPECIALIZATION(EIGTYPE, EIGPREFIX, MKLTYPE, MKLPREFIX) \ template< \ typename Index, \ int LhsStorageOrder, bool ConjugateLhs, \ int RhsStorageOrder, bool ConjugateRhs> \ struct general_matrix_matrix_product \ { \ static void run(Index rows, Index cols, Index depth, \ const EIGTYPE* _lhs, Index lhsStride, \ const EIGTYPE* _rhs, Index rhsStride, \ EIGTYPE* res, Index resStride, \ EIGTYPE alpha, \ level3_blocking& /*blocking*/, \ GemmParallelInfo* /*info = 0*/) \ { \ using std::conj; \ \ char transa, transb; \ MKL_INT m, n, k, lda, ldb, ldc; \ const EIGTYPE *a, *b; \ MKLTYPE alpha_, beta_; \ MatrixX##EIGPREFIX a_tmp, b_tmp; \ EIGTYPE myone(1);\ \ /* Set transpose options */ \ transa = (LhsStorageOrder==RowMajor) ? ((ConjugateLhs) ? 'C' : 'T') : 'N'; \ transb = (RhsStorageOrder==RowMajor) ? ((ConjugateRhs) ? 'C' : 'T') : 'N'; \ \ /* Set m, n, k */ \ m = (MKL_INT)rows; \ n = (MKL_INT)cols; \ k = (MKL_INT)depth; \ \ /* Set alpha_ & beta_ */ \ assign_scalar_eig2mkl(alpha_, alpha); \ assign_scalar_eig2mkl(beta_, myone); \ \ /* Set lda, ldb, ldc */ \ lda = (MKL_INT)lhsStride; \ ldb = (MKL_INT)rhsStride; \ ldc = (MKL_INT)resStride; \ \ /* Set a, b, c */ \ if ((LhsStorageOrder==ColMajor) && (ConjugateLhs)) { \ Map > lhs(_lhs,m,k,OuterStride<>(lhsStride)); \ a_tmp = lhs.conjugate(); \ a = a_tmp.data(); \ lda = a_tmp.outerStride(); \ } else a = _lhs; \ \ if ((RhsStorageOrder==ColMajor) && (ConjugateRhs)) { \ Map > rhs(_rhs,k,n,OuterStride<>(rhsStride)); \ b_tmp = rhs.conjugate(); \ b = b_tmp.data(); \ ldb = b_tmp.outerStride(); \ } else b = _rhs; \ \ MKLPREFIX##gemm(&transa, &transb, &m, &n, &k, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \ }}; GEMM_SPECIALIZATION(double, d, double, d) GEMM_SPECIALIZATION(float, f, float, s) GEMM_SPECIALIZATION(dcomplex, cd, MKL_Complex16, z) GEMM_SPECIALIZATION(scomplex, cf, MKL_Complex8, c) } // end namespase internal } // end namespace Eigen #endif // EIGEN_GENERAL_MATRIX_MATRIX_MKL_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/GeneralMatrixVector.h0000775000175000017500000005502012107032422024610 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_GENERAL_MATRIX_VECTOR_H #define EIGEN_GENERAL_MATRIX_VECTOR_H namespace Eigen { namespace internal { /* Optimized col-major matrix * vector product: * This algorithm processes 4 columns at onces that allows to both reduce * the number of load/stores of the result by a factor 4 and to reduce * the instruction dependency. Moreover, we know that all bands have the * same alignment pattern. * * Mixing type logic: C += alpha * A * B * | A | B |alpha| comments * |real |cplx |cplx | no vectorization * |real |cplx |real | alpha is converted to a cplx when calling the run function, no vectorization * |cplx |real |cplx | invalid, the caller has to do tmp: = A * B; C += alpha*tmp * |cplx |real |real | optimal case, vectorization possible via real-cplx mul */ template struct general_matrix_vector_product { typedef typename scalar_product_traits::ReturnType ResScalar; enum { Vectorizable = packet_traits::Vectorizable && packet_traits::Vectorizable && int(packet_traits::size)==int(packet_traits::size), LhsPacketSize = Vectorizable ? packet_traits::size : 1, RhsPacketSize = Vectorizable ? packet_traits::size : 1, ResPacketSize = Vectorizable ? packet_traits::size : 1 }; typedef typename packet_traits::type _LhsPacket; typedef typename packet_traits::type _RhsPacket; typedef typename packet_traits::type _ResPacket; typedef typename conditional::type LhsPacket; typedef typename conditional::type RhsPacket; typedef typename conditional::type ResPacket; EIGEN_DONT_INLINE static void run( Index rows, Index cols, const LhsScalar* lhs, Index lhsStride, const RhsScalar* rhs, Index rhsIncr, ResScalar* res, Index #ifdef EIGEN_INTERNAL_DEBUGGING resIncr #endif , RhsScalar alpha) { eigen_internal_assert(resIncr==1); #ifdef _EIGEN_ACCUMULATE_PACKETS #error _EIGEN_ACCUMULATE_PACKETS has already been defined #endif #define _EIGEN_ACCUMULATE_PACKETS(A0,A13,A2) \ pstore(&res[j], \ padd(pload(&res[j]), \ padd( \ padd(pcj.pmul(EIGEN_CAT(ploa , A0)(&lhs0[j]), ptmp0), \ pcj.pmul(EIGEN_CAT(ploa , A13)(&lhs1[j]), ptmp1)), \ padd(pcj.pmul(EIGEN_CAT(ploa , A2)(&lhs2[j]), ptmp2), \ pcj.pmul(EIGEN_CAT(ploa , A13)(&lhs3[j]), ptmp3)) ))) conj_helper cj; conj_helper pcj; if(ConjugateRhs) alpha = conj(alpha); enum { AllAligned = 0, EvenAligned, FirstAligned, NoneAligned }; const Index columnsAtOnce = 4; const Index peels = 2; const Index LhsPacketAlignedMask = LhsPacketSize-1; const Index ResPacketAlignedMask = ResPacketSize-1; const Index PeelAlignedMask = ResPacketSize*peels-1; const Index size = rows; // How many coeffs of the result do we have to skip to be aligned. // Here we assume data are at least aligned on the base scalar type. Index alignedStart = internal::first_aligned(res,size); Index alignedSize = ResPacketSize>1 ? alignedStart + ((size-alignedStart) & ~ResPacketAlignedMask) : 0; const Index peeledSize = peels>1 ? alignedStart + ((alignedSize-alignedStart) & ~PeelAlignedMask) : alignedStart; const Index alignmentStep = LhsPacketSize>1 ? (LhsPacketSize - lhsStride % LhsPacketSize) & LhsPacketAlignedMask : 0; Index alignmentPattern = alignmentStep==0 ? AllAligned : alignmentStep==(LhsPacketSize/2) ? EvenAligned : FirstAligned; // we cannot assume the first element is aligned because of sub-matrices const Index lhsAlignmentOffset = internal::first_aligned(lhs,size); // find how many columns do we have to skip to be aligned with the result (if possible) Index skipColumns = 0; // if the data cannot be aligned (TODO add some compile time tests when possible, e.g. for floats) if( (size_t(lhs)%sizeof(LhsScalar)) || (size_t(res)%sizeof(ResScalar)) ) { alignedSize = 0; alignedStart = 0; } else if (LhsPacketSize>1) { eigen_internal_assert(size_t(lhs+lhsAlignmentOffset)%sizeof(LhsPacket)==0 || size= cols) || LhsPacketSize > size || (size_t(lhs+alignedStart+lhsStride*skipColumns)%sizeof(LhsPacket))==0); } else if(Vectorizable) { alignedStart = 0; alignedSize = size; alignmentPattern = AllAligned; } Index offset1 = (FirstAligned && alignmentStep==1?3:1); Index offset3 = (FirstAligned && alignmentStep==1?1:3); Index columnBound = ((cols-skipColumns)/columnsAtOnce)*columnsAtOnce + skipColumns; for (Index i=skipColumns; i(alpha*rhs[i*rhsIncr]), ptmp1 = pset1(alpha*rhs[(i+offset1)*rhsIncr]), ptmp2 = pset1(alpha*rhs[(i+2)*rhsIncr]), ptmp3 = pset1(alpha*rhs[(i+offset3)*rhsIncr]); // this helps a lot generating better binary code const LhsScalar *lhs0 = lhs + i*lhsStride, *lhs1 = lhs + (i+offset1)*lhsStride, *lhs2 = lhs + (i+2)*lhsStride, *lhs3 = lhs + (i+offset3)*lhsStride; if (Vectorizable) { /* explicit vectorization */ // process initial unaligned coeffs for (Index j=0; jalignedStart) { switch(alignmentPattern) { case AllAligned: for (Index j = alignedStart; j1) { LhsPacket A00, A01, A02, A03, A10, A11, A12, A13; ResPacket T0, T1; A01 = pload(&lhs1[alignedStart-1]); A02 = pload(&lhs2[alignedStart-2]); A03 = pload(&lhs3[alignedStart-3]); for (Index j = alignedStart; j(&lhs1[j-1+LhsPacketSize]); palign<1>(A01,A11); A12 = pload(&lhs2[j-2+LhsPacketSize]); palign<2>(A02,A12); A13 = pload(&lhs3[j-3+LhsPacketSize]); palign<3>(A03,A13); A00 = pload(&lhs0[j]); A10 = pload(&lhs0[j+LhsPacketSize]); T0 = pcj.pmadd(A00, ptmp0, pload(&res[j])); T1 = pcj.pmadd(A10, ptmp0, pload(&res[j+ResPacketSize])); T0 = pcj.pmadd(A01, ptmp1, T0); A01 = pload(&lhs1[j-1+2*LhsPacketSize]); palign<1>(A11,A01); T0 = pcj.pmadd(A02, ptmp2, T0); A02 = pload(&lhs2[j-2+2*LhsPacketSize]); palign<2>(A12,A02); T0 = pcj.pmadd(A03, ptmp3, T0); pstore(&res[j],T0); A03 = pload(&lhs3[j-3+2*LhsPacketSize]); palign<3>(A13,A03); T1 = pcj.pmadd(A11, ptmp1, T1); T1 = pcj.pmadd(A12, ptmp2, T1); T1 = pcj.pmadd(A13, ptmp3, T1); pstore(&res[j+ResPacketSize],T1); } } for (Index j = peeledSize; j(alpha*rhs[k*rhsIncr]); const LhsScalar* lhs0 = lhs + k*lhsStride; if (Vectorizable) { /* explicit vectorization */ // process first unaligned result's coeffs for (Index j=0; j(&lhs0[i]), ptmp0, pload(&res[i]))); else for (Index i = alignedStart;i(&lhs0[i]), ptmp0, pload(&res[i]))); } // process remaining scalars (or all if no explicit vectorization) for (Index i=alignedSize; i struct general_matrix_vector_product { typedef typename scalar_product_traits::ReturnType ResScalar; enum { Vectorizable = packet_traits::Vectorizable && packet_traits::Vectorizable && int(packet_traits::size)==int(packet_traits::size), LhsPacketSize = Vectorizable ? packet_traits::size : 1, RhsPacketSize = Vectorizable ? packet_traits::size : 1, ResPacketSize = Vectorizable ? packet_traits::size : 1 }; typedef typename packet_traits::type _LhsPacket; typedef typename packet_traits::type _RhsPacket; typedef typename packet_traits::type _ResPacket; typedef typename conditional::type LhsPacket; typedef typename conditional::type RhsPacket; typedef typename conditional::type ResPacket; EIGEN_DONT_INLINE static void run( Index rows, Index cols, const LhsScalar* lhs, Index lhsStride, const RhsScalar* rhs, Index rhsIncr, ResScalar* res, Index resIncr, ResScalar alpha) { EIGEN_UNUSED_VARIABLE(rhsIncr); eigen_internal_assert(rhsIncr==1); #ifdef _EIGEN_ACCUMULATE_PACKETS #error _EIGEN_ACCUMULATE_PACKETS has already been defined #endif #define _EIGEN_ACCUMULATE_PACKETS(A0,A13,A2) {\ RhsPacket b = pload(&rhs[j]); \ ptmp0 = pcj.pmadd(EIGEN_CAT(ploa,A0) (&lhs0[j]), b, ptmp0); \ ptmp1 = pcj.pmadd(EIGEN_CAT(ploa,A13)(&lhs1[j]), b, ptmp1); \ ptmp2 = pcj.pmadd(EIGEN_CAT(ploa,A2) (&lhs2[j]), b, ptmp2); \ ptmp3 = pcj.pmadd(EIGEN_CAT(ploa,A13)(&lhs3[j]), b, ptmp3); } conj_helper cj; conj_helper pcj; enum { AllAligned=0, EvenAligned=1, FirstAligned=2, NoneAligned=3 }; const Index rowsAtOnce = 4; const Index peels = 2; const Index RhsPacketAlignedMask = RhsPacketSize-1; const Index LhsPacketAlignedMask = LhsPacketSize-1; const Index PeelAlignedMask = RhsPacketSize*peels-1; const Index depth = cols; // How many coeffs of the result do we have to skip to be aligned. // Here we assume data are at least aligned on the base scalar type // if that's not the case then vectorization is discarded, see below. Index alignedStart = internal::first_aligned(rhs, depth); Index alignedSize = RhsPacketSize>1 ? alignedStart + ((depth-alignedStart) & ~RhsPacketAlignedMask) : 0; const Index peeledSize = peels>1 ? alignedStart + ((alignedSize-alignedStart) & ~PeelAlignedMask) : alignedStart; const Index alignmentStep = LhsPacketSize>1 ? (LhsPacketSize - lhsStride % LhsPacketSize) & LhsPacketAlignedMask : 0; Index alignmentPattern = alignmentStep==0 ? AllAligned : alignmentStep==(LhsPacketSize/2) ? EvenAligned : FirstAligned; // we cannot assume the first element is aligned because of sub-matrices const Index lhsAlignmentOffset = internal::first_aligned(lhs,depth); // find how many rows do we have to skip to be aligned with rhs (if possible) Index skipRows = 0; // if the data cannot be aligned (TODO add some compile time tests when possible, e.g. for floats) if( (sizeof(LhsScalar)!=sizeof(RhsScalar)) || (size_t(lhs)%sizeof(LhsScalar)) || (size_t(rhs)%sizeof(RhsScalar)) ) { alignedSize = 0; alignedStart = 0; } else if (LhsPacketSize>1) { eigen_internal_assert(size_t(lhs+lhsAlignmentOffset)%sizeof(LhsPacket)==0 || depth= rows) || LhsPacketSize > depth || (size_t(lhs+alignedStart+lhsStride*skipRows)%sizeof(LhsPacket))==0); } else if(Vectorizable) { alignedStart = 0; alignedSize = depth; alignmentPattern = AllAligned; } Index offset1 = (FirstAligned && alignmentStep==1?3:1); Index offset3 = (FirstAligned && alignmentStep==1?1:3); Index rowBound = ((rows-skipRows)/rowsAtOnce)*rowsAtOnce + skipRows; for (Index i=skipRows; i(ResScalar(0)), ptmp1 = pset1(ResScalar(0)), ptmp2 = pset1(ResScalar(0)), ptmp3 = pset1(ResScalar(0)); // process initial unaligned coeffs // FIXME this loop get vectorized by the compiler ! for (Index j=0; jalignedStart) { switch(alignmentPattern) { case AllAligned: for (Index j = alignedStart; j1) { /* Here we proccess 4 rows with with two peeled iterations to hide * tghe overhead of unaligned loads. Moreover unaligned loads are handled * using special shift/move operations between the two aligned packets * overlaping the desired unaligned packet. This is *much* more efficient * than basic unaligned loads. */ LhsPacket A01, A02, A03, A11, A12, A13; A01 = pload(&lhs1[alignedStart-1]); A02 = pload(&lhs2[alignedStart-2]); A03 = pload(&lhs3[alignedStart-3]); for (Index j = alignedStart; j(&rhs[j]); A11 = pload(&lhs1[j-1+LhsPacketSize]); palign<1>(A01,A11); A12 = pload(&lhs2[j-2+LhsPacketSize]); palign<2>(A02,A12); A13 = pload(&lhs3[j-3+LhsPacketSize]); palign<3>(A03,A13); ptmp0 = pcj.pmadd(pload(&lhs0[j]), b, ptmp0); ptmp1 = pcj.pmadd(A01, b, ptmp1); A01 = pload(&lhs1[j-1+2*LhsPacketSize]); palign<1>(A11,A01); ptmp2 = pcj.pmadd(A02, b, ptmp2); A02 = pload(&lhs2[j-2+2*LhsPacketSize]); palign<2>(A12,A02); ptmp3 = pcj.pmadd(A03, b, ptmp3); A03 = pload(&lhs3[j-3+2*LhsPacketSize]); palign<3>(A13,A03); b = pload(&rhs[j+RhsPacketSize]); ptmp0 = pcj.pmadd(pload(&lhs0[j+LhsPacketSize]), b, ptmp0); ptmp1 = pcj.pmadd(A11, b, ptmp1); ptmp2 = pcj.pmadd(A12, b, ptmp2); ptmp3 = pcj.pmadd(A13, b, ptmp3); } } for (Index j = peeledSize; j(tmp0); const LhsScalar* lhs0 = lhs + i*lhsStride; // process first unaligned result's coeffs // FIXME this loop get vectorized by the compiler ! for (Index j=0; jalignedStart) { // process aligned rhs coeffs if ((size_t(lhs0+alignedStart)%sizeof(LhsPacket))==0) for (Index j = alignedStart;j(&lhs0[j]), pload(&rhs[j]), ptmp0); else for (Index j = alignedStart;j(&lhs0[j]), pload(&rhs[j]), ptmp0); tmp0 += predux(ptmp0); } // process remaining scalars // FIXME this loop get vectorized by the compiler ! for (Index j=alignedSize; j and std::complex types **********************************************************************/ // gemv specialization template struct general_matrix_vector_product_gemv : general_matrix_vector_product {}; #define EIGEN_MKL_GEMV_SPECIALIZE(Scalar) \ template \ struct general_matrix_vector_product { \ static EIGEN_DONT_INLINE void run( \ Index rows, Index cols, \ const Scalar* lhs, Index lhsStride, \ const Scalar* rhs, Index rhsIncr, \ Scalar* res, Index resIncr, Scalar alpha) \ { \ if (ConjugateLhs) { \ general_matrix_vector_product::run( \ rows, cols, lhs, lhsStride, rhs, rhsIncr, res, resIncr, alpha); \ } else { \ general_matrix_vector_product_gemv::run( \ rows, cols, lhs, lhsStride, rhs, rhsIncr, res, resIncr, alpha); \ } \ } \ }; \ template \ struct general_matrix_vector_product { \ static EIGEN_DONT_INLINE void run( \ Index rows, Index cols, \ const Scalar* lhs, Index lhsStride, \ const Scalar* rhs, Index rhsIncr, \ Scalar* res, Index resIncr, Scalar alpha) \ { \ general_matrix_vector_product_gemv::run( \ rows, cols, lhs, lhsStride, rhs, rhsIncr, res, resIncr, alpha); \ } \ }; \ EIGEN_MKL_GEMV_SPECIALIZE(double) EIGEN_MKL_GEMV_SPECIALIZE(float) EIGEN_MKL_GEMV_SPECIALIZE(dcomplex) EIGEN_MKL_GEMV_SPECIALIZE(scomplex) #define EIGEN_MKL_GEMV_SPECIALIZATION(EIGTYPE,MKLTYPE,MKLPREFIX) \ template \ struct general_matrix_vector_product_gemv \ { \ typedef Matrix GEMVVector;\ \ static EIGEN_DONT_INLINE void run( \ Index rows, Index cols, \ const EIGTYPE* lhs, Index lhsStride, \ const EIGTYPE* rhs, Index rhsIncr, \ EIGTYPE* res, Index resIncr, EIGTYPE alpha) \ { \ MKL_INT m=rows, n=cols, lda=lhsStride, incx=rhsIncr, incy=resIncr; \ MKLTYPE alpha_, beta_; \ const EIGTYPE *x_ptr, myone(1); \ char trans=(LhsStorageOrder==ColMajor) ? 'N' : (ConjugateLhs) ? 'C' : 'T'; \ if (LhsStorageOrder==RowMajor) { \ m=cols; \ n=rows; \ }\ assign_scalar_eig2mkl(alpha_, alpha); \ assign_scalar_eig2mkl(beta_, myone); \ GEMVVector x_tmp; \ if (ConjugateRhs) { \ Map > map_x(rhs,cols,1,InnerStride<>(incx)); \ x_tmp=map_x.conjugate(); \ x_ptr=x_tmp.data(); \ incx=1; \ } else x_ptr=rhs; \ MKLPREFIX##gemv(&trans, &m, &n, &alpha_, (const MKLTYPE*)lhs, &lda, (const MKLTYPE*)x_ptr, &incx, &beta_, (MKLTYPE*)res, &incy); \ }\ }; EIGEN_MKL_GEMV_SPECIALIZATION(double, double, d) EIGEN_MKL_GEMV_SPECIALIZATION(float, float, s) EIGEN_MKL_GEMV_SPECIALIZATION(dcomplex, MKL_Complex16, z) EIGEN_MKL_GEMV_SPECIALIZATION(scomplex, MKL_Complex8, c) } // end namespase internal } // end namespace Eigen #endif // EIGEN_GENERAL_MATRIX_VECTOR_MKL_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/GeneralMatrixMatrix.h0000775000175000017500000004060512107032422024615 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_GENERAL_MATRIX_MATRIX_H #define EIGEN_GENERAL_MATRIX_MATRIX_H namespace Eigen { namespace internal { template class level3_blocking; /* Specialization for a row-major destination matrix => simple transposition of the product */ template< typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs> struct general_matrix_matrix_product { typedef typename scalar_product_traits::ReturnType ResScalar; static EIGEN_STRONG_INLINE void run( Index rows, Index cols, Index depth, const LhsScalar* lhs, Index lhsStride, const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resStride, ResScalar alpha, level3_blocking& blocking, GemmParallelInfo* info = 0) { // transpose the product such that the result is column major general_matrix_matrix_product ::run(cols,rows,depth,rhs,rhsStride,lhs,lhsStride,res,resStride,alpha,blocking,info); } }; /* Specialization for a col-major destination matrix * => Blocking algorithm following Goto's paper */ template< typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs> struct general_matrix_matrix_product { typedef typename scalar_product_traits::ReturnType ResScalar; static void run(Index rows, Index cols, Index depth, const LhsScalar* _lhs, Index lhsStride, const RhsScalar* _rhs, Index rhsStride, ResScalar* res, Index resStride, ResScalar alpha, level3_blocking& blocking, GemmParallelInfo* info = 0) { const_blas_data_mapper lhs(_lhs,lhsStride); const_blas_data_mapper rhs(_rhs,rhsStride); typedef gebp_traits Traits; Index kc = blocking.kc(); // cache block size along the K direction Index mc = (std::min)(rows,blocking.mc()); // cache block size along the M direction //Index nc = blocking.nc(); // cache block size along the N direction gemm_pack_lhs pack_lhs; gemm_pack_rhs pack_rhs; gebp_kernel gebp; #ifdef EIGEN_HAS_OPENMP if(info) { // this is the parallel version! Index tid = omp_get_thread_num(); Index threads = omp_get_num_threads(); std::size_t sizeA = kc*mc; std::size_t sizeW = kc*Traits::WorkSpaceFactor; ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, sizeA, 0); ei_declare_aligned_stack_constructed_variable(RhsScalar, w, sizeW, 0); RhsScalar* blockB = blocking.blockB(); eigen_internal_assert(blockB!=0); // For each horizontal panel of the rhs, and corresponding vertical panel of the lhs... for(Index k=0; k rows of B', and cols of the A' // In order to reduce the chance that a thread has to wait for the other, // let's start by packing A'. pack_lhs(blockA, &lhs(0,k), lhsStride, actual_kc, mc); // Pack B_k to B' in a parallel fashion: // each thread packs the sub block B_k,j to B'_j where j is the thread id. // However, before copying to B'_j, we have to make sure that no other thread is still using it, // i.e., we test that info[tid].users equals 0. // Then, we set info[tid].users to the number of threads to mark that all other threads are going to use it. while(info[tid].users!=0) {} info[tid].users += threads; pack_rhs(blockB+info[tid].rhs_start*actual_kc, &rhs(k,info[tid].rhs_start), rhsStride, actual_kc, info[tid].rhs_length); // Notify the other threads that the part B'_j is ready to go. info[tid].sync = k; // Computes C_i += A' * B' per B'_j for(Index shift=0; shift0) while(info[j].sync!=k) {} gebp(res+info[j].rhs_start*resStride, resStride, blockA, blockB+info[j].rhs_start*actual_kc, mc, actual_kc, info[j].rhs_length, alpha, -1,-1,0,0, w); } // Then keep going as usual with the remaining A' for(Index i=mc; i Pack rhs's panel into a sequential chunk of memory (L2 caching) // Note that this panel will be read as many times as the number of blocks in the lhs's // vertical panel which is, in practice, a very low number. pack_rhs(blockB, &rhs(k2,0), rhsStride, actual_kc, cols); // For each mc x kc block of the lhs's vertical panel... // (==GEPP_VAR1) for(Index i2=0; i2 for "large" GEMM, i.e., * implementation of the high level wrapper to general_matrix_matrix_product **********************************************************************************/ template struct traits > : traits, Lhs, Rhs> > {}; template struct gemm_functor { gemm_functor(const Lhs& lhs, const Rhs& rhs, Dest& dest, Scalar actualAlpha, BlockingType& blocking) : m_lhs(lhs), m_rhs(rhs), m_dest(dest), m_actualAlpha(actualAlpha), m_blocking(blocking) {} void initParallelSession() const { m_blocking.allocateB(); } void operator() (Index row, Index rows, Index col=0, Index cols=-1, GemmParallelInfo* info=0) const { if(cols==-1) cols = m_rhs.cols(); Gemm::run(rows, cols, m_lhs.cols(), /*(const Scalar*)*/&m_lhs.coeffRef(row,0), m_lhs.outerStride(), /*(const Scalar*)*/&m_rhs.coeffRef(0,col), m_rhs.outerStride(), (Scalar*)&(m_dest.coeffRef(row,col)), m_dest.outerStride(), m_actualAlpha, m_blocking, info); } protected: const Lhs& m_lhs; const Rhs& m_rhs; Dest& m_dest; Scalar m_actualAlpha; BlockingType& m_blocking; }; template class gemm_blocking_space; template class level3_blocking { typedef _LhsScalar LhsScalar; typedef _RhsScalar RhsScalar; protected: LhsScalar* m_blockA; RhsScalar* m_blockB; RhsScalar* m_blockW; DenseIndex m_mc; DenseIndex m_nc; DenseIndex m_kc; public: level3_blocking() : m_blockA(0), m_blockB(0), m_blockW(0), m_mc(0), m_nc(0), m_kc(0) {} inline DenseIndex mc() const { return m_mc; } inline DenseIndex nc() const { return m_nc; } inline DenseIndex kc() const { return m_kc; } inline LhsScalar* blockA() { return m_blockA; } inline RhsScalar* blockB() { return m_blockB; } inline RhsScalar* blockW() { return m_blockW; } }; template class gemm_blocking_space : public level3_blocking< typename conditional::type, typename conditional::type> { enum { Transpose = StorageOrder==RowMajor, ActualRows = Transpose ? MaxCols : MaxRows, ActualCols = Transpose ? MaxRows : MaxCols }; typedef typename conditional::type LhsScalar; typedef typename conditional::type RhsScalar; typedef gebp_traits Traits; enum { SizeA = ActualRows * MaxDepth, SizeB = ActualCols * MaxDepth, SizeW = MaxDepth * Traits::WorkSpaceFactor }; EIGEN_ALIGN16 LhsScalar m_staticA[SizeA]; EIGEN_ALIGN16 RhsScalar m_staticB[SizeB]; EIGEN_ALIGN16 RhsScalar m_staticW[SizeW]; public: gemm_blocking_space(DenseIndex /*rows*/, DenseIndex /*cols*/, DenseIndex /*depth*/) { this->m_mc = ActualRows; this->m_nc = ActualCols; this->m_kc = MaxDepth; this->m_blockA = m_staticA; this->m_blockB = m_staticB; this->m_blockW = m_staticW; } inline void allocateA() {} inline void allocateB() {} inline void allocateW() {} inline void allocateAll() {} }; template class gemm_blocking_space : public level3_blocking< typename conditional::type, typename conditional::type> { enum { Transpose = StorageOrder==RowMajor }; typedef typename conditional::type LhsScalar; typedef typename conditional::type RhsScalar; typedef gebp_traits Traits; DenseIndex m_sizeA; DenseIndex m_sizeB; DenseIndex m_sizeW; public: gemm_blocking_space(DenseIndex rows, DenseIndex cols, DenseIndex depth) { this->m_mc = Transpose ? cols : rows; this->m_nc = Transpose ? rows : cols; this->m_kc = depth; computeProductBlockingSizes(this->m_kc, this->m_mc, this->m_nc); m_sizeA = this->m_mc * this->m_kc; m_sizeB = this->m_kc * this->m_nc; m_sizeW = this->m_kc*Traits::WorkSpaceFactor; } void allocateA() { if(this->m_blockA==0) this->m_blockA = aligned_new(m_sizeA); } void allocateB() { if(this->m_blockB==0) this->m_blockB = aligned_new(m_sizeB); } void allocateW() { if(this->m_blockW==0) this->m_blockW = aligned_new(m_sizeW); } void allocateAll() { allocateA(); allocateB(); allocateW(); } ~gemm_blocking_space() { aligned_delete(this->m_blockA, m_sizeA); aligned_delete(this->m_blockB, m_sizeB); aligned_delete(this->m_blockW, m_sizeW); } }; } // end namespace internal template class GeneralProduct : public ProductBase, Lhs, Rhs> { enum { MaxDepthAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(Lhs::MaxColsAtCompileTime,Rhs::MaxRowsAtCompileTime) }; public: EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct) typedef typename Lhs::Scalar LhsScalar; typedef typename Rhs::Scalar RhsScalar; typedef Scalar ResScalar; GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) { typedef internal::scalar_product_op BinOp; EIGEN_CHECK_BINARY_COMPATIBILIY(BinOp,LhsScalar,RhsScalar); } template void scaleAndAddTo(Dest& dst, Scalar alpha) const { eigen_assert(dst.rows()==m_lhs.rows() && dst.cols()==m_rhs.cols()); typename internal::add_const_on_value_type::type lhs = LhsBlasTraits::extract(m_lhs); typename internal::add_const_on_value_type::type rhs = RhsBlasTraits::extract(m_rhs); Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(m_lhs) * RhsBlasTraits::extractScalarFactor(m_rhs); typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,LhsScalar,RhsScalar, Dest::MaxRowsAtCompileTime,Dest::MaxColsAtCompileTime,MaxDepthAtCompileTime> BlockingType; typedef internal::gemm_functor< Scalar, Index, internal::general_matrix_matrix_product< Index, LhsScalar, (_ActualLhsType::Flags&RowMajorBit) ? RowMajor : ColMajor, bool(LhsBlasTraits::NeedToConjugate), RhsScalar, (_ActualRhsType::Flags&RowMajorBit) ? RowMajor : ColMajor, bool(RhsBlasTraits::NeedToConjugate), (Dest::Flags&RowMajorBit) ? RowMajor : ColMajor>, _ActualLhsType, _ActualRhsType, Dest, BlockingType> GemmFunctor; BlockingType blocking(dst.rows(), dst.cols(), lhs.cols()); internal::parallelize_gemm<(Dest::MaxRowsAtCompileTime>32 || Dest::MaxRowsAtCompileTime==Dynamic)>(GemmFunctor(lhs, rhs, dst, actualAlpha, blocking), this->rows(), this->cols(), Dest::Flags&RowMajorBit); } }; } // end namespace Eigen #endif // EIGEN_GENERAL_MATRIX_MATRIX_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/CoeffBasedProduct.h0000775000175000017500000004544612107032422024220 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2006-2008 Benoit Jacob // Copyright (C) 2008-2010 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_COEFFBASED_PRODUCT_H #define EIGEN_COEFFBASED_PRODUCT_H namespace Eigen { namespace internal { /********************************************************************************* * Coefficient based product implementation. * It is designed for the following use cases: * - small fixed sizes * - lazy products *********************************************************************************/ /* Since the all the dimensions of the product are small, here we can rely * on the generic Assign mechanism to evaluate the product per coeff (or packet). * * Note that here the inner-loops should always be unrolled. */ template struct product_coeff_impl; template struct product_packet_impl; template struct traits > { typedef MatrixXpr XprKind; typedef typename remove_all::type _LhsNested; typedef typename remove_all::type _RhsNested; typedef typename scalar_product_traits::ReturnType Scalar; typedef typename promote_storage_type::StorageKind, typename traits<_RhsNested>::StorageKind>::ret StorageKind; typedef typename promote_index_type::Index, typename traits<_RhsNested>::Index>::type Index; enum { LhsCoeffReadCost = _LhsNested::CoeffReadCost, RhsCoeffReadCost = _RhsNested::CoeffReadCost, LhsFlags = _LhsNested::Flags, RhsFlags = _RhsNested::Flags, RowsAtCompileTime = _LhsNested::RowsAtCompileTime, ColsAtCompileTime = _RhsNested::ColsAtCompileTime, InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(_LhsNested::ColsAtCompileTime, _RhsNested::RowsAtCompileTime), MaxRowsAtCompileTime = _LhsNested::MaxRowsAtCompileTime, MaxColsAtCompileTime = _RhsNested::MaxColsAtCompileTime, LhsRowMajor = LhsFlags & RowMajorBit, RhsRowMajor = RhsFlags & RowMajorBit, SameType = is_same::value, CanVectorizeRhs = RhsRowMajor && (RhsFlags & PacketAccessBit) && (ColsAtCompileTime == Dynamic || ( (ColsAtCompileTime % packet_traits::size) == 0 && (RhsFlags&AlignedBit) ) ), CanVectorizeLhs = (!LhsRowMajor) && (LhsFlags & PacketAccessBit) && (RowsAtCompileTime == Dynamic || ( (RowsAtCompileTime % packet_traits::size) == 0 && (LhsFlags&AlignedBit) ) ), EvalToRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1 : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0 : (RhsRowMajor && !CanVectorizeLhs), Flags = ((unsigned int)(LhsFlags | RhsFlags) & HereditaryBits & ~RowMajorBit) | (EvalToRowMajor ? RowMajorBit : 0) | NestingFlags | (LhsFlags & RhsFlags & AlignedBit) // TODO enable vectorization for mixed types | (SameType && (CanVectorizeLhs || CanVectorizeRhs) ? PacketAccessBit : 0), CoeffReadCost = InnerSize == Dynamic ? Dynamic : InnerSize * (NumTraits::MulCost + LhsCoeffReadCost + RhsCoeffReadCost) + (InnerSize - 1) * NumTraits::AddCost, /* CanVectorizeInner deserves special explanation. It does not affect the product flags. It is not used outside * of Product. If the Product itself is not a packet-access expression, there is still a chance that the inner * loop of the product might be vectorized. This is the meaning of CanVectorizeInner. Since it doesn't affect * the Flags, it is safe to make this value depend on ActualPacketAccessBit, that doesn't affect the ABI. */ CanVectorizeInner = SameType && LhsRowMajor && (!RhsRowMajor) && (LhsFlags & RhsFlags & ActualPacketAccessBit) && (LhsFlags & RhsFlags & AlignedBit) && (InnerSize % packet_traits::size == 0) }; }; } // end namespace internal template class CoeffBasedProduct : internal::no_assignment_operator, public MatrixBase > { public: typedef MatrixBase Base; EIGEN_DENSE_PUBLIC_INTERFACE(CoeffBasedProduct) typedef typename Base::PlainObject PlainObject; private: typedef typename internal::traits::_LhsNested _LhsNested; typedef typename internal::traits::_RhsNested _RhsNested; enum { PacketSize = internal::packet_traits::size, InnerSize = internal::traits::InnerSize, Unroll = CoeffReadCost != Dynamic && CoeffReadCost <= EIGEN_UNROLLING_LIMIT, CanVectorizeInner = internal::traits::CanVectorizeInner }; typedef internal::product_coeff_impl ScalarCoeffImpl; typedef CoeffBasedProduct LazyCoeffBasedProductType; public: inline CoeffBasedProduct(const CoeffBasedProduct& other) : Base(), m_lhs(other.m_lhs), m_rhs(other.m_rhs) {} template inline CoeffBasedProduct(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs) { // we don't allow taking products of matrices of different real types, as that wouldn't be vectorizable. // We still allow to mix T and complex. EIGEN_STATIC_ASSERT((internal::is_same::value), YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) eigen_assert(lhs.cols() == rhs.rows() && "invalid matrix product" && "if you wanted a coeff-wise or a dot product use the respective explicit functions"); } EIGEN_STRONG_INLINE Index rows() const { return m_lhs.rows(); } EIGEN_STRONG_INLINE Index cols() const { return m_rhs.cols(); } EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const { Scalar res; ScalarCoeffImpl::run(row, col, m_lhs, m_rhs, res); return res; } /* Allow index-based non-packet access. It is impossible though to allow index-based packed access, * which is why we don't set the LinearAccessBit. */ EIGEN_STRONG_INLINE const Scalar coeff(Index index) const { Scalar res; const Index row = RowsAtCompileTime == 1 ? 0 : index; const Index col = RowsAtCompileTime == 1 ? index : 0; ScalarCoeffImpl::run(row, col, m_lhs, m_rhs, res); return res; } template EIGEN_STRONG_INLINE const PacketScalar packet(Index row, Index col) const { PacketScalar res; internal::product_packet_impl ::run(row, col, m_lhs, m_rhs, res); return res; } // Implicit conversion to the nested type (trigger the evaluation of the product) EIGEN_STRONG_INLINE operator const PlainObject& () const { m_result.lazyAssign(*this); return m_result; } const _LhsNested& lhs() const { return m_lhs; } const _RhsNested& rhs() const { return m_rhs; } const Diagonal diagonal() const { return reinterpret_cast(*this); } template const Diagonal diagonal() const { return reinterpret_cast(*this); } const Diagonal diagonal(Index index) const { return reinterpret_cast(*this).diagonal(index); } protected: typename internal::add_const_on_value_type::type m_lhs; typename internal::add_const_on_value_type::type m_rhs; mutable PlainObject m_result; }; namespace internal { // here we need to overload the nested rule for products // such that the nested type is a const reference to a plain matrix template struct nested, N, PlainObject> { typedef PlainObject const& type; }; /*************************************************************************** * Normal product .coeff() implementation (with meta-unrolling) ***************************************************************************/ /************************************** *** Scalar path - no vectorization *** **************************************/ template struct product_coeff_impl { typedef typename Lhs::Index Index; static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, RetScalar &res) { product_coeff_impl::run(row, col, lhs, rhs, res); res += lhs.coeff(row, UnrollingIndex) * rhs.coeff(UnrollingIndex, col); } }; template struct product_coeff_impl { typedef typename Lhs::Index Index; static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, RetScalar &res) { res = lhs.coeff(row, 0) * rhs.coeff(0, col); } }; template struct product_coeff_impl { typedef typename Lhs::Index Index; static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, RetScalar& res) { eigen_assert(lhs.cols()>0 && "you are using a non initialized matrix"); res = lhs.coeff(row, 0) * rhs.coeff(0, col); for(Index i = 1; i < lhs.cols(); ++i) res += lhs.coeff(row, i) * rhs.coeff(i, col); } }; /******************************************* *** Scalar path with inner vectorization *** *******************************************/ template struct product_coeff_vectorized_unroller { typedef typename Lhs::Index Index; enum { PacketSize = packet_traits::size }; static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, typename Lhs::PacketScalar &pres) { product_coeff_vectorized_unroller::run(row, col, lhs, rhs, pres); pres = padd(pres, pmul( lhs.template packet(row, UnrollingIndex) , rhs.template packet(UnrollingIndex, col) )); } }; template struct product_coeff_vectorized_unroller<0, Lhs, Rhs, Packet> { typedef typename Lhs::Index Index; static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, typename Lhs::PacketScalar &pres) { pres = pmul(lhs.template packet(row, 0) , rhs.template packet(0, col)); } }; template struct product_coeff_impl { typedef typename Lhs::PacketScalar Packet; typedef typename Lhs::Index Index; enum { PacketSize = packet_traits::size }; static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, RetScalar &res) { Packet pres; product_coeff_vectorized_unroller::run(row, col, lhs, rhs, pres); product_coeff_impl::run(row, col, lhs, rhs, res); res = predux(pres); } }; template struct product_coeff_vectorized_dyn_selector { typedef typename Lhs::Index Index; static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res) { res = lhs.row(row).transpose().cwiseProduct(rhs.col(col)).sum(); } }; // NOTE the 3 following specializations are because taking .col(0) on a vector is a bit slower // NOTE maybe they are now useless since we have a specialization for Block template struct product_coeff_vectorized_dyn_selector { typedef typename Lhs::Index Index; static EIGEN_STRONG_INLINE void run(Index /*row*/, Index col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res) { res = lhs.transpose().cwiseProduct(rhs.col(col)).sum(); } }; template struct product_coeff_vectorized_dyn_selector { typedef typename Lhs::Index Index; static EIGEN_STRONG_INLINE void run(Index row, Index /*col*/, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res) { res = lhs.row(row).transpose().cwiseProduct(rhs).sum(); } }; template struct product_coeff_vectorized_dyn_selector { typedef typename Lhs::Index Index; static EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res) { res = lhs.transpose().cwiseProduct(rhs).sum(); } }; template struct product_coeff_impl { typedef typename Lhs::Index Index; static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res) { product_coeff_vectorized_dyn_selector::run(row, col, lhs, rhs, res); } }; /******************* *** Packet path *** *******************/ template struct product_packet_impl { typedef typename Lhs::Index Index; static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet &res) { product_packet_impl::run(row, col, lhs, rhs, res); res = pmadd(pset1(lhs.coeff(row, UnrollingIndex)), rhs.template packet(UnrollingIndex, col), res); } }; template struct product_packet_impl { typedef typename Lhs::Index Index; static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet &res) { product_packet_impl::run(row, col, lhs, rhs, res); res = pmadd(lhs.template packet(row, UnrollingIndex), pset1(rhs.coeff(UnrollingIndex, col)), res); } }; template struct product_packet_impl { typedef typename Lhs::Index Index; static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet &res) { res = pmul(pset1(lhs.coeff(row, 0)),rhs.template packet(0, col)); } }; template struct product_packet_impl { typedef typename Lhs::Index Index; static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet &res) { res = pmul(lhs.template packet(row, 0), pset1(rhs.coeff(0, col))); } }; template struct product_packet_impl { typedef typename Lhs::Index Index; static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet& res) { eigen_assert(lhs.cols()>0 && "you are using a non initialized matrix"); res = pmul(pset1(lhs.coeff(row, 0)),rhs.template packet(0, col)); for(Index i = 1; i < lhs.cols(); ++i) res = pmadd(pset1(lhs.coeff(row, i)), rhs.template packet(i, col), res); } }; template struct product_packet_impl { typedef typename Lhs::Index Index; static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet& res) { eigen_assert(lhs.cols()>0 && "you are using a non initialized matrix"); res = pmul(lhs.template packet(row, 0), pset1(rhs.coeff(0, col))); for(Index i = 1; i < lhs.cols(); ++i) res = pmadd(lhs.template packet(row, i), pset1(rhs.coeff(i, col)), res); } }; } // end namespace internal } // end namespace Eigen #endif // EIGEN_COEFFBASED_PRODUCT_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/CMakeLists.txt0000775000175000017500000000024612107032422023252 0ustar csheltoncsheltonFILE(GLOB Eigen_Core_Product_SRCS "*.h") INSTALL(FILES ${Eigen_Core_Product_SRCS} DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Core/products COMPONENT Devel ) ctbnrle.1.1.1/hdr/Eigen/src/Core/products/Parallelizer.h0000775000175000017500000001075412107032422023316 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2010 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_PARALLELIZER_H #define EIGEN_PARALLELIZER_H namespace Eigen { namespace internal { /** \internal */ inline void manage_multi_threading(Action action, int* v) { static EIGEN_UNUSED int m_maxThreads = -1; if(action==SetAction) { eigen_internal_assert(v!=0); m_maxThreads = *v; } else if(action==GetAction) { eigen_internal_assert(v!=0); #ifdef EIGEN_HAS_OPENMP if(m_maxThreads>0) *v = m_maxThreads; else *v = omp_get_max_threads(); #else *v = 1; #endif } else { eigen_internal_assert(false); } } /** \returns the max number of threads reserved for Eigen * \sa setNbThreads */ inline int nbThreads() { int ret; manage_multi_threading(GetAction, &ret); return ret; } /** Sets the max number of threads reserved for Eigen * \sa nbThreads */ inline void setNbThreads(int v) { manage_multi_threading(SetAction, &v); } template struct GemmParallelInfo { GemmParallelInfo() : sync(-1), users(0), rhs_start(0), rhs_length(0) {} int volatile sync; int volatile users; Index rhs_start; Index rhs_length; }; template void parallelize_gemm(const Functor& func, Index rows, Index cols, bool transpose) { // TODO when EIGEN_USE_BLAS is defined, // we should still enable OMP for other scalar types #if !(defined (EIGEN_HAS_OPENMP)) || defined (EIGEN_USE_BLAS) // FIXME the transpose variable is only needed to properly split // the matrix product when multithreading is enabled. This is a temporary // fix to support row-major destination matrices. This whole // parallelizer mechanism has to be redisigned anyway. EIGEN_UNUSED_VARIABLE(transpose); func(0,rows, 0,cols); #else // Dynamically check whether we should enable or disable OpenMP. // The conditions are: // - the max number of threads we can create is greater than 1 // - we are not already in a parallel code // - the sizes are large enough // 1- are we already in a parallel session? // FIXME omp_get_num_threads()>1 only works for openmp, what if the user does not use openmp? if((!Condition) || (omp_get_num_threads()>1)) return func(0,rows, 0,cols); Index size = transpose ? cols : rows; // 2- compute the maximal number of threads from the size of the product: // FIXME this has to be fine tuned Index max_threads = std::max(1,size / 32); // 3 - compute the number of threads we are going to use Index threads = std::min(nbThreads(), max_threads); if(threads==1) return func(0,rows, 0,cols); func.initParallelSession(); if(transpose) std::swap(rows,cols); Index blockCols = (cols / threads) & ~Index(0x3); Index blockRows = (rows / threads) & ~Index(0x7); GemmParallelInfo* info = new GemmParallelInfo[threads]; #pragma omp parallel for schedule(static,1) num_threads(threads) for(Index i=0; i // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H #define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H namespace Eigen { namespace internal { /********************************************************************** * This file implements a general A * B product while * evaluating only one triangular part of the product. * This is more general version of self adjoint product (C += A A^T) * as the level 3 SYRK Blas routine. **********************************************************************/ // forward declarations (defined at the end of this file) template struct tribb_kernel; /* Optimized matrix-matrix product evaluating only one triangular half */ template struct general_matrix_matrix_triangular_product; // as usual if the result is row major => we transpose the product template struct general_matrix_matrix_triangular_product { typedef typename scalar_product_traits::ReturnType ResScalar; static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* lhs, Index lhsStride, const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resStride, ResScalar alpha) { general_matrix_matrix_triangular_product ::run(size,depth,rhs,rhsStride,lhs,lhsStride,res,resStride,alpha); } }; template struct general_matrix_matrix_triangular_product { typedef typename scalar_product_traits::ReturnType ResScalar; static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* _lhs, Index lhsStride, const RhsScalar* _rhs, Index rhsStride, ResScalar* res, Index resStride, ResScalar alpha) { const_blas_data_mapper lhs(_lhs,lhsStride); const_blas_data_mapper rhs(_rhs,rhsStride); typedef gebp_traits Traits; Index kc = depth; // cache block size along the K direction Index mc = size; // cache block size along the M direction Index nc = size; // cache block size along the N direction computeProductBlockingSizes(kc, mc, nc); // !!! mc must be a multiple of nr: if(mc > Traits::nr) mc = (mc/Traits::nr)*Traits::nr; std::size_t sizeW = kc*Traits::WorkSpaceFactor; std::size_t sizeB = sizeW + kc*size; ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, kc*mc, 0); ei_declare_aligned_stack_constructed_variable(RhsScalar, allocatedBlockB, sizeB, 0); RhsScalar* blockB = allocatedBlockB + sizeW; gemm_pack_lhs pack_lhs; gemm_pack_rhs pack_rhs; gebp_kernel gebp; tribb_kernel sybb; for(Index k2=0; k2 processed with gebp or skipped // 2 - the actual_mc x actual_mc symmetric block => processed with a special kernel // 3 - after the diagonal => processed with gebp or skipped if (UpLo==Lower) gebp(res+i2, resStride, blockA, blockB, actual_mc, actual_kc, (std::min)(size,i2), alpha, -1, -1, 0, 0, allocatedBlockB); sybb(res+resStride*i2 + i2, resStride, blockA, blockB + actual_kc*i2, actual_mc, actual_kc, alpha, allocatedBlockB); if (UpLo==Upper) { Index j2 = i2+actual_mc; gebp(res+resStride*j2+i2, resStride, blockA, blockB+actual_kc*j2, actual_mc, actual_kc, (std::max)(Index(0), size-j2), alpha, -1, -1, 0, 0, allocatedBlockB); } } } } }; // Optimized packed Block * packed Block product kernel evaluating only one given triangular part // This kernel is built on top of the gebp kernel: // - the current destination block is processed per panel of actual_mc x BlockSize // where BlockSize is set to the minimal value allowing gebp to be as fast as possible // - then, as usual, each panel is split into three parts along the diagonal, // the sub blocks above and below the diagonal are processed as usual, // while the triangular block overlapping the diagonal is evaluated into a // small temporary buffer which is then accumulated into the result using a // triangular traversal. template struct tribb_kernel { typedef gebp_traits Traits; typedef typename Traits::ResScalar ResScalar; enum { BlockSize = EIGEN_PLAIN_ENUM_MAX(mr,nr) }; void operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index size, Index depth, ResScalar alpha, RhsScalar* workspace) { gebp_kernel gebp_kernel; Matrix buffer; // let's process the block per panel of actual_mc x BlockSize, // again, each is split into three parts, etc. for (Index j=0; j(BlockSize,size - j); const RhsScalar* actual_b = blockB+j*depth; if(UpLo==Upper) gebp_kernel(res+j*resStride, resStride, blockA, actual_b, j, depth, actualBlockSize, alpha, -1, -1, 0, 0, workspace); // selfadjoint micro block { Index i = j; buffer.setZero(); // 1 - apply the kernel on the temporary buffer gebp_kernel(buffer.data(), BlockSize, blockA+depth*i, actual_b, actualBlockSize, depth, actualBlockSize, alpha, -1, -1, 0, 0, workspace); // 2 - triangular accumulation for(Index j1=0; j1 template TriangularView& TriangularView::assignProduct(const ProductBase& prod, const Scalar& alpha) { typedef typename internal::remove_all::type Lhs; typedef internal::blas_traits LhsBlasTraits; typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs; typedef typename internal::remove_all::type _ActualLhs; typename internal::add_const_on_value_type::type actualLhs = LhsBlasTraits::extract(prod.lhs()); typedef typename internal::remove_all::type Rhs; typedef internal::blas_traits RhsBlasTraits; typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs; typedef typename internal::remove_all::type _ActualRhs; typename internal::add_const_on_value_type::type actualRhs = RhsBlasTraits::extract(prod.rhs()); typename ProductDerived::Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived()); internal::general_matrix_matrix_triangular_product ::run(m_matrix.cols(), actualLhs.cols(), &actualLhs.coeffRef(0,0), actualLhs.outerStride(), &actualRhs.coeffRef(0,0), actualRhs.outerStride(), const_cast(m_matrix.data()), m_matrix.outerStride(), actualAlpha); return *this; } } // end namespace Eigen #endif // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/GeneralBlockPanelKernel.h0000775000175000017500000013122112107032422025332 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_GENERAL_BLOCK_PANEL_H #define EIGEN_GENERAL_BLOCK_PANEL_H namespace Eigen { namespace internal { template class gebp_traits; /** \internal \returns b if a<=0, and returns a otherwise. */ inline std::ptrdiff_t manage_caching_sizes_helper(std::ptrdiff_t a, std::ptrdiff_t b) { return a<=0 ? b : a; } /** \internal */ inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1=0, std::ptrdiff_t* l2=0) { static std::ptrdiff_t m_l1CacheSize = manage_caching_sizes_helper(queryL1CacheSize(),8 * 1024); static std::ptrdiff_t m_l2CacheSize = manage_caching_sizes_helper(queryTopLevelCacheSize(),1*1024*1024); if(action==SetAction) { // set the cpu cache size and cache all block sizes from a global cache size in byte eigen_internal_assert(l1!=0 && l2!=0); m_l1CacheSize = *l1; m_l2CacheSize = *l2; } else if(action==GetAction) { eigen_internal_assert(l1!=0 && l2!=0); *l1 = m_l1CacheSize; *l2 = m_l2CacheSize; } else { eigen_internal_assert(false); } } /** \brief Computes the blocking parameters for a m x k times k x n matrix product * * \param[in,out] k Input: the third dimension of the product. Output: the blocking size along the same dimension. * \param[in,out] m Input: the number of rows of the left hand side. Output: the blocking size along the same dimension. * \param[in,out] n Input: the number of columns of the right hand side. Output: the blocking size along the same dimension. * * Given a m x k times k x n matrix product of scalar types \c LhsScalar and \c RhsScalar, * this function computes the blocking size parameters along the respective dimensions * for matrix products and related algorithms. The blocking sizes depends on various * parameters: * - the L1 and L2 cache sizes, * - the register level blocking sizes defined by gebp_traits, * - the number of scalars that fit into a packet (when vectorization is enabled). * * \sa setCpuCacheSizes */ template void computeProductBlockingSizes(std::ptrdiff_t& k, std::ptrdiff_t& m, std::ptrdiff_t& n) { EIGEN_UNUSED_VARIABLE(n); // Explanations: // Let's recall the product algorithms form kc x nc horizontal panels B' on the rhs and // mc x kc blocks A' on the lhs. A' has to fit into L2 cache. Moreover, B' is processed // per kc x nr vertical small panels where nr is the blocking size along the n dimension // at the register level. For vectorization purpose, these small vertical panels are unpacked, // e.g., each coefficient is replicated to fit a packet. This small vertical panel has to // stay in L1 cache. std::ptrdiff_t l1, l2; typedef gebp_traits Traits; enum { kdiv = KcFactor * 2 * Traits::nr * Traits::RhsProgress * sizeof(RhsScalar), mr = gebp_traits::mr, mr_mask = (0xffffffff/mr)*mr }; manage_caching_sizes(GetAction, &l1, &l2); k = std::min(k, l1/kdiv); std::ptrdiff_t _m = k>0 ? l2/(4 * sizeof(LhsScalar) * k) : 0; if(_m inline void computeProductBlockingSizes(std::ptrdiff_t& k, std::ptrdiff_t& m, std::ptrdiff_t& n) { computeProductBlockingSizes(k, m, n); } #ifdef EIGEN_HAS_FUSE_CJMADD #define MADD(CJ,A,B,C,T) C = CJ.pmadd(A,B,C); #else // FIXME (a bit overkill maybe ?) template struct gebp_madd_selector { EIGEN_ALWAYS_INLINE static void run(const CJ& cj, A& a, B& b, C& c, T& /*t*/) { c = cj.pmadd(a,b,c); } }; template struct gebp_madd_selector { EIGEN_ALWAYS_INLINE static void run(const CJ& cj, T& a, T& b, T& c, T& t) { t = b; t = cj.pmul(a,t); c = padd(c,t); } }; template EIGEN_STRONG_INLINE void gebp_madd(const CJ& cj, A& a, B& b, C& c, T& t) { gebp_madd_selector::run(cj,a,b,c,t); } #define MADD(CJ,A,B,C,T) gebp_madd(CJ,A,B,C,T); // #define MADD(CJ,A,B,C,T) T = B; T = CJ.pmul(A,T); C = padd(C,T); #endif /* Vectorization logic * real*real: unpack rhs to constant packets, ... * * cd*cd : unpack rhs to (b_r,b_r), (b_i,b_i), mul to get (a_r b_r,a_i b_r) (a_r b_i,a_i b_i), * storing each res packet into two packets (2x2), * at the end combine them: swap the second and addsub them * cf*cf : same but with 2x4 blocks * cplx*real : unpack rhs to constant packets, ... * real*cplx : load lhs as (a0,a0,a1,a1), and mul as usual */ template class gebp_traits { public: typedef _LhsScalar LhsScalar; typedef _RhsScalar RhsScalar; typedef typename scalar_product_traits::ReturnType ResScalar; enum { ConjLhs = _ConjLhs, ConjRhs = _ConjRhs, Vectorizable = packet_traits::Vectorizable && packet_traits::Vectorizable, LhsPacketSize = Vectorizable ? packet_traits::size : 1, RhsPacketSize = Vectorizable ? packet_traits::size : 1, ResPacketSize = Vectorizable ? packet_traits::size : 1, NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS, // register block size along the N direction (must be either 2 or 4) nr = NumberOfRegisters/4, // register block size along the M direction (currently, this one cannot be modified) mr = 2 * LhsPacketSize, WorkSpaceFactor = nr * RhsPacketSize, LhsProgress = LhsPacketSize, RhsProgress = RhsPacketSize }; typedef typename packet_traits::type _LhsPacket; typedef typename packet_traits::type _RhsPacket; typedef typename packet_traits::type _ResPacket; typedef typename conditional::type LhsPacket; typedef typename conditional::type RhsPacket; typedef typename conditional::type ResPacket; typedef ResPacket AccPacket; EIGEN_STRONG_INLINE void initAcc(AccPacket& p) { p = pset1(ResScalar(0)); } EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b) { for(DenseIndex k=0; k(&b[k*RhsPacketSize], rhs[k]); } EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const { dest = pload(b); } EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const { dest = pload(a); } EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, AccPacket& tmp) const { tmp = b; tmp = pmul(a,tmp); c = padd(c,tmp); } EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const { r = pmadd(c,alpha,r); } protected: // conj_helper cj; // conj_helper pcj; }; template class gebp_traits, RealScalar, _ConjLhs, false> { public: typedef std::complex LhsScalar; typedef RealScalar RhsScalar; typedef typename scalar_product_traits::ReturnType ResScalar; enum { ConjLhs = _ConjLhs, ConjRhs = false, Vectorizable = packet_traits::Vectorizable && packet_traits::Vectorizable, LhsPacketSize = Vectorizable ? packet_traits::size : 1, RhsPacketSize = Vectorizable ? packet_traits::size : 1, ResPacketSize = Vectorizable ? packet_traits::size : 1, NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS, nr = NumberOfRegisters/4, mr = 2 * LhsPacketSize, WorkSpaceFactor = nr*RhsPacketSize, LhsProgress = LhsPacketSize, RhsProgress = RhsPacketSize }; typedef typename packet_traits::type _LhsPacket; typedef typename packet_traits::type _RhsPacket; typedef typename packet_traits::type _ResPacket; typedef typename conditional::type LhsPacket; typedef typename conditional::type RhsPacket; typedef typename conditional::type ResPacket; typedef ResPacket AccPacket; EIGEN_STRONG_INLINE void initAcc(AccPacket& p) { p = pset1(ResScalar(0)); } EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b) { for(DenseIndex k=0; k(&b[k*RhsPacketSize], rhs[k]); } EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const { dest = pload(b); } EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const { dest = pload(a); } EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp) const { madd_impl(a, b, c, tmp, typename conditional::type()); } EIGEN_STRONG_INLINE void madd_impl(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp, const true_type&) const { tmp = b; tmp = pmul(a.v,tmp); c.v = padd(c.v,tmp); } EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const { c += a * b; } EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const { r = cj.pmadd(c,alpha,r); } protected: conj_helper cj; }; template class gebp_traits, std::complex, _ConjLhs, _ConjRhs > { public: typedef std::complex Scalar; typedef std::complex LhsScalar; typedef std::complex RhsScalar; typedef std::complex ResScalar; enum { ConjLhs = _ConjLhs, ConjRhs = _ConjRhs, Vectorizable = packet_traits::Vectorizable && packet_traits::Vectorizable, RealPacketSize = Vectorizable ? packet_traits::size : 1, ResPacketSize = Vectorizable ? packet_traits::size : 1, nr = 2, mr = 2 * ResPacketSize, WorkSpaceFactor = Vectorizable ? 2*nr*RealPacketSize : nr, LhsProgress = ResPacketSize, RhsProgress = Vectorizable ? 2*ResPacketSize : 1 }; typedef typename packet_traits::type RealPacket; typedef typename packet_traits::type ScalarPacket; struct DoublePacket { RealPacket first; RealPacket second; }; typedef typename conditional::type LhsPacket; typedef typename conditional::type RhsPacket; typedef typename conditional::type ResPacket; typedef typename conditional::type AccPacket; EIGEN_STRONG_INLINE void initAcc(Scalar& p) { p = Scalar(0); } EIGEN_STRONG_INLINE void initAcc(DoublePacket& p) { p.first = pset1(RealScalar(0)); p.second = pset1(RealScalar(0)); } /* Unpack the rhs coeff such that each complex coefficient is spread into * two packects containing respectively the real and imaginary coefficient * duplicated as many time as needed: (x+iy) => [x, ..., x] [y, ..., y] */ EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const Scalar* rhs, Scalar* b) { for(DenseIndex k=0; k((RealScalar*)&b[k*ResPacketSize*2+0], real(rhs[k])); pstore1((RealScalar*)&b[k*ResPacketSize*2+ResPacketSize], imag(rhs[k])); } else b[k] = rhs[k]; } } EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, ResPacket& dest) const { dest = *b; } EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, DoublePacket& dest) const { dest.first = pload((const RealScalar*)b); dest.second = pload((const RealScalar*)(b+ResPacketSize)); } // nothing special here EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const { dest = pload((const typename unpacket_traits::type*)(a)); } EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, DoublePacket& c, RhsPacket& /*tmp*/) const { c.first = padd(pmul(a,b.first), c.first); c.second = padd(pmul(a,b.second),c.second); } EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, ResPacket& c, RhsPacket& /*tmp*/) const { c = cj.pmadd(a,b,c); } EIGEN_STRONG_INLINE void acc(const Scalar& c, const Scalar& alpha, Scalar& r) const { r += alpha * c; } EIGEN_STRONG_INLINE void acc(const DoublePacket& c, const ResPacket& alpha, ResPacket& r) const { // assemble c ResPacket tmp; if((!ConjLhs)&&(!ConjRhs)) { tmp = pcplxflip(pconj(ResPacket(c.second))); tmp = padd(ResPacket(c.first),tmp); } else if((!ConjLhs)&&(ConjRhs)) { tmp = pconj(pcplxflip(ResPacket(c.second))); tmp = padd(ResPacket(c.first),tmp); } else if((ConjLhs)&&(!ConjRhs)) { tmp = pcplxflip(ResPacket(c.second)); tmp = padd(pconj(ResPacket(c.first)),tmp); } else if((ConjLhs)&&(ConjRhs)) { tmp = pcplxflip(ResPacket(c.second)); tmp = psub(pconj(ResPacket(c.first)),tmp); } r = pmadd(tmp,alpha,r); } protected: conj_helper cj; }; template class gebp_traits, false, _ConjRhs > { public: typedef std::complex Scalar; typedef RealScalar LhsScalar; typedef Scalar RhsScalar; typedef Scalar ResScalar; enum { ConjLhs = false, ConjRhs = _ConjRhs, Vectorizable = packet_traits::Vectorizable && packet_traits::Vectorizable, LhsPacketSize = Vectorizable ? packet_traits::size : 1, RhsPacketSize = Vectorizable ? packet_traits::size : 1, ResPacketSize = Vectorizable ? packet_traits::size : 1, NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS, nr = 4, mr = 2*ResPacketSize, WorkSpaceFactor = nr*RhsPacketSize, LhsProgress = ResPacketSize, RhsProgress = ResPacketSize }; typedef typename packet_traits::type _LhsPacket; typedef typename packet_traits::type _RhsPacket; typedef typename packet_traits::type _ResPacket; typedef typename conditional::type LhsPacket; typedef typename conditional::type RhsPacket; typedef typename conditional::type ResPacket; typedef ResPacket AccPacket; EIGEN_STRONG_INLINE void initAcc(AccPacket& p) { p = pset1(ResScalar(0)); } EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b) { for(DenseIndex k=0; k(&b[k*RhsPacketSize], rhs[k]); } EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const { dest = pload(b); } EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const { dest = ploaddup(a); } EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp) const { madd_impl(a, b, c, tmp, typename conditional::type()); } EIGEN_STRONG_INLINE void madd_impl(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp, const true_type&) const { tmp = b; tmp.v = pmul(a,tmp.v); c = padd(c,tmp); } EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const { c += a * b; } EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const { r = cj.pmadd(alpha,c,r); } protected: conj_helper cj; }; /* optimized GEneral packed Block * packed Panel product kernel * * Mixing type logic: C += A * B * | A | B | comments * |real |cplx | no vectorization yet, would require to pack A with duplication * |cplx |real | easy vectorization */ template struct gebp_kernel { typedef gebp_traits Traits; typedef typename Traits::ResScalar ResScalar; typedef typename Traits::LhsPacket LhsPacket; typedef typename Traits::RhsPacket RhsPacket; typedef typename Traits::ResPacket ResPacket; typedef typename Traits::AccPacket AccPacket; enum { Vectorizable = Traits::Vectorizable, LhsProgress = Traits::LhsProgress, RhsProgress = Traits::RhsProgress, ResPacketSize = Traits::ResPacketSize }; EIGEN_DONT_INLINE EIGEN_FLATTEN_ATTRIB void operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index rows, Index depth, Index cols, ResScalar alpha, Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0, RhsScalar* unpackedB = 0) { Traits traits; if(strideA==-1) strideA = depth; if(strideB==-1) strideB = depth; conj_helper cj; // conj_helper pcj; Index packet_cols = (cols/nr) * nr; const Index peeled_mc = (rows/mr)*mr; // FIXME: const Index peeled_mc2 = peeled_mc + (rows-peeled_mc >= LhsProgress ? LhsProgress : 0); const Index peeled_kc = (depth/4)*4; if(unpackedB==0) unpackedB = const_cast(blockB - strideB * nr * RhsProgress); // loops on each micro vertical panel of rhs (depth x nr) for(Index j2=0; j2 we select a mr x nr micro block of res which is entirely // stored into mr/packet_size x nr registers. for(Index i=0; i(alpha); R0 = ploadu(r0); R1 = ploadu(r1); R2 = ploadu(r2); R3 = ploadu(r3); R4 = ploadu(r0 + ResPacketSize); R5 = ploadu(r1 + ResPacketSize); R6 = ploadu(r2 + ResPacketSize); traits.acc(C0, alphav, R0); pstoreu(r0, R0); R0 = ploadu(r3 + ResPacketSize); traits.acc(C1, alphav, R1); traits.acc(C2, alphav, R2); traits.acc(C3, alphav, R3); traits.acc(C4, alphav, R4); traits.acc(C5, alphav, R5); traits.acc(C6, alphav, R6); traits.acc(C7, alphav, R0); pstoreu(r1, R1); pstoreu(r2, R2); pstoreu(r3, R3); pstoreu(r0 + ResPacketSize, R4); pstoreu(r1 + ResPacketSize, R5); pstoreu(r2 + ResPacketSize, R6); pstoreu(r3 + ResPacketSize, R0); } else { ResPacket R0, R1, R4; ResPacket alphav = pset1(alpha); R0 = ploadu(r0); R1 = ploadu(r1); R4 = ploadu(r0 + ResPacketSize); traits.acc(C0, alphav, R0); pstoreu(r0, R0); R0 = ploadu(r1 + ResPacketSize); traits.acc(C1, alphav, R1); traits.acc(C4, alphav, R4); traits.acc(C5, alphav, R0); pstoreu(r1, R1); pstoreu(r0 + ResPacketSize, R4); pstoreu(r1 + ResPacketSize, R0); } } if(rows-peeled_mc>=LhsProgress) { Index i = peeled_mc; const LhsScalar* blA = &blockA[i*strideA+offsetA*LhsProgress]; prefetch(&blA[0]); // gets res block as register AccPacket C0, C1, C2, C3; traits.initAcc(C0); traits.initAcc(C1); if(nr==4) traits.initAcc(C2); if(nr==4) traits.initAcc(C3); // performs "inner" product const RhsScalar* blB = unpackedB; for(Index k=0; k(alpha); ResScalar* r0 = &res[(j2+0)*resStride + i]; ResScalar* r1 = r0 + resStride; ResScalar* r2 = r1 + resStride; ResScalar* r3 = r2 + resStride; R0 = ploadu(r0); R1 = ploadu(r1); if(nr==4) R2 = ploadu(r2); if(nr==4) R3 = ploadu(r3); traits.acc(C0, alphav, R0); traits.acc(C1, alphav, R1); if(nr==4) traits.acc(C2, alphav, R2); if(nr==4) traits.acc(C3, alphav, R3); pstoreu(r0, R0); pstoreu(r1, R1); if(nr==4) pstoreu(r2, R2); if(nr==4) pstoreu(r3, R3); } for(Index i=peeled_mc2; i do the same but with nr==1 for(Index j2=packet_cols; j2(alpha); ResScalar* r0 = &res[(j2+0)*resStride + i]; R0 = ploadu(r0); R4 = ploadu(r0+ResPacketSize); traits.acc(C0, alphav, R0); traits.acc(C4, alphav, R4); pstoreu(r0, R0); pstoreu(r0+ResPacketSize, R4); } if(rows-peeled_mc>=LhsProgress) { Index i = peeled_mc; const LhsScalar* blA = &blockA[i*strideA+offsetA*LhsProgress]; prefetch(&blA[0]); AccPacket C0; traits.initAcc(C0); const RhsScalar* blB = unpackedB; for(Index k=0; k(alpha); ResPacket R0 = ploadu(&res[(j2+0)*resStride + i]); traits.acc(C0, alphav, R0); pstoreu(&res[(j2+0)*resStride + i], R0); } for(Index i=peeled_mc2; i struct gemm_pack_lhs { EIGEN_DONT_INLINE void operator()(Scalar* blockA, const Scalar* EIGEN_RESTRICT _lhs, Index lhsStride, Index depth, Index rows, Index stride=0, Index offset=0) { typedef typename packet_traits::type Packet; enum { PacketSize = packet_traits::size }; EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS"); eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)); eigen_assert( (StorageOrder==RowMajor) || ((Pack1%PacketSize)==0 && Pack1<=4*PacketSize) ); conj_if::IsComplex && Conjugate> cj; const_blas_data_mapper lhs(_lhs,lhsStride); Index count = 0; Index peeled_mc = (rows/Pack1)*Pack1; for(Index i=0; i=1*PacketSize) A = ploadu(&lhs(i+0*PacketSize, k)); if(Pack1>=2*PacketSize) B = ploadu(&lhs(i+1*PacketSize, k)); if(Pack1>=3*PacketSize) C = ploadu(&lhs(i+2*PacketSize, k)); if(Pack1>=4*PacketSize) D = ploadu(&lhs(i+3*PacketSize, k)); if(Pack1>=1*PacketSize) { pstore(blockA+count, cj.pconj(A)); count+=PacketSize; } if(Pack1>=2*PacketSize) { pstore(blockA+count, cj.pconj(B)); count+=PacketSize; } if(Pack1>=3*PacketSize) { pstore(blockA+count, cj.pconj(C)); count+=PacketSize; } if(Pack1>=4*PacketSize) { pstore(blockA+count, cj.pconj(D)); count+=PacketSize; } } } else { for(Index k=0; k=Pack2) { if(PanelMode) count += Pack2*offset; for(Index k=0; k struct gemm_pack_rhs { typedef typename packet_traits::type Packet; enum { PacketSize = packet_traits::size }; EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride=0, Index offset=0) { EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR"); eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)); conj_if::IsComplex && Conjugate> cj; Index packet_cols = (cols/nr) * nr; Index count = 0; for(Index j2=0; j2 struct gemm_pack_rhs { enum { PacketSize = packet_traits::size }; EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride=0, Index offset=0) { EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR"); eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)); conj_if::IsComplex && Conjugate> cj; Index packet_cols = (cols/nr) * nr; Index count = 0; for(Index j2=0; j2 // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_TRIANGULAR_SOLVER_MATRIX_H #define EIGEN_TRIANGULAR_SOLVER_MATRIX_H namespace Eigen { namespace internal { // if the rhs is row major, let's transpose the product template struct triangular_solve_matrix { static EIGEN_DONT_INLINE void run( Index size, Index cols, const Scalar* tri, Index triStride, Scalar* _other, Index otherStride) { triangular_solve_matrix< Scalar, Index, Side==OnTheLeft?OnTheRight:OnTheLeft, (Mode&UnitDiag) | ((Mode&Upper) ? Lower : Upper), NumTraits::IsComplex && Conjugate, TriStorageOrder==RowMajor ? ColMajor : RowMajor, ColMajor> ::run(size, cols, tri, triStride, _other, otherStride); } }; /* Optimized triangular solver with multiple right hand side and the triangular matrix on the left */ template struct triangular_solve_matrix { static EIGEN_DONT_INLINE void run( Index size, Index otherSize, const Scalar* _tri, Index triStride, Scalar* _other, Index otherStride) { Index cols = otherSize; const_blas_data_mapper tri(_tri,triStride); blas_data_mapper other(_other,otherStride); typedef gebp_traits Traits; enum { SmallPanelWidth = EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr), IsLower = (Mode&Lower) == Lower }; Index kc = size; // cache block size along the K direction Index mc = size; // cache block size along the M direction Index nc = cols; // cache block size along the N direction computeProductBlockingSizes(kc, mc, nc); std::size_t sizeW = kc*Traits::WorkSpaceFactor; std::size_t sizeB = sizeW + kc*cols; ei_declare_aligned_stack_constructed_variable(Scalar, blockA, kc*mc, 0); ei_declare_aligned_stack_constructed_variable(Scalar, allocatedBlockB, sizeB, 0); Scalar* blockB = allocatedBlockB + sizeW; Scalar* blockW = allocatedBlockB; conj_if conj; gebp_kernel gebp_kernel; gemm_pack_lhs pack_lhs; gemm_pack_rhs pack_rhs; // the goal here is to subdivise the Rhs panels such that we keep some cache // coherence when accessing the rhs elements std::ptrdiff_t l1, l2; manage_caching_sizes(GetAction, &l1, &l2); Index subcols = cols>0 ? l2/(4 * sizeof(Scalar) * otherStride) : 0; subcols = std::max((subcols/Traits::nr)*Traits::nr, Traits::nr); for(Index k2=IsLower ? 0 : size; IsLower ? k20; IsLower ? k2+=kc : k2-=kc) { const Index actual_kc = (std::min)(IsLower ? size-k2 : k2, kc); // We have selected and packed a big horizontal panel R1 of rhs. Let B be the packed copy of this panel, // and R2 the remaining part of rhs. The corresponding vertical panel of lhs is split into // A11 (the triangular part) and A21 the remaining rectangular part. // Then the high level algorithm is: // - B = R1 => general block copy (done during the next step) // - R1 = A11^-1 B => tricky part // - update B from the new R1 => actually this has to be performed continuously during the above step // - R2 -= A21 * B => GEPP // The tricky part: compute R1 = A11^-1 B while updating B from R1 // The idea is to split A11 into multiple small vertical panels. // Each panel can be split into a small triangular part T1k which is processed without optimization, // and the remaining small part T2k which is processed using gebp with appropriate block strides for(Index j2=0; j2(actual_kc-k1, SmallPanelWidth); // tr solve for (Index k=0; k0) { Index startTarget = IsLower ? k2+k1+actualPanelWidth : k2-actual_kc; pack_lhs(blockA, &tri(startTarget,startBlock), triStride, actualPanelWidth, lengthTarget); gebp_kernel(&other(startTarget,j2), otherStride, blockA, blockB+actual_kc*j2, lengthTarget, actualPanelWidth, actual_cols, Scalar(-1), actualPanelWidth, actual_kc, 0, blockBOffset, blockW); } } } // R2 -= A21 * B => GEPP { Index start = IsLower ? k2+kc : 0; Index end = IsLower ? size : k2-kc; for(Index i2=start; i20) { pack_lhs(blockA, &tri(i2, IsLower ? k2 : k2-kc), triStride, actual_kc, actual_mc); gebp_kernel(_other+i2, otherStride, blockA, blockB, actual_mc, actual_kc, cols, Scalar(-1)); } } } } } }; /* Optimized triangular solver with multiple left hand sides and the trinagular matrix on the right */ template struct triangular_solve_matrix { static EIGEN_DONT_INLINE void run( Index size, Index otherSize, const Scalar* _tri, Index triStride, Scalar* _other, Index otherStride) { Index rows = otherSize; const_blas_data_mapper rhs(_tri,triStride); blas_data_mapper lhs(_other,otherStride); typedef gebp_traits Traits; enum { RhsStorageOrder = TriStorageOrder, SmallPanelWidth = EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr), IsLower = (Mode&Lower) == Lower }; // Index kc = std::min(Traits::Max_kc/4,size); // cache block size along the K direction // Index mc = std::min(Traits::Max_mc,size); // cache block size along the M direction // check that !!!! Index kc = size; // cache block size along the K direction Index mc = size; // cache block size along the M direction Index nc = rows; // cache block size along the N direction computeProductBlockingSizes(kc, mc, nc); std::size_t sizeW = kc*Traits::WorkSpaceFactor; std::size_t sizeB = sizeW + kc*size; ei_declare_aligned_stack_constructed_variable(Scalar, blockA, kc*mc, 0); ei_declare_aligned_stack_constructed_variable(Scalar, allocatedBlockB, sizeB, 0); Scalar* blockB = allocatedBlockB + sizeW; conj_if conj; gebp_kernel gebp_kernel; gemm_pack_rhs pack_rhs; gemm_pack_rhs pack_rhs_panel; gemm_pack_lhs pack_lhs_panel; for(Index k2=IsLower ? size : 0; IsLower ? k2>0 : k20) pack_rhs(geb, &rhs(actual_k2,startPanel), triStride, actual_kc, rs); // triangular packing (we only pack the panels off the diagonal, // neglecting the blocks overlapping the diagonal { for (Index j2=0; j2(actual_kc-j2, SmallPanelWidth); Index actual_j2 = actual_k2 + j2; Index panelOffset = IsLower ? j2+actualPanelWidth : 0; Index panelLength = IsLower ? actual_kc-j2-actualPanelWidth : j2; if (panelLength>0) pack_rhs_panel(blockB+j2*actual_kc, &rhs(actual_k2+panelOffset, actual_j2), triStride, panelLength, actualPanelWidth, actual_kc, panelOffset); } } for(Index i2=0; i2 vertical panels of rhs) for (Index j2 = IsLower ? (actual_kc - ((actual_kc%SmallPanelWidth) ? Index(actual_kc%SmallPanelWidth) : Index(SmallPanelWidth))) : 0; IsLower ? j2>=0 : j2(actual_kc-j2, SmallPanelWidth); Index absolute_j2 = actual_k2 + j2; Index panelOffset = IsLower ? j2+actualPanelWidth : 0; Index panelLength = IsLower ? actual_kc - j2 - actualPanelWidth : j2; // GEBP if(panelLength>0) { gebp_kernel(&lhs(i2,absolute_j2), otherStride, blockA, blockB+j2*actual_kc, actual_mc, panelLength, actualPanelWidth, Scalar(-1), actual_kc, actual_kc, // strides panelOffset, panelOffset, // offsets allocatedBlockB); // workspace } // unblocked triangular solve for (Index k=0; k0) gebp_kernel(_other+i2+startPanel*otherStride, otherStride, blockA, geb, actual_mc, actual_kc, rs, Scalar(-1), -1, -1, 0, 0, allocatedBlockB); } } } }; } // end namespace internal } // end namespace Eigen #endif // EIGEN_TRIANGULAR_SOLVER_MATRIX_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/SelfadjointMatrixVector_MKL.h0000775000175000017500000001170612107032422026203 0ustar csheltoncshelton/* Copyright (c) 2011, Intel Corporation. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Intel Corporation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ******************************************************************************** * Content : Eigen bindings to Intel(R) MKL * Selfadjoint matrix-vector product functionality based on ?SYMV/HEMV. ******************************************************************************** */ #ifndef EIGEN_SELFADJOINT_MATRIX_VECTOR_MKL_H #define EIGEN_SELFADJOINT_MATRIX_VECTOR_MKL_H namespace Eigen { namespace internal { /********************************************************************** * This file implements selfadjoint matrix-vector multiplication using BLAS **********************************************************************/ // symv/hemv specialization template struct selfadjoint_matrix_vector_product_symv : selfadjoint_matrix_vector_product {}; #define EIGEN_MKL_SYMV_SPECIALIZE(Scalar) \ template \ struct selfadjoint_matrix_vector_product { \ static EIGEN_DONT_INLINE void run( \ Index size, const Scalar* lhs, Index lhsStride, \ const Scalar* _rhs, Index rhsIncr, Scalar* res, Scalar alpha) { \ enum {\ IsColMajor = StorageOrder==ColMajor \ }; \ if (IsColMajor == ConjugateLhs) {\ selfadjoint_matrix_vector_product::run( \ size, lhs, lhsStride, _rhs, rhsIncr, res, alpha); \ } else {\ selfadjoint_matrix_vector_product_symv::run( \ size, lhs, lhsStride, _rhs, rhsIncr, res, alpha); \ }\ } \ }; \ EIGEN_MKL_SYMV_SPECIALIZE(double) EIGEN_MKL_SYMV_SPECIALIZE(float) EIGEN_MKL_SYMV_SPECIALIZE(dcomplex) EIGEN_MKL_SYMV_SPECIALIZE(scomplex) #define EIGEN_MKL_SYMV_SPECIALIZATION(EIGTYPE,MKLTYPE,MKLFUNC) \ template \ struct selfadjoint_matrix_vector_product_symv \ { \ typedef Matrix SYMVVector;\ \ static EIGEN_DONT_INLINE void run( \ Index size, const EIGTYPE* lhs, Index lhsStride, \ const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* res, EIGTYPE alpha) \ { \ enum {\ IsRowMajor = StorageOrder==RowMajor ? 1 : 0, \ IsLower = UpLo == Lower ? 1 : 0 \ }; \ MKL_INT n=size, lda=lhsStride, incx=rhsIncr, incy=1; \ MKLTYPE alpha_, beta_; \ const EIGTYPE *x_ptr, myone(1); \ char uplo=(IsRowMajor) ? (IsLower ? 'U' : 'L') : (IsLower ? 'L' : 'U'); \ assign_scalar_eig2mkl(alpha_, alpha); \ assign_scalar_eig2mkl(beta_, myone); \ SYMVVector x_tmp; \ if (ConjugateRhs) { \ Map > map_x(_rhs,size,1,InnerStride<>(incx)); \ x_tmp=map_x.conjugate(); \ x_ptr=x_tmp.data(); \ incx=1; \ } else x_ptr=_rhs; \ MKLFUNC(&uplo, &n, &alpha_, (const MKLTYPE*)lhs, &lda, (const MKLTYPE*)x_ptr, &incx, &beta_, (MKLTYPE*)res, &incy); \ }\ }; EIGEN_MKL_SYMV_SPECIALIZATION(double, double, dsymv) EIGEN_MKL_SYMV_SPECIALIZATION(float, float, ssymv) EIGEN_MKL_SYMV_SPECIALIZATION(dcomplex, MKL_Complex16, zhemv) EIGEN_MKL_SYMV_SPECIALIZATION(scomplex, MKL_Complex8, chemv) } // end namespace internal } // end namespace Eigen #endif // EIGEN_SELFADJOINT_MATRIX_VECTOR_MKL_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/SelfadjointProduct.h0000775000175000017500000001474312107032422024475 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_SELFADJOINT_PRODUCT_H #define EIGEN_SELFADJOINT_PRODUCT_H /********************************************************************** * This file implements a self adjoint product: C += A A^T updating only * half of the selfadjoint matrix C. * It corresponds to the level 3 SYRK and level 2 SYR Blas routines. **********************************************************************/ namespace Eigen { template struct selfadjoint_rank1_update; template struct selfadjoint_rank1_update { static void run(Index size, Scalar* mat, Index stride, const Scalar* vec, Scalar alpha) { internal::conj_if cj; typedef Map > OtherMap; typedef typename internal::conditional::type ConjRhsType; for (Index i=0; i >(mat+stride*i+(UpLo==Lower ? i : 0), (UpLo==Lower ? size-i : (i+1))) += (alpha * cj(vec[i])) * ConjRhsType(OtherMap(vec+(UpLo==Lower ? i : 0),UpLo==Lower ? size-i : (i+1))); } } }; template struct selfadjoint_rank1_update { static void run(Index size, Scalar* mat, Index stride, const Scalar* vec, Scalar alpha) { selfadjoint_rank1_update::run(size,mat,stride,vec,alpha); } }; template struct selfadjoint_product_selector; template struct selfadjoint_product_selector { static void run(MatrixType& mat, const OtherType& other, typename MatrixType::Scalar alpha) { typedef typename MatrixType::Scalar Scalar; typedef typename MatrixType::Index Index; typedef internal::blas_traits OtherBlasTraits; typedef typename OtherBlasTraits::DirectLinearAccessType ActualOtherType; typedef typename internal::remove_all::type _ActualOtherType; typename internal::add_const_on_value_type::type actualOther = OtherBlasTraits::extract(other.derived()); Scalar actualAlpha = alpha * OtherBlasTraits::extractScalarFactor(other.derived()); enum { StorageOrder = (internal::traits::Flags&RowMajorBit) ? RowMajor : ColMajor, UseOtherDirectly = _ActualOtherType::InnerStrideAtCompileTime==1 }; internal::gemv_static_vector_if static_other; ei_declare_aligned_stack_constructed_variable(Scalar, actualOtherPtr, other.size(), (UseOtherDirectly ? const_cast(actualOther.data()) : static_other.data())); if(!UseOtherDirectly) Map(actualOtherPtr, actualOther.size()) = actualOther; selfadjoint_rank1_update::IsComplex, (!OtherBlasTraits::NeedToConjugate) && NumTraits::IsComplex> ::run(other.size(), mat.data(), mat.outerStride(), actualOtherPtr, actualAlpha); } }; template struct selfadjoint_product_selector { static void run(MatrixType& mat, const OtherType& other, typename MatrixType::Scalar alpha) { typedef typename MatrixType::Scalar Scalar; typedef typename MatrixType::Index Index; typedef internal::blas_traits OtherBlasTraits; typedef typename OtherBlasTraits::DirectLinearAccessType ActualOtherType; typedef typename internal::remove_all::type _ActualOtherType; typename internal::add_const_on_value_type::type actualOther = OtherBlasTraits::extract(other.derived()); Scalar actualAlpha = alpha * OtherBlasTraits::extractScalarFactor(other.derived()); enum { IsRowMajor = (internal::traits::Flags&RowMajorBit) ? 1 : 0 }; internal::general_matrix_matrix_triangular_product::IsComplex, Scalar, _ActualOtherType::Flags&RowMajorBit ? ColMajor : RowMajor, (!OtherBlasTraits::NeedToConjugate) && NumTraits::IsComplex, MatrixType::Flags&RowMajorBit ? RowMajor : ColMajor, UpLo> ::run(mat.cols(), actualOther.cols(), &actualOther.coeffRef(0,0), actualOther.outerStride(), &actualOther.coeffRef(0,0), actualOther.outerStride(), mat.data(), mat.outerStride(), actualAlpha); } }; // high level API template template SelfAdjointView& SelfAdjointView ::rankUpdate(const MatrixBase& u, Scalar alpha) { selfadjoint_product_selector::run(_expression().const_cast_derived(), u.derived(), alpha); return *this; } } // end namespace Eigen #endif // EIGEN_SELFADJOINT_PRODUCT_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/SelfadjointRank2Update.h0000775000175000017500000001115212107032422025164 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_SELFADJOINTRANK2UPTADE_H #define EIGEN_SELFADJOINTRANK2UPTADE_H namespace Eigen { namespace internal { /* Optimized selfadjoint matrix += alpha * uv' + conj(alpha)*vu' * It corresponds to the Level2 syr2 BLAS routine */ template struct selfadjoint_rank2_update_selector; template struct selfadjoint_rank2_update_selector { static void run(Scalar* mat, Index stride, const UType& u, const VType& v, Scalar alpha) { const Index size = u.size(); for (Index i=0; i >(mat+stride*i+i, size-i) += (conj(alpha) * conj(u.coeff(i))) * v.tail(size-i) + (alpha * conj(v.coeff(i))) * u.tail(size-i); } } }; template struct selfadjoint_rank2_update_selector { static void run(Scalar* mat, Index stride, const UType& u, const VType& v, Scalar alpha) { const Index size = u.size(); for (Index i=0; i >(mat+stride*i, i+1) += (conj(alpha) * conj(u.coeff(i))) * v.head(i+1) + (alpha * conj(v.coeff(i))) * u.head(i+1); } }; template struct conj_expr_if : conditional::Scalar>,T> > {}; } // end namespace internal template template SelfAdjointView& SelfAdjointView ::rankUpdate(const MatrixBase& u, const MatrixBase& v, Scalar alpha) { typedef internal::blas_traits UBlasTraits; typedef typename UBlasTraits::DirectLinearAccessType ActualUType; typedef typename internal::remove_all::type _ActualUType; typename internal::add_const_on_value_type::type actualU = UBlasTraits::extract(u.derived()); typedef internal::blas_traits VBlasTraits; typedef typename VBlasTraits::DirectLinearAccessType ActualVType; typedef typename internal::remove_all::type _ActualVType; typename internal::add_const_on_value_type::type actualV = VBlasTraits::extract(v.derived()); // If MatrixType is row major, then we use the routine for lower triangular in the upper triangular case and // vice versa, and take the complex conjugate of all coefficients and vector entries. enum { IsRowMajor = (internal::traits::Flags&RowMajorBit) ? 1 : 0 }; Scalar actualAlpha = alpha * UBlasTraits::extractScalarFactor(u.derived()) * internal::conj(VBlasTraits::extractScalarFactor(v.derived())); if (IsRowMajor) actualAlpha = internal::conj(actualAlpha); internal::selfadjoint_rank2_update_selector::type>::type, typename internal::remove_all::type>::type, (IsRowMajor ? int(UpLo==Upper ? Lower : Upper) : UpLo)> ::run(_expression().const_cast_derived().data(),_expression().outerStride(),actualU,actualV,actualAlpha); return *this; } } // end namespace Eigen #endif // EIGEN_SELFADJOINTRANK2UPTADE_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/TriangularMatrixVector_MKL.h0000775000175000017500000002323712107032422026053 0ustar csheltoncshelton/* Copyright (c) 2011, Intel Corporation. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Intel Corporation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ******************************************************************************** * Content : Eigen bindings to Intel(R) MKL * Triangular matrix-vector product functionality based on ?TRMV. ******************************************************************************** */ #ifndef EIGEN_TRIANGULAR_MATRIX_VECTOR_MKL_H #define EIGEN_TRIANGULAR_MATRIX_VECTOR_MKL_H namespace Eigen { namespace internal { /********************************************************************** * This file implements triangular matrix-vector multiplication using BLAS **********************************************************************/ // trmv/hemv specialization template struct triangular_matrix_vector_product_trmv : triangular_matrix_vector_product {}; #define EIGEN_MKL_TRMV_SPECIALIZE(Scalar) \ template \ struct triangular_matrix_vector_product { \ static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \ const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \ triangular_matrix_vector_product_trmv::run( \ _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \ } \ }; \ template \ struct triangular_matrix_vector_product { \ static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \ const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \ triangular_matrix_vector_product_trmv::run( \ _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \ } \ }; EIGEN_MKL_TRMV_SPECIALIZE(double) EIGEN_MKL_TRMV_SPECIALIZE(float) EIGEN_MKL_TRMV_SPECIALIZE(dcomplex) EIGEN_MKL_TRMV_SPECIALIZE(scomplex) // implements col-major: res += alpha * op(triangular) * vector #define EIGEN_MKL_TRMV_CM(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \ template \ struct triangular_matrix_vector_product_trmv { \ enum { \ IsLower = (Mode&Lower) == Lower, \ SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \ IsUnitDiag = (Mode&UnitDiag) ? 1 : 0, \ IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \ LowUp = IsLower ? Lower : Upper \ }; \ static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \ const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \ { \ if (ConjLhs || IsZeroDiag) { \ triangular_matrix_vector_product::run( \ _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \ return; \ }\ Index size = (std::min)(_rows,_cols); \ Index rows = IsLower ? _rows : size; \ Index cols = IsLower ? size : _cols; \ \ typedef VectorX##EIGPREFIX VectorRhs; \ EIGTYPE *x, *y;\ \ /* Set x*/ \ Map > rhs(_rhs,cols,InnerStride<>(rhsIncr)); \ VectorRhs x_tmp; \ if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \ x = x_tmp.data(); \ \ /* Square part handling */\ \ char trans, uplo, diag; \ MKL_INT m, n, lda, incx, incy; \ EIGTYPE const *a; \ MKLTYPE alpha_, beta_; \ assign_scalar_eig2mkl(alpha_, alpha); \ assign_scalar_eig2mkl(beta_, EIGTYPE(1)); \ \ /* Set m, n */ \ n = (MKL_INT)size; \ lda = lhsStride; \ incx = 1; \ incy = resIncr; \ \ /* Set uplo, trans and diag*/ \ trans = 'N'; \ uplo = IsLower ? 'L' : 'U'; \ diag = IsUnitDiag ? 'U' : 'N'; \ \ /* call ?TRMV*/ \ MKLPREFIX##trmv(&uplo, &trans, &diag, &n, (const MKLTYPE*)_lhs, &lda, (MKLTYPE*)x, &incx); \ \ /* Add op(a_tr)rhs into res*/ \ MKLPREFIX##axpy(&n, &alpha_,(const MKLTYPE*)x, &incx, (MKLTYPE*)_res, &incy); \ /* Non-square case - doesn't fit to MKL ?TRMV. Fall to default triangular product*/ \ if (size<(std::max)(rows,cols)) { \ typedef Matrix MatrixLhs; \ if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \ x = x_tmp.data(); \ if (size \ struct triangular_matrix_vector_product_trmv { \ enum { \ IsLower = (Mode&Lower) == Lower, \ SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \ IsUnitDiag = (Mode&UnitDiag) ? 1 : 0, \ IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \ LowUp = IsLower ? Lower : Upper \ }; \ static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \ const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \ { \ if (IsZeroDiag) { \ triangular_matrix_vector_product::run( \ _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \ return; \ }\ Index size = (std::min)(_rows,_cols); \ Index rows = IsLower ? _rows : size; \ Index cols = IsLower ? size : _cols; \ \ typedef VectorX##EIGPREFIX VectorRhs; \ EIGTYPE *x, *y;\ \ /* Set x*/ \ Map > rhs(_rhs,cols,InnerStride<>(rhsIncr)); \ VectorRhs x_tmp; \ if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \ x = x_tmp.data(); \ \ /* Square part handling */\ \ char trans, uplo, diag; \ MKL_INT m, n, lda, incx, incy; \ EIGTYPE const *a; \ MKLTYPE alpha_, beta_; \ assign_scalar_eig2mkl(alpha_, alpha); \ assign_scalar_eig2mkl(beta_, EIGTYPE(1)); \ \ /* Set m, n */ \ n = (MKL_INT)size; \ lda = lhsStride; \ incx = 1; \ incy = resIncr; \ \ /* Set uplo, trans and diag*/ \ trans = ConjLhs ? 'C' : 'T'; \ uplo = IsLower ? 'U' : 'L'; \ diag = IsUnitDiag ? 'U' : 'N'; \ \ /* call ?TRMV*/ \ MKLPREFIX##trmv(&uplo, &trans, &diag, &n, (const MKLTYPE*)_lhs, &lda, (MKLTYPE*)x, &incx); \ \ /* Add op(a_tr)rhs into res*/ \ MKLPREFIX##axpy(&n, &alpha_,(const MKLTYPE*)x, &incx, (MKLTYPE*)_res, &incy); \ /* Non-square case - doesn't fit to MKL ?TRMV. Fall to default triangular product*/ \ if (size<(std::max)(rows,cols)) { \ typedef Matrix MatrixLhs; \ if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \ x = x_tmp.data(); \ if (size // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_TRIANGULAR_SOLVER_VECTOR_H #define EIGEN_TRIANGULAR_SOLVER_VECTOR_H namespace Eigen { namespace internal { template struct triangular_solve_vector { static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs) { triangular_solve_vector::run(size, _lhs, lhsStride, rhs); } }; // forward and backward substitution, row-major, rhs is a vector template struct triangular_solve_vector { enum { IsLower = ((Mode&Lower)==Lower) }; static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs) { typedef Map, 0, OuterStride<> > LhsMap; const LhsMap lhs(_lhs,size,size,OuterStride<>(lhsStride)); typename internal::conditional< Conjugate, const CwiseUnaryOp,LhsMap>, const LhsMap&> ::type cjLhs(lhs); static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH; for(Index pi=IsLower ? 0 : size; IsLower ? pi0; IsLower ? pi+=PanelWidth : pi-=PanelWidth) { Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth); Index r = IsLower ? pi : size - pi; // remaining size if (r > 0) { // let's directly call the low level product function because: // 1 - it is faster to compile // 2 - it is slighlty faster at runtime Index startRow = IsLower ? pi : pi-actualPanelWidth; Index startCol = IsLower ? 0 : pi; general_matrix_vector_product::run( actualPanelWidth, r, &lhs.coeffRef(startRow,startCol), lhsStride, rhs + startCol, 1, rhs + startRow, 1, RhsScalar(-1)); } for(Index k=0; k0) rhs[i] -= (cjLhs.row(i).segment(s,k).transpose().cwiseProduct(Map >(rhs+s,k))).sum(); if(!(Mode & UnitDiag)) rhs[i] /= cjLhs(i,i); } } } }; // forward and backward substitution, column-major, rhs is a vector template struct triangular_solve_vector { enum { IsLower = ((Mode&Lower)==Lower) }; static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs) { typedef Map, 0, OuterStride<> > LhsMap; const LhsMap lhs(_lhs,size,size,OuterStride<>(lhsStride)); typename internal::conditional,LhsMap>, const LhsMap& >::type cjLhs(lhs); static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH; for(Index pi=IsLower ? 0 : size; IsLower ? pi0; IsLower ? pi+=PanelWidth : pi-=PanelWidth) { Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth); Index startBlock = IsLower ? pi : pi-actualPanelWidth; Index endBlock = IsLower ? pi + actualPanelWidth : 0; for(Index k=0; k0) Map >(rhs+s,r) -= rhs[i] * cjLhs.col(i).segment(s,r); } Index r = IsLower ? size - endBlock : startBlock; // remaining size if (r > 0) { // let's directly call the low level product function because: // 1 - it is faster to compile // 2 - it is slighlty faster at runtime general_matrix_vector_product::run( r, actualPanelWidth, &lhs.coeffRef(endBlock,startBlock), lhsStride, rhs+startBlock, 1, rhs+endBlock, 1, RhsScalar(-1)); } } } }; } // end namespace internal } // end namespace Eigen #endif // EIGEN_TRIANGULAR_SOLVER_VECTOR_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/TriangularMatrixMatrix.h0000775000175000017500000004104012107032422025342 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_TRIANGULAR_MATRIX_MATRIX_H #define EIGEN_TRIANGULAR_MATRIX_MATRIX_H namespace Eigen { namespace internal { // template // struct gemm_pack_lhs_triangular // { // Matrix::IsComplex && Conjugate> cj; // const_blas_data_mapper lhs(_lhs,lhsStride); // int count = 0; // const int peeled_mc = (rows/mr)*mr; // for(int i=0; i struct product_triangular_matrix_matrix; template struct product_triangular_matrix_matrix { static EIGEN_STRONG_INLINE void run( Index rows, Index cols, Index depth, const Scalar* lhs, Index lhsStride, const Scalar* rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha) { product_triangular_matrix_matrix ::run(cols, rows, depth, rhs, rhsStride, lhs, lhsStride, res, resStride, alpha); } }; // implements col-major += alpha * op(triangular) * op(general) template struct product_triangular_matrix_matrix { typedef gebp_traits Traits; enum { SmallPanelWidth = 2 * EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr), IsLower = (Mode&Lower) == Lower, SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1 }; static EIGEN_DONT_INLINE void run( Index _rows, Index _cols, Index _depth, const Scalar* _lhs, Index lhsStride, const Scalar* _rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha) { // strip zeros Index diagSize = (std::min)(_rows,_depth); Index rows = IsLower ? _rows : diagSize; Index depth = IsLower ? diagSize : _depth; Index cols = _cols; const_blas_data_mapper lhs(_lhs,lhsStride); const_blas_data_mapper rhs(_rhs,rhsStride); Index kc = depth; // cache block size along the K direction Index mc = rows; // cache block size along the M direction Index nc = cols; // cache block size along the N direction computeProductBlockingSizes(kc, mc, nc); std::size_t sizeW = kc*Traits::WorkSpaceFactor; std::size_t sizeB = sizeW + kc*cols; ei_declare_aligned_stack_constructed_variable(Scalar, blockA, kc*mc, 0); ei_declare_aligned_stack_constructed_variable(Scalar, allocatedBlockB, sizeB, 0); Scalar* blockB = allocatedBlockB + sizeW; Matrix triangularBuffer; triangularBuffer.setZero(); if((Mode&ZeroDiag)==ZeroDiag) triangularBuffer.diagonal().setZero(); else triangularBuffer.diagonal().setOnes(); gebp_kernel gebp_kernel; gemm_pack_lhs pack_lhs; gemm_pack_rhs pack_rhs; for(Index k2=IsLower ? depth : 0; IsLower ? k2>0 : k2rows)) { actual_kc = rows-k2; k2 = k2+actual_kc-kc; } pack_rhs(blockB, &rhs(actual_k2,0), rhsStride, actual_kc, cols); // the selected lhs's panel has to be split in three different parts: // 1 - the part which is zero => skip it // 2 - the diagonal block => special kernel // 3 - the dense panel below (lower case) or above (upper case) the diagonal block => GEPP // the block diagonal, if any: if(IsLower || actual_k2(actual_kc-k1, SmallPanelWidth); Index lengthTarget = IsLower ? actual_kc-k1-actualPanelWidth : k1; Index startBlock = actual_k2+k1; Index blockBOffset = k1; // => GEBP with the micro triangular block // The trick is to pack this micro block while filling the opposite triangular part with zeros. // To this end we do an extra triangular copy to a small temporary buffer for (Index k=0;k0) { Index startTarget = IsLower ? actual_k2+k1+actualPanelWidth : actual_k2; pack_lhs(blockA, &lhs(startTarget,startBlock), lhsStride, actualPanelWidth, lengthTarget); gebp_kernel(res+startTarget, resStride, blockA, blockB, lengthTarget, actualPanelWidth, cols, alpha, actualPanelWidth, actual_kc, 0, blockBOffset); } } } // the part below (lower case) or above (upper case) the diagonal => GEPP { Index start = IsLower ? k2 : 0; Index end = IsLower ? rows : (std::min)(actual_k2,rows); for(Index i2=start; i2() (blockA, &lhs(i2, actual_k2), lhsStride, actual_kc, actual_mc); gebp_kernel(res+i2, resStride, blockA, blockB, actual_mc, actual_kc, cols, alpha); } } } } }; // implements col-major += alpha * op(general) * op(triangular) template struct product_triangular_matrix_matrix { typedef gebp_traits Traits; enum { SmallPanelWidth = EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr), IsLower = (Mode&Lower) == Lower, SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1 }; static EIGEN_DONT_INLINE void run( Index _rows, Index _cols, Index _depth, const Scalar* _lhs, Index lhsStride, const Scalar* _rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha) { // strip zeros Index diagSize = (std::min)(_cols,_depth); Index rows = _rows; Index depth = IsLower ? _depth : diagSize; Index cols = IsLower ? diagSize : _cols; const_blas_data_mapper lhs(_lhs,lhsStride); const_blas_data_mapper rhs(_rhs,rhsStride); Index kc = depth; // cache block size along the K direction Index mc = rows; // cache block size along the M direction Index nc = cols; // cache block size along the N direction computeProductBlockingSizes(kc, mc, nc); std::size_t sizeW = kc*Traits::WorkSpaceFactor; std::size_t sizeB = sizeW + kc*cols; ei_declare_aligned_stack_constructed_variable(Scalar, blockA, kc*mc, 0); ei_declare_aligned_stack_constructed_variable(Scalar, allocatedBlockB, sizeB, 0); Scalar* blockB = allocatedBlockB + sizeW; Matrix triangularBuffer; triangularBuffer.setZero(); if((Mode&ZeroDiag)==ZeroDiag) triangularBuffer.diagonal().setZero(); else triangularBuffer.diagonal().setOnes(); gebp_kernel gebp_kernel; gemm_pack_lhs pack_lhs; gemm_pack_rhs pack_rhs; gemm_pack_rhs pack_rhs_panel; for(Index k2=IsLower ? 0 : depth; IsLower ? k20; IsLower ? k2+=kc : k2-=kc) { Index actual_kc = (std::min)(IsLower ? depth-k2 : k2, kc); Index actual_k2 = IsLower ? k2 : k2-actual_kc; // align blocks with the end of the triangular part for trapezoidal rhs if(IsLower && (k2cols)) { actual_kc = cols-k2; k2 = actual_k2 + actual_kc - kc; } // remaining size Index rs = IsLower ? (std::min)(cols,actual_k2) : cols - k2; // size of the triangular part Index ts = (IsLower && actual_k2>=cols) ? 0 : actual_kc; Scalar* geb = blockB+ts*ts; pack_rhs(geb, &rhs(actual_k2,IsLower ? 0 : k2), rhsStride, actual_kc, rs); // pack the triangular part of the rhs padding the unrolled blocks with zeros if(ts>0) { for (Index j2=0; j2(actual_kc-j2, SmallPanelWidth); Index actual_j2 = actual_k2 + j2; Index panelOffset = IsLower ? j2+actualPanelWidth : 0; Index panelLength = IsLower ? actual_kc-j2-actualPanelWidth : j2; // general part pack_rhs_panel(blockB+j2*actual_kc, &rhs(actual_k2+panelOffset, actual_j2), rhsStride, panelLength, actualPanelWidth, actual_kc, panelOffset); // append the triangular part via a temporary buffer for (Index j=0;j0) { for (Index j2=0; j2(actual_kc-j2, SmallPanelWidth); Index panelLength = IsLower ? actual_kc-j2 : j2+actualPanelWidth; Index blockOffset = IsLower ? j2 : 0; gebp_kernel(res+i2+(actual_k2+j2)*resStride, resStride, blockA, blockB+j2*actual_kc, actual_mc, panelLength, actualPanelWidth, alpha, actual_kc, actual_kc, // strides blockOffset, blockOffset,// offsets allocatedBlockB); // workspace } } gebp_kernel(res+i2+(IsLower ? 0 : k2)*resStride, resStride, blockA, geb, actual_mc, actual_kc, rs, alpha, -1, -1, 0, 0, allocatedBlockB); } } } }; /*************************************************************************** * Wrapper to product_triangular_matrix_matrix ***************************************************************************/ template struct traits > : traits, Lhs, Rhs> > {}; } // end namespace internal template struct TriangularProduct : public ProductBase, Lhs, Rhs > { EIGEN_PRODUCT_PUBLIC_INTERFACE(TriangularProduct) TriangularProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {} template void scaleAndAddTo(Dest& dst, Scalar alpha) const { typename internal::add_const_on_value_type::type lhs = LhsBlasTraits::extract(m_lhs); typename internal::add_const_on_value_type::type rhs = RhsBlasTraits::extract(m_rhs); Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(m_lhs) * RhsBlasTraits::extractScalarFactor(m_rhs); internal::product_triangular_matrix_matrix::Flags&RowMajorBit) ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate, (internal::traits<_ActualRhsType>::Flags&RowMajorBit) ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate, (internal::traits::Flags&RowMajorBit) ? RowMajor : ColMajor> ::run( lhs.rows(), rhs.cols(), lhs.cols(),// LhsIsTriangular ? rhs.cols() : lhs.rows(), // sizes &lhs.coeffRef(0,0), lhs.outerStride(), // lhs info &rhs.coeffRef(0,0), rhs.outerStride(), // rhs info &dst.coeffRef(0,0), dst.outerStride(), // result info actualAlpha // alpha ); } }; } // end namespace Eigen #endif // EIGEN_TRIANGULAR_MATRIX_MATRIX_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/TriangularMatrixMatrix_MKL.h0000775000175000017500000003105512107032422026052 0ustar csheltoncshelton/* Copyright (c) 2011, Intel Corporation. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Intel Corporation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ******************************************************************************** * Content : Eigen bindings to Intel(R) MKL * Triangular matrix * matrix product functionality based on ?TRMM. ******************************************************************************** */ #ifndef EIGEN_TRIANGULAR_MATRIX_MATRIX_MKL_H #define EIGEN_TRIANGULAR_MATRIX_MATRIX_MKL_H namespace Eigen { namespace internal { template struct product_triangular_matrix_matrix_trmm : product_triangular_matrix_matrix {}; // try to go to BLAS specialization #define EIGEN_MKL_TRMM_SPECIALIZE(Scalar, LhsIsTriangular) \ template \ struct product_triangular_matrix_matrix { \ static inline void run(Index _rows, Index _cols, Index _depth, const Scalar* _lhs, Index lhsStride,\ const Scalar* _rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha) { \ product_triangular_matrix_matrix_trmm::run( \ _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha); \ } \ }; EIGEN_MKL_TRMM_SPECIALIZE(double, true) EIGEN_MKL_TRMM_SPECIALIZE(double, false) EIGEN_MKL_TRMM_SPECIALIZE(dcomplex, true) EIGEN_MKL_TRMM_SPECIALIZE(dcomplex, false) EIGEN_MKL_TRMM_SPECIALIZE(float, true) EIGEN_MKL_TRMM_SPECIALIZE(float, false) EIGEN_MKL_TRMM_SPECIALIZE(scomplex, true) EIGEN_MKL_TRMM_SPECIALIZE(scomplex, false) // implements col-major += alpha * op(triangular) * op(general) #define EIGEN_MKL_TRMM_L(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \ template \ struct product_triangular_matrix_matrix_trmm \ { \ enum { \ IsLower = (Mode&Lower) == Lower, \ SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \ IsUnitDiag = (Mode&UnitDiag) ? 1 : 0, \ IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \ LowUp = IsLower ? Lower : Upper, \ conjA = ((LhsStorageOrder==ColMajor) && ConjugateLhs) ? 1 : 0 \ }; \ \ static EIGEN_DONT_INLINE void run( \ Index _rows, Index _cols, Index _depth, \ const EIGTYPE* _lhs, Index lhsStride, \ const EIGTYPE* _rhs, Index rhsStride, \ EIGTYPE* res, Index resStride, \ EIGTYPE alpha) \ { \ Index diagSize = (std::min)(_rows,_depth); \ Index rows = IsLower ? _rows : diagSize; \ Index depth = IsLower ? diagSize : _depth; \ Index cols = _cols; \ \ typedef Matrix MatrixLhs; \ typedef Matrix MatrixRhs; \ \ /* Non-square case - doesn't fit to MKL ?TRMM. Fall to default triangular product or call MKL ?GEMM*/ \ if (rows != depth) { \ \ int nthr = mkl_domain_get_max_threads(MKL_BLAS); \ \ if (((nthr==1) && (((std::max)(rows,depth)-diagSize)/(double)diagSize < 0.5))) { \ /* Most likely no benefit to call TRMM or GEMM from MKL*/ \ product_triangular_matrix_matrix::run( \ _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha); \ /*std::cout << "TRMM_L: A is not square! Go to Eigen TRMM implementation!\n";*/ \ } else { \ /* Make sense to call GEMM */ \ Map > lhsMap(_lhs,rows,depth,OuterStride<>(lhsStride)); \ MatrixLhs aa_tmp=lhsMap.template triangularView(); \ MKL_INT aStride = aa_tmp.outerStride(); \ gemm_blocking_space blocking(_rows,_cols,_depth); \ general_matrix_matrix_product::run( \ rows, cols, depth, aa_tmp.data(), aStride, _rhs, rhsStride, res, resStride, alpha, blocking, 0); \ \ /*std::cout << "TRMM_L: A is not square! Go to MKL GEMM implementation! " << nthr<<" \n";*/ \ } \ return; \ } \ char side = 'L', transa, uplo, diag = 'N'; \ EIGTYPE *b; \ const EIGTYPE *a; \ MKL_INT m, n, lda, ldb; \ MKLTYPE alpha_; \ \ /* Set alpha_*/ \ assign_scalar_eig2mkl(alpha_, alpha); \ \ /* Set m, n */ \ m = (MKL_INT)diagSize; \ n = (MKL_INT)cols; \ \ /* Set trans */ \ transa = (LhsStorageOrder==RowMajor) ? ((ConjugateLhs) ? 'C' : 'T') : 'N'; \ \ /* Set b, ldb */ \ Map > rhs(_rhs,depth,cols,OuterStride<>(rhsStride)); \ MatrixX##EIGPREFIX b_tmp; \ \ if (ConjugateRhs) b_tmp = rhs.conjugate(); else b_tmp = rhs; \ b = b_tmp.data(); \ ldb = b_tmp.outerStride(); \ \ /* Set uplo */ \ uplo = IsLower ? 'L' : 'U'; \ if (LhsStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \ /* Set a, lda */ \ Map > lhs(_lhs,rows,depth,OuterStride<>(lhsStride)); \ MatrixLhs a_tmp; \ \ if ((conjA!=0) || (SetDiag==0)) { \ if (conjA) a_tmp = lhs.conjugate(); else a_tmp = lhs; \ if (IsZeroDiag) \ a_tmp.diagonal().setZero(); \ else if (IsUnitDiag) \ a_tmp.diagonal().setOnes();\ a = a_tmp.data(); \ lda = a_tmp.outerStride(); \ } else { \ a = _lhs; \ lda = lhsStride; \ } \ /*std::cout << "TRMM_L: A is square! Go to MKL TRMM implementation! \n";*/ \ /* call ?trmm*/ \ MKLPREFIX##trmm(&side, &uplo, &transa, &diag, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (MKLTYPE*)b, &ldb); \ \ /* Add op(a_triangular)*b into res*/ \ Map > res_tmp(res,rows,cols,OuterStride<>(resStride)); \ res_tmp=res_tmp+b_tmp; \ } \ }; EIGEN_MKL_TRMM_L(double, double, d, d) EIGEN_MKL_TRMM_L(dcomplex, MKL_Complex16, cd, z) EIGEN_MKL_TRMM_L(float, float, f, s) EIGEN_MKL_TRMM_L(scomplex, MKL_Complex8, cf, c) // implements col-major += alpha * op(general) * op(triangular) #define EIGEN_MKL_TRMM_R(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \ template \ struct product_triangular_matrix_matrix_trmm \ { \ enum { \ IsLower = (Mode&Lower) == Lower, \ SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \ IsUnitDiag = (Mode&UnitDiag) ? 1 : 0, \ IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \ LowUp = IsLower ? Lower : Upper, \ conjA = ((RhsStorageOrder==ColMajor) && ConjugateRhs) ? 1 : 0 \ }; \ \ static EIGEN_DONT_INLINE void run( \ Index _rows, Index _cols, Index _depth, \ const EIGTYPE* _lhs, Index lhsStride, \ const EIGTYPE* _rhs, Index rhsStride, \ EIGTYPE* res, Index resStride, \ EIGTYPE alpha) \ { \ Index diagSize = (std::min)(_cols,_depth); \ Index rows = _rows; \ Index depth = IsLower ? _depth : diagSize; \ Index cols = IsLower ? diagSize : _cols; \ \ typedef Matrix MatrixLhs; \ typedef Matrix MatrixRhs; \ \ /* Non-square case - doesn't fit to MKL ?TRMM. Fall to default triangular product or call MKL ?GEMM*/ \ if (cols != depth) { \ \ int nthr = mkl_domain_get_max_threads(MKL_BLAS); \ \ if ((nthr==1) && (((std::max)(cols,depth)-diagSize)/(double)diagSize < 0.5)) { \ /* Most likely no benefit to call TRMM or GEMM from MKL*/ \ product_triangular_matrix_matrix::run( \ _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha); \ /*std::cout << "TRMM_R: A is not square! Go to Eigen TRMM implementation!\n";*/ \ } else { \ /* Make sense to call GEMM */ \ Map > rhsMap(_rhs,depth,cols, OuterStride<>(rhsStride)); \ MatrixRhs aa_tmp=rhsMap.template triangularView(); \ MKL_INT aStride = aa_tmp.outerStride(); \ gemm_blocking_space blocking(_rows,_cols,_depth); \ general_matrix_matrix_product::run( \ rows, cols, depth, _lhs, lhsStride, aa_tmp.data(), aStride, res, resStride, alpha, blocking, 0); \ \ /*std::cout << "TRMM_R: A is not square! Go to MKL GEMM implementation! " << nthr<<" \n";*/ \ } \ return; \ } \ char side = 'R', transa, uplo, diag = 'N'; \ EIGTYPE *b; \ const EIGTYPE *a; \ MKL_INT m, n, lda, ldb; \ MKLTYPE alpha_; \ \ /* Set alpha_*/ \ assign_scalar_eig2mkl(alpha_, alpha); \ \ /* Set m, n */ \ m = (MKL_INT)rows; \ n = (MKL_INT)diagSize; \ \ /* Set trans */ \ transa = (RhsStorageOrder==RowMajor) ? ((ConjugateRhs) ? 'C' : 'T') : 'N'; \ \ /* Set b, ldb */ \ Map > lhs(_lhs,rows,depth,OuterStride<>(lhsStride)); \ MatrixX##EIGPREFIX b_tmp; \ \ if (ConjugateLhs) b_tmp = lhs.conjugate(); else b_tmp = lhs; \ b = b_tmp.data(); \ ldb = b_tmp.outerStride(); \ \ /* Set uplo */ \ uplo = IsLower ? 'L' : 'U'; \ if (RhsStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \ /* Set a, lda */ \ Map > rhs(_rhs,depth,cols, OuterStride<>(rhsStride)); \ MatrixRhs a_tmp; \ \ if ((conjA!=0) || (SetDiag==0)) { \ if (conjA) a_tmp = rhs.conjugate(); else a_tmp = rhs; \ if (IsZeroDiag) \ a_tmp.diagonal().setZero(); \ else if (IsUnitDiag) \ a_tmp.diagonal().setOnes();\ a = a_tmp.data(); \ lda = a_tmp.outerStride(); \ } else { \ a = _rhs; \ lda = rhsStride; \ } \ /*std::cout << "TRMM_R: A is square! Go to MKL TRMM implementation! \n";*/ \ /* call ?trmm*/ \ MKLPREFIX##trmm(&side, &uplo, &transa, &diag, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (MKLTYPE*)b, &ldb); \ \ /* Add op(a_triangular)*b into res*/ \ Map > res_tmp(res,rows,cols,OuterStride<>(resStride)); \ res_tmp=res_tmp+b_tmp; \ } \ }; EIGEN_MKL_TRMM_R(double, double, d, d) EIGEN_MKL_TRMM_R(dcomplex, MKL_Complex16, cd, z) EIGEN_MKL_TRMM_R(float, float, f, s) EIGEN_MKL_TRMM_R(scomplex, MKL_Complex8, cf, c) } // end namespace internal } // end namespace Eigen #endif // EIGEN_TRIANGULAR_MATRIX_MATRIX_MKL_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_MKL.h0000775000175000017500000001461112107032422027347 0ustar csheltoncshelton/* Copyright (c) 2011, Intel Corporation. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Intel Corporation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ******************************************************************************** * Content : Eigen bindings to Intel(R) MKL * Level 3 BLAS SYRK/HERK implementation. ******************************************************************************** */ #ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_MKL_H #define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_MKL_H namespace Eigen { namespace internal { template struct general_matrix_matrix_rankupdate : general_matrix_matrix_triangular_product< Index,Scalar,AStorageOrder,ConjugateA,Scalar,AStorageOrder,ConjugateA,ResStorageOrder,UpLo,BuiltIn> {}; // try to go to BLAS specialization #define EIGEN_MKL_RANKUPDATE_SPECIALIZE(Scalar) \ template \ struct general_matrix_matrix_triangular_product { \ static EIGEN_STRONG_INLINE void run(Index size, Index depth,const Scalar* lhs, Index lhsStride, \ const Scalar* rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha) \ { \ if (lhs==rhs) { \ general_matrix_matrix_rankupdate \ ::run(size,depth,lhs,lhsStride,rhs,rhsStride,res,resStride,alpha); \ } else { \ general_matrix_matrix_triangular_product \ ::run(size,depth,lhs,lhsStride,rhs,rhsStride,res,resStride,alpha); \ } \ } \ }; EIGEN_MKL_RANKUPDATE_SPECIALIZE(double) //EIGEN_MKL_RANKUPDATE_SPECIALIZE(dcomplex) EIGEN_MKL_RANKUPDATE_SPECIALIZE(float) //EIGEN_MKL_RANKUPDATE_SPECIALIZE(scomplex) // SYRK for float/double #define EIGEN_MKL_RANKUPDATE_R(EIGTYPE, MKLTYPE, MKLFUNC) \ template \ struct general_matrix_matrix_rankupdate { \ enum { \ IsLower = (UpLo&Lower) == Lower, \ LowUp = IsLower ? Lower : Upper, \ conjA = ((AStorageOrder==ColMajor) && ConjugateA) ? 1 : 0 \ }; \ static EIGEN_STRONG_INLINE void run(Index size, Index depth,const EIGTYPE* lhs, Index lhsStride, \ const EIGTYPE* rhs, Index rhsStride, EIGTYPE* res, Index resStride, EIGTYPE alpha) \ { \ /* typedef Matrix MatrixRhs;*/ \ \ MKL_INT lda=lhsStride, ldc=resStride, n=size, k=depth; \ char uplo=(IsLower) ? 'L' : 'U', trans=(AStorageOrder==RowMajor) ? 'T':'N'; \ MKLTYPE alpha_, beta_; \ \ /* Set alpha_ & beta_ */ \ assign_scalar_eig2mkl(alpha_, alpha); \ assign_scalar_eig2mkl(beta_, EIGTYPE(1)); \ MKLFUNC(&uplo, &trans, &n, &k, &alpha_, lhs, &lda, &beta_, res, &ldc); \ } \ }; // HERK for complex data #define EIGEN_MKL_RANKUPDATE_C(EIGTYPE, MKLTYPE, RTYPE, MKLFUNC) \ template \ struct general_matrix_matrix_rankupdate { \ enum { \ IsLower = (UpLo&Lower) == Lower, \ LowUp = IsLower ? Lower : Upper, \ conjA = (((AStorageOrder==ColMajor) && ConjugateA) || ((AStorageOrder==RowMajor) && !ConjugateA)) ? 1 : 0 \ }; \ static EIGEN_STRONG_INLINE void run(Index size, Index depth,const EIGTYPE* lhs, Index lhsStride, \ const EIGTYPE* rhs, Index rhsStride, EIGTYPE* res, Index resStride, EIGTYPE alpha) \ { \ typedef Matrix MatrixType; \ \ MKL_INT lda=lhsStride, ldc=resStride, n=size, k=depth; \ char uplo=(IsLower) ? 'L' : 'U', trans=(AStorageOrder==RowMajor) ? 'C':'N'; \ RTYPE alpha_, beta_; \ const EIGTYPE* a_ptr; \ \ /* Set alpha_ & beta_ */ \ /* assign_scalar_eig2mkl(alpha_, alpha); */\ /* assign_scalar_eig2mkl(beta_, EIGTYPE(1));*/ \ alpha_ = alpha.real(); \ beta_ = 1.0; \ /* Copy with conjugation in some cases*/ \ MatrixType a; \ if (conjA) { \ Map > mapA(lhs,n,k,OuterStride<>(lhsStride)); \ a = mapA.conjugate(); \ lda = a.outerStride(); \ a_ptr = a.data(); \ } else a_ptr=lhs; \ MKLFUNC(&uplo, &trans, &n, &k, &alpha_, (MKLTYPE*)a_ptr, &lda, &beta_, (MKLTYPE*)res, &ldc); \ } \ }; EIGEN_MKL_RANKUPDATE_R(double, double, dsyrk) EIGEN_MKL_RANKUPDATE_R(float, float, ssyrk) //EIGEN_MKL_RANKUPDATE_C(dcomplex, MKL_Complex16, double, zherk) //EIGEN_MKL_RANKUPDATE_C(scomplex, MKL_Complex8, double, cherk) } // end namespace internal } // end namespace Eigen #endif // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_MKL_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/SelfadjointMatrixMatrix.h0000775000175000017500000003611012107032422025476 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_SELFADJOINT_MATRIX_MATRIX_H #define EIGEN_SELFADJOINT_MATRIX_MATRIX_H namespace Eigen { namespace internal { // pack a selfadjoint block diagonal for use with the gebp_kernel template struct symm_pack_lhs { template inline void pack(Scalar* blockA, const const_blas_data_mapper& lhs, Index cols, Index i, Index& count) { // normal copy for(Index k=0; k lhs(_lhs,lhsStride); Index count = 0; Index peeled_mc = (rows/Pack1)*Pack1; for(Index i=0; i(blockA, lhs, cols, i, count); } if(rows-peeled_mc>=Pack2) { pack(blockA, lhs, cols, peeled_mc, count); peeled_mc += Pack2; } // do the same with mr==1 for(Index i=peeled_mc; i struct symm_pack_rhs { enum { PacketSize = packet_traits::size }; void operator()(Scalar* blockB, const Scalar* _rhs, Index rhsStride, Index rows, Index cols, Index k2) { Index end_k = k2 + rows; Index count = 0; const_blas_data_mapper rhs(_rhs,rhsStride); Index packet_cols = (cols/nr)*nr; // first part: normal case for(Index j2=0; j2 the same with nr==1) for(Index j2=packet_cols; j2 struct product_selfadjoint_matrix; template struct product_selfadjoint_matrix { static EIGEN_STRONG_INLINE void run( Index rows, Index cols, const Scalar* lhs, Index lhsStride, const Scalar* rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha) { product_selfadjoint_matrix::IsComplex && EIGEN_LOGICAL_XOR(RhsSelfAdjoint,ConjugateRhs), EIGEN_LOGICAL_XOR(LhsSelfAdjoint,LhsStorageOrder==RowMajor) ? ColMajor : RowMajor, LhsSelfAdjoint, NumTraits::IsComplex && EIGEN_LOGICAL_XOR(LhsSelfAdjoint,ConjugateLhs), ColMajor> ::run(cols, rows, rhs, rhsStride, lhs, lhsStride, res, resStride, alpha); } }; template struct product_selfadjoint_matrix { static EIGEN_DONT_INLINE void run( Index rows, Index cols, const Scalar* _lhs, Index lhsStride, const Scalar* _rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha) { Index size = rows; const_blas_data_mapper lhs(_lhs,lhsStride); const_blas_data_mapper rhs(_rhs,rhsStride); typedef gebp_traits Traits; Index kc = size; // cache block size along the K direction Index mc = rows; // cache block size along the M direction Index nc = cols; // cache block size along the N direction computeProductBlockingSizes(kc, mc, nc); // kc must smaller than mc kc = (std::min)(kc,mc); std::size_t sizeW = kc*Traits::WorkSpaceFactor; std::size_t sizeB = sizeW + kc*cols; ei_declare_aligned_stack_constructed_variable(Scalar, blockA, kc*mc, 0); ei_declare_aligned_stack_constructed_variable(Scalar, allocatedBlockB, sizeB, 0); Scalar* blockB = allocatedBlockB + sizeW; gebp_kernel gebp_kernel; symm_pack_lhs pack_lhs; gemm_pack_rhs pack_rhs; gemm_pack_lhs pack_lhs_transposed; for(Index k2=0; k2 transposed packed copy // 2 - the diagonal block => special packed copy // 3 - the panel below the diagonal block => generic packed copy for(Index i2=0; i2() (blockA, &lhs(i2, k2), lhsStride, actual_kc, actual_mc); gebp_kernel(res+i2, resStride, blockA, blockB, actual_mc, actual_kc, cols, alpha); } } } }; // matrix * selfadjoint product template struct product_selfadjoint_matrix { static EIGEN_DONT_INLINE void run( Index rows, Index cols, const Scalar* _lhs, Index lhsStride, const Scalar* _rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha) { Index size = cols; const_blas_data_mapper lhs(_lhs,lhsStride); typedef gebp_traits Traits; Index kc = size; // cache block size along the K direction Index mc = rows; // cache block size along the M direction Index nc = cols; // cache block size along the N direction computeProductBlockingSizes(kc, mc, nc); std::size_t sizeW = kc*Traits::WorkSpaceFactor; std::size_t sizeB = sizeW + kc*cols; ei_declare_aligned_stack_constructed_variable(Scalar, blockA, kc*mc, 0); ei_declare_aligned_stack_constructed_variable(Scalar, allocatedBlockB, sizeB, 0); Scalar* blockB = allocatedBlockB + sizeW; gebp_kernel gebp_kernel; gemm_pack_lhs pack_lhs; symm_pack_rhs pack_rhs; for(Index k2=0; k2 GEPP for(Index i2=0; i2 struct traits > : traits, Lhs, Rhs> > {}; } template struct SelfadjointProductMatrix : public ProductBase, Lhs, Rhs > { EIGEN_PRODUCT_PUBLIC_INTERFACE(SelfadjointProductMatrix) SelfadjointProductMatrix(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {} enum { LhsIsUpper = (LhsMode&(Upper|Lower))==Upper, LhsIsSelfAdjoint = (LhsMode&SelfAdjoint)==SelfAdjoint, RhsIsUpper = (RhsMode&(Upper|Lower))==Upper, RhsIsSelfAdjoint = (RhsMode&SelfAdjoint)==SelfAdjoint }; template void scaleAndAddTo(Dest& dst, Scalar alpha) const { eigen_assert(dst.rows()==m_lhs.rows() && dst.cols()==m_rhs.cols()); typename internal::add_const_on_value_type::type lhs = LhsBlasTraits::extract(m_lhs); typename internal::add_const_on_value_type::type rhs = RhsBlasTraits::extract(m_rhs); Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(m_lhs) * RhsBlasTraits::extractScalarFactor(m_rhs); internal::product_selfadjoint_matrix::Flags &RowMajorBit) ? RowMajor : ColMajor, LhsIsSelfAdjoint, NumTraits::IsComplex && EIGEN_LOGICAL_XOR(LhsIsUpper,bool(LhsBlasTraits::NeedToConjugate)), EIGEN_LOGICAL_XOR(RhsIsUpper, internal::traits::Flags &RowMajorBit) ? RowMajor : ColMajor, RhsIsSelfAdjoint, NumTraits::IsComplex && EIGEN_LOGICAL_XOR(RhsIsUpper,bool(RhsBlasTraits::NeedToConjugate)), internal::traits::Flags&RowMajorBit ? RowMajor : ColMajor> ::run( lhs.rows(), rhs.cols(), // sizes &lhs.coeffRef(0,0), lhs.outerStride(), // lhs info &rhs.coeffRef(0,0), rhs.outerStride(), // rhs info &dst.coeffRef(0,0), dst.outerStride(), // result info actualAlpha // alpha ); } }; } // end namespace Eigen #endif // EIGEN_SELFADJOINT_MATRIX_MATRIX_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/TriangularSolverMatrix_MKL.h0000775000175000017500000001322512107032422026057 0ustar csheltoncshelton/* Copyright (c) 2011, Intel Corporation. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Intel Corporation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ******************************************************************************** * Content : Eigen bindings to Intel(R) MKL * Triangular matrix * matrix product functionality based on ?TRMM. ******************************************************************************** */ #ifndef EIGEN_TRIANGULAR_SOLVER_MATRIX_MKL_H #define EIGEN_TRIANGULAR_SOLVER_MATRIX_MKL_H namespace Eigen { namespace internal { // implements LeftSide op(triangular)^-1 * general #define EIGEN_MKL_TRSM_L(EIGTYPE, MKLTYPE, MKLPREFIX) \ template \ struct triangular_solve_matrix \ { \ enum { \ IsLower = (Mode&Lower) == Lower, \ IsUnitDiag = (Mode&UnitDiag) ? 1 : 0, \ IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \ conjA = ((TriStorageOrder==ColMajor) && Conjugate) ? 1 : 0 \ }; \ static EIGEN_DONT_INLINE void run( \ Index size, Index otherSize, \ const EIGTYPE* _tri, Index triStride, \ EIGTYPE* _other, Index otherStride) \ { \ MKL_INT m = size, n = otherSize, lda, ldb; \ char side = 'L', uplo, diag='N', transa; \ /* Set alpha_ */ \ MKLTYPE alpha; \ EIGTYPE myone(1); \ assign_scalar_eig2mkl(alpha, myone); \ ldb = otherStride;\ \ const EIGTYPE *a; \ /* Set trans */ \ transa = (TriStorageOrder==RowMajor) ? ((Conjugate) ? 'C' : 'T') : 'N'; \ /* Set uplo */ \ uplo = IsLower ? 'L' : 'U'; \ if (TriStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \ /* Set a, lda */ \ typedef Matrix MatrixTri; \ Map > tri(_tri,size,size,OuterStride<>(triStride)); \ MatrixTri a_tmp; \ \ if (conjA) { \ a_tmp = tri.conjugate(); \ a = a_tmp.data(); \ lda = a_tmp.outerStride(); \ } else { \ a = _tri; \ lda = triStride; \ } \ if (IsUnitDiag) diag='U'; \ /* call ?trsm*/ \ MKLPREFIX##trsm(&side, &uplo, &transa, &diag, &m, &n, &alpha, (const MKLTYPE*)a, &lda, (MKLTYPE*)_other, &ldb); \ } \ }; EIGEN_MKL_TRSM_L(double, double, d) EIGEN_MKL_TRSM_L(dcomplex, MKL_Complex16, z) EIGEN_MKL_TRSM_L(float, float, s) EIGEN_MKL_TRSM_L(scomplex, MKL_Complex8, c) // implements RightSide general * op(triangular)^-1 #define EIGEN_MKL_TRSM_R(EIGTYPE, MKLTYPE, MKLPREFIX) \ template \ struct triangular_solve_matrix \ { \ enum { \ IsLower = (Mode&Lower) == Lower, \ IsUnitDiag = (Mode&UnitDiag) ? 1 : 0, \ IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \ conjA = ((TriStorageOrder==ColMajor) && Conjugate) ? 1 : 0 \ }; \ static EIGEN_DONT_INLINE void run( \ Index size, Index otherSize, \ const EIGTYPE* _tri, Index triStride, \ EIGTYPE* _other, Index otherStride) \ { \ MKL_INT m = otherSize, n = size, lda, ldb; \ char side = 'R', uplo, diag='N', transa; \ /* Set alpha_ */ \ MKLTYPE alpha; \ EIGTYPE myone(1); \ assign_scalar_eig2mkl(alpha, myone); \ ldb = otherStride;\ \ const EIGTYPE *a; \ /* Set trans */ \ transa = (TriStorageOrder==RowMajor) ? ((Conjugate) ? 'C' : 'T') : 'N'; \ /* Set uplo */ \ uplo = IsLower ? 'L' : 'U'; \ if (TriStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \ /* Set a, lda */ \ typedef Matrix MatrixTri; \ Map > tri(_tri,size,size,OuterStride<>(triStride)); \ MatrixTri a_tmp; \ \ if (conjA) { \ a_tmp = tri.conjugate(); \ a = a_tmp.data(); \ lda = a_tmp.outerStride(); \ } else { \ a = _tri; \ lda = triStride; \ } \ if (IsUnitDiag) diag='U'; \ /* call ?trsm*/ \ MKLPREFIX##trsm(&side, &uplo, &transa, &diag, &m, &n, &alpha, (const MKLTYPE*)a, &lda, (MKLTYPE*)_other, &ldb); \ /*std::cout << "TRMS_L specialization!\n";*/ \ } \ }; EIGEN_MKL_TRSM_R(double, double, d) EIGEN_MKL_TRSM_R(dcomplex, MKL_Complex16, z) EIGEN_MKL_TRSM_R(float, float, s) EIGEN_MKL_TRSM_R(scomplex, MKL_Complex8, c) } // end namespace internal } // end namespace Eigen #endif // EIGEN_TRIANGULAR_SOLVER_MATRIX_MKL_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/SelfadjointMatrixVector.h0000775000175000017500000002566612107032422025512 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_SELFADJOINT_MATRIX_VECTOR_H #define EIGEN_SELFADJOINT_MATRIX_VECTOR_H namespace Eigen { namespace internal { /* Optimized selfadjoint matrix * vector product: * This algorithm processes 2 columns at onces that allows to both reduce * the number of load/stores of the result by a factor 2 and to reduce * the instruction dependency. */ template struct selfadjoint_matrix_vector_product; template struct selfadjoint_matrix_vector_product { static EIGEN_DONT_INLINE void run( Index size, const Scalar* lhs, Index lhsStride, const Scalar* _rhs, Index rhsIncr, Scalar* res, Scalar alpha) { typedef typename packet_traits::type Packet; typedef typename NumTraits::Real RealScalar; const Index PacketSize = sizeof(Packet)/sizeof(Scalar); enum { IsRowMajor = StorageOrder==RowMajor ? 1 : 0, IsLower = UpLo == Lower ? 1 : 0, FirstTriangular = IsRowMajor == IsLower }; conj_helper::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, IsRowMajor), ConjugateRhs> cj0; conj_helper::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> cj1; conj_helper::IsComplex, ConjugateRhs> cjd; conj_helper::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, IsRowMajor), ConjugateRhs> pcj0; conj_helper::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> pcj1; Scalar cjAlpha = ConjugateRhs ? conj(alpha) : alpha; // FIXME this copy is now handled outside product_selfadjoint_vector, so it could probably be removed. // if the rhs is not sequentially stored in memory we copy it to a temporary buffer, // this is because we need to extract packets ei_declare_aligned_stack_constructed_variable(Scalar,rhs,size,rhsIncr==1 ? const_cast(_rhs) : 0); if (rhsIncr!=1) { const Scalar* it = _rhs; for (Index i=0; i(t0); Scalar t1 = cjAlpha * rhs[j+1]; Packet ptmp1 = pset1(t1); Scalar t2(0); Packet ptmp2 = pset1(t2); Scalar t3(0); Packet ptmp3 = pset1(t3); size_t starti = FirstTriangular ? 0 : j+2; size_t endi = FirstTriangular ? j : size; size_t alignedStart = (starti) + internal::first_aligned(&res[starti], endi-starti); size_t alignedEnd = alignedStart + ((endi-alignedStart)/(PacketSize))*(PacketSize); // TODO make sure this product is a real * complex and that the rhs is properly conjugated if needed res[j] += cjd.pmul(internal::real(A0[j]), t0); res[j+1] += cjd.pmul(internal::real(A1[j+1]), t1); if(FirstTriangular) { res[j] += cj0.pmul(A1[j], t1); t3 += cj1.pmul(A1[j], rhs[j]); } else { res[j+1] += cj0.pmul(A0[j+1],t0); t2 += cj1.pmul(A0[j+1], rhs[j+1]); } for (size_t i=starti; i huge speed up) // gcc 4.2 does this optimization automatically. const Scalar* EIGEN_RESTRICT a0It = A0 + alignedStart; const Scalar* EIGEN_RESTRICT a1It = A1 + alignedStart; const Scalar* EIGEN_RESTRICT rhsIt = rhs + alignedStart; Scalar* EIGEN_RESTRICT resIt = res + alignedStart; for (size_t i=alignedStart; i(a0It); a0It += PacketSize; Packet A1i = ploadu(a1It); a1It += PacketSize; Packet Bi = ploadu(rhsIt); rhsIt += PacketSize; // FIXME should be aligned in most cases Packet Xi = pload (resIt); Xi = pcj0.pmadd(A0i,ptmp0, pcj0.pmadd(A1i,ptmp1,Xi)); ptmp2 = pcj1.pmadd(A0i, Bi, ptmp2); ptmp3 = pcj1.pmadd(A1i, Bi, ptmp3); pstore(resIt,Xi); resIt += PacketSize; } for (size_t i=alignedEnd; i struct traits > : traits, Lhs, Rhs> > {}; } template struct SelfadjointProductMatrix : public ProductBase, Lhs, Rhs > { EIGEN_PRODUCT_PUBLIC_INTERFACE(SelfadjointProductMatrix) enum { LhsUpLo = LhsMode&(Upper|Lower) }; SelfadjointProductMatrix(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {} template void scaleAndAddTo(Dest& dest, Scalar alpha) const { typedef typename Dest::Scalar ResScalar; typedef typename Base::RhsScalar RhsScalar; typedef Map, Aligned> MappedDest; eigen_assert(dest.rows()==m_lhs.rows() && dest.cols()==m_rhs.cols()); typename internal::add_const_on_value_type::type lhs = LhsBlasTraits::extract(m_lhs); typename internal::add_const_on_value_type::type rhs = RhsBlasTraits::extract(m_rhs); Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(m_lhs) * RhsBlasTraits::extractScalarFactor(m_rhs); enum { EvalToDest = (Dest::InnerStrideAtCompileTime==1), UseRhs = (_ActualRhsType::InnerStrideAtCompileTime==1) }; internal::gemv_static_vector_if static_dest; internal::gemv_static_vector_if static_rhs; ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(), EvalToDest ? dest.data() : static_dest.data()); ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,rhs.size(), UseRhs ? const_cast(rhs.data()) : static_rhs.data()); if(!EvalToDest) { #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN int size = dest.size(); EIGEN_DENSE_STORAGE_CTOR_PLUGIN #endif MappedDest(actualDestPtr, dest.size()) = dest; } if(!UseRhs) { #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN int size = rhs.size(); EIGEN_DENSE_STORAGE_CTOR_PLUGIN #endif Map(actualRhsPtr, rhs.size()) = rhs; } internal::selfadjoint_matrix_vector_product::Flags&RowMajorBit) ? RowMajor : ColMajor, int(LhsUpLo), bool(LhsBlasTraits::NeedToConjugate), bool(RhsBlasTraits::NeedToConjugate)>::run ( lhs.rows(), // size &lhs.coeffRef(0,0), lhs.outerStride(), // lhs info actualRhsPtr, 1, // rhs info actualDestPtr, // result info actualAlpha // scale factor ); if(!EvalToDest) dest = MappedDest(actualDestPtr, dest.size()); } }; namespace internal { template struct traits > : traits, Lhs, Rhs> > {}; } template struct SelfadjointProductMatrix : public ProductBase, Lhs, Rhs > { EIGEN_PRODUCT_PUBLIC_INTERFACE(SelfadjointProductMatrix) enum { RhsUpLo = RhsMode&(Upper|Lower) }; SelfadjointProductMatrix(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {} template void scaleAndAddTo(Dest& dest, Scalar alpha) const { // let's simply transpose the product Transpose destT(dest); SelfadjointProductMatrix, int(RhsUpLo)==Upper ? Lower : Upper, false, Transpose, 0, true>(m_rhs.transpose(), m_lhs.transpose()).scaleAndAddTo(destT, alpha); } }; } // end namespace Eigen #endif // EIGEN_SELFADJOINT_MATRIX_VECTOR_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/SelfadjointMatrixMatrix_MKL.h0000775000175000017500000002426312107032422026207 0ustar csheltoncshelton/* Copyright (c) 2011, Intel Corporation. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Intel Corporation nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ******************************************************************************** * Content : Eigen bindings to Intel(R) MKL * Self adjoint matrix * matrix product functionality based on ?SYMM/?HEMM. ******************************************************************************** */ #ifndef EIGEN_SELFADJOINT_MATRIX_MATRIX_MKL_H #define EIGEN_SELFADJOINT_MATRIX_MATRIX_MKL_H namespace Eigen { namespace internal { /* Optimized selfadjoint matrix * matrix (?SYMM/?HEMM) product */ #define EIGEN_MKL_SYMM_L(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \ template \ struct product_selfadjoint_matrix \ {\ \ static EIGEN_DONT_INLINE void run( \ Index rows, Index cols, \ const EIGTYPE* _lhs, Index lhsStride, \ const EIGTYPE* _rhs, Index rhsStride, \ EIGTYPE* res, Index resStride, \ EIGTYPE alpha) \ { \ char side='L', uplo='L'; \ MKL_INT m, n, lda, ldb, ldc; \ const EIGTYPE *a, *b; \ MKLTYPE alpha_, beta_; \ MatrixX##EIGPREFIX b_tmp; \ EIGTYPE myone(1);\ \ /* Set transpose options */ \ /* Set m, n, k */ \ m = (MKL_INT)rows; \ n = (MKL_INT)cols; \ \ /* Set alpha_ & beta_ */ \ assign_scalar_eig2mkl(alpha_, alpha); \ assign_scalar_eig2mkl(beta_, myone); \ \ /* Set lda, ldb, ldc */ \ lda = (MKL_INT)lhsStride; \ ldb = (MKL_INT)rhsStride; \ ldc = (MKL_INT)resStride; \ \ /* Set a, b, c */ \ if (LhsStorageOrder==RowMajor) uplo='U'; \ a = _lhs; \ \ if (RhsStorageOrder==RowMajor) { \ Map > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \ b_tmp = rhs.adjoint(); \ b = b_tmp.data(); \ ldb = b_tmp.outerStride(); \ } else b = _rhs; \ \ MKLPREFIX##symm(&side, &uplo, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \ \ } \ }; #define EIGEN_MKL_HEMM_L(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \ template \ struct product_selfadjoint_matrix \ {\ static EIGEN_DONT_INLINE void run( \ Index rows, Index cols, \ const EIGTYPE* _lhs, Index lhsStride, \ const EIGTYPE* _rhs, Index rhsStride, \ EIGTYPE* res, Index resStride, \ EIGTYPE alpha) \ { \ char side='L', uplo='L'; \ MKL_INT m, n, lda, ldb, ldc; \ const EIGTYPE *a, *b; \ MKLTYPE alpha_, beta_; \ MatrixX##EIGPREFIX b_tmp; \ Matrix a_tmp; \ EIGTYPE myone(1); \ \ /* Set transpose options */ \ /* Set m, n, k */ \ m = (MKL_INT)rows; \ n = (MKL_INT)cols; \ \ /* Set alpha_ & beta_ */ \ assign_scalar_eig2mkl(alpha_, alpha); \ assign_scalar_eig2mkl(beta_, myone); \ \ /* Set lda, ldb, ldc */ \ lda = (MKL_INT)lhsStride; \ ldb = (MKL_INT)rhsStride; \ ldc = (MKL_INT)resStride; \ \ /* Set a, b, c */ \ if (((LhsStorageOrder==ColMajor) && ConjugateLhs) || ((LhsStorageOrder==RowMajor) && (!ConjugateLhs))) { \ Map, 0, OuterStride<> > lhs(_lhs,m,m,OuterStride<>(lhsStride)); \ a_tmp = lhs.conjugate(); \ a = a_tmp.data(); \ lda = a_tmp.outerStride(); \ } else a = _lhs; \ if (LhsStorageOrder==RowMajor) uplo='U'; \ \ if (RhsStorageOrder==ColMajor && (!ConjugateRhs)) { \ b = _rhs; } \ else { \ if (RhsStorageOrder==ColMajor && ConjugateRhs) { \ Map > rhs(_rhs,m,n,OuterStride<>(rhsStride)); \ b_tmp = rhs.conjugate(); \ } else \ if (ConjugateRhs) { \ Map > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \ b_tmp = rhs.adjoint(); \ } else { \ Map > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \ b_tmp = rhs.transpose(); \ } \ b = b_tmp.data(); \ ldb = b_tmp.outerStride(); \ } \ \ MKLPREFIX##hemm(&side, &uplo, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \ \ } \ }; EIGEN_MKL_SYMM_L(double, double, d, d) EIGEN_MKL_SYMM_L(float, float, f, s) EIGEN_MKL_HEMM_L(dcomplex, MKL_Complex16, cd, z) EIGEN_MKL_HEMM_L(scomplex, MKL_Complex8, cf, c) /* Optimized matrix * selfadjoint matrix (?SYMM/?HEMM) product */ #define EIGEN_MKL_SYMM_R(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \ template \ struct product_selfadjoint_matrix \ {\ \ static EIGEN_DONT_INLINE void run( \ Index rows, Index cols, \ const EIGTYPE* _lhs, Index lhsStride, \ const EIGTYPE* _rhs, Index rhsStride, \ EIGTYPE* res, Index resStride, \ EIGTYPE alpha) \ { \ char side='R', uplo='L'; \ MKL_INT m, n, lda, ldb, ldc; \ const EIGTYPE *a, *b; \ MKLTYPE alpha_, beta_; \ MatrixX##EIGPREFIX b_tmp; \ EIGTYPE myone(1);\ \ /* Set m, n, k */ \ m = (MKL_INT)rows; \ n = (MKL_INT)cols; \ \ /* Set alpha_ & beta_ */ \ assign_scalar_eig2mkl(alpha_, alpha); \ assign_scalar_eig2mkl(beta_, myone); \ \ /* Set lda, ldb, ldc */ \ lda = (MKL_INT)rhsStride; \ ldb = (MKL_INT)lhsStride; \ ldc = (MKL_INT)resStride; \ \ /* Set a, b, c */ \ if (RhsStorageOrder==RowMajor) uplo='U'; \ a = _rhs; \ \ if (LhsStorageOrder==RowMajor) { \ Map > lhs(_lhs,n,m,OuterStride<>(rhsStride)); \ b_tmp = lhs.adjoint(); \ b = b_tmp.data(); \ ldb = b_tmp.outerStride(); \ } else b = _lhs; \ \ MKLPREFIX##symm(&side, &uplo, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \ \ } \ }; #define EIGEN_MKL_HEMM_R(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \ template \ struct product_selfadjoint_matrix \ {\ static EIGEN_DONT_INLINE void run( \ Index rows, Index cols, \ const EIGTYPE* _lhs, Index lhsStride, \ const EIGTYPE* _rhs, Index rhsStride, \ EIGTYPE* res, Index resStride, \ EIGTYPE alpha) \ { \ char side='R', uplo='L'; \ MKL_INT m, n, lda, ldb, ldc; \ const EIGTYPE *a, *b; \ MKLTYPE alpha_, beta_; \ MatrixX##EIGPREFIX b_tmp; \ Matrix a_tmp; \ EIGTYPE myone(1); \ \ /* Set m, n, k */ \ m = (MKL_INT)rows; \ n = (MKL_INT)cols; \ \ /* Set alpha_ & beta_ */ \ assign_scalar_eig2mkl(alpha_, alpha); \ assign_scalar_eig2mkl(beta_, myone); \ \ /* Set lda, ldb, ldc */ \ lda = (MKL_INT)rhsStride; \ ldb = (MKL_INT)lhsStride; \ ldc = (MKL_INT)resStride; \ \ /* Set a, b, c */ \ if (((RhsStorageOrder==ColMajor) && ConjugateRhs) || ((RhsStorageOrder==RowMajor) && (!ConjugateRhs))) { \ Map, 0, OuterStride<> > rhs(_rhs,n,n,OuterStride<>(rhsStride)); \ a_tmp = rhs.conjugate(); \ a = a_tmp.data(); \ lda = a_tmp.outerStride(); \ } else a = _rhs; \ if (RhsStorageOrder==RowMajor) uplo='U'; \ \ if (LhsStorageOrder==ColMajor && (!ConjugateLhs)) { \ b = _lhs; } \ else { \ if (LhsStorageOrder==ColMajor && ConjugateLhs) { \ Map > lhs(_lhs,m,n,OuterStride<>(lhsStride)); \ b_tmp = lhs.conjugate(); \ } else \ if (ConjugateLhs) { \ Map > lhs(_lhs,n,m,OuterStride<>(lhsStride)); \ b_tmp = lhs.adjoint(); \ } else { \ Map > lhs(_lhs,n,m,OuterStride<>(lhsStride)); \ b_tmp = lhs.transpose(); \ } \ b = b_tmp.data(); \ ldb = b_tmp.outerStride(); \ } \ \ MKLPREFIX##hemm(&side, &uplo, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \ } \ }; EIGEN_MKL_SYMM_R(double, double, d, d) EIGEN_MKL_SYMM_R(float, float, f, s) EIGEN_MKL_HEMM_R(dcomplex, MKL_Complex16, cd, z) EIGEN_MKL_HEMM_R(scomplex, MKL_Complex8, cf, c) } // end namespace internal } // end namespace Eigen #endif // EIGEN_SELFADJOINT_MATRIX_MATRIX_MKL_H ctbnrle.1.1.1/hdr/Eigen/src/Core/products/TriangularMatrixVector.h0000775000175000017500000003477712107032422025363 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_TRIANGULARMATRIXVECTOR_H #define EIGEN_TRIANGULARMATRIXVECTOR_H namespace Eigen { namespace internal { template struct triangular_matrix_vector_product; template struct triangular_matrix_vector_product { typedef typename scalar_product_traits::ReturnType ResScalar; enum { IsLower = ((Mode&Lower)==Lower), HasUnitDiag = (Mode & UnitDiag)==UnitDiag, HasZeroDiag = (Mode & ZeroDiag)==ZeroDiag }; static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride, const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, ResScalar alpha) { static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH; Index size = (std::min)(_rows,_cols); Index rows = IsLower ? _rows : (std::min)(_rows,_cols); Index cols = IsLower ? (std::min)(_rows,_cols) : _cols; typedef Map, 0, OuterStride<> > LhsMap; const LhsMap lhs(_lhs,rows,cols,OuterStride<>(lhsStride)); typename conj_expr_if::type cjLhs(lhs); typedef Map, 0, InnerStride<> > RhsMap; const RhsMap rhs(_rhs,cols,InnerStride<>(rhsIncr)); typename conj_expr_if::type cjRhs(rhs); typedef Map > ResMap; ResMap res(_res,rows); for (Index pi=0; pi0) res.segment(s,r) += (alpha * cjRhs.coeff(i)) * cjLhs.col(i).segment(s,r); if (HasUnitDiag) res.coeffRef(i) += alpha * cjRhs.coeff(i); } Index r = IsLower ? rows - pi - actualPanelWidth : pi; if (r>0) { Index s = IsLower ? pi+actualPanelWidth : 0; general_matrix_vector_product::run( r, actualPanelWidth, &lhs.coeffRef(s,pi), lhsStride, &rhs.coeffRef(pi), rhsIncr, &res.coeffRef(s), resIncr, alpha); } } if((!IsLower) && cols>size) { general_matrix_vector_product::run( rows, cols-size, &lhs.coeffRef(0,size), lhsStride, &rhs.coeffRef(size), rhsIncr, _res, resIncr, alpha); } } }; template struct triangular_matrix_vector_product { typedef typename scalar_product_traits::ReturnType ResScalar; enum { IsLower = ((Mode&Lower)==Lower), HasUnitDiag = (Mode & UnitDiag)==UnitDiag, HasZeroDiag = (Mode & ZeroDiag)==ZeroDiag }; static void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride, const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, ResScalar alpha) { static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH; Index diagSize = (std::min)(_rows,_cols); Index rows = IsLower ? _rows : diagSize; Index cols = IsLower ? diagSize : _cols; typedef Map, 0, OuterStride<> > LhsMap; const LhsMap lhs(_lhs,rows,cols,OuterStride<>(lhsStride)); typename conj_expr_if::type cjLhs(lhs); typedef Map > RhsMap; const RhsMap rhs(_rhs,cols); typename conj_expr_if::type cjRhs(rhs); typedef Map, 0, InnerStride<> > ResMap; ResMap res(_res,rows,InnerStride<>(resIncr)); for (Index pi=0; pi0) res.coeffRef(i) += alpha * (cjLhs.row(i).segment(s,r).cwiseProduct(cjRhs.segment(s,r).transpose())).sum(); if (HasUnitDiag) res.coeffRef(i) += alpha * cjRhs.coeff(i); } Index r = IsLower ? pi : cols - pi - actualPanelWidth; if (r>0) { Index s = IsLower ? 0 : pi + actualPanelWidth; general_matrix_vector_product::run( actualPanelWidth, r, &lhs.coeffRef(pi,s), lhsStride, &rhs.coeffRef(s), rhsIncr, &res.coeffRef(pi), resIncr, alpha); } } if(IsLower && rows>diagSize) { general_matrix_vector_product::run( rows-diagSize, cols, &lhs.coeffRef(diagSize,0), lhsStride, &rhs.coeffRef(0), rhsIncr, &res.coeffRef(diagSize), resIncr, alpha); } } }; /*************************************************************************** * Wrapper to product_triangular_vector ***************************************************************************/ template struct traits > : traits, Lhs, Rhs> > {}; template struct traits > : traits, Lhs, Rhs> > {}; template struct trmv_selector; } // end namespace internal template struct TriangularProduct : public ProductBase, Lhs, Rhs > { EIGEN_PRODUCT_PUBLIC_INTERFACE(TriangularProduct) TriangularProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {} template void scaleAndAddTo(Dest& dst, Scalar alpha) const { eigen_assert(dst.rows()==m_lhs.rows() && dst.cols()==m_rhs.cols()); internal::trmv_selector<(int(internal::traits::Flags)&RowMajorBit) ? RowMajor : ColMajor>::run(*this, dst, alpha); } }; template struct TriangularProduct : public ProductBase, Lhs, Rhs > { EIGEN_PRODUCT_PUBLIC_INTERFACE(TriangularProduct) TriangularProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {} template void scaleAndAddTo(Dest& dst, Scalar alpha) const { eigen_assert(dst.rows()==m_lhs.rows() && dst.cols()==m_rhs.cols()); typedef TriangularProduct<(Mode & (UnitDiag|ZeroDiag)) | ((Mode & Lower) ? Upper : Lower),true,Transpose,false,Transpose,true> TriangularProductTranspose; Transpose dstT(dst); internal::trmv_selector<(int(internal::traits::Flags)&RowMajorBit) ? ColMajor : RowMajor>::run( TriangularProductTranspose(m_rhs.transpose(),m_lhs.transpose()), dstT, alpha); } }; namespace internal { // TODO: find a way to factorize this piece of code with gemv_selector since the logic is exactly the same. template<> struct trmv_selector { template static void run(const TriangularProduct& prod, Dest& dest, typename TriangularProduct::Scalar alpha) { typedef TriangularProduct ProductType; typedef typename ProductType::Index Index; typedef typename ProductType::LhsScalar LhsScalar; typedef typename ProductType::RhsScalar RhsScalar; typedef typename ProductType::Scalar ResScalar; typedef typename ProductType::RealScalar RealScalar; typedef typename ProductType::ActualLhsType ActualLhsType; typedef typename ProductType::ActualRhsType ActualRhsType; typedef typename ProductType::LhsBlasTraits LhsBlasTraits; typedef typename ProductType::RhsBlasTraits RhsBlasTraits; typedef Map, Aligned> MappedDest; typename internal::add_const_on_value_type::type actualLhs = LhsBlasTraits::extract(prod.lhs()); typename internal::add_const_on_value_type::type actualRhs = RhsBlasTraits::extract(prod.rhs()); ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs()) * RhsBlasTraits::extractScalarFactor(prod.rhs()); enum { // FIXME find a way to allow an inner stride on the result if packet_traits::size==1 // on, the other hand it is good for the cache to pack the vector anyways... EvalToDestAtCompileTime = Dest::InnerStrideAtCompileTime==1, ComplexByReal = (NumTraits::IsComplex) && (!NumTraits::IsComplex), MightCannotUseDest = (Dest::InnerStrideAtCompileTime!=1) || ComplexByReal }; gemv_static_vector_if static_dest; bool alphaIsCompatible = (!ComplexByReal) || (imag(actualAlpha)==RealScalar(0)); bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible; RhsScalar compatibleAlpha = get_factor::run(actualAlpha); ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(), evalToDest ? dest.data() : static_dest.data()); if(!evalToDest) { #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN int size = dest.size(); EIGEN_DENSE_STORAGE_CTOR_PLUGIN #endif if(!alphaIsCompatible) { MappedDest(actualDestPtr, dest.size()).setZero(); compatibleAlpha = RhsScalar(1); } else MappedDest(actualDestPtr, dest.size()) = dest; } internal::triangular_matrix_vector_product ::run(actualLhs.rows(),actualLhs.cols(), actualLhs.data(),actualLhs.outerStride(), actualRhs.data(),actualRhs.innerStride(), actualDestPtr,1,compatibleAlpha); if (!evalToDest) { if(!alphaIsCompatible) dest += actualAlpha * MappedDest(actualDestPtr, dest.size()); else dest = MappedDest(actualDestPtr, dest.size()); } } }; template<> struct trmv_selector { template static void run(const TriangularProduct& prod, Dest& dest, typename TriangularProduct::Scalar alpha) { typedef TriangularProduct ProductType; typedef typename ProductType::LhsScalar LhsScalar; typedef typename ProductType::RhsScalar RhsScalar; typedef typename ProductType::Scalar ResScalar; typedef typename ProductType::Index Index; typedef typename ProductType::ActualLhsType ActualLhsType; typedef typename ProductType::ActualRhsType ActualRhsType; typedef typename ProductType::_ActualRhsType _ActualRhsType; typedef typename ProductType::LhsBlasTraits LhsBlasTraits; typedef typename ProductType::RhsBlasTraits RhsBlasTraits; typename add_const::type actualLhs = LhsBlasTraits::extract(prod.lhs()); typename add_const::type actualRhs = RhsBlasTraits::extract(prod.rhs()); ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs()) * RhsBlasTraits::extractScalarFactor(prod.rhs()); enum { DirectlyUseRhs = _ActualRhsType::InnerStrideAtCompileTime==1 }; gemv_static_vector_if static_rhs; ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(), DirectlyUseRhs ? const_cast(actualRhs.data()) : static_rhs.data()); if(!DirectlyUseRhs) { #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN int size = actualRhs.size(); EIGEN_DENSE_STORAGE_CTOR_PLUGIN #endif Map(actualRhsPtr, actualRhs.size()) = actualRhs; } internal::triangular_matrix_vector_product ::run(actualLhs.rows(),actualLhs.cols(), actualLhs.data(),actualLhs.outerStride(), actualRhsPtr,1, dest.data(),dest.innerStride(), actualAlpha); } }; } // end namespace internal } // end namespace Eigen #endif // EIGEN_TRIANGULARMATRIXVECTOR_H ctbnrle.1.1.1/hdr/Eigen/src/Core/EigenBase.h0000775000175000017500000001442712107032422020650 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Benoit Jacob // Copyright (C) 2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_EIGENBASE_H #define EIGEN_EIGENBASE_H namespace Eigen { /** Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T). * * In other words, an EigenBase object is an object that can be copied into a MatrixBase. * * Besides MatrixBase-derived classes, this also includes special matrix classes such as diagonal matrices, etc. * * Notice that this class is trivial, it is only used to disambiguate overloaded functions. * * \sa \ref TopicClassHierarchy */ template struct EigenBase { // typedef typename internal::plain_matrix_type::type PlainObject; typedef typename internal::traits::StorageKind StorageKind; typedef typename internal::traits::Index Index; /** \returns a reference to the derived object */ Derived& derived() { return *static_cast(this); } /** \returns a const reference to the derived object */ const Derived& derived() const { return *static_cast(this); } inline Derived& const_cast_derived() const { return *static_cast(const_cast(this)); } inline const Derived& const_derived() const { return *static_cast(this); } /** \returns the number of rows. \sa cols(), RowsAtCompileTime */ inline Index rows() const { return derived().rows(); } /** \returns the number of columns. \sa rows(), ColsAtCompileTime*/ inline Index cols() const { return derived().cols(); } /** \returns the number of coefficients, which is rows()*cols(). * \sa rows(), cols(), SizeAtCompileTime. */ inline Index size() const { return rows() * cols(); } /** \internal Don't use it, but do the equivalent: \code dst = *this; \endcode */ template inline void evalTo(Dest& dst) const { derived().evalTo(dst); } /** \internal Don't use it, but do the equivalent: \code dst += *this; \endcode */ template inline void addTo(Dest& dst) const { // This is the default implementation, // derived class can reimplement it in a more optimized way. typename Dest::PlainObject res(rows(),cols()); evalTo(res); dst += res; } /** \internal Don't use it, but do the equivalent: \code dst -= *this; \endcode */ template inline void subTo(Dest& dst) const { // This is the default implementation, // derived class can reimplement it in a more optimized way. typename Dest::PlainObject res(rows(),cols()); evalTo(res); dst -= res; } /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheRight(*this); \endcode */ template inline void applyThisOnTheRight(Dest& dst) const { // This is the default implementation, // derived class can reimplement it in a more optimized way. dst = dst * this->derived(); } /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheLeft(*this); \endcode */ template inline void applyThisOnTheLeft(Dest& dst) const { // This is the default implementation, // derived class can reimplement it in a more optimized way. dst = this->derived() * dst; } }; /*************************************************************************** * Implementation of matrix base methods ***************************************************************************/ /** \brief Copies the generic expression \a other into *this. * * \details The expression must provide a (templated) evalTo(Derived& dst) const * function which does the actual job. In practice, this allows any user to write * its own special matrix without having to modify MatrixBase * * \returns a reference to *this. */ template template Derived& DenseBase::operator=(const EigenBase &other) { other.derived().evalTo(derived()); return derived(); } template template Derived& DenseBase::operator+=(const EigenBase &other) { other.derived().addTo(derived()); return derived(); } template template Derived& DenseBase::operator-=(const EigenBase &other) { other.derived().subTo(derived()); return derived(); } /** replaces \c *this by \c *this * \a other. * * \returns a reference to \c *this */ template template inline Derived& MatrixBase::operator*=(const EigenBase &other) { other.derived().applyThisOnTheRight(derived()); return derived(); } /** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=() */ template template inline void MatrixBase::applyOnTheRight(const EigenBase &other) { other.derived().applyThisOnTheRight(derived()); } /** replaces \c *this by \c *this * \a other. */ template template inline void MatrixBase::applyOnTheLeft(const EigenBase &other) { other.derived().applyThisOnTheLeft(derived()); } } // end namespace Eigen #endif // EIGEN_EIGENBASE_H ctbnrle.1.1.1/hdr/Eigen/src/Core/Dot.h0000775000175000017500000002365212107032422017554 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2006-2008, 2010 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_DOT_H #define EIGEN_DOT_H namespace Eigen { namespace internal { // helper function for dot(). The problem is that if we put that in the body of dot(), then upon calling dot // with mismatched types, the compiler emits errors about failing to instantiate cwiseProduct BEFORE // looking at the static assertions. Thus this is a trick to get better compile errors. template struct dot_nocheck { typedef typename scalar_product_traits::Scalar,typename traits::Scalar>::ReturnType ResScalar; static inline ResScalar run(const MatrixBase& a, const MatrixBase& b) { return a.template binaryExpr::Scalar,typename traits::Scalar> >(b).sum(); } }; template struct dot_nocheck { typedef typename scalar_product_traits::Scalar,typename traits::Scalar>::ReturnType ResScalar; static inline ResScalar run(const MatrixBase& a, const MatrixBase& b) { return a.transpose().template binaryExpr::Scalar,typename traits::Scalar> >(b).sum(); } }; } // end namespace internal /** \returns the dot product of *this with other. * * \only_for_vectors * * \note If the scalar type is complex numbers, then this function returns the hermitian * (sesquilinear) dot product, conjugate-linear in the first variable and linear in the * second variable. * * \sa squaredNorm(), norm() */ template template typename internal::scalar_product_traits::Scalar,typename internal::traits::Scalar>::ReturnType MatrixBase::dot(const MatrixBase& other) const { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived) typedef internal::scalar_conj_product_op func; EIGEN_CHECK_BINARY_COMPATIBILIY(func,Scalar,typename OtherDerived::Scalar); eigen_assert(size() == other.size()); return internal::dot_nocheck::run(*this, other); } #ifdef EIGEN2_SUPPORT /** \returns the dot product of *this with other, with the Eigen2 convention that the dot product is linear in the first variable * (conjugating the second variable). Of course this only makes a difference in the complex case. * * This method is only available in EIGEN2_SUPPORT mode. * * \only_for_vectors * * \sa dot() */ template template typename internal::traits::Scalar MatrixBase::eigen2_dot(const MatrixBase& other) const { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived) EIGEN_STATIC_ASSERT((internal::is_same::value), YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) eigen_assert(size() == other.size()); return internal::dot_nocheck::run(other,*this); } #endif //---------- implementation of L2 norm and related functions ---------- /** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the Frobenius norm. * In both cases, it consists in the sum of the square of all the matrix entries. * For vectors, this is also equals to the dot product of \c *this with itself. * * \sa dot(), norm() */ template EIGEN_STRONG_INLINE typename NumTraits::Scalar>::Real MatrixBase::squaredNorm() const { return internal::real((*this).cwiseAbs2().sum()); } /** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm. * In both cases, it consists in the square root of the sum of the square of all the matrix entries. * For vectors, this is also equals to the square root of the dot product of \c *this with itself. * * \sa dot(), squaredNorm() */ template inline typename NumTraits::Scalar>::Real MatrixBase::norm() const { return internal::sqrt(squaredNorm()); } /** \returns an expression of the quotient of *this by its own norm. * * \only_for_vectors * * \sa norm(), normalize() */ template inline const typename MatrixBase::PlainObject MatrixBase::normalized() const { typedef typename internal::nested::type Nested; typedef typename internal::remove_reference::type _Nested; _Nested n(derived()); return n / n.norm(); } /** Normalizes the vector, i.e. divides it by its own norm. * * \only_for_vectors * * \sa norm(), normalized() */ template inline void MatrixBase::normalize() { *this /= norm(); } //---------- implementation of other norms ---------- namespace internal { template struct lpNorm_selector { typedef typename NumTraits::Scalar>::Real RealScalar; static inline RealScalar run(const MatrixBase& m) { return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p); } }; template struct lpNorm_selector { static inline typename NumTraits::Scalar>::Real run(const MatrixBase& m) { return m.cwiseAbs().sum(); } }; template struct lpNorm_selector { static inline typename NumTraits::Scalar>::Real run(const MatrixBase& m) { return m.norm(); } }; template struct lpNorm_selector { static inline typename NumTraits::Scalar>::Real run(const MatrixBase& m) { return m.cwiseAbs().maxCoeff(); } }; } // end namespace internal /** \returns the \f$ \ell^p \f$ norm of *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values * of the coefficients of *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$ * norm, that is the maximum of the absolute values of the coefficients of *this. * * \sa norm() */ template template inline typename NumTraits::Scalar>::Real MatrixBase::lpNorm() const { return internal::lpNorm_selector::run(*this); } //---------- implementation of isOrthogonal / isUnitary ---------- /** \returns true if *this is approximately orthogonal to \a other, * within the precision given by \a prec. * * Example: \include MatrixBase_isOrthogonal.cpp * Output: \verbinclude MatrixBase_isOrthogonal.out */ template template bool MatrixBase::isOrthogonal (const MatrixBase& other, RealScalar prec) const { typename internal::nested::type nested(derived()); typename internal::nested::type otherNested(other.derived()); return internal::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm(); } /** \returns true if *this is approximately an unitary matrix, * within the precision given by \a prec. In the case where the \a Scalar * type is real numbers, a unitary matrix is an orthogonal matrix, whence the name. * * \note This can be used to check whether a family of vectors forms an orthonormal basis. * Indeed, \c m.isUnitary() returns true if and only if the columns (equivalently, the rows) of m form an * orthonormal basis. * * Example: \include MatrixBase_isUnitary.cpp * Output: \verbinclude MatrixBase_isUnitary.out */ template bool MatrixBase::isUnitary(RealScalar prec) const { typename Derived::Nested nested(derived()); for(Index i = 0; i < cols(); ++i) { if(!internal::isApprox(nested.col(i).squaredNorm(), static_cast(1), prec)) return false; for(Index j = 0; j < i; ++j) if(!internal::isMuchSmallerThan(nested.col(i).dot(nested.col(j)), static_cast(1), prec)) return false; } return true; } } // end namespace Eigen #endif // EIGEN_DOT_H ctbnrle.1.1.1/hdr/Eigen/src/Core/Random.h0000775000175000017500000001402012107032422020233 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_RANDOM_H #define EIGEN_RANDOM_H namespace Eigen { namespace internal { template struct scalar_random_op { EIGEN_EMPTY_STRUCT_CTOR(scalar_random_op) template inline const Scalar operator() (Index, Index = 0) const { return random(); } }; template struct functor_traits > { enum { Cost = 5 * NumTraits::MulCost, PacketAccess = false, IsRepeatable = false }; }; } // end namespace internal /** \returns a random matrix expression * * The parameters \a rows and \a cols are the number of rows and of columns of * the returned matrix. Must be compatible with this MatrixBase type. * * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, * it is redundant to pass \a rows and \a cols as arguments, so Random() should be used * instead. * * Example: \include MatrixBase_random_int_int.cpp * Output: \verbinclude MatrixBase_random_int_int.out * * This expression has the "evaluate before nesting" flag so that it will be evaluated into * a temporary matrix whenever it is nested in a larger expression. This prevents unexpected * behavior with expressions involving random matrices. * * \sa MatrixBase::setRandom(), MatrixBase::Random(Index), MatrixBase::Random() */ template inline const CwiseNullaryOp::Scalar>, Derived> DenseBase::Random(Index rows, Index cols) { return NullaryExpr(rows, cols, internal::scalar_random_op()); } /** \returns a random vector expression * * The parameter \a size is the size of the returned vector. * Must be compatible with this MatrixBase type. * * \only_for_vectors * * This variant is meant to be used for dynamic-size vector types. For fixed-size types, * it is redundant to pass \a size as argument, so Random() should be used * instead. * * Example: \include MatrixBase_random_int.cpp * Output: \verbinclude MatrixBase_random_int.out * * This expression has the "evaluate before nesting" flag so that it will be evaluated into * a temporary vector whenever it is nested in a larger expression. This prevents unexpected * behavior with expressions involving random matrices. * * \sa MatrixBase::setRandom(), MatrixBase::Random(Index,Index), MatrixBase::Random() */ template inline const CwiseNullaryOp::Scalar>, Derived> DenseBase::Random(Index size) { return NullaryExpr(size, internal::scalar_random_op()); } /** \returns a fixed-size random matrix or vector expression * * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you * need to use the variants taking size arguments. * * Example: \include MatrixBase_random.cpp * Output: \verbinclude MatrixBase_random.out * * This expression has the "evaluate before nesting" flag so that it will be evaluated into * a temporary matrix whenever it is nested in a larger expression. This prevents unexpected * behavior with expressions involving random matrices. * * \sa MatrixBase::setRandom(), MatrixBase::Random(Index,Index), MatrixBase::Random(Index) */ template inline const CwiseNullaryOp::Scalar>, Derived> DenseBase::Random() { return NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_random_op()); } /** Sets all coefficients in this expression to random values. * * Example: \include MatrixBase_setRandom.cpp * Output: \verbinclude MatrixBase_setRandom.out * * \sa class CwiseNullaryOp, setRandom(Index), setRandom(Index,Index) */ template inline Derived& DenseBase::setRandom() { return *this = Random(rows(), cols()); } /** Resizes to the given \a size, and sets all coefficients in this expression to random values. * * \only_for_vectors * * Example: \include Matrix_setRandom_int.cpp * Output: \verbinclude Matrix_setRandom_int.out * * \sa MatrixBase::setRandom(), setRandom(Index,Index), class CwiseNullaryOp, MatrixBase::Random() */ template EIGEN_STRONG_INLINE Derived& PlainObjectBase::setRandom(Index size) { resize(size); return setRandom(); } /** Resizes to the given size, and sets all coefficients in this expression to random values. * * \param rows the new number of rows * \param cols the new number of columns * * Example: \include Matrix_setRandom_int_int.cpp * Output: \verbinclude Matrix_setRandom_int_int.out * * \sa MatrixBase::setRandom(), setRandom(Index), class CwiseNullaryOp, MatrixBase::Random() */ template EIGEN_STRONG_INLINE Derived& PlainObjectBase::setRandom(Index rows, Index cols) { resize(rows, cols); return setRandom(); } } // end namespace Eigen #endif // EIGEN_RANDOM_H ctbnrle.1.1.1/hdr/Eigen/src/Core/DenseBase.h0000775000175000017500000005755012107032422020663 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2007-2010 Benoit Jacob // Copyright (C) 2008-2010 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_DENSEBASE_H #define EIGEN_DENSEBASE_H namespace Eigen { /** \class DenseBase * \ingroup Core_Module * * \brief Base class for all dense matrices, vectors, and arrays * * This class is the base that is inherited by all dense objects (matrix, vector, arrays, * and related expression types). The common Eigen API for dense objects is contained in this class. * * \tparam Derived is the derived type, e.g., a matrix type or an expression. * * This class can be extended with the help of the plugin mechanism described on the page * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN. * * \sa \ref TopicClassHierarchy */ template class DenseBase #ifndef EIGEN_PARSED_BY_DOXYGEN : public internal::special_scalar_op_base::Scalar, typename NumTraits::Scalar>::Real> #else : public DenseCoeffsBase #endif // not EIGEN_PARSED_BY_DOXYGEN { public: using internal::special_scalar_op_base::Scalar, typename NumTraits::Scalar>::Real>::operator*; class InnerIterator; typedef typename internal::traits::StorageKind StorageKind; /** \brief The type of indices * \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE. * \sa \ref TopicPreprocessorDirectives. */ typedef typename internal::traits::Index Index; typedef typename internal::traits::Scalar Scalar; typedef typename internal::packet_traits::type PacketScalar; typedef typename NumTraits::Real RealScalar; typedef DenseCoeffsBase Base; using Base::derived; using Base::const_cast_derived; using Base::rows; using Base::cols; using Base::size; using Base::rowIndexByOuterInner; using Base::colIndexByOuterInner; using Base::coeff; using Base::coeffByOuterInner; using Base::packet; using Base::packetByOuterInner; using Base::writePacket; using Base::writePacketByOuterInner; using Base::coeffRef; using Base::coeffRefByOuterInner; using Base::copyCoeff; using Base::copyCoeffByOuterInner; using Base::copyPacket; using Base::copyPacketByOuterInner; using Base::operator(); using Base::operator[]; using Base::x; using Base::y; using Base::z; using Base::w; using Base::stride; using Base::innerStride; using Base::outerStride; using Base::rowStride; using Base::colStride; typedef typename Base::CoeffReturnType CoeffReturnType; enum { RowsAtCompileTime = internal::traits::RowsAtCompileTime, /**< The number of rows at compile-time. This is just a copy of the value provided * by the \a Derived type. If a value is not known at compile-time, * it is set to the \a Dynamic constant. * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */ ColsAtCompileTime = internal::traits::ColsAtCompileTime, /**< The number of columns at compile-time. This is just a copy of the value provided * by the \a Derived type. If a value is not known at compile-time, * it is set to the \a Dynamic constant. * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */ SizeAtCompileTime = (internal::size_at_compile_time::RowsAtCompileTime, internal::traits::ColsAtCompileTime>::ret), /**< This is equal to the number of coefficients, i.e. the number of * rows times the number of columns, or to \a Dynamic if this is not * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */ MaxRowsAtCompileTime = internal::traits::MaxRowsAtCompileTime, /**< This value is equal to the maximum possible number of rows that this expression * might have. If this expression might have an arbitrarily high number of rows, * this value is set to \a Dynamic. * * This value is useful to know when evaluating an expression, in order to determine * whether it is possible to avoid doing a dynamic memory allocation. * * \sa RowsAtCompileTime, MaxColsAtCompileTime, MaxSizeAtCompileTime */ MaxColsAtCompileTime = internal::traits::MaxColsAtCompileTime, /**< This value is equal to the maximum possible number of columns that this expression * might have. If this expression might have an arbitrarily high number of columns, * this value is set to \a Dynamic. * * This value is useful to know when evaluating an expression, in order to determine * whether it is possible to avoid doing a dynamic memory allocation. * * \sa ColsAtCompileTime, MaxRowsAtCompileTime, MaxSizeAtCompileTime */ MaxSizeAtCompileTime = (internal::size_at_compile_time::MaxRowsAtCompileTime, internal::traits::MaxColsAtCompileTime>::ret), /**< This value is equal to the maximum possible number of coefficients that this expression * might have. If this expression might have an arbitrarily high number of coefficients, * this value is set to \a Dynamic. * * This value is useful to know when evaluating an expression, in order to determine * whether it is possible to avoid doing a dynamic memory allocation. * * \sa SizeAtCompileTime, MaxRowsAtCompileTime, MaxColsAtCompileTime */ IsVectorAtCompileTime = internal::traits::MaxRowsAtCompileTime == 1 || internal::traits::MaxColsAtCompileTime == 1, /**< This is set to true if either the number of rows or the number of * columns is known at compile-time to be equal to 1. Indeed, in that case, * we are dealing with a column-vector (if there is only one column) or with * a row-vector (if there is only one row). */ Flags = internal::traits::Flags, /**< This stores expression \ref flags flags which may or may not be inherited by new expressions * constructed from this one. See the \ref flags "list of flags". */ IsRowMajor = int(Flags) & RowMajorBit, /**< True if this expression has row-major storage order. */ InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime) : int(IsRowMajor) ? int(ColsAtCompileTime) : int(RowsAtCompileTime), CoeffReadCost = internal::traits::CoeffReadCost, /**< This is a rough measure of how expensive it is to read one coefficient from * this expression. */ InnerStrideAtCompileTime = internal::inner_stride_at_compile_time::ret, OuterStrideAtCompileTime = internal::outer_stride_at_compile_time::ret }; enum { ThisConstantIsPrivateInPlainObjectBase }; /** \returns the number of nonzero coefficients which is in practice the number * of stored coefficients. */ inline Index nonZeros() const { return size(); } /** \returns true if either the number of rows or the number of columns is equal to 1. * In other words, this function returns * \code rows()==1 || cols()==1 \endcode * \sa rows(), cols(), IsVectorAtCompileTime. */ /** \returns the outer size. * * \note For a vector, this returns just 1. For a matrix (non-vector), this is the major dimension * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of columns for a * column-major matrix, and the number of rows for a row-major matrix. */ Index outerSize() const { return IsVectorAtCompileTime ? 1 : int(IsRowMajor) ? this->rows() : this->cols(); } /** \returns the inner size. * * \note For a vector, this is just the size. For a matrix (non-vector), this is the minor dimension * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of rows for a * column-major matrix, and the number of columns for a row-major matrix. */ Index innerSize() const { return IsVectorAtCompileTime ? this->size() : int(IsRowMajor) ? this->cols() : this->rows(); } /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does * nothing else. */ void resize(Index size) { EIGEN_ONLY_USED_FOR_DEBUG(size); eigen_assert(size == this->size() && "DenseBase::resize() does not actually allow to resize."); } /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does * nothing else. */ void resize(Index rows, Index cols) { EIGEN_ONLY_USED_FOR_DEBUG(rows); EIGEN_ONLY_USED_FOR_DEBUG(cols); eigen_assert(rows == this->rows() && cols == this->cols() && "DenseBase::resize() does not actually allow to resize."); } #ifndef EIGEN_PARSED_BY_DOXYGEN /** \internal Represents a matrix with all coefficients equal to one another*/ typedef CwiseNullaryOp,Derived> ConstantReturnType; /** \internal Represents a vector with linearly spaced coefficients that allows sequential access only. */ typedef CwiseNullaryOp,Derived> SequentialLinSpacedReturnType; /** \internal Represents a vector with linearly spaced coefficients that allows random access. */ typedef CwiseNullaryOp,Derived> RandomAccessLinSpacedReturnType; /** \internal the return type of MatrixBase::eigenvalues() */ typedef Matrix::Scalar>::Real, internal::traits::ColsAtCompileTime, 1> EigenvaluesReturnType; #endif // not EIGEN_PARSED_BY_DOXYGEN /** Copies \a other into *this. \returns a reference to *this. */ template Derived& operator=(const DenseBase& other); /** Special case of the template operator=, in order to prevent the compiler * from generating a default operator= (issue hit with g++ 4.1) */ Derived& operator=(const DenseBase& other); template Derived& operator=(const EigenBase &other); template Derived& operator+=(const EigenBase &other); template Derived& operator-=(const EigenBase &other); template Derived& operator=(const ReturnByValue& func); #ifndef EIGEN_PARSED_BY_DOXYGEN /** Copies \a other into *this without evaluating other. \returns a reference to *this. */ template Derived& lazyAssign(const DenseBase& other); #endif // not EIGEN_PARSED_BY_DOXYGEN CommaInitializer operator<< (const Scalar& s); template const Flagged flagged() const; template CommaInitializer operator<< (const DenseBase& other); Eigen::Transpose transpose(); typedef const Transpose ConstTransposeReturnType; ConstTransposeReturnType transpose() const; void transposeInPlace(); #ifndef EIGEN_NO_DEBUG protected: template void checkTransposeAliasing(const OtherDerived& other) const; public: #endif typedef VectorBlock SegmentReturnType; typedef const VectorBlock ConstSegmentReturnType; template struct FixedSegmentReturnType { typedef VectorBlock Type; }; template struct ConstFixedSegmentReturnType { typedef const VectorBlock Type; }; // Note: The "DenseBase::" prefixes are added to help MSVC9 to match these declarations with the later implementations. SegmentReturnType segment(Index start, Index size); typename DenseBase::ConstSegmentReturnType segment(Index start, Index size) const; SegmentReturnType head(Index size); typename DenseBase::ConstSegmentReturnType head(Index size) const; SegmentReturnType tail(Index size); typename DenseBase::ConstSegmentReturnType tail(Index size) const; template typename FixedSegmentReturnType::Type head(); template typename ConstFixedSegmentReturnType::Type head() const; template typename FixedSegmentReturnType::Type tail(); template typename ConstFixedSegmentReturnType::Type tail() const; template typename FixedSegmentReturnType::Type segment(Index start); template typename ConstFixedSegmentReturnType::Type segment(Index start) const; static const ConstantReturnType Constant(Index rows, Index cols, const Scalar& value); static const ConstantReturnType Constant(Index size, const Scalar& value); static const ConstantReturnType Constant(const Scalar& value); static const SequentialLinSpacedReturnType LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high); static const RandomAccessLinSpacedReturnType LinSpaced(Index size, const Scalar& low, const Scalar& high); static const SequentialLinSpacedReturnType LinSpaced(Sequential_t, const Scalar& low, const Scalar& high); static const RandomAccessLinSpacedReturnType LinSpaced(const Scalar& low, const Scalar& high); template static const CwiseNullaryOp NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func); template static const CwiseNullaryOp NullaryExpr(Index size, const CustomNullaryOp& func); template static const CwiseNullaryOp NullaryExpr(const CustomNullaryOp& func); static const ConstantReturnType Zero(Index rows, Index cols); static const ConstantReturnType Zero(Index size); static const ConstantReturnType Zero(); static const ConstantReturnType Ones(Index rows, Index cols); static const ConstantReturnType Ones(Index size); static const ConstantReturnType Ones(); void fill(const Scalar& value); Derived& setConstant(const Scalar& value); Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high); Derived& setLinSpaced(const Scalar& low, const Scalar& high); Derived& setZero(); Derived& setOnes(); Derived& setRandom(); template bool isApprox(const DenseBase& other, RealScalar prec = NumTraits::dummy_precision()) const; bool isMuchSmallerThan(const RealScalar& other, RealScalar prec = NumTraits::dummy_precision()) const; template bool isMuchSmallerThan(const DenseBase& other, RealScalar prec = NumTraits::dummy_precision()) const; bool isApproxToConstant(const Scalar& value, RealScalar prec = NumTraits::dummy_precision()) const; bool isConstant(const Scalar& value, RealScalar prec = NumTraits::dummy_precision()) const; bool isZero(RealScalar prec = NumTraits::dummy_precision()) const; bool isOnes(RealScalar prec = NumTraits::dummy_precision()) const; inline Derived& operator*=(const Scalar& other); inline Derived& operator/=(const Scalar& other); typedef typename internal::add_const_on_value_type::type>::type EvalReturnType; /** \returns the matrix or vector obtained by evaluating this expression. * * Notice that in the case of a plain matrix or vector (not an expression) this function just returns * a const reference, in order to avoid a useless copy. */ EIGEN_STRONG_INLINE EvalReturnType eval() const { // Even though MSVC does not honor strong inlining when the return type // is a dynamic matrix, we desperately need strong inlining for fixed // size types on MSVC. return typename internal::eval::type(derived()); } /** swaps *this with the expression \a other. * */ template void swap(const DenseBase& other, int = OtherDerived::ThisConstantIsPrivateInPlainObjectBase) { SwapWrapper(derived()).lazyAssign(other.derived()); } /** swaps *this with the matrix or array \a other. * */ template void swap(PlainObjectBase& other) { SwapWrapper(derived()).lazyAssign(other.derived()); } inline const NestByValue nestByValue() const; inline const ForceAlignedAccess forceAlignedAccess() const; inline ForceAlignedAccess forceAlignedAccess(); template inline const typename internal::conditional,Derived&>::type forceAlignedAccessIf() const; template inline typename internal::conditional,Derived&>::type forceAlignedAccessIf(); Scalar sum() const; Scalar mean() const; Scalar trace() const; Scalar prod() const; typename internal::traits::Scalar minCoeff() const; typename internal::traits::Scalar maxCoeff() const; template typename internal::traits::Scalar minCoeff(IndexType* row, IndexType* col) const; template typename internal::traits::Scalar maxCoeff(IndexType* row, IndexType* col) const; template typename internal::traits::Scalar minCoeff(IndexType* index) const; template typename internal::traits::Scalar maxCoeff(IndexType* index) const; template typename internal::result_of::Scalar)>::type redux(const BinaryOp& func) const; template void visit(Visitor& func) const; inline const WithFormat format(const IOFormat& fmt) const; /** \returns the unique coefficient of a 1x1 expression */ CoeffReturnType value() const { EIGEN_STATIC_ASSERT_SIZE_1x1(Derived) eigen_assert(this->rows() == 1 && this->cols() == 1); return derived().coeff(0,0); } /////////// Array module /////////// bool all(void) const; bool any(void) const; Index count() const; typedef VectorwiseOp RowwiseReturnType; typedef const VectorwiseOp ConstRowwiseReturnType; typedef VectorwiseOp ColwiseReturnType; typedef const VectorwiseOp ConstColwiseReturnType; ConstRowwiseReturnType rowwise() const; RowwiseReturnType rowwise(); ConstColwiseReturnType colwise() const; ColwiseReturnType colwise(); static const CwiseNullaryOp,Derived> Random(Index rows, Index cols); static const CwiseNullaryOp,Derived> Random(Index size); static const CwiseNullaryOp,Derived> Random(); template const Select select(const DenseBase& thenMatrix, const DenseBase& elseMatrix) const; template inline const Select select(const DenseBase& thenMatrix, typename ThenDerived::Scalar elseScalar) const; template inline const Select select(typename ElseDerived::Scalar thenScalar, const DenseBase& elseMatrix) const; template RealScalar lpNorm() const; template const Replicate replicate() const; const Replicate replicate(Index rowFacor,Index colFactor) const; typedef Reverse ReverseReturnType; typedef const Reverse ConstReverseReturnType; ReverseReturnType reverse(); ConstReverseReturnType reverse() const; void reverseInPlace(); #define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::DenseBase # include "../plugins/BlockMethods.h" # ifdef EIGEN_DENSEBASE_PLUGIN # include EIGEN_DENSEBASE_PLUGIN # endif #undef EIGEN_CURRENT_STORAGE_BASE_CLASS #ifdef EIGEN2_SUPPORT Block corner(CornerType type, Index cRows, Index cCols); const Block corner(CornerType type, Index cRows, Index cCols) const; template Block corner(CornerType type); template const Block corner(CornerType type) const; #endif // EIGEN2_SUPPORT // disable the use of evalTo for dense objects with a nice compilation error template inline void evalTo(Dest& ) const { EIGEN_STATIC_ASSERT((internal::is_same::value),THE_EVAL_EVALTO_FUNCTION_SHOULD_NEVER_BE_CALLED_FOR_DENSE_OBJECTS); } protected: /** Default constructor. Do nothing. */ DenseBase() { /* Just checks for self-consistency of the flags. * Only do it when debugging Eigen, as this borders on paranoiac and could slow compilation down */ #ifdef EIGEN_INTERNAL_DEBUGGING EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, int(IsRowMajor)) && EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, int(!IsRowMajor))), INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION) #endif } private: explicit DenseBase(int); DenseBase(int,int); template explicit DenseBase(const DenseBase&); }; } // end namespace Eigen #endif // EIGEN_DENSEBASE_H ctbnrle.1.1.1/hdr/Eigen/src/Core/NoAlias.h0000775000175000017500000001340412107032422020346 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_NOALIAS_H #define EIGEN_NOALIAS_H namespace Eigen { /** \class NoAlias * \ingroup Core_Module * * \brief Pseudo expression providing an operator = assuming no aliasing * * \param ExpressionType the type of the object on which to do the lazy assignment * * This class represents an expression with special assignment operators * assuming no aliasing between the target expression and the source expression. * More precisely it alloas to bypass the EvalBeforeAssignBit flag of the source expression. * It is the return type of MatrixBase::noalias() * and most of the time this is the only way it is used. * * \sa MatrixBase::noalias() */ template class StorageBase> class NoAlias { typedef typename ExpressionType::Scalar Scalar; public: NoAlias(ExpressionType& expression) : m_expression(expression) {} /** Behaves like MatrixBase::lazyAssign(other) * \sa MatrixBase::lazyAssign() */ template EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase& other) { return internal::assign_selector::run(m_expression,other.derived()); } /** \sa MatrixBase::operator+= */ template EIGEN_STRONG_INLINE ExpressionType& operator+=(const StorageBase& other) { typedef SelfCwiseBinaryOp, ExpressionType, OtherDerived> SelfAdder; SelfAdder tmp(m_expression); typedef typename internal::nested::type OtherDerivedNested; typedef typename internal::remove_all::type _OtherDerivedNested; internal::assign_selector::run(tmp,OtherDerivedNested(other.derived())); return m_expression; } /** \sa MatrixBase::operator-= */ template EIGEN_STRONG_INLINE ExpressionType& operator-=(const StorageBase& other) { typedef SelfCwiseBinaryOp, ExpressionType, OtherDerived> SelfAdder; SelfAdder tmp(m_expression); typedef typename internal::nested::type OtherDerivedNested; typedef typename internal::remove_all::type _OtherDerivedNested; internal::assign_selector::run(tmp,OtherDerivedNested(other.derived())); return m_expression; } #ifndef EIGEN_PARSED_BY_DOXYGEN template EIGEN_STRONG_INLINE ExpressionType& operator+=(const ProductBase& other) { other.derived().addTo(m_expression); return m_expression; } template EIGEN_STRONG_INLINE ExpressionType& operator-=(const ProductBase& other) { other.derived().subTo(m_expression); return m_expression; } template EIGEN_STRONG_INLINE ExpressionType& operator+=(const CoeffBasedProduct& other) { return m_expression.derived() += CoeffBasedProduct(other.lhs(), other.rhs()); } template EIGEN_STRONG_INLINE ExpressionType& operator-=(const CoeffBasedProduct& other) { return m_expression.derived() -= CoeffBasedProduct(other.lhs(), other.rhs()); } #endif protected: ExpressionType& m_expression; }; /** \returns a pseudo expression of \c *this with an operator= assuming * no aliasing between \c *this and the source expression. * * More precisely, noalias() allows to bypass the EvalBeforeAssignBit flag. * Currently, even though several expressions may alias, only product * expressions have this flag. Therefore, noalias() is only usefull when * the source expression contains a matrix product. * * Here are some examples where noalias is usefull: * \code * D.noalias() = A * B; * D.noalias() += A.transpose() * B; * D.noalias() -= 2 * A * B.adjoint(); * \endcode * * On the other hand the following example will lead to a \b wrong result: * \code * A.noalias() = A * B; * \endcode * because the result matrix A is also an operand of the matrix product. Therefore, * there is no alternative than evaluating A * B in a temporary, that is the default * behavior when you write: * \code * A = A * B; * \endcode * * \sa class NoAlias */ template NoAlias MatrixBase::noalias() { return derived(); } } // end namespace Eigen #endif // EIGEN_NOALIAS_H ctbnrle.1.1.1/hdr/Eigen/src/Core/ArrayWrapper.h0000775000175000017500000001726512107032422021450 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009-2010 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_ARRAYWRAPPER_H #define EIGEN_ARRAYWRAPPER_H namespace Eigen { /** \class ArrayWrapper * \ingroup Core_Module * * \brief Expression of a mathematical vector or matrix as an array object * * This class is the return type of MatrixBase::array(), and most of the time * this is the only way it is use. * * \sa MatrixBase::array(), class MatrixWrapper */ namespace internal { template struct traits > : public traits::type > { typedef ArrayXpr XprKind; }; } template class ArrayWrapper : public ArrayBase > { public: typedef ArrayBase Base; EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper) EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper) typedef typename internal::conditional< internal::is_lvalue::value, Scalar, const Scalar >::type ScalarWithConstIfNotLvalue; typedef typename internal::nested::type NestedExpressionType; inline ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {} inline Index rows() const { return m_expression.rows(); } inline Index cols() const { return m_expression.cols(); } inline Index outerStride() const { return m_expression.outerStride(); } inline Index innerStride() const { return m_expression.innerStride(); } inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); } inline const Scalar* data() const { return m_expression.data(); } inline CoeffReturnType coeff(Index row, Index col) const { return m_expression.coeff(row, col); } inline Scalar& coeffRef(Index row, Index col) { return m_expression.const_cast_derived().coeffRef(row, col); } inline const Scalar& coeffRef(Index row, Index col) const { return m_expression.const_cast_derived().coeffRef(row, col); } inline CoeffReturnType coeff(Index index) const { return m_expression.coeff(index); } inline Scalar& coeffRef(Index index) { return m_expression.const_cast_derived().coeffRef(index); } inline const Scalar& coeffRef(Index index) const { return m_expression.const_cast_derived().coeffRef(index); } template inline const PacketScalar packet(Index row, Index col) const { return m_expression.template packet(row, col); } template inline void writePacket(Index row, Index col, const PacketScalar& x) { m_expression.const_cast_derived().template writePacket(row, col, x); } template inline const PacketScalar packet(Index index) const { return m_expression.template packet(index); } template inline void writePacket(Index index, const PacketScalar& x) { m_expression.const_cast_derived().template writePacket(index, x); } template inline void evalTo(Dest& dst) const { dst = m_expression; } const typename internal::remove_all::type& nestedExpression() const { return m_expression; } protected: NestedExpressionType m_expression; }; /** \class MatrixWrapper * \ingroup Core_Module * * \brief Expression of an array as a mathematical vector or matrix * * This class is the return type of ArrayBase::matrix(), and most of the time * this is the only way it is use. * * \sa MatrixBase::matrix(), class ArrayWrapper */ namespace internal { template struct traits > : public traits::type > { typedef MatrixXpr XprKind; }; } template class MatrixWrapper : public MatrixBase > { public: typedef MatrixBase > Base; EIGEN_DENSE_PUBLIC_INTERFACE(MatrixWrapper) EIGEN_INHERIT_ASSIGNMENT_OPERATORS(MatrixWrapper) typedef typename internal::conditional< internal::is_lvalue::value, Scalar, const Scalar >::type ScalarWithConstIfNotLvalue; typedef typename internal::nested::type NestedExpressionType; inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {} inline Index rows() const { return m_expression.rows(); } inline Index cols() const { return m_expression.cols(); } inline Index outerStride() const { return m_expression.outerStride(); } inline Index innerStride() const { return m_expression.innerStride(); } inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); } inline const Scalar* data() const { return m_expression.data(); } inline CoeffReturnType coeff(Index row, Index col) const { return m_expression.coeff(row, col); } inline Scalar& coeffRef(Index row, Index col) { return m_expression.const_cast_derived().coeffRef(row, col); } inline const Scalar& coeffRef(Index row, Index col) const { return m_expression.derived().coeffRef(row, col); } inline CoeffReturnType coeff(Index index) const { return m_expression.coeff(index); } inline Scalar& coeffRef(Index index) { return m_expression.const_cast_derived().coeffRef(index); } inline const Scalar& coeffRef(Index index) const { return m_expression.const_cast_derived().coeffRef(index); } template inline const PacketScalar packet(Index row, Index col) const { return m_expression.template packet(row, col); } template inline void writePacket(Index row, Index col, const PacketScalar& x) { m_expression.const_cast_derived().template writePacket(row, col, x); } template inline const PacketScalar packet(Index index) const { return m_expression.template packet(index); } template inline void writePacket(Index index, const PacketScalar& x) { m_expression.const_cast_derived().template writePacket(index, x); } const typename internal::remove_all::type& nestedExpression() const { return m_expression; } protected: NestedExpressionType m_expression; }; } // end namespace Eigen #endif // EIGEN_ARRAYWRAPPER_H ctbnrle.1.1.1/hdr/Eigen/src/Core/Array.h0000775000175000017500000003013112107032422020072 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_ARRAY_H #define EIGEN_ARRAY_H namespace Eigen { /** \class Array * \ingroup Core_Module * * \brief General-purpose arrays with easy API for coefficient-wise operations * * The %Array class is very similar to the Matrix class. It provides * general-purpose one- and two-dimensional arrays. The difference between the * %Array and the %Matrix class is primarily in the API: the API for the * %Array class provides easy access to coefficient-wise operations, while the * API for the %Matrix class provides easy access to linear-algebra * operations. * * This class can be extended with the help of the plugin mechanism described on the page * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN. * * \sa \ref TutorialArrayClass, \ref TopicClassHierarchy */ namespace internal { template struct traits > : traits > { typedef ArrayXpr XprKind; typedef ArrayBase > XprBase; }; } template class Array : public PlainObjectBase > { public: typedef PlainObjectBase Base; EIGEN_DENSE_PUBLIC_INTERFACE(Array) enum { Options = _Options }; typedef typename Base::PlainObject PlainObject; protected: template friend struct internal::conservative_resize_like_impl; using Base::m_storage; public: using Base::base; using Base::coeff; using Base::coeffRef; /** * The usage of * using Base::operator=; * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped * the usage of 'using'. This should be done only for operator=. */ template EIGEN_STRONG_INLINE Array& operator=(const EigenBase &other) { return Base::operator=(other); } /** Copies the value of the expression \a other into \c *this with automatic resizing. * * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized), * it will be initialized. * * Note that copying a row-vector into a vector (and conversely) is allowed. * The resizing, if any, is then done in the appropriate way so that row-vectors * remain row-vectors and vectors remain vectors. */ template EIGEN_STRONG_INLINE Array& operator=(const ArrayBase& other) { return Base::_set(other); } /** This is a special case of the templated operator=. Its purpose is to * prevent a default operator= from hiding the templated operator=. */ EIGEN_STRONG_INLINE Array& operator=(const Array& other) { return Base::_set(other); } /** Default constructor. * * For fixed-size matrices, does nothing. * * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix * is called a null matrix. This constructor is the unique way to create null matrices: resizing * a matrix to 0 is not supported. * * \sa resize(Index,Index) */ EIGEN_STRONG_INLINE explicit Array() : Base() { Base::_check_template_params(); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED } #ifndef EIGEN_PARSED_BY_DOXYGEN // FIXME is it still needed ?? /** \internal */ Array(internal::constructor_without_unaligned_array_assert) : Base(internal::constructor_without_unaligned_array_assert()) { Base::_check_template_params(); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED } #endif /** Constructs a vector or row-vector with given dimension. \only_for_vectors * * Note that this is only useful for dynamic-size vectors. For fixed-size vectors, * it is redundant to pass the dimension here, so it makes more sense to use the default * constructor Matrix() instead. */ EIGEN_STRONG_INLINE explicit Array(Index dim) : Base(dim, RowsAtCompileTime == 1 ? 1 : dim, ColsAtCompileTime == 1 ? 1 : dim) { Base::_check_template_params(); EIGEN_STATIC_ASSERT_VECTOR_ONLY(Array) eigen_assert(dim >= 0); eigen_assert(SizeAtCompileTime == Dynamic || SizeAtCompileTime == dim); EIGEN_INITIALIZE_BY_ZERO_IF_THAT_OPTION_IS_ENABLED } #ifndef EIGEN_PARSED_BY_DOXYGEN template EIGEN_STRONG_INLINE Array(const T0& x, const T1& y) { Base::_check_template_params(); this->template _init2(x, y); } #else /** constructs an uninitialized matrix with \a rows rows and \a cols columns. * * This is useful for dynamic-size matrices. For fixed-size matrices, * it is redundant to pass these parameters, so one should use the default constructor * Matrix() instead. */ Array(Index rows, Index cols); /** constructs an initialized 2D vector with given coefficients */ Array(const Scalar& x, const Scalar& y); #endif /** constructs an initialized 3D vector with given coefficients */ EIGEN_STRONG_INLINE Array(const Scalar& x, const Scalar& y, const Scalar& z) { Base::_check_template_params(); EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3) m_storage.data()[0] = x; m_storage.data()[1] = y; m_storage.data()[2] = z; } /** constructs an initialized 4D vector with given coefficients */ EIGEN_STRONG_INLINE Array(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w) { Base::_check_template_params(); EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4) m_storage.data()[0] = x; m_storage.data()[1] = y; m_storage.data()[2] = z; m_storage.data()[3] = w; } explicit Array(const Scalar *data); /** Constructor copying the value of the expression \a other */ template EIGEN_STRONG_INLINE Array(const ArrayBase& other) : Base(other.rows() * other.cols(), other.rows(), other.cols()) { Base::_check_template_params(); Base::_set_noalias(other); } /** Copy constructor */ EIGEN_STRONG_INLINE Array(const Array& other) : Base(other.rows() * other.cols(), other.rows(), other.cols()) { Base::_check_template_params(); Base::_set_noalias(other); } /** Copy constructor with in-place evaluation */ template EIGEN_STRONG_INLINE Array(const ReturnByValue& other) { Base::_check_template_params(); Base::resize(other.rows(), other.cols()); other.evalTo(*this); } /** \sa MatrixBase::operator=(const EigenBase&) */ template EIGEN_STRONG_INLINE Array(const EigenBase &other) : Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols()) { Base::_check_template_params(); Base::resize(other.rows(), other.cols()); *this = other; } /** Override MatrixBase::swap() since for dynamic-sized matrices of same type it is enough to swap the * data pointers. */ template void swap(ArrayBase const & other) { this->_swap(other.derived()); } inline Index innerStride() const { return 1; } inline Index outerStride() const { return this->innerSize(); } #ifdef EIGEN_ARRAY_PLUGIN #include EIGEN_ARRAY_PLUGIN #endif private: template friend struct internal::matrix_swap_impl; }; /** \defgroup arraytypedefs Global array typedefs * \ingroup Core_Module * * Eigen defines several typedef shortcuts for most common 1D and 2D array types. * * The general patterns are the following: * * \c ArrayRowsColsType where \c Rows and \c Cols can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size, * and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd * for complex double. * * For example, \c Array33d is a fixed-size 3x3 array type of doubles, and \c ArrayXXf is a dynamic-size matrix of floats. * * There are also \c ArraySizeType which are self-explanatory. For example, \c Array4cf is * a fixed-size 1D array of 4 complex floats. * * \sa class Array */ #define EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix) \ /** \ingroup arraytypedefs */ \ typedef Array Array##SizeSuffix##SizeSuffix##TypeSuffix; \ /** \ingroup arraytypedefs */ \ typedef Array Array##SizeSuffix##TypeSuffix; #define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, Size) \ /** \ingroup arraytypedefs */ \ typedef Array Array##Size##X##TypeSuffix; \ /** \ingroup arraytypedefs */ \ typedef Array Array##X##Size##TypeSuffix; #define EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \ EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 2, 2) \ EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 3, 3) \ EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 4, 4) \ EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \ EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \ EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \ EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 4) EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(int, i) EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(float, f) EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(double, d) EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex, cf) EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex, cd) #undef EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES #undef EIGEN_MAKE_ARRAY_TYPEDEFS #undef EIGEN_MAKE_ARRAY_TYPEDEFS_LARGE #define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \ using Eigen::Matrix##SizeSuffix##TypeSuffix; \ using Eigen::Vector##SizeSuffix##TypeSuffix; \ using Eigen::RowVector##SizeSuffix##TypeSuffix; #define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(TypeSuffix) \ EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \ EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \ EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \ EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X) \ #define EIGEN_USING_ARRAY_TYPEDEFS \ EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(i) \ EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(f) \ EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(d) \ EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cf) \ EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cd) } // end namespace Eigen #endif // EIGEN_ARRAY_H ctbnrle.1.1.1/hdr/Eigen/src/Core/CwiseNullaryOp.h0000775000175000017500000007245012107032422021746 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2010 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_CWISE_NULLARY_OP_H #define EIGEN_CWISE_NULLARY_OP_H namespace Eigen { /** \class CwiseNullaryOp * \ingroup Core_Module * * \brief Generic expression of a matrix where all coefficients are defined by a functor * * \param NullaryOp template functor implementing the operator * \param PlainObjectType the underlying plain matrix/array type * * This class represents an expression of a generic nullary operator. * It is the return type of the Ones(), Zero(), Constant(), Identity() and Random() methods, * and most of the time this is the only way it is used. * * However, if you want to write a function returning such an expression, you * will need to use this class. * * \sa class CwiseUnaryOp, class CwiseBinaryOp, DenseBase::NullaryExpr() */ namespace internal { template struct traits > : traits { enum { Flags = (traits::Flags & ( HereditaryBits | (functor_has_linear_access::ret ? LinearAccessBit : 0) | (functor_traits::PacketAccess ? PacketAccessBit : 0))) | (functor_traits::IsRepeatable ? 0 : EvalBeforeNestingBit), CoeffReadCost = functor_traits::Cost }; }; } template class CwiseNullaryOp : internal::no_assignment_operator, public internal::dense_xpr_base< CwiseNullaryOp >::type { public: typedef typename internal::dense_xpr_base::type Base; EIGEN_DENSE_PUBLIC_INTERFACE(CwiseNullaryOp) CwiseNullaryOp(Index rows, Index cols, const NullaryOp& func = NullaryOp()) : m_rows(rows), m_cols(cols), m_functor(func) { eigen_assert(rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows) && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)); } EIGEN_STRONG_INLINE Index rows() const { return m_rows.value(); } EIGEN_STRONG_INLINE Index cols() const { return m_cols.value(); } EIGEN_STRONG_INLINE const Scalar coeff(Index rows, Index cols) const { return m_functor(rows, cols); } template EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const { return m_functor.packetOp(row, col); } EIGEN_STRONG_INLINE const Scalar coeff(Index index) const { return m_functor(index); } template EIGEN_STRONG_INLINE PacketScalar packet(Index index) const { return m_functor.packetOp(index); } /** \returns the functor representing the nullary operation */ const NullaryOp& functor() const { return m_functor; } protected: const internal::variable_if_dynamic m_rows; const internal::variable_if_dynamic m_cols; const NullaryOp m_functor; }; /** \returns an expression of a matrix defined by a custom functor \a func * * The parameters \a rows and \a cols are the number of rows and of columns of * the returned matrix. Must be compatible with this MatrixBase type. * * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used * instead. * * The template parameter \a CustomNullaryOp is the type of the functor. * * \sa class CwiseNullaryOp */ template template EIGEN_STRONG_INLINE const CwiseNullaryOp DenseBase::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func) { return CwiseNullaryOp(rows, cols, func); } /** \returns an expression of a matrix defined by a custom functor \a func * * The parameter \a size is the size of the returned vector. * Must be compatible with this MatrixBase type. * * \only_for_vectors * * This variant is meant to be used for dynamic-size vector types. For fixed-size types, * it is redundant to pass \a size as argument, so Zero() should be used * instead. * * The template parameter \a CustomNullaryOp is the type of the functor. * * \sa class CwiseNullaryOp */ template template EIGEN_STRONG_INLINE const CwiseNullaryOp DenseBase::NullaryExpr(Index size, const CustomNullaryOp& func) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) if(RowsAtCompileTime == 1) return CwiseNullaryOp(1, size, func); else return CwiseNullaryOp(size, 1, func); } /** \returns an expression of a matrix defined by a custom functor \a func * * This variant is only for fixed-size DenseBase types. For dynamic-size types, you * need to use the variants taking size arguments. * * The template parameter \a CustomNullaryOp is the type of the functor. * * \sa class CwiseNullaryOp */ template template EIGEN_STRONG_INLINE const CwiseNullaryOp DenseBase::NullaryExpr(const CustomNullaryOp& func) { return CwiseNullaryOp(RowsAtCompileTime, ColsAtCompileTime, func); } /** \returns an expression of a constant matrix of value \a value * * The parameters \a rows and \a cols are the number of rows and of columns of * the returned matrix. Must be compatible with this DenseBase type. * * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used * instead. * * The template parameter \a CustomNullaryOp is the type of the functor. * * \sa class CwiseNullaryOp */ template EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType DenseBase::Constant(Index rows, Index cols, const Scalar& value) { return DenseBase::NullaryExpr(rows, cols, internal::scalar_constant_op(value)); } /** \returns an expression of a constant matrix of value \a value * * The parameter \a size is the size of the returned vector. * Must be compatible with this DenseBase type. * * \only_for_vectors * * This variant is meant to be used for dynamic-size vector types. For fixed-size types, * it is redundant to pass \a size as argument, so Zero() should be used * instead. * * The template parameter \a CustomNullaryOp is the type of the functor. * * \sa class CwiseNullaryOp */ template EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType DenseBase::Constant(Index size, const Scalar& value) { return DenseBase::NullaryExpr(size, internal::scalar_constant_op(value)); } /** \returns an expression of a constant matrix of value \a value * * This variant is only for fixed-size DenseBase types. For dynamic-size types, you * need to use the variants taking size arguments. * * The template parameter \a CustomNullaryOp is the type of the functor. * * \sa class CwiseNullaryOp */ template EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType DenseBase::Constant(const Scalar& value) { EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived) return DenseBase::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_constant_op(value)); } /** * \brief Sets a linearly space vector. * * The function generates 'size' equally spaced values in the closed interval [low,high]. * This particular version of LinSpaced() uses sequential access, i.e. vector access is * assumed to be a(0), a(1), ..., a(size). This assumption allows for better vectorization * and yields faster code than the random access version. * * When size is set to 1, a vector of length 1 containing 'high' is returned. * * \only_for_vectors * * Example: \include DenseBase_LinSpaced_seq.cpp * Output: \verbinclude DenseBase_LinSpaced_seq.out * * \sa setLinSpaced(Index,const Scalar&,const Scalar&), LinSpaced(Index,Scalar,Scalar), CwiseNullaryOp */ template EIGEN_STRONG_INLINE const typename DenseBase::SequentialLinSpacedReturnType DenseBase::LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return DenseBase::NullaryExpr(size, internal::linspaced_op(low,high,size)); } /** * \copydoc DenseBase::LinSpaced(Sequential_t, Index, const Scalar&, const Scalar&) * Special version for fixed size types which does not require the size parameter. */ template EIGEN_STRONG_INLINE const typename DenseBase::SequentialLinSpacedReturnType DenseBase::LinSpaced(Sequential_t, const Scalar& low, const Scalar& high) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived) return DenseBase::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op(low,high,Derived::SizeAtCompileTime)); } /** * \brief Sets a linearly space vector. * * The function generates 'size' equally spaced values in the closed interval [low,high]. * When size is set to 1, a vector of length 1 containing 'high' is returned. * * \only_for_vectors * * Example: \include DenseBase_LinSpaced.cpp * Output: \verbinclude DenseBase_LinSpaced.out * * \sa setLinSpaced(Index,const Scalar&,const Scalar&), LinSpaced(Sequential_t,Index,const Scalar&,const Scalar&,Index), CwiseNullaryOp */ template EIGEN_STRONG_INLINE const typename DenseBase::RandomAccessLinSpacedReturnType DenseBase::LinSpaced(Index size, const Scalar& low, const Scalar& high) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return DenseBase::NullaryExpr(size, internal::linspaced_op(low,high,size)); } /** * \copydoc DenseBase::LinSpaced(Index, const Scalar&, const Scalar&) * Special version for fixed size types which does not require the size parameter. */ template EIGEN_STRONG_INLINE const typename DenseBase::RandomAccessLinSpacedReturnType DenseBase::LinSpaced(const Scalar& low, const Scalar& high) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived) return DenseBase::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op(low,high,Derived::SizeAtCompileTime)); } /** \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */ template bool DenseBase::isApproxToConstant (const Scalar& value, RealScalar prec) const { for(Index j = 0; j < cols(); ++j) for(Index i = 0; i < rows(); ++i) if(!internal::isApprox(this->coeff(i, j), value, prec)) return false; return true; } /** This is just an alias for isApproxToConstant(). * * \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */ template bool DenseBase::isConstant (const Scalar& value, RealScalar prec) const { return isApproxToConstant(value, prec); } /** Alias for setConstant(): sets all coefficients in this expression to \a value. * * \sa setConstant(), Constant(), class CwiseNullaryOp */ template EIGEN_STRONG_INLINE void DenseBase::fill(const Scalar& value) { setConstant(value); } /** Sets all coefficients in this expression to \a value. * * \sa fill(), setConstant(Index,const Scalar&), setConstant(Index,Index,const Scalar&), setZero(), setOnes(), Constant(), class CwiseNullaryOp, setZero(), setOnes() */ template EIGEN_STRONG_INLINE Derived& DenseBase::setConstant(const Scalar& value) { return derived() = Constant(rows(), cols(), value); } /** Resizes to the given \a size, and sets all coefficients in this expression to the given \a value. * * \only_for_vectors * * Example: \include Matrix_setConstant_int.cpp * Output: \verbinclude Matrix_setConstant_int.out * * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&) */ template EIGEN_STRONG_INLINE Derived& PlainObjectBase::setConstant(Index size, const Scalar& value) { resize(size); return setConstant(value); } /** Resizes to the given size, and sets all coefficients in this expression to the given \a value. * * \param rows the new number of rows * \param cols the new number of columns * \param value the value to which all coefficients are set * * Example: \include Matrix_setConstant_int_int.cpp * Output: \verbinclude Matrix_setConstant_int_int.out * * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&) */ template EIGEN_STRONG_INLINE Derived& PlainObjectBase::setConstant(Index rows, Index cols, const Scalar& value) { resize(rows, cols); return setConstant(value); } /** * \brief Sets a linearly space vector. * * The function generates 'size' equally spaced values in the closed interval [low,high]. * When size is set to 1, a vector of length 1 containing 'high' is returned. * * \only_for_vectors * * Example: \include DenseBase_setLinSpaced.cpp * Output: \verbinclude DenseBase_setLinSpaced.out * * \sa CwiseNullaryOp */ template EIGEN_STRONG_INLINE Derived& DenseBase::setLinSpaced(Index size, const Scalar& low, const Scalar& high) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return derived() = Derived::NullaryExpr(size, internal::linspaced_op(low,high,size)); } /** * \brief Sets a linearly space vector. * * The function fill *this with equally spaced values in the closed interval [low,high]. * When size is set to 1, a vector of length 1 containing 'high' is returned. * * \only_for_vectors * * \sa setLinSpaced(Index, const Scalar&, const Scalar&), CwiseNullaryOp */ template EIGEN_STRONG_INLINE Derived& DenseBase::setLinSpaced(const Scalar& low, const Scalar& high) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return setLinSpaced(size(), low, high); } // zero: /** \returns an expression of a zero matrix. * * The parameters \a rows and \a cols are the number of rows and of columns of * the returned matrix. Must be compatible with this MatrixBase type. * * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used * instead. * * Example: \include MatrixBase_zero_int_int.cpp * Output: \verbinclude MatrixBase_zero_int_int.out * * \sa Zero(), Zero(Index) */ template EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType DenseBase::Zero(Index rows, Index cols) { return Constant(rows, cols, Scalar(0)); } /** \returns an expression of a zero vector. * * The parameter \a size is the size of the returned vector. * Must be compatible with this MatrixBase type. * * \only_for_vectors * * This variant is meant to be used for dynamic-size vector types. For fixed-size types, * it is redundant to pass \a size as argument, so Zero() should be used * instead. * * Example: \include MatrixBase_zero_int.cpp * Output: \verbinclude MatrixBase_zero_int.out * * \sa Zero(), Zero(Index,Index) */ template EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType DenseBase::Zero(Index size) { return Constant(size, Scalar(0)); } /** \returns an expression of a fixed-size zero matrix or vector. * * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you * need to use the variants taking size arguments. * * Example: \include MatrixBase_zero.cpp * Output: \verbinclude MatrixBase_zero.out * * \sa Zero(Index), Zero(Index,Index) */ template EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType DenseBase::Zero() { return Constant(Scalar(0)); } /** \returns true if *this is approximately equal to the zero matrix, * within the precision given by \a prec. * * Example: \include MatrixBase_isZero.cpp * Output: \verbinclude MatrixBase_isZero.out * * \sa class CwiseNullaryOp, Zero() */ template bool DenseBase::isZero(RealScalar prec) const { for(Index j = 0; j < cols(); ++j) for(Index i = 0; i < rows(); ++i) if(!internal::isMuchSmallerThan(this->coeff(i, j), static_cast(1), prec)) return false; return true; } /** Sets all coefficients in this expression to zero. * * Example: \include MatrixBase_setZero.cpp * Output: \verbinclude MatrixBase_setZero.out * * \sa class CwiseNullaryOp, Zero() */ template EIGEN_STRONG_INLINE Derived& DenseBase::setZero() { return setConstant(Scalar(0)); } /** Resizes to the given \a size, and sets all coefficients in this expression to zero. * * \only_for_vectors * * Example: \include Matrix_setZero_int.cpp * Output: \verbinclude Matrix_setZero_int.out * * \sa DenseBase::setZero(), setZero(Index,Index), class CwiseNullaryOp, DenseBase::Zero() */ template EIGEN_STRONG_INLINE Derived& PlainObjectBase::setZero(Index size) { resize(size); return setConstant(Scalar(0)); } /** Resizes to the given size, and sets all coefficients in this expression to zero. * * \param rows the new number of rows * \param cols the new number of columns * * Example: \include Matrix_setZero_int_int.cpp * Output: \verbinclude Matrix_setZero_int_int.out * * \sa DenseBase::setZero(), setZero(Index), class CwiseNullaryOp, DenseBase::Zero() */ template EIGEN_STRONG_INLINE Derived& PlainObjectBase::setZero(Index rows, Index cols) { resize(rows, cols); return setConstant(Scalar(0)); } // ones: /** \returns an expression of a matrix where all coefficients equal one. * * The parameters \a rows and \a cols are the number of rows and of columns of * the returned matrix. Must be compatible with this MatrixBase type. * * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, * it is redundant to pass \a rows and \a cols as arguments, so Ones() should be used * instead. * * Example: \include MatrixBase_ones_int_int.cpp * Output: \verbinclude MatrixBase_ones_int_int.out * * \sa Ones(), Ones(Index), isOnes(), class Ones */ template EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType DenseBase::Ones(Index rows, Index cols) { return Constant(rows, cols, Scalar(1)); } /** \returns an expression of a vector where all coefficients equal one. * * The parameter \a size is the size of the returned vector. * Must be compatible with this MatrixBase type. * * \only_for_vectors * * This variant is meant to be used for dynamic-size vector types. For fixed-size types, * it is redundant to pass \a size as argument, so Ones() should be used * instead. * * Example: \include MatrixBase_ones_int.cpp * Output: \verbinclude MatrixBase_ones_int.out * * \sa Ones(), Ones(Index,Index), isOnes(), class Ones */ template EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType DenseBase::Ones(Index size) { return Constant(size, Scalar(1)); } /** \returns an expression of a fixed-size matrix or vector where all coefficients equal one. * * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you * need to use the variants taking size arguments. * * Example: \include MatrixBase_ones.cpp * Output: \verbinclude MatrixBase_ones.out * * \sa Ones(Index), Ones(Index,Index), isOnes(), class Ones */ template EIGEN_STRONG_INLINE const typename DenseBase::ConstantReturnType DenseBase::Ones() { return Constant(Scalar(1)); } /** \returns true if *this is approximately equal to the matrix where all coefficients * are equal to 1, within the precision given by \a prec. * * Example: \include MatrixBase_isOnes.cpp * Output: \verbinclude MatrixBase_isOnes.out * * \sa class CwiseNullaryOp, Ones() */ template bool DenseBase::isOnes (RealScalar prec) const { return isApproxToConstant(Scalar(1), prec); } /** Sets all coefficients in this expression to one. * * Example: \include MatrixBase_setOnes.cpp * Output: \verbinclude MatrixBase_setOnes.out * * \sa class CwiseNullaryOp, Ones() */ template EIGEN_STRONG_INLINE Derived& DenseBase::setOnes() { return setConstant(Scalar(1)); } /** Resizes to the given \a size, and sets all coefficients in this expression to one. * * \only_for_vectors * * Example: \include Matrix_setOnes_int.cpp * Output: \verbinclude Matrix_setOnes_int.out * * \sa MatrixBase::setOnes(), setOnes(Index,Index), class CwiseNullaryOp, MatrixBase::Ones() */ template EIGEN_STRONG_INLINE Derived& PlainObjectBase::setOnes(Index size) { resize(size); return setConstant(Scalar(1)); } /** Resizes to the given size, and sets all coefficients in this expression to one. * * \param rows the new number of rows * \param cols the new number of columns * * Example: \include Matrix_setOnes_int_int.cpp * Output: \verbinclude Matrix_setOnes_int_int.out * * \sa MatrixBase::setOnes(), setOnes(Index), class CwiseNullaryOp, MatrixBase::Ones() */ template EIGEN_STRONG_INLINE Derived& PlainObjectBase::setOnes(Index rows, Index cols) { resize(rows, cols); return setConstant(Scalar(1)); } // Identity: /** \returns an expression of the identity matrix (not necessarily square). * * The parameters \a rows and \a cols are the number of rows and of columns of * the returned matrix. Must be compatible with this MatrixBase type. * * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, * it is redundant to pass \a rows and \a cols as arguments, so Identity() should be used * instead. * * Example: \include MatrixBase_identity_int_int.cpp * Output: \verbinclude MatrixBase_identity_int_int.out * * \sa Identity(), setIdentity(), isIdentity() */ template EIGEN_STRONG_INLINE const typename MatrixBase::IdentityReturnType MatrixBase::Identity(Index rows, Index cols) { return DenseBase::NullaryExpr(rows, cols, internal::scalar_identity_op()); } /** \returns an expression of the identity matrix (not necessarily square). * * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you * need to use the variant taking size arguments. * * Example: \include MatrixBase_identity.cpp * Output: \verbinclude MatrixBase_identity.out * * \sa Identity(Index,Index), setIdentity(), isIdentity() */ template EIGEN_STRONG_INLINE const typename MatrixBase::IdentityReturnType MatrixBase::Identity() { EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived) return MatrixBase::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_identity_op()); } /** \returns true if *this is approximately equal to the identity matrix * (not necessarily square), * within the precision given by \a prec. * * Example: \include MatrixBase_isIdentity.cpp * Output: \verbinclude MatrixBase_isIdentity.out * * \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), setIdentity() */ template bool MatrixBase::isIdentity (RealScalar prec) const { for(Index j = 0; j < cols(); ++j) { for(Index i = 0; i < rows(); ++i) { if(i == j) { if(!internal::isApprox(this->coeff(i, j), static_cast(1), prec)) return false; } else { if(!internal::isMuchSmallerThan(this->coeff(i, j), static_cast(1), prec)) return false; } } } return true; } namespace internal { template=16)> struct setIdentity_impl { static EIGEN_STRONG_INLINE Derived& run(Derived& m) { return m = Derived::Identity(m.rows(), m.cols()); } }; template struct setIdentity_impl { typedef typename Derived::Index Index; static EIGEN_STRONG_INLINE Derived& run(Derived& m) { m.setZero(); const Index size = (std::min)(m.rows(), m.cols()); for(Index i = 0; i < size; ++i) m.coeffRef(i,i) = typename Derived::Scalar(1); return m; } }; } // end namespace internal /** Writes the identity expression (not necessarily square) into *this. * * Example: \include MatrixBase_setIdentity.cpp * Output: \verbinclude MatrixBase_setIdentity.out * * \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), isIdentity() */ template EIGEN_STRONG_INLINE Derived& MatrixBase::setIdentity() { return internal::setIdentity_impl::run(derived()); } /** \brief Resizes to the given size, and writes the identity expression (not necessarily square) into *this. * * \param rows the new number of rows * \param cols the new number of columns * * Example: \include Matrix_setIdentity_int_int.cpp * Output: \verbinclude Matrix_setIdentity_int_int.out * * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Identity() */ template EIGEN_STRONG_INLINE Derived& MatrixBase::setIdentity(Index rows, Index cols) { derived().resize(rows, cols); return setIdentity(); } /** \returns an expression of the i-th unit (basis) vector. * * \only_for_vectors * * \sa MatrixBase::Unit(Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() */ template EIGEN_STRONG_INLINE const typename MatrixBase::BasisReturnType MatrixBase::Unit(Index size, Index i) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return BasisReturnType(SquareMatrixType::Identity(size,size), i); } /** \returns an expression of the i-th unit (basis) vector. * * \only_for_vectors * * This variant is for fixed-size vector only. * * \sa MatrixBase::Unit(Index,Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() */ template EIGEN_STRONG_INLINE const typename MatrixBase::BasisReturnType MatrixBase::Unit(Index i) { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) return BasisReturnType(SquareMatrixType::Identity(),i); } /** \returns an expression of the X axis unit vector (1{,0}^*) * * \only_for_vectors * * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() */ template EIGEN_STRONG_INLINE const typename MatrixBase::BasisReturnType MatrixBase::UnitX() { return Derived::Unit(0); } /** \returns an expression of the Y axis unit vector (0,1{,0}^*) * * \only_for_vectors * * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() */ template EIGEN_STRONG_INLINE const typename MatrixBase::BasisReturnType MatrixBase::UnitY() { return Derived::Unit(1); } /** \returns an expression of the Z axis unit vector (0,0,1{,0}^*) * * \only_for_vectors * * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() */ template EIGEN_STRONG_INLINE const typename MatrixBase::BasisReturnType MatrixBase::UnitZ() { return Derived::Unit(2); } /** \returns an expression of the W axis unit vector (0,0,0,1) * * \only_for_vectors * * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() */ template EIGEN_STRONG_INLINE const typename MatrixBase::BasisReturnType MatrixBase::UnitW() { return Derived::Unit(3); } } // end namespace Eigen #endif // EIGEN_CWISE_NULLARY_OP_H ctbnrle.1.1.1/hdr/Eigen/src/Core/StableNorm.h0000775000175000017500000001560312107032422021071 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_STABLENORM_H #define EIGEN_STABLENORM_H namespace Eigen { namespace internal { template inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& scale, Scalar& invScale) { Scalar max = bl.cwiseAbs().maxCoeff(); if (max>scale) { ssq = ssq * abs2(scale/max); scale = max; invScale = Scalar(1)/scale; } // TODO if the max is much much smaller than the current scale, // then we can neglect this sub vector ssq += (bl*invScale).squaredNorm(); } } /** \returns the \em l2 norm of \c *this avoiding underflow and overflow. * This version use a blockwise two passes algorithm: * 1 - find the absolute largest coefficient \c s * 2 - compute \f$ s \Vert \frac{*this}{s} \Vert \f$ in a standard way * * For architecture/scalar types supporting vectorization, this version * is faster than blueNorm(). Otherwise the blueNorm() is much faster. * * \sa norm(), blueNorm(), hypotNorm() */ template inline typename NumTraits::Scalar>::Real MatrixBase::stableNorm() const { using std::min; const Index blockSize = 4096; RealScalar scale(0); RealScalar invScale(1); RealScalar ssq(0); // sum of square enum { Alignment = (int(Flags)&DirectAccessBit) || (int(Flags)&AlignedBit) ? 1 : 0 }; Index n = size(); Index bi = internal::first_aligned(derived()); if (bi>0) internal::stable_norm_kernel(this->head(bi), ssq, scale, invScale); for (; bisegment(bi,(min)(blockSize, n - bi)).template forceAlignedAccessIf(), ssq, scale, invScale); return scale * internal::sqrt(ssq); } /** \returns the \em l2 norm of \c *this using the Blue's algorithm. * A Portable Fortran Program to Find the Euclidean Norm of a Vector, * ACM TOMS, Vol 4, Issue 1, 1978. * * For architecture/scalar types without vectorization, this version * is much faster than stableNorm(). Otherwise the stableNorm() is faster. * * \sa norm(), stableNorm(), hypotNorm() */ template inline typename NumTraits::Scalar>::Real MatrixBase::blueNorm() const { using std::pow; using std::min; using std::max; static Index nmax = -1; static RealScalar b1, b2, s1m, s2m, overfl, rbig, relerr; if(nmax <= 0) { int nbig, ibeta, it, iemin, iemax, iexp; RealScalar abig, eps; // This program calculates the machine-dependent constants // bl, b2, slm, s2m, relerr overfl, nmax // from the "basic" machine-dependent numbers // nbig, ibeta, it, iemin, iemax, rbig. // The following define the basic machine-dependent constants. // For portability, the PORT subprograms "ilmaeh" and "rlmach" // are used. For any specific computer, each of the assignment // statements can be replaced nbig = (std::numeric_limits::max)(); // largest integer ibeta = std::numeric_limits::radix; // base for floating-point numbers it = std::numeric_limits::digits; // number of base-beta digits in mantissa iemin = std::numeric_limits::min_exponent; // minimum exponent iemax = std::numeric_limits::max_exponent; // maximum exponent rbig = (std::numeric_limits::max)(); // largest floating-point number iexp = -((1-iemin)/2); b1 = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // lower boundary of midrange iexp = (iemax + 1 - it)/2; b2 = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // upper boundary of midrange iexp = (2-iemin)/2; s1m = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // scaling factor for lower range iexp = - ((iemax+it)/2); s2m = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // scaling factor for upper range overfl = rbig*s2m; // overflow boundary for abig eps = RealScalar(pow(double(ibeta), 1-it)); relerr = internal::sqrt(eps); // tolerance for neglecting asml abig = RealScalar(1.0/eps - 1.0); if (RealScalar(nbig)>abig) nmax = int(abig); // largest safe n else nmax = nbig; } Index n = size(); RealScalar ab2 = b2 / RealScalar(n); RealScalar asml = RealScalar(0); RealScalar amed = RealScalar(0); RealScalar abig = RealScalar(0); for(Index j=0; j ab2) abig += internal::abs2(ax*s2m); else if(ax < b1) asml += internal::abs2(ax*s1m); else amed += internal::abs2(ax); } if(abig > RealScalar(0)) { abig = internal::sqrt(abig); if(abig > overfl) { eigen_assert(false && "overflow"); return rbig; } if(amed > RealScalar(0)) { abig = abig/s2m; amed = internal::sqrt(amed); } else return abig/s2m; } else if(asml > RealScalar(0)) { if (amed > RealScalar(0)) { abig = internal::sqrt(amed); amed = internal::sqrt(asml) / s1m; } else return internal::sqrt(asml)/s1m; } else return internal::sqrt(amed); asml = (min)(abig, amed); abig = (max)(abig, amed); if(asml <= abig*relerr) return abig; else return abig * internal::sqrt(RealScalar(1) + internal::abs2(asml/abig)); } /** \returns the \em l2 norm of \c *this avoiding undeflow and overflow. * This version use a concatenation of hypot() calls, and it is very slow. * * \sa norm(), stableNorm() */ template inline typename NumTraits::Scalar>::Real MatrixBase::hypotNorm() const { return this->cwiseAbs().redux(internal::scalar_hypot_op()); } } // end namespace Eigen #endif // EIGEN_STABLENORM_H ctbnrle.1.1.1/hdr/Eigen/src/Core/ArrayBase.h0000775000175000017500000002205512107032422020673 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_ARRAYBASE_H #define EIGEN_ARRAYBASE_H namespace Eigen { template class MatrixWrapper; /** \class ArrayBase * \ingroup Core_Module * * \brief Base class for all 1D and 2D array, and related expressions * * An array is similar to a dense vector or matrix. While matrices are mathematical * objects with well defined linear algebra operators, an array is just a collection * of scalar values arranged in a one or two dimensionnal fashion. As the main consequence, * all operations applied to an array are performed coefficient wise. Furthermore, * arrays support scalar math functions of the c++ standard library (e.g., std::sin(x)), and convenient * constructors allowing to easily write generic code working for both scalar values * and arrays. * * This class is the base that is inherited by all array expression types. * * \tparam Derived is the derived type, e.g., an array or an expression type. * * This class can be extended with the help of the plugin mechanism described on the page * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN. * * \sa class MatrixBase, \ref TopicClassHierarchy */ template class ArrayBase : public DenseBase { public: #ifndef EIGEN_PARSED_BY_DOXYGEN /** The base class for a given storage type. */ typedef ArrayBase StorageBaseType; typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl; using internal::special_scalar_op_base::Scalar, typename NumTraits::Scalar>::Real>::operator*; typedef typename internal::traits::StorageKind StorageKind; typedef typename internal::traits::Index Index; typedef typename internal::traits::Scalar Scalar; typedef typename internal::packet_traits::type PacketScalar; typedef typename NumTraits::Real RealScalar; typedef DenseBase Base; using Base::RowsAtCompileTime; using Base::ColsAtCompileTime; using Base::SizeAtCompileTime; using Base::MaxRowsAtCompileTime; using Base::MaxColsAtCompileTime; using Base::MaxSizeAtCompileTime; using Base::IsVectorAtCompileTime; using Base::Flags; using Base::CoeffReadCost; using Base::derived; using Base::const_cast_derived; using Base::rows; using Base::cols; using Base::size; using Base::coeff; using Base::coeffRef; using Base::lazyAssign; using Base::operator=; using Base::operator+=; using Base::operator-=; using Base::operator*=; using Base::operator/=; typedef typename Base::CoeffReturnType CoeffReturnType; #endif // not EIGEN_PARSED_BY_DOXYGEN #ifndef EIGEN_PARSED_BY_DOXYGEN /** \internal the plain matrix type corresponding to this expression. Note that is not necessarily * exactly the return type of eval(): in the case of plain matrices, the return type of eval() is a const * reference to a matrix, not a matrix! It is however guaranteed that the return type of eval() is either * PlainObject or const PlainObject&. */ typedef Array::Scalar, internal::traits::RowsAtCompileTime, internal::traits::ColsAtCompileTime, AutoAlign | (internal::traits::Flags&RowMajorBit ? RowMajor : ColMajor), internal::traits::MaxRowsAtCompileTime, internal::traits::MaxColsAtCompileTime > PlainObject; /** \internal Represents a matrix with all coefficients equal to one another*/ typedef CwiseNullaryOp,Derived> ConstantReturnType; #endif // not EIGEN_PARSED_BY_DOXYGEN #define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::ArrayBase # include "../plugins/CommonCwiseUnaryOps.h" # include "../plugins/MatrixCwiseUnaryOps.h" # include "../plugins/ArrayCwiseUnaryOps.h" # include "../plugins/CommonCwiseBinaryOps.h" # include "../plugins/MatrixCwiseBinaryOps.h" # include "../plugins/ArrayCwiseBinaryOps.h" # ifdef EIGEN_ARRAYBASE_PLUGIN # include EIGEN_ARRAYBASE_PLUGIN # endif #undef EIGEN_CURRENT_STORAGE_BASE_CLASS /** Special case of the template operator=, in order to prevent the compiler * from generating a default operator= (issue hit with g++ 4.1) */ Derived& operator=(const ArrayBase& other) { return internal::assign_selector::run(derived(), other.derived()); } Derived& operator+=(const Scalar& scalar) { return *this = derived() + scalar; } Derived& operator-=(const Scalar& scalar) { return *this = derived() - scalar; } template Derived& operator+=(const ArrayBase& other); template Derived& operator-=(const ArrayBase& other); template Derived& operator*=(const ArrayBase& other); template Derived& operator/=(const ArrayBase& other); public: ArrayBase& array() { return *this; } const ArrayBase& array() const { return *this; } /** \returns an \link MatrixBase Matrix \endlink expression of this array * \sa MatrixBase::array() */ MatrixWrapper matrix() { return derived(); } const MatrixWrapper matrix() const { return derived(); } // template // inline void evalTo(Dest& dst) const { dst = matrix(); } protected: ArrayBase() : Base() {} private: explicit ArrayBase(Index); ArrayBase(Index,Index); template explicit ArrayBase(const ArrayBase&); protected: // mixing arrays and matrices is not legal template Derived& operator+=(const MatrixBase& ) {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;} // mixing arrays and matrices is not legal template Derived& operator-=(const MatrixBase& ) {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;} }; /** replaces \c *this by \c *this - \a other. * * \returns a reference to \c *this */ template template EIGEN_STRONG_INLINE Derived & ArrayBase::operator-=(const ArrayBase &other) { SelfCwiseBinaryOp, Derived, OtherDerived> tmp(derived()); tmp = other.derived(); return derived(); } /** replaces \c *this by \c *this + \a other. * * \returns a reference to \c *this */ template template EIGEN_STRONG_INLINE Derived & ArrayBase::operator+=(const ArrayBase& other) { SelfCwiseBinaryOp, Derived, OtherDerived> tmp(derived()); tmp = other.derived(); return derived(); } /** replaces \c *this by \c *this * \a other coefficient wise. * * \returns a reference to \c *this */ template template EIGEN_STRONG_INLINE Derived & ArrayBase::operator*=(const ArrayBase& other) { SelfCwiseBinaryOp, Derived, OtherDerived> tmp(derived()); tmp = other.derived(); return derived(); } /** replaces \c *this by \c *this / \a other coefficient wise. * * \returns a reference to \c *this */ template template EIGEN_STRONG_INLINE Derived & ArrayBase::operator/=(const ArrayBase& other) { SelfCwiseBinaryOp, Derived, OtherDerived> tmp(derived()); tmp = other.derived(); return derived(); } } // end namespace Eigen #endif // EIGEN_ARRAYBASE_H ctbnrle.1.1.1/hdr/Eigen/src/Core/ProductBase.h0000775000175000017500000002576512107032422021250 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009-2010 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_PRODUCTBASE_H #define EIGEN_PRODUCTBASE_H namespace Eigen { /** \class ProductBase * \ingroup Core_Module * */ namespace internal { template struct traits > { typedef MatrixXpr XprKind; typedef typename remove_all<_Lhs>::type Lhs; typedef typename remove_all<_Rhs>::type Rhs; typedef typename scalar_product_traits::ReturnType Scalar; typedef typename promote_storage_type::StorageKind, typename traits::StorageKind>::ret StorageKind; typedef typename promote_index_type::Index, typename traits::Index>::type Index; enum { RowsAtCompileTime = traits::RowsAtCompileTime, ColsAtCompileTime = traits::ColsAtCompileTime, MaxRowsAtCompileTime = traits::MaxRowsAtCompileTime, MaxColsAtCompileTime = traits::MaxColsAtCompileTime, Flags = (MaxRowsAtCompileTime==1 ? RowMajorBit : 0) | EvalBeforeNestingBit | EvalBeforeAssigningBit | NestByRefBit, // Note that EvalBeforeNestingBit and NestByRefBit // are not used in practice because nested is overloaded for products CoeffReadCost = 0 // FIXME why is it needed ? }; }; } #define EIGEN_PRODUCT_PUBLIC_INTERFACE(Derived) \ typedef ProductBase Base; \ EIGEN_DENSE_PUBLIC_INTERFACE(Derived) \ typedef typename Base::LhsNested LhsNested; \ typedef typename Base::_LhsNested _LhsNested; \ typedef typename Base::LhsBlasTraits LhsBlasTraits; \ typedef typename Base::ActualLhsType ActualLhsType; \ typedef typename Base::_ActualLhsType _ActualLhsType; \ typedef typename Base::RhsNested RhsNested; \ typedef typename Base::_RhsNested _RhsNested; \ typedef typename Base::RhsBlasTraits RhsBlasTraits; \ typedef typename Base::ActualRhsType ActualRhsType; \ typedef typename Base::_ActualRhsType _ActualRhsType; \ using Base::m_lhs; \ using Base::m_rhs; template class ProductBase : public MatrixBase { public: typedef MatrixBase Base; EIGEN_DENSE_PUBLIC_INTERFACE(ProductBase) typedef typename Lhs::Nested LhsNested; typedef typename internal::remove_all::type _LhsNested; typedef internal::blas_traits<_LhsNested> LhsBlasTraits; typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType; typedef typename internal::remove_all::type _ActualLhsType; typedef typename internal::traits::Scalar LhsScalar; typedef typename Rhs::Nested RhsNested; typedef typename internal::remove_all::type _RhsNested; typedef internal::blas_traits<_RhsNested> RhsBlasTraits; typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType; typedef typename internal::remove_all::type _ActualRhsType; typedef typename internal::traits::Scalar RhsScalar; // Diagonal of a product: no need to evaluate the arguments because they are going to be evaluated only once typedef CoeffBasedProduct FullyLazyCoeffBaseProductType; public: typedef typename Base::PlainObject PlainObject; ProductBase(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs) { eigen_assert(lhs.cols() == rhs.rows() && "invalid matrix product" && "if you wanted a coeff-wise or a dot product use the respective explicit functions"); } inline Index rows() const { return m_lhs.rows(); } inline Index cols() const { return m_rhs.cols(); } template inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst,Scalar(1)); } template inline void addTo(Dest& dst) const { scaleAndAddTo(dst,Scalar(1)); } template inline void subTo(Dest& dst) const { scaleAndAddTo(dst,Scalar(-1)); } template inline void scaleAndAddTo(Dest& dst,Scalar alpha) const { derived().scaleAndAddTo(dst,alpha); } const _LhsNested& lhs() const { return m_lhs; } const _RhsNested& rhs() const { return m_rhs; } // Implicit conversion to the nested type (trigger the evaluation of the product) operator const PlainObject& () const { m_result.resize(m_lhs.rows(), m_rhs.cols()); derived().evalTo(m_result); return m_result; } const Diagonal diagonal() const { return FullyLazyCoeffBaseProductType(m_lhs, m_rhs); } template const Diagonal diagonal() const { return FullyLazyCoeffBaseProductType(m_lhs, m_rhs); } const Diagonal diagonal(Index index) const { return FullyLazyCoeffBaseProductType(m_lhs, m_rhs).diagonal(index); } // restrict coeff accessors to 1x1 expressions. No need to care about mutators here since this isnt a Lvalue expression typename Base::CoeffReturnType coeff(Index row, Index col) const { #ifdef EIGEN2_SUPPORT return lhs().row(row).cwiseProduct(rhs().col(col).transpose()).sum(); #else EIGEN_STATIC_ASSERT_SIZE_1x1(Derived) eigen_assert(this->rows() == 1 && this->cols() == 1); Matrix result = *this; return result.coeff(row,col); #endif } typename Base::CoeffReturnType coeff(Index i) const { EIGEN_STATIC_ASSERT_SIZE_1x1(Derived) eigen_assert(this->rows() == 1 && this->cols() == 1); Matrix result = *this; return result.coeff(i); } const Scalar& coeffRef(Index row, Index col) const { EIGEN_STATIC_ASSERT_SIZE_1x1(Derived) eigen_assert(this->rows() == 1 && this->cols() == 1); return derived().coeffRef(row,col); } const Scalar& coeffRef(Index i) const { EIGEN_STATIC_ASSERT_SIZE_1x1(Derived) eigen_assert(this->rows() == 1 && this->cols() == 1); return derived().coeffRef(i); } protected: LhsNested m_lhs; RhsNested m_rhs; mutable PlainObject m_result; }; // here we need to overload the nested rule for products // such that the nested type is a const reference to a plain matrix namespace internal { template struct nested, N, PlainObject> { typedef PlainObject const& type; }; } template class ScaledProduct; // Note that these two operator* functions are not defined as member // functions of ProductBase, because, otherwise we would have to // define all overloads defined in MatrixBase. Furthermore, Using // "using Base::operator*" would not work with MSVC. // // Also note that here we accept any compatible scalar types template const ScaledProduct operator*(const ProductBase& prod, typename Derived::Scalar x) { return ScaledProduct(prod.derived(), x); } template typename internal::enable_if::value, const ScaledProduct >::type operator*(const ProductBase& prod, typename Derived::RealScalar x) { return ScaledProduct(prod.derived(), x); } template const ScaledProduct operator*(typename Derived::Scalar x,const ProductBase& prod) { return ScaledProduct(prod.derived(), x); } template typename internal::enable_if::value, const ScaledProduct >::type operator*(typename Derived::RealScalar x,const ProductBase& prod) { return ScaledProduct(prod.derived(), x); } namespace internal { template struct traits > : traits, typename NestedProduct::_LhsNested, typename NestedProduct::_RhsNested> > { typedef typename traits::StorageKind StorageKind; }; } template class ScaledProduct : public ProductBase, typename NestedProduct::_LhsNested, typename NestedProduct::_RhsNested> { public: typedef ProductBase, typename NestedProduct::_LhsNested, typename NestedProduct::_RhsNested> Base; typedef typename Base::Scalar Scalar; typedef typename Base::PlainObject PlainObject; // EIGEN_PRODUCT_PUBLIC_INTERFACE(ScaledProduct) ScaledProduct(const NestedProduct& prod, Scalar x) : Base(prod.lhs(),prod.rhs()), m_prod(prod), m_alpha(x) {} template inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst, Scalar(1)); } template inline void addTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(1)); } template inline void subTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(-1)); } template inline void scaleAndAddTo(Dest& dst,Scalar alpha) const { m_prod.derived().scaleAndAddTo(dst,alpha * m_alpha); } const Scalar& alpha() const { return m_alpha; } protected: const NestedProduct& m_prod; Scalar m_alpha; }; /** \internal * Overloaded to perform an efficient C = (A*B).lazy() */ template template Derived& MatrixBase::lazyAssign(const ProductBase& other) { other.derived().evalTo(derived()); return derived(); } } // end namespace Eigen #endif // EIGEN_PRODUCTBASE_H ctbnrle.1.1.1/hdr/Eigen/src/Core/Visitor.h0000775000175000017500000001665612107032422020473 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_VISITOR_H #define EIGEN_VISITOR_H namespace Eigen { namespace internal { template struct visitor_impl { enum { col = (UnrollCount-1) / Derived::RowsAtCompileTime, row = (UnrollCount-1) % Derived::RowsAtCompileTime }; static inline void run(const Derived &mat, Visitor& visitor) { visitor_impl::run(mat, visitor); visitor(mat.coeff(row, col), row, col); } }; template struct visitor_impl { static inline void run(const Derived &mat, Visitor& visitor) { return visitor.init(mat.coeff(0, 0), 0, 0); } }; template struct visitor_impl { typedef typename Derived::Index Index; static inline void run(const Derived& mat, Visitor& visitor) { visitor.init(mat.coeff(0,0), 0, 0); for(Index i = 1; i < mat.rows(); ++i) visitor(mat.coeff(i, 0), i, 0); for(Index j = 1; j < mat.cols(); ++j) for(Index i = 0; i < mat.rows(); ++i) visitor(mat.coeff(i, j), i, j); } }; } // end namespace internal /** Applies the visitor \a visitor to the whole coefficients of the matrix or vector. * * The template parameter \a Visitor is the type of the visitor and provides the following interface: * \code * struct MyVisitor { * // called for the first coefficient * void init(const Scalar& value, Index i, Index j); * // called for all other coefficients * void operator() (const Scalar& value, Index i, Index j); * }; * \endcode * * \note compared to one or two \em for \em loops, visitors offer automatic * unrolling for small fixed size matrix. * * \sa minCoeff(Index*,Index*), maxCoeff(Index*,Index*), DenseBase::redux() */ template template void DenseBase::visit(Visitor& visitor) const { enum { unroll = SizeAtCompileTime != Dynamic && CoeffReadCost != Dynamic && (SizeAtCompileTime == 1 || internal::functor_traits::Cost != Dynamic) && SizeAtCompileTime * CoeffReadCost + (SizeAtCompileTime-1) * internal::functor_traits::Cost <= EIGEN_UNROLLING_LIMIT }; return internal::visitor_impl::run(derived(), visitor); } namespace internal { /** \internal * \brief Base class to implement min and max visitors */ template struct coeff_visitor { typedef typename Derived::Index Index; typedef typename Derived::Scalar Scalar; Index row, col; Scalar res; inline void init(const Scalar& value, Index i, Index j) { res = value; row = i; col = j; } }; /** \internal * \brief Visitor computing the min coefficient with its value and coordinates * * \sa DenseBase::minCoeff(Index*, Index*) */ template struct min_coeff_visitor : coeff_visitor { typedef typename Derived::Index Index; typedef typename Derived::Scalar Scalar; void operator() (const Scalar& value, Index i, Index j) { if(value < this->res) { this->res = value; this->row = i; this->col = j; } } }; template struct functor_traits > { enum { Cost = NumTraits::AddCost }; }; /** \internal * \brief Visitor computing the max coefficient with its value and coordinates * * \sa DenseBase::maxCoeff(Index*, Index*) */ template struct max_coeff_visitor : coeff_visitor { typedef typename Derived::Index Index; typedef typename Derived::Scalar Scalar; void operator() (const Scalar& value, Index i, Index j) { if(value > this->res) { this->res = value; this->row = i; this->col = j; } } }; template struct functor_traits > { enum { Cost = NumTraits::AddCost }; }; } // end namespace internal /** \returns the minimum of all coefficients of *this * and puts in *row and *col its location. * * \sa DenseBase::minCoeff(Index*), DenseBase::maxCoeff(Index*,Index*), DenseBase::visitor(), DenseBase::minCoeff() */ template template typename internal::traits::Scalar DenseBase::minCoeff(IndexType* row, IndexType* col) const { internal::min_coeff_visitor minVisitor; this->visit(minVisitor); *row = minVisitor.row; if (col) *col = minVisitor.col; return minVisitor.res; } /** \returns the minimum of all coefficients of *this * and puts in *index its location. * * \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::visitor(), DenseBase::minCoeff() */ template template typename internal::traits::Scalar DenseBase::minCoeff(IndexType* index) const { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) internal::min_coeff_visitor minVisitor; this->visit(minVisitor); *index = (RowsAtCompileTime==1) ? minVisitor.col : minVisitor.row; return minVisitor.res; } /** \returns the maximum of all coefficients of *this * and puts in *row and *col its location. * * \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visitor(), DenseBase::maxCoeff() */ template template typename internal::traits::Scalar DenseBase::maxCoeff(IndexType* row, IndexType* col) const { internal::max_coeff_visitor maxVisitor; this->visit(maxVisitor); *row = maxVisitor.row; if (col) *col = maxVisitor.col; return maxVisitor.res; } /** \returns the maximum of all coefficients of *this * and puts in *index its location. * * \sa DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visitor(), DenseBase::maxCoeff() */ template template typename internal::traits::Scalar DenseBase::maxCoeff(IndexType* index) const { EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) internal::max_coeff_visitor maxVisitor; this->visit(maxVisitor); *index = (RowsAtCompileTime==1) ? maxVisitor.col : maxVisitor.row; return maxVisitor.res; } } // end namespace Eigen #endif // EIGEN_VISITOR_H ctbnrle.1.1.1/hdr/Eigen/src/Core/GenericPacketMath.h0000775000175000017500000002744012107032422022343 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008 Gael Guennebaud // Copyright (C) 2006-2008 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_GENERIC_PACKET_MATH_H #define EIGEN_GENERIC_PACKET_MATH_H namespace Eigen { namespace internal { /** \internal * \file GenericPacketMath.h * * Default implementation for types not supported by the vectorization. * In practice these functions are provided to make easier the writing * of generic vectorized code. */ #ifndef EIGEN_DEBUG_ALIGNED_LOAD #define EIGEN_DEBUG_ALIGNED_LOAD #endif #ifndef EIGEN_DEBUG_UNALIGNED_LOAD #define EIGEN_DEBUG_UNALIGNED_LOAD #endif #ifndef EIGEN_DEBUG_ALIGNED_STORE #define EIGEN_DEBUG_ALIGNED_STORE #endif #ifndef EIGEN_DEBUG_UNALIGNED_STORE #define EIGEN_DEBUG_UNALIGNED_STORE #endif struct default_packet_traits { enum { HasAdd = 1, HasSub = 1, HasMul = 1, HasNegate = 1, HasAbs = 1, HasAbs2 = 1, HasMin = 1, HasMax = 1, HasConj = 1, HasSetLinear = 1, HasDiv = 0, HasSqrt = 0, HasExp = 0, HasLog = 0, HasPow = 0, HasSin = 0, HasCos = 0, HasTan = 0, HasASin = 0, HasACos = 0, HasATan = 0 }; }; template struct packet_traits : default_packet_traits { typedef T type; enum { Vectorizable = 0, size = 1, AlignedOnScalar = 0 }; enum { HasAdd = 0, HasSub = 0, HasMul = 0, HasNegate = 0, HasAbs = 0, HasAbs2 = 0, HasMin = 0, HasMax = 0, HasConj = 0, HasSetLinear = 0 }; }; /** \internal \returns a + b (coeff-wise) */ template inline Packet padd(const Packet& a, const Packet& b) { return a+b; } /** \internal \returns a - b (coeff-wise) */ template inline Packet psub(const Packet& a, const Packet& b) { return a-b; } /** \internal \returns -a (coeff-wise) */ template inline Packet pnegate(const Packet& a) { return -a; } /** \internal \returns conj(a) (coeff-wise) */ template inline Packet pconj(const Packet& a) { return conj(a); } /** \internal \returns a * b (coeff-wise) */ template inline Packet pmul(const Packet& a, const Packet& b) { return a*b; } /** \internal \returns a / b (coeff-wise) */ template inline Packet pdiv(const Packet& a, const Packet& b) { return a/b; } /** \internal \returns the min of \a a and \a b (coeff-wise) */ template inline Packet pmin(const Packet& a, const Packet& b) { using std::min; return (min)(a, b); } /** \internal \returns the max of \a a and \a b (coeff-wise) */ template inline Packet pmax(const Packet& a, const Packet& b) { using std::max; return (max)(a, b); } /** \internal \returns the absolute value of \a a */ template inline Packet pabs(const Packet& a) { return abs(a); } /** \internal \returns the bitwise and of \a a and \a b */ template inline Packet pand(const Packet& a, const Packet& b) { return a & b; } /** \internal \returns the bitwise or of \a a and \a b */ template inline Packet por(const Packet& a, const Packet& b) { return a | b; } /** \internal \returns the bitwise xor of \a a and \a b */ template inline Packet pxor(const Packet& a, const Packet& b) { return a ^ b; } /** \internal \returns the bitwise andnot of \a a and \a b */ template inline Packet pandnot(const Packet& a, const Packet& b) { return a & (!b); } /** \internal \returns a packet version of \a *from, from must be 16 bytes aligned */ template inline Packet pload(const typename unpacket_traits::type* from) { return *from; } /** \internal \returns a packet version of \a *from, (un-aligned load) */ template inline Packet ploadu(const typename unpacket_traits::type* from) { return *from; } /** \internal \returns a packet with elements of \a *from duplicated, e.g.: (from[0],from[0],from[1],from[1]) */ template inline Packet ploaddup(const typename unpacket_traits::type* from) { return *from; } /** \internal \returns a packet with constant coefficients \a a, e.g.: (a,a,a,a) */ template inline Packet pset1(const typename unpacket_traits::type& a) { return a; } /** \internal \brief Returns a packet with coefficients (a,a+1,...,a+packet_size-1). */ template inline typename packet_traits::type plset(const Scalar& a) { return a; } /** \internal copy the packet \a from to \a *to, \a to must be 16 bytes aligned */ template inline void pstore(Scalar* to, const Packet& from) { (*to) = from; } /** \internal copy the packet \a from to \a *to, (un-aligned store) */ template inline void pstoreu(Scalar* to, const Packet& from) { (*to) = from; } /** \internal tries to do cache prefetching of \a addr */ template inline void prefetch(const Scalar* addr) { #if !defined(_MSC_VER) __builtin_prefetch(addr); #endif } /** \internal \returns the first element of a packet */ template inline typename unpacket_traits::type pfirst(const Packet& a) { return a; } /** \internal \returns a packet where the element i contains the sum of the packet of \a vec[i] */ template inline Packet preduxp(const Packet* vecs) { return vecs[0]; } /** \internal \returns the sum of the elements of \a a*/ template inline typename unpacket_traits::type predux(const Packet& a) { return a; } /** \internal \returns the product of the elements of \a a*/ template inline typename unpacket_traits::type predux_mul(const Packet& a) { return a; } /** \internal \returns the min of the elements of \a a*/ template inline typename unpacket_traits::type predux_min(const Packet& a) { return a; } /** \internal \returns the max of the elements of \a a*/ template inline typename unpacket_traits::type predux_max(const Packet& a) { return a; } /** \internal \returns the reversed elements of \a a*/ template inline Packet preverse(const Packet& a) { return a; } /** \internal \returns \a a with real and imaginary part flipped (for complex type only) */ template inline Packet pcplxflip(const Packet& a) { return Packet(imag(a),real(a)); } /************************** * Special math functions ***************************/ /** \internal \returns the sine of \a a (coeff-wise) */ template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet psin(const Packet& a) { return sin(a); } /** \internal \returns the cosine of \a a (coeff-wise) */ template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pcos(const Packet& a) { return cos(a); } /** \internal \returns the tan of \a a (coeff-wise) */ template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet ptan(const Packet& a) { return tan(a); } /** \internal \returns the arc sine of \a a (coeff-wise) */ template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pasin(const Packet& a) { return asin(a); } /** \internal \returns the arc cosine of \a a (coeff-wise) */ template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pacos(const Packet& a) { return acos(a); } /** \internal \returns the exp of \a a (coeff-wise) */ template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet pexp(const Packet& a) { return exp(a); } /** \internal \returns the log of \a a (coeff-wise) */ template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet plog(const Packet& a) { return log(a); } /** \internal \returns the square-root of \a a (coeff-wise) */ template EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet psqrt(const Packet& a) { return sqrt(a); } /*************************************************************************** * The following functions might not have to be overwritten for vectorized types ***************************************************************************/ /** \internal copy a packet with constant coeficient \a a (e.g., [a,a,a,a]) to \a *to. \a to must be 16 bytes aligned */ // NOTE: this function must really be templated on the packet type (think about different packet types for the same scalar type) template inline void pstore1(typename unpacket_traits::type* to, const typename unpacket_traits::type& a) { pstore(to, pset1(a)); } /** \internal \returns a * b + c (coeff-wise) */ template inline Packet pmadd(const Packet& a, const Packet& b, const Packet& c) { return padd(pmul(a, b),c); } /** \internal \returns a packet version of \a *from. * If LoadMode equals #Aligned, \a from must be 16 bytes aligned */ template inline Packet ploadt(const typename unpacket_traits::type* from) { if(LoadMode == Aligned) return pload(from); else return ploadu(from); } /** \internal copy the packet \a from to \a *to. * If StoreMode equals #Aligned, \a to must be 16 bytes aligned */ template inline void pstoret(Scalar* to, const Packet& from) { if(LoadMode == Aligned) pstore(to, from); else pstoreu(to, from); } /** \internal default implementation of palign() allowing partial specialization */ template struct palign_impl { // by default data are aligned, so there is nothing to be done :) static inline void run(PacketType&, const PacketType&) {} }; /** \internal update \a first using the concatenation of the \a Offset last elements * of \a first and packet_size minus \a Offset first elements of \a second */ template inline void palign(PacketType& first, const PacketType& second) { palign_impl::run(first,second); } /*************************************************************************** * Fast complex products (GCC generates a function call which is very slow) ***************************************************************************/ template<> inline std::complex pmul(const std::complex& a, const std::complex& b) { return std::complex(real(a)*real(b) - imag(a)*imag(b), imag(a)*real(b) + real(a)*imag(b)); } template<> inline std::complex pmul(const std::complex& a, const std::complex& b) { return std::complex(real(a)*real(b) - imag(a)*imag(b), imag(a)*real(b) + real(a)*imag(b)); } } // end namespace internal } // end namespace Eigen #endif // EIGEN_GENERIC_PACKET_MATH_H ctbnrle.1.1.1/hdr/Eigen/src/Core/Transpose.h0000775000175000017500000003553212107032422021004 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2006-2008 Benoit Jacob // Copyright (C) 2009-2010 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_TRANSPOSE_H #define EIGEN_TRANSPOSE_H namespace Eigen { /** \class Transpose * \ingroup Core_Module * * \brief Expression of the transpose of a matrix * * \param MatrixType the type of the object of which we are taking the transpose * * This class represents an expression of the transpose of a matrix. * It is the return type of MatrixBase::transpose() and MatrixBase::adjoint() * and most of the time this is the only way it is used. * * \sa MatrixBase::transpose(), MatrixBase::adjoint() */ namespace internal { template struct traits > : traits { typedef typename MatrixType::Scalar Scalar; typedef typename nested::type MatrixTypeNested; typedef typename remove_reference::type MatrixTypeNestedPlain; typedef typename traits::StorageKind StorageKind; typedef typename traits::XprKind XprKind; enum { RowsAtCompileTime = MatrixType::ColsAtCompileTime, ColsAtCompileTime = MatrixType::RowsAtCompileTime, MaxRowsAtCompileTime = MatrixType::MaxColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxRowsAtCompileTime, FlagsLvalueBit = is_lvalue::value ? LvalueBit : 0, Flags0 = MatrixTypeNestedPlain::Flags & ~(LvalueBit | NestByRefBit), Flags1 = Flags0 | FlagsLvalueBit, Flags = Flags1 ^ RowMajorBit, CoeffReadCost = MatrixTypeNestedPlain::CoeffReadCost, InnerStrideAtCompileTime = inner_stride_at_compile_time::ret, OuterStrideAtCompileTime = outer_stride_at_compile_time::ret }; }; } template class TransposeImpl; template class Transpose : public TransposeImpl::StorageKind> { public: typedef typename TransposeImpl::StorageKind>::Base Base; EIGEN_GENERIC_PUBLIC_INTERFACE(Transpose) inline Transpose(MatrixType& matrix) : m_matrix(matrix) {} EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Transpose) inline Index rows() const { return m_matrix.cols(); } inline Index cols() const { return m_matrix.rows(); } /** \returns the nested expression */ const typename internal::remove_all::type& nestedExpression() const { return m_matrix; } /** \returns the nested expression */ typename internal::remove_all::type& nestedExpression() { return m_matrix.const_cast_derived(); } protected: typename MatrixType::Nested m_matrix; }; namespace internal { template::ret> struct TransposeImpl_base { typedef typename dense_xpr_base >::type type; }; template struct TransposeImpl_base { typedef typename dense_xpr_base >::type type; }; } // end namespace internal template class TransposeImpl : public internal::TransposeImpl_base::type { public: typedef typename internal::TransposeImpl_base::type Base; EIGEN_DENSE_PUBLIC_INTERFACE(Transpose) inline Index innerStride() const { return derived().nestedExpression().innerStride(); } inline Index outerStride() const { return derived().nestedExpression().outerStride(); } typedef typename internal::conditional< internal::is_lvalue::value, Scalar, const Scalar >::type ScalarWithConstIfNotLvalue; inline ScalarWithConstIfNotLvalue* data() { return derived().nestedExpression().data(); } inline const Scalar* data() const { return derived().nestedExpression().data(); } inline ScalarWithConstIfNotLvalue& coeffRef(Index row, Index col) { EIGEN_STATIC_ASSERT_LVALUE(MatrixType) return derived().nestedExpression().const_cast_derived().coeffRef(col, row); } inline ScalarWithConstIfNotLvalue& coeffRef(Index index) { EIGEN_STATIC_ASSERT_LVALUE(MatrixType) return derived().nestedExpression().const_cast_derived().coeffRef(index); } inline const Scalar& coeffRef(Index row, Index col) const { return derived().nestedExpression().coeffRef(col, row); } inline const Scalar& coeffRef(Index index) const { return derived().nestedExpression().coeffRef(index); } inline CoeffReturnType coeff(Index row, Index col) const { return derived().nestedExpression().coeff(col, row); } inline CoeffReturnType coeff(Index index) const { return derived().nestedExpression().coeff(index); } template inline const PacketScalar packet(Index row, Index col) const { return derived().nestedExpression().template packet(col, row); } template inline void writePacket(Index row, Index col, const PacketScalar& x) { derived().nestedExpression().const_cast_derived().template writePacket(col, row, x); } template inline const PacketScalar packet(Index index) const { return derived().nestedExpression().template packet(index); } template inline void writePacket(Index index, const PacketScalar& x) { derived().nestedExpression().const_cast_derived().template writePacket(index, x); } }; /** \returns an expression of the transpose of *this. * * Example: \include MatrixBase_transpose.cpp * Output: \verbinclude MatrixBase_transpose.out * * \warning If you want to replace a matrix by its own transpose, do \b NOT do this: * \code * m = m.transpose(); // bug!!! caused by aliasing effect * \endcode * Instead, use the transposeInPlace() method: * \code * m.transposeInPlace(); * \endcode * which gives Eigen good opportunities for optimization, or alternatively you can also do: * \code * m = m.transpose().eval(); * \endcode * * \sa transposeInPlace(), adjoint() */ template inline Transpose DenseBase::transpose() { return derived(); } /** This is the const version of transpose(). * * Make sure you read the warning for transpose() ! * * \sa transposeInPlace(), adjoint() */ template inline const typename DenseBase::ConstTransposeReturnType DenseBase::transpose() const { return ConstTransposeReturnType(derived()); } /** \returns an expression of the adjoint (i.e. conjugate transpose) of *this. * * Example: \include MatrixBase_adjoint.cpp * Output: \verbinclude MatrixBase_adjoint.out * * \warning If you want to replace a matrix by its own adjoint, do \b NOT do this: * \code * m = m.adjoint(); // bug!!! caused by aliasing effect * \endcode * Instead, use the adjointInPlace() method: * \code * m.adjointInPlace(); * \endcode * which gives Eigen good opportunities for optimization, or alternatively you can also do: * \code * m = m.adjoint().eval(); * \endcode * * \sa adjointInPlace(), transpose(), conjugate(), class Transpose, class internal::scalar_conjugate_op */ template inline const typename MatrixBase::AdjointReturnType MatrixBase::adjoint() const { return this->transpose(); // in the complex case, the .conjugate() is be implicit here // due to implicit conversion to return type } /*************************************************************************** * "in place" transpose implementation ***************************************************************************/ namespace internal { template struct inplace_transpose_selector; template struct inplace_transpose_selector { // square matrix static void run(MatrixType& m) { m.template triangularView().swap(m.transpose()); } }; template struct inplace_transpose_selector { // non square matrix static void run(MatrixType& m) { if (m.rows()==m.cols()) m.template triangularView().swap(m.transpose()); else m = m.transpose().eval(); } }; } // end namespace internal /** This is the "in place" version of transpose(): it replaces \c *this by its own transpose. * Thus, doing * \code * m.transposeInPlace(); * \endcode * has the same effect on m as doing * \code * m = m.transpose().eval(); * \endcode * and is faster and also safer because in the latter line of code, forgetting the eval() results * in a bug caused by aliasing. * * Notice however that this method is only useful if you want to replace a matrix by its own transpose. * If you just need the transpose of a matrix, use transpose(). * * \note if the matrix is not square, then \c *this must be a resizable matrix. * * \sa transpose(), adjoint(), adjointInPlace() */ template inline void DenseBase::transposeInPlace() { internal::inplace_transpose_selector::run(derived()); } /*************************************************************************** * "in place" adjoint implementation ***************************************************************************/ /** This is the "in place" version of adjoint(): it replaces \c *this by its own transpose. * Thus, doing * \code * m.adjointInPlace(); * \endcode * has the same effect on m as doing * \code * m = m.adjoint().eval(); * \endcode * and is faster and also safer because in the latter line of code, forgetting the eval() results * in a bug caused by aliasing. * * Notice however that this method is only useful if you want to replace a matrix by its own adjoint. * If you just need the adjoint of a matrix, use adjoint(). * * \note if the matrix is not square, then \c *this must be a resizable matrix. * * \sa transpose(), adjoint(), transposeInPlace() */ template inline void MatrixBase::adjointInPlace() { derived() = adjoint().eval(); } #ifndef EIGEN_NO_DEBUG // The following is to detect aliasing problems in most common cases. namespace internal { template struct blas_traits > : blas_traits { typedef SelfCwiseBinaryOp XprType; static inline const XprType extract(const XprType& x) { return x; } }; template struct check_transpose_aliasing_compile_time_selector { enum { ret = bool(blas_traits::IsTransposed) != DestIsTransposed }; }; template struct check_transpose_aliasing_compile_time_selector > { enum { ret = bool(blas_traits::IsTransposed) != DestIsTransposed || bool(blas_traits::IsTransposed) != DestIsTransposed }; }; template struct check_transpose_aliasing_run_time_selector { static bool run(const Scalar* dest, const OtherDerived& src) { return (bool(blas_traits::IsTransposed) != DestIsTransposed) && (dest!=0 && dest==(Scalar*)extract_data(src)); } }; template struct check_transpose_aliasing_run_time_selector > { static bool run(const Scalar* dest, const CwiseBinaryOp& src) { return ((blas_traits::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(Scalar*)extract_data(src.lhs()))) || ((blas_traits::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(Scalar*)extract_data(src.rhs()))); } }; // the following selector, checkTransposeAliasing_impl, based on MightHaveTransposeAliasing, // is because when the condition controlling the assert is known at compile time, ICC emits a warning. // This is actually a good warning: in expressions that don't have any transposing, the condition is // known at compile time to be false, and using that, we can avoid generating the code of the assert again // and again for all these expressions that don't need it. template::IsTransposed,OtherDerived>::ret > struct checkTransposeAliasing_impl { static void run(const Derived& dst, const OtherDerived& other) { eigen_assert((!check_transpose_aliasing_run_time_selector ::IsTransposed,OtherDerived> ::run(extract_data(dst), other)) && "aliasing detected during tranposition, use transposeInPlace() " "or evaluate the rhs into a temporary using .eval()"); } }; template struct checkTransposeAliasing_impl { static void run(const Derived&, const OtherDerived&) { } }; } // end namespace internal template template void DenseBase::checkTransposeAliasing(const OtherDerived& other) const { internal::checkTransposeAliasing_impl::run(derived(), other); } #endif } // end namespace Eigen #endif // EIGEN_TRANSPOSE_H ctbnrle.1.1.1/hdr/Eigen/src/Core/NumTraits.h0000775000175000017500000001543512107032422020754 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2006-2010 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_NUMTRAITS_H #define EIGEN_NUMTRAITS_H namespace Eigen { /** \class NumTraits * \ingroup Core_Module * * \brief Holds information about the various numeric (i.e. scalar) types allowed by Eigen. * * \param T the numeric type at hand * * This class stores enums, typedefs and static methods giving information about a numeric type. * * The provided data consists of: * \li A typedef \a Real, giving the "real part" type of \a T. If \a T is already real, * then \a Real is just a typedef to \a T. If \a T is \c std::complex then \a Real * is a typedef to \a U. * \li A typedef \a NonInteger, giving the type that should be used for operations producing non-integral values, * such as quotients, square roots, etc. If \a T is a floating-point type, then this typedef just gives * \a T again. Note however that many Eigen functions such as internal::sqrt simply refuse to * take integers. Outside of a few cases, Eigen doesn't do automatic type promotion. Thus, this typedef is * only intended as a helper for code that needs to explicitly promote types. * \li A typedef \a Nested giving the type to use to nest a value inside of the expression tree. If you don't know what * this means, just use \a T here. * \li An enum value \a IsComplex. It is equal to 1 if \a T is a \c std::complex * type, and to 0 otherwise. * \li An enum value \a IsInteger. It is equal to \c 1 if \a T is an integer type such as \c int, * and to \c 0 otherwise. * \li Enum values ReadCost, AddCost and MulCost representing a rough estimate of the number of CPU cycles needed * to by move / add / mul instructions respectively, assuming the data is already stored in CPU registers. * Stay vague here. No need to do architecture-specific stuff. * \li An enum value \a IsSigned. It is equal to \c 1 if \a T is a signed type and to 0 if \a T is unsigned. * \li An enum value \a RequireInitialization. It is equal to \c 1 if the constructor of the numeric type \a T must * be called, and to 0 if it is safe not to call it. Default is 0 if \a T is an arithmetic type, and 1 otherwise. * \li An epsilon() function which, unlike std::numeric_limits::epsilon(), returns a \a Real instead of a \a T. * \li A dummy_precision() function returning a weak epsilon value. It is mainly used as a default * value by the fuzzy comparison operators. * \li highest() and lowest() functions returning the highest and lowest possible values respectively. */ template struct GenericNumTraits { enum { IsInteger = std::numeric_limits::is_integer, IsSigned = std::numeric_limits::is_signed, IsComplex = 0, RequireInitialization = internal::is_arithmetic::value ? 0 : 1, ReadCost = 1, AddCost = 1, MulCost = 1 }; typedef T Real; typedef typename internal::conditional< IsInteger, typename internal::conditional::type, T >::type NonInteger; typedef T Nested; static inline Real epsilon() { return std::numeric_limits::epsilon(); } static inline Real dummy_precision() { // make sure to override this for floating-point types return Real(0); } static inline T highest() { return (std::numeric_limits::max)(); } static inline T lowest() { return IsInteger ? (std::numeric_limits::min)() : (-(std::numeric_limits::max)()); } #ifdef EIGEN2_SUPPORT enum { HasFloatingPoint = !IsInteger }; typedef NonInteger FloatingPoint; #endif }; template struct NumTraits : GenericNumTraits {}; template<> struct NumTraits : GenericNumTraits { static inline float dummy_precision() { return 1e-5f; } }; template<> struct NumTraits : GenericNumTraits { static inline double dummy_precision() { return 1e-12; } }; template<> struct NumTraits : GenericNumTraits { static inline long double dummy_precision() { return 1e-15l; } }; template struct NumTraits > : GenericNumTraits > { typedef _Real Real; enum { IsComplex = 1, RequireInitialization = NumTraits<_Real>::RequireInitialization, ReadCost = 2 * NumTraits<_Real>::ReadCost, AddCost = 2 * NumTraits::AddCost, MulCost = 4 * NumTraits::MulCost + 2 * NumTraits::AddCost }; static inline Real epsilon() { return NumTraits::epsilon(); } static inline Real dummy_precision() { return NumTraits::dummy_precision(); } }; template struct NumTraits > { typedef Array ArrayType; typedef typename NumTraits::Real RealScalar; typedef Array Real; typedef typename NumTraits::NonInteger NonIntegerScalar; typedef Array NonInteger; typedef ArrayType & Nested; enum { IsComplex = NumTraits::IsComplex, IsInteger = NumTraits::IsInteger, IsSigned = NumTraits::IsSigned, RequireInitialization = 1, ReadCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits::ReadCost, AddCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits::AddCost, MulCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits::MulCost }; }; } // end namespace Eigen #endif // EIGEN_NUMTRAITS_H ctbnrle.1.1.1/hdr/Eigen/src/Core/Select.h0000775000175000017500000001526512107032422020246 0ustar csheltoncshelton// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2010 Gael Guennebaud // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_SELECT_H #define EIGEN_SELECT_H namespace Eigen { /** \class Select * \ingroup Core_Module * * \brief Expression of a coefficient wise version of the C++ ternary operator ?: * * \param ConditionMatrixType the type of the \em condition expression which must be a boolean matrix * \param ThenMatrixType the type of the \em then expression * \param ElseMatrixType the type of the \em else expression * * This class represents an expression of a coefficient wise version of the C++ ternary operator ?:. * It is the return type of DenseBase::select() and most of the time this is the only way it is used. * * \sa DenseBase::select(const DenseBase&, const DenseBase&) const */ namespace internal { template struct traits > : traits { typedef typename traits::Scalar Scalar; typedef Dense StorageKind; typedef typename traits::XprKind XprKind; typedef typename ConditionMatrixType::Nested ConditionMatrixNested; typedef typename ThenMatrixType::Nested ThenMatrixNested; typedef typename ElseMatrixType::Nested ElseMatrixNested; enum { RowsAtCompileTime = ConditionMatrixType::RowsAtCompileTime, ColsAtCompileTime = ConditionMatrixType::ColsAtCompileTime, MaxRowsAtCompileTime = ConditionMatrixType::MaxRowsAtCompileTime, MaxColsAtCompileTime = ConditionMatrixType::MaxColsAtCompileTime, Flags = (unsigned int)ThenMatrixType::Flags & ElseMatrixType::Flags & HereditaryBits, CoeffReadCost = traits::type>::CoeffReadCost + EIGEN_SIZE_MAX(traits::type>::CoeffReadCost, traits::type>::CoeffReadCost) }; }; } template class Select : internal::no_assignment_operator, public internal::dense_xpr_base< Select >::type { public: typedef typename internal::dense_xpr_base * ::= * * ::= * table
IN : * [ ] , ; * ::= * ::= * ::= * ::= * ::= , * ::= * ::= <- * ::= * ::= , * ::= * ::= , * ::= * ::= ~ * * ::= * table
OUT : * ; * ::= * ::= * ::= , * ::= * ::= ~ */ TABLE *table_statement(MPL *mpl) { TABLE *tab; TABARG *last_arg, *arg; TABFLD *last_fld, *fld; TABIN *last_in, *in; TABOUT *last_out, *out; AVLNODE *node; int nflds; char name[MAX_LENGTH+1]; xassert(is_keyword(mpl, "table")); get_token(mpl /* solve */); /* symbolic name must follow the keyword table */ if (mpl->token == T_NAME) ; else if (is_reserved(mpl)) error(mpl, "invalid use of reserved keyword %s", mpl->image); else error(mpl, "symbolic name missing where expected"); /* there must be no other object with the same name */ if (avl_find_node(mpl->tree, mpl->image) != NULL) error(mpl, "%s multiply declared", mpl->image); /* create data table */ tab = alloc(TABLE); tab->name = dmp_get_atomv(mpl->pool, strlen(mpl->image)+1); strcpy(tab->name, mpl->image); get_token(mpl /* */); /* parse optional alias */ if (mpl->token == T_STRING) { tab->alias = dmp_get_atomv(mpl->pool, strlen(mpl->image)+1); strcpy(tab->alias, mpl->image); get_token(mpl /* */); } else tab->alias = NULL; /* parse optional indexing expression */ if (mpl->token == T_LBRACE) { /* this is output table */ tab->type = A_OUTPUT; tab->u.out.domain = indexing_expression(mpl); if (!is_keyword(mpl, "OUT")) error(mpl, "keyword OUT missing where expected"); get_token(mpl /* OUT */); } else { /* this is input table */ tab->type = A_INPUT; if (!is_keyword(mpl, "IN")) error(mpl, "keyword IN missing where expected"); get_token(mpl /* IN */); } /* parse argument list */ tab->arg = last_arg = NULL; for (;;) { /* create argument list entry */ arg = alloc(TABARG); /* parse argument expression */ if (mpl->token == T_COMMA || mpl->token == T_COLON || mpl->token == T_SEMICOLON) error(mpl, "argument expression missing where expected"); arg->code = expression_5(mpl); /* convert the result to symbolic type, if necessary */ if (arg->code->type == A_NUMERIC) arg->code = make_unary(mpl, O_CVTSYM, arg->code, A_SYMBOLIC, 0); /* check that now the result is of symbolic type */ if (arg->code->type != A_SYMBOLIC) error(mpl, "argument expression has invalid type"); /* add the entry to the end of the list */ arg->next = NULL; if (last_arg == NULL) tab->arg = arg; else last_arg->next = arg; last_arg = arg; /* argument expression has been parsed */ if (mpl->token == T_COMMA) get_token(mpl /* , */); else if (mpl->token == T_COLON || mpl->token == T_SEMICOLON) break; } xassert(tab->arg != NULL); /* argument list must end with colon */ if (mpl->token == T_COLON) get_token(mpl /* : */); else error(mpl, "colon missing where expected"); /* parse specific part of the table statement */ switch (tab->type) { case A_INPUT: goto input_table; case A_OUTPUT: goto output_table; default: xassert(tab != tab); } input_table: /* parse optional set name */ if (mpl->token == T_NAME) { node = avl_find_node(mpl->tree, mpl->image); if (node == NULL) error(mpl, "%s not defined", mpl->image); if (avl_get_node_type(node) != A_SET) error(mpl, "%s not a set", mpl->image); tab->u.in.set = (SET *)avl_get_node_link(node); if (tab->u.in.set->assign != NULL) error(mpl, "%s needs no data", mpl->image); if (tab->u.in.set->dim != 0) error(mpl, "%s must be a simple set", mpl->image); get_token(mpl /* */); if (mpl->token == T_INPUT) get_token(mpl /* <- */); else error(mpl, "delimiter <- missing where expected"); } else if (is_reserved(mpl)) error(mpl, "invalid use of reserved keyword %s", mpl->image); else tab->u.in.set = NULL; /* parse field list */ tab->u.in.fld = last_fld = NULL; nflds = 0; if (mpl->token == T_LBRACKET) get_token(mpl /* [ */); else error(mpl, "field list missing where expected"); for (;;) { /* create field list entry */ fld = alloc(TABFLD); /* parse field name */ if (mpl->token == T_NAME) ; else if (is_reserved(mpl)) error(mpl, "invalid use of reserved keyword %s", mpl->image); else error(mpl, "field name missing where expected"); fld->name = dmp_get_atomv(mpl->pool, strlen(mpl->image)+1); strcpy(fld->name, mpl->image); get_token(mpl /* */); /* add the entry to the end of the list */ fld->next = NULL; if (last_fld == NULL) tab->u.in.fld = fld; else last_fld->next = fld; last_fld = fld; nflds++; /* field name has been parsed */ if (mpl->token == T_COMMA) get_token(mpl /* , */); else if (mpl->token == T_RBRACKET) break; else error(mpl, "syntax error in field list"); } /* check that the set dimen is equal to the number of fields */ if (tab->u.in.set != NULL && tab->u.in.set->dimen != nflds) error(mpl, "there must be %d field%s rather than %d", tab->u.in.set->dimen, tab->u.in.set->dimen == 1 ? "" : "s", nflds); get_token(mpl /* ] */); /* parse optional input list */ tab->u.in.list = last_in = NULL; while (mpl->token == T_COMMA) { get_token(mpl /* , */); /* create input list entry */ in = alloc(TABIN); /* parse parameter name */ if (mpl->token == T_NAME) ; else if (is_reserved(mpl)) error(mpl, "invalid use of reserved keyword %s", mpl->image); else error(mpl, "parameter name missing where expected"); node = avl_find_node(mpl->tree, mpl->image); if (node == NULL) error(mpl, "%s not defined", mpl->image); if (avl_get_node_type(node) != A_PARAMETER) error(mpl, "%s not a parameter", mpl->image); in->par = (PARAMETER *)avl_get_node_link(node); if (in->par->dim != nflds) error(mpl, "%s must have %d subscript%s rather than %d", mpl->image, nflds, nflds == 1 ? "" : "s", in->par->dim); if (in->par->assign != NULL) error(mpl, "%s needs no data", mpl->image); get_token(mpl /* */); /* parse optional field name */ if (mpl->token == T_TILDE) { get_token(mpl /* ~ */); /* parse field name */ if (mpl->token == T_NAME) ; else if (is_reserved(mpl)) error(mpl, "invalid use of reserved keyword %s", mpl->image); else error(mpl, "field name missing where expected"); xassert(strlen(mpl->image) < sizeof(name)); strcpy(name, mpl->image); get_token(mpl /* */); } else { /* field name is the same as the parameter name */ xassert(strlen(in->par->name) < sizeof(name)); strcpy(name, in->par->name); } /* assign field name */ in->name = dmp_get_atomv(mpl->pool, strlen(name)+1); strcpy(in->name, name); /* add the entry to the end of the list */ in->next = NULL; if (last_in == NULL) tab->u.in.list = in; else last_in->next = in; last_in = in; } goto end_of_table; output_table: /* parse output list */ tab->u.out.list = last_out = NULL; for (;;) { /* create output list entry */ out = alloc(TABOUT); /* parse expression */ if (mpl->token == T_COMMA || mpl->token == T_SEMICOLON) error(mpl, "expression missing where expected"); if (mpl->token == T_NAME) { xassert(strlen(mpl->image) < sizeof(name)); strcpy(name, mpl->image); } else name[0] = '\0'; out->code = expression_5(mpl); /* parse optional field name */ if (mpl->token == T_TILDE) { get_token(mpl /* ~ */); /* parse field name */ if (mpl->token == T_NAME) ; else if (is_reserved(mpl)) error(mpl, "invalid use of reserved keyword %s", mpl->image); else error(mpl, "field name missing where expected"); xassert(strlen(mpl->image) < sizeof(name)); strcpy(name, mpl->image); get_token(mpl /* */); } /* assign field name */ if (name[0] == '\0') error(mpl, "field name required"); out->name = dmp_get_atomv(mpl->pool, strlen(name)+1); strcpy(out->name, name); /* add the entry to the end of the list */ out->next = NULL; if (last_out == NULL) tab->u.out.list = out; else last_out->next = out; last_out = out; /* output item has been parsed */ if (mpl->token == T_COMMA) get_token(mpl /* , */); else if (mpl->token == T_SEMICOLON) break; else error(mpl, "syntax error in output list"); } /* close the domain scope */ close_scope(mpl,tab->u.out.domain); end_of_table: /* the table statement must end with semicolon */ if (mpl->token != T_SEMICOLON) error(mpl, "syntax error in table statement"); get_token(mpl /* ; */); return tab; } #endif /*---------------------------------------------------------------------- -- solve_statement - parse solve statement. -- -- This routine parses solve statement using the syntax: -- -- ::= solve ; -- -- The solve statement can be used at most once. */ void *solve_statement(MPL *mpl) { xassert(is_keyword(mpl, "solve")); if (mpl->flag_s) error(mpl, "at most one solve statement allowed"); mpl->flag_s = 1; get_token(mpl /* solve */); /* semicolon must follow solve statement */ if (mpl->token != T_SEMICOLON) error(mpl, "syntax error in solve statement"); get_token(mpl /* ; */); return NULL; } /*---------------------------------------------------------------------- -- check_statement - parse check statement. -- -- This routine parses check statement using the syntax: -- -- ::= check : ; -- ::= -- ::= -- -- If is omitted, colon following it may also be omitted. */ CHECK *check_statement(MPL *mpl) { CHECK *chk; xassert(is_keyword(mpl, "check")); /* create check descriptor */ chk = alloc(CHECK); chk->domain = NULL; chk->code = NULL; get_token(mpl /* check */); /* parse optional indexing expression */ if (mpl->token == T_LBRACE) { chk->domain = indexing_expression(mpl); #if 0 if (mpl->token != T_COLON) error(mpl, "colon missing where expected"); #endif } /* skip optional colon */ if (mpl->token == T_COLON) get_token(mpl /* : */); /* parse logical expression */ chk->code = expression_13(mpl); if (chk->code->type != A_LOGICAL) error(mpl, "expression has invalid type"); xassert(chk->code->dim == 0); /* close the domain scope */ if (chk->domain != NULL) close_scope(mpl, chk->domain); /* the check statement has been completely parsed */ if (mpl->token != T_SEMICOLON) error(mpl, "syntax error in check statement"); get_token(mpl /* ; */); return chk; } #if 1 /* 15/V-2010 */ /*---------------------------------------------------------------------- -- display_statement - parse display statement. -- -- This routine parses display statement using the syntax: -- -- ::= display : ; -- ::= display ; -- ::= -- ::= -- ::= -- ::= , -- ::= -- ::= -- ::= [ ] -- ::= -- ::= [ ] -- ::= -- ::= [ ] -- ::= -- ::= [ ] -- ::= */ DISPLAY *display_statement(MPL *mpl) { DISPLAY *dpy; DISPLAY1 *entry, *last_entry; xassert(is_keyword(mpl, "display")); /* create display descriptor */ dpy = alloc(DISPLAY); dpy->domain = NULL; dpy->list = last_entry = NULL; get_token(mpl /* display */); /* parse optional indexing expression */ if (mpl->token == T_LBRACE) dpy->domain = indexing_expression(mpl); /* skip optional colon */ if (mpl->token == T_COLON) get_token(mpl /* : */); /* parse display list */ for (;;) { /* create new display entry */ entry = alloc(DISPLAY1); entry->type = 0; entry->next = NULL; /* and append it to the display list */ if (dpy->list == NULL) dpy->list = entry; else last_entry->next = entry; last_entry = entry; /* parse display entry */ if (mpl->token == T_NAME) { AVLNODE *node; int next_token; get_token(mpl /* */); next_token = mpl->token; unget_token(mpl); if (!(next_token == T_COMMA || next_token == T_SEMICOLON)) { /* symbolic name begins expression */ goto expr; } /* display entry is dummy index or model object */ node = avl_find_node(mpl->tree, mpl->image); if (node == NULL) error(mpl, "%s not defined", mpl->image); entry->type = avl_get_node_type(node); switch (avl_get_node_type(node)) { case A_INDEX: entry->u.slot = (DOMAIN_SLOT *)avl_get_node_link(node); break; case A_SET: entry->u.set = (SET *)avl_get_node_link(node); break; case A_PARAMETER: entry->u.par = (PARAMETER *)avl_get_node_link(node); break; case A_VARIABLE: entry->u.var = (VARIABLE *)avl_get_node_link(node); if (!mpl->flag_s) error(mpl, "invalid reference to variable %s above" " solve statement", entry->u.var->name); break; case A_CONSTRAINT: entry->u.con = (CONSTRAINT *)avl_get_node_link(node); if (!mpl->flag_s) error(mpl, "invalid reference to %s %s above solve" " statement", entry->u.con->type == A_CONSTRAINT ? "constraint" : "objective", entry->u.con->name); break; default: xassert(node != node); } get_token(mpl /* */); } else expr: { /* display entry is expression */ entry->type = A_EXPRESSION; entry->u.code = expression_13(mpl); } /* check a token that follows the entry parsed */ if (mpl->token == T_COMMA) get_token(mpl /* , */); else break; } /* close the domain scope */ if (dpy->domain != NULL) close_scope(mpl, dpy->domain); /* the display statement has been completely parsed */ if (mpl->token != T_SEMICOLON) error(mpl, "syntax error in display statement"); get_token(mpl /* ; */); return dpy; } #endif /*---------------------------------------------------------------------- -- printf_statement - parse printf statement. -- -- This routine parses print statement using the syntax: -- -- ::= ; -- ::= > ; -- ::= >> ; -- ::= printf : -- ::= printf -- ::= -- ::= -- ::= -- ::= -- ::= , -- ::= -- ::= */ PRINTF *printf_statement(MPL *mpl) { PRINTF *prt; PRINTF1 *entry, *last_entry; xassert(is_keyword(mpl, "printf")); /* create printf descriptor */ prt = alloc(PRINTF); prt->domain = NULL; prt->fmt = NULL; prt->list = last_entry = NULL; get_token(mpl /* printf */); /* parse optional indexing expression */ if (mpl->token == T_LBRACE) { prt->domain = indexing_expression(mpl); #if 0 if (mpl->token != T_COLON) error(mpl, "colon missing where expected"); #endif } /* skip optional colon */ if (mpl->token == T_COLON) get_token(mpl /* : */); /* parse expression for format string */ prt->fmt = expression_5(mpl); /* convert it to symbolic type, if necessary */ if (prt->fmt->type == A_NUMERIC) prt->fmt = make_unary(mpl, O_CVTSYM, prt->fmt, A_SYMBOLIC, 0); /* check that now the expression is of symbolic type */ if (prt->fmt->type != A_SYMBOLIC) error(mpl, "format expression has invalid type"); /* parse printf list */ while (mpl->token == T_COMMA) { get_token(mpl /* , */); /* create new printf entry */ entry = alloc(PRINTF1); entry->code = NULL; entry->next = NULL; /* and append it to the printf list */ if (prt->list == NULL) prt->list = entry; else last_entry->next = entry; last_entry = entry; /* parse printf entry */ entry->code = expression_9(mpl); if (!(entry->code->type == A_NUMERIC || entry->code->type == A_SYMBOLIC || entry->code->type == A_LOGICAL)) error(mpl, "only numeric, symbolic, or logical expression a" "llowed"); } /* close the domain scope */ if (prt->domain != NULL) close_scope(mpl, prt->domain); #if 1 /* 14/VII-2006 */ /* parse optional redirection */ prt->fname = NULL, prt->app = 0; if (mpl->token == T_GT || mpl->token == T_APPEND) { prt->app = (mpl->token == T_APPEND); get_token(mpl /* > or >> */); /* parse expression for file name string */ prt->fname = expression_5(mpl); /* convert it to symbolic type, if necessary */ if (prt->fname->type == A_NUMERIC) prt->fname = make_unary(mpl, O_CVTSYM, prt->fname, A_SYMBOLIC, 0); /* check that now the expression is of symbolic type */ if (prt->fname->type != A_SYMBOLIC) error(mpl, "file name expression has invalid type"); } #endif /* the printf statement has been completely parsed */ if (mpl->token != T_SEMICOLON) error(mpl, "syntax error in printf statement"); get_token(mpl /* ; */); return prt; } /*---------------------------------------------------------------------- -- for_statement - parse for statement. -- -- This routine parses for statement using the syntax: -- -- ::= for -- ::= for { } -- ::= -- ::= -- ::= -- ::= -- ::= -- ::= -- ::= */ FOR *for_statement(MPL *mpl) { FOR *fur; STATEMENT *stmt, *last_stmt; xassert(is_keyword(mpl, "for")); /* create for descriptor */ fur = alloc(FOR); fur->domain = NULL; fur->list = last_stmt = NULL; get_token(mpl /* for */); /* parse indexing expression */ if (mpl->token != T_LBRACE) error(mpl, "indexing expression missing where expected"); fur->domain = indexing_expression(mpl); /* skip optional colon */ if (mpl->token == T_COLON) get_token(mpl /* : */); /* parse for statement body */ if (mpl->token != T_LBRACE) { /* parse simple statement */ fur->list = simple_statement(mpl, 1); } else { /* parse compound statement */ get_token(mpl /* { */); while (mpl->token != T_RBRACE) { /* parse statement */ stmt = simple_statement(mpl, 1); /* and append it to the end of the statement list */ if (last_stmt == NULL) fur->list = stmt; else last_stmt->next = stmt; last_stmt = stmt; } get_token(mpl /* } */); } /* close the domain scope */ xassert(fur->domain != NULL); close_scope(mpl, fur->domain); /* the for statement has been completely parsed */ return fur; } /*---------------------------------------------------------------------- -- end_statement - parse end statement. -- -- This routine parses end statement using the syntax: -- -- ::= end ; */ void end_statement(MPL *mpl) { if (!mpl->flag_d && is_keyword(mpl, "end") || mpl->flag_d && is_literal(mpl, "end")) { get_token(mpl /* end */); if (mpl->token == T_SEMICOLON) get_token(mpl /* ; */); else warning(mpl, "no semicolon following end statement; missing" " semicolon inserted"); } else warning(mpl, "unexpected end of file; missing end statement in" "serted"); if (mpl->token != T_EOF) warning(mpl, "some text detected beyond end statement; text ig" "nored"); return; } /*---------------------------------------------------------------------- -- simple_statement - parse simple statement. -- -- This routine parses simple statement using the syntax: -- -- ::= -- ::= -- ::= -- ::= -- ::= -- ::= -- ::= -- ::= -- ::= -- ::= -- -- If the flag spec is set, some statements cannot be used. */ STATEMENT *simple_statement(MPL *mpl, int spec) { STATEMENT *stmt; stmt = alloc(STATEMENT); stmt->line = mpl->line; stmt->next = NULL; if (is_keyword(mpl, "set")) { if (spec) error(mpl, "set statement not allowed here"); stmt->type = A_SET; stmt->u.set = set_statement(mpl); } else if (is_keyword(mpl, "param")) { if (spec) error(mpl, "parameter statement not allowed here"); stmt->type = A_PARAMETER; stmt->u.par = parameter_statement(mpl); } else if (is_keyword(mpl, "var")) { if (spec) error(mpl, "variable statement not allowed here"); stmt->type = A_VARIABLE; stmt->u.var = variable_statement(mpl); } else if (is_keyword(mpl, "subject") || is_keyword(mpl, "subj") || mpl->token == T_SPTP) { if (spec) error(mpl, "constraint statement not allowed here"); stmt->type = A_CONSTRAINT; stmt->u.con = constraint_statement(mpl); } else if (is_keyword(mpl, "minimize") || is_keyword(mpl, "maximize")) { if (spec) error(mpl, "objective statement not allowed here"); stmt->type = A_CONSTRAINT; stmt->u.con = objective_statement(mpl); } #if 1 /* 11/II-2008 */ else if (is_keyword(mpl, "table")) { if (spec) error(mpl, "table statement not allowed here"); stmt->type = A_TABLE; stmt->u.tab = table_statement(mpl); } #endif else if (is_keyword(mpl, "solve")) { if (spec) error(mpl, "solve statement not allowed here"); stmt->type = A_SOLVE; stmt->u.slv = solve_statement(mpl); } else if (is_keyword(mpl, "check")) { stmt->type = A_CHECK; stmt->u.chk = check_statement(mpl); } else if (is_keyword(mpl, "display")) { stmt->type = A_DISPLAY; stmt->u.dpy = display_statement(mpl); } else if (is_keyword(mpl, "printf")) { stmt->type = A_PRINTF; stmt->u.prt = printf_statement(mpl); } else if (is_keyword(mpl, "for")) { stmt->type = A_FOR; stmt->u.fur = for_statement(mpl); } else if (mpl->token == T_NAME) { if (spec) error(mpl, "constraint statement not allowed here"); stmt->type = A_CONSTRAINT; stmt->u.con = constraint_statement(mpl); } else if (is_reserved(mpl)) error(mpl, "invalid use of reserved keyword %s", mpl->image); else error(mpl, "syntax error in model section"); return stmt; } /*---------------------------------------------------------------------- -- model_section - parse model section. -- -- This routine parses model section using the syntax: -- -- ::= -- ::= -- -- Parsing model section is terminated by either the keyword 'data', or -- the keyword 'end', or the end of file. */ void model_section(MPL *mpl) { STATEMENT *stmt, *last_stmt; xassert(mpl->model == NULL); last_stmt = NULL; while (!(mpl->token == T_EOF || is_keyword(mpl, "data") || is_keyword(mpl, "end"))) { /* parse statement */ stmt = simple_statement(mpl, 0); /* and append it to the end of the statement list */ if (last_stmt == NULL) mpl->model = stmt; else last_stmt->next = stmt; last_stmt = stmt; } return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpssx.h0000644000175000017500000004013611632352620020134 0ustar csheltoncshelton/* glpssx.h (simplex method, bignum arithmetic) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPSSX_H #define GLPSSX_H #include "glpbfx.h" #include "glpenv.h" typedef struct SSX SSX; struct SSX { /* simplex solver workspace */ /*---------------------------------------------------------------------- // LP PROBLEM DATA // // It is assumed that LP problem has the following statement: // // minimize (or maximize) // // z = c[1]*x[1] + ... + c[m+n]*x[m+n] + c[0] (1) // // subject to equality constraints // // x[1] - a[1,1]*x[m+1] - ... - a[1,n]*x[m+n] = 0 // // . . . . . . . (2) // // x[m] - a[m,1]*x[m+1] + ... - a[m,n]*x[m+n] = 0 // // and bounds of variables // // l[1] <= x[1] <= u[1] // // . . . . . . . (3) // // l[m+n] <= x[m+n] <= u[m+n] // // where: // x[1], ..., x[m] - auxiliary variables; // x[m+1], ..., x[m+n] - structural variables; // z - objective function; // c[1], ..., c[m+n] - coefficients of the objective function; // c[0] - constant term of the objective function; // a[1,1], ..., a[m,n] - constraint coefficients; // l[1], ..., l[m+n] - lower bounds of variables; // u[1], ..., u[m+n] - upper bounds of variables. // // Bounds of variables can be finite as well as inifinite. Besides, // lower and upper bounds can be equal to each other. So the following // five types of variables are possible: // // Bounds of variable Type of variable // ------------------------------------------------- // -inf < x[k] < +inf Free (unbounded) variable // l[k] <= x[k] < +inf Variable with lower bound // -inf < x[k] <= u[k] Variable with upper bound // l[k] <= x[k] <= u[k] Double-bounded variable // l[k] = x[k] = u[k] Fixed variable // // Using vector-matrix notations the LP problem (1)-(3) can be written // as follows: // // minimize (or maximize) // // z = c * x + c[0] (4) // // subject to equality constraints // // xR - A * xS = 0 (5) // // and bounds of variables // // l <= x <= u (6) // // where: // xR - vector of auxiliary variables; // xS - vector of structural variables; // x = (xR, xS) - vector of all variables; // z - objective function; // c - vector of objective coefficients; // c[0] - constant term of the objective function; // A - matrix of constraint coefficients (has m rows // and n columns); // l - vector of lower bounds of variables; // u - vector of upper bounds of variables. // // The simplex method makes no difference between auxiliary and // structural variables, so it is convenient to think the system of // equality constraints (5) written in a homogeneous form: // // (I | -A) * x = 0, (7) // // where (I | -A) is an augmented (m+n)xm constraint matrix, I is mxm // unity matrix whose columns correspond to auxiliary variables, and A // is the original mxn constraint matrix whose columns correspond to // structural variables. Note that only the matrix A is stored. ----------------------------------------------------------------------*/ int m; /* number of rows (auxiliary variables), m > 0 */ int n; /* number of columns (structural variables), n > 0 */ int *type; /* int type[1+m+n]; */ /* type[0] is not used; type[k], 1 <= k <= m+n, is the type of variable x[k]: */ #define SSX_FR 0 /* free (unbounded) variable */ #define SSX_LO 1 /* variable with lower bound */ #define SSX_UP 2 /* variable with upper bound */ #define SSX_DB 3 /* double-bounded variable */ #define SSX_FX 4 /* fixed variable */ mpq_t *lb; /* mpq_t lb[1+m+n]; alias: l */ /* lb[0] is not used; lb[k], 1 <= k <= m+n, is an lower bound of variable x[k]; if x[k] has no lower bound, lb[k] is zero */ mpq_t *ub; /* mpq_t ub[1+m+n]; alias: u */ /* ub[0] is not used; ub[k], 1 <= k <= m+n, is an upper bound of variable x[k]; if x[k] has no upper bound, ub[k] is zero; if x[k] is of fixed type, ub[k] is equal to lb[k] */ int dir; /* optimization direction (sense of the objective function): */ #define SSX_MIN 0 /* minimization */ #define SSX_MAX 1 /* maximization */ mpq_t *coef; /* mpq_t coef[1+m+n]; alias: c */ /* coef[0] is a constant term of the objective function; coef[k], 1 <= k <= m+n, is a coefficient of the objective function at variable x[k]; note that auxiliary variables also may have non-zero objective coefficients */ int *A_ptr; /* int A_ptr[1+n+1]; */ int *A_ind; /* int A_ind[A_ptr[n+1]]; */ mpq_t *A_val; /* mpq_t A_val[A_ptr[n+1]]; */ /* constraint matrix A (see (5)) in storage-by-columns format */ /*---------------------------------------------------------------------- // LP BASIS AND CURRENT BASIC SOLUTION // // The LP basis is defined by the following partition of the augmented // constraint matrix (7): // // (B | N) = (I | -A) * Q, (8) // // where B is a mxm non-singular basis matrix whose columns correspond // to basic variables xB, N is a mxn matrix whose columns correspond to // non-basic variables xN, and Q is a permutation (m+n)x(m+n) matrix. // // From (7) and (8) it follows that // // (I | -A) * x = (I | -A) * Q * Q' * x = (B | N) * (xB, xN), // // therefore // // (xB, xN) = Q' * x, (9) // // where x is the vector of all variables in the original order, xB is // a vector of basic variables, xN is a vector of non-basic variables, // Q' = inv(Q) is a matrix transposed to Q. // // Current values of non-basic variables xN[j], j = 1, ..., n, are not // stored; they are defined implicitly by their statuses as follows: // // 0, if xN[j] is free variable // lN[j], if xN[j] is on its lower bound (10) // uN[j], if xN[j] is on its upper bound // lN[j] = uN[j], if xN[j] is fixed variable // // where lN[j] and uN[j] are lower and upper bounds of xN[j]. // // Current values of basic variables xB[i], i = 1, ..., m, are computed // as follows: // // beta = - inv(B) * N * xN, (11) // // where current values of xN are defined by (10). // // Current values of simplex multipliers pi[i], i = 1, ..., m (which // are values of Lagrange multipliers for equality constraints (7) also // called shadow prices) are computed as follows: // // pi = inv(B') * cB, (12) // // where B' is a matrix transposed to B, cB is a vector of objective // coefficients at basic variables xB. // // Current values of reduced costs d[j], j = 1, ..., n, (which are // values of Langrange multipliers for active inequality constraints // corresponding to non-basic variables) are computed as follows: // // d = cN - N' * pi, (13) // // where N' is a matrix transposed to N, cN is a vector of objective // coefficients at non-basic variables xN. ----------------------------------------------------------------------*/ int *stat; /* int stat[1+m+n]; */ /* stat[0] is not used; stat[k], 1 <= k <= m+n, is the status of variable x[k]: */ #define SSX_BS 0 /* basic variable */ #define SSX_NL 1 /* non-basic variable on lower bound */ #define SSX_NU 2 /* non-basic variable on upper bound */ #define SSX_NF 3 /* non-basic free variable */ #define SSX_NS 4 /* non-basic fixed variable */ int *Q_row; /* int Q_row[1+m+n]; */ /* matrix Q in row-like format; Q_row[0] is not used; Q_row[i] = j means that q[i,j] = 1 */ int *Q_col; /* int Q_col[1+m+n]; */ /* matrix Q in column-like format; Q_col[0] is not used; Q_col[j] = i means that q[i,j] = 1 */ /* if k-th column of the matrix (I | A) is k'-th column of the matrix (B | N), then Q_row[k] = k' and Q_col[k'] = k; if x[k] is xB[i], then Q_row[k] = i and Q_col[i] = k; if x[k] is xN[j], then Q_row[k] = m+j and Q_col[m+j] = k */ BFX *binv; /* invertable form of the basis matrix B */ mpq_t *bbar; /* mpq_t bbar[1+m]; alias: beta */ /* bbar[0] is a value of the objective function; bbar[i], 1 <= i <= m, is a value of basic variable xB[i] */ mpq_t *pi; /* mpq_t pi[1+m]; */ /* pi[0] is not used; pi[i], 1 <= i <= m, is a simplex multiplier corresponding to i-th row (equality constraint) */ mpq_t *cbar; /* mpq_t cbar[1+n]; alias: d */ /* cbar[0] is not used; cbar[j], 1 <= j <= n, is a reduced cost of non-basic variable xN[j] */ /*---------------------------------------------------------------------- // SIMPLEX TABLE // // Due to (8) and (9) the system of equality constraints (7) for the // current basis can be written as follows: // // xB = A~ * xN, (14) // // where // // A~ = - inv(B) * N (15) // // is a mxn matrix called the simplex table. // // The revised simplex method uses only two components of A~, namely, // pivot column corresponding to non-basic variable xN[q] chosen to // enter the basis, and pivot row corresponding to basic variable xB[p] // chosen to leave the basis. // // Pivot column alfa_q is q-th column of A~, so // // alfa_q = A~ * e[q] = - inv(B) * N * e[q] = - inv(B) * N[q], (16) // // where N[q] is q-th column of the matrix N. // // Pivot row alfa_p is p-th row of A~ or, equivalently, p-th column of // A~', a matrix transposed to A~, so // // alfa_p = A~' * e[p] = - N' * inv(B') * e[p] = - N' * rho_p, (17) // // where (*)' means transposition, and // // rho_p = inv(B') * e[p], (18) // // is p-th column of inv(B') or, that is the same, p-th row of inv(B). ----------------------------------------------------------------------*/ int p; /* number of basic variable xB[p], 1 <= p <= m, chosen to leave the basis */ mpq_t *rho; /* mpq_t rho[1+m]; */ /* p-th row of the inverse inv(B); see (18) */ mpq_t *ap; /* mpq_t ap[1+n]; */ /* p-th row of the simplex table; see (17) */ int q; /* number of non-basic variable xN[q], 1 <= q <= n, chosen to enter the basis */ mpq_t *aq; /* mpq_t aq[1+m]; */ /* q-th column of the simplex table; see (16) */ /*--------------------------------------------------------------------*/ int q_dir; /* direction in which non-basic variable xN[q] should change on moving to the adjacent vertex of the polyhedron: +1 means that xN[q] increases -1 means that xN[q] decreases */ int p_stat; /* non-basic status which should be assigned to basic variable xB[p] when it has left the basis and become xN[q] */ mpq_t delta; /* actual change of xN[q] in the adjacent basis (it has the same sign as q_dir) */ /*--------------------------------------------------------------------*/ int it_lim; /* simplex iterations limit; if this value is positive, it is decreased by one each time when one simplex iteration has been performed, and reaching zero value signals the solver to stop the search; negative value means no iterations limit */ int it_cnt; /* simplex iterations count; this count is increased by one each time when one simplex iteration has been performed */ double tm_lim; /* searching time limit, in seconds; if this value is positive, it is decreased each time when one simplex iteration has been performed by the amount of time spent for the iteration, and reaching zero value signals the solver to stop the search; negative value means no time limit */ double out_frq; /* output frequency, in seconds; this parameter specifies how frequently the solver sends information about the progress of the search to the standard output */ glp_long tm_beg; /* starting time of the search, in seconds; the total time of the search is the difference between xtime() and tm_beg */ glp_long tm_lag; /* the most recent time, in seconds, at which the progress of the the search was displayed */ }; #define ssx_create _glp_ssx_create #define ssx_factorize _glp_ssx_factorize #define ssx_get_xNj _glp_ssx_get_xNj #define ssx_eval_bbar _glp_ssx_eval_bbar #define ssx_eval_pi _glp_ssx_eval_pi #define ssx_eval_dj _glp_ssx_eval_dj #define ssx_eval_cbar _glp_ssx_eval_cbar #define ssx_eval_rho _glp_ssx_eval_rho #define ssx_eval_row _glp_ssx_eval_row #define ssx_eval_col _glp_ssx_eval_col #define ssx_chuzc _glp_ssx_chuzc #define ssx_chuzr _glp_ssx_chuzr #define ssx_update_bbar _glp_ssx_update_bbar #define ssx_update_pi _glp_ssx_update_pi #define ssx_update_cbar _glp_ssx_update_cbar #define ssx_change_basis _glp_ssx_change_basis #define ssx_delete _glp_ssx_delete #define ssx_phase_I _glp_ssx_phase_I #define ssx_phase_II _glp_ssx_phase_II #define ssx_driver _glp_ssx_driver SSX *ssx_create(int m, int n, int nnz); /* create simplex solver workspace */ int ssx_factorize(SSX *ssx); /* factorize the current basis matrix */ void ssx_get_xNj(SSX *ssx, int j, mpq_t x); /* determine value of non-basic variable */ void ssx_eval_bbar(SSX *ssx); /* compute values of basic variables */ void ssx_eval_pi(SSX *ssx); /* compute values of simplex multipliers */ void ssx_eval_dj(SSX *ssx, int j, mpq_t dj); /* compute reduced cost of non-basic variable */ void ssx_eval_cbar(SSX *ssx); /* compute reduced costs of all non-basic variables */ void ssx_eval_rho(SSX *ssx); /* compute p-th row of the inverse */ void ssx_eval_row(SSX *ssx); /* compute pivot row of the simplex table */ void ssx_eval_col(SSX *ssx); /* compute pivot column of the simplex table */ void ssx_chuzc(SSX *ssx); /* choose pivot column */ void ssx_chuzr(SSX *ssx); /* choose pivot row */ void ssx_update_bbar(SSX *ssx); /* update values of basic variables */ void ssx_update_pi(SSX *ssx); /* update simplex multipliers */ void ssx_update_cbar(SSX *ssx); /* update reduced costs of non-basic variables */ void ssx_change_basis(SSX *ssx); /* change current basis to adjacent one */ void ssx_delete(SSX *ssx); /* delete simplex solver workspace */ int ssx_phase_I(SSX *ssx); /* find primal feasible solution */ int ssx_phase_II(SSX *ssx); /* find optimal solution */ int ssx_driver(SSX *ssx); /* base driver to exact simplex method */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/Makefile.am0000644000175000017500000000372211632352620020477 0ustar csheltoncshelton## Process this file with automake to produce Makefile.in ## include_HEADERS = glpk.h lib_LTLIBRARIES = libglpk.la libglpk_la_LDFLAGS = -version-info 32:0:32 \ -export-symbols-regex '^(glp_|_glp_lpx_).*' libglpk_la_SOURCES = \ glpapi01.c \ glpapi02.c \ glpapi03.c \ glpapi04.c \ glpapi05.c \ glpapi06.c \ glpapi07.c \ glpapi08.c \ glpapi09.c \ glpapi10.c \ glpapi11.c \ glpapi12.c \ glpapi13.c \ glpapi14.c \ glpapi15.c \ glpapi16.c \ glpapi17.c \ glpapi18.c \ glpapi19.c \ glpapi20.c \ glpapi21.c \ glpavl.c \ glpbfd.c \ glpbfx.c \ glpcpx.c \ glpdmp.c \ glpdmx.c \ glpenv01.c \ glpenv02.c \ glpenv03.c \ glpenv04.c \ glpenv05.c \ glpenv06.c \ glpenv07.c \ glpenv08.c \ glpfhv.c \ glpgmp.c \ glphbm.c \ glpini01.c \ glpini02.c \ glpios01.c \ glpios02.c \ glpios03.c \ glpios04.c \ glpios05.c \ glpios06.c \ glpios07.c \ glpios08.c \ glpios09.c \ glpios10.c \ glpios11.c \ glpios12.c \ glpipm.c \ glplib01.c \ glplib02.c \ glplib03.c \ glplpf.c \ glplpx01.c \ glplpx02.c \ glplpx03.c \ glpluf.c \ glplux.c \ glpmat.c \ glpmpl01.c \ glpmpl02.c \ glpmpl03.c \ glpmpl04.c \ glpmpl05.c \ glpmpl06.c \ glpmps.c \ glpnet01.c \ glpnet02.c \ glpnet03.c \ glpnet04.c \ glpnet05.c \ glpnet06.c \ glpnet07.c \ glpnet08.c \ glpnet09.c \ glpnpp01.c \ glpnpp02.c \ glpnpp03.c \ glpnpp04.c \ glpnpp05.c \ glpnpp06.c \ glpqmd.c \ glprgr.c \ glprng01.c \ glprng02.c \ glpscf.c \ glpscl.c \ glpsdf.c \ glpspm.c \ glpspx01.c \ glpspx02.c \ glpsql.c \ glpssx01.c \ glpssx02.c \ glptsp.c \ amd/amd_1.c \ amd/amd_2.c \ amd/amd_aat.c \ amd/amd_control.c \ amd/amd_defaults.c \ amd/amd_dump.c \ amd/amd_info.c \ amd/amd_order.c \ amd/amd_post_tree.c \ amd/amd_postorder.c \ amd/amd_preprocess.c \ amd/amd_valid.c \ colamd/colamd.c \ minisat/minisat.c \ zlib/adler32.c \ zlib/compress.c \ zlib/crc32.c \ zlib/deflate.c \ zlib/gzclose.c \ zlib/gzlib.c \ zlib/gzread.c \ zlib/gzwrite.c \ zlib/infback.c \ zlib/inffast.c \ zlib/inflate.c \ zlib/inftrees.c \ zlib/trees.c \ zlib/uncompr.c \ zlib/zio.c \ zlib/zutil.c ## eof ## ctbnrle.1.1.1/glpk/glpk-4.47/src/glpios10.c0000644000175000017500000002746311632352620020255 0ustar csheltoncshelton/* glpios10.c (feasibility pump heuristic) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpios.h" #include "glprng.h" /*********************************************************************** * NAME * * ios_feas_pump - feasibility pump heuristic * * SYNOPSIS * * #include "glpios.h" * void ios_feas_pump(glp_tree *T); * * DESCRIPTION * * The routine ios_feas_pump is a simple implementation of the Feasi- * bility Pump heuristic. * * REFERENCES * * M.Fischetti, F.Glover, and A.Lodi. "The feasibility pump." Math. * Program., Ser. A 104, pp. 91-104 (2005). */ struct VAR { /* binary variable */ int j; /* ordinal number */ int x; /* value in the rounded solution (0 or 1) */ double d; /* sorting key */ }; static int fcmp(const void *x, const void *y) { /* comparison routine */ const struct VAR *vx = x, *vy = y; if (vx->d > vy->d) return -1; else if (vx->d < vy->d) return +1; else return 0; } void ios_feas_pump(glp_tree *T) { glp_prob *P = T->mip; int n = P->n; glp_prob *lp = NULL; struct VAR *var = NULL; RNG *rand = NULL; GLPCOL *col; glp_smcp parm; int j, k, new_x, nfail, npass, nv, ret, stalling; double dist, tol; xassert(glp_get_status(P) == GLP_OPT); /* this heuristic is applied only once on the root level */ if (!(T->curr->level == 0 && T->curr->solved == 1)) goto done; /* determine number of binary variables */ nv = 0; for (j = 1; j <= n; j++) { col = P->col[j]; /* if x[j] is continuous, skip it */ if (col->kind == GLP_CV) continue; /* if x[j] is fixed, skip it */ if (col->type == GLP_FX) continue; /* x[j] is non-fixed integer */ xassert(col->kind == GLP_IV); if (col->type == GLP_DB && col->lb == 0.0 && col->ub == 1.0) { /* x[j] is binary */ nv++; } else { /* x[j] is general integer */ if (T->parm->msg_lev >= GLP_MSG_ALL) xprintf("FPUMP heuristic cannot be applied due to genera" "l integer variables\n"); goto done; } } /* there must be at least one binary variable */ if (nv == 0) goto done; if (T->parm->msg_lev >= GLP_MSG_ALL) xprintf("Applying FPUMP heuristic...\n"); /* build the list of binary variables */ var = xcalloc(1+nv, sizeof(struct VAR)); k = 0; for (j = 1; j <= n; j++) { col = P->col[j]; if (col->kind == GLP_IV && col->type == GLP_DB) var[++k].j = j; } xassert(k == nv); /* create working problem object */ lp = glp_create_prob(); more: /* copy the original problem object to keep it intact */ glp_copy_prob(lp, P, GLP_OFF); /* we are interested to find an integer feasible solution, which is better than the best known one */ if (P->mip_stat == GLP_FEAS) { int *ind; double *val, bnd; /* add a row and make it identical to the objective row */ glp_add_rows(lp, 1); ind = xcalloc(1+n, sizeof(int)); val = xcalloc(1+n, sizeof(double)); for (j = 1; j <= n; j++) { ind[j] = j; val[j] = P->col[j]->coef; } glp_set_mat_row(lp, lp->m, n, ind, val); xfree(ind); xfree(val); /* introduce upper (minimization) or lower (maximization) bound to the original objective function; note that this additional constraint is not violated at the optimal point to LP relaxation */ #if 0 /* modified by xypron */ if (P->dir == GLP_MIN) { bnd = P->mip_obj - 0.10 * (1.0 + fabs(P->mip_obj)); if (bnd < P->obj_val) bnd = P->obj_val; glp_set_row_bnds(lp, lp->m, GLP_UP, 0.0, bnd - P->c0); } else if (P->dir == GLP_MAX) { bnd = P->mip_obj + 0.10 * (1.0 + fabs(P->mip_obj)); if (bnd > P->obj_val) bnd = P->obj_val; glp_set_row_bnds(lp, lp->m, GLP_LO, bnd - P->c0, 0.0); } else xassert(P != P); #else bnd = 0.1 * P->obj_val + 0.9 * P->mip_obj; /* xprintf("bnd = %f\n", bnd); */ if (P->dir == GLP_MIN) glp_set_row_bnds(lp, lp->m, GLP_UP, 0.0, bnd - P->c0); else if (P->dir == GLP_MAX) glp_set_row_bnds(lp, lp->m, GLP_LO, bnd - P->c0, 0.0); else xassert(P != P); #endif } /* reset pass count */ npass = 0; /* invalidate the rounded point */ for (k = 1; k <= nv; k++) var[k].x = -1; pass: /* next pass starts here */ npass++; if (T->parm->msg_lev >= GLP_MSG_ALL) xprintf("Pass %d\n", npass); /* initialize minimal distance between the basic point and the rounded one obtained during this pass */ dist = DBL_MAX; /* reset failure count (the number of succeeded iterations failed to improve the distance) */ nfail = 0; /* if it is not the first pass, perturb the last rounded point rather than construct it from the basic solution */ if (npass > 1) { double rho, temp; if (rand == NULL) rand = rng_create_rand(); for (k = 1; k <= nv; k++) { j = var[k].j; col = lp->col[j]; rho = rng_uniform(rand, -0.3, 0.7); if (rho < 0.0) rho = 0.0; temp = fabs((double)var[k].x - col->prim); if (temp + rho > 0.5) var[k].x = 1 - var[k].x; } goto skip; } loop: /* innermost loop begins here */ /* round basic solution (which is assumed primal feasible) */ stalling = 1; for (k = 1; k <= nv; k++) { col = lp->col[var[k].j]; if (col->prim < 0.5) { /* rounded value is 0 */ new_x = 0; } else { /* rounded value is 1 */ new_x = 1; } if (var[k].x != new_x) { stalling = 0; var[k].x = new_x; } } /* if the rounded point has not changed (stalling), choose and flip some its entries heuristically */ if (stalling) { /* compute d[j] = |x[j] - round(x[j])| */ for (k = 1; k <= nv; k++) { col = lp->col[var[k].j]; var[k].d = fabs(col->prim - (double)var[k].x); } /* sort the list of binary variables by descending d[j] */ qsort(&var[1], nv, sizeof(struct VAR), fcmp); /* choose and flip some rounded components */ for (k = 1; k <= nv; k++) { if (k >= 5 && var[k].d < 0.35 || k >= 10) break; var[k].x = 1 - var[k].x; } } skip: /* check if the time limit has been exhausted */ if (T->parm->tm_lim < INT_MAX && (double)(T->parm->tm_lim - 1) <= 1000.0 * xdifftime(xtime(), T->tm_beg)) goto done; /* build the objective, which is the distance between the current (basic) point and the rounded one */ lp->dir = GLP_MIN; lp->c0 = 0.0; for (j = 1; j <= n; j++) lp->col[j]->coef = 0.0; for (k = 1; k <= nv; k++) { j = var[k].j; if (var[k].x == 0) lp->col[j]->coef = +1.0; else { lp->col[j]->coef = -1.0; lp->c0 += 1.0; } } /* minimize the distance with the simplex method */ glp_init_smcp(&parm); if (T->parm->msg_lev <= GLP_MSG_ERR) parm.msg_lev = T->parm->msg_lev; else if (T->parm->msg_lev <= GLP_MSG_ALL) { parm.msg_lev = GLP_MSG_ON; parm.out_dly = 10000; } ret = glp_simplex(lp, &parm); if (ret != 0) { if (T->parm->msg_lev >= GLP_MSG_ERR) xprintf("Warning: glp_simplex returned %d\n", ret); goto done; } ret = glp_get_status(lp); if (ret != GLP_OPT) { if (T->parm->msg_lev >= GLP_MSG_ERR) xprintf("Warning: glp_get_status returned %d\n", ret); goto done; } if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("delta = %g\n", lp->obj_val); /* check if the basic solution is integer feasible; note that it may be so even if the minimial distance is positive */ tol = 0.3 * T->parm->tol_int; for (k = 1; k <= nv; k++) { col = lp->col[var[k].j]; if (tol < col->prim && col->prim < 1.0 - tol) break; } if (k > nv) { /* okay; the basic solution seems to be integer feasible */ double *x = xcalloc(1+n, sizeof(double)); for (j = 1; j <= n; j++) { x[j] = lp->col[j]->prim; if (P->col[j]->kind == GLP_IV) x[j] = floor(x[j] + 0.5); } #if 1 /* modified by xypron */ /* reset direction and right-hand side of objective */ lp->c0 = P->c0; lp->dir = P->dir; /* fix integer variables */ for (k = 1; k <= nv; k++) { lp->col[var[k].j]->lb = x[var[k].j]; lp->col[var[k].j]->ub = x[var[k].j]; lp->col[var[k].j]->type = GLP_FX; } /* copy original objective function */ for (j = 1; j <= n; j++) lp->col[j]->coef = P->col[j]->coef; /* solve original LP and copy result */ ret = glp_simplex(lp, &parm); if (ret != 0) { if (T->parm->msg_lev >= GLP_MSG_ERR) xprintf("Warning: glp_simplex returned %d\n", ret); goto done; } ret = glp_get_status(lp); if (ret != GLP_OPT) { if (T->parm->msg_lev >= GLP_MSG_ERR) xprintf("Warning: glp_get_status returned %d\n", ret); goto done; } for (j = 1; j <= n; j++) if (P->col[j]->kind != GLP_IV) x[j] = lp->col[j]->prim; #endif ret = glp_ios_heur_sol(T, x); xfree(x); if (ret == 0) { /* the integer solution is accepted */ if (ios_is_hopeful(T, T->curr->bound)) { /* it is reasonable to apply the heuristic once again */ goto more; } else { /* the best known integer feasible solution just found is close to optimal solution to LP relaxation */ goto done; } } } /* the basic solution is fractional */ if (dist == DBL_MAX || lp->obj_val <= dist - 1e-6 * (1.0 + dist)) { /* the distance is reducing */ nfail = 0, dist = lp->obj_val; } else { /* improving the distance failed */ nfail++; } if (nfail < 3) goto loop; if (npass < 5) goto pass; done: /* delete working objects */ if (lp != NULL) glp_delete_prob(lp); if (var != NULL) xfree(var); if (rand != NULL) rng_delete_rand(rand); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpnpp04.c0000644000175000017500000014253711632352620020263 0ustar csheltoncshelton/* glpnpp04.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpnpp.h" /*********************************************************************** * NAME * * npp_binarize_prob - binarize MIP problem * * SYNOPSIS * * #include "glpnpp.h" * int npp_binarize_prob(NPP *npp); * * DESCRIPTION * * The routine npp_binarize_prob replaces in the original MIP problem * every integer variable: * * l[q] <= x[q] <= u[q], (1) * * where l[q] < u[q], by an equivalent sum of binary variables. * * RETURNS * * The routine returns the number of integer variables for which the * transformation failed, because u[q] - l[q] > d_max. * * PROBLEM TRANSFORMATION * * If variable x[q] has non-zero lower bound, it is first processed * with the routine npp_lbnd_col. Thus, we can assume that: * * 0 <= x[q] <= u[q]. (2) * * If u[q] = 1, variable x[q] is already binary, so further processing * is not needed. Let, therefore, that 2 <= u[q] <= d_max, and n be a * smallest integer such that u[q] <= 2^n - 1 (n >= 2, since u[q] >= 2). * Then variable x[q] can be replaced by the following sum: * * n-1 * x[q] = sum 2^k x[k], (3) * k=0 * * where x[k] are binary columns (variables). If u[q] < 2^n - 1, the * following additional inequality constraint must be also included in * the transformed problem: * * n-1 * sum 2^k x[k] <= u[q]. (4) * k=0 * * Note: Assuming that in the transformed problem x[q] becomes binary * variable x[0], this transformation causes new n-1 binary variables * to appear. * * Substituting x[q] from (3) to the objective row gives: * * z = sum c[j] x[j] + c[0] = * j * * = sum c[j] x[j] + c[q] x[q] + c[0] = * j!=q * n-1 * = sum c[j] x[j] + c[q] sum 2^k x[k] + c[0] = * j!=q k=0 * n-1 * = sum c[j] x[j] + sum c[k] x[k] + c[0], * j!=q k=0 * * where: * * c[k] = 2^k c[q], k = 0, ..., n-1. (5) * * And substituting x[q] from (3) to i-th constraint row i gives: * * L[i] <= sum a[i,j] x[j] <= U[i] ==> * j * * L[i] <= sum a[i,j] x[j] + a[i,q] x[q] <= U[i] ==> * j!=q * n-1 * L[i] <= sum a[i,j] x[j] + a[i,q] sum 2^k x[k] <= U[i] ==> * j!=q k=0 * n-1 * L[i] <= sum a[i,j] x[j] + sum a[i,k] x[k] <= U[i], * j!=q k=0 * * where: * * a[i,k] = 2^k a[i,q], k = 0, ..., n-1. (6) * * RECOVERING SOLUTION * * Value of variable x[q] is computed with formula (3). */ struct binarize { int q; /* column reference number for x[q] = x[0] */ int j; /* column reference number for x[1]; x[2] has reference number j+1, x[3] - j+2, etc. */ int n; /* total number of binary variables, n >= 2 */ }; static int rcv_binarize_prob(NPP *npp, void *info); int npp_binarize_prob(NPP *npp) { /* binarize MIP problem */ struct binarize *info; NPPROW *row; NPPCOL *col, *bin; NPPAIJ *aij; int u, n, k, temp, nfails, nvars, nbins, nrows; /* new variables will be added to the end of the column list, so we go from the end to beginning of the column list */ nfails = nvars = nbins = nrows = 0; for (col = npp->c_tail; col != NULL; col = col->prev) { /* skip continuous variable */ if (!col->is_int) continue; /* skip fixed variable */ if (col->lb == col->ub) continue; /* skip binary variable */ if (col->lb == 0.0 && col->ub == 1.0) continue; /* check if the transformation is applicable */ if (col->lb < -1e6 || col->ub > +1e6 || col->ub - col->lb > 4095.0) { /* unfortunately, not */ nfails++; continue; } /* process integer non-binary variable x[q] */ nvars++; /* make x[q] non-negative, if its lower bound is non-zero */ if (col->lb != 0.0) npp_lbnd_col(npp, col); /* now 0 <= x[q] <= u[q] */ xassert(col->lb == 0.0); u = (int)col->ub; xassert(col->ub == (double)u); /* if x[q] is binary, further processing is not needed */ if (u == 1) continue; /* determine smallest n such that u <= 2^n - 1 (thus, n is the number of binary variables needed) */ n = 2, temp = 4; while (u >= temp) n++, temp += temp; nbins += n; /* create transformation stack entry */ info = npp_push_tse(npp, rcv_binarize_prob, sizeof(struct binarize)); info->q = col->j; info->j = 0; /* will be set below */ info->n = n; /* if u < 2^n - 1, we need one additional row for (4) */ if (u < temp - 1) { row = npp_add_row(npp), nrows++; row->lb = -DBL_MAX, row->ub = u; } else row = NULL; /* in the transformed problem variable x[q] becomes binary variable x[0], so its objective and constraint coefficients are not changed */ col->ub = 1.0; /* include x[0] into constraint (4) */ if (row != NULL) npp_add_aij(npp, row, col, 1.0); /* add other binary variables x[1], ..., x[n-1] */ for (k = 1, temp = 2; k < n; k++, temp += temp) { /* add new binary variable x[k] */ bin = npp_add_col(npp); bin->is_int = 1; bin->lb = 0.0, bin->ub = 1.0; bin->coef = (double)temp * col->coef; /* store column reference number for x[1] */ if (info->j == 0) info->j = bin->j; else xassert(info->j + (k-1) == bin->j); /* duplicate constraint coefficients for x[k]; this also automatically includes x[k] into constraint (4) */ for (aij = col->ptr; aij != NULL; aij = aij->c_next) npp_add_aij(npp, aij->row, bin, (double)temp * aij->val); } } if (nvars > 0) xprintf("%d integer variable(s) were replaced by %d binary one" "s\n", nvars, nbins); if (nrows > 0) xprintf("%d row(s) were added due to binarization\n", nrows); if (nfails > 0) xprintf("Binarization failed for %d integer variable(s)\n", nfails); return nfails; } static int rcv_binarize_prob(NPP *npp, void *_info) { /* recovery binarized variable */ struct binarize *info = _info; int k, temp; double sum; /* compute value of x[q]; see formula (3) */ sum = npp->c_value[info->q]; for (k = 1, temp = 2; k < info->n; k++, temp += temp) sum += (double)temp * npp->c_value[info->j + (k-1)]; npp->c_value[info->q] = sum; return 0; } /**********************************************************************/ struct elem { /* linear form element a[j] x[j] */ double aj; /* non-zero coefficient value */ NPPCOL *xj; /* pointer to variable (column) */ struct elem *next; /* pointer to another term */ }; static struct elem *copy_form(NPP *npp, NPPROW *row, double s) { /* copy linear form */ NPPAIJ *aij; struct elem *ptr, *e; ptr = NULL; for (aij = row->ptr; aij != NULL; aij = aij->r_next) { e = dmp_get_atom(npp->pool, sizeof(struct elem)); e->aj = s * aij->val; e->xj = aij->col; e->next = ptr; ptr = e; } return ptr; } static void drop_form(NPP *npp, struct elem *ptr) { /* drop linear form */ struct elem *e; while (ptr != NULL) { e = ptr; ptr = e->next; dmp_free_atom(npp->pool, e, sizeof(struct elem)); } return; } /*********************************************************************** * NAME * * npp_is_packing - test if constraint is packing inequality * * SYNOPSIS * * #include "glpnpp.h" * int npp_is_packing(NPP *npp, NPPROW *row); * * RETURNS * * If the specified row (constraint) is packing inequality (see below), * the routine npp_is_packing returns non-zero. Otherwise, it returns * zero. * * PACKING INEQUALITIES * * In canonical format the packing inequality is the following: * * sum x[j] <= 1, (1) * j in J * * where all variables x[j] are binary. This inequality expresses the * condition that in any integer feasible solution at most one variable * from set J can take non-zero (unity) value while other variables * must be equal to zero. W.l.o.g. it is assumed that |J| >= 2, because * if J is empty or |J| = 1, the inequality (1) is redundant. * * In general case the packing inequality may include original variables * x[j] as well as their complements x~[j]: * * sum x[j] + sum x~[j] <= 1, (2) * j in Jp j in Jn * * where Jp and Jn are not intersected. Therefore, using substitution * x~[j] = 1 - x[j] gives the packing inequality in generalized format: * * sum x[j] - sum x[j] <= 1 - |Jn|. (3) * j in Jp j in Jn */ int npp_is_packing(NPP *npp, NPPROW *row) { /* test if constraint is packing inequality */ NPPCOL *col; NPPAIJ *aij; int b; xassert(npp == npp); if (!(row->lb == -DBL_MAX && row->ub != +DBL_MAX)) return 0; b = 1; for (aij = row->ptr; aij != NULL; aij = aij->r_next) { col = aij->col; if (!(col->is_int && col->lb == 0.0 && col->ub == 1.0)) return 0; if (aij->val == +1.0) ; else if (aij->val == -1.0) b--; else return 0; } if (row->ub != (double)b) return 0; return 1; } /*********************************************************************** * NAME * * npp_hidden_packing - identify hidden packing inequality * * SYNOPSIS * * #include "glpnpp.h" * int npp_hidden_packing(NPP *npp, NPPROW *row); * * DESCRIPTION * * The routine npp_hidden_packing processes specified inequality * constraint, which includes only binary variables, and the number of * the variables is not less than two. If the original inequality is * equivalent to a packing inequality, the routine replaces it by this * equivalent inequality. If the original constraint is double-sided * inequality, it is replaced by a pair of single-sided inequalities, * if necessary. * * RETURNS * * If the original inequality constraint was replaced by equivalent * packing inequality, the routine npp_hidden_packing returns non-zero. * Otherwise, it returns zero. * * PROBLEM TRANSFORMATION * * Consider an inequality constraint: * * sum a[j] x[j] <= b, (1) * j in J * * where all variables x[j] are binary, and |J| >= 2. (In case of '>=' * inequality it can be transformed to '<=' format by multiplying both * its sides by -1.) * * Let Jp = {j: a[j] > 0}, Jn = {j: a[j] < 0}. Performing substitution * x[j] = 1 - x~[j] for all j in Jn, we have: * * sum a[j] x[j] <= b ==> * j in J * * sum a[j] x[j] + sum a[j] x[j] <= b ==> * j in Jp j in Jn * * sum a[j] x[j] + sum a[j] (1 - x~[j]) <= b ==> * j in Jp j in Jn * * sum a[j] x[j] - sum a[j] x~[j] <= b - sum a[j]. * j in Jp j in Jn j in Jn * * Thus, meaning the transformation above, we can assume that in * inequality (1) all coefficients a[j] are positive. Moreover, we can * assume that a[j] <= b. In fact, let a[j] > b; then the following * three cases are possible: * * 1) b < 0. In this case inequality (1) is infeasible, so the problem * has no feasible solution (see the routine npp_analyze_row); * * 2) b = 0. In this case inequality (1) is a forcing inequality on its * upper bound (see the routine npp_forcing row), from which it * follows that all variables x[j] should be fixed at zero; * * 3) b > 0. In this case inequality (1) defines an implied zero upper * bound for variable x[j] (see the routine npp_implied_bounds), from * which it follows that x[j] should be fixed at zero. * * It is assumed that all three cases listed above have been recognized * by the routine npp_process_prob, which performs basic MIP processing * prior to a call the routine npp_hidden_packing. So, if one of these * cases occurs, we should just skip processing such constraint. * * Thus, let 0 < a[j] <= b. Then it is obvious that constraint (1) is * equivalent to packing inquality only if: * * a[j] + a[k] > b + eps (2) * * for all j, k in J, j != k, where eps is an absolute tolerance for * row (linear form) value. Checking the condition (2) for all j and k, * j != k, requires time O(|J|^2). However, this time can be reduced to * O(|J|), if use minimal a[j] and a[k], in which case it is sufficient * to check the condition (2) only once. * * Once the original inequality (1) is replaced by equivalent packing * inequality, we need to perform back substitution x~[j] = 1 - x[j] for * all j in Jn (see above). * * RECOVERING SOLUTION * * None needed. */ static int hidden_packing(NPP *npp, struct elem *ptr, double *_b) { /* process inequality constraint: sum a[j] x[j] <= b; 0 - specified row is NOT hidden packing inequality; 1 - specified row is packing inequality; 2 - specified row is hidden packing inequality. */ struct elem *e, *ej, *ek; int neg; double b = *_b, eps; xassert(npp == npp); /* a[j] must be non-zero, x[j] must be binary, for all j in J */ for (e = ptr; e != NULL; e = e->next) { xassert(e->aj != 0.0); xassert(e->xj->is_int); xassert(e->xj->lb == 0.0 && e->xj->ub == 1.0); } /* check if the specified inequality constraint already has the form of packing inequality */ neg = 0; /* neg is |Jn| */ for (e = ptr; e != NULL; e = e->next) { if (e->aj == +1.0) ; else if (e->aj == -1.0) neg++; else break; } if (e == NULL) { /* all coefficients a[j] are +1 or -1; check rhs b */ if (b == (double)(1 - neg)) { /* it is packing inequality; no processing is needed */ return 1; } } /* substitute x[j] = 1 - x~[j] for all j in Jn to make all a[j] positive; the result is a~[j] = |a[j]| and new rhs b */ for (e = ptr; e != NULL; e = e->next) if (e->aj < 0) b -= e->aj; /* now a[j] > 0 for all j in J (actually |a[j]| are used) */ /* if a[j] > b, skip processing--this case must not appear */ for (e = ptr; e != NULL; e = e->next) if (fabs(e->aj) > b) return 0; /* now 0 < a[j] <= b for all j in J */ /* find two minimal coefficients a[j] and a[k], j != k */ ej = NULL; for (e = ptr; e != NULL; e = e->next) if (ej == NULL || fabs(ej->aj) > fabs(e->aj)) ej = e; xassert(ej != NULL); ek = NULL; for (e = ptr; e != NULL; e = e->next) if (e != ej) if (ek == NULL || fabs(ek->aj) > fabs(e->aj)) ek = e; xassert(ek != NULL); /* the specified constraint is equivalent to packing inequality iff a[j] + a[k] > b + eps */ eps = 1e-3 + 1e-6 * fabs(b); if (fabs(ej->aj) + fabs(ek->aj) <= b + eps) return 0; /* perform back substitution x~[j] = 1 - x[j] and construct the final equivalent packing inequality in generalized format */ b = 1.0; for (e = ptr; e != NULL; e = e->next) { if (e->aj > 0.0) e->aj = +1.0; else /* e->aj < 0.0 */ e->aj = -1.0, b -= 1.0; } *_b = b; return 2; } int npp_hidden_packing(NPP *npp, NPPROW *row) { /* identify hidden packing inequality */ NPPROW *copy; NPPAIJ *aij; struct elem *ptr, *e; int kase, ret, count = 0; double b; /* the row must be inequality constraint */ xassert(row->lb < row->ub); for (kase = 0; kase <= 1; kase++) { if (kase == 0) { /* process row upper bound */ if (row->ub == +DBL_MAX) continue; ptr = copy_form(npp, row, +1.0); b = + row->ub; } else { /* process row lower bound */ if (row->lb == -DBL_MAX) continue; ptr = copy_form(npp, row, -1.0); b = - row->lb; } /* now the inequality has the form "sum a[j] x[j] <= b" */ ret = hidden_packing(npp, ptr, &b); xassert(0 <= ret && ret <= 2); if (kase == 1 && ret == 1 || ret == 2) { /* the original inequality has been identified as hidden packing inequality */ count++; #ifdef GLP_DEBUG xprintf("Original constraint:\n"); for (aij = row->ptr; aij != NULL; aij = aij->r_next) xprintf(" %+g x%d", aij->val, aij->col->j); if (row->lb != -DBL_MAX) xprintf(", >= %g", row->lb); if (row->ub != +DBL_MAX) xprintf(", <= %g", row->ub); xprintf("\n"); xprintf("Equivalent packing inequality:\n"); for (e = ptr; e != NULL; e = e->next) xprintf(" %sx%d", e->aj > 0.0 ? "+" : "-", e->xj->j); xprintf(", <= %g\n", b); #endif if (row->lb == -DBL_MAX || row->ub == +DBL_MAX) { /* the original row is single-sided inequality; no copy is needed */ copy = NULL; } else { /* the original row is double-sided inequality; we need to create its copy for other bound before replacing it with the equivalent inequality */ copy = npp_add_row(npp); if (kase == 0) { /* the copy is for lower bound */ copy->lb = row->lb, copy->ub = +DBL_MAX; } else { /* the copy is for upper bound */ copy->lb = -DBL_MAX, copy->ub = row->ub; } /* copy original row coefficients */ for (aij = row->ptr; aij != NULL; aij = aij->r_next) npp_add_aij(npp, copy, aij->col, aij->val); } /* replace the original inequality by equivalent one */ npp_erase_row(npp, row); row->lb = -DBL_MAX, row->ub = b; for (e = ptr; e != NULL; e = e->next) npp_add_aij(npp, row, e->xj, e->aj); /* continue processing lower bound for the copy */ if (copy != NULL) row = copy; } drop_form(npp, ptr); } return count; } /*********************************************************************** * NAME * * npp_implied_packing - identify implied packing inequality * * SYNOPSIS * * #include "glpnpp.h" * int npp_implied_packing(NPP *npp, NPPROW *row, int which, * NPPCOL *var[], char set[]); * * DESCRIPTION * * The routine npp_implied_packing processes specified row (constraint) * of general format: * * L <= sum a[j] x[j] <= U. (1) * j * * If which = 0, only lower bound L, which must exist, is considered, * while upper bound U is ignored. Similarly, if which = 1, only upper * bound U, which must exist, is considered, while lower bound L is * ignored. Thus, if the specified row is a double-sided inequality or * equality constraint, this routine should be called twice for both * lower and upper bounds. * * The routine npp_implied_packing attempts to find a non-trivial (i.e. * having not less than two binary variables) packing inequality: * * sum x[j] - sum x[j] <= 1 - |Jn|, (2) * j in Jp j in Jn * * which is relaxation of the constraint (1) in the sense that any * solution satisfying to that constraint also satisfies to the packing * inequality (2). If such relaxation exists, the routine stores * pointers to descriptors of corresponding binary variables and their * flags, resp., to locations var[1], var[2], ..., var[len] and set[1], * set[2], ..., set[len], where set[j] = 0 means that j in Jp and * set[j] = 1 means that j in Jn. * * RETURNS * * The routine npp_implied_packing returns len, which is the total * number of binary variables in the packing inequality found, len >= 2. * However, if the relaxation does not exist, the routine returns zero. * * ALGORITHM * * If which = 0, the constraint coefficients (1) are multiplied by -1 * and b is assigned -L; if which = 1, the constraint coefficients (1) * are not changed and b is assigned +U. In both cases the specified * constraint gets the following format: * * sum a[j] x[j] <= b. (3) * j * * (Note that (3) is a relaxation of (1), because one of bounds L or U * is ignored.) * * Let J be set of binary variables, Kp be set of non-binary (integer * or continuous) variables with a[j] > 0, and Kn be set of non-binary * variables with a[j] < 0. Then the inequality (3) can be written as * follows: * * sum a[j] x[j] <= b - sum a[j] x[j] - sum a[j] x[j]. (4) * j in J j in Kp j in Kn * * To get rid of non-binary variables we can replace the inequality (4) * by the following relaxed inequality: * * sum a[j] x[j] <= b~, (5) * j in J * * where: * * b~ = sup(b - sum a[j] x[j] - sum a[j] x[j]) = * j in Kp j in Kn * * = b - inf sum a[j] x[j] - inf sum a[j] x[j] = (6) * j in Kp j in Kn * * = b - sum a[j] l[j] - sum a[j] u[j]. * j in Kp j in Kn * * Note that if lower bound l[j] (if j in Kp) or upper bound u[j] * (if j in Kn) of some non-binary variable x[j] does not exist, then * formally b = +oo, in which case further analysis is not performed. * * Let Bp = {j in J: a[j] > 0}, Bn = {j in J: a[j] < 0}. To make all * the inequality coefficients in (5) positive, we replace all x[j] in * Bn by their complementaries, substituting x[j] = 1 - x~[j] for all * j in Bn, that gives: * * sum a[j] x[j] - sum a[j] x~[j] <= b~ - sum a[j]. (7) * j in Bp j in Bn j in Bn * * This inequality is a relaxation of the original constraint (1), and * it is a binary knapsack inequality. Writing it in the standard format * we have: * * sum alfa[j] z[j] <= beta, (8) * j in J * * where: * ( + a[j], if j in Bp, * alfa[j] = < (9) * ( - a[j], if j in Bn, * * ( x[j], if j in Bp, * z[j] = < (10) * ( 1 - x[j], if j in Bn, * * beta = b~ - sum a[j]. (11) * j in Bn * * In the inequality (8) all coefficients are positive, therefore, the * packing relaxation to be found for this inequality is the following: * * sum z[j] <= 1. (12) * j in P * * It is obvious that set P within J, which we would like to find, must * satisfy to the following condition: * * alfa[j] + alfa[k] > beta + eps for all j, k in P, j != k, (13) * * where eps is an absolute tolerance for value of the linear form. * Thus, it is natural to take P = {j: alpha[j] > (beta + eps) / 2}. * Moreover, if in the equality (8) there exist coefficients alfa[k], * for which alfa[k] <= (beta + eps) / 2, but which, nevertheless, * satisfies to the condition (13) for all j in P, *one* corresponding * variable z[k] (having, for example, maximal coefficient alfa[k]) can * be included in set P, that allows increasing the number of binary * variables in (12) by one. * * Once the set P has been built, for the inequality (12) we need to * perform back substitution according to (10) in order to express it * through the original binary variables. As the result of such back * substitution the relaxed packing inequality get its final format (2), * where Jp = J intersect Bp, and Jn = J intersect Bn. */ int npp_implied_packing(NPP *npp, NPPROW *row, int which, NPPCOL *var[], char set[]) { struct elem *ptr, *e, *i, *k; int len = 0; double b, eps; /* build inequality (3) */ if (which == 0) { ptr = copy_form(npp, row, -1.0); xassert(row->lb != -DBL_MAX); b = - row->lb; } else if (which == 1) { ptr = copy_form(npp, row, +1.0); xassert(row->ub != +DBL_MAX); b = + row->ub; } /* remove non-binary variables to build relaxed inequality (5); compute its right-hand side b~ with formula (6) */ for (e = ptr; e != NULL; e = e->next) { if (!(e->xj->is_int && e->xj->lb == 0.0 && e->xj->ub == 1.0)) { /* x[j] is non-binary variable */ if (e->aj > 0.0) { if (e->xj->lb == -DBL_MAX) goto done; b -= e->aj * e->xj->lb; } else /* e->aj < 0.0 */ { if (e->xj->ub == +DBL_MAX) goto done; b -= e->aj * e->xj->ub; } /* a[j] = 0 means that variable x[j] is removed */ e->aj = 0.0; } } /* substitute x[j] = 1 - x~[j] to build knapsack inequality (8); compute its right-hand side beta with formula (11) */ for (e = ptr; e != NULL; e = e->next) if (e->aj < 0.0) b -= e->aj; /* if beta is close to zero, the knapsack inequality is either infeasible or forcing inequality; this must never happen, so we skip further analysis */ if (b < 1e-3) goto done; /* build set P as well as sets Jp and Jn, and determine x[k] as explained above in comments to the routine */ eps = 1e-3 + 1e-6 * b; i = k = NULL; for (e = ptr; e != NULL; e = e->next) { /* note that alfa[j] = |a[j]| */ if (fabs(e->aj) > 0.5 * (b + eps)) { /* alfa[j] > (b + eps) / 2; include x[j] in set P, i.e. in set Jp or Jn */ var[++len] = e->xj; set[len] = (char)(e->aj > 0.0 ? 0 : 1); /* alfa[i] = min alfa[j] over all j included in set P */ if (i == NULL || fabs(i->aj) > fabs(e->aj)) i = e; } else if (fabs(e->aj) >= 1e-3) { /* alfa[k] = max alfa[j] over all j not included in set P; we skip coefficient a[j] if it is close to zero to avoid numerically unreliable results */ if (k == NULL || fabs(k->aj) < fabs(e->aj)) k = e; } } /* if alfa[k] satisfies to condition (13) for all j in P, include x[k] in P */ if (i != NULL && k != NULL && fabs(i->aj) + fabs(k->aj) > b + eps) { var[++len] = k->xj; set[len] = (char)(k->aj > 0.0 ? 0 : 1); } /* trivial packing inequality being redundant must never appear, so we just ignore it */ if (len < 2) len = 0; done: drop_form(npp, ptr); return len; } /*********************************************************************** * NAME * * npp_is_covering - test if constraint is covering inequality * * SYNOPSIS * * #include "glpnpp.h" * int npp_is_covering(NPP *npp, NPPROW *row); * * RETURNS * * If the specified row (constraint) is covering inequality (see below), * the routine npp_is_covering returns non-zero. Otherwise, it returns * zero. * * COVERING INEQUALITIES * * In canonical format the covering inequality is the following: * * sum x[j] >= 1, (1) * j in J * * where all variables x[j] are binary. This inequality expresses the * condition that in any integer feasible solution variables in set J * cannot be all equal to zero at the same time, i.e. at least one * variable must take non-zero (unity) value. W.l.o.g. it is assumed * that |J| >= 2, because if J is empty, the inequality (1) is * infeasible, and if |J| = 1, the inequality (1) is a forcing row. * * In general case the covering inequality may include original * variables x[j] as well as their complements x~[j]: * * sum x[j] + sum x~[j] >= 1, (2) * j in Jp j in Jn * * where Jp and Jn are not intersected. Therefore, using substitution * x~[j] = 1 - x[j] gives the packing inequality in generalized format: * * sum x[j] - sum x[j] >= 1 - |Jn|. (3) * j in Jp j in Jn * * (May note that the inequality (3) cuts off infeasible solutions, * where x[j] = 0 for all j in Jp and x[j] = 1 for all j in Jn.) * * NOTE: If |J| = 2, the inequality (3) is equivalent to packing * inequality (see the routine npp_is_packing). */ int npp_is_covering(NPP *npp, NPPROW *row) { /* test if constraint is covering inequality */ NPPCOL *col; NPPAIJ *aij; int b; xassert(npp == npp); if (!(row->lb != -DBL_MAX && row->ub == +DBL_MAX)) return 0; b = 1; for (aij = row->ptr; aij != NULL; aij = aij->r_next) { col = aij->col; if (!(col->is_int && col->lb == 0.0 && col->ub == 1.0)) return 0; if (aij->val == +1.0) ; else if (aij->val == -1.0) b--; else return 0; } if (row->lb != (double)b) return 0; return 1; } /*********************************************************************** * NAME * * npp_hidden_covering - identify hidden covering inequality * * SYNOPSIS * * #include "glpnpp.h" * int npp_hidden_covering(NPP *npp, NPPROW *row); * * DESCRIPTION * * The routine npp_hidden_covering processes specified inequality * constraint, which includes only binary variables, and the number of * the variables is not less than three. If the original inequality is * equivalent to a covering inequality (see below), the routine * replaces it by the equivalent inequality. If the original constraint * is double-sided inequality, it is replaced by a pair of single-sided * inequalities, if necessary. * * RETURNS * * If the original inequality constraint was replaced by equivalent * covering inequality, the routine npp_hidden_covering returns * non-zero. Otherwise, it returns zero. * * PROBLEM TRANSFORMATION * * Consider an inequality constraint: * * sum a[j] x[j] >= b, (1) * j in J * * where all variables x[j] are binary, and |J| >= 3. (In case of '<=' * inequality it can be transformed to '>=' format by multiplying both * its sides by -1.) * * Let Jp = {j: a[j] > 0}, Jn = {j: a[j] < 0}. Performing substitution * x[j] = 1 - x~[j] for all j in Jn, we have: * * sum a[j] x[j] >= b ==> * j in J * * sum a[j] x[j] + sum a[j] x[j] >= b ==> * j in Jp j in Jn * * sum a[j] x[j] + sum a[j] (1 - x~[j]) >= b ==> * j in Jp j in Jn * * sum m a[j] x[j] - sum a[j] x~[j] >= b - sum a[j]. * j in Jp j in Jn j in Jn * * Thus, meaning the transformation above, we can assume that in * inequality (1) all coefficients a[j] are positive. Moreover, we can * assume that b > 0, because otherwise the inequality (1) would be * redundant (see the routine npp_analyze_row). It is then obvious that * constraint (1) is equivalent to covering inequality only if: * * a[j] >= b, (2) * * for all j in J. * * Once the original inequality (1) is replaced by equivalent covering * inequality, we need to perform back substitution x~[j] = 1 - x[j] for * all j in Jn (see above). * * RECOVERING SOLUTION * * None needed. */ static int hidden_covering(NPP *npp, struct elem *ptr, double *_b) { /* process inequality constraint: sum a[j] x[j] >= b; 0 - specified row is NOT hidden covering inequality; 1 - specified row is covering inequality; 2 - specified row is hidden covering inequality. */ struct elem *e; int neg; double b = *_b, eps; xassert(npp == npp); /* a[j] must be non-zero, x[j] must be binary, for all j in J */ for (e = ptr; e != NULL; e = e->next) { xassert(e->aj != 0.0); xassert(e->xj->is_int); xassert(e->xj->lb == 0.0 && e->xj->ub == 1.0); } /* check if the specified inequality constraint already has the form of covering inequality */ neg = 0; /* neg is |Jn| */ for (e = ptr; e != NULL; e = e->next) { if (e->aj == +1.0) ; else if (e->aj == -1.0) neg++; else break; } if (e == NULL) { /* all coefficients a[j] are +1 or -1; check rhs b */ if (b == (double)(1 - neg)) { /* it is covering inequality; no processing is needed */ return 1; } } /* substitute x[j] = 1 - x~[j] for all j in Jn to make all a[j] positive; the result is a~[j] = |a[j]| and new rhs b */ for (e = ptr; e != NULL; e = e->next) if (e->aj < 0) b -= e->aj; /* now a[j] > 0 for all j in J (actually |a[j]| are used) */ /* if b <= 0, skip processing--this case must not appear */ if (b < 1e-3) return 0; /* now a[j] > 0 for all j in J, and b > 0 */ /* the specified constraint is equivalent to covering inequality iff a[j] >= b for all j in J */ eps = 1e-9 + 1e-12 * fabs(b); for (e = ptr; e != NULL; e = e->next) if (fabs(e->aj) < b - eps) return 0; /* perform back substitution x~[j] = 1 - x[j] and construct the final equivalent covering inequality in generalized format */ b = 1.0; for (e = ptr; e != NULL; e = e->next) { if (e->aj > 0.0) e->aj = +1.0; else /* e->aj < 0.0 */ e->aj = -1.0, b -= 1.0; } *_b = b; return 2; } int npp_hidden_covering(NPP *npp, NPPROW *row) { /* identify hidden covering inequality */ NPPROW *copy; NPPAIJ *aij; struct elem *ptr, *e; int kase, ret, count = 0; double b; /* the row must be inequality constraint */ xassert(row->lb < row->ub); for (kase = 0; kase <= 1; kase++) { if (kase == 0) { /* process row lower bound */ if (row->lb == -DBL_MAX) continue; ptr = copy_form(npp, row, +1.0); b = + row->lb; } else { /* process row upper bound */ if (row->ub == +DBL_MAX) continue; ptr = copy_form(npp, row, -1.0); b = - row->ub; } /* now the inequality has the form "sum a[j] x[j] >= b" */ ret = hidden_covering(npp, ptr, &b); xassert(0 <= ret && ret <= 2); if (kase == 1 && ret == 1 || ret == 2) { /* the original inequality has been identified as hidden covering inequality */ count++; #ifdef GLP_DEBUG xprintf("Original constraint:\n"); for (aij = row->ptr; aij != NULL; aij = aij->r_next) xprintf(" %+g x%d", aij->val, aij->col->j); if (row->lb != -DBL_MAX) xprintf(", >= %g", row->lb); if (row->ub != +DBL_MAX) xprintf(", <= %g", row->ub); xprintf("\n"); xprintf("Equivalent covering inequality:\n"); for (e = ptr; e != NULL; e = e->next) xprintf(" %sx%d", e->aj > 0.0 ? "+" : "-", e->xj->j); xprintf(", >= %g\n", b); #endif if (row->lb == -DBL_MAX || row->ub == +DBL_MAX) { /* the original row is single-sided inequality; no copy is needed */ copy = NULL; } else { /* the original row is double-sided inequality; we need to create its copy for other bound before replacing it with the equivalent inequality */ copy = npp_add_row(npp); if (kase == 0) { /* the copy is for upper bound */ copy->lb = -DBL_MAX, copy->ub = row->ub; } else { /* the copy is for lower bound */ copy->lb = row->lb, copy->ub = +DBL_MAX; } /* copy original row coefficients */ for (aij = row->ptr; aij != NULL; aij = aij->r_next) npp_add_aij(npp, copy, aij->col, aij->val); } /* replace the original inequality by equivalent one */ npp_erase_row(npp, row); row->lb = b, row->ub = +DBL_MAX; for (e = ptr; e != NULL; e = e->next) npp_add_aij(npp, row, e->xj, e->aj); /* continue processing upper bound for the copy */ if (copy != NULL) row = copy; } drop_form(npp, ptr); } return count; } /*********************************************************************** * NAME * * npp_is_partitioning - test if constraint is partitioning equality * * SYNOPSIS * * #include "glpnpp.h" * int npp_is_partitioning(NPP *npp, NPPROW *row); * * RETURNS * * If the specified row (constraint) is partitioning equality (see * below), the routine npp_is_partitioning returns non-zero. Otherwise, * it returns zero. * * PARTITIONING EQUALITIES * * In canonical format the partitioning equality is the following: * * sum x[j] = 1, (1) * j in J * * where all variables x[j] are binary. This equality expresses the * condition that in any integer feasible solution exactly one variable * in set J must take non-zero (unity) value while other variables must * be equal to zero. W.l.o.g. it is assumed that |J| >= 2, because if * J is empty, the inequality (1) is infeasible, and if |J| = 1, the * inequality (1) is a fixing row. * * In general case the partitioning equality may include original * variables x[j] as well as their complements x~[j]: * * sum x[j] + sum x~[j] = 1, (2) * j in Jp j in Jn * * where Jp and Jn are not intersected. Therefore, using substitution * x~[j] = 1 - x[j] leads to the partitioning equality in generalized * format: * * sum x[j] - sum x[j] = 1 - |Jn|. (3) * j in Jp j in Jn */ int npp_is_partitioning(NPP *npp, NPPROW *row) { /* test if constraint is partitioning equality */ NPPCOL *col; NPPAIJ *aij; int b; xassert(npp == npp); if (row->lb != row->ub) return 0; b = 1; for (aij = row->ptr; aij != NULL; aij = aij->r_next) { col = aij->col; if (!(col->is_int && col->lb == 0.0 && col->ub == 1.0)) return 0; if (aij->val == +1.0) ; else if (aij->val == -1.0) b--; else return 0; } if (row->lb != (double)b) return 0; return 1; } /*********************************************************************** * NAME * * npp_reduce_ineq_coef - reduce inequality constraint coefficients * * SYNOPSIS * * #include "glpnpp.h" * int npp_reduce_ineq_coef(NPP *npp, NPPROW *row); * * DESCRIPTION * * The routine npp_reduce_ineq_coef processes specified inequality * constraint attempting to replace it by an equivalent constraint, * where magnitude of coefficients at binary variables is smaller than * in the original constraint. If the inequality is double-sided, it is * replaced by a pair of single-sided inequalities, if necessary. * * RETURNS * * The routine npp_reduce_ineq_coef returns the number of coefficients * reduced. * * BACKGROUND * * Consider an inequality constraint: * * sum a[j] x[j] >= b. (1) * j in J * * (In case of '<=' inequality it can be transformed to '>=' format by * multiplying both its sides by -1.) Let x[k] be a binary variable; * other variables can be integer as well as continuous. We can write * constraint (1) as follows: * * a[k] x[k] + t[k] >= b, (2) * * where: * * t[k] = sum a[j] x[j]. (3) * j in J\{k} * * Since x[k] is binary, constraint (2) is equivalent to disjunction of * the following two constraints: * * x[k] = 0, t[k] >= b (4) * * OR * * x[k] = 1, t[k] >= b - a[k]. (5) * * Let also that for the partial sum t[k] be known some its implied * lower bound inf t[k]. * * Case a[k] > 0. Let inf t[k] < b, since otherwise both constraints * (4) and (5) and therefore constraint (2) are redundant. * If inf t[k] > b - a[k], only constraint (5) is redundant, in which * case it can be replaced with the following redundant and therefore * equivalent constraint: * * t[k] >= b - a'[k] = inf t[k], (6) * * where: * * a'[k] = b - inf t[k]. (7) * * Thus, the original constraint (2) is equivalent to the following * constraint with coefficient at variable x[k] changed: * * a'[k] x[k] + t[k] >= b. (8) * * From inf t[k] < b it follows that a'[k] > 0, i.e. the coefficient * at x[k] keeps its sign. And from inf t[k] > b - a[k] it follows that * a'[k] < a[k], i.e. the coefficient reduces in magnitude. * * Case a[k] < 0. Let inf t[k] < b - a[k], since otherwise both * constraints (4) and (5) and therefore constraint (2) are redundant. * If inf t[k] > b, only constraint (4) is redundant, in which case it * can be replaced with the following redundant and therefore equivalent * constraint: * * t[k] >= b' = inf t[k]. (9) * * Rewriting constraint (5) as follows: * * t[k] >= b - a[k] = b' - a'[k], (10) * * where: * * a'[k] = a[k] + b' - b = a[k] + inf t[k] - b, (11) * * we can see that disjunction of constraint (9) and (10) is equivalent * to disjunction of constraint (4) and (5), from which it follows that * the original constraint (2) is equivalent to the following constraint * with both coefficient at variable x[k] and right-hand side changed: * * a'[k] x[k] + t[k] >= b'. (12) * * From inf t[k] < b - a[k] it follows that a'[k] < 0, i.e. the * coefficient at x[k] keeps its sign. And from inf t[k] > b it follows * that a'[k] > a[k], i.e. the coefficient reduces in magnitude. * * PROBLEM TRANSFORMATION * * In the routine npp_reduce_ineq_coef the following implied lower * bound of the partial sum (3) is used: * * inf t[k] = sum a[j] l[j] + sum a[j] u[j], (13) * j in Jp\{k} k in Jn\{k} * * where Jp = {j : a[j] > 0}, Jn = {j : a[j] < 0}, l[j] and u[j] are * lower and upper bounds, resp., of variable x[j]. * * In order to compute inf t[k] more efficiently, the following formula, * which is equivalent to (13), is actually used: * * ( h - a[k] l[k] = h, if a[k] > 0, * inf t[k] = < (14) * ( h - a[k] u[k] = h - a[k], if a[k] < 0, * * where: * * h = sum a[j] l[j] + sum a[j] u[j] (15) * j in Jp j in Jn * * is the implied lower bound of row (1). * * Reduction of positive coefficient (a[k] > 0) does not change value * of h, since l[k] = 0. In case of reduction of negative coefficient * (a[k] < 0) from (11) it follows that: * * delta a[k] = a'[k] - a[k] = inf t[k] - b (> 0), (16) * * so new value of h (accounting that u[k] = 1) can be computed as * follows: * * h := h + delta a[k] = h + (inf t[k] - b). (17) * * RECOVERING SOLUTION * * None needed. */ static int reduce_ineq_coef(NPP *npp, struct elem *ptr, double *_b) { /* process inequality constraint: sum a[j] x[j] >= b */ /* returns: the number of coefficients reduced */ struct elem *e; int count = 0; double h, inf_t, new_a, b = *_b; xassert(npp == npp); /* compute h; see (15) */ h = 0.0; for (e = ptr; e != NULL; e = e->next) { if (e->aj > 0.0) { if (e->xj->lb == -DBL_MAX) goto done; h += e->aj * e->xj->lb; } else /* e->aj < 0.0 */ { if (e->xj->ub == +DBL_MAX) goto done; h += e->aj * e->xj->ub; } } /* perform reduction of coefficients at binary variables */ for (e = ptr; e != NULL; e = e->next) { /* skip non-binary variable */ if (!(e->xj->is_int && e->xj->lb == 0.0 && e->xj->ub == 1.0)) continue; if (e->aj > 0.0) { /* compute inf t[k]; see (14) */ inf_t = h; if (b - e->aj < inf_t && inf_t < b) { /* compute reduced coefficient a'[k]; see (7) */ new_a = b - inf_t; if (new_a >= +1e-3 && e->aj - new_a >= 0.01 * (1.0 + e->aj)) { /* accept a'[k] */ #ifdef GLP_DEBUG xprintf("+"); #endif e->aj = new_a; count++; } } } else /* e->aj < 0.0 */ { /* compute inf t[k]; see (14) */ inf_t = h - e->aj; if (b < inf_t && inf_t < b - e->aj) { /* compute reduced coefficient a'[k]; see (11) */ new_a = e->aj + (inf_t - b); if (new_a <= -1e-3 && new_a - e->aj >= 0.01 * (1.0 - e->aj)) { /* accept a'[k] */ #ifdef GLP_DEBUG xprintf("-"); #endif e->aj = new_a; /* update h; see (17) */ h += (inf_t - b); /* compute b'; see (9) */ b = inf_t; count++; } } } } *_b = b; done: return count; } int npp_reduce_ineq_coef(NPP *npp, NPPROW *row) { /* reduce inequality constraint coefficients */ NPPROW *copy; NPPAIJ *aij; struct elem *ptr, *e; int kase, count[2]; double b; /* the row must be inequality constraint */ xassert(row->lb < row->ub); count[0] = count[1] = 0; for (kase = 0; kase <= 1; kase++) { if (kase == 0) { /* process row lower bound */ if (row->lb == -DBL_MAX) continue; #ifdef GLP_DEBUG xprintf("L"); #endif ptr = copy_form(npp, row, +1.0); b = + row->lb; } else { /* process row upper bound */ if (row->ub == +DBL_MAX) continue; #ifdef GLP_DEBUG xprintf("U"); #endif ptr = copy_form(npp, row, -1.0); b = - row->ub; } /* now the inequality has the form "sum a[j] x[j] >= b" */ count[kase] = reduce_ineq_coef(npp, ptr, &b); if (count[kase] > 0) { /* the original inequality has been replaced by equivalent one with coefficients reduced */ if (row->lb == -DBL_MAX || row->ub == +DBL_MAX) { /* the original row is single-sided inequality; no copy is needed */ copy = NULL; } else { /* the original row is double-sided inequality; we need to create its copy for other bound before replacing it with the equivalent inequality */ #ifdef GLP_DEBUG xprintf("*"); #endif copy = npp_add_row(npp); if (kase == 0) { /* the copy is for upper bound */ copy->lb = -DBL_MAX, copy->ub = row->ub; } else { /* the copy is for lower bound */ copy->lb = row->lb, copy->ub = +DBL_MAX; } /* copy original row coefficients */ for (aij = row->ptr; aij != NULL; aij = aij->r_next) npp_add_aij(npp, copy, aij->col, aij->val); } /* replace the original inequality by equivalent one */ npp_erase_row(npp, row); row->lb = b, row->ub = +DBL_MAX; for (e = ptr; e != NULL; e = e->next) npp_add_aij(npp, row, e->xj, e->aj); /* continue processing upper bound for the copy */ if (copy != NULL) row = copy; } drop_form(npp, ptr); } return count[0] + count[1]; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpqmd.c0000644000175000017500000004356511632352620020104 0ustar csheltoncshelton/* glpqmd.c (quotient minimum degree algorithm) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * THIS CODE IS THE RESULT OF TRANSLATION OF THE FORTRAN SUBROUTINES * GENQMD, QMDRCH, QMDQT, QMDUPD, AND QMDMRG FROM THE BOOK: * * ALAN GEORGE, JOSEPH W-H LIU. COMPUTER SOLUTION OF LARGE SPARSE * POSITIVE DEFINITE SYSTEMS. PRENTICE-HALL, 1981. * * THE TRANSLATION HAS BEEN DONE WITH THE PERMISSION OF THE AUTHORS * OF THE ORIGINAL FORTRAN SUBROUTINES: ALAN GEORGE AND JOSEPH LIU, * UNIVERSITY OF WATERLOO, WATERLOO, ONTARIO, CANADA. * * The translation was made by Andrew Makhorin . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpqmd.h" /*********************************************************************** * NAME * * genqmd - GENeral Quotient Minimum Degree algorithm * * SYNOPSIS * * #include "glpqmd.h" * void genqmd(int *neqns, int xadj[], int adjncy[], int perm[], * int invp[], int deg[], int marker[], int rchset[], int nbrhd[], * int qsize[], int qlink[], int *nofsub); * * PURPOSE * * This routine implements the minimum degree algorithm. It makes use * of the implicit representation of the elimination graph by quotient * graphs, and the notion of indistinguishable nodes. * * CAUTION * * The adjancy vector adjncy will be destroyed. * * INPUT PARAMETERS * * neqns - number of equations; * (xadj, adjncy) - * the adjancy structure. * * OUTPUT PARAMETERS * * perm - the minimum degree ordering; * invp - the inverse of perm. * * WORKING PARAMETERS * * deg - the degree vector. deg[i] is negative means node i has been * numbered; * marker - a marker vector, where marker[i] is negative means node i * has been merged with another nodeand thus can be ignored; * rchset - vector used for the reachable set; * nbrhd - vector used for neighborhood set; * qsize - vector used to store the size of indistinguishable * supernodes; * qlink - vector used to store indistinguishable nodes, i, qlink[i], * qlink[qlink[i]], ... are the members of the supernode * represented by i. * * PROGRAM SUBROUTINES * * qmdrch, qmdqt, qmdupd. ***********************************************************************/ void genqmd(int *_neqns, int xadj[], int adjncy[], int perm[], int invp[], int deg[], int marker[], int rchset[], int nbrhd[], int qsize[], int qlink[], int *_nofsub) { int inode, ip, irch, j, mindeg, ndeg, nhdsze, node, np, num, nump1, nxnode, rchsze, search, thresh; # define neqns (*_neqns) # define nofsub (*_nofsub) /* Initialize degree vector and other working variables. */ mindeg = neqns; nofsub = 0; for (node = 1; node <= neqns; node++) { perm[node] = node; invp[node] = node; marker[node] = 0; qsize[node] = 1; qlink[node] = 0; ndeg = xadj[node+1] - xadj[node]; deg[node] = ndeg; if (ndeg < mindeg) mindeg = ndeg; } num = 0; /* Perform threshold search to get a node of min degree. Variable search point to where search should start. */ s200: search = 1; thresh = mindeg; mindeg = neqns; s300: nump1 = num + 1; if (nump1 > search) search = nump1; for (j = search; j <= neqns; j++) { node = perm[j]; if (marker[node] >= 0) { ndeg = deg[node]; if (ndeg <= thresh) goto s500; if (ndeg < mindeg) mindeg = ndeg; } } goto s200; /* Node has minimum degree. Find its reachable sets by calling qmdrch. */ s500: search = j; nofsub += deg[node]; marker[node] = 1; qmdrch(&node, xadj, adjncy, deg, marker, &rchsze, rchset, &nhdsze, nbrhd); /* Eliminate all nodes indistinguishable from node. They are given by node, qlink[node], ... . */ nxnode = node; s600: num++; np = invp[nxnode]; ip = perm[num]; perm[np] = ip; invp[ip] = np; perm[num] = nxnode; invp[nxnode] = num; deg[nxnode] = -1; nxnode = qlink[nxnode]; if (nxnode > 0) goto s600; if (rchsze > 0) { /* Update the degrees of the nodes in the reachable set and identify indistinguishable nodes. */ qmdupd(xadj, adjncy, &rchsze, rchset, deg, qsize, qlink, marker, &rchset[rchsze+1], &nbrhd[nhdsze+1]); /* Reset marker value of nodes in reach set. Update threshold value for cyclic search. Also call qmdqt to form new quotient graph. */ marker[node] = 0; for (irch = 1; irch <= rchsze; irch++) { inode = rchset[irch]; if (marker[inode] >= 0) { marker[inode] = 0; ndeg = deg[inode]; if (ndeg < mindeg) mindeg = ndeg; if (ndeg <= thresh) { mindeg = thresh; thresh = ndeg; search = invp[inode]; } } } if (nhdsze > 0) qmdqt(&node, xadj, adjncy, marker, &rchsze, rchset, nbrhd); } if (num < neqns) goto s300; return; # undef neqns # undef nofsub } /*********************************************************************** * NAME * * qmdrch - Quotient MD ReaCHable set * * SYNOPSIS * * #include "glpqmd.h" * void qmdrch(int *root, int xadj[], int adjncy[], int deg[], * int marker[], int *rchsze, int rchset[], int *nhdsze, * int nbrhd[]); * * PURPOSE * * This subroutine determines the reachable set of a node through a * given subset. The adjancy structure is assumed to be stored in a * quotient graph format. * * INPUT PARAMETERS * * root - the given node not in the subset; * (xadj, adjncy) - * the adjancy structure pair; * deg - the degree vector. deg[i] < 0 means the node belongs to the * given subset. * * OUTPUT PARAMETERS * * (rchsze, rchset) - * the reachable set; * (nhdsze, nbrhd) - * the neighborhood set. * * UPDATED PARAMETERS * * marker - the marker vector for reach and nbrhd sets. > 0 means the * node is in reach set. < 0 means the node has been merged * with others in the quotient or it is in nbrhd set. ***********************************************************************/ void qmdrch(int *_root, int xadj[], int adjncy[], int deg[], int marker[], int *_rchsze, int rchset[], int *_nhdsze, int nbrhd[]) { int i, istop, istrt, j, jstop, jstrt, nabor, node; # define root (*_root) # define rchsze (*_rchsze) # define nhdsze (*_nhdsze) /* Loop through the neighbors of root in the quotient graph. */ nhdsze = 0; rchsze = 0; istrt = xadj[root]; istop = xadj[root+1] - 1; if (istop < istrt) return; for (i = istrt; i <= istop; i++) { nabor = adjncy[i]; if (nabor == 0) return; if (marker[nabor] == 0) { if (deg[nabor] >= 0) { /* Include nabor into the reachable set. */ rchsze++; rchset[rchsze] = nabor; marker[nabor] = 1; goto s600; } /* nabor has been eliminated. Find nodes reachable from it. */ marker[nabor] = -1; nhdsze++; nbrhd[nhdsze] = nabor; s300: jstrt = xadj[nabor]; jstop = xadj[nabor+1] - 1; for (j = jstrt; j <= jstop; j++) { node = adjncy[j]; nabor = - node; if (node < 0) goto s300; if (node == 0) goto s600; if (marker[node] == 0) { rchsze++; rchset[rchsze] = node; marker[node] = 1; } } } s600: ; } return; # undef root # undef rchsze # undef nhdsze } /*********************************************************************** * NAME * * qmdqt - Quotient MD Quotient graph Transformation * * SYNOPSIS * * #include "glpqmd.h" * void qmdqt(int *root, int xadj[], int adjncy[], int marker[], * int *rchsze, int rchset[], int nbrhd[]); * * PURPOSE * * This subroutine performs the quotient graph transformation after a * node has been eliminated. * * INPUT PARAMETERS * * root - the node just eliminated. It becomes the representative of * the new supernode; * (xadj, adjncy) - * the adjancy structure; * (rchsze, rchset) - * the reachable set of root in the old quotient graph; * nbrhd - the neighborhood set which will be merged with root to form * the new supernode; * marker - the marker vector. * * UPDATED PARAMETERS * * adjncy - becomes the adjncy of the quotient graph. ***********************************************************************/ void qmdqt(int *_root, int xadj[], int adjncy[], int marker[], int *_rchsze, int rchset[], int nbrhd[]) { int inhd, irch, j, jstop, jstrt, link, nabor, node; # define root (*_root) # define rchsze (*_rchsze) irch = 0; inhd = 0; node = root; s100: jstrt = xadj[node]; jstop = xadj[node+1] - 2; if (jstop >= jstrt) { /* Place reach nodes into the adjacent list of node. */ for (j = jstrt; j <= jstop; j++) { irch++; adjncy[j] = rchset[irch]; if (irch >= rchsze) goto s400; } } /* Link to other space provided by the nbrhd set. */ link = adjncy[jstop+1]; node = - link; if (link >= 0) { inhd++; node = nbrhd[inhd]; adjncy[jstop+1] = - node; } goto s100; /* All reachable nodes have been saved. End the adjacent list. Add root to the neighborhood list of each node in the reach set. */ s400: adjncy[j+1] = 0; for (irch = 1; irch <= rchsze; irch++) { node = rchset[irch]; if (marker[node] >= 0) { jstrt = xadj[node]; jstop = xadj[node+1] - 1; for (j = jstrt; j <= jstop; j++) { nabor = adjncy[j]; if (marker[nabor] < 0) { adjncy[j] = root; goto s600; } } } s600: ; } return; # undef root # undef rchsze } /*********************************************************************** * NAME * * qmdupd - Quotient MD UPDate * * SYNOPSIS * * #include "glpqmd.h" * void qmdupd(int xadj[], int adjncy[], int *nlist, int list[], * int deg[], int qsize[], int qlink[], int marker[], int rchset[], * int nbrhd[]); * * PURPOSE * * This routine performs degree update for a set of nodes in the minimum * degree algorithm. * * INPUT PARAMETERS * * (xadj, adjncy) - * the adjancy structure; * (nlist, list) - * the list of nodes whose degree has to be updated. * * UPDATED PARAMETERS * * deg - the degree vector; * qsize - size of indistinguishable supernodes; * qlink - linked list for indistinguishable nodes; * marker - used to mark those nodes in reach/nbrhd sets. * * WORKING PARAMETERS * * rchset - the reachable set; * nbrhd - the neighborhood set. * * PROGRAM SUBROUTINES * * qmdmrg. ***********************************************************************/ void qmdupd(int xadj[], int adjncy[], int *_nlist, int list[], int deg[], int qsize[], int qlink[], int marker[], int rchset[], int nbrhd[]) { int deg0, deg1, il, inhd, inode, irch, j, jstop, jstrt, mark, nabor, nhdsze, node, rchsze; # define nlist (*_nlist) /* Find all eliminated supernodes that are adjacent to some nodes in the given list. Put them into (nhdsze, nbrhd). deg0 contains the number of nodes in the list. */ if (nlist <= 0) return; deg0 = 0; nhdsze = 0; for (il = 1; il <= nlist; il++) { node = list[il]; deg0 += qsize[node]; jstrt = xadj[node]; jstop = xadj[node+1] - 1; for (j = jstrt; j <= jstop; j++) { nabor = adjncy[j]; if (marker[nabor] == 0 && deg[nabor] < 0) { marker[nabor] = -1; nhdsze++; nbrhd[nhdsze] = nabor; } } } /* Merge indistinguishable nodes in the list by calling the subroutine qmdmrg. */ if (nhdsze > 0) qmdmrg(xadj, adjncy, deg, qsize, qlink, marker, °0, &nhdsze, nbrhd, rchset, &nbrhd[nhdsze+1]); /* Find the new degrees of the nodes that have not been merged. */ for (il = 1; il <= nlist; il++) { node = list[il]; mark = marker[node]; if (mark == 0 || mark == 1) { marker[node] = 2; qmdrch(&node, xadj, adjncy, deg, marker, &rchsze, rchset, &nhdsze, nbrhd); deg1 = deg0; if (rchsze > 0) { for (irch = 1; irch <= rchsze; irch++) { inode = rchset[irch]; deg1 += qsize[inode]; marker[inode] = 0; } } deg[node] = deg1 - 1; if (nhdsze > 0) { for (inhd = 1; inhd <= nhdsze; inhd++) { inode = nbrhd[inhd]; marker[inode] = 0; } } } } return; # undef nlist } /*********************************************************************** * NAME * * qmdmrg - Quotient MD MeRGe * * SYNOPSIS * * #include "qmdmrg.h" * void qmdmrg(int xadj[], int adjncy[], int deg[], int qsize[], * int qlink[], int marker[], int *deg0, int *nhdsze, int nbrhd[], * int rchset[], int ovrlp[]); * * PURPOSE * * This routine merges indistinguishable nodes in the minimum degree * ordering algorithm. It also computes the new degrees of these new * supernodes. * * INPUT PARAMETERS * * (xadj, adjncy) - * the adjancy structure; * deg0 - the number of nodes in the given set; * (nhdsze, nbrhd) - * the set of eliminated supernodes adjacent to some nodes in * the set. * * UPDATED PARAMETERS * * deg - the degree vector; * qsize - size of indistinguishable nodes; * qlink - linked list for indistinguishable nodes; * marker - the given set is given by those nodes with marker value set * to 1. Those nodes with degree updated will have marker value * set to 2. * * WORKING PARAMETERS * * rchset - the reachable set; * ovrlp - temp vector to store the intersection of two reachable sets. ***********************************************************************/ void qmdmrg(int xadj[], int adjncy[], int deg[], int qsize[], int qlink[], int marker[], int *_deg0, int *_nhdsze, int nbrhd[], int rchset[], int ovrlp[]) { int deg1, head, inhd, iov, irch, j, jstop, jstrt, link, lnode, mark, mrgsze, nabor, node, novrlp, rchsze, root; # define deg0 (*_deg0) # define nhdsze (*_nhdsze) /* Initialization. */ if (nhdsze <= 0) return; for (inhd = 1; inhd <= nhdsze; inhd++) { root = nbrhd[inhd]; marker[root] = 0; } /* Loop through each eliminated supernode in the set (nhdsze, nbrhd). */ for (inhd = 1; inhd <= nhdsze; inhd++) { root = nbrhd[inhd]; marker[root] = -1; rchsze = 0; novrlp = 0; deg1 = 0; s200: jstrt = xadj[root]; jstop = xadj[root+1] - 1; /* Determine the reachable set and its intersection with the input reachable set. */ for (j = jstrt; j <= jstop; j++) { nabor = adjncy[j]; root = - nabor; if (nabor < 0) goto s200; if (nabor == 0) break; mark = marker[nabor]; if (mark == 0) { rchsze++; rchset[rchsze] = nabor; deg1 += qsize[nabor]; marker[nabor] = 1; } else if (mark == 1) { novrlp++; ovrlp[novrlp] = nabor; marker[nabor] = 2; } } /* From the overlapped set, determine the nodes that can be merged together. */ head = 0; mrgsze = 0; for (iov = 1; iov <= novrlp; iov++) { node = ovrlp[iov]; jstrt = xadj[node]; jstop = xadj[node+1] - 1; for (j = jstrt; j <= jstop; j++) { nabor = adjncy[j]; if (marker[nabor] == 0) { marker[node] = 1; goto s1100; } } /* Node belongs to the new merged supernode. Update the vectors qlink and qsize. */ mrgsze += qsize[node]; marker[node] = -1; lnode = node; s900: link = qlink[lnode]; if (link > 0) { lnode = link; goto s900; } qlink[lnode] = head; head = node; s1100: ; } if (head > 0) { qsize[head] = mrgsze; deg[head] = deg0 + deg1 - 1; marker[head] = 2; } /* Reset marker values. */ root = nbrhd[inhd]; marker[root] = 0; if (rchsze > 0) { for (irch = 1; irch <= rchsze; irch++) { node = rchset[irch]; marker[node] = 0; } } } return; # undef deg0 # undef nhdsze } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glplpf.h0000644000175000017500000001713011632352620020076 0ustar csheltoncshelton/* glplpf.h (LP basis factorization, Schur complement version) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPLPF_H #define GLPLPF_H #include "glpscf.h" #include "glpluf.h" /*********************************************************************** * The structure LPF defines the factorization of the basis mxm matrix * B, where m is the number of rows in corresponding problem instance. * * This factorization is the following septet: * * [B] = (L0, U0, R, S, C, P, Q), (1) * * and is based on the following main equality: * * ( B F^) ( B0 F ) ( L0 0 ) ( U0 R ) * ( ) = P ( ) Q = P ( ) ( ) Q, (2) * ( G^ H^) ( G H ) ( S I ) ( 0 C ) * * where: * * B is the current basis matrix (not stored); * * F^, G^, H^ are some additional matrices (not stored); * * B0 is some initial basis matrix (not stored); * * F, G, H are some additional matrices (not stored); * * P, Q are permutation matrices (stored in both row- and column-like * formats); * * L0, U0 are some matrices that defines a factorization of the initial * basis matrix B0 = L0 * U0 (stored in an invertable form); * * R is a matrix defined from L0 * R = F, so R = inv(L0) * F (stored in * a column-wise sparse format); * * S is a matrix defined from S * U0 = G, so S = G * inv(U0) (stored in * a row-wise sparse format); * * C is the Schur complement for matrix (B0 F G H). It is defined from * S * R + C = H, so C = H - S * R = H - G * inv(U0) * inv(L0) * F = * = H - G * inv(B0) * F. Matrix C is stored in an invertable form. * * REFERENCES * * 1. M.A.Saunders, "LUSOL: A basis package for constrained optimiza- * tion," SCCM, Stanford University, 2006. * * 2. M.A.Saunders, "Notes 5: Basis Updates," CME 318, Stanford Univer- * sity, Spring 2006. * * 3. M.A.Saunders, "Notes 6: LUSOL---a Basis Factorization Package," * ibid. */ typedef struct LPF LPF; struct LPF { /* LP basis factorization */ int valid; /* the factorization is valid only if this flag is set */ /*--------------------------------------------------------------*/ /* initial basis matrix B0 */ int m0_max; /* maximal value of m0 (increased automatically, if necessary) */ int m0; /* the order of B0 */ LUF *luf; /* LU-factorization of B0 */ /*--------------------------------------------------------------*/ /* current basis matrix B */ int m; /* the order of B */ double *B; /* double B[1+m*m]; */ /* B in dense format stored by rows and used only for debugging; normally this array is not allocated */ /*--------------------------------------------------------------*/ /* augmented matrix (B0 F G H) of the order m0+n */ int n_max; /* maximal number of additional rows and columns */ int n; /* current number of additional rows and columns */ /*--------------------------------------------------------------*/ /* m0xn matrix R in column-wise format */ int *R_ptr; /* int R_ptr[1+n_max]; */ /* R_ptr[j], 1 <= j <= n, is a pointer to j-th column */ int *R_len; /* int R_len[1+n_max]; */ /* R_len[j], 1 <= j <= n, is the length of j-th column */ /*--------------------------------------------------------------*/ /* nxm0 matrix S in row-wise format */ int *S_ptr; /* int S_ptr[1+n_max]; */ /* S_ptr[i], 1 <= i <= n, is a pointer to i-th row */ int *S_len; /* int S_len[1+n_max]; */ /* S_len[i], 1 <= i <= n, is the length of i-th row */ /*--------------------------------------------------------------*/ /* Schur complement C of the order n */ SCF *scf; /* SCF scf[1:n_max]; */ /* factorization of the Schur complement */ /*--------------------------------------------------------------*/ /* matrix P of the order m0+n */ int *P_row; /* int P_row[1+m0_max+n_max]; */ /* P_row[i] = j means that P[i,j] = 1 */ int *P_col; /* int P_col[1+m0_max+n_max]; */ /* P_col[j] = i means that P[i,j] = 1 */ /*--------------------------------------------------------------*/ /* matrix Q of the order m0+n */ int *Q_row; /* int Q_row[1+m0_max+n_max]; */ /* Q_row[i] = j means that Q[i,j] = 1 */ int *Q_col; /* int Q_col[1+m0_max+n_max]; */ /* Q_col[j] = i means that Q[i,j] = 1 */ /*--------------------------------------------------------------*/ /* Sparse Vector Area (SVA) is a set of locations intended to store sparse vectors which represent columns of matrix R and rows of matrix S; each location is a doublet (ind, val), where ind is an index, val is a numerical value of a sparse vector element; in the whole each sparse vector is a set of adjacent locations defined by a pointer to its first element and its length, i.e. the number of its elements */ int v_size; /* the SVA size, in locations; locations are numbered by integers 1, 2, ..., v_size, and location 0 is not used */ int v_ptr; /* pointer to the first available location */ int *v_ind; /* int v_ind[1+v_size]; */ /* v_ind[k], 1 <= k <= v_size, is the index field of location k */ double *v_val; /* double v_val[1+v_size]; */ /* v_val[k], 1 <= k <= v_size, is the value field of location k */ /*--------------------------------------------------------------*/ double *work1; /* double work1[1+m0+n_max]; */ /* working array */ double *work2; /* double work2[1+m0+n_max]; */ /* working array */ }; /* return codes: */ #define LPF_ESING 1 /* singular matrix */ #define LPF_ECOND 2 /* ill-conditioned matrix */ #define LPF_ELIMIT 3 /* update limit reached */ #define lpf_create_it _glp_lpf_create_it LPF *lpf_create_it(void); /* create LP basis factorization */ #define lpf_factorize _glp_lpf_factorize int lpf_factorize(LPF *lpf, int m, const int bh[], int (*col) (void *info, int j, int ind[], double val[]), void *info); /* compute LP basis factorization */ #define lpf_ftran _glp_lpf_ftran void lpf_ftran(LPF *lpf, double x[]); /* perform forward transformation (solve system B*x = b) */ #define lpf_btran _glp_lpf_btran void lpf_btran(LPF *lpf, double x[]); /* perform backward transformation (solve system B'*x = b) */ #define lpf_update_it _glp_lpf_update_it int lpf_update_it(LPF *lpf, int j, int bh, int len, const int ind[], const double val[]); /* update LP basis factorization */ #define lpf_delete_it _glp_lpf_delete_it void lpf_delete_it(LPF *lpf); /* delete LP basis factorization */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpgmp.h0000644000175000017500000001432311632352620020101 0ustar csheltoncshelton/* glpgmp.h (bignum arithmetic) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPGMP_H #define GLPGMP_H #ifdef HAVE_CONFIG_H #include #endif #ifdef HAVE_GMP /* use GNU MP bignum library */ #include #define gmp_pool_count _glp_gmp_pool_count #define gmp_free_mem _glp_gmp_free_mem int gmp_pool_count(void); void gmp_free_mem(void); #else /* use GLPK bignum module */ /*---------------------------------------------------------------------- // INTEGER NUMBERS // // Depending on its magnitude an integer number of arbitrary precision // is represented either in short format or in long format. // // Short format corresponds to the int type and allows representing // integer numbers in the range [-(2^31-1), +(2^31-1)]. Note that for // the most negative number of int type the short format is not used. // // In long format integer numbers are represented using the positional // system with the base (radix) 2^16 = 65536: // // x = (-1)^s sum{j in 0..n-1} d[j] * 65536^j, // // where x is the integer to be represented, s is its sign (+1 or -1), // d[j] are its digits (0 <= d[j] <= 65535). // // RATIONAL NUMBERS // // A rational number is represented as an irreducible fraction: // // p / q, // // where p (numerator) and q (denominator) are integer numbers (q > 0) // having no common divisors. */ struct mpz { /* integer number */ int val; /* if ptr is a null pointer, the number is in short format, and val is its value; otherwise, the number is in long format, and val is its sign (+1 or -1) */ struct mpz_seg *ptr; /* pointer to the linked list of the number segments ordered in ascending of powers of the base */ }; struct mpz_seg { /* integer number segment */ unsigned short d[6]; /* six digits of the number ordered in ascending of powers of the base */ struct mpz_seg *next; /* pointer to the next number segment */ }; struct mpq { /* rational number (p / q) */ struct mpz p; /* numerator */ struct mpz q; /* denominator */ }; typedef struct mpz *mpz_t; typedef struct mpq *mpq_t; #define gmp_get_atom _glp_gmp_get_atom #define gmp_free_atom _glp_gmp_free_atom #define gmp_pool_count _glp_gmp_pool_count #define gmp_get_work _glp_gmp_get_work #define gmp_free_mem _glp_gmp_free_mem #define _mpz_init _glp_mpz_init #define mpz_clear _glp_mpz_clear #define mpz_set _glp_mpz_set #define mpz_set_si _glp_mpz_set_si #define mpz_get_d _glp_mpz_get_d #define mpz_get_d_2exp _glp_mpz_get_d_2exp #define mpz_swap _glp_mpz_swap #define mpz_add _glp_mpz_add #define mpz_sub _glp_mpz_sub #define mpz_mul _glp_mpz_mul #define mpz_neg _glp_mpz_neg #define mpz_abs _glp_mpz_abs #define mpz_div _glp_mpz_div #define mpz_gcd _glp_mpz_gcd #define mpz_cmp _glp_mpz_cmp #define mpz_sgn _glp_mpz_sgn #define mpz_out_str _glp_mpz_out_str #define _mpq_init _glp_mpq_init #define mpq_clear _glp_mpq_clear #define mpq_canonicalize _glp_mpq_canonicalize #define mpq_set _glp_mpq_set #define mpq_set_si _glp_mpq_set_si #define mpq_get_d _glp_mpq_get_d #define mpq_set_d _glp_mpq_set_d #define mpq_add _glp_mpq_add #define mpq_sub _glp_mpq_sub #define mpq_mul _glp_mpq_mul #define mpq_div _glp_mpq_div #define mpq_neg _glp_mpq_neg #define mpq_abs _glp_mpq_abs #define mpq_cmp _glp_mpq_cmp #define mpq_sgn _glp_mpq_sgn #define mpq_out_str _glp_mpq_out_str void *gmp_get_atom(int size); void gmp_free_atom(void *ptr, int size); int gmp_pool_count(void); unsigned short *gmp_get_work(int size); void gmp_free_mem(void); mpz_t _mpz_init(void); #define mpz_init(x) (void)((x) = _mpz_init()) void mpz_clear(mpz_t x); void mpz_set(mpz_t z, mpz_t x); void mpz_set_si(mpz_t x, int val); double mpz_get_d(mpz_t x); double mpz_get_d_2exp(int *exp, mpz_t x); void mpz_swap(mpz_t x, mpz_t y); void mpz_add(mpz_t, mpz_t, mpz_t); void mpz_sub(mpz_t, mpz_t, mpz_t); void mpz_mul(mpz_t, mpz_t, mpz_t); void mpz_neg(mpz_t z, mpz_t x); void mpz_abs(mpz_t z, mpz_t x); void mpz_div(mpz_t q, mpz_t r, mpz_t x, mpz_t y); void mpz_gcd(mpz_t z, mpz_t x, mpz_t y); int mpz_cmp(mpz_t x, mpz_t y); int mpz_sgn(mpz_t x); int mpz_out_str(void *fp, int base, mpz_t x); mpq_t _mpq_init(void); #define mpq_init(x) (void)((x) = _mpq_init()) void mpq_clear(mpq_t x); void mpq_canonicalize(mpq_t x); void mpq_set(mpq_t z, mpq_t x); void mpq_set_si(mpq_t x, int p, unsigned int q); double mpq_get_d(mpq_t x); void mpq_set_d(mpq_t x, double val); void mpq_add(mpq_t z, mpq_t x, mpq_t y); void mpq_sub(mpq_t z, mpq_t x, mpq_t y); void mpq_mul(mpq_t z, mpq_t x, mpq_t y); void mpq_div(mpq_t z, mpq_t x, mpq_t y); void mpq_neg(mpq_t z, mpq_t x); void mpq_abs(mpq_t z, mpq_t x); int mpq_cmp(mpq_t x, mpq_t y); int mpq_sgn(mpq_t x); int mpq_out_str(void *fp, int base, mpq_t x); #endif #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glptsp.h0000644000175000017500000001052511632352620020124 0ustar csheltoncshelton/* glptsp.h (TSP format) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPTSP_H #define GLPTSP_H typedef struct TSP TSP; struct TSP { /* TSP (or related problem) instance in the format described in the report [G.Reinelt, TSPLIB 95] */ /*--------------------------------------------------------------*/ /* the specification part */ char *name; /* identifies the data file */ int type; /* specifies the type of data: */ #define TSP_UNDEF 0 /* undefined */ #define TSP_TSP 1 /* symmetric TSP */ #define TSP_ATSP 2 /* asymmetric TSP */ #define TSP_TOUR 3 /* collection of tours */ char *comment; /* additional comments (usually the name of the contributor or creator of the problem instance is given here) */ int dimension; /* for a TSP or ATSP, the dimension is the number of its nodes for a TOUR it is the dimension of the corresponding problem */ int edge_weight_type; /* specifies how the edge weights (or distances) are given: */ #define TSP_UNDEF 0 /* undefined */ #define TSP_EXPLICIT 1 /* listed explicitly */ #define TSP_EUC_2D 2 /* Eucl. distances in 2-D */ #define TSP_CEIL_2D 3 /* Eucl. distances in 2-D rounded up */ #define TSP_GEO 4 /* geographical distances */ #define TSP_ATT 5 /* special distance function */ int edge_weight_format; /* describes the format of the edge weights if they are given explicitly: */ #define TSP_UNDEF 0 /* undefined */ #define TSP_FUNCTION 1 /* given by a function */ #define TSP_FULL_MATRIX 2 /* given by a full matrix */ #define TSP_UPPER_ROW 3 /* upper triangulat matrix (row-wise without diagonal entries) */ #define TSP_LOWER_DIAG_ROW 4 /* lower triangular matrix (row-wise including diagonal entries) */ int display_data_type; /* specifies how a graphical display of the nodes can be obtained: */ #define TSP_UNDEF 0 /* undefined */ #define TSP_COORD_DISPLAY 1 /* display is generated from the node coordinates */ #define TSP_TWOD_DISPLAY 2 /* explicit coordinates in 2-D are given */ /*--------------------------------------------------------------*/ /* data part */ /* NODE_COORD_SECTION: */ double *node_x_coord; /* double node_x_coord[1+dimension]; */ double *node_y_coord; /* double node_y_coord[1+dimension]; */ /* DISPLAY_DATA_SECTION: */ double *dply_x_coord; /* double dply_x_coord[1+dimension]; */ double *dply_y_coord; /* double dply_y_coord[1+dimension]; */ /* TOUR_SECTION: */ int *tour; /* int tour[1+dimension]; */ /* EDGE_WEIGHT_SECTION: */ int *edge_weight; /* int edge_weight[1+dimension*dimension]; */ }; #define tsp_read_data _glp_tsp_read_data #define tsp_free_data _glp_tsp_free_data #define tsp_distance _glp_tsp_distance TSP *tsp_read_data(char *fname); /* read TSP instance data */ void tsp_free_data(TSP *tsp); /* free TSP instance data */ int tsp_distance(TSP *tsp, int i, int j); /* compute distance between two nodes */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpios03.c0000644000175000017500000012431611632352620020252 0ustar csheltoncshelton/* glpios03.c (branch-and-cut driver) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpios.h" /*********************************************************************** * show_progress - display current progress of the search * * This routine displays some information about current progress of the * search. * * The information includes: * * the current number of iterations performed by the simplex solver; * * the objective value for the best known integer feasible solution, * which is upper (minimization) or lower (maximization) global bound * for optimal solution of the original mip problem; * * the best local bound for active nodes, which is lower (minimization) * or upper (maximization) global bound for optimal solution of the * original mip problem; * * the relative mip gap, in percents; * * the number of open (active) subproblems; * * the number of completely explored subproblems, i.e. whose nodes have * been removed from the tree. */ static void show_progress(glp_tree *T, int bingo) { int p; double temp; char best_mip[50], best_bound[50], *rho, rel_gap[50]; /* format the best known integer feasible solution */ if (T->mip->mip_stat == GLP_FEAS) sprintf(best_mip, "%17.9e", T->mip->mip_obj); else sprintf(best_mip, "%17s", "not found yet"); /* determine reference number of an active subproblem whose local bound is best */ p = ios_best_node(T); /* format the best bound */ if (p == 0) sprintf(best_bound, "%17s", "tree is empty"); else { temp = T->slot[p].node->bound; if (temp == -DBL_MAX) sprintf(best_bound, "%17s", "-inf"); else if (temp == +DBL_MAX) sprintf(best_bound, "%17s", "+inf"); else sprintf(best_bound, "%17.9e", temp); } /* choose the relation sign between global bounds */ if (T->mip->dir == GLP_MIN) rho = ">="; else if (T->mip->dir == GLP_MAX) rho = "<="; else xassert(T != T); /* format the relative mip gap */ temp = ios_relative_gap(T); if (temp == 0.0) sprintf(rel_gap, " 0.0%%"); else if (temp < 0.001) sprintf(rel_gap, "< 0.1%%"); else if (temp <= 9.999) sprintf(rel_gap, "%5.1f%%", 100.0 * temp); else sprintf(rel_gap, "%6s", ""); /* display progress of the search */ xprintf("+%6d: %s %s %s %s %s (%d; %d)\n", T->mip->it_cnt, bingo ? ">>>>>" : "mip =", best_mip, rho, best_bound, rel_gap, T->a_cnt, T->t_cnt - T->n_cnt); T->tm_lag = xtime(); return; } /*********************************************************************** * is_branch_hopeful - check if specified branch is hopeful * * This routine checks if the specified subproblem can have an integer * optimal solution which is better than the best known one. * * The check is based on comparison of the local objective bound stored * in the subproblem descriptor and the incumbent objective value which * is the global objective bound. * * If there is a chance that the specified subproblem can have a better * integer optimal solution, the routine returns non-zero. Otherwise, if * the corresponding branch can pruned, zero is returned. */ static int is_branch_hopeful(glp_tree *T, int p) { xassert(1 <= p && p <= T->nslots); xassert(T->slot[p].node != NULL); return ios_is_hopeful(T, T->slot[p].node->bound); } /*********************************************************************** * check_integrality - check integrality of basic solution * * This routine checks if the basic solution of LP relaxation of the * current subproblem satisfies to integrality conditions, i.e. that all * variables of integer kind have integral primal values. (The solution * is assumed to be optimal.) * * For each variable of integer kind the routine computes the following * quantity: * * ii(x[j]) = min(x[j] - floor(x[j]), ceil(x[j]) - x[j]), (1) * * which is a measure of the integer infeasibility (non-integrality) of * x[j] (for example, ii(2.1) = 0.1, ii(3.7) = 0.3, ii(5.0) = 0). It is * understood that 0 <= ii(x[j]) <= 0.5, and variable x[j] is integer * feasible if ii(x[j]) = 0. However, due to floating-point arithmetic * the routine checks less restrictive condition: * * ii(x[j]) <= tol_int, (2) * * where tol_int is a given tolerance (small positive number) and marks * each variable which does not satisfy to (2) as integer infeasible by * setting its fractionality flag. * * In order to characterize integer infeasibility of the basic solution * in the whole the routine computes two parameters: ii_cnt, which is * the number of variables with the fractionality flag set, and ii_sum, * which is the sum of integer infeasibilities (1). */ static void check_integrality(glp_tree *T) { glp_prob *mip = T->mip; int j, type, ii_cnt = 0; double lb, ub, x, temp1, temp2, ii_sum = 0.0; /* walk through the set of columns (structural variables) */ for (j = 1; j <= mip->n; j++) { GLPCOL *col = mip->col[j]; T->non_int[j] = 0; /* if the column is not integer, skip it */ if (col->kind != GLP_IV) continue; /* if the column is non-basic, it is integer feasible */ if (col->stat != GLP_BS) continue; /* obtain the type and bounds of the column */ type = col->type, lb = col->lb, ub = col->ub; /* obtain value of the column in optimal basic solution */ x = col->prim; /* if the column's primal value is close to the lower bound, the column is integer feasible within given tolerance */ if (type == GLP_LO || type == GLP_DB || type == GLP_FX) { temp1 = lb - T->parm->tol_int; temp2 = lb + T->parm->tol_int; if (temp1 <= x && x <= temp2) continue; #if 0 /* the lower bound must not be violated */ xassert(x >= lb); #else if (x < lb) continue; #endif } /* if the column's primal value is close to the upper bound, the column is integer feasible within given tolerance */ if (type == GLP_UP || type == GLP_DB || type == GLP_FX) { temp1 = ub - T->parm->tol_int; temp2 = ub + T->parm->tol_int; if (temp1 <= x && x <= temp2) continue; #if 0 /* the upper bound must not be violated */ xassert(x <= ub); #else if (x > ub) continue; #endif } /* if the column's primal value is close to nearest integer, the column is integer feasible within given tolerance */ temp1 = floor(x + 0.5) - T->parm->tol_int; temp2 = floor(x + 0.5) + T->parm->tol_int; if (temp1 <= x && x <= temp2) continue; /* otherwise the column is integer infeasible */ T->non_int[j] = 1; /* increase the number of fractional-valued columns */ ii_cnt++; /* compute the sum of integer infeasibilities */ temp1 = x - floor(x); temp2 = ceil(x) - x; xassert(temp1 > 0.0 && temp2 > 0.0); ii_sum += (temp1 <= temp2 ? temp1 : temp2); } /* store ii_cnt and ii_sum to the current problem descriptor */ xassert(T->curr != NULL); T->curr->ii_cnt = ii_cnt; T->curr->ii_sum = ii_sum; /* and also display these parameters */ if (T->parm->msg_lev >= GLP_MSG_DBG) { if (ii_cnt == 0) xprintf("There are no fractional columns\n"); else if (ii_cnt == 1) xprintf("There is one fractional column, integer infeasibil" "ity is %.3e\n", ii_sum); else xprintf("There are %d fractional columns, integer infeasibi" "lity is %.3e\n", ii_cnt, ii_sum); } return; } /*********************************************************************** * record_solution - record better integer feasible solution * * This routine records optimal basic solution of LP relaxation of the * current subproblem, which being integer feasible is better than the * best known integer feasible solution. */ static void record_solution(glp_tree *T) { glp_prob *mip = T->mip; int i, j; mip->mip_stat = GLP_FEAS; mip->mip_obj = mip->obj_val; for (i = 1; i <= mip->m; i++) { GLPROW *row = mip->row[i]; row->mipx = row->prim; } for (j = 1; j <= mip->n; j++) { GLPCOL *col = mip->col[j]; if (col->kind == GLP_CV) col->mipx = col->prim; else if (col->kind == GLP_IV) { /* value of the integer column must be integral */ col->mipx = floor(col->prim + 0.5); } else xassert(col != col); } T->sol_cnt++; return; } /*********************************************************************** * fix_by_red_cost - fix non-basic integer columns by reduced costs * * This routine fixes some non-basic integer columns if their reduced * costs indicate that increasing (decreasing) the column at least by * one involves the objective value becoming worse than the incumbent * objective value. */ static void fix_by_red_cost(glp_tree *T) { glp_prob *mip = T->mip; int j, stat, fixed = 0; double obj, lb, ub, dj; /* the global bound must exist */ xassert(T->mip->mip_stat == GLP_FEAS); /* basic solution of LP relaxation must be optimal */ xassert(mip->pbs_stat == GLP_FEAS && mip->dbs_stat == GLP_FEAS); /* determine the objective function value */ obj = mip->obj_val; /* walk through the column list */ for (j = 1; j <= mip->n; j++) { GLPCOL *col = mip->col[j]; /* if the column is not integer, skip it */ if (col->kind != GLP_IV) continue; /* obtain bounds of j-th column */ lb = col->lb, ub = col->ub; /* and determine its status and reduced cost */ stat = col->stat, dj = col->dual; /* analyze the reduced cost */ switch (mip->dir) { case GLP_MIN: /* minimization */ if (stat == GLP_NL) { /* j-th column is non-basic on its lower bound */ if (dj < 0.0) dj = 0.0; if (obj + dj >= mip->mip_obj) glp_set_col_bnds(mip, j, GLP_FX, lb, lb), fixed++; } else if (stat == GLP_NU) { /* j-th column is non-basic on its upper bound */ if (dj > 0.0) dj = 0.0; if (obj - dj >= mip->mip_obj) glp_set_col_bnds(mip, j, GLP_FX, ub, ub), fixed++; } break; case GLP_MAX: /* maximization */ if (stat == GLP_NL) { /* j-th column is non-basic on its lower bound */ if (dj > 0.0) dj = 0.0; if (obj + dj <= mip->mip_obj) glp_set_col_bnds(mip, j, GLP_FX, lb, lb), fixed++; } else if (stat == GLP_NU) { /* j-th column is non-basic on its upper bound */ if (dj < 0.0) dj = 0.0; if (obj - dj <= mip->mip_obj) glp_set_col_bnds(mip, j, GLP_FX, ub, ub), fixed++; } break; default: xassert(T != T); } } if (T->parm->msg_lev >= GLP_MSG_DBG) { if (fixed == 0) /* nothing to say */; else if (fixed == 1) xprintf("One column has been fixed by reduced cost\n"); else xprintf("%d columns have been fixed by reduced costs\n", fixed); } /* fixing non-basic columns on their current bounds does not change the basic solution */ xassert(mip->pbs_stat == GLP_FEAS && mip->dbs_stat == GLP_FEAS); return; } /*********************************************************************** * branch_on - perform branching on specified variable * * This routine performs branching on j-th column (structural variable) * of the current subproblem. The specified column must be of integer * kind and must have a fractional value in optimal basic solution of * LP relaxation of the current subproblem (i.e. only columns for which * the flag non_int[j] is set are valid candidates to branch on). * * Let x be j-th structural variable, and beta be its primal fractional * value in the current basic solution. Branching on j-th variable is * dividing the current subproblem into two new subproblems, which are * identical to the current subproblem with the following exception: in * the first subproblem that begins the down-branch x has a new upper * bound x <= floor(beta), and in the second subproblem that begins the * up-branch x has a new lower bound x >= ceil(beta). * * Depending on estimation of local bounds for down- and up-branches * this routine returns the following: * * 0 - both branches have been created; * 1 - one branch is hopeless and has been pruned, so now the current * subproblem is other branch; * 2 - both branches are hopeless and have been pruned; new subproblem * selection is needed to continue the search. */ static int branch_on(glp_tree *T, int j, int next) { glp_prob *mip = T->mip; IOSNPD *node; int m = mip->m; int n = mip->n; int type, dn_type, up_type, dn_bad, up_bad, p, ret, clone[1+2]; double lb, ub, beta, new_ub, new_lb, dn_lp, up_lp, dn_bnd, up_bnd; /* determine bounds and value of x[j] in optimal solution to LP relaxation of the current subproblem */ xassert(1 <= j && j <= n); type = mip->col[j]->type; lb = mip->col[j]->lb; ub = mip->col[j]->ub; beta = mip->col[j]->prim; /* determine new bounds of x[j] for down- and up-branches */ new_ub = floor(beta); new_lb = ceil(beta); switch (type) { case GLP_FR: dn_type = GLP_UP; up_type = GLP_LO; break; case GLP_LO: xassert(lb <= new_ub); dn_type = (lb == new_ub ? GLP_FX : GLP_DB); xassert(lb + 1.0 <= new_lb); up_type = GLP_LO; break; case GLP_UP: xassert(new_ub <= ub - 1.0); dn_type = GLP_UP; xassert(new_lb <= ub); up_type = (new_lb == ub ? GLP_FX : GLP_DB); break; case GLP_DB: xassert(lb <= new_ub && new_ub <= ub - 1.0); dn_type = (lb == new_ub ? GLP_FX : GLP_DB); xassert(lb + 1.0 <= new_lb && new_lb <= ub); up_type = (new_lb == ub ? GLP_FX : GLP_DB); break; default: xassert(type != type); } /* compute local bounds to LP relaxation for both branches */ ios_eval_degrad(T, j, &dn_lp, &up_lp); /* and improve them by rounding */ dn_bnd = ios_round_bound(T, dn_lp); up_bnd = ios_round_bound(T, up_lp); /* check local bounds for down- and up-branches */ dn_bad = !ios_is_hopeful(T, dn_bnd); up_bad = !ios_is_hopeful(T, up_bnd); if (dn_bad && up_bad) { if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("Both down- and up-branches are hopeless\n"); ret = 2; goto done; } else if (up_bad) { if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("Up-branch is hopeless\n"); glp_set_col_bnds(mip, j, dn_type, lb, new_ub); T->curr->lp_obj = dn_lp; if (mip->dir == GLP_MIN) { if (T->curr->bound < dn_bnd) T->curr->bound = dn_bnd; } else if (mip->dir == GLP_MAX) { if (T->curr->bound > dn_bnd) T->curr->bound = dn_bnd; } else xassert(mip != mip); ret = 1; goto done; } else if (dn_bad) { if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("Down-branch is hopeless\n"); glp_set_col_bnds(mip, j, up_type, new_lb, ub); T->curr->lp_obj = up_lp; if (mip->dir == GLP_MIN) { if (T->curr->bound < up_bnd) T->curr->bound = up_bnd; } else if (mip->dir == GLP_MAX) { if (T->curr->bound > up_bnd) T->curr->bound = up_bnd; } else xassert(mip != mip); ret = 1; goto done; } /* both down- and up-branches seem to be hopeful */ if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("Branching on column %d, primal value is %.9e\n", j, beta); /* determine the reference number of the current subproblem */ xassert(T->curr != NULL); p = T->curr->p; T->curr->br_var = j; T->curr->br_val = beta; /* freeze the current subproblem */ ios_freeze_node(T); /* create two clones of the current subproblem; the first clone begins the down-branch, the second one begins the up-branch */ ios_clone_node(T, p, 2, clone); if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("Node %d begins down branch, node %d begins up branch " "\n", clone[1], clone[2]); /* set new upper bound of j-th column in the down-branch */ node = T->slot[clone[1]].node; xassert(node != NULL); xassert(node->up != NULL); xassert(node->b_ptr == NULL); node->b_ptr = dmp_get_atom(T->pool, sizeof(IOSBND)); node->b_ptr->k = m + j; node->b_ptr->type = (unsigned char)dn_type; node->b_ptr->lb = lb; node->b_ptr->ub = new_ub; node->b_ptr->next = NULL; node->lp_obj = dn_lp; if (mip->dir == GLP_MIN) { if (node->bound < dn_bnd) node->bound = dn_bnd; } else if (mip->dir == GLP_MAX) { if (node->bound > dn_bnd) node->bound = dn_bnd; } else xassert(mip != mip); /* set new lower bound of j-th column in the up-branch */ node = T->slot[clone[2]].node; xassert(node != NULL); xassert(node->up != NULL); xassert(node->b_ptr == NULL); node->b_ptr = dmp_get_atom(T->pool, sizeof(IOSBND)); node->b_ptr->k = m + j; node->b_ptr->type = (unsigned char)up_type; node->b_ptr->lb = new_lb; node->b_ptr->ub = ub; node->b_ptr->next = NULL; node->lp_obj = up_lp; if (mip->dir == GLP_MIN) { if (node->bound < up_bnd) node->bound = up_bnd; } else if (mip->dir == GLP_MAX) { if (node->bound > up_bnd) node->bound = up_bnd; } else xassert(mip != mip); /* suggest the subproblem to be solved next */ xassert(T->child == 0); if (next == GLP_NO_BRNCH) T->child = 0; else if (next == GLP_DN_BRNCH) T->child = clone[1]; else if (next == GLP_UP_BRNCH) T->child = clone[2]; else xassert(next != next); ret = 0; done: return ret; } /*********************************************************************** * cleanup_the_tree - prune hopeless branches from the tree * * This routine walks through the active list and checks the local * bound for every active subproblem. If the local bound indicates that * the subproblem cannot have integer optimal solution better than the * incumbent objective value, the routine deletes such subproblem that, * in turn, involves pruning the corresponding branch of the tree. */ static void cleanup_the_tree(glp_tree *T) { IOSNPD *node, *next_node; int count = 0; /* the global bound must exist */ xassert(T->mip->mip_stat == GLP_FEAS); /* walk through the list of active subproblems */ for (node = T->head; node != NULL; node = next_node) { /* deleting some active problem node may involve deleting its parents recursively; however, all its parents being created *before* it are always *precede* it in the node list, so the next problem node is never affected by such deletion */ next_node = node->next; /* if the branch is hopeless, prune it */ if (!is_branch_hopeful(T, node->p)) ios_delete_node(T, node->p), count++; } if (T->parm->msg_lev >= GLP_MSG_DBG) { if (count == 1) xprintf("One hopeless branch has been pruned\n"); else if (count > 1) xprintf("%d hopeless branches have been pruned\n", count); } return; } /**********************************************************************/ static void generate_cuts(glp_tree *T) { /* generate generic cuts with built-in generators */ if (!(T->parm->mir_cuts == GLP_ON || T->parm->gmi_cuts == GLP_ON || T->parm->cov_cuts == GLP_ON || T->parm->clq_cuts == GLP_ON)) goto done; #if 1 /* 20/IX-2008 */ { int i, max_cuts, added_cuts; max_cuts = T->n; if (max_cuts < 1000) max_cuts = 1000; added_cuts = 0; for (i = T->orig_m+1; i <= T->mip->m; i++) { if (T->mip->row[i]->origin == GLP_RF_CUT) added_cuts++; } /* xprintf("added_cuts = %d\n", added_cuts); */ if (added_cuts >= max_cuts) goto done; } #endif /* generate and add to POOL all cuts violated by x* */ if (T->parm->gmi_cuts == GLP_ON) { if (T->curr->changed < 5) ios_gmi_gen(T); } if (T->parm->mir_cuts == GLP_ON) { xassert(T->mir_gen != NULL); ios_mir_gen(T, T->mir_gen); } if (T->parm->cov_cuts == GLP_ON) { /* cover cuts works well along with mir cuts */ /*if (T->round <= 5)*/ ios_cov_gen(T); } if (T->parm->clq_cuts == GLP_ON) { if (T->clq_gen != NULL) { if (T->curr->level == 0 && T->curr->changed < 50 || T->curr->level > 0 && T->curr->changed < 5) ios_clq_gen(T, T->clq_gen); } } done: return; } /**********************************************************************/ static void remove_cuts(glp_tree *T) { /* remove inactive cuts (some valueable globally valid cut might be saved in the global cut pool) */ int i, cnt = 0, *num = NULL; xassert(T->curr != NULL); for (i = T->orig_m+1; i <= T->mip->m; i++) { if (T->mip->row[i]->origin == GLP_RF_CUT && T->mip->row[i]->level == T->curr->level && T->mip->row[i]->stat == GLP_BS) { if (num == NULL) num = xcalloc(1+T->mip->m, sizeof(int)); num[++cnt] = i; } } if (cnt > 0) { glp_del_rows(T->mip, cnt, num); #if 0 xprintf("%d inactive cut(s) removed\n", cnt); #endif xfree(num); xassert(glp_factorize(T->mip) == 0); } return; } /**********************************************************************/ static void display_cut_info(glp_tree *T) { glp_prob *mip = T->mip; int i, gmi = 0, mir = 0, cov = 0, clq = 0, app = 0; for (i = mip->m; i > 0; i--) { GLPROW *row; row = mip->row[i]; /* if (row->level < T->curr->level) break; */ if (row->origin == GLP_RF_CUT) { if (row->klass == GLP_RF_GMI) gmi++; else if (row->klass == GLP_RF_MIR) mir++; else if (row->klass == GLP_RF_COV) cov++; else if (row->klass == GLP_RF_CLQ) clq++; else app++; } } xassert(T->curr != NULL); if (gmi + mir + cov + clq + app > 0) { xprintf("Cuts on level %d:", T->curr->level); if (gmi > 0) xprintf(" gmi = %d;", gmi); if (mir > 0) xprintf(" mir = %d;", mir); if (cov > 0) xprintf(" cov = %d;", cov); if (clq > 0) xprintf(" clq = %d;", clq); if (app > 0) xprintf(" app = %d;", app); xprintf("\n"); } return; } /*********************************************************************** * NAME * * ios_driver - branch-and-cut driver * * SYNOPSIS * * #include "glpios.h" * int ios_driver(glp_tree *T); * * DESCRIPTION * * The routine ios_driver is a branch-and-cut driver. It controls the * MIP solution process. * * RETURNS * * 0 The MIP problem instance has been successfully solved. This code * does not necessarily mean that the solver has found optimal * solution. It only means that the solution process was successful. * * GLP_EFAIL * The search was prematurely terminated due to the solver failure. * * GLP_EMIPGAP * The search was prematurely terminated, because the relative mip * gap tolerance has been reached. * * GLP_ETMLIM * The search was prematurely terminated, because the time limit has * been exceeded. * * GLP_ESTOP * The search was prematurely terminated by application. */ int ios_driver(glp_tree *T) { int p, curr_p, p_stat, d_stat, ret; #if 1 /* carry out to glp_tree */ int pred_p = 0; /* if the current subproblem has been just created due to branching, pred_p is the reference number of its parent subproblem, otherwise pred_p is zero */ #endif glp_long ttt = T->tm_beg; #if 0 ((glp_iocp *)T->parm)->msg_lev = GLP_MSG_DBG; #endif /* on entry to the B&B driver it is assumed that the active list contains the only active (i.e. root) subproblem, which is the original MIP problem to be solved */ loop: /* main loop starts here */ /* at this point the current subproblem does not exist */ xassert(T->curr == NULL); /* if the active list is empty, the search is finished */ if (T->head == NULL) { if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("Active list is empty!\n"); xassert(dmp_in_use(T->pool).lo == 0); ret = 0; goto done; } /* select some active subproblem to continue the search */ xassert(T->next_p == 0); /* let the application program select subproblem */ if (T->parm->cb_func != NULL) { xassert(T->reason == 0); T->reason = GLP_ISELECT; T->parm->cb_func(T, T->parm->cb_info); T->reason = 0; if (T->stop) { ret = GLP_ESTOP; goto done; } } if (T->next_p != 0) { /* the application program has selected something */ ; } else if (T->a_cnt == 1) { /* the only active subproblem exists, so select it */ xassert(T->head->next == NULL); T->next_p = T->head->p; } else if (T->child != 0) { /* select one of branching childs suggested by the branching heuristic */ T->next_p = T->child; } else { /* select active subproblem as specified by the backtracking technique option */ T->next_p = ios_choose_node(T); } /* the active subproblem just selected becomes current */ ios_revive_node(T, T->next_p); T->next_p = T->child = 0; /* invalidate pred_p, if it is not the reference number of the parent of the current subproblem */ if (T->curr->up != NULL && T->curr->up->p != pred_p) pred_p = 0; /* determine the reference number of the current subproblem */ p = T->curr->p; if (T->parm->msg_lev >= GLP_MSG_DBG) { xprintf("-----------------------------------------------------" "-------------------\n"); xprintf("Processing node %d at level %d\n", p, T->curr->level); } /* if it is the root subproblem, initialize cut generators */ if (p == 1) { if (T->parm->gmi_cuts == GLP_ON) { if (T->parm->msg_lev >= GLP_MSG_ALL) xprintf("Gomory's cuts enabled\n"); } if (T->parm->mir_cuts == GLP_ON) { if (T->parm->msg_lev >= GLP_MSG_ALL) xprintf("MIR cuts enabled\n"); xassert(T->mir_gen == NULL); T->mir_gen = ios_mir_init(T); } if (T->parm->cov_cuts == GLP_ON) { if (T->parm->msg_lev >= GLP_MSG_ALL) xprintf("Cover cuts enabled\n"); } if (T->parm->clq_cuts == GLP_ON) { xassert(T->clq_gen == NULL); if (T->parm->msg_lev >= GLP_MSG_ALL) xprintf("Clique cuts enabled\n"); T->clq_gen = ios_clq_init(T); } } more: /* minor loop starts here */ /* at this point the current subproblem needs either to be solved for the first time or re-optimized due to reformulation */ /* display current progress of the search */ if (T->parm->msg_lev >= GLP_MSG_DBG || T->parm->msg_lev >= GLP_MSG_ON && (double)(T->parm->out_frq - 1) <= 1000.0 * xdifftime(xtime(), T->tm_lag)) show_progress(T, 0); if (T->parm->msg_lev >= GLP_MSG_ALL && xdifftime(xtime(), ttt) >= 60.0) { glp_long total; glp_mem_usage(NULL, NULL, &total, NULL); xprintf("Time used: %.1f secs. Memory used: %.1f Mb.\n", xdifftime(xtime(), T->tm_beg), xltod(total) / 1048576.0); ttt = xtime(); } /* check the mip gap */ if (T->parm->mip_gap > 0.0 && ios_relative_gap(T) <= T->parm->mip_gap) { if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("Relative gap tolerance reached; search terminated " "\n"); ret = GLP_EMIPGAP; goto done; } /* check if the time limit has been exhausted */ if (T->parm->tm_lim < INT_MAX && (double)(T->parm->tm_lim - 1) <= 1000.0 * xdifftime(xtime(), T->tm_beg)) { if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("Time limit exhausted; search terminated\n"); ret = GLP_ETMLIM; goto done; } /* let the application program preprocess the subproblem */ if (T->parm->cb_func != NULL) { xassert(T->reason == 0); T->reason = GLP_IPREPRO; T->parm->cb_func(T, T->parm->cb_info); T->reason = 0; if (T->stop) { ret = GLP_ESTOP; goto done; } } /* perform basic preprocessing */ if (T->parm->pp_tech == GLP_PP_NONE) ; else if (T->parm->pp_tech == GLP_PP_ROOT) { if (T->curr->level == 0) { if (ios_preprocess_node(T, 100)) goto fath; } } else if (T->parm->pp_tech == GLP_PP_ALL) { if (ios_preprocess_node(T, T->curr->level == 0 ? 100 : 10)) goto fath; } else xassert(T != T); /* preprocessing may improve the global bound */ if (!is_branch_hopeful(T, p)) { xprintf("*** not tested yet ***\n"); goto fath; } /* solve LP relaxation of the current subproblem */ if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("Solving LP relaxation...\n"); ret = ios_solve_node(T); if (!(ret == 0 || ret == GLP_EOBJLL || ret == GLP_EOBJUL)) { if (T->parm->msg_lev >= GLP_MSG_ERR) xprintf("ios_driver: unable to solve current LP relaxation;" " glp_simplex returned %d\n", ret); ret = GLP_EFAIL; goto done; } /* analyze status of the basic solution to LP relaxation found */ p_stat = T->mip->pbs_stat; d_stat = T->mip->dbs_stat; if (p_stat == GLP_FEAS && d_stat == GLP_FEAS) { /* LP relaxation has optimal solution */ if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("Found optimal solution to LP relaxation\n"); } else if (d_stat == GLP_NOFEAS) { /* LP relaxation has no dual feasible solution */ /* since the current subproblem cannot have a larger feasible region than its parent, there is something wrong */ if (T->parm->msg_lev >= GLP_MSG_ERR) xprintf("ios_driver: current LP relaxation has no dual feas" "ible solution\n"); ret = GLP_EFAIL; goto done; } else if (p_stat == GLP_INFEAS && d_stat == GLP_FEAS) { /* LP relaxation has no primal solution which is better than the incumbent objective value */ xassert(T->mip->mip_stat == GLP_FEAS); if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("LP relaxation has no solution better than incumben" "t objective value\n"); /* prune the branch */ goto fath; } else if (p_stat == GLP_NOFEAS) { /* LP relaxation has no primal feasible solution */ if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("LP relaxation has no feasible solution\n"); /* prune the branch */ goto fath; } else { /* other cases cannot appear */ xassert(T->mip != T->mip); } /* at this point basic solution to LP relaxation of the current subproblem is optimal */ xassert(p_stat == GLP_FEAS && d_stat == GLP_FEAS); xassert(T->curr != NULL); T->curr->lp_obj = T->mip->obj_val; /* thus, it defines a local bound to integer optimal solution of the current subproblem */ { double bound = T->mip->obj_val; /* some local bound to the current subproblem could be already set before, so we should only improve it */ bound = ios_round_bound(T, bound); if (T->mip->dir == GLP_MIN) { if (T->curr->bound < bound) T->curr->bound = bound; } else if (T->mip->dir == GLP_MAX) { if (T->curr->bound > bound) T->curr->bound = bound; } else xassert(T->mip != T->mip); if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("Local bound is %.9e\n", bound); } /* if the local bound indicates that integer optimal solution of the current subproblem cannot be better than the global bound, prune the branch */ if (!is_branch_hopeful(T, p)) { if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("Current branch is hopeless and can be pruned\n"); goto fath; } /* let the application program generate additional rows ("lazy" constraints) */ xassert(T->reopt == 0); xassert(T->reinv == 0); if (T->parm->cb_func != NULL) { xassert(T->reason == 0); T->reason = GLP_IROWGEN; T->parm->cb_func(T, T->parm->cb_info); T->reason = 0; if (T->stop) { ret = GLP_ESTOP; goto done; } if (T->reopt) { /* some rows were added; re-optimization is needed */ T->reopt = T->reinv = 0; goto more; } if (T->reinv) { /* no rows were added, however, some inactive rows were removed */ T->reinv = 0; xassert(glp_factorize(T->mip) == 0); } } /* check if the basic solution is integer feasible */ check_integrality(T); /* if the basic solution satisfies to all integrality conditions, it is a new, better integer feasible solution */ if (T->curr->ii_cnt == 0) { if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("New integer feasible solution found\n"); if (T->parm->msg_lev >= GLP_MSG_ALL) display_cut_info(T); record_solution(T); if (T->parm->msg_lev >= GLP_MSG_ON) show_progress(T, 1); /* make the application program happy */ if (T->parm->cb_func != NULL) { xassert(T->reason == 0); T->reason = GLP_IBINGO; T->parm->cb_func(T, T->parm->cb_info); T->reason = 0; if (T->stop) { ret = GLP_ESTOP; goto done; } } /* since the current subproblem has been fathomed, prune its branch */ goto fath; } /* at this point basic solution to LP relaxation of the current subproblem is optimal, but integer infeasible */ /* try to fix some non-basic structural variables of integer kind on their current bounds due to reduced costs */ if (T->mip->mip_stat == GLP_FEAS) fix_by_red_cost(T); /* let the application program try to find some solution to the original MIP with a primal heuristic */ if (T->parm->cb_func != NULL) { xassert(T->reason == 0); T->reason = GLP_IHEUR; T->parm->cb_func(T, T->parm->cb_info); T->reason = 0; if (T->stop) { ret = GLP_ESTOP; goto done; } /* check if the current branch became hopeless */ if (!is_branch_hopeful(T, p)) { if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("Current branch became hopeless and can be prune" "d\n"); goto fath; } } /* try to find solution with the feasibility pump heuristic */ if (T->parm->fp_heur) { xassert(T->reason == 0); T->reason = GLP_IHEUR; ios_feas_pump(T); T->reason = 0; /* check if the current branch became hopeless */ if (!is_branch_hopeful(T, p)) { if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("Current branch became hopeless and can be prune" "d\n"); goto fath; } } /* it's time to generate cutting planes */ xassert(T->local != NULL); xassert(T->local->size == 0); /* let the application program generate some cuts; note that it can add cuts either to the local cut pool or directly to the current subproblem */ if (T->parm->cb_func != NULL) { xassert(T->reason == 0); T->reason = GLP_ICUTGEN; T->parm->cb_func(T, T->parm->cb_info); T->reason = 0; if (T->stop) { ret = GLP_ESTOP; goto done; } } /* try to generate generic cuts with built-in generators (as suggested by Matteo Fischetti et al. the built-in cuts are not generated at each branching node; an intense attempt of generating new cuts is only made at the root node, and then a moderate effort is spent after each backtracking step) */ if (T->curr->level == 0 || pred_p == 0) { xassert(T->reason == 0); T->reason = GLP_ICUTGEN; generate_cuts(T); T->reason = 0; } /* if the local cut pool is not empty, select useful cuts and add them to the current subproblem */ if (T->local->size > 0) { xassert(T->reason == 0); T->reason = GLP_ICUTGEN; ios_process_cuts(T); T->reason = 0; } /* clear the local cut pool */ ios_clear_pool(T, T->local); /* perform re-optimization, if necessary */ if (T->reopt) { T->reopt = 0; T->curr->changed++; goto more; } /* no cuts were generated; remove inactive cuts */ remove_cuts(T); if (T->parm->msg_lev >= GLP_MSG_ALL && T->curr->level == 0) display_cut_info(T); /* update history information used on pseudocost branching */ if (T->pcost != NULL) ios_pcost_update(T); /* it's time to perform branching */ xassert(T->br_var == 0); xassert(T->br_sel == 0); /* let the application program choose variable to branch on */ if (T->parm->cb_func != NULL) { xassert(T->reason == 0); xassert(T->br_var == 0); xassert(T->br_sel == 0); T->reason = GLP_IBRANCH; T->parm->cb_func(T, T->parm->cb_info); T->reason = 0; if (T->stop) { ret = GLP_ESTOP; goto done; } } /* if nothing has been chosen, choose some variable as specified by the branching technique option */ if (T->br_var == 0) T->br_var = ios_choose_var(T, &T->br_sel); /* perform actual branching */ curr_p = T->curr->p; ret = branch_on(T, T->br_var, T->br_sel); T->br_var = T->br_sel = 0; if (ret == 0) { /* both branches have been created */ pred_p = curr_p; goto loop; } else if (ret == 1) { /* one branch is hopeless and has been pruned, so now the current subproblem is other branch */ /* the current subproblem should be considered as a new one, since one bound of the branching variable was changed */ T->curr->solved = T->curr->changed = 0; goto more; } else if (ret == 2) { /* both branches are hopeless and have been pruned; new subproblem selection is needed to continue the search */ goto fath; } else xassert(ret != ret); fath: /* the current subproblem has been fathomed */ if (T->parm->msg_lev >= GLP_MSG_DBG) xprintf("Node %d fathomed\n", p); /* freeze the current subproblem */ ios_freeze_node(T); /* and prune the corresponding branch of the tree */ ios_delete_node(T, p); /* if a new integer feasible solution has just been found, other branches may become hopeless and therefore must be pruned */ if (T->mip->mip_stat == GLP_FEAS) cleanup_the_tree(T); /* new subproblem selection is needed due to backtracking */ pred_p = 0; goto loop; done: /* display progress of the search on exit from the solver */ if (T->parm->msg_lev >= GLP_MSG_ON) show_progress(T, 0); if (T->mir_gen != NULL) ios_mir_term(T->mir_gen), T->mir_gen = NULL; if (T->clq_gen != NULL) ios_clq_term(T->clq_gen), T->clq_gen = NULL; /* return to the calling program */ return ret; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpapi09.c0000644000175000017500000005755211632352620020246 0ustar csheltoncshelton/* glpapi09.c (mixed integer programming routines) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpios.h" #include "glpnpp.h" /*********************************************************************** * NAME * * glp_set_col_kind - set (change) column kind * * SYNOPSIS * * void glp_set_col_kind(glp_prob *mip, int j, int kind); * * DESCRIPTION * * The routine glp_set_col_kind sets (changes) the kind of j-th column * (structural variable) as specified by the parameter kind: * * GLP_CV - continuous variable; * GLP_IV - integer variable; * GLP_BV - binary variable. */ void glp_set_col_kind(glp_prob *mip, int j, int kind) { GLPCOL *col; if (!(1 <= j && j <= mip->n)) xerror("glp_set_col_kind: j = %d; column number out of range\n" , j); col = mip->col[j]; switch (kind) { case GLP_CV: col->kind = GLP_CV; break; case GLP_IV: col->kind = GLP_IV; break; case GLP_BV: col->kind = GLP_IV; if (!(col->type == GLP_DB && col->lb == 0.0 && col->ub == 1.0)) glp_set_col_bnds(mip, j, GLP_DB, 0.0, 1.0); break; default: xerror("glp_set_col_kind: j = %d; kind = %d; invalid column" " kind\n", j, kind); } return; } /*********************************************************************** * NAME * * glp_get_col_kind - retrieve column kind * * SYNOPSIS * * int glp_get_col_kind(glp_prob *mip, int j); * * RETURNS * * The routine glp_get_col_kind returns the kind of j-th column, i.e. * the kind of corresponding structural variable, as follows: * * GLP_CV - continuous variable; * GLP_IV - integer variable; * GLP_BV - binary variable */ int glp_get_col_kind(glp_prob *mip, int j) { GLPCOL *col; int kind; if (!(1 <= j && j <= mip->n)) xerror("glp_get_col_kind: j = %d; column number out of range\n" , j); col = mip->col[j]; kind = col->kind; switch (kind) { case GLP_CV: break; case GLP_IV: if (col->type == GLP_DB && col->lb == 0.0 && col->ub == 1.0) kind = GLP_BV; break; default: xassert(kind != kind); } return kind; } /*********************************************************************** * NAME * * glp_get_num_int - retrieve number of integer columns * * SYNOPSIS * * int glp_get_num_int(glp_prob *mip); * * RETURNS * * The routine glp_get_num_int returns the current number of columns, * which are marked as integer. */ int glp_get_num_int(glp_prob *mip) { GLPCOL *col; int j, count = 0; for (j = 1; j <= mip->n; j++) { col = mip->col[j]; if (col->kind == GLP_IV) count++; } return count; } /*********************************************************************** * NAME * * glp_get_num_bin - retrieve number of binary columns * * SYNOPSIS * * int glp_get_num_bin(glp_prob *mip); * * RETURNS * * The routine glp_get_num_bin returns the current number of columns, * which are marked as binary. */ int glp_get_num_bin(glp_prob *mip) { GLPCOL *col; int j, count = 0; for (j = 1; j <= mip->n; j++) { col = mip->col[j]; if (col->kind == GLP_IV && col->type == GLP_DB && col->lb == 0.0 && col->ub == 1.0) count++; } return count; } /*********************************************************************** * NAME * * glp_intopt - solve MIP problem with the branch-and-bound method * * SYNOPSIS * * int glp_intopt(glp_prob *P, const glp_iocp *parm); * * DESCRIPTION * * The routine glp_intopt is a driver to the MIP solver based on the * branch-and-bound method. * * On entry the problem object should contain optimal solution to LP * relaxation (which can be obtained with the routine glp_simplex). * * The MIP solver has a set of control parameters. Values of the control * parameters can be passed in a structure glp_iocp, which the parameter * parm points to. * * The parameter parm can be specified as NULL, in which case the MIP * solver uses default settings. * * RETURNS * * 0 The MIP problem instance has been successfully solved. This code * does not necessarily mean that the solver has found optimal * solution. It only means that the solution process was successful. * * GLP_EBOUND * Unable to start the search, because some double-bounded variables * have incorrect bounds or some integer variables have non-integer * (fractional) bounds. * * GLP_EROOT * Unable to start the search, because optimal basis for initial LP * relaxation is not provided. * * GLP_EFAIL * The search was prematurely terminated due to the solver failure. * * GLP_EMIPGAP * The search was prematurely terminated, because the relative mip * gap tolerance has been reached. * * GLP_ETMLIM * The search was prematurely terminated, because the time limit has * been exceeded. * * GLP_ENOPFS * The MIP problem instance has no primal feasible solution (only if * the MIP presolver is used). * * GLP_ENODFS * LP relaxation of the MIP problem instance has no dual feasible * solution (only if the MIP presolver is used). * * GLP_ESTOP * The search was prematurely terminated by application. */ static int solve_mip(glp_prob *P, const glp_iocp *parm) { /* solve MIP directly without using the preprocessor */ glp_tree *T; int ret; /* optimal basis to LP relaxation must be provided */ if (glp_get_status(P) != GLP_OPT) { if (parm->msg_lev >= GLP_MSG_ERR) xprintf("glp_intopt: optimal basis to initial LP relaxation" " not provided\n"); ret = GLP_EROOT; goto done; } /* it seems all is ok */ if (parm->msg_lev >= GLP_MSG_ALL) xprintf("Integer optimization begins...\n"); /* create the branch-and-bound tree */ T = ios_create_tree(P, parm); /* solve the problem instance */ ret = ios_driver(T); /* delete the branch-and-bound tree */ ios_delete_tree(T); /* analyze exit code reported by the mip driver */ if (ret == 0) { if (P->mip_stat == GLP_FEAS) { if (parm->msg_lev >= GLP_MSG_ALL) xprintf("INTEGER OPTIMAL SOLUTION FOUND\n"); P->mip_stat = GLP_OPT; } else { if (parm->msg_lev >= GLP_MSG_ALL) xprintf("PROBLEM HAS NO INTEGER FEASIBLE SOLUTION\n"); P->mip_stat = GLP_NOFEAS; } } else if (ret == GLP_EMIPGAP) { if (parm->msg_lev >= GLP_MSG_ALL) xprintf("RELATIVE MIP GAP TOLERANCE REACHED; SEARCH TERMINA" "TED\n"); } else if (ret == GLP_ETMLIM) { if (parm->msg_lev >= GLP_MSG_ALL) xprintf("TIME LIMIT EXCEEDED; SEARCH TERMINATED\n"); } else if (ret == GLP_EFAIL) { if (parm->msg_lev >= GLP_MSG_ERR) xprintf("glp_intopt: cannot solve current LP relaxation\n"); } else if (ret == GLP_ESTOP) { if (parm->msg_lev >= GLP_MSG_ALL) xprintf("SEARCH TERMINATED BY APPLICATION\n"); } else xassert(ret != ret); done: return ret; } static int preprocess_and_solve_mip(glp_prob *P, const glp_iocp *parm) { /* solve MIP using the preprocessor */ ENV *env = get_env_ptr(); int term_out = env->term_out; NPP *npp; glp_prob *mip = NULL; glp_bfcp bfcp; glp_smcp smcp; int ret; if (parm->msg_lev >= GLP_MSG_ALL) xprintf("Preprocessing...\n"); /* create preprocessor workspace */ npp = npp_create_wksp(); /* load original problem into the preprocessor workspace */ npp_load_prob(npp, P, GLP_OFF, GLP_MIP, GLP_OFF); /* process MIP prior to applying the branch-and-bound method */ if (!term_out || parm->msg_lev < GLP_MSG_ALL) env->term_out = GLP_OFF; else env->term_out = GLP_ON; ret = npp_integer(npp, parm); env->term_out = term_out; if (ret == 0) ; else if (ret == GLP_ENOPFS) { if (parm->msg_lev >= GLP_MSG_ALL) xprintf("PROBLEM HAS NO PRIMAL FEASIBLE SOLUTION\n"); } else if (ret == GLP_ENODFS) { if (parm->msg_lev >= GLP_MSG_ALL) xprintf("LP RELAXATION HAS NO DUAL FEASIBLE SOLUTION\n"); } else xassert(ret != ret); if (ret != 0) goto done; /* build transformed MIP */ mip = glp_create_prob(); npp_build_prob(npp, mip); /* if the transformed MIP is empty, it has empty solution, which is optimal */ if (mip->m == 0 && mip->n == 0) { mip->mip_stat = GLP_OPT; mip->mip_obj = mip->c0; if (parm->msg_lev >= GLP_MSG_ALL) { xprintf("Objective value = %17.9e\n", mip->mip_obj); xprintf("INTEGER OPTIMAL SOLUTION FOUND BY MIP PREPROCESSOR" "\n"); } goto post; } /* display some statistics */ if (parm->msg_lev >= GLP_MSG_ALL) { int ni = glp_get_num_int(mip); int nb = glp_get_num_bin(mip); char s[50]; xprintf("%d row%s, %d column%s, %d non-zero%s\n", mip->m, mip->m == 1 ? "" : "s", mip->n, mip->n == 1 ? "" : "s", mip->nnz, mip->nnz == 1 ? "" : "s"); if (nb == 0) strcpy(s, "none of"); else if (ni == 1 && nb == 1) strcpy(s, ""); else if (nb == 1) strcpy(s, "one of"); else if (nb == ni) strcpy(s, "all of"); else sprintf(s, "%d of", nb); xprintf("%d integer variable%s, %s which %s binary\n", ni, ni == 1 ? "" : "s", s, nb == 1 ? "is" : "are"); } /* inherit basis factorization control parameters */ glp_get_bfcp(P, &bfcp); glp_set_bfcp(mip, &bfcp); /* scale the transformed problem */ if (!term_out || parm->msg_lev < GLP_MSG_ALL) env->term_out = GLP_OFF; else env->term_out = GLP_ON; glp_scale_prob(mip, GLP_SF_GM | GLP_SF_EQ | GLP_SF_2N | GLP_SF_SKIP); env->term_out = term_out; /* build advanced initial basis */ if (!term_out || parm->msg_lev < GLP_MSG_ALL) env->term_out = GLP_OFF; else env->term_out = GLP_ON; glp_adv_basis(mip, 0); env->term_out = term_out; /* solve initial LP relaxation */ if (parm->msg_lev >= GLP_MSG_ALL) xprintf("Solving LP relaxation...\n"); glp_init_smcp(&smcp); smcp.msg_lev = parm->msg_lev; mip->it_cnt = P->it_cnt; ret = glp_simplex(mip, &smcp); P->it_cnt = mip->it_cnt; if (ret != 0) { if (parm->msg_lev >= GLP_MSG_ERR) xprintf("glp_intopt: cannot solve LP relaxation\n"); ret = GLP_EFAIL; goto done; } /* check status of the basic solution */ ret = glp_get_status(mip); if (ret == GLP_OPT) ret = 0; else if (ret == GLP_NOFEAS) ret = GLP_ENOPFS; else if (ret == GLP_UNBND) ret = GLP_ENODFS; else xassert(ret != ret); if (ret != 0) goto done; /* solve the transformed MIP */ mip->it_cnt = P->it_cnt; ret = solve_mip(mip, parm); P->it_cnt = mip->it_cnt; /* only integer feasible solution can be postprocessed */ if (!(mip->mip_stat == GLP_OPT || mip->mip_stat == GLP_FEAS)) { P->mip_stat = mip->mip_stat; goto done; } /* postprocess solution from the transformed MIP */ post: npp_postprocess(npp, mip); /* the transformed MIP is no longer needed */ glp_delete_prob(mip), mip = NULL; /* store solution to the original problem */ npp_unload_sol(npp, P); done: /* delete the transformed MIP, if it exists */ if (mip != NULL) glp_delete_prob(mip); /* delete preprocessor workspace */ npp_delete_wksp(npp); return ret; } #ifndef HAVE_ALIEN_SOLVER /* 28/V-2010 */ int _glp_intopt1(glp_prob *P, const glp_iocp *parm) { xassert(P == P); xassert(parm == parm); xprintf("glp_intopt: no alien solver is available\n"); return GLP_EFAIL; } #endif int glp_intopt(glp_prob *P, const glp_iocp *parm) { /* solve MIP problem with the branch-and-bound method */ glp_iocp _parm; int i, j, ret; /* check problem object */ if (P == NULL || P->magic != GLP_PROB_MAGIC) xerror("glp_intopt: P = %p; invalid problem object\n", P); if (P->tree != NULL) xerror("glp_intopt: operation not allowed\n"); /* check control parameters */ if (parm == NULL) parm = &_parm, glp_init_iocp((glp_iocp *)parm); if (!(parm->msg_lev == GLP_MSG_OFF || parm->msg_lev == GLP_MSG_ERR || parm->msg_lev == GLP_MSG_ON || parm->msg_lev == GLP_MSG_ALL || parm->msg_lev == GLP_MSG_DBG)) xerror("glp_intopt: msg_lev = %d; invalid parameter\n", parm->msg_lev); if (!(parm->br_tech == GLP_BR_FFV || parm->br_tech == GLP_BR_LFV || parm->br_tech == GLP_BR_MFV || parm->br_tech == GLP_BR_DTH || parm->br_tech == GLP_BR_PCH)) xerror("glp_intopt: br_tech = %d; invalid parameter\n", parm->br_tech); if (!(parm->bt_tech == GLP_BT_DFS || parm->bt_tech == GLP_BT_BFS || parm->bt_tech == GLP_BT_BLB || parm->bt_tech == GLP_BT_BPH)) xerror("glp_intopt: bt_tech = %d; invalid parameter\n", parm->bt_tech); if (!(0.0 < parm->tol_int && parm->tol_int < 1.0)) xerror("glp_intopt: tol_int = %g; invalid parameter\n", parm->tol_int); if (!(0.0 < parm->tol_obj && parm->tol_obj < 1.0)) xerror("glp_intopt: tol_obj = %g; invalid parameter\n", parm->tol_obj); if (parm->tm_lim < 0) xerror("glp_intopt: tm_lim = %d; invalid parameter\n", parm->tm_lim); if (parm->out_frq < 0) xerror("glp_intopt: out_frq = %d; invalid parameter\n", parm->out_frq); if (parm->out_dly < 0) xerror("glp_intopt: out_dly = %d; invalid parameter\n", parm->out_dly); if (!(0 <= parm->cb_size && parm->cb_size <= 256)) xerror("glp_intopt: cb_size = %d; invalid parameter\n", parm->cb_size); if (!(parm->pp_tech == GLP_PP_NONE || parm->pp_tech == GLP_PP_ROOT || parm->pp_tech == GLP_PP_ALL)) xerror("glp_intopt: pp_tech = %d; invalid parameter\n", parm->pp_tech); if (parm->mip_gap < 0.0) xerror("glp_intopt: mip_gap = %g; invalid parameter\n", parm->mip_gap); if (!(parm->mir_cuts == GLP_ON || parm->mir_cuts == GLP_OFF)) xerror("glp_intopt: mir_cuts = %d; invalid parameter\n", parm->mir_cuts); if (!(parm->gmi_cuts == GLP_ON || parm->gmi_cuts == GLP_OFF)) xerror("glp_intopt: gmi_cuts = %d; invalid parameter\n", parm->gmi_cuts); if (!(parm->cov_cuts == GLP_ON || parm->cov_cuts == GLP_OFF)) xerror("glp_intopt: cov_cuts = %d; invalid parameter\n", parm->cov_cuts); if (!(parm->clq_cuts == GLP_ON || parm->clq_cuts == GLP_OFF)) xerror("glp_intopt: clq_cuts = %d; invalid parameter\n", parm->clq_cuts); if (!(parm->presolve == GLP_ON || parm->presolve == GLP_OFF)) xerror("glp_intopt: presolve = %d; invalid parameter\n", parm->presolve); if (!(parm->binarize == GLP_ON || parm->binarize == GLP_OFF)) xerror("glp_intopt: binarize = %d; invalid parameter\n", parm->binarize); if (!(parm->fp_heur == GLP_ON || parm->fp_heur == GLP_OFF)) xerror("glp_intopt: fp_heur = %d; invalid parameter\n", parm->fp_heur); #if 1 /* 28/V-2010 */ if (!(parm->alien == GLP_ON || parm->alien == GLP_OFF)) xerror("glp_intopt: alien = %d; invalid parameter\n", parm->alien); #endif /* integer solution is currently undefined */ P->mip_stat = GLP_UNDEF; P->mip_obj = 0.0; /* check bounds of double-bounded variables */ for (i = 1; i <= P->m; i++) { GLPROW *row = P->row[i]; if (row->type == GLP_DB && row->lb >= row->ub) { if (parm->msg_lev >= GLP_MSG_ERR) xprintf("glp_intopt: row %d: lb = %g, ub = %g; incorrect" " bounds\n", i, row->lb, row->ub); ret = GLP_EBOUND; goto done; } } for (j = 1; j <= P->n; j++) { GLPCOL *col = P->col[j]; if (col->type == GLP_DB && col->lb >= col->ub) { if (parm->msg_lev >= GLP_MSG_ERR) xprintf("glp_intopt: column %d: lb = %g, ub = %g; incorr" "ect bounds\n", j, col->lb, col->ub); ret = GLP_EBOUND; goto done; } } /* bounds of all integer variables must be integral */ for (j = 1; j <= P->n; j++) { GLPCOL *col = P->col[j]; if (col->kind != GLP_IV) continue; if (col->type == GLP_LO || col->type == GLP_DB) { if (col->lb != floor(col->lb)) { if (parm->msg_lev >= GLP_MSG_ERR) xprintf("glp_intopt: integer column %d has non-intege" "r lower bound %g\n", j, col->lb); ret = GLP_EBOUND; goto done; } } if (col->type == GLP_UP || col->type == GLP_DB) { if (col->ub != floor(col->ub)) { if (parm->msg_lev >= GLP_MSG_ERR) xprintf("glp_intopt: integer column %d has non-intege" "r upper bound %g\n", j, col->ub); ret = GLP_EBOUND; goto done; } } if (col->type == GLP_FX) { if (col->lb != floor(col->lb)) { if (parm->msg_lev >= GLP_MSG_ERR) xprintf("glp_intopt: integer column %d has non-intege" "r fixed value %g\n", j, col->lb); ret = GLP_EBOUND; goto done; } } } /* solve MIP problem */ if (parm->msg_lev >= GLP_MSG_ALL) { int ni = glp_get_num_int(P); int nb = glp_get_num_bin(P); char s[50]; xprintf("GLPK Integer Optimizer, v%s\n", glp_version()); xprintf("%d row%s, %d column%s, %d non-zero%s\n", P->m, P->m == 1 ? "" : "s", P->n, P->n == 1 ? "" : "s", P->nnz, P->nnz == 1 ? "" : "s"); if (nb == 0) strcpy(s, "none of"); else if (ni == 1 && nb == 1) strcpy(s, ""); else if (nb == 1) strcpy(s, "one of"); else if (nb == ni) strcpy(s, "all of"); else sprintf(s, "%d of", nb); xprintf("%d integer variable%s, %s which %s binary\n", ni, ni == 1 ? "" : "s", s, nb == 1 ? "is" : "are"); } #if 1 /* 28/V-2010 */ if (parm->alien) { /* use alien integer optimizer */ ret = _glp_intopt1(P, parm); goto done; } #endif if (!parm->presolve) ret = solve_mip(P, parm); else ret = preprocess_and_solve_mip(P, parm); done: /* return to the application program */ return ret; } /*********************************************************************** * NAME * * glp_init_iocp - initialize integer optimizer control parameters * * SYNOPSIS * * void glp_init_iocp(glp_iocp *parm); * * DESCRIPTION * * The routine glp_init_iocp initializes control parameters, which are * used by the integer optimizer, with default values. * * Default values of the control parameters are stored in a glp_iocp * structure, which the parameter parm points to. */ void glp_init_iocp(glp_iocp *parm) { parm->msg_lev = GLP_MSG_ALL; parm->br_tech = GLP_BR_DTH; parm->bt_tech = GLP_BT_BLB; parm->tol_int = 1e-5; parm->tol_obj = 1e-7; parm->tm_lim = INT_MAX; parm->out_frq = 5000; parm->out_dly = 10000; parm->cb_func = NULL; parm->cb_info = NULL; parm->cb_size = 0; parm->pp_tech = GLP_PP_ALL; parm->mip_gap = 0.0; parm->mir_cuts = GLP_OFF; parm->gmi_cuts = GLP_OFF; parm->cov_cuts = GLP_OFF; parm->clq_cuts = GLP_OFF; parm->presolve = GLP_OFF; parm->binarize = GLP_OFF; parm->fp_heur = GLP_OFF; #if 1 /* 28/V-2010 */ parm->alien = GLP_OFF; #endif return; } /*********************************************************************** * NAME * * glp_mip_status - retrieve status of MIP solution * * SYNOPSIS * * int glp_mip_status(glp_prob *mip); * * RETURNS * * The routine lpx_mip_status reports the status of MIP solution found * by the branch-and-bound solver as follows: * * GLP_UNDEF - MIP solution is undefined; * GLP_OPT - MIP solution is integer optimal; * GLP_FEAS - MIP solution is integer feasible but its optimality * (or non-optimality) has not been proven, perhaps due to * premature termination of the search; * GLP_NOFEAS - problem has no integer feasible solution (proven by the * solver). */ int glp_mip_status(glp_prob *mip) { int mip_stat = mip->mip_stat; return mip_stat; } /*********************************************************************** * NAME * * glp_mip_obj_val - retrieve objective value (MIP solution) * * SYNOPSIS * * double glp_mip_obj_val(glp_prob *mip); * * RETURNS * * The routine glp_mip_obj_val returns value of the objective function * for MIP solution. */ double glp_mip_obj_val(glp_prob *mip) { /*struct LPXCPS *cps = mip->cps;*/ double z; z = mip->mip_obj; /*if (cps->round && fabs(z) < 1e-9) z = 0.0;*/ return z; } /*********************************************************************** * NAME * * glp_mip_row_val - retrieve row value (MIP solution) * * SYNOPSIS * * double glp_mip_row_val(glp_prob *mip, int i); * * RETURNS * * The routine glp_mip_row_val returns value of the auxiliary variable * associated with i-th row. */ double glp_mip_row_val(glp_prob *mip, int i) { /*struct LPXCPS *cps = mip->cps;*/ double mipx; if (!(1 <= i && i <= mip->m)) xerror("glp_mip_row_val: i = %d; row number out of range\n", i) ; mipx = mip->row[i]->mipx; /*if (cps->round && fabs(mipx) < 1e-9) mipx = 0.0;*/ return mipx; } /*********************************************************************** * NAME * * glp_mip_col_val - retrieve column value (MIP solution) * * SYNOPSIS * * double glp_mip_col_val(glp_prob *mip, int j); * * RETURNS * * The routine glp_mip_col_val returns value of the structural variable * associated with j-th column. */ double glp_mip_col_val(glp_prob *mip, int j) { /*struct LPXCPS *cps = mip->cps;*/ double mipx; if (!(1 <= j && j <= mip->n)) xerror("glp_mip_col_val: j = %d; column number out of range\n", j); mipx = mip->col[j]->mipx; /*if (cps->round && fabs(mipx) < 1e-9) mipx = 0.0;*/ return mipx; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpnpp02.c0000644000175000017500000012624211632352620020254 0ustar csheltoncshelton/* glpnpp02.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpnpp.h" /*********************************************************************** * NAME * * npp_free_row - process free (unbounded) row * * SYNOPSIS * * #include "glpnpp.h" * void npp_free_row(NPP *npp, NPPROW *p); * * DESCRIPTION * * The routine npp_free_row processes row p, which is free (i.e. has * no finite bounds): * * -inf < sum a[p,j] x[j] < +inf. (1) * j * * PROBLEM TRANSFORMATION * * Constraint (1) cannot be active, so it is redundant and can be * removed from the original problem. * * Removing row p leads to removing a column of multiplier pi[p] for * this row in the dual system. Since row p has no bounds, pi[p] = 0, * so removing the column does not affect the dual solution. * * RECOVERING BASIC SOLUTION * * In solution to the original problem row p is inactive constraint, * so it is assigned status GLP_BS, and multiplier pi[p] is assigned * zero value. * * RECOVERING INTERIOR-POINT SOLUTION * * In solution to the original problem row p is inactive constraint, * so its multiplier pi[p] is assigned zero value. * * RECOVERING MIP SOLUTION * * None needed. */ struct free_row { /* free (unbounded) row */ int p; /* row reference number */ }; static int rcv_free_row(NPP *npp, void *info); void npp_free_row(NPP *npp, NPPROW *p) { /* process free (unbounded) row */ struct free_row *info; /* the row must be free */ xassert(p->lb == -DBL_MAX && p->ub == +DBL_MAX); /* create transformation stack entry */ info = npp_push_tse(npp, rcv_free_row, sizeof(struct free_row)); info->p = p->i; /* remove the row from the problem */ npp_del_row(npp, p); return; } static int rcv_free_row(NPP *npp, void *_info) { /* recover free (unbounded) row */ struct free_row *info = _info; if (npp->sol == GLP_SOL) npp->r_stat[info->p] = GLP_BS; if (npp->sol != GLP_MIP) npp->r_pi[info->p] = 0.0; return 0; } /*********************************************************************** * NAME * * npp_geq_row - process row of 'not less than' type * * SYNOPSIS * * #include "glpnpp.h" * void npp_geq_row(NPP *npp, NPPROW *p); * * DESCRIPTION * * The routine npp_geq_row processes row p, which is 'not less than' * inequality constraint: * * L[p] <= sum a[p,j] x[j] (<= U[p]), (1) * j * * where L[p] < U[p], and upper bound may not exist (U[p] = +oo). * * PROBLEM TRANSFORMATION * * Constraint (1) can be replaced by equality constraint: * * sum a[p,j] x[j] - s = L[p], (2) * j * * where * * 0 <= s (<= U[p] - L[p]) (3) * * is a non-negative surplus variable. * * Since in the primal system there appears column s having the only * non-zero coefficient in row p, in the dual system there appears a * new row: * * (-1) pi[p] + lambda = 0, (4) * * where (-1) is coefficient of column s in row p, pi[p] is multiplier * of row p, lambda is multiplier of column q, 0 is coefficient of * column s in the objective row. * * RECOVERING BASIC SOLUTION * * Status of row p in solution to the original problem is determined * by its status and status of column q in solution to the transformed * problem as follows: * * +--------------------------------------+------------------+ * | Transformed problem | Original problem | * +-----------------+--------------------+------------------+ * | Status of row p | Status of column s | Status of row p | * +-----------------+--------------------+------------------+ * | GLP_BS | GLP_BS | N/A | * | GLP_BS | GLP_NL | GLP_BS | * | GLP_BS | GLP_NU | GLP_BS | * | GLP_NS | GLP_BS | GLP_BS | * | GLP_NS | GLP_NL | GLP_NL | * | GLP_NS | GLP_NU | GLP_NU | * +-----------------+--------------------+------------------+ * * Value of row multiplier pi[p] in solution to the original problem * is the same as in solution to the transformed problem. * * 1. In solution to the transformed problem row p and column q cannot * be basic at the same time; otherwise the basis matrix would have * two linear dependent columns: unity column of auxiliary variable * of row p and unity column of variable s. * * 2. Though in the transformed problem row p is equality constraint, * it may be basic due to primal degenerate solution. * * RECOVERING INTERIOR-POINT SOLUTION * * Value of row multiplier pi[p] in solution to the original problem * is the same as in solution to the transformed problem. * * RECOVERING MIP SOLUTION * * None needed. */ struct ineq_row { /* inequality constraint row */ int p; /* row reference number */ int s; /* column reference number for slack/surplus variable */ }; static int rcv_geq_row(NPP *npp, void *info); void npp_geq_row(NPP *npp, NPPROW *p) { /* process row of 'not less than' type */ struct ineq_row *info; NPPCOL *s; /* the row must have lower bound */ xassert(p->lb != -DBL_MAX); xassert(p->lb < p->ub); /* create column for surplus variable */ s = npp_add_col(npp); s->lb = 0.0; s->ub = (p->ub == +DBL_MAX ? +DBL_MAX : p->ub - p->lb); /* and add it to the transformed problem */ npp_add_aij(npp, p, s, -1.0); /* create transformation stack entry */ info = npp_push_tse(npp, rcv_geq_row, sizeof(struct ineq_row)); info->p = p->i; info->s = s->j; /* replace the row by equality constraint */ p->ub = p->lb; return; } static int rcv_geq_row(NPP *npp, void *_info) { /* recover row of 'not less than' type */ struct ineq_row *info = _info; if (npp->sol == GLP_SOL) { if (npp->r_stat[info->p] == GLP_BS) { if (npp->c_stat[info->s] == GLP_BS) { npp_error(); return 1; } else if (npp->c_stat[info->s] == GLP_NL || npp->c_stat[info->s] == GLP_NU) npp->r_stat[info->p] = GLP_BS; else { npp_error(); return 1; } } else if (npp->r_stat[info->p] == GLP_NS) { if (npp->c_stat[info->s] == GLP_BS) npp->r_stat[info->p] = GLP_BS; else if (npp->c_stat[info->s] == GLP_NL) npp->r_stat[info->p] = GLP_NL; else if (npp->c_stat[info->s] == GLP_NU) npp->r_stat[info->p] = GLP_NU; else { npp_error(); return 1; } } else { npp_error(); return 1; } } return 0; } /*********************************************************************** * NAME * * npp_leq_row - process row of 'not greater than' type * * SYNOPSIS * * #include "glpnpp.h" * void npp_leq_row(NPP *npp, NPPROW *p); * * DESCRIPTION * * The routine npp_leq_row processes row p, which is 'not greater than' * inequality constraint: * * (L[p] <=) sum a[p,j] x[j] <= U[p], (1) * j * * where L[p] < U[p], and lower bound may not exist (L[p] = +oo). * * PROBLEM TRANSFORMATION * * Constraint (1) can be replaced by equality constraint: * * sum a[p,j] x[j] + s = L[p], (2) * j * * where * * 0 <= s (<= U[p] - L[p]) (3) * * is a non-negative slack variable. * * Since in the primal system there appears column s having the only * non-zero coefficient in row p, in the dual system there appears a * new row: * * (+1) pi[p] + lambda = 0, (4) * * where (+1) is coefficient of column s in row p, pi[p] is multiplier * of row p, lambda is multiplier of column q, 0 is coefficient of * column s in the objective row. * * RECOVERING BASIC SOLUTION * * Status of row p in solution to the original problem is determined * by its status and status of column q in solution to the transformed * problem as follows: * * +--------------------------------------+------------------+ * | Transformed problem | Original problem | * +-----------------+--------------------+------------------+ * | Status of row p | Status of column s | Status of row p | * +-----------------+--------------------+------------------+ * | GLP_BS | GLP_BS | N/A | * | GLP_BS | GLP_NL | GLP_BS | * | GLP_BS | GLP_NU | GLP_BS | * | GLP_NS | GLP_BS | GLP_BS | * | GLP_NS | GLP_NL | GLP_NU | * | GLP_NS | GLP_NU | GLP_NL | * +-----------------+--------------------+------------------+ * * Value of row multiplier pi[p] in solution to the original problem * is the same as in solution to the transformed problem. * * 1. In solution to the transformed problem row p and column q cannot * be basic at the same time; otherwise the basis matrix would have * two linear dependent columns: unity column of auxiliary variable * of row p and unity column of variable s. * * 2. Though in the transformed problem row p is equality constraint, * it may be basic due to primal degeneracy. * * RECOVERING INTERIOR-POINT SOLUTION * * Value of row multiplier pi[p] in solution to the original problem * is the same as in solution to the transformed problem. * * RECOVERING MIP SOLUTION * * None needed. */ static int rcv_leq_row(NPP *npp, void *info); void npp_leq_row(NPP *npp, NPPROW *p) { /* process row of 'not greater than' type */ struct ineq_row *info; NPPCOL *s; /* the row must have upper bound */ xassert(p->ub != +DBL_MAX); xassert(p->lb < p->ub); /* create column for slack variable */ s = npp_add_col(npp); s->lb = 0.0; s->ub = (p->lb == -DBL_MAX ? +DBL_MAX : p->ub - p->lb); /* and add it to the transformed problem */ npp_add_aij(npp, p, s, +1.0); /* create transformation stack entry */ info = npp_push_tse(npp, rcv_leq_row, sizeof(struct ineq_row)); info->p = p->i; info->s = s->j; /* replace the row by equality constraint */ p->lb = p->ub; return; } static int rcv_leq_row(NPP *npp, void *_info) { /* recover row of 'not greater than' type */ struct ineq_row *info = _info; if (npp->sol == GLP_SOL) { if (npp->r_stat[info->p] == GLP_BS) { if (npp->c_stat[info->s] == GLP_BS) { npp_error(); return 1; } else if (npp->c_stat[info->s] == GLP_NL || npp->c_stat[info->s] == GLP_NU) npp->r_stat[info->p] = GLP_BS; else { npp_error(); return 1; } } else if (npp->r_stat[info->p] == GLP_NS) { if (npp->c_stat[info->s] == GLP_BS) npp->r_stat[info->p] = GLP_BS; else if (npp->c_stat[info->s] == GLP_NL) npp->r_stat[info->p] = GLP_NU; else if (npp->c_stat[info->s] == GLP_NU) npp->r_stat[info->p] = GLP_NL; else { npp_error(); return 1; } } else { npp_error(); return 1; } } return 0; } /*********************************************************************** * NAME * * npp_free_col - process free (unbounded) column * * SYNOPSIS * * #include "glpnpp.h" * void npp_free_col(NPP *npp, NPPCOL *q); * * DESCRIPTION * * The routine npp_free_col processes column q, which is free (i.e. has * no finite bounds): * * -oo < x[q] < +oo. (1) * * PROBLEM TRANSFORMATION * * Free (unbounded) variable can be replaced by the difference of two * non-negative variables: * * x[q] = s' - s'', s', s'' >= 0. (2) * * Assuming that in the transformed problem x[q] becomes s', * transformation (2) causes new column s'' to appear, which differs * from column s' only in the sign of coefficients in constraint and * objective rows. Thus, if in the dual system the following row * corresponds to column s': * * sum a[i,q] pi[i] + lambda' = c[q], (3) * i * * the row which corresponds to column s'' is the following: * * sum (-a[i,q]) pi[i] + lambda'' = -c[q]. (4) * i * * Then from (3) and (4) it follows that: * * lambda' + lambda'' = 0 => lambda' = lmabda'' = 0, (5) * * where lambda' and lambda'' are multipliers for columns s' and s'', * resp. * * RECOVERING BASIC SOLUTION * * With respect to (5) status of column q in solution to the original * problem is determined by statuses of columns s' and s'' in solution * to the transformed problem as follows: * * +--------------------------------------+------------------+ * | Transformed problem | Original problem | * +------------------+-------------------+------------------+ * | Status of col s' | Status of col s'' | Status of col q | * +------------------+-------------------+------------------+ * | GLP_BS | GLP_BS | N/A | * | GLP_BS | GLP_NL | GLP_BS | * | GLP_NL | GLP_BS | GLP_BS | * | GLP_NL | GLP_NL | GLP_NF | * +------------------+-------------------+------------------+ * * Value of column q is computed with formula (2). * * 1. In solution to the transformed problem columns s' and s'' cannot * be basic at the same time, because they differ only in the sign, * hence, are linear dependent. * * 2. Though column q is free, it can be non-basic due to dual * degeneracy. * * 3. If column q is integral, columns s' and s'' are also integral. * * RECOVERING INTERIOR-POINT SOLUTION * * Value of column q is computed with formula (2). * * RECOVERING MIP SOLUTION * * Value of column q is computed with formula (2). */ struct free_col { /* free (unbounded) column */ int q; /* column reference number for variables x[q] and s' */ int s; /* column reference number for variable s'' */ }; static int rcv_free_col(NPP *npp, void *info); void npp_free_col(NPP *npp, NPPCOL *q) { /* process free (unbounded) column */ struct free_col *info; NPPCOL *s; NPPAIJ *aij; /* the column must be free */ xassert(q->lb == -DBL_MAX && q->ub == +DBL_MAX); /* variable x[q] becomes s' */ q->lb = 0.0, q->ub = +DBL_MAX; /* create variable s'' */ s = npp_add_col(npp); s->is_int = q->is_int; s->lb = 0.0, s->ub = +DBL_MAX; /* duplicate objective coefficient */ s->coef = -q->coef; /* duplicate column of the constraint matrix */ for (aij = q->ptr; aij != NULL; aij = aij->c_next) npp_add_aij(npp, aij->row, s, -aij->val); /* create transformation stack entry */ info = npp_push_tse(npp, rcv_free_col, sizeof(struct free_col)); info->q = q->j; info->s = s->j; return; } static int rcv_free_col(NPP *npp, void *_info) { /* recover free (unbounded) column */ struct free_col *info = _info; if (npp->sol == GLP_SOL) { if (npp->c_stat[info->q] == GLP_BS) { if (npp->c_stat[info->s] == GLP_BS) { npp_error(); return 1; } else if (npp->c_stat[info->s] == GLP_NL) npp->c_stat[info->q] = GLP_BS; else { npp_error(); return -1; } } else if (npp->c_stat[info->q] == GLP_NL) { if (npp->c_stat[info->s] == GLP_BS) npp->c_stat[info->q] = GLP_BS; else if (npp->c_stat[info->s] == GLP_NL) npp->c_stat[info->q] = GLP_NF; else { npp_error(); return -1; } } else { npp_error(); return -1; } } /* compute value of x[q] with formula (2) */ npp->c_value[info->q] -= npp->c_value[info->s]; return 0; } /*********************************************************************** * NAME * * npp_lbnd_col - process column with (non-zero) lower bound * * SYNOPSIS * * #include "glpnpp.h" * void npp_lbnd_col(NPP *npp, NPPCOL *q); * * DESCRIPTION * * The routine npp_lbnd_col processes column q, which has (non-zero) * lower bound: * * l[q] <= x[q] (<= u[q]), (1) * * where l[q] < u[q], and upper bound may not exist (u[q] = +oo). * * PROBLEM TRANSFORMATION * * Column q can be replaced as follows: * * x[q] = l[q] + s, (2) * * where * * 0 <= s (<= u[q] - l[q]) (3) * * is a non-negative variable. * * Substituting x[q] from (2) into the objective row, we have: * * z = sum c[j] x[j] + c0 = * j * * = sum c[j] x[j] + c[q] x[q] + c0 = * j!=q * * = sum c[j] x[j] + c[q] (l[q] + s) + c0 = * j!=q * * = sum c[j] x[j] + c[q] s + c~0, * * where * * c~0 = c0 + c[q] l[q] (4) * * is the constant term of the objective in the transformed problem. * Similarly, substituting x[q] into constraint row i, we have: * * L[i] <= sum a[i,j] x[j] <= U[i] ==> * j * * L[i] <= sum a[i,j] x[j] + a[i,q] x[q] <= U[i] ==> * j!=q * * L[i] <= sum a[i,j] x[j] + a[i,q] (l[q] + s) <= U[i] ==> * j!=q * * L~[i] <= sum a[i,j] x[j] + a[i,q] s <= U~[i], * j!=q * * where * * L~[i] = L[i] - a[i,q] l[q], U~[i] = U[i] - a[i,q] l[q] (5) * * are lower and upper bounds of row i in the transformed problem, * resp. * * Transformation (2) does not affect the dual system. * * RECOVERING BASIC SOLUTION * * Status of column q in solution to the original problem is the same * as in solution to the transformed problem (GLP_BS, GLP_NL or GLP_NU). * Value of column q is computed with formula (2). * * RECOVERING INTERIOR-POINT SOLUTION * * Value of column q is computed with formula (2). * * RECOVERING MIP SOLUTION * * Value of column q is computed with formula (2). */ struct bnd_col { /* bounded column */ int q; /* column reference number for variables x[q] and s */ double bnd; /* lower/upper bound l[q] or u[q] */ }; static int rcv_lbnd_col(NPP *npp, void *info); void npp_lbnd_col(NPP *npp, NPPCOL *q) { /* process column with (non-zero) lower bound */ struct bnd_col *info; NPPROW *i; NPPAIJ *aij; /* the column must have non-zero lower bound */ xassert(q->lb != 0.0); xassert(q->lb != -DBL_MAX); xassert(q->lb < q->ub); /* create transformation stack entry */ info = npp_push_tse(npp, rcv_lbnd_col, sizeof(struct bnd_col)); info->q = q->j; info->bnd = q->lb; /* substitute x[q] into objective row */ npp->c0 += q->coef * q->lb; /* substitute x[q] into constraint rows */ for (aij = q->ptr; aij != NULL; aij = aij->c_next) { i = aij->row; if (i->lb == i->ub) i->ub = (i->lb -= aij->val * q->lb); else { if (i->lb != -DBL_MAX) i->lb -= aij->val * q->lb; if (i->ub != +DBL_MAX) i->ub -= aij->val * q->lb; } } /* column x[q] becomes column s */ if (q->ub != +DBL_MAX) q->ub -= q->lb; q->lb = 0.0; return; } static int rcv_lbnd_col(NPP *npp, void *_info) { /* recover column with (non-zero) lower bound */ struct bnd_col *info = _info; if (npp->sol == GLP_SOL) { if (npp->c_stat[info->q] == GLP_BS || npp->c_stat[info->q] == GLP_NL || npp->c_stat[info->q] == GLP_NU) npp->c_stat[info->q] = npp->c_stat[info->q]; else { npp_error(); return 1; } } /* compute value of x[q] with formula (2) */ npp->c_value[info->q] = info->bnd + npp->c_value[info->q]; return 0; } /*********************************************************************** * NAME * * npp_ubnd_col - process column with upper bound * * SYNOPSIS * * #include "glpnpp.h" * void npp_ubnd_col(NPP *npp, NPPCOL *q); * * DESCRIPTION * * The routine npp_ubnd_col processes column q, which has upper bound: * * (l[q] <=) x[q] <= u[q], (1) * * where l[q] < u[q], and lower bound may not exist (l[q] = -oo). * * PROBLEM TRANSFORMATION * * Column q can be replaced as follows: * * x[q] = u[q] - s, (2) * * where * * 0 <= s (<= u[q] - l[q]) (3) * * is a non-negative variable. * * Substituting x[q] from (2) into the objective row, we have: * * z = sum c[j] x[j] + c0 = * j * * = sum c[j] x[j] + c[q] x[q] + c0 = * j!=q * * = sum c[j] x[j] + c[q] (u[q] - s) + c0 = * j!=q * * = sum c[j] x[j] - c[q] s + c~0, * * where * * c~0 = c0 + c[q] u[q] (4) * * is the constant term of the objective in the transformed problem. * Similarly, substituting x[q] into constraint row i, we have: * * L[i] <= sum a[i,j] x[j] <= U[i] ==> * j * * L[i] <= sum a[i,j] x[j] + a[i,q] x[q] <= U[i] ==> * j!=q * * L[i] <= sum a[i,j] x[j] + a[i,q] (u[q] - s) <= U[i] ==> * j!=q * * L~[i] <= sum a[i,j] x[j] - a[i,q] s <= U~[i], * j!=q * * where * * L~[i] = L[i] - a[i,q] u[q], U~[i] = U[i] - a[i,q] u[q] (5) * * are lower and upper bounds of row i in the transformed problem, * resp. * * Note that in the transformed problem coefficients c[q] and a[i,q] * change their sign. Thus, the row of the dual system corresponding to * column q: * * sum a[i,q] pi[i] + lambda[q] = c[q] (6) * i * * in the transformed problem becomes the following: * * sum (-a[i,q]) pi[i] + lambda[s] = -c[q]. (7) * i * * Therefore: * * lambda[q] = - lambda[s], (8) * * where lambda[q] is multiplier for column q, lambda[s] is multiplier * for column s. * * RECOVERING BASIC SOLUTION * * With respect to (8) status of column q in solution to the original * problem is determined by status of column s in solution to the * transformed problem as follows: * * +-----------------------+--------------------+ * | Status of column s | Status of column q | * | (transformed problem) | (original problem) | * +-----------------------+--------------------+ * | GLP_BS | GLP_BS | * | GLP_NL | GLP_NU | * | GLP_NU | GLP_NL | * +-----------------------+--------------------+ * * Value of column q is computed with formula (2). * * RECOVERING INTERIOR-POINT SOLUTION * * Value of column q is computed with formula (2). * * RECOVERING MIP SOLUTION * * Value of column q is computed with formula (2). */ static int rcv_ubnd_col(NPP *npp, void *info); void npp_ubnd_col(NPP *npp, NPPCOL *q) { /* process column with upper bound */ struct bnd_col *info; NPPROW *i; NPPAIJ *aij; /* the column must have upper bound */ xassert(q->ub != +DBL_MAX); xassert(q->lb < q->ub); /* create transformation stack entry */ info = npp_push_tse(npp, rcv_ubnd_col, sizeof(struct bnd_col)); info->q = q->j; info->bnd = q->ub; /* substitute x[q] into objective row */ npp->c0 += q->coef * q->ub; q->coef = -q->coef; /* substitute x[q] into constraint rows */ for (aij = q->ptr; aij != NULL; aij = aij->c_next) { i = aij->row; if (i->lb == i->ub) i->ub = (i->lb -= aij->val * q->ub); else { if (i->lb != -DBL_MAX) i->lb -= aij->val * q->ub; if (i->ub != +DBL_MAX) i->ub -= aij->val * q->ub; } aij->val = -aij->val; } /* column x[q] becomes column s */ if (q->lb != -DBL_MAX) q->ub -= q->lb; else q->ub = +DBL_MAX; q->lb = 0.0; return; } static int rcv_ubnd_col(NPP *npp, void *_info) { /* recover column with upper bound */ struct bnd_col *info = _info; if (npp->sol == GLP_BS) { if (npp->c_stat[info->q] == GLP_BS) npp->c_stat[info->q] = GLP_BS; else if (npp->c_stat[info->q] == GLP_NL) npp->c_stat[info->q] = GLP_NU; else if (npp->c_stat[info->q] == GLP_NU) npp->c_stat[info->q] = GLP_NL; else { npp_error(); return 1; } } /* compute value of x[q] with formula (2) */ npp->c_value[info->q] = info->bnd - npp->c_value[info->q]; return 0; } /*********************************************************************** * NAME * * npp_dbnd_col - process non-negative column with upper bound * * SYNOPSIS * * #include "glpnpp.h" * void npp_dbnd_col(NPP *npp, NPPCOL *q); * * DESCRIPTION * * The routine npp_dbnd_col processes column q, which is non-negative * and has upper bound: * * 0 <= x[q] <= u[q], (1) * * where u[q] > 0. * * PROBLEM TRANSFORMATION * * Upper bound of column q can be replaced by the following equality * constraint: * * x[q] + s = u[q], (2) * * where s >= 0 is a non-negative complement variable. * * Since in the primal system along with new row (2) there appears a * new column s having the only non-zero coefficient in this row, in * the dual system there appears a new row: * * (+1)pi + lambda[s] = 0, (3) * * where (+1) is coefficient at column s in row (2), pi is multiplier * for row (2), lambda[s] is multiplier for column s, 0 is coefficient * at column s in the objective row. * * RECOVERING BASIC SOLUTION * * Status of column q in solution to the original problem is determined * by its status and status of column s in solution to the transformed * problem as follows: * * +-----------------------------------+------------------+ * | Transformed problem | Original problem | * +-----------------+-----------------+------------------+ * | Status of col q | Status of col s | Status of col q | * +-----------------+-----------------+------------------+ * | GLP_BS | GLP_BS | GLP_BS | * | GLP_BS | GLP_NL | GLP_NU | * | GLP_NL | GLP_BS | GLP_NL | * | GLP_NL | GLP_NL | GLP_NL (*) | * +-----------------+-----------------+------------------+ * * Value of column q in solution to the original problem is the same as * in solution to the transformed problem. * * 1. Formally, in solution to the transformed problem columns q and s * cannot be non-basic at the same time, since the constraint (2) * would be violated. However, if u[q] is close to zero, violation * may be less than a working precision even if both columns q and s * are non-basic. In this degenerate case row (2) can be only basic, * i.e. non-active constraint (otherwise corresponding row of the * basis matrix would be zero). This allows to pivot out auxiliary * variable and pivot in column s, in which case the row becomes * active while column s becomes basic. * * 2. If column q is integral, column s is also integral. * * RECOVERING INTERIOR-POINT SOLUTION * * Value of column q in solution to the original problem is the same as * in solution to the transformed problem. * * RECOVERING MIP SOLUTION * * Value of column q in solution to the original problem is the same as * in solution to the transformed problem. */ struct dbnd_col { /* double-bounded column */ int q; /* column reference number for variable x[q] */ int s; /* column reference number for complement variable s */ }; static int rcv_dbnd_col(NPP *npp, void *info); void npp_dbnd_col(NPP *npp, NPPCOL *q) { /* process non-negative column with upper bound */ struct dbnd_col *info; NPPROW *p; NPPCOL *s; /* the column must be non-negative with upper bound */ xassert(q->lb == 0.0); xassert(q->ub > 0.0); xassert(q->ub != +DBL_MAX); /* create variable s */ s = npp_add_col(npp); s->is_int = q->is_int; s->lb = 0.0, s->ub = +DBL_MAX; /* create equality constraint (2) */ p = npp_add_row(npp); p->lb = p->ub = q->ub; npp_add_aij(npp, p, q, +1.0); npp_add_aij(npp, p, s, +1.0); /* create transformation stack entry */ info = npp_push_tse(npp, rcv_dbnd_col, sizeof(struct dbnd_col)); info->q = q->j; info->s = s->j; /* remove upper bound of x[q] */ q->ub = +DBL_MAX; return; } static int rcv_dbnd_col(NPP *npp, void *_info) { /* recover non-negative column with upper bound */ struct dbnd_col *info = _info; if (npp->sol == GLP_BS) { if (npp->c_stat[info->q] == GLP_BS) { if (npp->c_stat[info->s] == GLP_BS) npp->c_stat[info->q] = GLP_BS; else if (npp->c_stat[info->s] == GLP_NL) npp->c_stat[info->q] = GLP_NU; else { npp_error(); return 1; } } else if (npp->c_stat[info->q] == GLP_NL) { if (npp->c_stat[info->s] == GLP_BS || npp->c_stat[info->s] == GLP_NL) npp->c_stat[info->q] = GLP_NL; else { npp_error(); return 1; } } else { npp_error(); return 1; } } return 0; } /*********************************************************************** * NAME * * npp_fixed_col - process fixed column * * SYNOPSIS * * #include "glpnpp.h" * void npp_fixed_col(NPP *npp, NPPCOL *q); * * DESCRIPTION * * The routine npp_fixed_col processes column q, which is fixed: * * x[q] = s[q], (1) * * where s[q] is a fixed column value. * * PROBLEM TRANSFORMATION * * The value of a fixed column can be substituted into the objective * and constraint rows that allows removing the column from the problem. * * Substituting x[q] = s[q] into the objective row, we have: * * z = sum c[j] x[j] + c0 = * j * * = sum c[j] x[j] + c[q] x[q] + c0 = * j!=q * * = sum c[j] x[j] + c[q] s[q] + c0 = * j!=q * * = sum c[j] x[j] + c~0, * j!=q * * where * * c~0 = c0 + c[q] s[q] (2) * * is the constant term of the objective in the transformed problem. * Similarly, substituting x[q] = s[q] into constraint row i, we have: * * L[i] <= sum a[i,j] x[j] <= U[i] ==> * j * * L[i] <= sum a[i,j] x[j] + a[i,q] x[q] <= U[i] ==> * j!=q * * L[i] <= sum a[i,j] x[j] + a[i,q] s[q] <= U[i] ==> * j!=q * * L~[i] <= sum a[i,j] x[j] + a[i,q] s <= U~[i], * j!=q * * where * * L~[i] = L[i] - a[i,q] s[q], U~[i] = U[i] - a[i,q] s[q] (3) * * are lower and upper bounds of row i in the transformed problem, * resp. * * RECOVERING BASIC SOLUTION * * Column q is assigned status GLP_NS and its value is assigned s[q]. * * RECOVERING INTERIOR-POINT SOLUTION * * Value of column q is assigned s[q]. * * RECOVERING MIP SOLUTION * * Value of column q is assigned s[q]. */ struct fixed_col { /* fixed column */ int q; /* column reference number for variable x[q] */ double s; /* value, at which x[q] is fixed */ }; static int rcv_fixed_col(NPP *npp, void *info); void npp_fixed_col(NPP *npp, NPPCOL *q) { /* process fixed column */ struct fixed_col *info; NPPROW *i; NPPAIJ *aij; /* the column must be fixed */ xassert(q->lb == q->ub); /* create transformation stack entry */ info = npp_push_tse(npp, rcv_fixed_col, sizeof(struct fixed_col)); info->q = q->j; info->s = q->lb; /* substitute x[q] = s[q] into objective row */ npp->c0 += q->coef * q->lb; /* substitute x[q] = s[q] into constraint rows */ for (aij = q->ptr; aij != NULL; aij = aij->c_next) { i = aij->row; if (i->lb == i->ub) i->ub = (i->lb -= aij->val * q->lb); else { if (i->lb != -DBL_MAX) i->lb -= aij->val * q->lb; if (i->ub != +DBL_MAX) i->ub -= aij->val * q->lb; } } /* remove the column from the problem */ npp_del_col(npp, q); return; } static int rcv_fixed_col(NPP *npp, void *_info) { /* recover fixed column */ struct fixed_col *info = _info; if (npp->sol == GLP_SOL) npp->c_stat[info->q] = GLP_NS; npp->c_value[info->q] = info->s; return 0; } /*********************************************************************** * NAME * * npp_make_equality - process row with almost identical bounds * * SYNOPSIS * * #include "glpnpp.h" * int npp_make_equality(NPP *npp, NPPROW *p); * * DESCRIPTION * * The routine npp_make_equality processes row p: * * L[p] <= sum a[p,j] x[j] <= U[p], (1) * j * * where -oo < L[p] < U[p] < +oo, i.e. which is double-sided inequality * constraint. * * RETURNS * * 0 - row bounds have not been changed; * * 1 - row has been replaced by equality constraint. * * PROBLEM TRANSFORMATION * * If bounds of row (1) are very close to each other: * * U[p] - L[p] <= eps, (2) * * where eps is an absolute tolerance for row value, the row can be * replaced by the following almost equivalent equiality constraint: * * sum a[p,j] x[j] = b, (3) * j * * where b = (L[p] + U[p]) / 2. If the right-hand side in (3) happens * to be very close to its nearest integer: * * |b - floor(b + 0.5)| <= eps, (4) * * it is reasonable to use this nearest integer as the right-hand side. * * RECOVERING BASIC SOLUTION * * Status of row p in solution to the original problem is determined * by its status and the sign of its multiplier pi[p] in solution to * the transformed problem as follows: * * +-----------------------+---------+--------------------+ * | Status of row p | Sign of | Status of row p | * | (transformed problem) | pi[p] | (original problem) | * +-----------------------+---------+--------------------+ * | GLP_BS | + / - | GLP_BS | * | GLP_NS | + | GLP_NL | * | GLP_NS | - | GLP_NU | * +-----------------------+---------+--------------------+ * * Value of row multiplier pi[p] in solution to the original problem is * the same as in solution to the transformed problem. * * RECOVERING INTERIOR POINT SOLUTION * * Value of row multiplier pi[p] in solution to the original problem is * the same as in solution to the transformed problem. * * RECOVERING MIP SOLUTION * * None needed. */ struct make_equality { /* row with almost identical bounds */ int p; /* row reference number */ }; static int rcv_make_equality(NPP *npp, void *info); int npp_make_equality(NPP *npp, NPPROW *p) { /* process row with almost identical bounds */ struct make_equality *info; double b, eps, nint; /* the row must be double-sided inequality */ xassert(p->lb != -DBL_MAX); xassert(p->ub != +DBL_MAX); xassert(p->lb < p->ub); /* check row bounds */ eps = 1e-9 + 1e-12 * fabs(p->lb); if (p->ub - p->lb > eps) return 0; /* row bounds are very close to each other */ /* create transformation stack entry */ info = npp_push_tse(npp, rcv_make_equality, sizeof(struct make_equality)); info->p = p->i; /* compute right-hand side */ b = 0.5 * (p->ub + p->lb); nint = floor(b + 0.5); if (fabs(b - nint) <= eps) b = nint; /* replace row p by almost equivalent equality constraint */ p->lb = p->ub = b; return 1; } int rcv_make_equality(NPP *npp, void *_info) { /* recover row with almost identical bounds */ struct make_equality *info = _info; if (npp->sol == GLP_SOL) { if (npp->r_stat[info->p] == GLP_BS) npp->r_stat[info->p] = GLP_BS; else if (npp->r_stat[info->p] == GLP_NS) { if (npp->r_pi[info->p] >= 0.0) npp->r_stat[info->p] = GLP_NL; else npp->r_stat[info->p] = GLP_NU; } else { npp_error(); return 1; } } return 0; } /*********************************************************************** * NAME * * npp_make_fixed - process column with almost identical bounds * * SYNOPSIS * * #include "glpnpp.h" * int npp_make_fixed(NPP *npp, NPPCOL *q); * * DESCRIPTION * * The routine npp_make_fixed processes column q: * * l[q] <= x[q] <= u[q], (1) * * where -oo < l[q] < u[q] < +oo, i.e. which has both lower and upper * bounds. * * RETURNS * * 0 - column bounds have not been changed; * * 1 - column has been fixed. * * PROBLEM TRANSFORMATION * * If bounds of column (1) are very close to each other: * * u[q] - l[q] <= eps, (2) * * where eps is an absolute tolerance for column value, the column can * be fixed: * * x[q] = s[q], (3) * * where s[q] = (l[q] + u[q]) / 2. And if the fixed column value s[q] * happens to be very close to its nearest integer: * * |s[q] - floor(s[q] + 0.5)| <= eps, (4) * * it is reasonable to use this nearest integer as the fixed value. * * RECOVERING BASIC SOLUTION * * In the dual system of the original (as well as transformed) problem * column q corresponds to the following row: * * sum a[i,q] pi[i] + lambda[q] = c[q]. (5) * i * * Since multipliers pi[i] are known for all rows from solution to the * transformed problem, formula (5) allows computing value of multiplier * (reduced cost) for column q: * * lambda[q] = c[q] - sum a[i,q] pi[i]. (6) * i * * Status of column q in solution to the original problem is determined * by its status and the sign of its multiplier lambda[q] in solution to * the transformed problem as follows: * * +-----------------------+-----------+--------------------+ * | Status of column q | Sign of | Status of column q | * | (transformed problem) | lambda[q] | (original problem) | * +-----------------------+-----------+--------------------+ * | GLP_BS | + / - | GLP_BS | * | GLP_NS | + | GLP_NL | * | GLP_NS | - | GLP_NU | * +-----------------------+-----------+--------------------+ * * Value of column q in solution to the original problem is the same as * in solution to the transformed problem. * * RECOVERING INTERIOR POINT SOLUTION * * Value of column q in solution to the original problem is the same as * in solution to the transformed problem. * * RECOVERING MIP SOLUTION * * None needed. */ struct make_fixed { /* column with almost identical bounds */ int q; /* column reference number */ double c; /* objective coefficient at x[q] */ NPPLFE *ptr; /* list of non-zero coefficients a[i,q] */ }; static int rcv_make_fixed(NPP *npp, void *info); int npp_make_fixed(NPP *npp, NPPCOL *q) { /* process column with almost identical bounds */ struct make_fixed *info; NPPAIJ *aij; NPPLFE *lfe; double s, eps, nint; /* the column must be double-bounded */ xassert(q->lb != -DBL_MAX); xassert(q->ub != +DBL_MAX); xassert(q->lb < q->ub); /* check column bounds */ eps = 1e-9 + 1e-12 * fabs(q->lb); if (q->ub - q->lb > eps) return 0; /* column bounds are very close to each other */ /* create transformation stack entry */ info = npp_push_tse(npp, rcv_make_fixed, sizeof(struct make_fixed)); info->q = q->j; info->c = q->coef; info->ptr = NULL; /* save column coefficients a[i,q] (needed for basic solution only) */ if (npp->sol == GLP_SOL) { for (aij = q->ptr; aij != NULL; aij = aij->c_next) { lfe = dmp_get_atom(npp->stack, sizeof(NPPLFE)); lfe->ref = aij->row->i; lfe->val = aij->val; lfe->next = info->ptr; info->ptr = lfe; } } /* compute column fixed value */ s = 0.5 * (q->ub + q->lb); nint = floor(s + 0.5); if (fabs(s - nint) <= eps) s = nint; /* make column q fixed */ q->lb = q->ub = s; return 1; } static int rcv_make_fixed(NPP *npp, void *_info) { /* recover column with almost identical bounds */ struct make_fixed *info = _info; NPPLFE *lfe; double lambda; if (npp->sol == GLP_SOL) { if (npp->c_stat[info->q] == GLP_BS) npp->c_stat[info->q] = GLP_BS; else if (npp->c_stat[info->q] == GLP_NS) { /* compute multiplier for column q with formula (6) */ lambda = info->c; for (lfe = info->ptr; lfe != NULL; lfe = lfe->next) lambda -= lfe->val * npp->r_pi[lfe->ref]; /* assign status to non-basic column */ if (lambda >= 0.0) npp->c_stat[info->q] = GLP_NL; else npp->c_stat[info->q] = GLP_NU; } else { npp_error(); return 1; } } return 0; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpios04.c0000644000175000017500000001664611632352620020261 0ustar csheltoncshelton/* glpios04.c (operations on sparse vectors) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpios.h" /*********************************************************************** * NAME * * ios_create_vec - create sparse vector * * SYNOPSIS * * #include "glpios.h" * IOSVEC *ios_create_vec(int n); * * DESCRIPTION * * The routine ios_create_vec creates a sparse vector of dimension n, * which initially is a null vector. * * RETURNS * * The routine returns a pointer to the vector created. */ IOSVEC *ios_create_vec(int n) { IOSVEC *v; xassert(n >= 0); v = xmalloc(sizeof(IOSVEC)); v->n = n; v->nnz = 0; v->pos = xcalloc(1+n, sizeof(int)); memset(&v->pos[1], 0, n * sizeof(int)); v->ind = xcalloc(1+n, sizeof(int)); v->val = xcalloc(1+n, sizeof(double)); return v; } /*********************************************************************** * NAME * * ios_check_vec - check that sparse vector has correct representation * * SYNOPSIS * * #include "glpios.h" * void ios_check_vec(IOSVEC *v); * * DESCRIPTION * * The routine ios_check_vec checks that a sparse vector specified by * the parameter v has correct representation. * * NOTE * * Complexity of this operation is O(n). */ void ios_check_vec(IOSVEC *v) { int j, k, nnz; xassert(v->n >= 0); nnz = 0; for (j = v->n; j >= 1; j--) { k = v->pos[j]; xassert(0 <= k && k <= v->nnz); if (k != 0) { xassert(v->ind[k] == j); nnz++; } } xassert(v->nnz == nnz); return; } /*********************************************************************** * NAME * * ios_get_vj - retrieve component of sparse vector * * SYNOPSIS * * #include "glpios.h" * double ios_get_vj(IOSVEC *v, int j); * * RETURNS * * The routine ios_get_vj returns j-th component of a sparse vector * specified by the parameter v. */ double ios_get_vj(IOSVEC *v, int j) { int k; xassert(1 <= j && j <= v->n); k = v->pos[j]; xassert(0 <= k && k <= v->nnz); return (k == 0 ? 0.0 : v->val[k]); } /*********************************************************************** * NAME * * ios_set_vj - set/change component of sparse vector * * SYNOPSIS * * #include "glpios.h" * void ios_set_vj(IOSVEC *v, int j, double val); * * DESCRIPTION * * The routine ios_set_vj assigns val to j-th component of a sparse * vector specified by the parameter v. */ void ios_set_vj(IOSVEC *v, int j, double val) { int k; xassert(1 <= j && j <= v->n); k = v->pos[j]; if (val == 0.0) { if (k != 0) { /* remove j-th component */ v->pos[j] = 0; if (k < v->nnz) { v->pos[v->ind[v->nnz]] = k; v->ind[k] = v->ind[v->nnz]; v->val[k] = v->val[v->nnz]; } v->nnz--; } } else { if (k == 0) { /* create j-th component */ k = ++(v->nnz); v->pos[j] = k; v->ind[k] = j; } v->val[k] = val; } return; } /*********************************************************************** * NAME * * ios_clear_vec - set all components of sparse vector to zero * * SYNOPSIS * * #include "glpios.h" * void ios_clear_vec(IOSVEC *v); * * DESCRIPTION * * The routine ios_clear_vec sets all components of a sparse vector * specified by the parameter v to zero. */ void ios_clear_vec(IOSVEC *v) { int k; for (k = 1; k <= v->nnz; k++) v->pos[v->ind[k]] = 0; v->nnz = 0; return; } /*********************************************************************** * NAME * * ios_clean_vec - remove zero or small components from sparse vector * * SYNOPSIS * * #include "glpios.h" * void ios_clean_vec(IOSVEC *v, double eps); * * DESCRIPTION * * The routine ios_clean_vec removes zero components and components * whose magnitude is less than eps from a sparse vector specified by * the parameter v. If eps is 0.0, only zero components are removed. */ void ios_clean_vec(IOSVEC *v, double eps) { int k, nnz; nnz = 0; for (k = 1; k <= v->nnz; k++) { if (fabs(v->val[k]) == 0.0 || fabs(v->val[k]) < eps) { /* remove component */ v->pos[v->ind[k]] = 0; } else { /* keep component */ nnz++; v->pos[v->ind[k]] = nnz; v->ind[nnz] = v->ind[k]; v->val[nnz] = v->val[k]; } } v->nnz = nnz; return; } /*********************************************************************** * NAME * * ios_copy_vec - copy sparse vector (x := y) * * SYNOPSIS * * #include "glpios.h" * void ios_copy_vec(IOSVEC *x, IOSVEC *y); * * DESCRIPTION * * The routine ios_copy_vec copies a sparse vector specified by the * parameter y to a sparse vector specified by the parameter x. */ void ios_copy_vec(IOSVEC *x, IOSVEC *y) { int j; xassert(x != y); xassert(x->n == y->n); ios_clear_vec(x); x->nnz = y->nnz; memcpy(&x->ind[1], &y->ind[1], x->nnz * sizeof(int)); memcpy(&x->val[1], &y->val[1], x->nnz * sizeof(double)); for (j = 1; j <= x->nnz; j++) x->pos[x->ind[j]] = j; return; } /*********************************************************************** * NAME * * ios_linear_comb - compute linear combination (x := x + a * y) * * SYNOPSIS * * #include "glpios.h" * void ios_linear_comb(IOSVEC *x, double a, IOSVEC *y); * * DESCRIPTION * * The routine ios_linear_comb computes the linear combination * * x := x + a * y, * * where x and y are sparse vectors, a is a scalar. */ void ios_linear_comb(IOSVEC *x, double a, IOSVEC *y) { int j, k; double xj, yj; xassert(x != y); xassert(x->n == y->n); for (k = 1; k <= y->nnz; k++) { j = y->ind[k]; xj = ios_get_vj(x, j); yj = y->val[k]; ios_set_vj(x, j, xj + a * yj); } return; } /*********************************************************************** * NAME * * ios_delete_vec - delete sparse vector * * SYNOPSIS * * #include "glpios.h" * void ios_delete_vec(IOSVEC *v); * * DESCRIPTION * * The routine ios_delete_vec deletes a sparse vector specified by the * parameter v freeing all the memory allocated to this object. */ void ios_delete_vec(IOSVEC *v) { /* delete sparse vector */ xfree(v->pos); xfree(v->ind); xfree(v->val); xfree(v); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glprgr.c0000644000175000017500000001416211632352620020104 0ustar csheltoncshelton/* glprgr.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #define _GLPSTD_ERRNO #define _GLPSTD_STDIO #include "glpenv.h" #include "glprgr.h" #define xfault xerror /*********************************************************************** * NAME * * rgr_write_bmp16 - write 16-color raster image in BMP file format * * SYNOPSIS * * #include "glprgr.h" * int rgr_write_bmp16(const char *fname, int m, int n, const char * map[]); * * DESCRIPTION * * The routine rgr_write_bmp16 writes 16-color raster image in * uncompressed BMP file format (Windows bitmap) to a binary file whose * name is specified by the character string fname. * * The parameters m and n specify, respectively, the number of rows and * the numbers of columns (i.e. height and width) of the raster image. * * The character array map has m*n elements. Elements map[0, ..., n-1] * correspond to the first (top) scanline, elements map[n, ..., 2*n-1] * correspond to the second scanline, etc. * * Each element of the array map specifies a color of the corresponding * pixel as 8-bit binary number XXXXIRGB, where four high-order bits (X) * are ignored, I is high intensity bit, R is red color bit, G is green * color bit, and B is blue color bit. Thus, all 16 possible colors are * coded as following hexadecimal numbers: * * 0x00 = black 0x08 = dark gray * 0x01 = blue 0x09 = bright blue * 0x02 = green 0x0A = bright green * 0x03 = cyan 0x0B = bright cyan * 0x04 = red 0x0C = bright red * 0x05 = magenta 0x0D = bright magenta * 0x06 = brown 0x0E = yellow * 0x07 = light gray 0x0F = white * * RETURNS * * If no error occured, the routine returns zero; otherwise, it prints * an appropriate error message and returns non-zero. */ static void put_byte(FILE *fp, int c) { fputc(c, fp); return; } static void put_word(FILE *fp, int w) { /* big endian */ put_byte(fp, w); put_byte(fp, w >> 8); return; } static void put_dword(FILE *fp, int d) { /* big endian */ put_word(fp, d); put_word(fp, d >> 16); return; } int rgr_write_bmp16(const char *fname, int m, int n, const char map[]) { FILE *fp; int offset, bmsize, i, j, b, ret = 0; if (!(1 <= m && m <= 32767)) xfault("rgr_write_bmp16: m = %d; invalid height\n", m); if (!(1 <= n && n <= 32767)) xfault("rgr_write_bmp16: n = %d; invalid width\n", n); fp = fopen(fname, "wb"); if (fp == NULL) { xprintf("rgr_write_bmp16: unable to create `%s' - %s\n", fname, strerror(errno)); ret = 1; goto fini; } offset = 14 + 40 + 16 * 4; bmsize = (4 * n + 31) / 32; /* struct BMPFILEHEADER (14 bytes) */ /* UINT bfType */ put_byte(fp, 'B'), put_byte(fp, 'M'); /* DWORD bfSize */ put_dword(fp, offset + bmsize * 4); /* UINT bfReserved1 */ put_word(fp, 0); /* UNIT bfReserved2 */ put_word(fp, 0); /* DWORD bfOffBits */ put_dword(fp, offset); /* struct BMPINFOHEADER (40 bytes) */ /* DWORD biSize */ put_dword(fp, 40); /* LONG biWidth */ put_dword(fp, n); /* LONG biHeight */ put_dword(fp, m); /* WORD biPlanes */ put_word(fp, 1); /* WORD biBitCount */ put_word(fp, 4); /* DWORD biCompression */ put_dword(fp, 0 /* BI_RGB */); /* DWORD biSizeImage */ put_dword(fp, 0); /* LONG biXPelsPerMeter */ put_dword(fp, 2953 /* 75 dpi */); /* LONG biYPelsPerMeter */ put_dword(fp, 2953 /* 75 dpi */); /* DWORD biClrUsed */ put_dword(fp, 0); /* DWORD biClrImportant */ put_dword(fp, 0); /* struct RGBQUAD (16 * 4 = 64 bytes) */ /* CGA-compatible colors: */ /* 0x00 = black */ put_dword(fp, 0x000000); /* 0x01 = blue */ put_dword(fp, 0x000080); /* 0x02 = green */ put_dword(fp, 0x008000); /* 0x03 = cyan */ put_dword(fp, 0x008080); /* 0x04 = red */ put_dword(fp, 0x800000); /* 0x05 = magenta */ put_dword(fp, 0x800080); /* 0x06 = brown */ put_dword(fp, 0x808000); /* 0x07 = light gray */ put_dword(fp, 0xC0C0C0); /* 0x08 = dark gray */ put_dword(fp, 0x808080); /* 0x09 = bright blue */ put_dword(fp, 0x0000FF); /* 0x0A = bright green */ put_dword(fp, 0x00FF00); /* 0x0B = bright cyan */ put_dword(fp, 0x00FFFF); /* 0x0C = bright red */ put_dword(fp, 0xFF0000); /* 0x0D = bright magenta */ put_dword(fp, 0xFF00FF); /* 0x0E = yellow */ put_dword(fp, 0xFFFF00); /* 0x0F = white */ put_dword(fp, 0xFFFFFF); /* pixel data bits */ b = 0; for (i = m - 1; i >= 0; i--) { for (j = 0; j < ((n + 7) / 8) * 8; j++) { b <<= 4; b |= (j < n ? map[i * n + j] & 15 : 0); if (j & 1) put_byte(fp, b); } } fflush(fp); if (ferror(fp)) { xprintf("rgr_write_bmp16: write error on `%s' - %s\n", fname, strerror(errno)); ret = 1; } fini: if (fp != NULL) fclose(fp); return ret; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpnet09.c0000644000175000017500000002230711632352620020251 0ustar csheltoncshelton/* glpnet09.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" #include "glpnet.h" /*********************************************************************** * NAME * * kellerman - cover edges by cliques with Kellerman's heuristic * * SYNOPSIS * * #include "glpnet.h" * int kellerman(int n, int (*func)(void *info, int i, int ind[]), * void *info, glp_graph *H); * * DESCRIPTION * * The routine kellerman implements Kellerman's heuristic algorithm * to find a minimal set of cliques which cover all edges of specified * graph G = (V, E). * * The parameter n specifies the number of vertices |V|, n >= 0. * * Formal routine func specifies the set of edges E in the following * way. Running the routine kellerman calls the routine func and passes * to it parameter i, which is the number of some vertex, 1 <= i <= n. * In response the routine func should store numbers of all vertices * adjacent to vertex i to locations ind[1], ind[2], ..., ind[len] and * return the value of len, which is the number of adjacent vertices, * 0 <= len <= n. Self-loops are allowed, but ignored. Multiple edges * are not allowed. * * The parameter info is a transit pointer (magic cookie) passed to the * formal routine func as its first parameter. * * The result provided by the routine kellerman is the bipartite graph * H = (V union C, F), which defines the covering found. (The program * object of type glp_graph specified by the parameter H should be * previously created with the routine glp_create_graph. On entry the * routine kellerman erases the content of this object with the routine * glp_erase_graph.) Vertices of first part V correspond to vertices of * the graph G and have the same ordinal numbers 1, 2, ..., n. Vertices * of second part C correspond to cliques and have ordinal numbers * n+1, n+2, ..., n+k, where k is the total number of cliques in the * edge covering found. Every edge f in F in the program object H is * represented as arc f = (i->j), where i in V and j in C, which means * that vertex i of the graph G is in clique C[j], 1 <= j <= k. (Thus, * if two vertices of the graph G are in the same clique, these vertices * are adjacent in G, and corresponding edge is covered by that clique.) * * RETURNS * * The routine Kellerman returns k, the total number of cliques in the * edge covering found. * * REFERENCE * * For more details see: glpk/doc/notes/keller.pdf (in Russian). */ struct set { /* set of vertices */ int size; /* size (cardinality) of the set, 0 <= card <= n */ int *list; /* int list[1+n]; */ /* the set contains vertices list[1,...,size] */ int *pos; /* int pos[1+n]; */ /* pos[i] > 0 means that vertex i is in the set and list[pos[i]] = i; pos[i] = 0 means that vertex i is not in the set */ }; int kellerman(int n, int (*func)(void *info, int i, int ind[]), void *info, void /* glp_graph */ *H_) { glp_graph *H = H_; struct set W_, *W = &W_, V_, *V = &V_; glp_arc *a; int i, j, k, m, t, len, card, best; xassert(n >= 0); /* H := (V, 0; 0), where V is the set of vertices of graph G */ glp_erase_graph(H, H->v_size, H->a_size); glp_add_vertices(H, n); /* W := 0 */ W->size = 0; W->list = xcalloc(1+n, sizeof(int)); W->pos = xcalloc(1+n, sizeof(int)); memset(&W->pos[1], 0, sizeof(int) * n); /* V := 0 */ V->size = 0; V->list = xcalloc(1+n, sizeof(int)); V->pos = xcalloc(1+n, sizeof(int)); memset(&V->pos[1], 0, sizeof(int) * n); /* main loop */ for (i = 1; i <= n; i++) { /* W must be empty */ xassert(W->size == 0); /* W := { j : i > j and (i,j) in E } */ len = func(info, i, W->list); xassert(0 <= len && len <= n); for (t = 1; t <= len; t++) { j = W->list[t]; xassert(1 <= j && j <= n); if (j >= i) continue; xassert(W->pos[j] == 0); W->list[++W->size] = j, W->pos[j] = W->size; } /* on i-th iteration we need to cover edges (i,j) for all j in W */ /* if W is empty, it is a special case */ if (W->size == 0) { /* set k := k + 1 and create new clique C[k] = { i } */ k = glp_add_vertices(H, 1) - n; glp_add_arc(H, i, n + k); continue; } /* try to include vertex i into existing cliques */ /* V must be empty */ xassert(V->size == 0); /* k is the number of cliques found so far */ k = H->nv - n; for (m = 1; m <= k; m++) { /* do while V != W; since here V is within W, we can use equivalent condition: do while |V| < |W| */ if (V->size == W->size) break; /* check if C[m] is within W */ for (a = H->v[n + m]->in; a != NULL; a = a->h_next) { j = a->tail->i; if (W->pos[j] == 0) break; } if (a != NULL) continue; /* C[m] is within W, expand clique C[m] with vertex i */ /* C[m] := C[m] union {i} */ glp_add_arc(H, i, n + m); /* V is a set of vertices whose incident edges are already covered by existing cliques */ /* V := V union C[m] */ for (a = H->v[n + m]->in; a != NULL; a = a->h_next) { j = a->tail->i; if (V->pos[j] == 0) V->list[++V->size] = j, V->pos[j] = V->size; } } /* remove from set W the vertices whose incident edges are already covered by existing cliques */ /* W := W \ V, V := 0 */ for (t = 1; t <= V->size; t++) { j = V->list[t], V->pos[j] = 0; if (W->pos[j] != 0) { /* remove vertex j from W */ if (W->pos[j] != W->size) { int jj = W->list[W->size]; W->list[W->pos[j]] = jj; W->pos[jj] = W->pos[j]; } W->size--, W->pos[j] = 0; } } V->size = 0; /* now set W contains only vertices whose incident edges are still not covered by existing cliques; create new cliques to cover remaining edges until set W becomes empty */ while (W->size > 0) { /* find clique C[m], 1 <= m <= k, which shares maximal number of vertices with W; to break ties choose clique having smallest number m */ m = 0, best = -1; k = H->nv - n; for (t = 1; t <= k; t++) { /* compute cardinality of intersection of W and C[t] */ card = 0; for (a = H->v[n + t]->in; a != NULL; a = a->h_next) { j = a->tail->i; if (W->pos[j] != 0) card++; } if (best < card) m = t, best = card; } xassert(m > 0); /* set k := k + 1 and create new clique: C[k] := (W intersect C[m]) union { i }, which covers all edges incident to vertices from (W intersect C[m]) */ k = glp_add_vertices(H, 1) - n; for (a = H->v[n + m]->in; a != NULL; a = a->h_next) { j = a->tail->i; if (W->pos[j] != 0) { /* vertex j is in both W and C[m]; include it in new clique C[k] */ glp_add_arc(H, j, n + k); /* remove vertex j from W, since edge (i,j) will be covered by new clique C[k] */ if (W->pos[j] != W->size) { int jj = W->list[W->size]; W->list[W->pos[j]] = jj; W->pos[jj] = W->pos[j]; } W->size--, W->pos[j] = 0; } } /* include vertex i to new clique C[k] to cover edges (i,j) incident to all vertices j just removed from W */ glp_add_arc(H, i, n + k); } } /* free working arrays */ xfree(W->list); xfree(W->pos); xfree(V->list); xfree(V->pos); /* return the number of cliques in the edge covering found */ return H->nv - n; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpenv08.c0000644000175000017500000000744011632352620020253 0ustar csheltoncshelton/* glpenv08.c (shared library support) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifdef HAVE_CONFIG_H #include #endif #include "glpenv.h" /* GNU version ********************************************************/ #if defined(HAVE_LTDL) #include void *xdlopen(const char *module) { void *h = NULL; if (lt_dlinit() != 0) { lib_err_msg(lt_dlerror()); goto done; } h = lt_dlopen(module); if (h == NULL) { lib_err_msg(lt_dlerror()); if (lt_dlexit() != 0) xerror("xdlopen: %s\n", lt_dlerror()); } done: return h; } void *xdlsym(void *h, const char *symbol) { void *ptr; xassert(h != NULL); ptr = lt_dlsym(h, symbol); if (ptr == NULL) xerror("xdlsym: %s: %s\n", symbol, lt_dlerror()); return ptr; } void xdlclose(void *h) { xassert(h != NULL); if (lt_dlclose(h) != 0) xerror("xdlclose: %s\n", lt_dlerror()); if (lt_dlexit() != 0) xerror("xdlclose: %s\n", lt_dlerror()); return; } /* POSIX version ******************************************************/ #elif defined(HAVE_DLFCN) #include void *xdlopen(const char *module) { void *h; h = dlopen(module, RTLD_NOW); if (h == NULL) lib_err_msg(dlerror()); return h; } void *xdlsym(void *h, const char *symbol) { void *ptr; xassert(h != NULL); ptr = dlsym(h, symbol); if (ptr == NULL) xerror("xdlsym: %s: %s\n", symbol, dlerror()); return ptr; } void xdlclose(void *h) { xassert(h != NULL); if (dlclose(h) != 0) xerror("xdlclose: %s\n", dlerror()); return; } /* Windows version ****************************************************/ #elif defined(__WOE__) #include void *xdlopen(const char *module) { void *h; h = LoadLibrary(module); if (h == NULL) { char msg[20]; sprintf(msg, "Error %d", GetLastError()); lib_err_msg(msg); } return h; } void *xdlsym(void *h, const char *symbol) { void *ptr; xassert(h != NULL); ptr = GetProcAddress(h, symbol); if (ptr == NULL) xerror("xdlsym: %s: Error %d\n", symbol, GetLastError()); return ptr; } void xdlclose(void *h) { xassert(h != NULL); if (!FreeLibrary(h)) xerror("xdlclose: Error %d\n", GetLastError()); return; } /* NULL version *******************************************************/ #else void *xdlopen(const char *module) { xassert(module == module); lib_err_msg("Shared libraries not supported"); return NULL; } void *xdlsym(void *h, const char *symbol) { xassert(h != h); xassert(symbol != symbol); return NULL; } void xdlclose(void *h) { xassert(h != h); return; } #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpluf.c0000644000175000017500000021357111632352620020105 0ustar csheltoncshelton/* glpluf.c (LU-factorization) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpenv.h" #include "glpluf.h" #define xfault xerror /* CAUTION: DO NOT CHANGE THE LIMIT BELOW */ #define N_MAX 100000000 /* = 100*10^6 */ /* maximal order of the original matrix */ /*********************************************************************** * NAME * * luf_create_it - create LU-factorization * * SYNOPSIS * * #include "glpluf.h" * LUF *luf_create_it(void); * * DESCRIPTION * * The routine luf_create_it creates a program object, which represents * LU-factorization of a square matrix. * * RETURNS * * The routine luf_create_it returns a pointer to the object created. */ LUF *luf_create_it(void) { LUF *luf; luf = xmalloc(sizeof(LUF)); luf->n_max = luf->n = 0; luf->valid = 0; luf->fr_ptr = luf->fr_len = NULL; luf->fc_ptr = luf->fc_len = NULL; luf->vr_ptr = luf->vr_len = luf->vr_cap = NULL; luf->vr_piv = NULL; luf->vc_ptr = luf->vc_len = luf->vc_cap = NULL; luf->pp_row = luf->pp_col = NULL; luf->qq_row = luf->qq_col = NULL; luf->sv_size = 0; luf->sv_beg = luf->sv_end = 0; luf->sv_ind = NULL; luf->sv_val = NULL; luf->sv_head = luf->sv_tail = 0; luf->sv_prev = luf->sv_next = NULL; luf->vr_max = NULL; luf->rs_head = luf->rs_prev = luf->rs_next = NULL; luf->cs_head = luf->cs_prev = luf->cs_next = NULL; luf->flag = NULL; luf->work = NULL; luf->new_sva = 0; luf->piv_tol = 0.10; luf->piv_lim = 4; luf->suhl = 1; luf->eps_tol = 1e-15; luf->max_gro = 1e+10; luf->nnz_a = luf->nnz_f = luf->nnz_v = 0; luf->max_a = luf->big_v = 0.0; luf->rank = 0; return luf; } /*********************************************************************** * NAME * * luf_defrag_sva - defragment the sparse vector area * * SYNOPSIS * * #include "glpluf.h" * void luf_defrag_sva(LUF *luf); * * DESCRIPTION * * The routine luf_defrag_sva defragments the sparse vector area (SVA) * gathering all unused locations in one continuous extent. In order to * do that the routine moves all unused locations from the left part of * SVA (which contains rows and columns of the matrix V) to the middle * part (which contains free locations). This is attained by relocating * elements of rows and columns of the matrix V toward the beginning of * the left part. * * NOTE that this "garbage collection" involves changing row and column * pointers of the matrix V. */ void luf_defrag_sva(LUF *luf) { int n = luf->n; int *vr_ptr = luf->vr_ptr; int *vr_len = luf->vr_len; int *vr_cap = luf->vr_cap; int *vc_ptr = luf->vc_ptr; int *vc_len = luf->vc_len; int *vc_cap = luf->vc_cap; int *sv_ind = luf->sv_ind; double *sv_val = luf->sv_val; int *sv_next = luf->sv_next; int sv_beg = 1; int i, j, k; /* skip rows and columns, which do not need to be relocated */ for (k = luf->sv_head; k != 0; k = sv_next[k]) { if (k <= n) { /* i-th row of the matrix V */ i = k; if (vr_ptr[i] != sv_beg) break; vr_cap[i] = vr_len[i]; sv_beg += vr_cap[i]; } else { /* j-th column of the matrix V */ j = k - n; if (vc_ptr[j] != sv_beg) break; vc_cap[j] = vc_len[j]; sv_beg += vc_cap[j]; } } /* relocate other rows and columns in order to gather all unused locations in one continuous extent */ for (k = k; k != 0; k = sv_next[k]) { if (k <= n) { /* i-th row of the matrix V */ i = k; memmove(&sv_ind[sv_beg], &sv_ind[vr_ptr[i]], vr_len[i] * sizeof(int)); memmove(&sv_val[sv_beg], &sv_val[vr_ptr[i]], vr_len[i] * sizeof(double)); vr_ptr[i] = sv_beg; vr_cap[i] = vr_len[i]; sv_beg += vr_cap[i]; } else { /* j-th column of the matrix V */ j = k - n; memmove(&sv_ind[sv_beg], &sv_ind[vc_ptr[j]], vc_len[j] * sizeof(int)); memmove(&sv_val[sv_beg], &sv_val[vc_ptr[j]], vc_len[j] * sizeof(double)); vc_ptr[j] = sv_beg; vc_cap[j] = vc_len[j]; sv_beg += vc_cap[j]; } } /* set new pointer to the beginning of the free part */ luf->sv_beg = sv_beg; return; } /*********************************************************************** * NAME * * luf_enlarge_row - enlarge row capacity * * SYNOPSIS * * #include "glpluf.h" * int luf_enlarge_row(LUF *luf, int i, int cap); * * DESCRIPTION * * The routine luf_enlarge_row enlarges capacity of the i-th row of the * matrix V to cap locations (assuming that its current capacity is less * than cap). In order to do that the routine relocates elements of the * i-th row to the end of the left part of SVA (which contains rows and * columns of the matrix V) and then expands the left part by allocating * cap free locations from the free part. If there are less than cap * free locations, the routine defragments the sparse vector area. * * Due to "garbage collection" this operation may change row and column * pointers of the matrix V. * * RETURNS * * If no error occured, the routine returns zero. Otherwise, in case of * overflow of the sparse vector area, the routine returns non-zero. */ int luf_enlarge_row(LUF *luf, int i, int cap) { int n = luf->n; int *vr_ptr = luf->vr_ptr; int *vr_len = luf->vr_len; int *vr_cap = luf->vr_cap; int *vc_cap = luf->vc_cap; int *sv_ind = luf->sv_ind; double *sv_val = luf->sv_val; int *sv_prev = luf->sv_prev; int *sv_next = luf->sv_next; int ret = 0; int cur, k, kk; xassert(1 <= i && i <= n); xassert(vr_cap[i] < cap); /* if there are less than cap free locations, defragment SVA */ if (luf->sv_end - luf->sv_beg < cap) { luf_defrag_sva(luf); if (luf->sv_end - luf->sv_beg < cap) { ret = 1; goto done; } } /* save current capacity of the i-th row */ cur = vr_cap[i]; /* copy existing elements to the beginning of the free part */ memmove(&sv_ind[luf->sv_beg], &sv_ind[vr_ptr[i]], vr_len[i] * sizeof(int)); memmove(&sv_val[luf->sv_beg], &sv_val[vr_ptr[i]], vr_len[i] * sizeof(double)); /* set new pointer and new capacity of the i-th row */ vr_ptr[i] = luf->sv_beg; vr_cap[i] = cap; /* set new pointer to the beginning of the free part */ luf->sv_beg += cap; /* now the i-th row starts in the rightmost location among other rows and columns of the matrix V, so its node should be moved to the end of the row/column linked list */ k = i; /* remove the i-th row node from the linked list */ if (sv_prev[k] == 0) luf->sv_head = sv_next[k]; else { /* capacity of the previous row/column can be increased at the expense of old locations of the i-th row */ kk = sv_prev[k]; if (kk <= n) vr_cap[kk] += cur; else vc_cap[kk-n] += cur; sv_next[sv_prev[k]] = sv_next[k]; } if (sv_next[k] == 0) luf->sv_tail = sv_prev[k]; else sv_prev[sv_next[k]] = sv_prev[k]; /* insert the i-th row node to the end of the linked list */ sv_prev[k] = luf->sv_tail; sv_next[k] = 0; if (sv_prev[k] == 0) luf->sv_head = k; else sv_next[sv_prev[k]] = k; luf->sv_tail = k; done: return ret; } /*********************************************************************** * NAME * * luf_enlarge_col - enlarge column capacity * * SYNOPSIS * * #include "glpluf.h" * int luf_enlarge_col(LUF *luf, int j, int cap); * * DESCRIPTION * * The routine luf_enlarge_col enlarges capacity of the j-th column of * the matrix V to cap locations (assuming that its current capacity is * less than cap). In order to do that the routine relocates elements * of the j-th column to the end of the left part of SVA (which contains * rows and columns of the matrix V) and then expands the left part by * allocating cap free locations from the free part. If there are less * than cap free locations, the routine defragments the sparse vector * area. * * Due to "garbage collection" this operation may change row and column * pointers of the matrix V. * * RETURNS * * If no error occured, the routine returns zero. Otherwise, in case of * overflow of the sparse vector area, the routine returns non-zero. */ int luf_enlarge_col(LUF *luf, int j, int cap) { int n = luf->n; int *vr_cap = luf->vr_cap; int *vc_ptr = luf->vc_ptr; int *vc_len = luf->vc_len; int *vc_cap = luf->vc_cap; int *sv_ind = luf->sv_ind; double *sv_val = luf->sv_val; int *sv_prev = luf->sv_prev; int *sv_next = luf->sv_next; int ret = 0; int cur, k, kk; xassert(1 <= j && j <= n); xassert(vc_cap[j] < cap); /* if there are less than cap free locations, defragment SVA */ if (luf->sv_end - luf->sv_beg < cap) { luf_defrag_sva(luf); if (luf->sv_end - luf->sv_beg < cap) { ret = 1; goto done; } } /* save current capacity of the j-th column */ cur = vc_cap[j]; /* copy existing elements to the beginning of the free part */ memmove(&sv_ind[luf->sv_beg], &sv_ind[vc_ptr[j]], vc_len[j] * sizeof(int)); memmove(&sv_val[luf->sv_beg], &sv_val[vc_ptr[j]], vc_len[j] * sizeof(double)); /* set new pointer and new capacity of the j-th column */ vc_ptr[j] = luf->sv_beg; vc_cap[j] = cap; /* set new pointer to the beginning of the free part */ luf->sv_beg += cap; /* now the j-th column starts in the rightmost location among other rows and columns of the matrix V, so its node should be moved to the end of the row/column linked list */ k = n + j; /* remove the j-th column node from the linked list */ if (sv_prev[k] == 0) luf->sv_head = sv_next[k]; else { /* capacity of the previous row/column can be increased at the expense of old locations of the j-th column */ kk = sv_prev[k]; if (kk <= n) vr_cap[kk] += cur; else vc_cap[kk-n] += cur; sv_next[sv_prev[k]] = sv_next[k]; } if (sv_next[k] == 0) luf->sv_tail = sv_prev[k]; else sv_prev[sv_next[k]] = sv_prev[k]; /* insert the j-th column node to the end of the linked list */ sv_prev[k] = luf->sv_tail; sv_next[k] = 0; if (sv_prev[k] == 0) luf->sv_head = k; else sv_next[sv_prev[k]] = k; luf->sv_tail = k; done: return ret; } /*********************************************************************** * reallocate - reallocate LU-factorization arrays * * This routine reallocates arrays, whose size depends of n, the order * of the matrix A to be factorized. */ static void reallocate(LUF *luf, int n) { int n_max = luf->n_max; luf->n = n; if (n <= n_max) goto done; if (luf->fr_ptr != NULL) xfree(luf->fr_ptr); if (luf->fr_len != NULL) xfree(luf->fr_len); if (luf->fc_ptr != NULL) xfree(luf->fc_ptr); if (luf->fc_len != NULL) xfree(luf->fc_len); if (luf->vr_ptr != NULL) xfree(luf->vr_ptr); if (luf->vr_len != NULL) xfree(luf->vr_len); if (luf->vr_cap != NULL) xfree(luf->vr_cap); if (luf->vr_piv != NULL) xfree(luf->vr_piv); if (luf->vc_ptr != NULL) xfree(luf->vc_ptr); if (luf->vc_len != NULL) xfree(luf->vc_len); if (luf->vc_cap != NULL) xfree(luf->vc_cap); if (luf->pp_row != NULL) xfree(luf->pp_row); if (luf->pp_col != NULL) xfree(luf->pp_col); if (luf->qq_row != NULL) xfree(luf->qq_row); if (luf->qq_col != NULL) xfree(luf->qq_col); if (luf->sv_prev != NULL) xfree(luf->sv_prev); if (luf->sv_next != NULL) xfree(luf->sv_next); if (luf->vr_max != NULL) xfree(luf->vr_max); if (luf->rs_head != NULL) xfree(luf->rs_head); if (luf->rs_prev != NULL) xfree(luf->rs_prev); if (luf->rs_next != NULL) xfree(luf->rs_next); if (luf->cs_head != NULL) xfree(luf->cs_head); if (luf->cs_prev != NULL) xfree(luf->cs_prev); if (luf->cs_next != NULL) xfree(luf->cs_next); if (luf->flag != NULL) xfree(luf->flag); if (luf->work != NULL) xfree(luf->work); luf->n_max = n_max = n + 100; luf->fr_ptr = xcalloc(1+n_max, sizeof(int)); luf->fr_len = xcalloc(1+n_max, sizeof(int)); luf->fc_ptr = xcalloc(1+n_max, sizeof(int)); luf->fc_len = xcalloc(1+n_max, sizeof(int)); luf->vr_ptr = xcalloc(1+n_max, sizeof(int)); luf->vr_len = xcalloc(1+n_max, sizeof(int)); luf->vr_cap = xcalloc(1+n_max, sizeof(int)); luf->vr_piv = xcalloc(1+n_max, sizeof(double)); luf->vc_ptr = xcalloc(1+n_max, sizeof(int)); luf->vc_len = xcalloc(1+n_max, sizeof(int)); luf->vc_cap = xcalloc(1+n_max, sizeof(int)); luf->pp_row = xcalloc(1+n_max, sizeof(int)); luf->pp_col = xcalloc(1+n_max, sizeof(int)); luf->qq_row = xcalloc(1+n_max, sizeof(int)); luf->qq_col = xcalloc(1+n_max, sizeof(int)); luf->sv_prev = xcalloc(1+n_max+n_max, sizeof(int)); luf->sv_next = xcalloc(1+n_max+n_max, sizeof(int)); luf->vr_max = xcalloc(1+n_max, sizeof(double)); luf->rs_head = xcalloc(1+n_max, sizeof(int)); luf->rs_prev = xcalloc(1+n_max, sizeof(int)); luf->rs_next = xcalloc(1+n_max, sizeof(int)); luf->cs_head = xcalloc(1+n_max, sizeof(int)); luf->cs_prev = xcalloc(1+n_max, sizeof(int)); luf->cs_next = xcalloc(1+n_max, sizeof(int)); luf->flag = xcalloc(1+n_max, sizeof(int)); luf->work = xcalloc(1+n_max, sizeof(double)); done: return; } /*********************************************************************** * initialize - initialize LU-factorization data structures * * This routine initializes data structures for subsequent computing * the LU-factorization of a given matrix A, which is specified by the * formal routine col. On exit V = A and F = P = Q = I, where I is the * unity matrix. (Row-wise representation of the matrix F is not used * at the factorization stage and therefore is not initialized.) * * If no error occured, the routine returns zero. Otherwise, in case of * overflow of the sparse vector area, the routine returns non-zero. */ static int initialize(LUF *luf, int (*col)(void *info, int j, int rn[], double aj[]), void *info) { int n = luf->n; int *fc_ptr = luf->fc_ptr; int *fc_len = luf->fc_len; int *vr_ptr = luf->vr_ptr; int *vr_len = luf->vr_len; int *vr_cap = luf->vr_cap; int *vc_ptr = luf->vc_ptr; int *vc_len = luf->vc_len; int *vc_cap = luf->vc_cap; int *pp_row = luf->pp_row; int *pp_col = luf->pp_col; int *qq_row = luf->qq_row; int *qq_col = luf->qq_col; int *sv_ind = luf->sv_ind; double *sv_val = luf->sv_val; int *sv_prev = luf->sv_prev; int *sv_next = luf->sv_next; double *vr_max = luf->vr_max; int *rs_head = luf->rs_head; int *rs_prev = luf->rs_prev; int *rs_next = luf->rs_next; int *cs_head = luf->cs_head; int *cs_prev = luf->cs_prev; int *cs_next = luf->cs_next; int *flag = luf->flag; double *work = luf->work; int ret = 0; int i, i_ptr, j, j_beg, j_end, k, len, nnz, sv_beg, sv_end, ptr; double big, val; /* free all locations of the sparse vector area */ sv_beg = 1; sv_end = luf->sv_size + 1; /* (row-wise representation of the matrix F is not initialized, because it is not used at the factorization stage) */ /* build the matrix F in column-wise format (initially F = I) */ for (j = 1; j <= n; j++) { fc_ptr[j] = sv_end; fc_len[j] = 0; } /* clear rows of the matrix V; clear the flag array */ for (i = 1; i <= n; i++) vr_len[i] = vr_cap[i] = 0, flag[i] = 0; /* build the matrix V in column-wise format (initially V = A); count non-zeros in rows of this matrix; count total number of non-zeros; compute largest of absolute values of elements */ nnz = 0; big = 0.0; for (j = 1; j <= n; j++) { int *rn = pp_row; double *aj = work; /* obtain j-th column of the matrix A */ len = col(info, j, rn, aj); if (!(0 <= len && len <= n)) xfault("luf_factorize: j = %d; len = %d; invalid column len" "gth\n", j, len); /* check for free locations */ if (sv_end - sv_beg < len) { /* overflow of the sparse vector area */ ret = 1; goto done; } /* set pointer to the j-th column */ vc_ptr[j] = sv_beg; /* set length of the j-th column */ vc_len[j] = vc_cap[j] = len; /* count total number of non-zeros */ nnz += len; /* walk through elements of the j-th column */ for (ptr = 1; ptr <= len; ptr++) { /* get row index and numerical value of a[i,j] */ i = rn[ptr]; val = aj[ptr]; if (!(1 <= i && i <= n)) xfault("luf_factorize: i = %d; j = %d; invalid row index" "\n", i, j); if (flag[i]) xfault("luf_factorize: i = %d; j = %d; duplicate element" " not allowed\n", i, j); if (val == 0.0) xfault("luf_factorize: i = %d; j = %d; zero element not " "allowed\n", i, j); /* add new element v[i,j] = a[i,j] to j-th column */ sv_ind[sv_beg] = i; sv_val[sv_beg] = val; sv_beg++; /* big := max(big, |a[i,j]|) */ if (val < 0.0) val = - val; if (big < val) big = val; /* mark non-zero in the i-th position of the j-th column */ flag[i] = 1; /* increase length of the i-th row */ vr_cap[i]++; } /* reset all non-zero marks */ for (ptr = 1; ptr <= len; ptr++) flag[rn[ptr]] = 0; } /* allocate rows of the matrix V */ for (i = 1; i <= n; i++) { /* get length of the i-th row */ len = vr_cap[i]; /* check for free locations */ if (sv_end - sv_beg < len) { /* overflow of the sparse vector area */ ret = 1; goto done; } /* set pointer to the i-th row */ vr_ptr[i] = sv_beg; /* reserve locations for the i-th row */ sv_beg += len; } /* build the matrix V in row-wise format using representation of this matrix in column-wise format */ for (j = 1; j <= n; j++) { /* walk through elements of the j-th column */ j_beg = vc_ptr[j]; j_end = j_beg + vc_len[j] - 1; for (k = j_beg; k <= j_end; k++) { /* get row index and numerical value of v[i,j] */ i = sv_ind[k]; val = sv_val[k]; /* store element in the i-th row */ i_ptr = vr_ptr[i] + vr_len[i]; sv_ind[i_ptr] = j; sv_val[i_ptr] = val; /* increase count of the i-th row */ vr_len[i]++; } } /* initialize the matrices P and Q (initially P = Q = I) */ for (k = 1; k <= n; k++) pp_row[k] = pp_col[k] = qq_row[k] = qq_col[k] = k; /* set sva partitioning pointers */ luf->sv_beg = sv_beg; luf->sv_end = sv_end; /* the initial physical order of rows and columns of the matrix V is n+1, ..., n+n, 1, ..., n (firstly columns, then rows) */ luf->sv_head = n+1; luf->sv_tail = n; for (i = 1; i <= n; i++) { sv_prev[i] = i-1; sv_next[i] = i+1; } sv_prev[1] = n+n; sv_next[n] = 0; for (j = 1; j <= n; j++) { sv_prev[n+j] = n+j-1; sv_next[n+j] = n+j+1; } sv_prev[n+1] = 0; sv_next[n+n] = 1; /* clear working arrays */ for (k = 1; k <= n; k++) { flag[k] = 0; work[k] = 0.0; } /* initialize some statistics */ luf->nnz_a = nnz; luf->nnz_f = 0; luf->nnz_v = nnz; luf->max_a = big; luf->big_v = big; luf->rank = -1; /* initially the active submatrix is the entire matrix V */ /* largest of absolute values of elements in each active row is unknown yet */ for (i = 1; i <= n; i++) vr_max[i] = -1.0; /* build linked lists of active rows */ for (len = 0; len <= n; len++) rs_head[len] = 0; for (i = 1; i <= n; i++) { len = vr_len[i]; rs_prev[i] = 0; rs_next[i] = rs_head[len]; if (rs_next[i] != 0) rs_prev[rs_next[i]] = i; rs_head[len] = i; } /* build linked lists of active columns */ for (len = 0; len <= n; len++) cs_head[len] = 0; for (j = 1; j <= n; j++) { len = vc_len[j]; cs_prev[j] = 0; cs_next[j] = cs_head[len]; if (cs_next[j] != 0) cs_prev[cs_next[j]] = j; cs_head[len] = j; } done: /* return to the factorizing routine */ return ret; } /*********************************************************************** * find_pivot - choose a pivot element * * This routine chooses a pivot element in the active submatrix of the * matrix U = P*V*Q. * * It is assumed that on entry the matrix U has the following partially * triangularized form: * * 1 k n * 1 x x x x x x x x x x * . x x x x x x x x x * . . x x x x x x x x * . . . x x x x x x x * k . . . . * * * * * * * . . . . * * * * * * * . . . . * * * * * * * . . . . * * * * * * * . . . . * * * * * * * n . . . . * * * * * * * * where rows and columns k, k+1, ..., n belong to the active submatrix * (elements of the active submatrix are marked by '*'). * * Since the matrix U = P*V*Q is not stored, the routine works with the * matrix V. It is assumed that the row-wise representation corresponds * to the matrix V, but the column-wise representation corresponds to * the active submatrix of the matrix V, i.e. elements of the matrix V, * which doesn't belong to the active submatrix, are missing from the * column linked lists. It is also assumed that each active row of the * matrix V is in the set R[len], where len is number of non-zeros in * the row, and each active column of the matrix V is in the set C[len], * where len is number of non-zeros in the column (in the latter case * only elements of the active submatrix are counted; such elements are * marked by '*' on the figure above). * * For the reason of numerical stability the routine applies so called * threshold pivoting proposed by J.Reid. It is assumed that an element * v[i,j] can be selected as a pivot candidate if it is not very small * (in absolute value) among other elements in the same row, i.e. if it * satisfies to the stability condition |v[i,j]| >= tol * max|v[i,*]|, * where 0 < tol < 1 is a given tolerance. * * In order to keep sparsity of the matrix V the routine uses Markowitz * strategy, trying to choose such element v[p,q], which satisfies to * the stability condition (see above) and has smallest Markowitz cost * (nr[p]-1) * (nc[q]-1), where nr[p] and nc[q] are numbers of non-zero * elements, respectively, in the p-th row and in the q-th column of the * active submatrix. * * In order to reduce the search, i.e. not to walk through all elements * of the active submatrix, the routine exploits a technique proposed by * I.Duff. This technique is based on using the sets R[len] and C[len] * of active rows and columns. * * If the pivot element v[p,q] has been chosen, the routine stores its * indices to the locations *p and *q and returns zero. Otherwise, if * the active submatrix is empty and therefore the pivot element can't * be chosen, the routine returns non-zero. */ static int find_pivot(LUF *luf, int *_p, int *_q) { int n = luf->n; int *vr_ptr = luf->vr_ptr; int *vr_len = luf->vr_len; int *vc_ptr = luf->vc_ptr; int *vc_len = luf->vc_len; int *sv_ind = luf->sv_ind; double *sv_val = luf->sv_val; double *vr_max = luf->vr_max; int *rs_head = luf->rs_head; int *rs_next = luf->rs_next; int *cs_head = luf->cs_head; int *cs_prev = luf->cs_prev; int *cs_next = luf->cs_next; double piv_tol = luf->piv_tol; int piv_lim = luf->piv_lim; int suhl = luf->suhl; int p, q, len, i, i_beg, i_end, i_ptr, j, j_beg, j_end, j_ptr, ncand, next_j, min_p, min_q, min_len; double best, cost, big, temp; /* initially no pivot candidates have been found so far */ p = q = 0, best = DBL_MAX, ncand = 0; /* if in the active submatrix there is a column that has the only non-zero (column singleton), choose it as pivot */ j = cs_head[1]; if (j != 0) { xassert(vc_len[j] == 1); p = sv_ind[vc_ptr[j]], q = j; goto done; } /* if in the active submatrix there is a row that has the only non-zero (row singleton), choose it as pivot */ i = rs_head[1]; if (i != 0) { xassert(vr_len[i] == 1); p = i, q = sv_ind[vr_ptr[i]]; goto done; } /* there are no singletons in the active submatrix; walk through other non-empty rows and columns */ for (len = 2; len <= n; len++) { /* consider active columns that have len non-zeros */ for (j = cs_head[len]; j != 0; j = next_j) { /* the j-th column has len non-zeros */ j_beg = vc_ptr[j]; j_end = j_beg + vc_len[j] - 1; /* save pointer to the next column with the same length */ next_j = cs_next[j]; /* find an element in the j-th column, which is placed in a row with minimal number of non-zeros and satisfies to the stability condition (such element may not exist) */ min_p = min_q = 0, min_len = INT_MAX; for (j_ptr = j_beg; j_ptr <= j_end; j_ptr++) { /* get row index of v[i,j] */ i = sv_ind[j_ptr]; i_beg = vr_ptr[i]; i_end = i_beg + vr_len[i] - 1; /* if the i-th row is not shorter than that one, where minimal element is currently placed, skip v[i,j] */ if (vr_len[i] >= min_len) continue; /* determine the largest of absolute values of elements in the i-th row */ big = vr_max[i]; if (big < 0.0) { /* the largest value is unknown yet; compute it */ for (i_ptr = i_beg; i_ptr <= i_end; i_ptr++) { temp = sv_val[i_ptr]; if (temp < 0.0) temp = - temp; if (big < temp) big = temp; } vr_max[i] = big; } /* find v[i,j] in the i-th row */ for (i_ptr = vr_ptr[i]; sv_ind[i_ptr] != j; i_ptr++); xassert(i_ptr <= i_end); /* if v[i,j] doesn't satisfy to the stability condition, skip it */ temp = sv_val[i_ptr]; if (temp < 0.0) temp = - temp; if (temp < piv_tol * big) continue; /* v[i,j] is better than the current minimal element */ min_p = i, min_q = j, min_len = vr_len[i]; /* if Markowitz cost of the current minimal element is not greater than (len-1)**2, it can be chosen right now; this heuristic reduces the search and works well in many cases */ if (min_len <= len) { p = min_p, q = min_q; goto done; } } /* the j-th column has been scanned */ if (min_p != 0) { /* the minimal element is a next pivot candidate */ ncand++; /* compute its Markowitz cost */ cost = (double)(min_len - 1) * (double)(len - 1); /* choose between the minimal element and the current candidate */ if (cost < best) p = min_p, q = min_q, best = cost; /* if piv_lim candidates have been considered, there are doubts that a much better candidate exists; therefore it's time to terminate the search */ if (ncand == piv_lim) goto done; } else { /* the j-th column has no elements, which satisfy to the stability condition; Uwe Suhl suggests to exclude such column from the further consideration until it becomes a column singleton; in hard cases this significantly reduces a time needed for pivot searching */ if (suhl) { /* remove the j-th column from the active set */ if (cs_prev[j] == 0) cs_head[len] = cs_next[j]; else cs_next[cs_prev[j]] = cs_next[j]; if (cs_next[j] == 0) /* nop */; else cs_prev[cs_next[j]] = cs_prev[j]; /* the following assignment is used to avoid an error when the routine eliminate (see below) will try to remove the j-th column from the active set */ cs_prev[j] = cs_next[j] = j; } } } /* consider active rows that have len non-zeros */ for (i = rs_head[len]; i != 0; i = rs_next[i]) { /* the i-th row has len non-zeros */ i_beg = vr_ptr[i]; i_end = i_beg + vr_len[i] - 1; /* determine the largest of absolute values of elements in the i-th row */ big = vr_max[i]; if (big < 0.0) { /* the largest value is unknown yet; compute it */ for (i_ptr = i_beg; i_ptr <= i_end; i_ptr++) { temp = sv_val[i_ptr]; if (temp < 0.0) temp = - temp; if (big < temp) big = temp; } vr_max[i] = big; } /* find an element in the i-th row, which is placed in a column with minimal number of non-zeros and satisfies to the stability condition (such element always exists) */ min_p = min_q = 0, min_len = INT_MAX; for (i_ptr = i_beg; i_ptr <= i_end; i_ptr++) { /* get column index of v[i,j] */ j = sv_ind[i_ptr]; /* if the j-th column is not shorter than that one, where minimal element is currently placed, skip v[i,j] */ if (vc_len[j] >= min_len) continue; /* if v[i,j] doesn't satisfy to the stability condition, skip it */ temp = sv_val[i_ptr]; if (temp < 0.0) temp = - temp; if (temp < piv_tol * big) continue; /* v[i,j] is better than the current minimal element */ min_p = i, min_q = j, min_len = vc_len[j]; /* if Markowitz cost of the current minimal element is not greater than (len-1)**2, it can be chosen right now; this heuristic reduces the search and works well in many cases */ if (min_len <= len) { p = min_p, q = min_q; goto done; } } /* the i-th row has been scanned */ if (min_p != 0) { /* the minimal element is a next pivot candidate */ ncand++; /* compute its Markowitz cost */ cost = (double)(len - 1) * (double)(min_len - 1); /* choose between the minimal element and the current candidate */ if (cost < best) p = min_p, q = min_q, best = cost; /* if piv_lim candidates have been considered, there are doubts that a much better candidate exists; therefore it's time to terminate the search */ if (ncand == piv_lim) goto done; } else { /* this can't be because this can never be */ xassert(min_p != min_p); } } } done: /* bring the pivot to the factorizing routine */ *_p = p, *_q = q; return (p == 0); } /*********************************************************************** * eliminate - perform gaussian elimination. * * This routine performs elementary gaussian transformations in order * to eliminate subdiagonal elements in the k-th column of the matrix * U = P*V*Q using the pivot element u[k,k], where k is the number of * the current elimination step. * * The parameters p and q are, respectively, row and column indices of * the element v[p,q], which corresponds to the element u[k,k]. * * Each time when the routine applies the elementary transformation to * a non-pivot row of the matrix V, it stores the corresponding element * to the matrix F in order to keep the main equality A = F*V. * * The routine assumes that on entry the matrices L = P*F*inv(P) and * U = P*V*Q are the following: * * 1 k 1 k n * 1 1 . . . . . . . . . 1 x x x x x x x x x x * x 1 . . . . . . . . . x x x x x x x x x * x x 1 . . . . . . . . . x x x x x x x x * x x x 1 . . . . . . . . . x x x x x x x * k x x x x 1 . . . . . k . . . . * * * * * * * x x x x _ 1 . . . . . . . . # * * * * * * x x x x _ . 1 . . . . . . . # * * * * * * x x x x _ . . 1 . . . . . . # * * * * * * x x x x _ . . . 1 . . . . . # * * * * * * n x x x x _ . . . . 1 n . . . . # * * * * * * * matrix L matrix U * * where rows and columns of the matrix U with numbers k, k+1, ..., n * form the active submatrix (eliminated elements are marked by '#' and * other elements of the active submatrix are marked by '*'). Note that * each eliminated non-zero element u[i,k] of the matrix U gives the * corresponding element l[i,k] of the matrix L (marked by '_'). * * Actually all operations are performed on the matrix V. Should note * that the row-wise representation corresponds to the matrix V, but the * column-wise representation corresponds to the active submatrix of the * matrix V, i.e. elements of the matrix V, which doesn't belong to the * active submatrix, are missing from the column linked lists. * * Let u[k,k] = v[p,q] be the pivot. In order to eliminate subdiagonal * elements u[i',k] = v[i,q], i' = k+1, k+2, ..., n, the routine applies * the following elementary gaussian transformations: * * (i-th row of V) := (i-th row of V) - f[i,p] * (p-th row of V), * * where f[i,p] = v[i,q] / v[p,q] is a gaussian multiplier. * * Additionally, in order to keep the main equality A = F*V, each time * when the routine applies the transformation to i-th row of the matrix * V, it also adds f[i,p] as a new element to the matrix F. * * IMPORTANT: On entry the working arrays flag and work should contain * zeros. This status is provided by the routine on exit. * * If no error occured, the routine returns zero. Otherwise, in case of * overflow of the sparse vector area, the routine returns non-zero. */ static int eliminate(LUF *luf, int p, int q) { int n = luf->n; int *fc_ptr = luf->fc_ptr; int *fc_len = luf->fc_len; int *vr_ptr = luf->vr_ptr; int *vr_len = luf->vr_len; int *vr_cap = luf->vr_cap; double *vr_piv = luf->vr_piv; int *vc_ptr = luf->vc_ptr; int *vc_len = luf->vc_len; int *vc_cap = luf->vc_cap; int *sv_ind = luf->sv_ind; double *sv_val = luf->sv_val; int *sv_prev = luf->sv_prev; int *sv_next = luf->sv_next; double *vr_max = luf->vr_max; int *rs_head = luf->rs_head; int *rs_prev = luf->rs_prev; int *rs_next = luf->rs_next; int *cs_head = luf->cs_head; int *cs_prev = luf->cs_prev; int *cs_next = luf->cs_next; int *flag = luf->flag; double *work = luf->work; double eps_tol = luf->eps_tol; /* at this stage the row-wise representation of the matrix F is not used, so fr_len can be used as a working array */ int *ndx = luf->fr_len; int ret = 0; int len, fill, i, i_beg, i_end, i_ptr, j, j_beg, j_end, j_ptr, k, p_beg, p_end, p_ptr, q_beg, q_end, q_ptr; double fip, val, vpq, temp; xassert(1 <= p && p <= n); xassert(1 <= q && q <= n); /* remove the p-th (pivot) row from the active set; this row will never return there */ if (rs_prev[p] == 0) rs_head[vr_len[p]] = rs_next[p]; else rs_next[rs_prev[p]] = rs_next[p]; if (rs_next[p] == 0) ; else rs_prev[rs_next[p]] = rs_prev[p]; /* remove the q-th (pivot) column from the active set; this column will never return there */ if (cs_prev[q] == 0) cs_head[vc_len[q]] = cs_next[q]; else cs_next[cs_prev[q]] = cs_next[q]; if (cs_next[q] == 0) ; else cs_prev[cs_next[q]] = cs_prev[q]; /* find the pivot v[p,q] = u[k,k] in the p-th row */ p_beg = vr_ptr[p]; p_end = p_beg + vr_len[p] - 1; for (p_ptr = p_beg; sv_ind[p_ptr] != q; p_ptr++) /* nop */; xassert(p_ptr <= p_end); /* store value of the pivot */ vpq = (vr_piv[p] = sv_val[p_ptr]); /* remove the pivot from the p-th row */ sv_ind[p_ptr] = sv_ind[p_end]; sv_val[p_ptr] = sv_val[p_end]; vr_len[p]--; p_end--; /* find the pivot v[p,q] = u[k,k] in the q-th column */ q_beg = vc_ptr[q]; q_end = q_beg + vc_len[q] - 1; for (q_ptr = q_beg; sv_ind[q_ptr] != p; q_ptr++) /* nop */; xassert(q_ptr <= q_end); /* remove the pivot from the q-th column */ sv_ind[q_ptr] = sv_ind[q_end]; vc_len[q]--; q_end--; /* walk through the p-th (pivot) row, which doesn't contain the pivot v[p,q] already, and do the following... */ for (p_ptr = p_beg; p_ptr <= p_end; p_ptr++) { /* get column index of v[p,j] */ j = sv_ind[p_ptr]; /* store v[p,j] to the working array */ flag[j] = 1; work[j] = sv_val[p_ptr]; /* remove the j-th column from the active set; this column will return there later with new length */ if (cs_prev[j] == 0) cs_head[vc_len[j]] = cs_next[j]; else cs_next[cs_prev[j]] = cs_next[j]; if (cs_next[j] == 0) ; else cs_prev[cs_next[j]] = cs_prev[j]; /* find v[p,j] in the j-th column */ j_beg = vc_ptr[j]; j_end = j_beg + vc_len[j] - 1; for (j_ptr = j_beg; sv_ind[j_ptr] != p; j_ptr++) /* nop */; xassert(j_ptr <= j_end); /* since v[p,j] leaves the active submatrix, remove it from the j-th column; however, v[p,j] is kept in the p-th row */ sv_ind[j_ptr] = sv_ind[j_end]; vc_len[j]--; } /* walk through the q-th (pivot) column, which doesn't contain the pivot v[p,q] already, and perform gaussian elimination */ while (q_beg <= q_end) { /* element v[i,q] should be eliminated */ /* get row index of v[i,q] */ i = sv_ind[q_beg]; /* remove the i-th row from the active set; later this row will return there with new length */ if (rs_prev[i] == 0) rs_head[vr_len[i]] = rs_next[i]; else rs_next[rs_prev[i]] = rs_next[i]; if (rs_next[i] == 0) ; else rs_prev[rs_next[i]] = rs_prev[i]; /* find v[i,q] in the i-th row */ i_beg = vr_ptr[i]; i_end = i_beg + vr_len[i] - 1; for (i_ptr = i_beg; sv_ind[i_ptr] != q; i_ptr++) /* nop */; xassert(i_ptr <= i_end); /* compute gaussian multiplier f[i,p] = v[i,q] / v[p,q] */ fip = sv_val[i_ptr] / vpq; /* since v[i,q] should be eliminated, remove it from the i-th row */ sv_ind[i_ptr] = sv_ind[i_end]; sv_val[i_ptr] = sv_val[i_end]; vr_len[i]--; i_end--; /* and from the q-th column */ sv_ind[q_beg] = sv_ind[q_end]; vc_len[q]--; q_end--; /* perform gaussian transformation: (i-th row) := (i-th row) - f[i,p] * (p-th row) note that now the p-th row, which is in the working array, doesn't contain the pivot v[p,q], and the i-th row doesn't contain the eliminated element v[i,q] */ /* walk through the i-th row and transform existing non-zero elements */ fill = vr_len[p]; for (i_ptr = i_beg; i_ptr <= i_end; i_ptr++) { /* get column index of v[i,j] */ j = sv_ind[i_ptr]; /* v[i,j] := v[i,j] - f[i,p] * v[p,j] */ if (flag[j]) { /* v[p,j] != 0 */ temp = (sv_val[i_ptr] -= fip * work[j]); if (temp < 0.0) temp = - temp; flag[j] = 0; fill--; /* since both v[i,j] and v[p,j] exist */ if (temp == 0.0 || temp < eps_tol) { /* new v[i,j] is closer to zero; replace it by exact zero, i.e. remove it from the active submatrix */ /* remove v[i,j] from the i-th row */ sv_ind[i_ptr] = sv_ind[i_end]; sv_val[i_ptr] = sv_val[i_end]; vr_len[i]--; i_ptr--; i_end--; /* find v[i,j] in the j-th column */ j_beg = vc_ptr[j]; j_end = j_beg + vc_len[j] - 1; for (j_ptr = j_beg; sv_ind[j_ptr] != i; j_ptr++); xassert(j_ptr <= j_end); /* remove v[i,j] from the j-th column */ sv_ind[j_ptr] = sv_ind[j_end]; vc_len[j]--; } else { /* v_big := max(v_big, |v[i,j]|) */ if (luf->big_v < temp) luf->big_v = temp; } } } /* now flag is the pattern of the set v[p,*] \ v[i,*], and fill is number of non-zeros in this set; therefore up to fill new non-zeros may appear in the i-th row */ if (vr_len[i] + fill > vr_cap[i]) { /* enlarge the i-th row */ if (luf_enlarge_row(luf, i, vr_len[i] + fill)) { /* overflow of the sparse vector area */ ret = 1; goto done; } /* defragmentation may change row and column pointers of the matrix V */ p_beg = vr_ptr[p]; p_end = p_beg + vr_len[p] - 1; q_beg = vc_ptr[q]; q_end = q_beg + vc_len[q] - 1; } /* walk through the p-th (pivot) row and create new elements of the i-th row that appear due to fill-in; column indices of these new elements are accumulated in the array ndx */ len = 0; for (p_ptr = p_beg; p_ptr <= p_end; p_ptr++) { /* get column index of v[p,j], which may cause fill-in */ j = sv_ind[p_ptr]; if (flag[j]) { /* compute new non-zero v[i,j] = 0 - f[i,p] * v[p,j] */ temp = (val = - fip * work[j]); if (temp < 0.0) temp = - temp; if (temp == 0.0 || temp < eps_tol) /* if v[i,j] is closer to zero; just ignore it */; else { /* add v[i,j] to the i-th row */ i_ptr = vr_ptr[i] + vr_len[i]; sv_ind[i_ptr] = j; sv_val[i_ptr] = val; vr_len[i]++; /* remember column index of v[i,j] */ ndx[++len] = j; /* big_v := max(big_v, |v[i,j]|) */ if (luf->big_v < temp) luf->big_v = temp; } } else { /* there is no fill-in, because v[i,j] already exists in the i-th row; restore the flag of the element v[p,j], which was reset before */ flag[j] = 1; } } /* add new non-zeros v[i,j] to the corresponding columns */ for (k = 1; k <= len; k++) { /* get column index of new non-zero v[i,j] */ j = ndx[k]; /* one free location is needed in the j-th column */ if (vc_len[j] + 1 > vc_cap[j]) { /* enlarge the j-th column */ if (luf_enlarge_col(luf, j, vc_len[j] + 10)) { /* overflow of the sparse vector area */ ret = 1; goto done; } /* defragmentation may change row and column pointers of the matrix V */ p_beg = vr_ptr[p]; p_end = p_beg + vr_len[p] - 1; q_beg = vc_ptr[q]; q_end = q_beg + vc_len[q] - 1; } /* add new non-zero v[i,j] to the j-th column */ j_ptr = vc_ptr[j] + vc_len[j]; sv_ind[j_ptr] = i; vc_len[j]++; } /* now the i-th row has been completely transformed, therefore it can return to the active set with new length */ rs_prev[i] = 0; rs_next[i] = rs_head[vr_len[i]]; if (rs_next[i] != 0) rs_prev[rs_next[i]] = i; rs_head[vr_len[i]] = i; /* the largest of absolute values of elements in the i-th row is currently unknown */ vr_max[i] = -1.0; /* at least one free location is needed to store the gaussian multiplier */ if (luf->sv_end - luf->sv_beg < 1) { /* there are no free locations at all; defragment SVA */ luf_defrag_sva(luf); if (luf->sv_end - luf->sv_beg < 1) { /* overflow of the sparse vector area */ ret = 1; goto done; } /* defragmentation may change row and column pointers of the matrix V */ p_beg = vr_ptr[p]; p_end = p_beg + vr_len[p] - 1; q_beg = vc_ptr[q]; q_end = q_beg + vc_len[q] - 1; } /* add the element f[i,p], which is the gaussian multiplier, to the matrix F */ luf->sv_end--; sv_ind[luf->sv_end] = i; sv_val[luf->sv_end] = fip; fc_len[p]++; /* end of elimination loop */ } /* at this point the q-th (pivot) column should be empty */ xassert(vc_len[q] == 0); /* reset capacity of the q-th column */ vc_cap[q] = 0; /* remove node of the q-th column from the addressing list */ k = n + q; if (sv_prev[k] == 0) luf->sv_head = sv_next[k]; else sv_next[sv_prev[k]] = sv_next[k]; if (sv_next[k] == 0) luf->sv_tail = sv_prev[k]; else sv_prev[sv_next[k]] = sv_prev[k]; /* the p-th column of the matrix F has been completely built; set its pointer */ fc_ptr[p] = luf->sv_end; /* walk through the p-th (pivot) row and do the following... */ for (p_ptr = p_beg; p_ptr <= p_end; p_ptr++) { /* get column index of v[p,j] */ j = sv_ind[p_ptr]; /* erase v[p,j] from the working array */ flag[j] = 0; work[j] = 0.0; /* the j-th column has been completely transformed, therefore it can return to the active set with new length; however the special case c_prev[j] = c_next[j] = j means that the routine find_pivot excluded the j-th column from the active set due to Uwe Suhl's rule, and therefore in this case the column can return to the active set only if it is a column singleton */ if (!(vc_len[j] != 1 && cs_prev[j] == j && cs_next[j] == j)) { cs_prev[j] = 0; cs_next[j] = cs_head[vc_len[j]]; if (cs_next[j] != 0) cs_prev[cs_next[j]] = j; cs_head[vc_len[j]] = j; } } done: /* return to the factorizing routine */ return ret; } /*********************************************************************** * build_v_cols - build the matrix V in column-wise format * * This routine builds the column-wise representation of the matrix V * using its row-wise representation. * * If no error occured, the routine returns zero. Otherwise, in case of * overflow of the sparse vector area, the routine returns non-zero. */ static int build_v_cols(LUF *luf) { int n = luf->n; int *vr_ptr = luf->vr_ptr; int *vr_len = luf->vr_len; int *vc_ptr = luf->vc_ptr; int *vc_len = luf->vc_len; int *vc_cap = luf->vc_cap; int *sv_ind = luf->sv_ind; double *sv_val = luf->sv_val; int *sv_prev = luf->sv_prev; int *sv_next = luf->sv_next; int ret = 0; int i, i_beg, i_end, i_ptr, j, j_ptr, k, nnz; /* it is assumed that on entry all columns of the matrix V are empty, i.e. vc_len[j] = vc_cap[j] = 0 for all j = 1, ..., n, and have been removed from the addressing list */ /* count non-zeros in columns of the matrix V; count total number of non-zeros in this matrix */ nnz = 0; for (i = 1; i <= n; i++) { /* walk through elements of the i-th row and count non-zeros in the corresponding columns */ i_beg = vr_ptr[i]; i_end = i_beg + vr_len[i] - 1; for (i_ptr = i_beg; i_ptr <= i_end; i_ptr++) vc_cap[sv_ind[i_ptr]]++; /* count total number of non-zeros */ nnz += vr_len[i]; } /* store total number of non-zeros */ luf->nnz_v = nnz; /* check for free locations */ if (luf->sv_end - luf->sv_beg < nnz) { /* overflow of the sparse vector area */ ret = 1; goto done; } /* allocate columns of the matrix V */ for (j = 1; j <= n; j++) { /* set pointer to the j-th column */ vc_ptr[j] = luf->sv_beg; /* reserve locations for the j-th column */ luf->sv_beg += vc_cap[j]; } /* build the matrix V in column-wise format using this matrix in row-wise format */ for (i = 1; i <= n; i++) { /* walk through elements of the i-th row */ i_beg = vr_ptr[i]; i_end = i_beg + vr_len[i] - 1; for (i_ptr = i_beg; i_ptr <= i_end; i_ptr++) { /* get column index */ j = sv_ind[i_ptr]; /* store element in the j-th column */ j_ptr = vc_ptr[j] + vc_len[j]; sv_ind[j_ptr] = i; sv_val[j_ptr] = sv_val[i_ptr]; /* increase length of the j-th column */ vc_len[j]++; } } /* now columns are placed in the sparse vector area behind rows in the order n+1, n+2, ..., n+n; so insert column nodes in the addressing list using this order */ for (k = n+1; k <= n+n; k++) { sv_prev[k] = k-1; sv_next[k] = k+1; } sv_prev[n+1] = luf->sv_tail; sv_next[luf->sv_tail] = n+1; sv_next[n+n] = 0; luf->sv_tail = n+n; done: /* return to the factorizing routine */ return ret; } /*********************************************************************** * build_f_rows - build the matrix F in row-wise format * * This routine builds the row-wise representation of the matrix F using * its column-wise representation. * * If no error occured, the routine returns zero. Otherwise, in case of * overflow of the sparse vector area, the routine returns non-zero. */ static int build_f_rows(LUF *luf) { int n = luf->n; int *fr_ptr = luf->fr_ptr; int *fr_len = luf->fr_len; int *fc_ptr = luf->fc_ptr; int *fc_len = luf->fc_len; int *sv_ind = luf->sv_ind; double *sv_val = luf->sv_val; int ret = 0; int i, j, j_beg, j_end, j_ptr, ptr, nnz; /* clear rows of the matrix F */ for (i = 1; i <= n; i++) fr_len[i] = 0; /* count non-zeros in rows of the matrix F; count total number of non-zeros in this matrix */ nnz = 0; for (j = 1; j <= n; j++) { /* walk through elements of the j-th column and count non-zeros in the corresponding rows */ j_beg = fc_ptr[j]; j_end = j_beg + fc_len[j] - 1; for (j_ptr = j_beg; j_ptr <= j_end; j_ptr++) fr_len[sv_ind[j_ptr]]++; /* increase total number of non-zeros */ nnz += fc_len[j]; } /* store total number of non-zeros */ luf->nnz_f = nnz; /* check for free locations */ if (luf->sv_end - luf->sv_beg < nnz) { /* overflow of the sparse vector area */ ret = 1; goto done; } /* allocate rows of the matrix F */ for (i = 1; i <= n; i++) { /* set pointer to the end of the i-th row; later this pointer will be set to the beginning of the i-th row */ fr_ptr[i] = luf->sv_end; /* reserve locations for the i-th row */ luf->sv_end -= fr_len[i]; } /* build the matrix F in row-wise format using this matrix in column-wise format */ for (j = 1; j <= n; j++) { /* walk through elements of the j-th column */ j_beg = fc_ptr[j]; j_end = j_beg + fc_len[j] - 1; for (j_ptr = j_beg; j_ptr <= j_end; j_ptr++) { /* get row index */ i = sv_ind[j_ptr]; /* store element in the i-th row */ ptr = --fr_ptr[i]; sv_ind[ptr] = j; sv_val[ptr] = sv_val[j_ptr]; } } done: /* return to the factorizing routine */ return ret; } /*********************************************************************** * NAME * * luf_factorize - compute LU-factorization * * SYNOPSIS * * #include "glpluf.h" * int luf_factorize(LUF *luf, int n, int (*col)(void *info, int j, * int ind[], double val[]), void *info); * * DESCRIPTION * * The routine luf_factorize computes LU-factorization of a specified * square matrix A. * * The parameter luf specifies LU-factorization program object created * by the routine luf_create_it. * * The parameter n specifies the order of A, n > 0. * * The formal routine col specifies the matrix A to be factorized. To * obtain j-th column of A the routine luf_factorize calls the routine * col with the parameter j (1 <= j <= n). In response the routine col * should store row indices and numerical values of non-zero elements * of j-th column of A to locations ind[1,...,len] and val[1,...,len], * respectively, where len is the number of non-zeros in j-th column * returned on exit. Neither zero nor duplicate elements are allowed. * * The parameter info is a transit pointer passed to the routine col. * * RETURNS * * 0 LU-factorization has been successfully computed. * * LUF_ESING * The specified matrix is singular within the working precision. * (On some elimination step the active submatrix is exactly zero, * so no pivot can be chosen.) * * LUF_ECOND * The specified matrix is ill-conditioned. * (On some elimination step too intensive growth of elements of the * active submatix has been detected.) * * If matrix A is well scaled, the return code LUF_ECOND may also mean * that the threshold pivoting tolerance piv_tol should be increased. * * In case of non-zero return code the factorization becomes invalid. * It should not be used in other operations until the cause of failure * has been eliminated and the factorization has been recomputed again * with the routine luf_factorize. * * REPAIRING SINGULAR MATRIX * * If the routine luf_factorize returns non-zero code, it provides all * necessary information that can be used for "repairing" the matrix A, * where "repairing" means replacing linearly dependent columns of the * matrix A by appropriate columns of the unity matrix. This feature is * needed when this routine is used for factorizing the basis matrix * within the simplex method procedure. * * On exit linearly dependent columns of the (partially transformed) * matrix U have numbers rank+1, rank+2, ..., n, where rank is estimated * rank of the matrix A stored by the routine to the member luf->rank. * The correspondence between columns of A and U is the same as between * columns of V and U. Thus, linearly dependent columns of the matrix A * have numbers qq_col[rank+1], qq_col[rank+2], ..., qq_col[n], where * qq_col is the column-like representation of the permutation matrix Q. * It is understood that each j-th linearly dependent column of the * matrix U should be replaced by the unity vector, where all elements * are zero except the unity diagonal element u[j,j]. On the other hand * j-th row of the matrix U corresponds to the row of the matrix V (and * therefore of the matrix A) with the number pp_row[j], where pp_row is * the row-like representation of the permutation matrix P. Thus, each * j-th linearly dependent column of the matrix U should be replaced by * column of the unity matrix with the number pp_row[j]. * * The code that repairs the matrix A may look like follows: * * for (j = rank+1; j <= n; j++) * { replace the column qq_col[j] of the matrix A by the column * pp_row[j] of the unity matrix; * } * * where rank, pp_row, and qq_col are members of the structure LUF. */ int luf_factorize(LUF *luf, int n, int (*col)(void *info, int j, int ind[], double val[]), void *info) { int *pp_row, *pp_col, *qq_row, *qq_col; double max_gro = luf->max_gro; int i, j, k, p, q, t, ret; if (n < 1) xfault("luf_factorize: n = %d; invalid parameter\n", n); if (n > N_MAX) xfault("luf_factorize: n = %d; matrix too big\n", n); /* invalidate the factorization */ luf->valid = 0; /* reallocate arrays, if necessary */ reallocate(luf, n); pp_row = luf->pp_row; pp_col = luf->pp_col; qq_row = luf->qq_row; qq_col = luf->qq_col; /* estimate initial size of the SVA, if not specified */ if (luf->sv_size == 0 && luf->new_sva == 0) luf->new_sva = 5 * (n + 10); more: /* reallocate the sparse vector area, if required */ if (luf->new_sva > 0) { if (luf->sv_ind != NULL) xfree(luf->sv_ind); if (luf->sv_val != NULL) xfree(luf->sv_val); luf->sv_size = luf->new_sva; luf->sv_ind = xcalloc(1+luf->sv_size, sizeof(int)); luf->sv_val = xcalloc(1+luf->sv_size, sizeof(double)); luf->new_sva = 0; } /* initialize LU-factorization data structures */ if (initialize(luf, col, info)) { /* overflow of the sparse vector area */ luf->new_sva = luf->sv_size + luf->sv_size; xassert(luf->new_sva > luf->sv_size); goto more; } /* main elimination loop */ for (k = 1; k <= n; k++) { /* choose a pivot element v[p,q] */ if (find_pivot(luf, &p, &q)) { /* no pivot can be chosen, because the active submatrix is exactly zero */ luf->rank = k - 1; ret = LUF_ESING; goto done; } /* let v[p,q] correspond to u[i',j']; permute k-th and i'-th rows and k-th and j'-th columns of the matrix U = P*V*Q to move the element u[i',j'] to the position u[k,k] */ i = pp_col[p], j = qq_row[q]; xassert(k <= i && i <= n && k <= j && j <= n); /* permute k-th and i-th rows of the matrix U */ t = pp_row[k]; pp_row[i] = t, pp_col[t] = i; pp_row[k] = p, pp_col[p] = k; /* permute k-th and j-th columns of the matrix U */ t = qq_col[k]; qq_col[j] = t, qq_row[t] = j; qq_col[k] = q, qq_row[q] = k; /* eliminate subdiagonal elements of k-th column of the matrix U = P*V*Q using the pivot element u[k,k] = v[p,q] */ if (eliminate(luf, p, q)) { /* overflow of the sparse vector area */ luf->new_sva = luf->sv_size + luf->sv_size; xassert(luf->new_sva > luf->sv_size); goto more; } /* check relative growth of elements of the matrix V */ if (luf->big_v > max_gro * luf->max_a) { /* the growth is too intensive, therefore most probably the matrix A is ill-conditioned */ luf->rank = k - 1; ret = LUF_ECOND; goto done; } } /* now the matrix U = P*V*Q is upper triangular, the matrix V has been built in row-wise format, and the matrix F has been built in column-wise format */ /* defragment the sparse vector area in order to merge all free locations in one continuous extent */ luf_defrag_sva(luf); /* build the matrix V in column-wise format */ if (build_v_cols(luf)) { /* overflow of the sparse vector area */ luf->new_sva = luf->sv_size + luf->sv_size; xassert(luf->new_sva > luf->sv_size); goto more; } /* build the matrix F in row-wise format */ if (build_f_rows(luf)) { /* overflow of the sparse vector area */ luf->new_sva = luf->sv_size + luf->sv_size; xassert(luf->new_sva > luf->sv_size); goto more; } /* the LU-factorization has been successfully computed */ luf->valid = 1; luf->rank = n; ret = 0; /* if there are few free locations in the sparse vector area, try increasing its size in the future */ t = 3 * (n + luf->nnz_v) + 2 * luf->nnz_f; if (luf->sv_size < t) { luf->new_sva = luf->sv_size; while (luf->new_sva < t) { k = luf->new_sva; luf->new_sva = k + k; xassert(luf->new_sva > k); } } done: /* return to the calling program */ return ret; } /*********************************************************************** * NAME * * luf_f_solve - solve system F*x = b or F'*x = b * * SYNOPSIS * * #include "glpluf.h" * void luf_f_solve(LUF *luf, int tr, double x[]); * * DESCRIPTION * * The routine luf_f_solve solves either the system F*x = b (if the * flag tr is zero) or the system F'*x = b (if the flag tr is non-zero), * where the matrix F is a component of LU-factorization specified by * the parameter luf, F' is a matrix transposed to F. * * On entry the array x should contain elements of the right-hand side * vector b in locations x[1], ..., x[n], where n is the order of the * matrix F. On exit this array will contain elements of the solution * vector x in the same locations. */ void luf_f_solve(LUF *luf, int tr, double x[]) { int n = luf->n; int *fr_ptr = luf->fr_ptr; int *fr_len = luf->fr_len; int *fc_ptr = luf->fc_ptr; int *fc_len = luf->fc_len; int *pp_row = luf->pp_row; int *sv_ind = luf->sv_ind; double *sv_val = luf->sv_val; int i, j, k, beg, end, ptr; double xk; if (!luf->valid) xfault("luf_f_solve: LU-factorization is not valid\n"); if (!tr) { /* solve the system F*x = b */ for (j = 1; j <= n; j++) { k = pp_row[j]; xk = x[k]; if (xk != 0.0) { beg = fc_ptr[k]; end = beg + fc_len[k] - 1; for (ptr = beg; ptr <= end; ptr++) x[sv_ind[ptr]] -= sv_val[ptr] * xk; } } } else { /* solve the system F'*x = b */ for (i = n; i >= 1; i--) { k = pp_row[i]; xk = x[k]; if (xk != 0.0) { beg = fr_ptr[k]; end = beg + fr_len[k] - 1; for (ptr = beg; ptr <= end; ptr++) x[sv_ind[ptr]] -= sv_val[ptr] * xk; } } } return; } /*********************************************************************** * NAME * * luf_v_solve - solve system V*x = b or V'*x = b * * SYNOPSIS * * #include "glpluf.h" * void luf_v_solve(LUF *luf, int tr, double x[]); * * DESCRIPTION * * The routine luf_v_solve solves either the system V*x = b (if the * flag tr is zero) or the system V'*x = b (if the flag tr is non-zero), * where the matrix V is a component of LU-factorization specified by * the parameter luf, V' is a matrix transposed to V. * * On entry the array x should contain elements of the right-hand side * vector b in locations x[1], ..., x[n], where n is the order of the * matrix V. On exit this array will contain elements of the solution * vector x in the same locations. */ void luf_v_solve(LUF *luf, int tr, double x[]) { int n = luf->n; int *vr_ptr = luf->vr_ptr; int *vr_len = luf->vr_len; double *vr_piv = luf->vr_piv; int *vc_ptr = luf->vc_ptr; int *vc_len = luf->vc_len; int *pp_row = luf->pp_row; int *qq_col = luf->qq_col; int *sv_ind = luf->sv_ind; double *sv_val = luf->sv_val; double *b = luf->work; int i, j, k, beg, end, ptr; double temp; if (!luf->valid) xfault("luf_v_solve: LU-factorization is not valid\n"); for (k = 1; k <= n; k++) b[k] = x[k], x[k] = 0.0; if (!tr) { /* solve the system V*x = b */ for (k = n; k >= 1; k--) { i = pp_row[k], j = qq_col[k]; temp = b[i]; if (temp != 0.0) { x[j] = (temp /= vr_piv[i]); beg = vc_ptr[j]; end = beg + vc_len[j] - 1; for (ptr = beg; ptr <= end; ptr++) b[sv_ind[ptr]] -= sv_val[ptr] * temp; } } } else { /* solve the system V'*x = b */ for (k = 1; k <= n; k++) { i = pp_row[k], j = qq_col[k]; temp = b[j]; if (temp != 0.0) { x[i] = (temp /= vr_piv[i]); beg = vr_ptr[i]; end = beg + vr_len[i] - 1; for (ptr = beg; ptr <= end; ptr++) b[sv_ind[ptr]] -= sv_val[ptr] * temp; } } } return; } /*********************************************************************** * NAME * * luf_a_solve - solve system A*x = b or A'*x = b * * SYNOPSIS * * #include "glpluf.h" * void luf_a_solve(LUF *luf, int tr, double x[]); * * DESCRIPTION * * The routine luf_a_solve solves either the system A*x = b (if the * flag tr is zero) or the system A'*x = b (if the flag tr is non-zero), * where the parameter luf specifies LU-factorization of the matrix A, * A' is a matrix transposed to A. * * On entry the array x should contain elements of the right-hand side * vector b in locations x[1], ..., x[n], where n is the order of the * matrix A. On exit this array will contain elements of the solution * vector x in the same locations. */ void luf_a_solve(LUF *luf, int tr, double x[]) { if (!luf->valid) xfault("luf_a_solve: LU-factorization is not valid\n"); if (!tr) { /* A = F*V, therefore inv(A) = inv(V)*inv(F) */ luf_f_solve(luf, 0, x); luf_v_solve(luf, 0, x); } else { /* A' = V'*F', therefore inv(A') = inv(F')*inv(V') */ luf_v_solve(luf, 1, x); luf_f_solve(luf, 1, x); } return; } /*********************************************************************** * NAME * * luf_delete_it - delete LU-factorization * * SYNOPSIS * * #include "glpluf.h" * void luf_delete_it(LUF *luf); * * DESCRIPTION * * The routine luf_delete deletes LU-factorization specified by the * parameter luf and frees all the memory allocated to this program * object. */ void luf_delete_it(LUF *luf) { if (luf->fr_ptr != NULL) xfree(luf->fr_ptr); if (luf->fr_len != NULL) xfree(luf->fr_len); if (luf->fc_ptr != NULL) xfree(luf->fc_ptr); if (luf->fc_len != NULL) xfree(luf->fc_len); if (luf->vr_ptr != NULL) xfree(luf->vr_ptr); if (luf->vr_len != NULL) xfree(luf->vr_len); if (luf->vr_cap != NULL) xfree(luf->vr_cap); if (luf->vr_piv != NULL) xfree(luf->vr_piv); if (luf->vc_ptr != NULL) xfree(luf->vc_ptr); if (luf->vc_len != NULL) xfree(luf->vc_len); if (luf->vc_cap != NULL) xfree(luf->vc_cap); if (luf->pp_row != NULL) xfree(luf->pp_row); if (luf->pp_col != NULL) xfree(luf->pp_col); if (luf->qq_row != NULL) xfree(luf->qq_row); if (luf->qq_col != NULL) xfree(luf->qq_col); if (luf->sv_ind != NULL) xfree(luf->sv_ind); if (luf->sv_val != NULL) xfree(luf->sv_val); if (luf->sv_prev != NULL) xfree(luf->sv_prev); if (luf->sv_next != NULL) xfree(luf->sv_next); if (luf->vr_max != NULL) xfree(luf->vr_max); if (luf->rs_head != NULL) xfree(luf->rs_head); if (luf->rs_prev != NULL) xfree(luf->rs_prev); if (luf->rs_next != NULL) xfree(luf->rs_next); if (luf->cs_head != NULL) xfree(luf->cs_head); if (luf->cs_prev != NULL) xfree(luf->cs_prev); if (luf->cs_next != NULL) xfree(luf->cs_next); if (luf->flag != NULL) xfree(luf->flag); if (luf->work != NULL) xfree(luf->work); xfree(luf); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpenv07.c0000644000175000017500000003707311632352620020257 0ustar csheltoncshelton/* glpenv07.c (stream input/output) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifdef HAVE_CONFIG_H #include #endif #include "glpenv.h" /*********************************************************************** * NAME * * lib_err_msg - save error message string * * SYNOPSIS * * #include "glpenv.h" * void lib_err_msg(const char *msg); * * DESCRIPTION * * The routine lib_err_msg saves an error message string specified by * the parameter msg. The message is obtained by some library routines * with a call to strerror(errno). */ void lib_err_msg(const char *msg) { ENV *env = get_env_ptr(); int len = strlen(msg); if (len >= IOERR_MSG_SIZE) len = IOERR_MSG_SIZE - 1; memcpy(env->ioerr_msg, msg, len); if (len > 0 && env->ioerr_msg[len-1] == '\n') len--; env->ioerr_msg[len] = '\0'; return; } /*********************************************************************** * NAME * * xerrmsg - retrieve error message string * * SYNOPSIS * * #include "glpenv.h" * const char *xerrmsg(void); * * RETURNS * * The routine xerrmsg returns a pointer to an error message string * previously set by some library routine to indicate an error. */ const char *xerrmsg(void) { ENV *env = get_env_ptr(); return env->ioerr_msg; } /*********************************************************************** * NAME * * xfopen - open a stream * * SYNOPSIS * * #include "glpenv.h" * XFILE *xfopen(const char *fname, const char *mode); * * DESCRIPTION * * The routine xfopen opens the file whose name is a string pointed to * by fname and associates a stream with it. * * The parameter mode points to a string, which indicates the open mode * and should be one of the following: * * "r" open text file for reading; * "w" truncate to zero length or create text file for writing; * "rb" open binary file for reading; * "wb" truncate to zero length or create binary file for writing. * * RETURNS * * The routine xfopen returns a pointer to the object controlling the * stream. If the open operation fails, xfopen returns NULL. */ static void *c_fopen(const char *fname, const char *mode); static void *z_fopen(const char *fname, const char *mode); static int is_gz_file(const char *fname) { char *ext = strrchr(fname, '.'); return ext != NULL && strcmp(ext, ".gz") == 0; } XFILE *xfopen(const char *fname, const char *mode) { ENV *env = get_env_ptr(); XFILE *fp; int type; void *fh; if (!is_gz_file(fname)) { type = FH_FILE; fh = c_fopen(fname, mode); } else { type = FH_ZLIB; fh = z_fopen(fname, mode); } if (fh == NULL) { fp = NULL; goto done; } fp = xmalloc(sizeof(XFILE)); fp->type = type; fp->fh = fh; fp->prev = NULL; fp->next = env->file_ptr; if (fp->next != NULL) fp->next->prev = fp; env->file_ptr = fp; done: return fp; } /*********************************************************************** * NAME * * xfgetc - read character from the stream * * SYNOPSIS * * #include "glpenv.h" * int xfgetc(XFILE *fp); * * DESCRIPTION * * If the end-of-file indicator for the input stream pointed to by fp * is not set and a next character is present, the routine xfgetc * obtains that character as an unsigned char converted to an int and * advances the associated file position indicator for the stream (if * defined). * * RETURNS * * If the end-of-file indicator for the stream is set, or if the * stream is at end-of-file, the end-of-file indicator for the stream * is set and the routine xfgetc returns XEOF. Otherwise, the routine * xfgetc returns the next character from the input stream pointed to * by fp. If a read error occurs, the error indicator for the stream is * set and the xfgetc routine returns XEOF. * * Note: An end-of-file and a read error can be distinguished by use of * the routines xfeof and xferror. */ static int c_fgetc(void *fh); static int z_fgetc(void *fh); int xfgetc(XFILE *fp) { int c; switch (fp->type) { case FH_FILE: c = c_fgetc(fp->fh); break; case FH_ZLIB: c = z_fgetc(fp->fh); break; default: xassert(fp != fp); } return c; } /*********************************************************************** * NAME * * xfputc - write character to the stream * * SYNOPSIS * * #include "glpenv.h" * int xfputc(int c, XFILE *fp); * * DESCRIPTION * * The routine xfputc writes the character specified by c (converted * to an unsigned char) to the output stream pointed to by fp, at the * position indicated by the associated file position indicator (if * defined), and advances the indicator appropriately. * * RETURNS * * The routine xfputc returns the character written. If a write error * occurs, the error indicator for the stream is set and xfputc returns * XEOF. */ static int c_fputc(int c, void *fh); static int z_fputc(int c, void *fh); int xfputc(int c, XFILE *fp) { switch (fp->type) { case FH_FILE: c = c_fputc(c, fp->fh); break; case FH_ZLIB: c = z_fputc(c, fp->fh); break; default: xassert(fp != fp); } return c; } /*********************************************************************** * NAME * * xferror - test error indicator for the stream * * SYNOPSIS * * #include "glpenv.h" * int xferror(XFILE *fp); * * DESCRIPTION * * The routine xferror tests the error indicator for the stream * pointed to by fp. * * RETURNS * * The routine xferror returns non-zero if and only if the error * indicator is set for the stream. */ static int c_ferror(void *fh); static int z_ferror(void *fh); int xferror(XFILE *fp) { int ret; switch (fp->type) { case FH_FILE: ret = c_ferror(fp->fh); break; case FH_ZLIB: ret = z_ferror(fp->fh); break; default: xassert(fp != fp); } return ret; } /*********************************************************************** * NAME * * xfeof - test end-of-file indicator for the stream * * SYNOPSIS * * #include "glpenv.h" * int xfeof(XFILE *fp); * * DESCRIPTION * * The routine xfeof tests the end-of-file indicator for the stream * pointed to by fp. * * RETURNS * * The routine xfeof returns non-zero if and only if the end-of-file * indicator is set for the stream. */ static int c_feof(void *fh); static int z_feof(void *fh); int xfeof(XFILE *fp) { int ret; switch (fp->type) { case FH_FILE: ret = c_feof(fp->fh); break; case FH_ZLIB: ret = z_feof(fp->fh); break; default: xassert(fp != fp); } return ret; } int xfprintf(XFILE *file, const char *fmt, ...) { ENV *env = get_env_ptr(); int cnt, j; va_list arg; va_start(arg, fmt); cnt = vsprintf(env->term_buf, fmt, arg); va_end(arg); for (j = 0; j < cnt; j++) { if (xfputc(env->term_buf[j], file) < 0) { cnt = -1; break; } } return cnt; } /*********************************************************************** * NAME * * xfflush - flush the stream * * SYNOPSIS * * #include "glpenv.h" * int xfflush(XFILE *fp); * * DESCRIPTION * * The routine xfflush causes any unwritten data for the output stream * pointed to by fp to be written to the associated file. * * RETURNS * * The routine xfflush returns zero if the stream was successfully * flushed. Otherwise, xfflush sets the error indicator for the stream * and returns XEOF. */ static int c_fflush(void *fh); static int z_fflush(void *fh); int xfflush(XFILE *fp) { int ret; switch (fp->type) { case FH_FILE: ret = c_fflush(fp->fh); break; case FH_ZLIB: ret = z_fflush(fp->fh); break; default: xassert(fp != fp); } return ret; } /*********************************************************************** * NAME * * xfclose - close the stream * * SYNOPSIS * * #include "glpenv.h" * int xfclose(XFILE *fp); * * DESCRIPTION * * A successful call to the routine xfclose causes the stream pointed * to by fp to be flushed and the associated file to be closed. Whether * or not the call succeeds, the stream is disassociated from the file. * * RETURNS * * The routine xfclose returns zero if the stream was successfully * closed, or XEOF if any errors were detected. */ static int c_fclose(void *fh); static int z_fclose(void *fh); int xfclose(XFILE *fp) { ENV *env = get_env_ptr(); int ret; switch (fp->type) { case FH_FILE: ret = c_fclose(fp->fh); break; case FH_ZLIB: ret = z_fclose(fp->fh); break; default: xassert(fp != fp); } fp->type = 0xF00BAD; if (fp->prev == NULL) env->file_ptr = fp->next; else fp->prev->next = fp->next; if (fp->next == NULL) ; else fp->next->prev = fp->prev; xfree(fp); return ret; } /*********************************************************************** * The following routines implement stream input/output based on the * standard C streams. */ static void *c_fopen(const char *fname, const char *mode) { FILE *fh; if (strcmp(fname, "/dev/stdin") == 0) fh = stdin; else if (strcmp(fname, "/dev/stdout") == 0) fh = stdout; else if (strcmp(fname, "/dev/stderr") == 0) fh = stderr; else fh = fopen(fname, mode); if (fh == NULL) lib_err_msg(strerror(errno)); return fh; } static int c_fgetc(void *_fh) { FILE *fh = _fh; int c; if (ferror(fh) || feof(fh)) { c = XEOF; goto done; } c = fgetc(fh); if (ferror(fh)) { lib_err_msg(strerror(errno)); c = XEOF; } else if (feof(fh)) c = XEOF; else xassert(0x00 <= c && c <= 0xFF); done: return c; } static int c_fputc(int c, void *_fh) { FILE *fh = _fh; if (ferror(fh)) { c = XEOF; goto done; } c = (unsigned char)c; fputc(c, fh); if (ferror(fh)) { lib_err_msg(strerror(errno)); c = XEOF; } done: return c; } static int c_ferror(void *_fh) { FILE *fh = _fh; return ferror(fh); } static int c_feof(void *_fh) { FILE *fh = _fh; return feof(fh); } static int c_fflush(void *_fh) { FILE *fh = _fh; int ret; ret = fflush(fh); if (ret != 0) { lib_err_msg(strerror(errno)); ret = XEOF; } return ret; } static int c_fclose(void *_fh) { FILE *fh = _fh; int ret; if (fh == stdin) ret = 0; else if (fh == stdout || fh == stderr) fflush(fh), ret = 0; else ret = fclose(fh); if (ret != 0) { lib_err_msg(strerror(errno)); ret = XEOF; } return ret; } /*********************************************************************** * The following routines implement stream input/output based on the * zlib library, which provides processing .gz files "on the fly". */ /*#ifndef HAVE_ZLIB*/ #if 0 /* 27/IV-2011 */ static void *z_fopen(const char *fname, const char *mode) { xassert(fname == fname); xassert(mode == mode); lib_err_msg("Compressed files not supported"); return NULL; } static int z_fgetc(void *fh) { xassert(fh != fh); return 0; } static int z_fputc(int c, void *fh) { xassert(c != c); xassert(fh != fh); return 0; } static int z_ferror(void *fh) { xassert(fh != fh); return 0; } static int z_feof(void *fh) { xassert(fh != fh); return 0; } static int z_fflush(void *fh) { xassert(fh != fh); return 0; } static int z_fclose(void *fh) { xassert(fh != fh); return 0; } #else #include "zlib/zlib.h" struct z_file { /* .gz file handle */ gzFile file; /* pointer to .gz stream */ int err; /* i/o error indicator */ int eof; /* end-of-file indicator */ }; static void *z_fopen(const char *fname, const char *mode) { struct z_file *fh; gzFile file; if (strcmp(mode, "r") == 0 || strcmp(mode, "rb") == 0) mode = "rb"; else if (strcmp(mode, "w") == 0 || strcmp(mode, "wb") == 0) mode = "wb"; else { lib_err_msg("Invalid open mode"); fh = NULL; goto done; } file = gzopen(fname, mode); if (file == NULL) { lib_err_msg(strerror(errno)); fh = NULL; goto done; } fh = xmalloc(sizeof(struct z_file)); fh->file = file; fh->err = fh->eof = 0; done: return fh; } static int z_fgetc(void *_fh) { struct z_file *fh = _fh; int c; if (fh->err || fh->eof) { c = XEOF; goto done; } c = gzgetc(fh->file); if (c < 0) { int errnum; const char *msg; msg = gzerror(fh->file, &errnum); if (errnum == Z_STREAM_END) fh->eof = 1; else if (errnum == Z_ERRNO) { fh->err = 1; lib_err_msg(strerror(errno)); } else { fh->err = 1; lib_err_msg(msg); } c = XEOF; } else xassert(0x00 <= c && c <= 0xFF); done: return c; } static int z_fputc(int c, void *_fh) { struct z_file *fh = _fh; if (fh->err) { c = XEOF; goto done; } c = (unsigned char)c; if (gzputc(fh->file, c) < 0) { int errnum; const char *msg; fh->err = 1; msg = gzerror(fh->file, &errnum); if (errnum == Z_ERRNO) lib_err_msg(strerror(errno)); else lib_err_msg(msg); c = XEOF; } done: return c; } static int z_ferror(void *_fh) { struct z_file *fh = _fh; return fh->err; } static int z_feof(void *_fh) { struct z_file *fh = _fh; return fh->eof; } static int z_fflush(void *_fh) { struct z_file *fh = _fh; int ret; ret = gzflush(fh->file, Z_FINISH); if (ret == Z_OK) ret = 0; else { int errnum; const char *msg; fh->err = 1; msg = gzerror(fh->file, &errnum); if (errnum == Z_ERRNO) lib_err_msg(strerror(errno)); else lib_err_msg(msg); ret = XEOF; } return ret; } static int z_fclose(void *_fh) { struct z_file *fh = _fh; gzclose(fh->file); xfree(fh); return 0; } #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpluf.h0000644000175000017500000003547011632352620020112 0ustar csheltoncshelton/* glpluf.h (LU-factorization) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPLUF_H #define GLPLUF_H /*********************************************************************** * The structure LUF defines LU-factorization of a square matrix A and * is the following quartet: * * [A] = (F, V, P, Q), (1) * * where F and V are such matrices that * * A = F * V, (2) * * and P and Q are such permutation matrices that the matrix * * L = P * F * inv(P) (3) * * is lower triangular with unity diagonal, and the matrix * * U = P * V * Q (4) * * is upper triangular. All the matrices have the order n. * * Matrices F and V are stored in row- and column-wise sparse format * as row and column linked lists of non-zero elements. Unity elements * on the main diagonal of matrix F are not stored. Pivot elements of * matrix V (which correspond to diagonal elements of matrix U) are * stored separately in an ordinary array. * * Permutation matrices P and Q are stored in ordinary arrays in both * row- and column-like formats. * * Matrices L and U are completely defined by matrices F, V, P, and Q * and therefore not stored explicitly. * * The factorization (1)-(4) is a version of LU-factorization. Indeed, * from (3) and (4) it follows that: * * F = inv(P) * L * P, * * U = inv(P) * U * inv(Q), * * and substitution into (2) leads to: * * A = F * V = inv(P) * L * U * inv(Q). * * For more details see the program documentation. */ typedef struct LUF LUF; struct LUF { /* LU-factorization of a square matrix */ int n_max; /* maximal value of n (increased automatically, if necessary) */ int n; /* the order of matrices A, F, V, P, Q */ int valid; /* the factorization is valid only if this flag is set */ /*--------------------------------------------------------------*/ /* matrix F in row-wise format */ int *fr_ptr; /* int fr_ptr[1+n_max]; */ /* fr_ptr[i], i = 1,...,n, is a pointer to the first element of i-th row in SVA */ int *fr_len; /* int fr_len[1+n_max]; */ /* fr_len[i], i = 1,...,n, is the number of elements in i-th row (except unity diagonal element) */ /*--------------------------------------------------------------*/ /* matrix F in column-wise format */ int *fc_ptr; /* int fc_ptr[1+n_max]; */ /* fc_ptr[j], j = 1,...,n, is a pointer to the first element of j-th column in SVA */ int *fc_len; /* int fc_len[1+n_max]; */ /* fc_len[j], j = 1,...,n, is the number of elements in j-th column (except unity diagonal element) */ /*--------------------------------------------------------------*/ /* matrix V in row-wise format */ int *vr_ptr; /* int vr_ptr[1+n_max]; */ /* vr_ptr[i], i = 1,...,n, is a pointer to the first element of i-th row in SVA */ int *vr_len; /* int vr_len[1+n_max]; */ /* vr_len[i], i = 1,...,n, is the number of elements in i-th row (except pivot element) */ int *vr_cap; /* int vr_cap[1+n_max]; */ /* vr_cap[i], i = 1,...,n, is the capacity of i-th row, i.e. maximal number of elements which can be stored in the row without relocating it, vr_cap[i] >= vr_len[i] */ double *vr_piv; /* double vr_piv[1+n_max]; */ /* vr_piv[p], p = 1,...,n, is the pivot element v[p,q] which corresponds to a diagonal element of matrix U = P*V*Q */ /*--------------------------------------------------------------*/ /* matrix V in column-wise format */ int *vc_ptr; /* int vc_ptr[1+n_max]; */ /* vc_ptr[j], j = 1,...,n, is a pointer to the first element of j-th column in SVA */ int *vc_len; /* int vc_len[1+n_max]; */ /* vc_len[j], j = 1,...,n, is the number of elements in j-th column (except pivot element) */ int *vc_cap; /* int vc_cap[1+n_max]; */ /* vc_cap[j], j = 1,...,n, is the capacity of j-th column, i.e. maximal number of elements which can be stored in the column without relocating it, vc_cap[j] >= vc_len[j] */ /*--------------------------------------------------------------*/ /* matrix P */ int *pp_row; /* int pp_row[1+n_max]; */ /* pp_row[i] = j means that P[i,j] = 1 */ int *pp_col; /* int pp_col[1+n_max]; */ /* pp_col[j] = i means that P[i,j] = 1 */ /* if i-th row or column of matrix F is i'-th row or column of matrix L, or if i-th row of matrix V is i'-th row of matrix U, then pp_row[i'] = i and pp_col[i] = i' */ /*--------------------------------------------------------------*/ /* matrix Q */ int *qq_row; /* int qq_row[1+n_max]; */ /* qq_row[i] = j means that Q[i,j] = 1 */ int *qq_col; /* int qq_col[1+n_max]; */ /* qq_col[j] = i means that Q[i,j] = 1 */ /* if j-th column of matrix V is j'-th column of matrix U, then qq_row[j] = j' and qq_col[j'] = j */ /*--------------------------------------------------------------*/ /* the Sparse Vector Area (SVA) is a set of locations used to store sparse vectors representing rows and columns of matrices F and V; each location is a doublet (ind, val), where ind is an index, and val is a numerical value of a sparse vector element; in the whole each sparse vector is a set of adjacent locations defined by a pointer to the first element and the number of elements; these pointer and number are stored in the corresponding matrix data structure (see above); the left part of SVA is used to store rows and columns of matrix V, and its right part is used to store rows and columns of matrix F; the middle part of SVA contains free (unused) locations */ int sv_size; /* the size of SVA, in locations; all locations are numbered by integers 1, ..., n, and location 0 is not used; if necessary, the SVA size is automatically increased */ int sv_beg, sv_end; /* SVA partitioning pointers: locations from 1 to sv_beg-1 belong to the left part locations from sv_beg to sv_end-1 belong to the middle part locations from sv_end to sv_size belong to the right part the size of the middle part is (sv_end - sv_beg) */ int *sv_ind; /* sv_ind[1+sv_size]; */ /* sv_ind[k], 1 <= k <= sv_size, is the index field of k-th location */ double *sv_val; /* sv_val[1+sv_size]; */ /* sv_val[k], 1 <= k <= sv_size, is the value field of k-th location */ /*--------------------------------------------------------------*/ /* in order to efficiently defragment the left part of SVA there is a doubly linked list of rows and columns of matrix V, where rows are numbered by 1, ..., n, while columns are numbered by n+1, ..., n+n, that allows uniquely identifying each row and column of V by only one integer; in this list rows and columns are ordered by ascending their pointers vr_ptr and vc_ptr */ int sv_head; /* the number of leftmost row/column */ int sv_tail; /* the number of rightmost row/column */ int *sv_prev; /* int sv_prev[1+n_max+n_max]; */ /* sv_prev[k], k = 1,...,n+n, is the number of a row/column which precedes k-th row/column */ int *sv_next; /* int sv_next[1+n_max+n_max]; */ /* sv_next[k], k = 1,...,n+n, is the number of a row/column which succedes k-th row/column */ /*--------------------------------------------------------------*/ /* working segment (used only during factorization) */ double *vr_max; /* int vr_max[1+n_max]; */ /* vr_max[i], 1 <= i <= n, is used only if i-th row of matrix V is active (i.e. belongs to the active submatrix), and is the largest magnitude of elements in i-th row; if vr_max[i] < 0, the largest magnitude is not known yet and should be computed by the pivoting routine */ /*--------------------------------------------------------------*/ /* in order to efficiently implement Markowitz strategy and Duff search technique there are two families {R[0], R[1], ..., R[n]} and {C[0], C[1], ..., C[n]}; member R[k] is the set of active rows of matrix V, which have k non-zeros, and member C[k] is the set of active columns of V, which have k non-zeros in the active submatrix (i.e. in the active rows); each set R[k] and C[k] is implemented as a separate doubly linked list */ int *rs_head; /* int rs_head[1+n_max]; */ /* rs_head[k], 0 <= k <= n, is the number of first active row, which has k non-zeros */ int *rs_prev; /* int rs_prev[1+n_max]; */ /* rs_prev[i], 1 <= i <= n, is the number of previous row, which has the same number of non-zeros as i-th row */ int *rs_next; /* int rs_next[1+n_max]; */ /* rs_next[i], 1 <= i <= n, is the number of next row, which has the same number of non-zeros as i-th row */ int *cs_head; /* int cs_head[1+n_max]; */ /* cs_head[k], 0 <= k <= n, is the number of first active column, which has k non-zeros (in the active rows) */ int *cs_prev; /* int cs_prev[1+n_max]; */ /* cs_prev[j], 1 <= j <= n, is the number of previous column, which has the same number of non-zeros (in the active rows) as j-th column */ int *cs_next; /* int cs_next[1+n_max]; */ /* cs_next[j], 1 <= j <= n, is the number of next column, which has the same number of non-zeros (in the active rows) as j-th column */ /* (end of working segment) */ /*--------------------------------------------------------------*/ /* working arrays */ int *flag; /* int flag[1+n_max]; */ /* integer working array */ double *work; /* double work[1+n_max]; */ /* floating-point working array */ /*--------------------------------------------------------------*/ /* control parameters */ int new_sva; /* new required size of the sparse vector area, in locations; set automatically by the factorizing routine */ double piv_tol; /* threshold pivoting tolerance, 0 < piv_tol < 1; element v[i,j] of the active submatrix fits to be pivot if it satisfies to the stability criterion |v[i,j]| >= piv_tol * max |v[i,*]|, i.e. if it is not very small in the magnitude among other elements in the same row; decreasing this parameter gives better sparsity at the expense of numerical accuracy and vice versa */ int piv_lim; /* maximal allowable number of pivot candidates to be considered; if piv_lim pivot candidates have been considered, the pivoting routine terminates the search with the best candidate found */ int suhl; /* if this flag is set, the pivoting routine applies a heuristic proposed by Uwe Suhl: if a column of the active submatrix has no eligible pivot candidates (i.e. all its elements do not satisfy to the stability criterion), the routine excludes it from futher consideration until it becomes column singleton; in many cases this allows reducing the time needed for pivot searching */ double eps_tol; /* epsilon tolerance; each element of the active submatrix, whose magnitude is less than eps_tol, is replaced by exact zero */ double max_gro; /* maximal allowable growth of elements of matrix V during all the factorization process; if on some eliminaion step the ratio big_v / max_a (see below) becomes greater than max_gro, matrix A is considered as ill-conditioned (assuming that the pivoting tolerance piv_tol has an appropriate value) */ /*--------------------------------------------------------------*/ /* some statistics */ int nnz_a; /* the number of non-zeros in matrix A */ int nnz_f; /* the number of non-zeros in matrix F (except diagonal elements, which are not stored) */ int nnz_v; /* the number of non-zeros in matrix V (except its pivot elements, which are stored in a separate array) */ double max_a; /* the largest magnitude of elements of matrix A */ double big_v; /* the largest magnitude of elements of matrix V appeared in the active submatrix during all the factorization process */ int rank; /* estimated rank of matrix A */ }; /* return codes: */ #define LUF_ESING 1 /* singular matrix */ #define LUF_ECOND 2 /* ill-conditioned matrix */ #define luf_create_it _glp_luf_create_it LUF *luf_create_it(void); /* create LU-factorization */ #define luf_defrag_sva _glp_luf_defrag_sva void luf_defrag_sva(LUF *luf); /* defragment the sparse vector area */ #define luf_enlarge_row _glp_luf_enlarge_row int luf_enlarge_row(LUF *luf, int i, int cap); /* enlarge row capacity */ #define luf_enlarge_col _glp_luf_enlarge_col int luf_enlarge_col(LUF *luf, int j, int cap); /* enlarge column capacity */ #define luf_factorize _glp_luf_factorize int luf_factorize(LUF *luf, int n, int (*col)(void *info, int j, int ind[], double val[]), void *info); /* compute LU-factorization */ #define luf_f_solve _glp_luf_f_solve void luf_f_solve(LUF *luf, int tr, double x[]); /* solve system F*x = b or F'*x = b */ #define luf_v_solve _glp_luf_v_solve void luf_v_solve(LUF *luf, int tr, double x[]); /* solve system V*x = b or V'*x = b */ #define luf_a_solve _glp_luf_a_solve void luf_a_solve(LUF *luf, int tr, double x[]); /* solve system A*x = b or A'*x = b */ #define luf_delete_it _glp_luf_delete_it void luf_delete_it(LUF *luf); /* delete LU-factorization */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpapi01.c0000644000175000017500000014725311632352620020234 0ustar csheltoncshelton/* glpapi01.c (problem creating and modifying routines) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpios.h" /* CAUTION: DO NOT CHANGE THE LIMITS BELOW */ #define M_MAX 100000000 /* = 100*10^6 */ /* maximal number of rows in the problem object */ #define N_MAX 100000000 /* = 100*10^6 */ /* maximal number of columns in the problem object */ #define NNZ_MAX 500000000 /* = 500*10^6 */ /* maximal number of constraint coefficients in the problem object */ /*********************************************************************** * NAME * * glp_create_prob - create problem object * * SYNOPSIS * * glp_prob *glp_create_prob(void); * * DESCRIPTION * * The routine glp_create_prob creates a new problem object, which is * initially "empty", i.e. has no rows and columns. * * RETURNS * * The routine returns a pointer to the object created, which should be * used in any subsequent operations on this object. */ static void create_prob(glp_prob *lp) { lp->magic = GLP_PROB_MAGIC; lp->pool = dmp_create_pool(); #if 0 /* 17/XI-2009 */ lp->cps = xmalloc(sizeof(struct LPXCPS)); lpx_reset_parms(lp); #else lp->parms = NULL; #endif lp->tree = NULL; #if 0 lp->lwa = 0; lp->cwa = NULL; #endif /* LP/MIP data */ lp->name = NULL; lp->obj = NULL; lp->dir = GLP_MIN; lp->c0 = 0.0; lp->m_max = 100; lp->n_max = 200; lp->m = lp->n = 0; lp->nnz = 0; lp->row = xcalloc(1+lp->m_max, sizeof(GLPROW *)); lp->col = xcalloc(1+lp->n_max, sizeof(GLPCOL *)); lp->r_tree = lp->c_tree = NULL; /* basis factorization */ lp->valid = 0; lp->head = xcalloc(1+lp->m_max, sizeof(int)); lp->bfcp = NULL; lp->bfd = NULL; /* basic solution (LP) */ lp->pbs_stat = lp->dbs_stat = GLP_UNDEF; lp->obj_val = 0.0; lp->it_cnt = 0; lp->some = 0; /* interior-point solution (LP) */ lp->ipt_stat = GLP_UNDEF; lp->ipt_obj = 0.0; /* integer solution (MIP) */ lp->mip_stat = GLP_UNDEF; lp->mip_obj = 0.0; return; } glp_prob *glp_create_prob(void) { glp_prob *lp; lp = xmalloc(sizeof(glp_prob)); create_prob(lp); return lp; } /*********************************************************************** * NAME * * glp_set_prob_name - assign (change) problem name * * SYNOPSIS * * void glp_set_prob_name(glp_prob *lp, const char *name); * * DESCRIPTION * * The routine glp_set_prob_name assigns a given symbolic name (1 up to * 255 characters) to the specified problem object. * * If the parameter name is NULL or empty string, the routine erases an * existing symbolic name of the problem object. */ void glp_set_prob_name(glp_prob *lp, const char *name) { glp_tree *tree = lp->tree; if (tree != NULL && tree->reason != 0) xerror("glp_set_prob_name: operation not allowed\n"); if (lp->name != NULL) { dmp_free_atom(lp->pool, lp->name, strlen(lp->name)+1); lp->name = NULL; } if (!(name == NULL || name[0] == '\0')) { int k; for (k = 0; name[k] != '\0'; k++) { if (k == 256) xerror("glp_set_prob_name: problem name too long\n"); if (iscntrl((unsigned char)name[k])) xerror("glp_set_prob_name: problem name contains invalid" " character(s)\n"); } lp->name = dmp_get_atom(lp->pool, strlen(name)+1); strcpy(lp->name, name); } return; } /*********************************************************************** * NAME * * glp_set_obj_name - assign (change) objective function name * * SYNOPSIS * * void glp_set_obj_name(glp_prob *lp, const char *name); * * DESCRIPTION * * The routine glp_set_obj_name assigns a given symbolic name (1 up to * 255 characters) to the objective function of the specified problem * object. * * If the parameter name is NULL or empty string, the routine erases an * existing name of the objective function. */ void glp_set_obj_name(glp_prob *lp, const char *name) { glp_tree *tree = lp->tree; if (tree != NULL && tree->reason != 0) xerror("glp_set_obj_name: operation not allowed\n"); if (lp->obj != NULL) { dmp_free_atom(lp->pool, lp->obj, strlen(lp->obj)+1); lp->obj = NULL; } if (!(name == NULL || name[0] == '\0')) { int k; for (k = 0; name[k] != '\0'; k++) { if (k == 256) xerror("glp_set_obj_name: objective name too long\n"); if (iscntrl((unsigned char)name[k])) xerror("glp_set_obj_name: objective name contains invali" "d character(s)\n"); } lp->obj = dmp_get_atom(lp->pool, strlen(name)+1); strcpy(lp->obj, name); } return; } /*********************************************************************** * NAME * * glp_set_obj_dir - set (change) optimization direction flag * * SYNOPSIS * * void glp_set_obj_dir(glp_prob *lp, int dir); * * DESCRIPTION * * The routine glp_set_obj_dir sets (changes) optimization direction * flag (i.e. "sense" of the objective function) as specified by the * parameter dir: * * GLP_MIN - minimization; * GLP_MAX - maximization. */ void glp_set_obj_dir(glp_prob *lp, int dir) { glp_tree *tree = lp->tree; if (tree != NULL && tree->reason != 0) xerror("glp_set_obj_dir: operation not allowed\n"); if (!(dir == GLP_MIN || dir == GLP_MAX)) xerror("glp_set_obj_dir: dir = %d; invalid direction flag\n", dir); lp->dir = dir; return; } /*********************************************************************** * NAME * * glp_add_rows - add new rows to problem object * * SYNOPSIS * * int glp_add_rows(glp_prob *lp, int nrs); * * DESCRIPTION * * The routine glp_add_rows adds nrs rows (constraints) to the specified * problem object. New rows are always added to the end of the row list, * so the ordinal numbers of existing rows remain unchanged. * * Being added each new row is initially free (unbounded) and has empty * list of the constraint coefficients. * * RETURNS * * The routine glp_add_rows returns the ordinal number of the first new * row added to the problem object. */ int glp_add_rows(glp_prob *lp, int nrs) { glp_tree *tree = lp->tree; GLPROW *row; int m_new, i; /* determine new number of rows */ if (nrs < 1) xerror("glp_add_rows: nrs = %d; invalid number of rows\n", nrs); if (nrs > M_MAX - lp->m) xerror("glp_add_rows: nrs = %d; too many rows\n", nrs); m_new = lp->m + nrs; /* increase the room, if necessary */ if (lp->m_max < m_new) { GLPROW **save = lp->row; while (lp->m_max < m_new) { lp->m_max += lp->m_max; xassert(lp->m_max > 0); } lp->row = xcalloc(1+lp->m_max, sizeof(GLPROW *)); memcpy(&lp->row[1], &save[1], lp->m * sizeof(GLPROW *)); xfree(save); /* do not forget about the basis header */ xfree(lp->head); lp->head = xcalloc(1+lp->m_max, sizeof(int)); } /* add new rows to the end of the row list */ for (i = lp->m+1; i <= m_new; i++) { /* create row descriptor */ lp->row[i] = row = dmp_get_atom(lp->pool, sizeof(GLPROW)); row->i = i; row->name = NULL; row->node = NULL; #if 1 /* 20/IX-2008 */ row->level = 0; row->origin = 0; row->klass = 0; if (tree != NULL) { switch (tree->reason) { case 0: break; case GLP_IROWGEN: xassert(tree->curr != NULL); row->level = tree->curr->level; row->origin = GLP_RF_LAZY; break; case GLP_ICUTGEN: xassert(tree->curr != NULL); row->level = tree->curr->level; row->origin = GLP_RF_CUT; break; default: xassert(tree != tree); } } #endif row->type = GLP_FR; row->lb = row->ub = 0.0; row->ptr = NULL; row->rii = 1.0; row->stat = GLP_BS; #if 0 row->bind = -1; #else row->bind = 0; #endif row->prim = row->dual = 0.0; row->pval = row->dval = 0.0; row->mipx = 0.0; } /* set new number of rows */ lp->m = m_new; /* invalidate the basis factorization */ lp->valid = 0; #if 1 if (tree != NULL && tree->reason != 0) tree->reopt = 1; #endif /* return the ordinal number of the first row added */ return m_new - nrs + 1; } /*********************************************************************** * NAME * * glp_add_cols - add new columns to problem object * * SYNOPSIS * * int glp_add_cols(glp_prob *lp, int ncs); * * DESCRIPTION * * The routine glp_add_cols adds ncs columns (structural variables) to * the specified problem object. New columns are always added to the end * of the column list, so the ordinal numbers of existing columns remain * unchanged. * * Being added each new column is initially fixed at zero and has empty * list of the constraint coefficients. * * RETURNS * * The routine glp_add_cols returns the ordinal number of the first new * column added to the problem object. */ int glp_add_cols(glp_prob *lp, int ncs) { glp_tree *tree = lp->tree; GLPCOL *col; int n_new, j; if (tree != NULL && tree->reason != 0) xerror("glp_add_cols: operation not allowed\n"); /* determine new number of columns */ if (ncs < 1) xerror("glp_add_cols: ncs = %d; invalid number of columns\n", ncs); if (ncs > N_MAX - lp->n) xerror("glp_add_cols: ncs = %d; too many columns\n", ncs); n_new = lp->n + ncs; /* increase the room, if necessary */ if (lp->n_max < n_new) { GLPCOL **save = lp->col; while (lp->n_max < n_new) { lp->n_max += lp->n_max; xassert(lp->n_max > 0); } lp->col = xcalloc(1+lp->n_max, sizeof(GLPCOL *)); memcpy(&lp->col[1], &save[1], lp->n * sizeof(GLPCOL *)); xfree(save); } /* add new columns to the end of the column list */ for (j = lp->n+1; j <= n_new; j++) { /* create column descriptor */ lp->col[j] = col = dmp_get_atom(lp->pool, sizeof(GLPCOL)); col->j = j; col->name = NULL; col->node = NULL; col->kind = GLP_CV; col->type = GLP_FX; col->lb = col->ub = 0.0; col->coef = 0.0; col->ptr = NULL; col->sjj = 1.0; col->stat = GLP_NS; #if 0 col->bind = -1; #else col->bind = 0; /* the basis may remain valid */ #endif col->prim = col->dual = 0.0; col->pval = col->dval = 0.0; col->mipx = 0.0; } /* set new number of columns */ lp->n = n_new; /* return the ordinal number of the first column added */ return n_new - ncs + 1; } /*********************************************************************** * NAME * * glp_set_row_name - assign (change) row name * * SYNOPSIS * * void glp_set_row_name(glp_prob *lp, int i, const char *name); * * DESCRIPTION * * The routine glp_set_row_name assigns a given symbolic name (1 up to * 255 characters) to i-th row (auxiliary variable) of the specified * problem object. * * If the parameter name is NULL or empty string, the routine erases an * existing name of i-th row. */ void glp_set_row_name(glp_prob *lp, int i, const char *name) { glp_tree *tree = lp->tree; GLPROW *row; if (!(1 <= i && i <= lp->m)) xerror("glp_set_row_name: i = %d; row number out of range\n", i); row = lp->row[i]; if (tree != NULL && tree->reason != 0) { xassert(tree->curr != NULL); xassert(row->level == tree->curr->level); } if (row->name != NULL) { if (row->node != NULL) { xassert(lp->r_tree != NULL); avl_delete_node(lp->r_tree, row->node); row->node = NULL; } dmp_free_atom(lp->pool, row->name, strlen(row->name)+1); row->name = NULL; } if (!(name == NULL || name[0] == '\0')) { int k; for (k = 0; name[k] != '\0'; k++) { if (k == 256) xerror("glp_set_row_name: i = %d; row name too long\n", i); if (iscntrl((unsigned char)name[k])) xerror("glp_set_row_name: i = %d: row name contains inva" "lid character(s)\n", i); } row->name = dmp_get_atom(lp->pool, strlen(name)+1); strcpy(row->name, name); if (lp->r_tree != NULL) { xassert(row->node == NULL); row->node = avl_insert_node(lp->r_tree, row->name); avl_set_node_link(row->node, row); } } return; } /*********************************************************************** * NAME * * glp_set_col_name - assign (change) column name * * SYNOPSIS * * void glp_set_col_name(glp_prob *lp, int j, const char *name); * * DESCRIPTION * * The routine glp_set_col_name assigns a given symbolic name (1 up to * 255 characters) to j-th column (structural variable) of the specified * problem object. * * If the parameter name is NULL or empty string, the routine erases an * existing name of j-th column. */ void glp_set_col_name(glp_prob *lp, int j, const char *name) { glp_tree *tree = lp->tree; GLPCOL *col; if (tree != NULL && tree->reason != 0) xerror("glp_set_col_name: operation not allowed\n"); if (!(1 <= j && j <= lp->n)) xerror("glp_set_col_name: j = %d; column number out of range\n" , j); col = lp->col[j]; if (col->name != NULL) { if (col->node != NULL) { xassert(lp->c_tree != NULL); avl_delete_node(lp->c_tree, col->node); col->node = NULL; } dmp_free_atom(lp->pool, col->name, strlen(col->name)+1); col->name = NULL; } if (!(name == NULL || name[0] == '\0')) { int k; for (k = 0; name[k] != '\0'; k++) { if (k == 256) xerror("glp_set_col_name: j = %d; column name too long\n" , j); if (iscntrl((unsigned char)name[k])) xerror("glp_set_col_name: j = %d: column name contains i" "nvalid character(s)\n", j); } col->name = dmp_get_atom(lp->pool, strlen(name)+1); strcpy(col->name, name); if (lp->c_tree != NULL && col->name != NULL) { xassert(col->node == NULL); col->node = avl_insert_node(lp->c_tree, col->name); avl_set_node_link(col->node, col); } } return; } /*********************************************************************** * NAME * * glp_set_row_bnds - set (change) row bounds * * SYNOPSIS * * void glp_set_row_bnds(glp_prob *lp, int i, int type, double lb, * double ub); * * DESCRIPTION * * The routine glp_set_row_bnds sets (changes) the type and bounds of * i-th row (auxiliary variable) of the specified problem object. * * Parameters type, lb, and ub specify the type, lower bound, and upper * bound, respectively, as follows: * * Type Bounds Comments * ------------------------------------------------------ * GLP_FR -inf < x < +inf Free variable * GLP_LO lb <= x < +inf Variable with lower bound * GLP_UP -inf < x <= ub Variable with upper bound * GLP_DB lb <= x <= ub Double-bounded variable * GLP_FX x = lb Fixed variable * * where x is the auxiliary variable associated with i-th row. * * If the row has no lower bound, the parameter lb is ignored. If the * row has no upper bound, the parameter ub is ignored. If the row is * an equality constraint (i.e. the corresponding auxiliary variable is * of fixed type), only the parameter lb is used while the parameter ub * is ignored. */ void glp_set_row_bnds(glp_prob *lp, int i, int type, double lb, double ub) { GLPROW *row; if (!(1 <= i && i <= lp->m)) xerror("glp_set_row_bnds: i = %d; row number out of range\n", i); row = lp->row[i]; row->type = type; switch (type) { case GLP_FR: row->lb = row->ub = 0.0; if (row->stat != GLP_BS) row->stat = GLP_NF; break; case GLP_LO: row->lb = lb, row->ub = 0.0; if (row->stat != GLP_BS) row->stat = GLP_NL; break; case GLP_UP: row->lb = 0.0, row->ub = ub; if (row->stat != GLP_BS) row->stat = GLP_NU; break; case GLP_DB: row->lb = lb, row->ub = ub; if (!(row->stat == GLP_BS || row->stat == GLP_NL || row->stat == GLP_NU)) row->stat = (fabs(lb) <= fabs(ub) ? GLP_NL : GLP_NU); break; case GLP_FX: row->lb = row->ub = lb; if (row->stat != GLP_BS) row->stat = GLP_NS; break; default: xerror("glp_set_row_bnds: i = %d; type = %d; invalid row ty" "pe\n", i, type); } return; } /*********************************************************************** * NAME * * glp_set_col_bnds - set (change) column bounds * * SYNOPSIS * * void glp_set_col_bnds(glp_prob *lp, int j, int type, double lb, * double ub); * * DESCRIPTION * * The routine glp_set_col_bnds sets (changes) the type and bounds of * j-th column (structural variable) of the specified problem object. * * Parameters type, lb, and ub specify the type, lower bound, and upper * bound, respectively, as follows: * * Type Bounds Comments * ------------------------------------------------------ * GLP_FR -inf < x < +inf Free variable * GLP_LO lb <= x < +inf Variable with lower bound * GLP_UP -inf < x <= ub Variable with upper bound * GLP_DB lb <= x <= ub Double-bounded variable * GLP_FX x = lb Fixed variable * * where x is the structural variable associated with j-th column. * * If the column has no lower bound, the parameter lb is ignored. If the * column has no upper bound, the parameter ub is ignored. If the column * is of fixed type, only the parameter lb is used while the parameter * ub is ignored. */ void glp_set_col_bnds(glp_prob *lp, int j, int type, double lb, double ub) { GLPCOL *col; if (!(1 <= j && j <= lp->n)) xerror("glp_set_col_bnds: j = %d; column number out of range\n" , j); col = lp->col[j]; col->type = type; switch (type) { case GLP_FR: col->lb = col->ub = 0.0; if (col->stat != GLP_BS) col->stat = GLP_NF; break; case GLP_LO: col->lb = lb, col->ub = 0.0; if (col->stat != GLP_BS) col->stat = GLP_NL; break; case GLP_UP: col->lb = 0.0, col->ub = ub; if (col->stat != GLP_BS) col->stat = GLP_NU; break; case GLP_DB: col->lb = lb, col->ub = ub; if (!(col->stat == GLP_BS || col->stat == GLP_NL || col->stat == GLP_NU)) col->stat = (fabs(lb) <= fabs(ub) ? GLP_NL : GLP_NU); break; case GLP_FX: col->lb = col->ub = lb; if (col->stat != GLP_BS) col->stat = GLP_NS; break; default: xerror("glp_set_col_bnds: j = %d; type = %d; invalid column" " type\n", j, type); } return; } /*********************************************************************** * NAME * * glp_set_obj_coef - set (change) obj. coefficient or constant term * * SYNOPSIS * * void glp_set_obj_coef(glp_prob *lp, int j, double coef); * * DESCRIPTION * * The routine glp_set_obj_coef sets (changes) objective coefficient at * j-th column (structural variable) of the specified problem object. * * If the parameter j is 0, the routine sets (changes) the constant term * ("shift") of the objective function. */ void glp_set_obj_coef(glp_prob *lp, int j, double coef) { glp_tree *tree = lp->tree; if (tree != NULL && tree->reason != 0) xerror("glp_set_obj_coef: operation not allowed\n"); if (!(0 <= j && j <= lp->n)) xerror("glp_set_obj_coef: j = %d; column number out of range\n" , j); if (j == 0) lp->c0 = coef; else lp->col[j]->coef = coef; return; } /*********************************************************************** * NAME * * glp_set_mat_row - set (replace) row of the constraint matrix * * SYNOPSIS * * void glp_set_mat_row(glp_prob *lp, int i, int len, const int ind[], * const double val[]); * * DESCRIPTION * * The routine glp_set_mat_row stores (replaces) the contents of i-th * row of the constraint matrix of the specified problem object. * * Column indices and numeric values of new row elements must be placed * in locations ind[1], ..., ind[len] and val[1], ..., val[len], where * 0 <= len <= n is the new length of i-th row, n is the current number * of columns in the problem object. Elements with identical column * indices are not allowed. Zero elements are allowed, but they are not * stored in the constraint matrix. * * If the parameter len is zero, the parameters ind and/or val can be * specified as NULL. */ void glp_set_mat_row(glp_prob *lp, int i, int len, const int ind[], const double val[]) { glp_tree *tree = lp->tree; GLPROW *row; GLPCOL *col; GLPAIJ *aij, *next; int j, k; /* obtain pointer to i-th row */ if (!(1 <= i && i <= lp->m)) xerror("glp_set_mat_row: i = %d; row number out of range\n", i); row = lp->row[i]; if (tree != NULL && tree->reason != 0) { xassert(tree->curr != NULL); xassert(row->level == tree->curr->level); } /* remove all existing elements from i-th row */ while (row->ptr != NULL) { /* take next element in the row */ aij = row->ptr; /* remove the element from the row list */ row->ptr = aij->r_next; /* obtain pointer to corresponding column */ col = aij->col; /* remove the element from the column list */ if (aij->c_prev == NULL) col->ptr = aij->c_next; else aij->c_prev->c_next = aij->c_next; if (aij->c_next == NULL) ; else aij->c_next->c_prev = aij->c_prev; /* return the element to the memory pool */ dmp_free_atom(lp->pool, aij, sizeof(GLPAIJ)), lp->nnz--; /* if the corresponding column is basic, invalidate the basis factorization */ if (col->stat == GLP_BS) lp->valid = 0; } /* store new contents of i-th row */ if (!(0 <= len && len <= lp->n)) xerror("glp_set_mat_row: i = %d; len = %d; invalid row length " "\n", i, len); if (len > NNZ_MAX - lp->nnz) xerror("glp_set_mat_row: i = %d; len = %d; too many constraint" " coefficients\n", i, len); for (k = 1; k <= len; k++) { /* take number j of corresponding column */ j = ind[k]; /* obtain pointer to j-th column */ if (!(1 <= j && j <= lp->n)) xerror("glp_set_mat_row: i = %d; ind[%d] = %d; column index" " out of range\n", i, k, j); col = lp->col[j]; /* if there is element with the same column index, it can only be found in the beginning of j-th column list */ if (col->ptr != NULL && col->ptr->row->i == i) xerror("glp_set_mat_row: i = %d; ind[%d] = %d; duplicate co" "lumn indices not allowed\n", i, k, j); /* create new element */ aij = dmp_get_atom(lp->pool, sizeof(GLPAIJ)), lp->nnz++; aij->row = row; aij->col = col; aij->val = val[k]; /* add the new element to the beginning of i-th row and j-th column lists */ aij->r_prev = NULL; aij->r_next = row->ptr; aij->c_prev = NULL; aij->c_next = col->ptr; if (aij->r_next != NULL) aij->r_next->r_prev = aij; if (aij->c_next != NULL) aij->c_next->c_prev = aij; row->ptr = col->ptr = aij; /* if the corresponding column is basic, invalidate the basis factorization */ if (col->stat == GLP_BS && aij->val != 0.0) lp->valid = 0; } /* remove zero elements from i-th row */ for (aij = row->ptr; aij != NULL; aij = next) { next = aij->r_next; if (aij->val == 0.0) { /* remove the element from the row list */ if (aij->r_prev == NULL) row->ptr = next; else aij->r_prev->r_next = next; if (next == NULL) ; else next->r_prev = aij->r_prev; /* remove the element from the column list */ xassert(aij->c_prev == NULL); aij->col->ptr = aij->c_next; if (aij->c_next != NULL) aij->c_next->c_prev = NULL; /* return the element to the memory pool */ dmp_free_atom(lp->pool, aij, sizeof(GLPAIJ)), lp->nnz--; } } return; } /*********************************************************************** * NAME * * glp_set_mat_col - set (replace) column of the constraint matrix * * SYNOPSIS * * void glp_set_mat_col(glp_prob *lp, int j, int len, const int ind[], * const double val[]); * * DESCRIPTION * * The routine glp_set_mat_col stores (replaces) the contents of j-th * column of the constraint matrix of the specified problem object. * * Row indices and numeric values of new column elements must be placed * in locations ind[1], ..., ind[len] and val[1], ..., val[len], where * 0 <= len <= m is the new length of j-th column, m is the current * number of rows in the problem object. Elements with identical column * indices are not allowed. Zero elements are allowed, but they are not * stored in the constraint matrix. * * If the parameter len is zero, the parameters ind and/or val can be * specified as NULL. */ void glp_set_mat_col(glp_prob *lp, int j, int len, const int ind[], const double val[]) { glp_tree *tree = lp->tree; GLPROW *row; GLPCOL *col; GLPAIJ *aij, *next; int i, k; if (tree != NULL && tree->reason != 0) xerror("glp_set_mat_col: operation not allowed\n"); /* obtain pointer to j-th column */ if (!(1 <= j && j <= lp->n)) xerror("glp_set_mat_col: j = %d; column number out of range\n", j); col = lp->col[j]; /* remove all existing elements from j-th column */ while (col->ptr != NULL) { /* take next element in the column */ aij = col->ptr; /* remove the element from the column list */ col->ptr = aij->c_next; /* obtain pointer to corresponding row */ row = aij->row; /* remove the element from the row list */ if (aij->r_prev == NULL) row->ptr = aij->r_next; else aij->r_prev->r_next = aij->r_next; if (aij->r_next == NULL) ; else aij->r_next->r_prev = aij->r_prev; /* return the element to the memory pool */ dmp_free_atom(lp->pool, aij, sizeof(GLPAIJ)), lp->nnz--; } /* store new contents of j-th column */ if (!(0 <= len && len <= lp->m)) xerror("glp_set_mat_col: j = %d; len = %d; invalid column leng" "th\n", j, len); if (len > NNZ_MAX - lp->nnz) xerror("glp_set_mat_col: j = %d; len = %d; too many constraint" " coefficients\n", j, len); for (k = 1; k <= len; k++) { /* take number i of corresponding row */ i = ind[k]; /* obtain pointer to i-th row */ if (!(1 <= i && i <= lp->m)) xerror("glp_set_mat_col: j = %d; ind[%d] = %d; row index ou" "t of range\n", j, k, i); row = lp->row[i]; /* if there is element with the same row index, it can only be found in the beginning of i-th row list */ if (row->ptr != NULL && row->ptr->col->j == j) xerror("glp_set_mat_col: j = %d; ind[%d] = %d; duplicate ro" "w indices not allowed\n", j, k, i); /* create new element */ aij = dmp_get_atom(lp->pool, sizeof(GLPAIJ)), lp->nnz++; aij->row = row; aij->col = col; aij->val = val[k]; /* add the new element to the beginning of i-th row and j-th column lists */ aij->r_prev = NULL; aij->r_next = row->ptr; aij->c_prev = NULL; aij->c_next = col->ptr; if (aij->r_next != NULL) aij->r_next->r_prev = aij; if (aij->c_next != NULL) aij->c_next->c_prev = aij; row->ptr = col->ptr = aij; } /* remove zero elements from j-th column */ for (aij = col->ptr; aij != NULL; aij = next) { next = aij->c_next; if (aij->val == 0.0) { /* remove the element from the row list */ xassert(aij->r_prev == NULL); aij->row->ptr = aij->r_next; if (aij->r_next != NULL) aij->r_next->r_prev = NULL; /* remove the element from the column list */ if (aij->c_prev == NULL) col->ptr = next; else aij->c_prev->c_next = next; if (next == NULL) ; else next->c_prev = aij->c_prev; /* return the element to the memory pool */ dmp_free_atom(lp->pool, aij, sizeof(GLPAIJ)), lp->nnz--; } } /* if j-th column is basic, invalidate the basis factorization */ if (col->stat == GLP_BS) lp->valid = 0; return; } /*********************************************************************** * NAME * * glp_load_matrix - load (replace) the whole constraint matrix * * SYNOPSIS * * void glp_load_matrix(glp_prob *lp, int ne, const int ia[], * const int ja[], const double ar[]); * * DESCRIPTION * * The routine glp_load_matrix loads the constraint matrix passed in * the arrays ia, ja, and ar into the specified problem object. Before * loading the current contents of the constraint matrix is destroyed. * * Constraint coefficients (elements of the constraint matrix) must be * specified as triplets (ia[k], ja[k], ar[k]) for k = 1, ..., ne, * where ia[k] is the row index, ja[k] is the column index, ar[k] is a * numeric value of corresponding constraint coefficient. The parameter * ne specifies the total number of (non-zero) elements in the matrix * to be loaded. Coefficients with identical indices are not allowed. * Zero coefficients are allowed, however, they are not stored in the * constraint matrix. * * If the parameter ne is zero, the parameters ia, ja, and ar can be * specified as NULL. */ void glp_load_matrix(glp_prob *lp, int ne, const int ia[], const int ja[], const double ar[]) { glp_tree *tree = lp->tree; GLPROW *row; GLPCOL *col; GLPAIJ *aij, *next; int i, j, k; if (tree != NULL && tree->reason != 0) xerror("glp_load_matrix: operation not allowed\n"); /* clear the constraint matrix */ for (i = 1; i <= lp->m; i++) { row = lp->row[i]; while (row->ptr != NULL) { aij = row->ptr; row->ptr = aij->r_next; dmp_free_atom(lp->pool, aij, sizeof(GLPAIJ)), lp->nnz--; } } xassert(lp->nnz == 0); for (j = 1; j <= lp->n; j++) lp->col[j]->ptr = NULL; /* load the new contents of the constraint matrix and build its row lists */ if (ne < 0) xerror("glp_load_matrix: ne = %d; invalid number of constraint" " coefficients\n", ne); if (ne > NNZ_MAX) xerror("glp_load_matrix: ne = %d; too many constraint coeffici" "ents\n", ne); for (k = 1; k <= ne; k++) { /* take indices of new element */ i = ia[k], j = ja[k]; /* obtain pointer to i-th row */ if (!(1 <= i && i <= lp->m)) xerror("glp_load_matrix: ia[%d] = %d; row index out of rang" "e\n", k, i); row = lp->row[i]; /* obtain pointer to j-th column */ if (!(1 <= j && j <= lp->n)) xerror("glp_load_matrix: ja[%d] = %d; column index out of r" "ange\n", k, j); col = lp->col[j]; /* create new element */ aij = dmp_get_atom(lp->pool, sizeof(GLPAIJ)), lp->nnz++; aij->row = row; aij->col = col; aij->val = ar[k]; /* add the new element to the beginning of i-th row list */ aij->r_prev = NULL; aij->r_next = row->ptr; if (aij->r_next != NULL) aij->r_next->r_prev = aij; row->ptr = aij; } xassert(lp->nnz == ne); /* build column lists of the constraint matrix and check elements with identical indices */ for (i = 1; i <= lp->m; i++) { for (aij = lp->row[i]->ptr; aij != NULL; aij = aij->r_next) { /* obtain pointer to corresponding column */ col = aij->col; /* if there is element with identical indices, it can only be found in the beginning of j-th column list */ if (col->ptr != NULL && col->ptr->row->i == i) { for (k = 1; k <= ne; k++) if (ia[k] == i && ja[k] == col->j) break; xerror("glp_load_mat: ia[%d] = %d; ja[%d] = %d; duplicat" "e indices not allowed\n", k, i, k, col->j); } /* add the element to the beginning of j-th column list */ aij->c_prev = NULL; aij->c_next = col->ptr; if (aij->c_next != NULL) aij->c_next->c_prev = aij; col->ptr = aij; } } /* remove zero elements from the constraint matrix */ for (i = 1; i <= lp->m; i++) { row = lp->row[i]; for (aij = row->ptr; aij != NULL; aij = next) { next = aij->r_next; if (aij->val == 0.0) { /* remove the element from the row list */ if (aij->r_prev == NULL) row->ptr = next; else aij->r_prev->r_next = next; if (next == NULL) ; else next->r_prev = aij->r_prev; /* remove the element from the column list */ if (aij->c_prev == NULL) aij->col->ptr = aij->c_next; else aij->c_prev->c_next = aij->c_next; if (aij->c_next == NULL) ; else aij->c_next->c_prev = aij->c_prev; /* return the element to the memory pool */ dmp_free_atom(lp->pool, aij, sizeof(GLPAIJ)), lp->nnz--; } } } /* invalidate the basis factorization */ lp->valid = 0; return; } /*********************************************************************** * NAME * * glp_check_dup - check for duplicate elements in sparse matrix * * SYNOPSIS * * int glp_check_dup(int m, int n, int ne, const int ia[], * const int ja[]); * * DESCRIPTION * * The routine glp_check_dup checks for duplicate elements (that is, * elements with identical indices) in a sparse matrix specified in the * coordinate format. * * The parameters m and n specifies, respectively, the number of rows * and columns in the matrix, m >= 0, n >= 0. * * The parameter ne specifies the number of (structurally) non-zero * elements in the matrix, ne >= 0. * * Elements of the matrix are specified as doublets (ia[k],ja[k]) for * k = 1,...,ne, where ia[k] is a row index, ja[k] is a column index. * * The routine glp_check_dup can be used prior to a call to the routine * glp_load_matrix to check that the constraint matrix to be loaded has * no duplicate elements. * * RETURNS * * The routine glp_check_dup returns one of the following values: * * 0 - the matrix has no duplicate elements; * * -k - indices ia[k] or/and ja[k] are out of range; * * +k - element (ia[k],ja[k]) is duplicate. */ int glp_check_dup(int m, int n, int ne, const int ia[], const int ja[]) { int i, j, k, *ptr, *next, ret; char *flag; if (m < 0) xerror("glp_check_dup: m = %d; invalid parameter\n"); if (n < 0) xerror("glp_check_dup: n = %d; invalid parameter\n"); if (ne < 0) xerror("glp_check_dup: ne = %d; invalid parameter\n"); if (ne > 0 && ia == NULL) xerror("glp_check_dup: ia = %p; invalid parameter\n", ia); if (ne > 0 && ja == NULL) xerror("glp_check_dup: ja = %p; invalid parameter\n", ja); for (k = 1; k <= ne; k++) { i = ia[k], j = ja[k]; if (!(1 <= i && i <= m && 1 <= j && j <= n)) { ret = -k; goto done; } } if (m == 0 || n == 0) { ret = 0; goto done; } /* allocate working arrays */ ptr = xcalloc(1+m, sizeof(int)); next = xcalloc(1+ne, sizeof(int)); flag = xcalloc(1+n, sizeof(char)); /* build row lists */ for (i = 1; i <= m; i++) ptr[i] = 0; for (k = 1; k <= ne; k++) { i = ia[k]; next[k] = ptr[i]; ptr[i] = k; } /* clear column flags */ for (j = 1; j <= n; j++) flag[j] = 0; /* check for duplicate elements */ for (i = 1; i <= m; i++) { for (k = ptr[i]; k != 0; k = next[k]) { j = ja[k]; if (flag[j]) { /* find first element (i,j) */ for (k = 1; k <= ne; k++) if (ia[k] == i && ja[k] == j) break; xassert(k <= ne); /* find next (duplicate) element (i,j) */ for (k++; k <= ne; k++) if (ia[k] == i && ja[k] == j) break; xassert(k <= ne); ret = +k; goto skip; } flag[j] = 1; } /* clear column flags */ for (k = ptr[i]; k != 0; k = next[k]) flag[ja[k]] = 0; } /* no duplicate element found */ ret = 0; skip: /* free working arrays */ xfree(ptr); xfree(next); xfree(flag); done: return ret; } /*********************************************************************** * NAME * * glp_sort_matrix - sort elements of the constraint matrix * * SYNOPSIS * * void glp_sort_matrix(glp_prob *P); * * DESCRIPTION * * The routine glp_sort_matrix sorts elements of the constraint matrix * rebuilding its row and column linked lists. On exit from the routine * the constraint matrix is not changed, however, elements in the row * linked lists become ordered by ascending column indices, and the * elements in the column linked lists become ordered by ascending row * indices. */ void glp_sort_matrix(glp_prob *P) { GLPAIJ *aij; int i, j; if (P == NULL || P->magic != GLP_PROB_MAGIC) xerror("glp_sort_matrix: P = %p; invalid problem object\n", P); /* rebuild row linked lists */ for (i = P->m; i >= 1; i--) P->row[i]->ptr = NULL; for (j = P->n; j >= 1; j--) { for (aij = P->col[j]->ptr; aij != NULL; aij = aij->c_next) { i = aij->row->i; aij->r_prev = NULL; aij->r_next = P->row[i]->ptr; if (aij->r_next != NULL) aij->r_next->r_prev = aij; P->row[i]->ptr = aij; } } /* rebuild column linked lists */ for (j = P->n; j >= 1; j--) P->col[j]->ptr = NULL; for (i = P->m; i >= 1; i--) { for (aij = P->row[i]->ptr; aij != NULL; aij = aij->r_next) { j = aij->col->j; aij->c_prev = NULL; aij->c_next = P->col[j]->ptr; if (aij->c_next != NULL) aij->c_next->c_prev = aij; P->col[j]->ptr = aij; } } return; } /*********************************************************************** * NAME * * glp_del_rows - delete rows from problem object * * SYNOPSIS * * void glp_del_rows(glp_prob *lp, int nrs, const int num[]); * * DESCRIPTION * * The routine glp_del_rows deletes rows from the specified problem * object. Ordinal numbers of rows to be deleted should be placed in * locations num[1], ..., num[nrs], where nrs > 0. * * Note that deleting rows involves changing ordinal numbers of other * rows remaining in the problem object. New ordinal numbers of the * remaining rows are assigned under the assumption that the original * order of rows is not changed. */ void glp_del_rows(glp_prob *lp, int nrs, const int num[]) { glp_tree *tree = lp->tree; GLPROW *row; int i, k, m_new; /* mark rows to be deleted */ if (!(1 <= nrs && nrs <= lp->m)) xerror("glp_del_rows: nrs = %d; invalid number of rows\n", nrs); for (k = 1; k <= nrs; k++) { /* take the number of row to be deleted */ i = num[k]; /* obtain pointer to i-th row */ if (!(1 <= i && i <= lp->m)) xerror("glp_del_rows: num[%d] = %d; row number out of range" "\n", k, i); row = lp->row[i]; if (tree != NULL && tree->reason != 0) { if (!(tree->reason == GLP_IROWGEN || tree->reason == GLP_ICUTGEN)) xerror("glp_del_rows: operation not allowed\n"); xassert(tree->curr != NULL); if (row->level != tree->curr->level) xerror("glp_del_rows: num[%d] = %d; invalid attempt to d" "elete row created not in current subproblem\n", k,i); if (row->stat != GLP_BS) xerror("glp_del_rows: num[%d] = %d; invalid attempt to d" "elete active row (constraint)\n", k, i); tree->reinv = 1; } /* check that the row is not marked yet */ if (row->i == 0) xerror("glp_del_rows: num[%d] = %d; duplicate row numbers n" "ot allowed\n", k, i); /* erase symbolic name assigned to the row */ glp_set_row_name(lp, i, NULL); xassert(row->node == NULL); /* erase corresponding row of the constraint matrix */ glp_set_mat_row(lp, i, 0, NULL, NULL); xassert(row->ptr == NULL); /* mark the row to be deleted */ row->i = 0; } /* delete all marked rows from the row list */ m_new = 0; for (i = 1; i <= lp->m; i++) { /* obtain pointer to i-th row */ row = lp->row[i]; /* check if the row is marked */ if (row->i == 0) { /* it is marked, delete it */ dmp_free_atom(lp->pool, row, sizeof(GLPROW)); } else { /* it is not marked; keep it */ row->i = ++m_new; lp->row[row->i] = row; } } /* set new number of rows */ lp->m = m_new; /* invalidate the basis factorization */ lp->valid = 0; return; } /*********************************************************************** * NAME * * glp_del_cols - delete columns from problem object * * SYNOPSIS * * void glp_del_cols(glp_prob *lp, int ncs, const int num[]); * * DESCRIPTION * * The routine glp_del_cols deletes columns from the specified problem * object. Ordinal numbers of columns to be deleted should be placed in * locations num[1], ..., num[ncs], where ncs > 0. * * Note that deleting columns involves changing ordinal numbers of * other columns remaining in the problem object. New ordinal numbers * of the remaining columns are assigned under the assumption that the * original order of columns is not changed. */ void glp_del_cols(glp_prob *lp, int ncs, const int num[]) { glp_tree *tree = lp->tree; GLPCOL *col; int j, k, n_new; if (tree != NULL && tree->reason != 0) xerror("glp_del_cols: operation not allowed\n"); /* mark columns to be deleted */ if (!(1 <= ncs && ncs <= lp->n)) xerror("glp_del_cols: ncs = %d; invalid number of columns\n", ncs); for (k = 1; k <= ncs; k++) { /* take the number of column to be deleted */ j = num[k]; /* obtain pointer to j-th column */ if (!(1 <= j && j <= lp->n)) xerror("glp_del_cols: num[%d] = %d; column number out of ra" "nge", k, j); col = lp->col[j]; /* check that the column is not marked yet */ if (col->j == 0) xerror("glp_del_cols: num[%d] = %d; duplicate column number" "s not allowed\n", k, j); /* erase symbolic name assigned to the column */ glp_set_col_name(lp, j, NULL); xassert(col->node == NULL); /* erase corresponding column of the constraint matrix */ glp_set_mat_col(lp, j, 0, NULL, NULL); xassert(col->ptr == NULL); /* mark the column to be deleted */ col->j = 0; /* if it is basic, invalidate the basis factorization */ if (col->stat == GLP_BS) lp->valid = 0; } /* delete all marked columns from the column list */ n_new = 0; for (j = 1; j <= lp->n; j++) { /* obtain pointer to j-th column */ col = lp->col[j]; /* check if the column is marked */ if (col->j == 0) { /* it is marked; delete it */ dmp_free_atom(lp->pool, col, sizeof(GLPCOL)); } else { /* it is not marked; keep it */ col->j = ++n_new; lp->col[col->j] = col; } } /* set new number of columns */ lp->n = n_new; /* if the basis header is still valid, adjust it */ if (lp->valid) { int m = lp->m; int *head = lp->head; for (j = 1; j <= n_new; j++) { k = lp->col[j]->bind; if (k != 0) { xassert(1 <= k && k <= m); head[k] = m + j; } } } return; } /*********************************************************************** * NAME * * glp_copy_prob - copy problem object content * * SYNOPSIS * * void glp_copy_prob(glp_prob *dest, glp_prob *prob, int names); * * DESCRIPTION * * The routine glp_copy_prob copies the content of the problem object * prob to the problem object dest. * * The parameter names is a flag. If it is non-zero, the routine also * copies all symbolic names; otherwise, if it is zero, symbolic names * are not copied. */ void glp_copy_prob(glp_prob *dest, glp_prob *prob, int names) { glp_tree *tree = dest->tree; glp_bfcp bfcp; int i, j, len, *ind; double *val; if (tree != NULL && tree->reason != 0) xerror("glp_copy_prob: operation not allowed\n"); if (dest == prob) xerror("glp_copy_prob: copying problem object to itself not al" "lowed\n"); if (!(names == GLP_ON || names == GLP_OFF)) xerror("glp_copy_prob: names = %d; invalid parameter\n", names); glp_erase_prob(dest); if (names && prob->name != NULL) glp_set_prob_name(dest, prob->name); if (names && prob->obj != NULL) glp_set_obj_name(dest, prob->obj); dest->dir = prob->dir; dest->c0 = prob->c0; if (prob->m > 0) glp_add_rows(dest, prob->m); if (prob->n > 0) glp_add_cols(dest, prob->n); glp_get_bfcp(prob, &bfcp); glp_set_bfcp(dest, &bfcp); dest->pbs_stat = prob->pbs_stat; dest->dbs_stat = prob->dbs_stat; dest->obj_val = prob->obj_val; dest->some = prob->some; dest->ipt_stat = prob->ipt_stat; dest->ipt_obj = prob->ipt_obj; dest->mip_stat = prob->mip_stat; dest->mip_obj = prob->mip_obj; for (i = 1; i <= prob->m; i++) { GLPROW *to = dest->row[i]; GLPROW *from = prob->row[i]; if (names && from->name != NULL) glp_set_row_name(dest, i, from->name); to->type = from->type; to->lb = from->lb; to->ub = from->ub; to->rii = from->rii; to->stat = from->stat; to->prim = from->prim; to->dual = from->dual; to->pval = from->pval; to->dval = from->dval; to->mipx = from->mipx; } ind = xcalloc(1+prob->m, sizeof(int)); val = xcalloc(1+prob->m, sizeof(double)); for (j = 1; j <= prob->n; j++) { GLPCOL *to = dest->col[j]; GLPCOL *from = prob->col[j]; if (names && from->name != NULL) glp_set_col_name(dest, j, from->name); to->kind = from->kind; to->type = from->type; to->lb = from->lb; to->ub = from->ub; to->coef = from->coef; len = glp_get_mat_col(prob, j, ind, val); glp_set_mat_col(dest, j, len, ind, val); to->sjj = from->sjj; to->stat = from->stat; to->prim = from->prim; to->dual = from->dual; to->pval = from->pval; to->dval = from->dval; to->mipx = from->mipx; } xfree(ind); xfree(val); return; } /*********************************************************************** * NAME * * glp_erase_prob - erase problem object content * * SYNOPSIS * * void glp_erase_prob(glp_prob *lp); * * DESCRIPTION * * The routine glp_erase_prob erases the content of the specified * problem object. The effect of this operation is the same as if the * problem object would be deleted with the routine glp_delete_prob and * then created anew with the routine glp_create_prob, with exception * that the handle (pointer) to the problem object remains valid. */ static void delete_prob(glp_prob *lp); void glp_erase_prob(glp_prob *lp) { glp_tree *tree = lp->tree; if (tree != NULL && tree->reason != 0) xerror("glp_erase_prob: operation not allowed\n"); delete_prob(lp); create_prob(lp); return; } /*********************************************************************** * NAME * * glp_delete_prob - delete problem object * * SYNOPSIS * * void glp_delete_prob(glp_prob *lp); * * DESCRIPTION * * The routine glp_delete_prob deletes the specified problem object and * frees all the memory allocated to it. */ static void delete_prob(glp_prob *lp) { lp->magic = 0x3F3F3F3F; dmp_delete_pool(lp->pool); #if 0 /* 17/XI-2009 */ xfree(lp->cps); #else if (lp->parms != NULL) xfree(lp->parms); #endif xassert(lp->tree == NULL); #if 0 if (lp->cwa != NULL) xfree(lp->cwa); #endif xfree(lp->row); xfree(lp->col); if (lp->r_tree != NULL) avl_delete_tree(lp->r_tree); if (lp->c_tree != NULL) avl_delete_tree(lp->c_tree); xfree(lp->head); if (lp->bfcp != NULL) xfree(lp->bfcp); if (lp->bfd != NULL) bfd_delete_it(lp->bfd); return; } void glp_delete_prob(glp_prob *lp) { glp_tree *tree = lp->tree; if (tree != NULL && tree->reason != 0) xerror("glp_delete_prob: operation not allowed\n"); delete_prob(lp); xfree(lp); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpios.h0000644000175000017500000005357411632352620020123 0ustar csheltoncshelton/* glpios.h (integer optimization suite) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPIOS_H #define GLPIOS_H #define GLP_TREE_DEFINED typedef struct glp_tree glp_tree; #include "glpapi.h" typedef struct IOSLOT IOSLOT; typedef struct IOSNPD IOSNPD; typedef struct IOSBND IOSBND; typedef struct IOSTAT IOSTAT; typedef struct IOSROW IOSROW; typedef struct IOSAIJ IOSAIJ; typedef struct IOSPOOL IOSPOOL; typedef struct IOSCUT IOSCUT; struct glp_tree { /* branch-and-bound tree */ int magic; /* magic value used for debugging */ DMP *pool; /* memory pool to store all IOS components */ int n; /* number of columns (variables) */ /*--------------------------------------------------------------*/ /* problem components corresponding to the original MIP and its LP relaxation (used to restore the original problem object on exit from the solver) */ int orig_m; /* number of rows */ unsigned char *orig_type; /* uchar orig_type[1+orig_m+n]; */ /* types of all variables */ double *orig_lb; /* double orig_lb[1+orig_m+n]; */ /* lower bounds of all variables */ double *orig_ub; /* double orig_ub[1+orig_m+n]; */ /* upper bounds of all variables */ unsigned char *orig_stat; /* uchar orig_stat[1+orig_m+n]; */ /* statuses of all variables */ double *orig_prim; /* double orig_prim[1+orig_m+n]; */ /* primal values of all variables */ double *orig_dual; /* double orig_dual[1+orig_m+n]; */ /* dual values of all variables */ double orig_obj; /* optimal objective value for LP relaxation */ /*--------------------------------------------------------------*/ /* branch-and-bound tree */ int nslots; /* length of the array of slots (enlarged automatically) */ int avail; /* index of the first free slot; 0 means all slots are in use */ IOSLOT *slot; /* IOSLOT slot[1+nslots]; */ /* array of slots: slot[0] is not used; slot[p], 1 <= p <= nslots, either contains a pointer to some node of the branch-and-bound tree, in which case p is used on API level as the reference number of corresponding subproblem, or is free; all free slots are linked into single linked list; slot[1] always contains a pointer to the root node (it is free only if the tree is empty) */ IOSNPD *head; /* pointer to the head of the active list */ IOSNPD *tail; /* pointer to the tail of the active list */ /* the active list is a doubly linked list of active subproblems which correspond to leaves of the tree; all subproblems in the active list are ordered chronologically (each a new subproblem is always added to the tail of the list) */ int a_cnt; /* current number of active nodes (including the current one) */ int n_cnt; /* current number of all (active and inactive) nodes */ int t_cnt; /* total number of nodes including those which have been already removed from the tree; this count is increased by one whenever a new node is created and never decreased */ /*--------------------------------------------------------------*/ /* problem components corresponding to the root subproblem */ int root_m; /* number of rows */ unsigned char *root_type; /* uchar root_type[1+root_m+n]; */ /* types of all variables */ double *root_lb; /* double root_lb[1+root_m+n]; */ /* lower bounds of all variables */ double *root_ub; /* double root_ub[1+root_m+n]; */ /* upper bounds of all variables */ unsigned char *root_stat; /* uchar root_stat[1+root_m+n]; */ /* statuses of all variables */ /*--------------------------------------------------------------*/ /* current subproblem and its LP relaxation */ IOSNPD *curr; /* pointer to the current subproblem (which can be only active); NULL means the current subproblem does not exist */ glp_prob *mip; /* original problem object passed to the solver; if the current subproblem exists, its LP segment corresponds to LP relaxation of the current subproblem; if the current subproblem does not exist, its LP segment corresponds to LP relaxation of the root subproblem (note that the root subproblem may differ from the original MIP, because it may be preprocessed and/or may have additional rows) */ unsigned char *non_int; /* uchar non_int[1+n]; */ /* these column flags are set each time when LP relaxation of the current subproblem has been solved; non_int[0] is not used; non_int[j], 1 <= j <= n, is j-th column flag; if this flag is set, corresponding variable is required to be integer, but its value in basic solution is fractional */ /*--------------------------------------------------------------*/ /* problem components corresponding to the parent (predecessor) subproblem for the current subproblem; used to inspect changes on freezing the current subproblem */ int pred_m; /* number of rows */ int pred_max; /* length of the following four arrays (enlarged automatically), pred_max >= pred_m + n */ unsigned char *pred_type; /* uchar pred_type[1+pred_m+n]; */ /* types of all variables */ double *pred_lb; /* double pred_lb[1+pred_m+n]; */ /* lower bounds of all variables */ double *pred_ub; /* double pred_ub[1+pred_m+n]; */ /* upper bounds of all variables */ unsigned char *pred_stat; /* uchar pred_stat[1+pred_m+n]; */ /* statuses of all variables */ /****************************************************************/ /* built-in cut generators segment */ IOSPOOL *local; /* local cut pool */ void *mir_gen; /* pointer to working area used by the MIR cut generator */ void *clq_gen; /* pointer to working area used by the clique cut generator */ /*--------------------------------------------------------------*/ void *pcost; /* pointer to working area used on pseudocost branching */ int *iwrk; /* int iwrk[1+n]; */ /* working array */ double *dwrk; /* double dwrk[1+n]; */ /* working array */ /*--------------------------------------------------------------*/ /* control parameters and statistics */ const glp_iocp *parm; /* copy of control parameters passed to the solver */ glp_long tm_beg; /* starting time of the search, in seconds; the total time of the search is the difference between xtime() and tm_beg */ glp_long tm_lag; /* the most recent time, in seconds, at which the progress of the the search was displayed */ int sol_cnt; /* number of integer feasible solutions found */ /*--------------------------------------------------------------*/ /* advanced solver interface */ int reason; /* flag indicating the reason why the callback routine is being called (see glpk.h) */ int stop; /* flag indicating that the callback routine requires premature termination of the search */ int next_p; /* reference number of active subproblem selected to continue the search; 0 means no subproblem has been selected */ int reopt; /* flag indicating that the current LP relaxation needs to be re-optimized */ int reinv; /* flag indicating that some (non-active) rows were removed from the current LP relaxation, so if there no new rows appear, the basis must be re-factorized */ int br_var; /* the number of variable chosen to branch on */ int br_sel; /* flag indicating which branch (subproblem) is suggested to be selected to continue the search: GLP_DN_BRNCH - select down-branch GLP_UP_BRNCH - select up-branch GLP_NO_BRNCH - use general selection technique */ int child; /* subproblem reference number corresponding to br_sel */ }; struct IOSLOT { /* node subproblem slot */ IOSNPD *node; /* pointer to subproblem descriptor; NULL means free slot */ int next; /* index of another free slot (only if this slot is free) */ }; struct IOSNPD { /* node subproblem descriptor */ int p; /* subproblem reference number (it is the index to corresponding slot, i.e. slot[p] points to this descriptor) */ IOSNPD *up; /* pointer to the parent subproblem; NULL means this node is the root of the tree, in which case p = 1 */ int level; /* node level (the root node has level 0) */ int count; /* if count = 0, this subproblem is active; if count > 0, this subproblem is inactive, in which case count is the number of its child subproblems */ /* the following three linked lists are destroyed on reviving and built anew on freezing the subproblem: */ IOSBND *b_ptr; /* linked list of rows and columns of the parent subproblem whose types and bounds were changed */ IOSTAT *s_ptr; /* linked list of rows and columns of the parent subproblem whose statuses were changed */ IOSROW *r_ptr; /* linked list of rows (cuts) added to the parent subproblem */ int solved; /* how many times LP relaxation of this subproblem was solved; for inactive subproblem this count is always non-zero; for active subproblem, which is not current, this count may be non-zero, if the subproblem was temporarily suspended */ double lp_obj; /* optimal objective value to LP relaxation of this subproblem; on creating a subproblem this value is inherited from its parent; for the root subproblem, which has no parent, this value is initially set to -DBL_MAX (minimization) or +DBL_MAX (maximization); each time the subproblem is re-optimized, this value is appropriately changed */ double bound; /* local lower (minimization) or upper (maximization) bound for integer optimal solution to *this* subproblem; this bound is local in the sense that only subproblems in the subtree rooted at this node cannot have better integer feasible solutions; on creating a subproblem its local bound is inherited from its parent and then can be made stronger (never weaker); for the root subproblem its local bound is initially set to -DBL_MAX (minimization) or +DBL_MAX (maximization) and then improved as the root LP relaxation has been solved */ /* the following two quantities are defined only if LP relaxation of this subproblem was solved at least once (solved > 0): */ int ii_cnt; /* number of integer variables whose value in optimal solution to LP relaxation of this subproblem is fractional */ double ii_sum; /* sum of integer infeasibilities */ #if 1 /* 30/XI-2009 */ int changed; /* how many times this subproblem was re-formulated (by adding cutting plane constraints) */ #endif int br_var; /* ordinal number of branching variable, 1 <= br_var <= n, used to split this subproblem; 0 means that either this subproblem is active or branching was made on a constraint */ double br_val; /* (fractional) value of branching variable in optimal solution to final LP relaxation of this subproblem */ void *data; /* char data[tree->cb_size]; */ /* pointer to the application-specific data */ IOSNPD *temp; /* working pointer used by some routines */ IOSNPD *prev; /* pointer to previous subproblem in the active list */ IOSNPD *next; /* pointer to next subproblem in the active list */ }; struct IOSBND { /* bounds change entry */ int k; /* ordinal number of corresponding row (1 <= k <= m) or column (m+1 <= k <= m+n), where m and n are the number of rows and columns, resp., in the parent subproblem */ unsigned char type; /* new type */ double lb; /* new lower bound */ double ub; /* new upper bound */ IOSBND *next; /* pointer to next entry for the same subproblem */ }; struct IOSTAT { /* status change entry */ int k; /* ordinal number of corresponding row (1 <= k <= m) or column (m+1 <= k <= m+n), where m and n are the number of rows and columns, resp., in the parent subproblem */ unsigned char stat; /* new status */ IOSTAT *next; /* pointer to next entry for the same subproblem */ }; struct IOSROW { /* row (constraint) addition entry */ char *name; /* row name or NULL */ unsigned char origin; /* row origin flag (see glp_attr.origin) */ unsigned char klass; /* row class descriptor (see glp_attr.klass) */ unsigned char type; /* row type (GLP_LO, GLP_UP, etc.) */ double lb; /* row lower bound */ double ub; /* row upper bound */ IOSAIJ *ptr; /* pointer to the row coefficient list */ double rii; /* row scale factor */ unsigned char stat; /* row status (GLP_BS, GLP_NL, etc.) */ IOSROW *next; /* pointer to next entry for the same subproblem */ }; struct IOSAIJ { /* constraint coefficient */ int j; /* variable (column) number, 1 <= j <= n */ double val; /* non-zero coefficient value */ IOSAIJ *next; /* pointer to next coefficient for the same row */ }; struct IOSPOOL { /* cut pool */ int size; /* pool size = number of cuts in the pool */ IOSCUT *head; /* pointer to the first cut */ IOSCUT *tail; /* pointer to the last cut */ int ord; /* ordinal number of the current cut, 1 <= ord <= size */ IOSCUT *curr; /* pointer to the current cut */ }; struct IOSCUT { /* cut (cutting plane constraint) */ char *name; /* cut name or NULL */ unsigned char klass; /* cut class descriptor (see glp_attr.klass) */ IOSAIJ *ptr; /* pointer to the cut coefficient list */ unsigned char type; /* cut type: GLP_LO: sum a[j] * x[j] >= b GLP_UP: sum a[j] * x[j] <= b GLP_FX: sum a[j] * x[j] = b */ double rhs; /* cut right-hand side */ IOSCUT *prev; /* pointer to previous cut */ IOSCUT *next; /* pointer to next cut */ }; #define ios_create_tree _glp_ios_create_tree glp_tree *ios_create_tree(glp_prob *mip, const glp_iocp *parm); /* create branch-and-bound tree */ #define ios_revive_node _glp_ios_revive_node void ios_revive_node(glp_tree *tree, int p); /* revive specified subproblem */ #define ios_freeze_node _glp_ios_freeze_node void ios_freeze_node(glp_tree *tree); /* freeze current subproblem */ #define ios_clone_node _glp_ios_clone_node void ios_clone_node(glp_tree *tree, int p, int nnn, int ref[]); /* clone specified subproblem */ #define ios_delete_node _glp_ios_delete_node void ios_delete_node(glp_tree *tree, int p); /* delete specified subproblem */ #define ios_delete_tree _glp_ios_delete_tree void ios_delete_tree(glp_tree *tree); /* delete branch-and-bound tree */ #define ios_eval_degrad _glp_ios_eval_degrad void ios_eval_degrad(glp_tree *tree, int j, double *dn, double *up); /* estimate obj. degrad. for down- and up-branches */ #define ios_round_bound _glp_ios_round_bound double ios_round_bound(glp_tree *tree, double bound); /* improve local bound by rounding */ #define ios_is_hopeful _glp_ios_is_hopeful int ios_is_hopeful(glp_tree *tree, double bound); /* check if subproblem is hopeful */ #define ios_best_node _glp_ios_best_node int ios_best_node(glp_tree *tree); /* find active node with best local bound */ #define ios_relative_gap _glp_ios_relative_gap double ios_relative_gap(glp_tree *tree); /* compute relative mip gap */ #define ios_solve_node _glp_ios_solve_node int ios_solve_node(glp_tree *tree); /* solve LP relaxation of current subproblem */ #define ios_create_pool _glp_ios_create_pool IOSPOOL *ios_create_pool(glp_tree *tree); /* create cut pool */ #define ios_add_row _glp_ios_add_row int ios_add_row(glp_tree *tree, IOSPOOL *pool, const char *name, int klass, int flags, int len, const int ind[], const double val[], int type, double rhs); /* add row (constraint) to the cut pool */ #define ios_find_row _glp_ios_find_row IOSCUT *ios_find_row(IOSPOOL *pool, int i); /* find row (constraint) in the cut pool */ #define ios_del_row _glp_ios_del_row void ios_del_row(glp_tree *tree, IOSPOOL *pool, int i); /* remove row (constraint) from the cut pool */ #define ios_clear_pool _glp_ios_clear_pool void ios_clear_pool(glp_tree *tree, IOSPOOL *pool); /* remove all rows (constraints) from the cut pool */ #define ios_delete_pool _glp_ios_delete_pool void ios_delete_pool(glp_tree *tree, IOSPOOL *pool); /* delete cut pool */ #define ios_preprocess_node _glp_ios_preprocess_node int ios_preprocess_node(glp_tree *tree, int max_pass); /* preprocess current subproblem */ #define ios_driver _glp_ios_driver int ios_driver(glp_tree *tree); /* branch-and-bound driver */ /**********************************************************************/ typedef struct IOSVEC IOSVEC; struct IOSVEC { /* sparse vector v = (v[j]) */ int n; /* dimension, n >= 0 */ int nnz; /* number of non-zero components, 0 <= nnz <= n */ int *pos; /* int pos[1+n]; */ /* pos[j] = k, 1 <= j <= n, is position of (non-zero) v[j] in the arrays ind and val, where 1 <= k <= nnz; pos[j] = 0 means that v[j] is structural zero */ int *ind; /* int ind[1+n]; */ /* ind[k] = j, 1 <= k <= nnz, is index of v[j] */ double *val; /* double val[1+n]; */ /* val[k], 1 <= k <= nnz, is a numeric value of v[j] */ }; #define ios_create_vec _glp_ios_create_vec IOSVEC *ios_create_vec(int n); /* create sparse vector */ #define ios_check_vec _glp_ios_check_vec void ios_check_vec(IOSVEC *v); /* check that sparse vector has correct representation */ #define ios_get_vj _glp_ios_get_vj double ios_get_vj(IOSVEC *v, int j); /* retrieve component of sparse vector */ #define ios_set_vj _glp_ios_set_vj void ios_set_vj(IOSVEC *v, int j, double val); /* set/change component of sparse vector */ #define ios_clear_vec _glp_ios_clear_vec void ios_clear_vec(IOSVEC *v); /* set all components of sparse vector to zero */ #define ios_clean_vec _glp_ios_clean_vec void ios_clean_vec(IOSVEC *v, double eps); /* remove zero or small components from sparse vector */ #define ios_copy_vec _glp_ios_copy_vec void ios_copy_vec(IOSVEC *x, IOSVEC *y); /* copy sparse vector (x := y) */ #define ios_linear_comb _glp_ios_linear_comb void ios_linear_comb(IOSVEC *x, double a, IOSVEC *y); /* compute linear combination (x := x + a * y) */ #define ios_delete_vec _glp_ios_delete_vec void ios_delete_vec(IOSVEC *v); /* delete sparse vector */ /**********************************************************************/ #define ios_gmi_gen _glp_ios_gmi_gen void ios_gmi_gen(glp_tree *tree); /* generate Gomory's mixed integer cuts */ #define ios_mir_init _glp_ios_mir_init void *ios_mir_init(glp_tree *tree); /* initialize MIR cut generator */ #define ios_mir_gen _glp_ios_mir_gen void ios_mir_gen(glp_tree *tree, void *gen); /* generate MIR cuts */ #define ios_mir_term _glp_ios_mir_term void ios_mir_term(void *gen); /* terminate MIR cut generator */ #define ios_cov_gen _glp_ios_cov_gen void ios_cov_gen(glp_tree *tree); /* generate mixed cover cuts */ #define ios_clq_init _glp_ios_clq_init void *ios_clq_init(glp_tree *tree); /* initialize clique cut generator */ #define ios_clq_gen _glp_ios_clq_gen void ios_clq_gen(glp_tree *tree, void *gen); /* generate clique cuts */ #define ios_clq_term _glp_ios_clq_term void ios_clq_term(void *gen); /* terminate clique cut generator */ #define ios_pcost_init _glp_ios_pcost_init void *ios_pcost_init(glp_tree *tree); /* initialize working data used on pseudocost branching */ #define ios_pcost_branch _glp_ios_pcost_branch int ios_pcost_branch(glp_tree *T, int *next); /* choose branching variable with pseudocost branching */ #define ios_pcost_update _glp_ios_pcost_update void ios_pcost_update(glp_tree *tree); /* update history information for pseudocost branching */ #define ios_pcost_free _glp_ios_pcost_free void ios_pcost_free(glp_tree *tree); /* free working area used on pseudocost branching */ #define ios_feas_pump _glp_ios_feas_pump void ios_feas_pump(glp_tree *T); /* feasibility pump heuristic */ #define ios_process_cuts _glp_ios_process_cuts void ios_process_cuts(glp_tree *T); /* process cuts stored in the local cut pool */ #define ios_choose_node _glp_ios_choose_node int ios_choose_node(glp_tree *T); /* select subproblem to continue the search */ #define ios_choose_var _glp_ios_choose_var int ios_choose_var(glp_tree *T, int *next); /* select variable to branch on */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpapi07.c0000644000175000017500000003505011632352620020231 0ustar csheltoncshelton/* glpapi07.c (exact simplex solver) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" #include "glpssx.h" /*********************************************************************** * NAME * * glp_exact - solve LP problem in exact arithmetic * * SYNOPSIS * * int glp_exact(glp_prob *lp, const glp_smcp *parm); * * DESCRIPTION * * The routine glp_exact is a tentative implementation of the primal * two-phase simplex method based on exact (rational) arithmetic. It is * similar to the routine glp_simplex, however, for all internal * computations it uses arithmetic of rational numbers, which is exact * in mathematical sense, i.e. free of round-off errors unlike floating * point arithmetic. * * Note that the routine glp_exact uses inly two control parameters * passed in the structure glp_smcp, namely, it_lim and tm_lim. * * RETURNS * * 0 The LP problem instance has been successfully solved. This code * does not necessarily mean that the solver has found optimal * solution. It only means that the solution process was successful. * * GLP_EBADB * Unable to start the search, because the initial basis specified * in the problem object is invalid--the number of basic (auxiliary * and structural) variables is not the same as the number of rows in * the problem object. * * GLP_ESING * Unable to start the search, because the basis matrix correspodning * to the initial basis is exactly singular. * * GLP_EBOUND * Unable to start the search, because some double-bounded variables * have incorrect bounds. * * GLP_EFAIL * The problem has no rows/columns. * * GLP_EITLIM * The search was prematurely terminated, because the simplex * iteration limit has been exceeded. * * GLP_ETMLIM * The search was prematurely terminated, because the time limit has * been exceeded. */ static void set_d_eps(mpq_t x, double val) { /* convert double val to rational x obtaining a more adequate fraction than provided by mpq_set_d due to allowing a small approximation error specified by a given relative tolerance; for example, mpq_set_d would give the following 1/3 ~= 0.333333333333333314829616256247391... -> -> 6004799503160661/18014398509481984 while this routine gives exactly 1/3 */ int s, n, j; double f, p, q, eps = 1e-9; mpq_t temp; xassert(-DBL_MAX <= val && val <= +DBL_MAX); #if 1 /* 30/VII-2008 */ if (val == floor(val)) { /* if val is integral, do not approximate */ mpq_set_d(x, val); goto done; } #endif if (val > 0.0) s = +1; else if (val < 0.0) s = -1; else { mpq_set_si(x, 0, 1); goto done; } f = frexp(fabs(val), &n); /* |val| = f * 2^n, where 0.5 <= f < 1.0 */ fp2rat(f, 0.1 * eps, &p, &q); /* f ~= p / q, where p and q are integers */ mpq_init(temp); mpq_set_d(x, p); mpq_set_d(temp, q); mpq_div(x, x, temp); mpq_set_si(temp, 1, 1); for (j = 1; j <= abs(n); j++) mpq_add(temp, temp, temp); if (n > 0) mpq_mul(x, x, temp); else if (n < 0) mpq_div(x, x, temp); mpq_clear(temp); if (s < 0) mpq_neg(x, x); /* check that the desired tolerance has been attained */ xassert(fabs(val - mpq_get_d(x)) <= eps * (1.0 + fabs(val))); done: return; } static void load_data(SSX *ssx, LPX *lp) { /* load LP problem data into simplex solver workspace */ int m = ssx->m; int n = ssx->n; int nnz = ssx->A_ptr[n+1]-1; int j, k, type, loc, len, *ind; double lb, ub, coef, *val; xassert(lpx_get_num_rows(lp) == m); xassert(lpx_get_num_cols(lp) == n); xassert(lpx_get_num_nz(lp) == nnz); /* types and bounds of rows and columns */ for (k = 1; k <= m+n; k++) { if (k <= m) { type = lpx_get_row_type(lp, k); lb = lpx_get_row_lb(lp, k); ub = lpx_get_row_ub(lp, k); } else { type = lpx_get_col_type(lp, k-m); lb = lpx_get_col_lb(lp, k-m); ub = lpx_get_col_ub(lp, k-m); } switch (type) { case LPX_FR: type = SSX_FR; break; case LPX_LO: type = SSX_LO; break; case LPX_UP: type = SSX_UP; break; case LPX_DB: type = SSX_DB; break; case LPX_FX: type = SSX_FX; break; default: xassert(type != type); } ssx->type[k] = type; set_d_eps(ssx->lb[k], lb); set_d_eps(ssx->ub[k], ub); } /* optimization direction */ switch (lpx_get_obj_dir(lp)) { case LPX_MIN: ssx->dir = SSX_MIN; break; case LPX_MAX: ssx->dir = SSX_MAX; break; default: xassert(lp != lp); } /* objective coefficients */ for (k = 0; k <= m+n; k++) { if (k == 0) coef = lpx_get_obj_coef(lp, 0); else if (k <= m) coef = 0.0; else coef = lpx_get_obj_coef(lp, k-m); set_d_eps(ssx->coef[k], coef); } /* constraint coefficients */ ind = xcalloc(1+m, sizeof(int)); val = xcalloc(1+m, sizeof(double)); loc = 0; for (j = 1; j <= n; j++) { ssx->A_ptr[j] = loc+1; len = lpx_get_mat_col(lp, j, ind, val); for (k = 1; k <= len; k++) { loc++; ssx->A_ind[loc] = ind[k]; set_d_eps(ssx->A_val[loc], val[k]); } } xassert(loc == nnz); xfree(ind); xfree(val); return; } static int load_basis(SSX *ssx, LPX *lp) { /* load current LP basis into simplex solver workspace */ int m = ssx->m; int n = ssx->n; int *type = ssx->type; int *stat = ssx->stat; int *Q_row = ssx->Q_row; int *Q_col = ssx->Q_col; int i, j, k; xassert(lpx_get_num_rows(lp) == m); xassert(lpx_get_num_cols(lp) == n); /* statuses of rows and columns */ for (k = 1; k <= m+n; k++) { if (k <= m) stat[k] = lpx_get_row_stat(lp, k); else stat[k] = lpx_get_col_stat(lp, k-m); switch (stat[k]) { case LPX_BS: stat[k] = SSX_BS; break; case LPX_NL: stat[k] = SSX_NL; xassert(type[k] == SSX_LO || type[k] == SSX_DB); break; case LPX_NU: stat[k] = SSX_NU; xassert(type[k] == SSX_UP || type[k] == SSX_DB); break; case LPX_NF: stat[k] = SSX_NF; xassert(type[k] == SSX_FR); break; case LPX_NS: stat[k] = SSX_NS; xassert(type[k] == SSX_FX); break; default: xassert(stat != stat); } } /* build permutation matix Q */ i = j = 0; for (k = 1; k <= m+n; k++) { if (stat[k] == SSX_BS) { i++; if (i > m) return 1; Q_row[k] = i, Q_col[i] = k; } else { j++; if (j > n) return 1; Q_row[k] = m+j, Q_col[m+j] = k; } } xassert(i == m && j == n); return 0; } int glp_exact(glp_prob *lp, const glp_smcp *parm) { glp_smcp _parm; SSX *ssx; int m = lpx_get_num_rows(lp); int n = lpx_get_num_cols(lp); int nnz = lpx_get_num_nz(lp); int i, j, k, type, pst, dst, ret, *stat; double lb, ub, *prim, *dual, sum; if (parm == NULL) parm = &_parm, glp_init_smcp((glp_smcp *)parm); /* check control parameters */ if (parm->it_lim < 0) xerror("glp_exact: it_lim = %d; invalid parameter\n", parm->it_lim); if (parm->tm_lim < 0) xerror("glp_exact: tm_lim = %d; invalid parameter\n", parm->tm_lim); /* the problem must have at least one row and one column */ if (!(m > 0 && n > 0)) { xprintf("glp_exact: problem has no rows/columns\n"); return GLP_EFAIL; } #if 1 /* basic solution is currently undefined */ lp->pbs_stat = lp->dbs_stat = GLP_UNDEF; lp->obj_val = 0.0; lp->some = 0; #endif /* check that all double-bounded variables have correct bounds */ for (k = 1; k <= m+n; k++) { if (k <= m) { type = lpx_get_row_type(lp, k); lb = lpx_get_row_lb(lp, k); ub = lpx_get_row_ub(lp, k); } else { type = lpx_get_col_type(lp, k-m); lb = lpx_get_col_lb(lp, k-m); ub = lpx_get_col_ub(lp, k-m); } if (type == LPX_DB && lb >= ub) { xprintf("glp_exact: %s %d has invalid bounds\n", k <= m ? "row" : "column", k <= m ? k : k-m); return GLP_EBOUND; } } /* create the simplex solver workspace */ xprintf("glp_exact: %d rows, %d columns, %d non-zeros\n", m, n, nnz); #ifdef HAVE_GMP xprintf("GNU MP bignum library is being used\n"); #else xprintf("GLPK bignum module is being used\n"); xprintf("(Consider installing GNU MP to attain a much better perf" "ormance.)\n"); #endif ssx = ssx_create(m, n, nnz); /* load LP problem data into the workspace */ load_data(ssx, lp); /* load current LP basis into the workspace */ if (load_basis(ssx, lp)) { xprintf("glp_exact: initial LP basis is invalid\n"); ret = GLP_EBADB; goto done; } /* inherit some control parameters from the LP object */ #if 0 ssx->it_lim = lpx_get_int_parm(lp, LPX_K_ITLIM); ssx->it_cnt = lpx_get_int_parm(lp, LPX_K_ITCNT); ssx->tm_lim = lpx_get_real_parm(lp, LPX_K_TMLIM); #else ssx->it_lim = parm->it_lim; ssx->it_cnt = lp->it_cnt; ssx->tm_lim = (double)parm->tm_lim / 1000.0; #endif ssx->out_frq = 5.0; ssx->tm_beg = xtime(); ssx->tm_lag = xlset(0); /* solve LP */ ret = ssx_driver(ssx); /* copy back some statistics to the LP object */ #if 0 lpx_set_int_parm(lp, LPX_K_ITLIM, ssx->it_lim); lpx_set_int_parm(lp, LPX_K_ITCNT, ssx->it_cnt); lpx_set_real_parm(lp, LPX_K_TMLIM, ssx->tm_lim); #else lp->it_cnt = ssx->it_cnt; #endif /* analyze the return code */ switch (ret) { case 0: /* optimal solution found */ ret = 0; pst = LPX_P_FEAS, dst = LPX_D_FEAS; break; case 1: /* problem has no feasible solution */ ret = 0; pst = LPX_P_NOFEAS, dst = LPX_D_INFEAS; break; case 2: /* problem has unbounded solution */ ret = 0; pst = LPX_P_FEAS, dst = LPX_D_NOFEAS; #if 1 xassert(1 <= ssx->q && ssx->q <= n); lp->some = ssx->Q_col[m + ssx->q]; xassert(1 <= lp->some && lp->some <= m+n); #endif break; case 3: /* iteration limit exceeded (phase I) */ ret = GLP_EITLIM; pst = LPX_P_INFEAS, dst = LPX_D_INFEAS; break; case 4: /* iteration limit exceeded (phase II) */ ret = GLP_EITLIM; pst = LPX_P_FEAS, dst = LPX_D_INFEAS; break; case 5: /* time limit exceeded (phase I) */ ret = GLP_ETMLIM; pst = LPX_P_INFEAS, dst = LPX_D_INFEAS; break; case 6: /* time limit exceeded (phase II) */ ret = GLP_ETMLIM; pst = LPX_P_FEAS, dst = LPX_D_INFEAS; break; case 7: /* initial basis matrix is singular */ ret = GLP_ESING; goto done; default: xassert(ret != ret); } /* obtain final basic solution components */ stat = xcalloc(1+m+n, sizeof(int)); prim = xcalloc(1+m+n, sizeof(double)); dual = xcalloc(1+m+n, sizeof(double)); for (k = 1; k <= m+n; k++) { if (ssx->stat[k] == SSX_BS) { i = ssx->Q_row[k]; /* x[k] = xB[i] */ xassert(1 <= i && i <= m); stat[k] = LPX_BS; prim[k] = mpq_get_d(ssx->bbar[i]); dual[k] = 0.0; } else { j = ssx->Q_row[k] - m; /* x[k] = xN[j] */ xassert(1 <= j && j <= n); switch (ssx->stat[k]) { case SSX_NF: stat[k] = LPX_NF; prim[k] = 0.0; break; case SSX_NL: stat[k] = LPX_NL; prim[k] = mpq_get_d(ssx->lb[k]); break; case SSX_NU: stat[k] = LPX_NU; prim[k] = mpq_get_d(ssx->ub[k]); break; case SSX_NS: stat[k] = LPX_NS; prim[k] = mpq_get_d(ssx->lb[k]); break; default: xassert(ssx != ssx); } dual[k] = mpq_get_d(ssx->cbar[j]); } } /* and store them into the LP object */ pst = pst - LPX_P_UNDEF + GLP_UNDEF; dst = dst - LPX_D_UNDEF + GLP_UNDEF; for (k = 1; k <= m+n; k++) stat[k] = stat[k] - LPX_BS + GLP_BS; sum = lpx_get_obj_coef(lp, 0); for (j = 1; j <= n; j++) sum += lpx_get_obj_coef(lp, j) * prim[m+j]; lpx_put_solution(lp, 1, &pst, &dst, &sum, &stat[0], &prim[0], &dual[0], &stat[m], &prim[m], &dual[m]); xfree(stat); xfree(prim); xfree(dual); done: /* delete the simplex solver workspace */ ssx_delete(ssx); /* return to the application program */ return ret; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpios02.c0000644000175000017500000006452511632352620020256 0ustar csheltoncshelton/* glpios02.c (preprocess current subproblem) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpios.h" /*********************************************************************** * prepare_row_info - prepare row info to determine implied bounds * * Given a row (linear form) * * n * sum a[j] * x[j] (1) * j=1 * * and bounds of columns (variables) * * l[j] <= x[j] <= u[j] (2) * * this routine computes f_min, j_min, f_max, j_max needed to determine * implied bounds. * * ALGORITHM * * Let J+ = {j : a[j] > 0} and J- = {j : a[j] < 0}. * * Parameters f_min and j_min are computed as follows: * * 1) if there is no x[k] such that k in J+ and l[k] = -inf or k in J- * and u[k] = +inf, then * * f_min := sum a[j] * l[j] + sum a[j] * u[j] * j in J+ j in J- * (3) * j_min := 0 * * 2) if there is exactly one x[k] such that k in J+ and l[k] = -inf * or k in J- and u[k] = +inf, then * * f_min := sum a[j] * l[j] + sum a[j] * u[j] * j in J+\{k} j in J-\{k} * (4) * j_min := k * * 3) if there are two or more x[k] such that k in J+ and l[k] = -inf * or k in J- and u[k] = +inf, then * * f_min := -inf * (5) * j_min := 0 * * Parameters f_max and j_max are computed in a similar way as follows: * * 1) if there is no x[k] such that k in J+ and u[k] = +inf or k in J- * and l[k] = -inf, then * * f_max := sum a[j] * u[j] + sum a[j] * l[j] * j in J+ j in J- * (6) * j_max := 0 * * 2) if there is exactly one x[k] such that k in J+ and u[k] = +inf * or k in J- and l[k] = -inf, then * * f_max := sum a[j] * u[j] + sum a[j] * l[j] * j in J+\{k} j in J-\{k} * (7) * j_max := k * * 3) if there are two or more x[k] such that k in J+ and u[k] = +inf * or k in J- and l[k] = -inf, then * * f_max := +inf * (8) * j_max := 0 */ struct f_info { int j_min, j_max; double f_min, f_max; }; static void prepare_row_info(int n, const double a[], const double l[], const double u[], struct f_info *f) { int j, j_min, j_max; double f_min, f_max; xassert(n >= 0); /* determine f_min and j_min */ f_min = 0.0, j_min = 0; for (j = 1; j <= n; j++) { if (a[j] > 0.0) { if (l[j] == -DBL_MAX) { if (j_min == 0) j_min = j; else { f_min = -DBL_MAX, j_min = 0; break; } } else f_min += a[j] * l[j]; } else if (a[j] < 0.0) { if (u[j] == +DBL_MAX) { if (j_min == 0) j_min = j; else { f_min = -DBL_MAX, j_min = 0; break; } } else f_min += a[j] * u[j]; } else xassert(a != a); } f->f_min = f_min, f->j_min = j_min; /* determine f_max and j_max */ f_max = 0.0, j_max = 0; for (j = 1; j <= n; j++) { if (a[j] > 0.0) { if (u[j] == +DBL_MAX) { if (j_max == 0) j_max = j; else { f_max = +DBL_MAX, j_max = 0; break; } } else f_max += a[j] * u[j]; } else if (a[j] < 0.0) { if (l[j] == -DBL_MAX) { if (j_max == 0) j_max = j; else { f_max = +DBL_MAX, j_max = 0; break; } } else f_max += a[j] * l[j]; } else xassert(a != a); } f->f_max = f_max, f->j_max = j_max; return; } /*********************************************************************** * row_implied_bounds - determine row implied bounds * * Given a row (linear form) * * n * sum a[j] * x[j] * j=1 * * and bounds of columns (variables) * * l[j] <= x[j] <= u[j] * * this routine determines implied bounds of the row. * * ALGORITHM * * Let J+ = {j : a[j] > 0} and J- = {j : a[j] < 0}. * * The implied lower bound of the row is computed as follows: * * L' := sum a[j] * l[j] + sum a[j] * u[j] (9) * j in J+ j in J- * * and as it follows from (3), (4), and (5): * * L' := if j_min = 0 then f_min else -inf (10) * * The implied upper bound of the row is computed as follows: * * U' := sum a[j] * u[j] + sum a[j] * l[j] (11) * j in J+ j in J- * * and as it follows from (6), (7), and (8): * * U' := if j_max = 0 then f_max else +inf (12) * * The implied bounds are stored in locations LL and UU. */ static void row_implied_bounds(const struct f_info *f, double *LL, double *UU) { *LL = (f->j_min == 0 ? f->f_min : -DBL_MAX); *UU = (f->j_max == 0 ? f->f_max : +DBL_MAX); return; } /*********************************************************************** * col_implied_bounds - determine column implied bounds * * Given a row (constraint) * * n * L <= sum a[j] * x[j] <= U (13) * j=1 * * and bounds of columns (variables) * * l[j] <= x[j] <= u[j] * * this routine determines implied bounds of variable x[k]. * * It is assumed that if L != -inf, the lower bound of the row can be * active, and if U != +inf, the upper bound of the row can be active. * * ALGORITHM * * From (13) it follows that * * L <= sum a[j] * x[j] + a[k] * x[k] <= U * j!=k * or * * L - sum a[j] * x[j] <= a[k] * x[k] <= U - sum a[j] * x[j] * j!=k j!=k * * Thus, if the row lower bound L can be active, implied lower bound of * term a[k] * x[k] can be determined as follows: * * ilb(a[k] * x[k]) = min(L - sum a[j] * x[j]) = * j!=k * (14) * = L - max sum a[j] * x[j] * j!=k * * where, as it follows from (6), (7), and (8) * * / f_max - a[k] * u[k], j_max = 0, a[k] > 0 * | * | f_max - a[k] * l[k], j_max = 0, a[k] < 0 * max sum a[j] * x[j] = { * j!=k | f_max, j_max = k * | * \ +inf, j_max != 0 * * and if the upper bound U can be active, implied upper bound of term * a[k] * x[k] can be determined as follows: * * iub(a[k] * x[k]) = max(U - sum a[j] * x[j]) = * j!=k * (15) * = U - min sum a[j] * x[j] * j!=k * * where, as it follows from (3), (4), and (5) * * / f_min - a[k] * l[k], j_min = 0, a[k] > 0 * | * | f_min - a[k] * u[k], j_min = 0, a[k] < 0 * min sum a[j] * x[j] = { * j!=k | f_min, j_min = k * | * \ -inf, j_min != 0 * * Since * * ilb(a[k] * x[k]) <= a[k] * x[k] <= iub(a[k] * x[k]) * * implied lower and upper bounds of x[k] are determined as follows: * * l'[k] := if a[k] > 0 then ilb / a[k] else ulb / a[k] (16) * * u'[k] := if a[k] > 0 then ulb / a[k] else ilb / a[k] (17) * * The implied bounds are stored in locations ll and uu. */ static void col_implied_bounds(const struct f_info *f, int n, const double a[], double L, double U, const double l[], const double u[], int k, double *ll, double *uu) { double ilb, iub; xassert(n >= 0); xassert(1 <= k && k <= n); /* determine implied lower bound of term a[k] * x[k] (14) */ if (L == -DBL_MAX || f->f_max == +DBL_MAX) ilb = -DBL_MAX; else if (f->j_max == 0) { if (a[k] > 0.0) { xassert(u[k] != +DBL_MAX); ilb = L - (f->f_max - a[k] * u[k]); } else if (a[k] < 0.0) { xassert(l[k] != -DBL_MAX); ilb = L - (f->f_max - a[k] * l[k]); } else xassert(a != a); } else if (f->j_max == k) ilb = L - f->f_max; else ilb = -DBL_MAX; /* determine implied upper bound of term a[k] * x[k] (15) */ if (U == +DBL_MAX || f->f_min == -DBL_MAX) iub = +DBL_MAX; else if (f->j_min == 0) { if (a[k] > 0.0) { xassert(l[k] != -DBL_MAX); iub = U - (f->f_min - a[k] * l[k]); } else if (a[k] < 0.0) { xassert(u[k] != +DBL_MAX); iub = U - (f->f_min - a[k] * u[k]); } else xassert(a != a); } else if (f->j_min == k) iub = U - f->f_min; else iub = +DBL_MAX; /* determine implied bounds of x[k] (16) and (17) */ #if 1 /* do not use a[k] if it has small magnitude to prevent wrong implied bounds; for example, 1e-15 * x1 >= x2 + x3, where x1 >= -10, x2, x3 >= 0, would lead to wrong conclusion that x1 >= 0 */ if (fabs(a[k]) < 1e-6) *ll = -DBL_MAX, *uu = +DBL_MAX; else #endif if (a[k] > 0.0) { *ll = (ilb == -DBL_MAX ? -DBL_MAX : ilb / a[k]); *uu = (iub == +DBL_MAX ? +DBL_MAX : iub / a[k]); } else if (a[k] < 0.0) { *ll = (iub == +DBL_MAX ? -DBL_MAX : iub / a[k]); *uu = (ilb == -DBL_MAX ? +DBL_MAX : ilb / a[k]); } else xassert(a != a); return; } /*********************************************************************** * check_row_bounds - check and relax original row bounds * * Given a row (constraint) * * n * L <= sum a[j] * x[j] <= U * j=1 * * and bounds of columns (variables) * * l[j] <= x[j] <= u[j] * * this routine checks the original row bounds L and U for feasibility * and redundancy. If the original lower bound L or/and upper bound U * cannot be active due to bounds of variables, the routine remove them * replacing by -inf or/and +inf, respectively. * * If no primal infeasibility is detected, the routine returns zero, * otherwise non-zero. */ static int check_row_bounds(const struct f_info *f, double *L_, double *U_) { int ret = 0; double L = *L_, U = *U_, LL, UU; /* determine implied bounds of the row */ row_implied_bounds(f, &LL, &UU); /* check if the original lower bound is infeasible */ if (L != -DBL_MAX) { double eps = 1e-3 * (1.0 + fabs(L)); if (UU < L - eps) { ret = 1; goto done; } } /* check if the original upper bound is infeasible */ if (U != +DBL_MAX) { double eps = 1e-3 * (1.0 + fabs(U)); if (LL > U + eps) { ret = 1; goto done; } } /* check if the original lower bound is redundant */ if (L != -DBL_MAX) { double eps = 1e-12 * (1.0 + fabs(L)); if (LL > L - eps) { /* it cannot be active, so remove it */ *L_ = -DBL_MAX; } } /* check if the original upper bound is redundant */ if (U != +DBL_MAX) { double eps = 1e-12 * (1.0 + fabs(U)); if (UU < U + eps) { /* it cannot be active, so remove it */ *U_ = +DBL_MAX; } } done: return ret; } /*********************************************************************** * check_col_bounds - check and tighten original column bounds * * Given a row (constraint) * * n * L <= sum a[j] * x[j] <= U * j=1 * * and bounds of columns (variables) * * l[j] <= x[j] <= u[j] * * for column (variable) x[j] this routine checks the original column * bounds l[j] and u[j] for feasibility and redundancy. If the original * lower bound l[j] or/and upper bound u[j] cannot be active due to * bounds of the constraint and other variables, the routine tighten * them replacing by corresponding implied bounds, if possible. * * NOTE: It is assumed that if L != -inf, the row lower bound can be * active, and if U != +inf, the row upper bound can be active. * * The flag means that variable x[j] is required to be integer. * * New actual bounds for x[j] are stored in locations lj and uj. * * If no primal infeasibility is detected, the routine returns zero, * otherwise non-zero. */ static int check_col_bounds(const struct f_info *f, int n, const double a[], double L, double U, const double l[], const double u[], int flag, int j, double *_lj, double *_uj) { int ret = 0; double lj, uj, ll, uu; xassert(n >= 0); xassert(1 <= j && j <= n); lj = l[j], uj = u[j]; /* determine implied bounds of the column */ col_implied_bounds(f, n, a, L, U, l, u, j, &ll, &uu); /* if x[j] is integral, round its implied bounds */ if (flag) { if (ll != -DBL_MAX) ll = (ll - floor(ll) < 1e-3 ? floor(ll) : ceil(ll)); if (uu != +DBL_MAX) uu = (ceil(uu) - uu < 1e-3 ? ceil(uu) : floor(uu)); } /* check if the original lower bound is infeasible */ if (lj != -DBL_MAX) { double eps = 1e-3 * (1.0 + fabs(lj)); if (uu < lj - eps) { ret = 1; goto done; } } /* check if the original upper bound is infeasible */ if (uj != +DBL_MAX) { double eps = 1e-3 * (1.0 + fabs(uj)); if (ll > uj + eps) { ret = 1; goto done; } } /* check if the original lower bound is redundant */ if (ll != -DBL_MAX) { double eps = 1e-3 * (1.0 + fabs(ll)); if (lj < ll - eps) { /* it cannot be active, so tighten it */ lj = ll; } } /* check if the original upper bound is redundant */ if (uu != +DBL_MAX) { double eps = 1e-3 * (1.0 + fabs(uu)); if (uj > uu + eps) { /* it cannot be active, so tighten it */ uj = uu; } } /* due to round-off errors it may happen that lj > uj (although lj < uj + eps, since no primal infeasibility is detected), so adjuct the new actual bounds to provide lj <= uj */ if (!(lj == -DBL_MAX || uj == +DBL_MAX)) { double t1 = fabs(lj), t2 = fabs(uj); double eps = 1e-10 * (1.0 + (t1 <= t2 ? t1 : t2)); if (lj > uj - eps) { if (lj == l[j]) uj = lj; else if (uj == u[j]) lj = uj; else if (t1 <= t2) uj = lj; else lj = uj; } } *_lj = lj, *_uj = uj; done: return ret; } /*********************************************************************** * check_efficiency - check if change in column bounds is efficient * * Given the original bounds of a column l and u and its new actual * bounds l' and u' (possibly tighten by the routine check_col_bounds) * this routine checks if the change in the column bounds is efficient * enough. If so, the routine returns non-zero, otherwise zero. * * The flag means that the variable is required to be integer. */ static int check_efficiency(int flag, double l, double u, double ll, double uu) { int eff = 0; /* check efficiency for lower bound */ if (l < ll) { if (flag || l == -DBL_MAX) eff++; else { double r; if (u == +DBL_MAX) r = 1.0 + fabs(l); else r = 1.0 + (u - l); if (ll - l >= 0.25 * r) eff++; } } /* check efficiency for upper bound */ if (u > uu) { if (flag || u == +DBL_MAX) eff++; else { double r; if (l == -DBL_MAX) r = 1.0 + fabs(u); else r = 1.0 + (u - l); if (u - uu >= 0.25 * r) eff++; } } return eff; } /*********************************************************************** * basic_preprocessing - perform basic preprocessing * * This routine performs basic preprocessing of the specified MIP that * includes relaxing some row bounds and tightening some column bounds. * * On entry the arrays L and U contains original row bounds, and the * arrays l and u contains original column bounds: * * L[0] is the lower bound of the objective row; * L[i], i = 1,...,m, is the lower bound of i-th row; * U[0] is the upper bound of the objective row; * U[i], i = 1,...,m, is the upper bound of i-th row; * l[0] is not used; * l[j], j = 1,...,n, is the lower bound of j-th column; * u[0] is not used; * u[j], j = 1,...,n, is the upper bound of j-th column. * * On exit the arrays L, U, l, and u contain new actual bounds of rows * and column in the same locations. * * The parameters nrs and num specify an initial list of rows to be * processed: * * nrs is the number of rows in the initial list, 0 <= nrs <= m+1; * num[0] is not used; * num[1,...,nrs] are row numbers (0 means the objective row). * * The parameter max_pass specifies the maximal number of times that * each row can be processed, max_pass > 0. * * If no primal infeasibility is detected, the routine returns zero, * otherwise non-zero. */ static int basic_preprocessing(glp_prob *mip, double L[], double U[], double l[], double u[], int nrs, const int num[], int max_pass) { int m = mip->m; int n = mip->n; struct f_info f; int i, j, k, len, size, ret = 0; int *ind, *list, *mark, *pass; double *val, *lb, *ub; xassert(0 <= nrs && nrs <= m+1); xassert(max_pass > 0); /* allocate working arrays */ ind = xcalloc(1+n, sizeof(int)); list = xcalloc(1+m+1, sizeof(int)); mark = xcalloc(1+m+1, sizeof(int)); memset(&mark[0], 0, (m+1) * sizeof(int)); pass = xcalloc(1+m+1, sizeof(int)); memset(&pass[0], 0, (m+1) * sizeof(int)); val = xcalloc(1+n, sizeof(double)); lb = xcalloc(1+n, sizeof(double)); ub = xcalloc(1+n, sizeof(double)); /* initialize the list of rows to be processed */ size = 0; for (k = 1; k <= nrs; k++) { i = num[k]; xassert(0 <= i && i <= m); /* duplicate row numbers are not allowed */ xassert(!mark[i]); list[++size] = i, mark[i] = 1; } xassert(size == nrs); /* process rows in the list until it becomes empty */ while (size > 0) { /* get a next row from the list */ i = list[size--], mark[i] = 0; /* increase the row processing count */ pass[i]++; /* if the row is free, skip it */ if (L[i] == -DBL_MAX && U[i] == +DBL_MAX) continue; /* obtain coefficients of the row */ len = 0; if (i == 0) { for (j = 1; j <= n; j++) { GLPCOL *col = mip->col[j]; if (col->coef != 0.0) len++, ind[len] = j, val[len] = col->coef; } } else { GLPROW *row = mip->row[i]; GLPAIJ *aij; for (aij = row->ptr; aij != NULL; aij = aij->r_next) len++, ind[len] = aij->col->j, val[len] = aij->val; } /* determine lower and upper bounds of columns corresponding to non-zero row coefficients */ for (k = 1; k <= len; k++) j = ind[k], lb[k] = l[j], ub[k] = u[j]; /* prepare the row info to determine implied bounds */ prepare_row_info(len, val, lb, ub, &f); /* check and relax bounds of the row */ if (check_row_bounds(&f, &L[i], &U[i])) { /* the feasible region is empty */ ret = 1; goto done; } /* if the row became free, drop it */ if (L[i] == -DBL_MAX && U[i] == +DBL_MAX) continue; /* process columns having non-zero coefficients in the row */ for (k = 1; k <= len; k++) { GLPCOL *col; int flag, eff; double ll, uu; /* take a next column in the row */ j = ind[k], col = mip->col[j]; flag = col->kind != GLP_CV; /* check and tighten bounds of the column */ if (check_col_bounds(&f, len, val, L[i], U[i], lb, ub, flag, k, &ll, &uu)) { /* the feasible region is empty */ ret = 1; goto done; } /* check if change in the column bounds is efficient */ eff = check_efficiency(flag, l[j], u[j], ll, uu); /* set new actual bounds of the column */ l[j] = ll, u[j] = uu; /* if the change is efficient, add all rows affected by the corresponding column, to the list */ if (eff > 0) { GLPAIJ *aij; for (aij = col->ptr; aij != NULL; aij = aij->c_next) { int ii = aij->row->i; /* if the row was processed maximal number of times, skip it */ if (pass[ii] >= max_pass) continue; /* if the row is free, skip it */ if (L[ii] == -DBL_MAX && U[ii] == +DBL_MAX) continue; /* put the row into the list */ if (mark[ii] == 0) { xassert(size <= m); list[++size] = ii, mark[ii] = 1; } } } } } done: /* free working arrays */ xfree(ind); xfree(list); xfree(mark); xfree(pass); xfree(val); xfree(lb); xfree(ub); return ret; } /*********************************************************************** * NAME * * ios_preprocess_node - preprocess current subproblem * * SYNOPSIS * * #include "glpios.h" * int ios_preprocess_node(glp_tree *tree, int max_pass); * * DESCRIPTION * * The routine ios_preprocess_node performs basic preprocessing of the * current subproblem. * * RETURNS * * If no primal infeasibility is detected, the routine returns zero, * otherwise non-zero. */ int ios_preprocess_node(glp_tree *tree, int max_pass) { glp_prob *mip = tree->mip; int m = mip->m; int n = mip->n; int i, j, nrs, *num, ret = 0; double *L, *U, *l, *u; /* the current subproblem must exist */ xassert(tree->curr != NULL); /* determine original row bounds */ L = xcalloc(1+m, sizeof(double)); U = xcalloc(1+m, sizeof(double)); switch (mip->mip_stat) { case GLP_UNDEF: L[0] = -DBL_MAX, U[0] = +DBL_MAX; break; case GLP_FEAS: switch (mip->dir) { case GLP_MIN: L[0] = -DBL_MAX, U[0] = mip->mip_obj - mip->c0; break; case GLP_MAX: L[0] = mip->mip_obj - mip->c0, U[0] = +DBL_MAX; break; default: xassert(mip != mip); } break; default: xassert(mip != mip); } for (i = 1; i <= m; i++) { L[i] = glp_get_row_lb(mip, i); U[i] = glp_get_row_ub(mip, i); } /* determine original column bounds */ l = xcalloc(1+n, sizeof(double)); u = xcalloc(1+n, sizeof(double)); for (j = 1; j <= n; j++) { l[j] = glp_get_col_lb(mip, j); u[j] = glp_get_col_ub(mip, j); } /* build the initial list of rows to be analyzed */ nrs = m + 1; num = xcalloc(1+nrs, sizeof(int)); for (i = 1; i <= nrs; i++) num[i] = i - 1; /* perform basic preprocessing */ if (basic_preprocessing(mip , L, U, l, u, nrs, num, max_pass)) { ret = 1; goto done; } /* set new actual (relaxed) row bounds */ for (i = 1; i <= m; i++) { /* consider only non-active rows to keep dual feasibility */ if (glp_get_row_stat(mip, i) == GLP_BS) { if (L[i] == -DBL_MAX && U[i] == +DBL_MAX) glp_set_row_bnds(mip, i, GLP_FR, 0.0, 0.0); else if (U[i] == +DBL_MAX) glp_set_row_bnds(mip, i, GLP_LO, L[i], 0.0); else if (L[i] == -DBL_MAX) glp_set_row_bnds(mip, i, GLP_UP, 0.0, U[i]); } } /* set new actual (tightened) column bounds */ for (j = 1; j <= n; j++) { int type; if (l[j] == -DBL_MAX && u[j] == +DBL_MAX) type = GLP_FR; else if (u[j] == +DBL_MAX) type = GLP_LO; else if (l[j] == -DBL_MAX) type = GLP_UP; else if (l[j] != u[j]) type = GLP_DB; else type = GLP_FX; glp_set_col_bnds(mip, j, type, l[j], u[j]); } done: /* free working arrays and return */ xfree(L); xfree(U); xfree(l); xfree(u); xfree(num); return ret; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpapi10.c0000644000175000017500000002305111632352620020221 0ustar csheltoncshelton/* glpapi10.c (solution checking routines) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" void _glp_check_kkt(glp_prob *P, int sol, int cond, double *_ae_max, int *_ae_ind, double *_re_max, int *_re_ind) { /* check feasibility and optimality conditions */ int m = P->m; int n = P->n; GLPROW *row; GLPCOL *col; GLPAIJ *aij; int i, j, ae_ind, re_ind; double e, sp, sn, t, ae_max, re_max; if (!(sol == GLP_SOL || sol == GLP_IPT || sol == GLP_MIP)) xerror("glp_check_kkt: sol = %d; invalid solution indicator\n", sol); if (!(cond == GLP_KKT_PE || cond == GLP_KKT_PB || cond == GLP_KKT_DE || cond == GLP_KKT_DB || cond == GLP_KKT_CS)) xerror("glp_check_kkt: cond = %d; invalid condition indicator " "\n", cond); ae_max = re_max = 0.0; ae_ind = re_ind = 0; if (cond == GLP_KKT_PE) { /* xR - A * xS = 0 */ for (i = 1; i <= m; i++) { row = P->row[i]; sp = sn = 0.0; /* t := xR[i] */ if (sol == GLP_SOL) t = row->prim; else if (sol == GLP_IPT) t = row->pval; else if (sol == GLP_MIP) t = row->mipx; else xassert(sol != sol); if (t >= 0.0) sp += t; else sn -= t; for (aij = row->ptr; aij != NULL; aij = aij->r_next) { col = aij->col; /* t := - a[i,j] * xS[j] */ if (sol == GLP_SOL) t = - aij->val * col->prim; else if (sol == GLP_IPT) t = - aij->val * col->pval; else if (sol == GLP_MIP) t = - aij->val * col->mipx; else xassert(sol != sol); if (t >= 0.0) sp += t; else sn -= t; } /* absolute error */ e = fabs(sp - sn); if (ae_max < e) ae_max = e, ae_ind = i; /* relative error */ e /= (1.0 + sp + sn); if (re_max < e) re_max = e, re_ind = i; } } else if (cond == GLP_KKT_PB) { /* lR <= xR <= uR */ for (i = 1; i <= m; i++) { row = P->row[i]; /* t := xR[i] */ if (sol == GLP_SOL) t = row->prim; else if (sol == GLP_IPT) t = row->pval; else if (sol == GLP_MIP) t = row->mipx; else xassert(sol != sol); /* check lower bound */ if (row->type == GLP_LO || row->type == GLP_DB || row->type == GLP_FX) { if (t < row->lb) { /* absolute error */ e = row->lb - t; if (ae_max < e) ae_max = e, ae_ind = i; /* relative error */ e /= (1.0 + fabs(row->lb)); if (re_max < e) re_max = e, re_ind = i; } } /* check upper bound */ if (row->type == GLP_UP || row->type == GLP_DB || row->type == GLP_FX) { if (t > row->ub) { /* absolute error */ e = t - row->ub; if (ae_max < e) ae_max = e, ae_ind = i; /* relative error */ e /= (1.0 + fabs(row->ub)); if (re_max < e) re_max = e, re_ind = i; } } } /* lS <= xS <= uS */ for (j = 1; j <= n; j++) { col = P->col[j]; /* t := xS[j] */ if (sol == GLP_SOL) t = col->prim; else if (sol == GLP_IPT) t = col->pval; else if (sol == GLP_MIP) t = col->mipx; else xassert(sol != sol); /* check lower bound */ if (col->type == GLP_LO || col->type == GLP_DB || col->type == GLP_FX) { if (t < col->lb) { /* absolute error */ e = col->lb - t; if (ae_max < e) ae_max = e, ae_ind = m+j; /* relative error */ e /= (1.0 + fabs(col->lb)); if (re_max < e) re_max = e, re_ind = m+j; } } /* check upper bound */ if (col->type == GLP_UP || col->type == GLP_DB || col->type == GLP_FX) { if (t > col->ub) { /* absolute error */ e = t - col->ub; if (ae_max < e) ae_max = e, ae_ind = m+j; /* relative error */ e /= (1.0 + fabs(col->ub)); if (re_max < e) re_max = e, re_ind = m+j; } } } } else if (cond == GLP_KKT_DE) { /* A' * (lambdaR - cR) + (lambdaS - cS) = 0 */ for (j = 1; j <= n; j++) { col = P->col[j]; sp = sn = 0.0; /* t := lambdaS[j] - cS[j] */ if (sol == GLP_SOL) t = col->dual - col->coef; else if (sol == GLP_IPT) t = col->dval - col->coef; else xassert(sol != sol); if (t >= 0.0) sp += t; else sn -= t; for (aij = col->ptr; aij != NULL; aij = aij->c_next) { row = aij->row; /* t := a[i,j] * (lambdaR[i] - cR[i]) */ if (sol == GLP_SOL) t = aij->val * row->dual; else if (sol == GLP_IPT) t = aij->val * row->dval; else xassert(sol != sol); if (t >= 0.0) sp += t; else sn -= t; } /* absolute error */ e = fabs(sp - sn); if (ae_max < e) ae_max = e, ae_ind = m+j; /* relative error */ e /= (1.0 + sp + sn); if (re_max < e) re_max = e, re_ind = m+j; } } else if (cond == GLP_KKT_DB) { /* check lambdaR */ for (i = 1; i <= m; i++) { row = P->row[i]; /* t := lambdaR[i] */ if (sol == GLP_SOL) t = row->dual; else if (sol == GLP_IPT) t = row->dval; else xassert(sol != sol); /* correct sign */ if (P->dir == GLP_MIN) t = + t; else if (P->dir == GLP_MAX) t = - t; else xassert(P != P); /* check for positivity */ if (row->type == GLP_FR || row->type == GLP_LO) { if (t < 0.0) { e = - t; if (ae_max < e) ae_max = re_max = e, ae_ind = re_ind = i; } } /* check for negativity */ if (row->type == GLP_FR || row->type == GLP_UP) { if (t > 0.0) { e = + t; if (ae_max < e) ae_max = re_max = e, ae_ind = re_ind = i; } } } /* check lambdaS */ for (j = 1; j <= n; j++) { col = P->col[j]; /* t := lambdaS[j] */ if (sol == GLP_SOL) t = col->dual; else if (sol == GLP_IPT) t = col->dval; else xassert(sol != sol); /* correct sign */ if (P->dir == GLP_MIN) t = + t; else if (P->dir == GLP_MAX) t = - t; else xassert(P != P); /* check for positivity */ if (col->type == GLP_FR || col->type == GLP_LO) { if (t < 0.0) { e = - t; if (ae_max < e) ae_max = re_max = e, ae_ind = re_ind = m+j; } } /* check for negativity */ if (col->type == GLP_FR || col->type == GLP_UP) { if (t > 0.0) { e = + t; if (ae_max < e) ae_max = re_max = e, ae_ind = re_ind = m+j; } } } } else xassert(cond != cond); if (_ae_max != NULL) *_ae_max = ae_max; if (_ae_ind != NULL) *_ae_ind = ae_ind; if (_re_max != NULL) *_re_max = re_max; if (_re_ind != NULL) *_re_ind = re_ind; return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glplpf.c0000644000175000017500000007533611632352620020105 0ustar csheltoncshelton/* glplpf.c (LP basis factorization, Schur complement version) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glplpf.h" #include "glpenv.h" #define xfault xerror #define _GLPLPF_DEBUG 0 /* CAUTION: DO NOT CHANGE THE LIMIT BELOW */ #define M_MAX 100000000 /* = 100*10^6 */ /* maximal order of the basis matrix */ /*********************************************************************** * NAME * * lpf_create_it - create LP basis factorization * * SYNOPSIS * * #include "glplpf.h" * LPF *lpf_create_it(void); * * DESCRIPTION * * The routine lpf_create_it creates a program object, which represents * a factorization of LP basis. * * RETURNS * * The routine lpf_create_it returns a pointer to the object created. */ LPF *lpf_create_it(void) { LPF *lpf; #if _GLPLPF_DEBUG xprintf("lpf_create_it: warning: debug mode enabled\n"); #endif lpf = xmalloc(sizeof(LPF)); lpf->valid = 0; lpf->m0_max = lpf->m0 = 0; lpf->luf = luf_create_it(); lpf->m = 0; lpf->B = NULL; lpf->n_max = 50; lpf->n = 0; lpf->R_ptr = lpf->R_len = NULL; lpf->S_ptr = lpf->S_len = NULL; lpf->scf = NULL; lpf->P_row = lpf->P_col = NULL; lpf->Q_row = lpf->Q_col = NULL; lpf->v_size = 1000; lpf->v_ptr = 0; lpf->v_ind = NULL; lpf->v_val = NULL; lpf->work1 = lpf->work2 = NULL; return lpf; } /*********************************************************************** * NAME * * lpf_factorize - compute LP basis factorization * * SYNOPSIS * * #include "glplpf.h" * int lpf_factorize(LPF *lpf, int m, const int bh[], int (*col) * (void *info, int j, int ind[], double val[]), void *info); * * DESCRIPTION * * The routine lpf_factorize computes the factorization of the basis * matrix B specified by the routine col. * * The parameter lpf specified the basis factorization data structure * created with the routine lpf_create_it. * * The parameter m specifies the order of B, m > 0. * * The array bh specifies the basis header: bh[j], 1 <= j <= m, is the * number of j-th column of B in some original matrix. The array bh is * optional and can be specified as NULL. * * The formal routine col specifies the matrix B to be factorized. To * obtain j-th column of A the routine lpf_factorize calls the routine * col with the parameter j (1 <= j <= n). In response the routine col * should store row indices and numerical values of non-zero elements * of j-th column of B to locations ind[1,...,len] and val[1,...,len], * respectively, where len is the number of non-zeros in j-th column * returned on exit. Neither zero nor duplicate elements are allowed. * * The parameter info is a transit pointer passed to the routine col. * * RETURNS * * 0 The factorization has been successfully computed. * * LPF_ESING * The specified matrix is singular within the working precision. * * LPF_ECOND * The specified matrix is ill-conditioned. * * For more details see comments to the routine luf_factorize. */ int lpf_factorize(LPF *lpf, int m, const int bh[], int (*col) (void *info, int j, int ind[], double val[]), void *info) { int k, ret; #if _GLPLPF_DEBUG int i, j, len, *ind; double *B, *val; #endif xassert(bh == bh); if (m < 1) xfault("lpf_factorize: m = %d; invalid parameter\n", m); if (m > M_MAX) xfault("lpf_factorize: m = %d; matrix too big\n", m); lpf->m0 = lpf->m = m; /* invalidate the factorization */ lpf->valid = 0; /* allocate/reallocate arrays, if necessary */ if (lpf->R_ptr == NULL) lpf->R_ptr = xcalloc(1+lpf->n_max, sizeof(int)); if (lpf->R_len == NULL) lpf->R_len = xcalloc(1+lpf->n_max, sizeof(int)); if (lpf->S_ptr == NULL) lpf->S_ptr = xcalloc(1+lpf->n_max, sizeof(int)); if (lpf->S_len == NULL) lpf->S_len = xcalloc(1+lpf->n_max, sizeof(int)); if (lpf->scf == NULL) lpf->scf = scf_create_it(lpf->n_max); if (lpf->v_ind == NULL) lpf->v_ind = xcalloc(1+lpf->v_size, sizeof(int)); if (lpf->v_val == NULL) lpf->v_val = xcalloc(1+lpf->v_size, sizeof(double)); if (lpf->m0_max < m) { if (lpf->P_row != NULL) xfree(lpf->P_row); if (lpf->P_col != NULL) xfree(lpf->P_col); if (lpf->Q_row != NULL) xfree(lpf->Q_row); if (lpf->Q_col != NULL) xfree(lpf->Q_col); if (lpf->work1 != NULL) xfree(lpf->work1); if (lpf->work2 != NULL) xfree(lpf->work2); lpf->m0_max = m + 100; lpf->P_row = xcalloc(1+lpf->m0_max+lpf->n_max, sizeof(int)); lpf->P_col = xcalloc(1+lpf->m0_max+lpf->n_max, sizeof(int)); lpf->Q_row = xcalloc(1+lpf->m0_max+lpf->n_max, sizeof(int)); lpf->Q_col = xcalloc(1+lpf->m0_max+lpf->n_max, sizeof(int)); lpf->work1 = xcalloc(1+lpf->m0_max+lpf->n_max, sizeof(double)); lpf->work2 = xcalloc(1+lpf->m0_max+lpf->n_max, sizeof(double)); } /* try to factorize the basis matrix */ switch (luf_factorize(lpf->luf, m, col, info)) { case 0: break; case LUF_ESING: ret = LPF_ESING; goto done; case LUF_ECOND: ret = LPF_ECOND; goto done; default: xassert(lpf != lpf); } /* the basis matrix has been successfully factorized */ lpf->valid = 1; #if _GLPLPF_DEBUG /* store the basis matrix for debugging */ if (lpf->B != NULL) xfree(lpf->B); xassert(m <= 32767); lpf->B = B = xcalloc(1+m*m, sizeof(double)); ind = xcalloc(1+m, sizeof(int)); val = xcalloc(1+m, sizeof(double)); for (k = 1; k <= m * m; k++) B[k] = 0.0; for (j = 1; j <= m; j++) { len = col(info, j, ind, val); xassert(0 <= len && len <= m); for (k = 1; k <= len; k++) { i = ind[k]; xassert(1 <= i && i <= m); xassert(B[(i - 1) * m + j] == 0.0); xassert(val[k] != 0.0); B[(i - 1) * m + j] = val[k]; } } xfree(ind); xfree(val); #endif /* B = B0, so there are no additional rows/columns */ lpf->n = 0; /* reset the Schur complement factorization */ scf_reset_it(lpf->scf); /* P := Q := I */ for (k = 1; k <= m; k++) { lpf->P_row[k] = lpf->P_col[k] = k; lpf->Q_row[k] = lpf->Q_col[k] = k; } /* make all SVA locations free */ lpf->v_ptr = 1; ret = 0; done: /* return to the calling program */ return ret; } /*********************************************************************** * The routine r_prod computes the product y := y + alpha * R * x, * where x is a n-vector, alpha is a scalar, y is a m0-vector. * * Since matrix R is available by columns, the product is computed as * a linear combination: * * y := y + alpha * (R[1] * x[1] + ... + R[n] * x[n]), * * where R[j] is j-th column of R. */ static void r_prod(LPF *lpf, double y[], double a, const double x[]) { int n = lpf->n; int *R_ptr = lpf->R_ptr; int *R_len = lpf->R_len; int *v_ind = lpf->v_ind; double *v_val = lpf->v_val; int j, beg, end, ptr; double t; for (j = 1; j <= n; j++) { if (x[j] == 0.0) continue; /* y := y + alpha * R[j] * x[j] */ t = a * x[j]; beg = R_ptr[j]; end = beg + R_len[j]; for (ptr = beg; ptr < end; ptr++) y[v_ind[ptr]] += t * v_val[ptr]; } return; } /*********************************************************************** * The routine rt_prod computes the product y := y + alpha * R' * x, * where R' is a matrix transposed to R, x is a m0-vector, alpha is a * scalar, y is a n-vector. * * Since matrix R is available by columns, the product components are * computed as inner products: * * y[j] := y[j] + alpha * (j-th column of R) * x * * for j = 1, 2, ..., n. */ static void rt_prod(LPF *lpf, double y[], double a, const double x[]) { int n = lpf->n; int *R_ptr = lpf->R_ptr; int *R_len = lpf->R_len; int *v_ind = lpf->v_ind; double *v_val = lpf->v_val; int j, beg, end, ptr; double t; for (j = 1; j <= n; j++) { /* t := (j-th column of R) * x */ t = 0.0; beg = R_ptr[j]; end = beg + R_len[j]; for (ptr = beg; ptr < end; ptr++) t += v_val[ptr] * x[v_ind[ptr]]; /* y[j] := y[j] + alpha * t */ y[j] += a * t; } return; } /*********************************************************************** * The routine s_prod computes the product y := y + alpha * S * x, * where x is a m0-vector, alpha is a scalar, y is a n-vector. * * Since matrix S is available by rows, the product components are * computed as inner products: * * y[i] = y[i] + alpha * (i-th row of S) * x * * for i = 1, 2, ..., n. */ static void s_prod(LPF *lpf, double y[], double a, const double x[]) { int n = lpf->n; int *S_ptr = lpf->S_ptr; int *S_len = lpf->S_len; int *v_ind = lpf->v_ind; double *v_val = lpf->v_val; int i, beg, end, ptr; double t; for (i = 1; i <= n; i++) { /* t := (i-th row of S) * x */ t = 0.0; beg = S_ptr[i]; end = beg + S_len[i]; for (ptr = beg; ptr < end; ptr++) t += v_val[ptr] * x[v_ind[ptr]]; /* y[i] := y[i] + alpha * t */ y[i] += a * t; } return; } /*********************************************************************** * The routine st_prod computes the product y := y + alpha * S' * x, * where S' is a matrix transposed to S, x is a n-vector, alpha is a * scalar, y is m0-vector. * * Since matrix R is available by rows, the product is computed as a * linear combination: * * y := y + alpha * (S'[1] * x[1] + ... + S'[n] * x[n]), * * where S'[i] is i-th row of S. */ static void st_prod(LPF *lpf, double y[], double a, const double x[]) { int n = lpf->n; int *S_ptr = lpf->S_ptr; int *S_len = lpf->S_len; int *v_ind = lpf->v_ind; double *v_val = lpf->v_val; int i, beg, end, ptr; double t; for (i = 1; i <= n; i++) { if (x[i] == 0.0) continue; /* y := y + alpha * S'[i] * x[i] */ t = a * x[i]; beg = S_ptr[i]; end = beg + S_len[i]; for (ptr = beg; ptr < end; ptr++) y[v_ind[ptr]] += t * v_val[ptr]; } return; } #if _GLPLPF_DEBUG /*********************************************************************** * The routine check_error computes the maximal relative error between * left- and right-hand sides for the system B * x = b (if tr is zero) * or B' * x = b (if tr is non-zero), where B' is a matrix transposed * to B. (This routine is intended for debugging only.) */ static void check_error(LPF *lpf, int tr, const double x[], const double b[]) { int m = lpf->m; double *B = lpf->B; int i, j; double d, dmax = 0.0, s, t, tmax; for (i = 1; i <= m; i++) { s = 0.0; tmax = 1.0; for (j = 1; j <= m; j++) { if (!tr) t = B[m * (i - 1) + j] * x[j]; else t = B[m * (j - 1) + i] * x[j]; if (tmax < fabs(t)) tmax = fabs(t); s += t; } d = fabs(s - b[i]) / tmax; if (dmax < d) dmax = d; } if (dmax > 1e-8) xprintf("%s: dmax = %g; relative error too large\n", !tr ? "lpf_ftran" : "lpf_btran", dmax); return; } #endif /*********************************************************************** * NAME * * lpf_ftran - perform forward transformation (solve system B*x = b) * * SYNOPSIS * * #include "glplpf.h" * void lpf_ftran(LPF *lpf, double x[]); * * DESCRIPTION * * The routine lpf_ftran performs forward transformation, i.e. solves * the system B*x = b, where B is the basis matrix, x is the vector of * unknowns to be computed, b is the vector of right-hand sides. * * On entry elements of the vector b should be stored in dense format * in locations x[1], ..., x[m], where m is the number of rows. On exit * the routine stores elements of the vector x in the same locations. * * BACKGROUND * * Solution of the system B * x = b can be obtained by solving the * following augmented system: * * ( B F^) ( x ) ( b ) * ( ) ( ) = ( ) * ( G^ H^) ( y ) ( 0 ) * * which, using the main equality, can be written as follows: * * ( L0 0 ) ( U0 R ) ( x ) ( b ) * P ( ) ( ) Q ( ) = ( ) * ( S I ) ( 0 C ) ( y ) ( 0 ) * * therefore, * * ( x ) ( U0 R )-1 ( L0 0 )-1 ( b ) * ( ) = Q' ( ) ( ) P' ( ) * ( y ) ( 0 C ) ( S I ) ( 0 ) * * Thus, computing the solution includes the following steps: * * 1. Compute * * ( f ) ( b ) * ( ) = P' ( ) * ( g ) ( 0 ) * * 2. Solve the system * * ( f1 ) ( L0 0 )-1 ( f ) ( L0 0 ) ( f1 ) ( f ) * ( ) = ( ) ( ) => ( ) ( ) = ( ) * ( g1 ) ( S I ) ( g ) ( S I ) ( g1 ) ( g ) * * from which it follows that: * * { L0 * f1 = f f1 = inv(L0) * f * { => * { S * f1 + g1 = g g1 = g - S * f1 * * 3. Solve the system * * ( f2 ) ( U0 R )-1 ( f1 ) ( U0 R ) ( f2 ) ( f1 ) * ( ) = ( ) ( ) => ( ) ( ) = ( ) * ( g2 ) ( 0 C ) ( g1 ) ( 0 C ) ( g2 ) ( g1 ) * * from which it follows that: * * { U0 * f2 + R * g2 = f1 f2 = inv(U0) * (f1 - R * g2) * { => * { C * g2 = g1 g2 = inv(C) * g1 * * 4. Compute * * ( x ) ( f2 ) * ( ) = Q' ( ) * ( y ) ( g2 ) */ void lpf_ftran(LPF *lpf, double x[]) { int m0 = lpf->m0; int m = lpf->m; int n = lpf->n; int *P_col = lpf->P_col; int *Q_col = lpf->Q_col; double *fg = lpf->work1; double *f = fg; double *g = fg + m0; int i, ii; #if _GLPLPF_DEBUG double *b; #endif if (!lpf->valid) xfault("lpf_ftran: the factorization is not valid\n"); xassert(0 <= m && m <= m0 + n); #if _GLPLPF_DEBUG /* save the right-hand side vector */ b = xcalloc(1+m, sizeof(double)); for (i = 1; i <= m; i++) b[i] = x[i]; #endif /* (f g) := inv(P) * (b 0) */ for (i = 1; i <= m0 + n; i++) fg[i] = ((ii = P_col[i]) <= m ? x[ii] : 0.0); /* f1 := inv(L0) * f */ luf_f_solve(lpf->luf, 0, f); /* g1 := g - S * f1 */ s_prod(lpf, g, -1.0, f); /* g2 := inv(C) * g1 */ scf_solve_it(lpf->scf, 0, g); /* f2 := inv(U0) * (f1 - R * g2) */ r_prod(lpf, f, -1.0, g); luf_v_solve(lpf->luf, 0, f); /* (x y) := inv(Q) * (f2 g2) */ for (i = 1; i <= m; i++) x[i] = fg[Q_col[i]]; #if _GLPLPF_DEBUG /* check relative error in solution */ check_error(lpf, 0, x, b); xfree(b); #endif return; } /*********************************************************************** * NAME * * lpf_btran - perform backward transformation (solve system B'*x = b) * * SYNOPSIS * * #include "glplpf.h" * void lpf_btran(LPF *lpf, double x[]); * * DESCRIPTION * * The routine lpf_btran performs backward transformation, i.e. solves * the system B'*x = b, where B' is a matrix transposed to the basis * matrix B, x is the vector of unknowns to be computed, b is the vector * of right-hand sides. * * On entry elements of the vector b should be stored in dense format * in locations x[1], ..., x[m], where m is the number of rows. On exit * the routine stores elements of the vector x in the same locations. * * BACKGROUND * * Solution of the system B' * x = b, where B' is a matrix transposed * to B, can be obtained by solving the following augmented system: * * ( B F^)T ( x ) ( b ) * ( ) ( ) = ( ) * ( G^ H^) ( y ) ( 0 ) * * which, using the main equality, can be written as follows: * * T ( U0 R )T ( L0 0 )T T ( x ) ( b ) * Q ( ) ( ) P ( ) = ( ) * ( 0 C ) ( S I ) ( y ) ( 0 ) * * or, equivalently, as follows: * * ( U'0 0 ) ( L'0 S') ( x ) ( b ) * Q' ( ) ( ) P' ( ) = ( ) * ( R' C') ( 0 I ) ( y ) ( 0 ) * * therefore, * * ( x ) ( L'0 S')-1 ( U'0 0 )-1 ( b ) * ( ) = P ( ) ( ) Q ( ) * ( y ) ( 0 I ) ( R' C') ( 0 ) * * Thus, computing the solution includes the following steps: * * 1. Compute * * ( f ) ( b ) * ( ) = Q ( ) * ( g ) ( 0 ) * * 2. Solve the system * * ( f1 ) ( U'0 0 )-1 ( f ) ( U'0 0 ) ( f1 ) ( f ) * ( ) = ( ) ( ) => ( ) ( ) = ( ) * ( g1 ) ( R' C') ( g ) ( R' C') ( g1 ) ( g ) * * from which it follows that: * * { U'0 * f1 = f f1 = inv(U'0) * f * { => * { R' * f1 + C' * g1 = g g1 = inv(C') * (g - R' * f1) * * 3. Solve the system * * ( f2 ) ( L'0 S')-1 ( f1 ) ( L'0 S') ( f2 ) ( f1 ) * ( ) = ( ) ( ) => ( ) ( ) = ( ) * ( g2 ) ( 0 I ) ( g1 ) ( 0 I ) ( g2 ) ( g1 ) * * from which it follows that: * * { L'0 * f2 + S' * g2 = f1 * { => f2 = inv(L'0) * ( f1 - S' * g2) * { g2 = g1 * * 4. Compute * * ( x ) ( f2 ) * ( ) = P ( ) * ( y ) ( g2 ) */ void lpf_btran(LPF *lpf, double x[]) { int m0 = lpf->m0; int m = lpf->m; int n = lpf->n; int *P_row = lpf->P_row; int *Q_row = lpf->Q_row; double *fg = lpf->work1; double *f = fg; double *g = fg + m0; int i, ii; #if _GLPLPF_DEBUG double *b; #endif if (!lpf->valid) xfault("lpf_btran: the factorization is not valid\n"); xassert(0 <= m && m <= m0 + n); #if _GLPLPF_DEBUG /* save the right-hand side vector */ b = xcalloc(1+m, sizeof(double)); for (i = 1; i <= m; i++) b[i] = x[i]; #endif /* (f g) := Q * (b 0) */ for (i = 1; i <= m0 + n; i++) fg[i] = ((ii = Q_row[i]) <= m ? x[ii] : 0.0); /* f1 := inv(U'0) * f */ luf_v_solve(lpf->luf, 1, f); /* g1 := inv(C') * (g - R' * f1) */ rt_prod(lpf, g, -1.0, f); scf_solve_it(lpf->scf, 1, g); /* g2 := g1 */ g = g; /* f2 := inv(L'0) * (f1 - S' * g2) */ st_prod(lpf, f, -1.0, g); luf_f_solve(lpf->luf, 1, f); /* (x y) := P * (f2 g2) */ for (i = 1; i <= m; i++) x[i] = fg[P_row[i]]; #if _GLPLPF_DEBUG /* check relative error in solution */ check_error(lpf, 1, x, b); xfree(b); #endif return; } /*********************************************************************** * The routine enlarge_sva enlarges the Sparse Vector Area to new_size * locations by reallocating the arrays v_ind and v_val. */ static void enlarge_sva(LPF *lpf, int new_size) { int v_size = lpf->v_size; int used = lpf->v_ptr - 1; int *v_ind = lpf->v_ind; double *v_val = lpf->v_val; xassert(v_size < new_size); while (v_size < new_size) v_size += v_size; lpf->v_size = v_size; lpf->v_ind = xcalloc(1+v_size, sizeof(int)); lpf->v_val = xcalloc(1+v_size, sizeof(double)); xassert(used >= 0); memcpy(&lpf->v_ind[1], &v_ind[1], used * sizeof(int)); memcpy(&lpf->v_val[1], &v_val[1], used * sizeof(double)); xfree(v_ind); xfree(v_val); return; } /*********************************************************************** * NAME * * lpf_update_it - update LP basis factorization * * SYNOPSIS * * #include "glplpf.h" * int lpf_update_it(LPF *lpf, int j, int bh, int len, const int ind[], * const double val[]); * * DESCRIPTION * * The routine lpf_update_it updates the factorization of the basis * matrix B after replacing its j-th column by a new vector. * * The parameter j specifies the number of column of B, which has been * replaced, 1 <= j <= m, where m is the order of B. * * The parameter bh specifies the basis header entry for the new column * of B, which is the number of the new column in some original matrix. * This parameter is optional and can be specified as 0. * * Row indices and numerical values of non-zero elements of the new * column of B should be placed in locations ind[1], ..., ind[len] and * val[1], ..., val[len], resp., where len is the number of non-zeros * in the column. Neither zero nor duplicate elements are allowed. * * RETURNS * * 0 The factorization has been successfully updated. * * LPF_ESING * New basis B is singular within the working precision. * * LPF_ELIMIT * Maximal number of additional rows and columns has been reached. * * BACKGROUND * * Let j-th column of the current basis matrix B have to be replaced by * a new column a. This replacement is equivalent to removing the old * j-th column by fixing it at zero and introducing the new column as * follows: * * ( B F^| a ) * ( B F^) ( | ) * ( ) ---> ( G^ H^| 0 ) * ( G^ H^) (-------+---) * ( e'j 0 | 0 ) * * where ej is a unit vector with 1 in j-th position which used to fix * the old j-th column of B (at zero). Then using the main equality we * have: * * ( B F^| a ) ( B0 F | f ) * ( | ) ( P 0 ) ( | ) ( Q 0 ) * ( G^ H^| 0 ) = ( ) ( G H | g ) ( ) = * (-------+---) ( 0 1 ) (-------+---) ( 0 1 ) * ( e'j 0 | 0 ) ( v' w'| 0 ) * * [ ( B0 F )| ( f ) ] [ ( B0 F ) | ( f ) ] * [ P ( )| P ( ) ] ( Q 0 ) [ P ( ) Q| P ( ) ] * = [ ( G H )| ( g ) ] ( ) = [ ( G H ) | ( g ) ] * [------------+-------- ] ( 0 1 ) [-------------+---------] * [ ( v' w')| 0 ] [ ( v' w') Q| 0 ] * * where: * * ( a ) ( f ) ( f ) ( a ) * ( ) = P ( ) => ( ) = P' * ( ) * ( 0 ) ( g ) ( g ) ( 0 ) * * ( ej ) ( v ) ( v ) ( ej ) * ( e'j 0 ) = ( v' w' ) Q => ( ) = Q' ( ) => ( ) = Q ( ) * ( 0 ) ( w ) ( w ) ( 0 ) * * On the other hand: * * ( B0| F f ) * ( P 0 ) (---+------) ( Q 0 ) ( B0 new F ) * ( ) ( G | H g ) ( ) = new P ( ) new Q * ( 0 1 ) ( | ) ( 0 1 ) ( new G new H ) * ( v'| w' 0 ) * * where: * ( G ) ( H g ) * new F = ( F f ), new G = ( ), new H = ( ), * ( v') ( w' 0 ) * * ( P 0 ) ( Q 0 ) * new P = ( ) , new Q = ( ) . * ( 0 1 ) ( 0 1 ) * * The factorization structure for the new augmented matrix remains the * same, therefore: * * ( B0 new F ) ( L0 0 ) ( U0 new R ) * new P ( ) new Q = ( ) ( ) * ( new G new H ) ( new S I ) ( 0 new C ) * * where: * * new F = L0 * new R => * * new R = inv(L0) * new F = inv(L0) * (F f) = ( R inv(L0)*f ) * * new G = new S * U0 => * * ( G ) ( S ) * new S = new G * inv(U0) = ( ) * inv(U0) = ( ) * ( v') ( v'*inv(U0) ) * * new H = new S * new R + new C => * * new C = new H - new S * new R = * * ( H g ) ( S ) * = ( ) - ( ) * ( R inv(L0)*f ) = * ( w' 0 ) ( v'*inv(U0) ) * * ( H - S*R g - S*inv(L0)*f ) ( C x ) * = ( ) = ( ) * ( w'- v'*inv(U0)*R -v'*inv(U0)*inv(L0)*f) ( y' z ) * * Note that new C is resulted by expanding old C with new column x, * row y', and diagonal element z, where: * * x = g - S * inv(L0) * f = g - S * (new column of R) * * y = w - R'* inv(U'0)* v = w - R'* (new row of S) * * z = - (new row of S) * (new column of R) * * Finally, to replace old B by new B we have to permute j-th and last * (just added) columns of the matrix * * ( B F^| a ) * ( | ) * ( G^ H^| 0 ) * (-------+---) * ( e'j 0 | 0 ) * * and to keep the main equality do the same for matrix Q. */ int lpf_update_it(LPF *lpf, int j, int bh, int len, const int ind[], const double val[]) { int m0 = lpf->m0; int m = lpf->m; #if _GLPLPF_DEBUG double *B = lpf->B; #endif int n = lpf->n; int *R_ptr = lpf->R_ptr; int *R_len = lpf->R_len; int *S_ptr = lpf->S_ptr; int *S_len = lpf->S_len; int *P_row = lpf->P_row; int *P_col = lpf->P_col; int *Q_row = lpf->Q_row; int *Q_col = lpf->Q_col; int v_ptr = lpf->v_ptr; int *v_ind = lpf->v_ind; double *v_val = lpf->v_val; double *a = lpf->work2; /* new column */ double *fg = lpf->work1, *f = fg, *g = fg + m0; double *vw = lpf->work2, *v = vw, *w = vw + m0; double *x = g, *y = w, z; int i, ii, k, ret; xassert(bh == bh); if (!lpf->valid) xfault("lpf_update_it: the factorization is not valid\n"); if (!(1 <= j && j <= m)) xfault("lpf_update_it: j = %d; column number out of range\n", j); xassert(0 <= m && m <= m0 + n); /* check if the basis factorization can be expanded */ if (n == lpf->n_max) { lpf->valid = 0; ret = LPF_ELIMIT; goto done; } /* convert new j-th column of B to dense format */ for (i = 1; i <= m; i++) a[i] = 0.0; for (k = 1; k <= len; k++) { i = ind[k]; if (!(1 <= i && i <= m)) xfault("lpf_update_it: ind[%d] = %d; row number out of rang" "e\n", k, i); if (a[i] != 0.0) xfault("lpf_update_it: ind[%d] = %d; duplicate row index no" "t allowed\n", k, i); if (val[k] == 0.0) xfault("lpf_update_it: val[%d] = %g; zero element not allow" "ed\n", k, val[k]); a[i] = val[k]; } #if _GLPLPF_DEBUG /* change column in the basis matrix for debugging */ for (i = 1; i <= m; i++) B[(i - 1) * m + j] = a[i]; #endif /* (f g) := inv(P) * (a 0) */ for (i = 1; i <= m0+n; i++) fg[i] = ((ii = P_col[i]) <= m ? a[ii] : 0.0); /* (v w) := Q * (ej 0) */ for (i = 1; i <= m0+n; i++) vw[i] = 0.0; vw[Q_col[j]] = 1.0; /* f1 := inv(L0) * f (new column of R) */ luf_f_solve(lpf->luf, 0, f); /* v1 := inv(U'0) * v (new row of S) */ luf_v_solve(lpf->luf, 1, v); /* we need at most 2 * m0 available locations in the SVA to store new column of matrix R and new row of matrix S */ if (lpf->v_size < v_ptr + m0 + m0) { enlarge_sva(lpf, v_ptr + m0 + m0); v_ind = lpf->v_ind; v_val = lpf->v_val; } /* store new column of R */ R_ptr[n+1] = v_ptr; for (i = 1; i <= m0; i++) { if (f[i] != 0.0) v_ind[v_ptr] = i, v_val[v_ptr] = f[i], v_ptr++; } R_len[n+1] = v_ptr - lpf->v_ptr; lpf->v_ptr = v_ptr; /* store new row of S */ S_ptr[n+1] = v_ptr; for (i = 1; i <= m0; i++) { if (v[i] != 0.0) v_ind[v_ptr] = i, v_val[v_ptr] = v[i], v_ptr++; } S_len[n+1] = v_ptr - lpf->v_ptr; lpf->v_ptr = v_ptr; /* x := g - S * f1 (new column of C) */ s_prod(lpf, x, -1.0, f); /* y := w - R' * v1 (new row of C) */ rt_prod(lpf, y, -1.0, v); /* z := - v1 * f1 (new diagonal element of C) */ z = 0.0; for (i = 1; i <= m0; i++) z -= v[i] * f[i]; /* update factorization of new matrix C */ switch (scf_update_exp(lpf->scf, x, y, z)) { case 0: break; case SCF_ESING: lpf->valid = 0; ret = LPF_ESING; goto done; case SCF_ELIMIT: xassert(lpf != lpf); default: xassert(lpf != lpf); } /* expand matrix P */ P_row[m0+n+1] = P_col[m0+n+1] = m0+n+1; /* expand matrix Q */ Q_row[m0+n+1] = Q_col[m0+n+1] = m0+n+1; /* permute j-th and last (just added) column of matrix Q */ i = Q_col[j], ii = Q_col[m0+n+1]; Q_row[i] = m0+n+1, Q_col[m0+n+1] = i; Q_row[ii] = j, Q_col[j] = ii; /* increase the number of additional rows and columns */ lpf->n++; xassert(lpf->n <= lpf->n_max); /* the factorization has been successfully updated */ ret = 0; done: /* return to the calling program */ return ret; } /*********************************************************************** * NAME * * lpf_delete_it - delete LP basis factorization * * SYNOPSIS * * #include "glplpf.h" * void lpf_delete_it(LPF *lpf) * * DESCRIPTION * * The routine lpf_delete_it deletes LP basis factorization specified * by the parameter lpf and frees all memory allocated to this program * object. */ void lpf_delete_it(LPF *lpf) { luf_delete_it(lpf->luf); #if _GLPLPF_DEBUG if (lpf->B != NULL) xfree(lpf->B); #else xassert(lpf->B == NULL); #endif if (lpf->R_ptr != NULL) xfree(lpf->R_ptr); if (lpf->R_len != NULL) xfree(lpf->R_len); if (lpf->S_ptr != NULL) xfree(lpf->S_ptr); if (lpf->S_len != NULL) xfree(lpf->S_len); if (lpf->scf != NULL) scf_delete_it(lpf->scf); if (lpf->P_row != NULL) xfree(lpf->P_row); if (lpf->P_col != NULL) xfree(lpf->P_col); if (lpf->Q_row != NULL) xfree(lpf->Q_row); if (lpf->Q_col != NULL) xfree(lpf->Q_col); if (lpf->v_ind != NULL) xfree(lpf->v_ind); if (lpf->v_val != NULL) xfree(lpf->v_val); if (lpf->work1 != NULL) xfree(lpf->work1); if (lpf->work2 != NULL) xfree(lpf->work2); xfree(lpf); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpapi08.c0000644000175000017500000003030711632352620020232 0ustar csheltoncshelton/* glpapi08.c (interior-point method routines) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" #include "glpipm.h" #include "glpnpp.h" /*********************************************************************** * NAME * * glp_interior - solve LP problem with the interior-point method * * SYNOPSIS * * int glp_interior(glp_prob *P, const glp_iptcp *parm); * * The routine glp_interior is a driver to the LP solver based on the * interior-point method. * * The interior-point solver has a set of control parameters. Values of * the control parameters can be passed in a structure glp_iptcp, which * the parameter parm points to. * * Currently this routine implements an easy variant of the primal-dual * interior-point method based on Mehrotra's technique. * * This routine transforms the original LP problem to an equivalent LP * problem in the standard formulation (all constraints are equalities, * all variables are non-negative), calls the routine ipm_main to solve * the transformed problem, and then transforms an obtained solution to * the solution of the original problem. * * RETURNS * * 0 The LP problem instance has been successfully solved. This code * does not necessarily mean that the solver has found optimal * solution. It only means that the solution process was successful. * * GLP_EFAIL * The problem has no rows/columns. * * GLP_ENOCVG * Very slow convergence or divergence. * * GLP_EITLIM * Iteration limit exceeded. * * GLP_EINSTAB * Numerical instability on solving Newtonian system. */ static void transform(NPP *npp) { /* transform LP to the standard formulation */ NPPROW *row, *prev_row; NPPCOL *col, *prev_col; for (row = npp->r_tail; row != NULL; row = prev_row) { prev_row = row->prev; if (row->lb == -DBL_MAX && row->ub == +DBL_MAX) npp_free_row(npp, row); else if (row->lb == -DBL_MAX) npp_leq_row(npp, row); else if (row->ub == +DBL_MAX) npp_geq_row(npp, row); else if (row->lb != row->ub) { if (fabs(row->lb) < fabs(row->ub)) npp_geq_row(npp, row); else npp_leq_row(npp, row); } } for (col = npp->c_tail; col != NULL; col = prev_col) { prev_col = col->prev; if (col->lb == -DBL_MAX && col->ub == +DBL_MAX) npp_free_col(npp, col); else if (col->lb == -DBL_MAX) npp_ubnd_col(npp, col); else if (col->ub == +DBL_MAX) { if (col->lb != 0.0) npp_lbnd_col(npp, col); } else if (col->lb != col->ub) { if (fabs(col->lb) < fabs(col->ub)) { if (col->lb != 0.0) npp_lbnd_col(npp, col); } else npp_ubnd_col(npp, col); npp_dbnd_col(npp, col); } else npp_fixed_col(npp, col); } for (row = npp->r_head; row != NULL; row = row->next) xassert(row->lb == row->ub); for (col = npp->c_head; col != NULL; col = col->next) xassert(col->lb == 0.0 && col->ub == +DBL_MAX); return; } int glp_interior(glp_prob *P, const glp_iptcp *parm) { glp_iptcp _parm; GLPROW *row; GLPCOL *col; NPP *npp = NULL; glp_prob *prob = NULL; int i, j, ret; /* check control parameters */ if (parm == NULL) glp_init_iptcp(&_parm), parm = &_parm; if (!(parm->msg_lev == GLP_MSG_OFF || parm->msg_lev == GLP_MSG_ERR || parm->msg_lev == GLP_MSG_ON || parm->msg_lev == GLP_MSG_ALL)) xerror("glp_interior: msg_lev = %d; invalid parameter\n", parm->msg_lev); if (!(parm->ord_alg == GLP_ORD_NONE || parm->ord_alg == GLP_ORD_QMD || parm->ord_alg == GLP_ORD_AMD || parm->ord_alg == GLP_ORD_SYMAMD)) xerror("glp_interior: ord_alg = %d; invalid parameter\n", parm->ord_alg); /* interior-point solution is currently undefined */ P->ipt_stat = GLP_UNDEF; P->ipt_obj = 0.0; /* check bounds of double-bounded variables */ for (i = 1; i <= P->m; i++) { row = P->row[i]; if (row->type == GLP_DB && row->lb >= row->ub) { if (parm->msg_lev >= GLP_MSG_ERR) xprintf("glp_interior: row %d: lb = %g, ub = %g; incorre" "ct bounds\n", i, row->lb, row->ub); ret = GLP_EBOUND; goto done; } } for (j = 1; j <= P->n; j++) { col = P->col[j]; if (col->type == GLP_DB && col->lb >= col->ub) { if (parm->msg_lev >= GLP_MSG_ERR) xprintf("glp_interior: column %d: lb = %g, ub = %g; inco" "rrect bounds\n", j, col->lb, col->ub); ret = GLP_EBOUND; goto done; } } /* transform LP to the standard formulation */ if (parm->msg_lev >= GLP_MSG_ALL) xprintf("Original LP has %d row(s), %d column(s), and %d non-z" "ero(s)\n", P->m, P->n, P->nnz); npp = npp_create_wksp(); npp_load_prob(npp, P, GLP_OFF, GLP_IPT, GLP_ON); transform(npp); prob = glp_create_prob(); npp_build_prob(npp, prob); if (parm->msg_lev >= GLP_MSG_ALL) xprintf("Working LP has %d row(s), %d column(s), and %d non-ze" "ro(s)\n", prob->m, prob->n, prob->nnz); #if 1 /* currently empty problem cannot be solved */ if (!(prob->m > 0 && prob->n > 0)) { if (parm->msg_lev >= GLP_MSG_ERR) xprintf("glp_interior: unable to solve empty problem\n"); ret = GLP_EFAIL; goto done; } #endif /* scale the resultant LP */ { ENV *env = get_env_ptr(); int term_out = env->term_out; env->term_out = GLP_OFF; glp_scale_prob(prob, GLP_SF_EQ); env->term_out = term_out; } /* warn about dense columns */ if (parm->msg_lev >= GLP_MSG_ON && prob->m >= 200) { int len, cnt = 0; for (j = 1; j <= prob->n; j++) { len = glp_get_mat_col(prob, j, NULL, NULL); if ((double)len >= 0.20 * (double)prob->m) cnt++; } if (cnt == 1) xprintf("WARNING: PROBLEM HAS ONE DENSE COLUMN\n"); else if (cnt > 0) xprintf("WARNING: PROBLEM HAS %d DENSE COLUMNS\n", cnt); } /* solve the transformed LP */ ret = ipm_solve(prob, parm); /* postprocess solution from the transformed LP */ npp_postprocess(npp, prob); /* and store solution to the original LP */ npp_unload_sol(npp, P); done: /* free working program objects */ if (npp != NULL) npp_delete_wksp(npp); if (prob != NULL) glp_delete_prob(prob); /* return to the application program */ return ret; } /*********************************************************************** * NAME * * glp_init_iptcp - initialize interior-point solver control parameters * * SYNOPSIS * * void glp_init_iptcp(glp_iptcp *parm); * * DESCRIPTION * * The routine glp_init_iptcp initializes control parameters, which are * used by the interior-point solver, with default values. * * Default values of the control parameters are stored in the glp_iptcp * structure, which the parameter parm points to. */ void glp_init_iptcp(glp_iptcp *parm) { parm->msg_lev = GLP_MSG_ALL; parm->ord_alg = GLP_ORD_AMD; return; } /*********************************************************************** * NAME * * glp_ipt_status - retrieve status of interior-point solution * * SYNOPSIS * * int glp_ipt_status(glp_prob *lp); * * RETURNS * * The routine glp_ipt_status reports the status of solution found by * the interior-point solver as follows: * * GLP_UNDEF - interior-point solution is undefined; * GLP_OPT - interior-point solution is optimal; * GLP_INFEAS - interior-point solution is infeasible; * GLP_NOFEAS - no feasible solution exists. */ int glp_ipt_status(glp_prob *lp) { int ipt_stat = lp->ipt_stat; return ipt_stat; } /*********************************************************************** * NAME * * glp_ipt_obj_val - retrieve objective value (interior point) * * SYNOPSIS * * double glp_ipt_obj_val(glp_prob *lp); * * RETURNS * * The routine glp_ipt_obj_val returns value of the objective function * for interior-point solution. */ double glp_ipt_obj_val(glp_prob *lp) { /*struct LPXCPS *cps = lp->cps;*/ double z; z = lp->ipt_obj; /*if (cps->round && fabs(z) < 1e-9) z = 0.0;*/ return z; } /*********************************************************************** * NAME * * glp_ipt_row_prim - retrieve row primal value (interior point) * * SYNOPSIS * * double glp_ipt_row_prim(glp_prob *lp, int i); * * RETURNS * * The routine glp_ipt_row_prim returns primal value of the auxiliary * variable associated with i-th row. */ double glp_ipt_row_prim(glp_prob *lp, int i) { /*struct LPXCPS *cps = lp->cps;*/ double pval; if (!(1 <= i && i <= lp->m)) xerror("glp_ipt_row_prim: i = %d; row number out of range\n", i); pval = lp->row[i]->pval; /*if (cps->round && fabs(pval) < 1e-9) pval = 0.0;*/ return pval; } /*********************************************************************** * NAME * * glp_ipt_row_dual - retrieve row dual value (interior point) * * SYNOPSIS * * double glp_ipt_row_dual(glp_prob *lp, int i); * * RETURNS * * The routine glp_ipt_row_dual returns dual value (i.e. reduced cost) * of the auxiliary variable associated with i-th row. */ double glp_ipt_row_dual(glp_prob *lp, int i) { /*struct LPXCPS *cps = lp->cps;*/ double dval; if (!(1 <= i && i <= lp->m)) xerror("glp_ipt_row_dual: i = %d; row number out of range\n", i); dval = lp->row[i]->dval; /*if (cps->round && fabs(dval) < 1e-9) dval = 0.0;*/ return dval; } /*********************************************************************** * NAME * * glp_ipt_col_prim - retrieve column primal value (interior point) * * SYNOPSIS * * double glp_ipt_col_prim(glp_prob *lp, int j); * * RETURNS * * The routine glp_ipt_col_prim returns primal value of the structural * variable associated with j-th column. */ double glp_ipt_col_prim(glp_prob *lp, int j) { /*struct LPXCPS *cps = lp->cps;*/ double pval; if (!(1 <= j && j <= lp->n)) xerror("glp_ipt_col_prim: j = %d; column number out of range\n" , j); pval = lp->col[j]->pval; /*if (cps->round && fabs(pval) < 1e-9) pval = 0.0;*/ return pval; } /*********************************************************************** * NAME * * glp_ipt_col_dual - retrieve column dual value (interior point) * * SYNOPSIS * * #include "glplpx.h" * double glp_ipt_col_dual(glp_prob *lp, int j); * * RETURNS * * The routine glp_ipt_col_dual returns dual value (i.e. reduced cost) * of the structural variable associated with j-th column. */ double glp_ipt_col_dual(glp_prob *lp, int j) { /*struct LPXCPS *cps = lp->cps;*/ double dval; if (!(1 <= j && j <= lp->n)) xerror("glp_ipt_col_dual: j = %d; column number out of range\n" , j); dval = lp->col[j]->dval; /*if (cps->round && fabs(dval) < 1e-9) dval = 0.0;*/ return dval; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpios12.c0000644000175000017500000001326611632352620020253 0ustar csheltoncshelton/* glpios12.c (node selection heuristics) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpios.h" /*********************************************************************** * NAME * * ios_choose_node - select subproblem to continue the search * * SYNOPSIS * * #include "glpios.h" * int ios_choose_node(glp_tree *T); * * DESCRIPTION * * The routine ios_choose_node selects a subproblem from the active * list to continue the search. The choice depends on the backtracking * technique option. * * RETURNS * * The routine ios_choose_node return the reference number of the * subproblem selected. */ static int most_feas(glp_tree *T); static int best_proj(glp_tree *T); static int best_node(glp_tree *T); int ios_choose_node(glp_tree *T) { int p; if (T->parm->bt_tech == GLP_BT_DFS) { /* depth first search */ xassert(T->tail != NULL); p = T->tail->p; } else if (T->parm->bt_tech == GLP_BT_BFS) { /* breadth first search */ xassert(T->head != NULL); p = T->head->p; } else if (T->parm->bt_tech == GLP_BT_BLB) { /* select node with best local bound */ p = best_node(T); } else if (T->parm->bt_tech == GLP_BT_BPH) { if (T->mip->mip_stat == GLP_UNDEF) { /* "most integer feasible" subproblem */ p = most_feas(T); } else { /* best projection heuristic */ p = best_proj(T); } } else xassert(T != T); return p; } static int most_feas(glp_tree *T) { /* select subproblem whose parent has minimal sum of integer infeasibilities */ IOSNPD *node; int p; double best; p = 0, best = DBL_MAX; for (node = T->head; node != NULL; node = node->next) { xassert(node->up != NULL); if (best > node->up->ii_sum) p = node->p, best = node->up->ii_sum; } return p; } static int best_proj(glp_tree *T) { /* select subproblem using the best projection heuristic */ IOSNPD *root, *node; int p; double best, deg, obj; /* the global bound must exist */ xassert(T->mip->mip_stat == GLP_FEAS); /* obtain pointer to the root node, which must exist */ root = T->slot[1].node; xassert(root != NULL); /* deg estimates degradation of the objective function per unit of the sum of integer infeasibilities */ xassert(root->ii_sum > 0.0); deg = (T->mip->mip_obj - root->bound) / root->ii_sum; /* nothing has been selected so far */ p = 0, best = DBL_MAX; /* walk through the list of active subproblems */ for (node = T->head; node != NULL; node = node->next) { xassert(node->up != NULL); /* obj estimates optimal objective value if the sum of integer infeasibilities were zero */ obj = node->up->bound + deg * node->up->ii_sum; if (T->mip->dir == GLP_MAX) obj = - obj; /* select the subproblem which has the best estimated optimal objective value */ if (best > obj) p = node->p, best = obj; } return p; } static int best_node(glp_tree *T) { /* select subproblem with best local bound */ IOSNPD *node, *best = NULL; double bound, eps; switch (T->mip->dir) { case GLP_MIN: bound = +DBL_MAX; for (node = T->head; node != NULL; node = node->next) if (bound > node->bound) bound = node->bound; xassert(bound != +DBL_MAX); eps = 0.001 * (1.0 + fabs(bound)); for (node = T->head; node != NULL; node = node->next) { if (node->bound <= bound + eps) { xassert(node->up != NULL); if (best == NULL || #if 1 best->up->ii_sum > node->up->ii_sum) best = node; #else best->lp_obj > node->lp_obj) best = node; #endif } } break; case GLP_MAX: bound = -DBL_MAX; for (node = T->head; node != NULL; node = node->next) if (bound < node->bound) bound = node->bound; xassert(bound != -DBL_MAX); eps = 0.001 * (1.0 + fabs(bound)); for (node = T->head; node != NULL; node = node->next) { if (node->bound >= bound - eps) { xassert(node->up != NULL); if (best == NULL || #if 1 best->up->ii_sum > node->up->ii_sum) best = node; #else best->lp_obj < node->lp_obj) best = node; #endif } } break; default: xassert(T != T); } xassert(best != NULL); return best->p; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpios09.c0000644000175000017500000006312511632352620020260 0ustar csheltoncshelton/* glpios09.c (branching heuristics) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpios.h" /*********************************************************************** * NAME * * ios_choose_var - select variable to branch on * * SYNOPSIS * * #include "glpios.h" * int ios_choose_var(glp_tree *T, int *next); * * The routine ios_choose_var chooses a variable from the candidate * list to branch on. Additionally the routine provides a flag stored * in the location next to suggests which of the child subproblems * should be solved next. * * RETURNS * * The routine ios_choose_var returns the ordinal number of the column * choosen. */ static int branch_first(glp_tree *T, int *next); static int branch_last(glp_tree *T, int *next); static int branch_mostf(glp_tree *T, int *next); static int branch_drtom(glp_tree *T, int *next); int ios_choose_var(glp_tree *T, int *next) { int j; if (T->parm->br_tech == GLP_BR_FFV) { /* branch on first fractional variable */ j = branch_first(T, next); } else if (T->parm->br_tech == GLP_BR_LFV) { /* branch on last fractional variable */ j = branch_last(T, next); } else if (T->parm->br_tech == GLP_BR_MFV) { /* branch on most fractional variable */ j = branch_mostf(T, next); } else if (T->parm->br_tech == GLP_BR_DTH) { /* branch using the heuristic by Dreebeck and Tomlin */ j = branch_drtom(T, next); } else if (T->parm->br_tech == GLP_BR_PCH) { /* hybrid pseudocost heuristic */ j = ios_pcost_branch(T, next); } else xassert(T != T); return j; } /*********************************************************************** * branch_first - choose first branching variable * * This routine looks up the list of structural variables and chooses * the first one, which is of integer kind and has fractional value in * optimal solution to the current LP relaxation. * * This routine also selects the branch to be solved next where integer * infeasibility of the chosen variable is less than in other one. */ static int branch_first(glp_tree *T, int *_next) { int j, next; double beta; /* choose the column to branch on */ for (j = 1; j <= T->n; j++) if (T->non_int[j]) break; xassert(1 <= j && j <= T->n); /* select the branch to be solved next */ beta = glp_get_col_prim(T->mip, j); if (beta - floor(beta) < ceil(beta) - beta) next = GLP_DN_BRNCH; else next = GLP_UP_BRNCH; *_next = next; return j; } /*********************************************************************** * branch_last - choose last branching variable * * This routine looks up the list of structural variables and chooses * the last one, which is of integer kind and has fractional value in * optimal solution to the current LP relaxation. * * This routine also selects the branch to be solved next where integer * infeasibility of the chosen variable is less than in other one. */ static int branch_last(glp_tree *T, int *_next) { int j, next; double beta; /* choose the column to branch on */ for (j = T->n; j >= 1; j--) if (T->non_int[j]) break; xassert(1 <= j && j <= T->n); /* select the branch to be solved next */ beta = glp_get_col_prim(T->mip, j); if (beta - floor(beta) < ceil(beta) - beta) next = GLP_DN_BRNCH; else next = GLP_UP_BRNCH; *_next = next; return j; } /*********************************************************************** * branch_mostf - choose most fractional branching variable * * This routine looks up the list of structural variables and chooses * that one, which is of integer kind and has most fractional value in * optimal solution to the current LP relaxation. * * This routine also selects the branch to be solved next where integer * infeasibility of the chosen variable is less than in other one. * * (Alexander Martin notices that "...most infeasible is as good as * random...".) */ static int branch_mostf(glp_tree *T, int *_next) { int j, jj, next; double beta, most, temp; /* choose the column to branch on */ jj = 0, most = DBL_MAX; for (j = 1; j <= T->n; j++) { if (T->non_int[j]) { beta = glp_get_col_prim(T->mip, j); temp = floor(beta) + 0.5; if (most > fabs(beta - temp)) { jj = j, most = fabs(beta - temp); if (beta < temp) next = GLP_DN_BRNCH; else next = GLP_UP_BRNCH; } } } *_next = next; return jj; } /*********************************************************************** * branch_drtom - choose branching var using Driebeck-Tomlin heuristic * * This routine chooses a structural variable, which is required to be * integral and has fractional value in optimal solution of the current * LP relaxation, using a heuristic proposed by Driebeck and Tomlin. * * The routine also selects the branch to be solved next, again due to * Driebeck and Tomlin. * * This routine is based on the heuristic proposed in: * * Driebeck N.J. An algorithm for the solution of mixed-integer * programming problems, Management Science, 12: 576-87 (1966); * * and improved in: * * Tomlin J.A. Branch and bound methods for integer and non-convex * programming, in J.Abadie (ed.), Integer and Nonlinear Programming, * North-Holland, Amsterdam, pp. 437-50 (1970). * * Must note that this heuristic is time-expensive, because computing * one-step degradation (see the routine below) requires one BTRAN for * each fractional-valued structural variable. */ static int branch_drtom(glp_tree *T, int *_next) { glp_prob *mip = T->mip; int m = mip->m; int n = mip->n; char *non_int = T->non_int; int j, jj, k, t, next, kase, len, stat, *ind; double x, dk, alfa, delta_j, delta_k, delta_z, dz_dn, dz_up, dd_dn, dd_up, degrad, *val; /* basic solution of LP relaxation must be optimal */ xassert(glp_get_status(mip) == GLP_OPT); /* allocate working arrays */ ind = xcalloc(1+n, sizeof(int)); val = xcalloc(1+n, sizeof(double)); /* nothing has been chosen so far */ jj = 0, degrad = -1.0; /* walk through the list of columns (structural variables) */ for (j = 1; j <= n; j++) { /* if j-th column is not marked as fractional, skip it */ if (!non_int[j]) continue; /* obtain (fractional) value of j-th column in basic solution of LP relaxation */ x = glp_get_col_prim(mip, j); /* since the value of j-th column is fractional, the column is basic; compute corresponding row of the simplex table */ len = glp_eval_tab_row(mip, m+j, ind, val); /* the following fragment computes a change in the objective function: delta Z = new Z - old Z, where old Z is the objective value in the current optimal basis, and new Z is the objective value in the adjacent basis, for two cases: 1) if new upper bound ub' = floor(x[j]) is introduced for j-th column (down branch); 2) if new lower bound lb' = ceil(x[j]) is introduced for j-th column (up branch); since in both cases the solution remaining dual feasible becomes primal infeasible, one implicit simplex iteration is performed to determine the change delta Z; it is obvious that new Z, which is never better than old Z, is a lower (minimization) or upper (maximization) bound of the objective function for down- and up-branches. */ for (kase = -1; kase <= +1; kase += 2) { /* if kase < 0, the new upper bound of x[j] is introduced; in this case x[j] should decrease in order to leave the basis and go to its new upper bound */ /* if kase > 0, the new lower bound of x[j] is introduced; in this case x[j] should increase in order to leave the basis and go to its new lower bound */ /* apply the dual ratio test in order to determine which auxiliary or structural variable should enter the basis to keep dual feasibility */ k = glp_dual_rtest(mip, len, ind, val, kase, 1e-9); if (k != 0) k = ind[k]; /* if no non-basic variable has been chosen, LP relaxation of corresponding branch being primal infeasible and dual unbounded has no primal feasible solution; in this case the change delta Z is formally set to infinity */ if (k == 0) { delta_z = (T->mip->dir == GLP_MIN ? +DBL_MAX : -DBL_MAX); goto skip; } /* row of the simplex table that corresponds to non-basic variable x[k] choosen by the dual ratio test is: x[j] = ... + alfa * x[k] + ... where alfa is the influence coefficient (an element of the simplex table row) */ /* determine the coefficient alfa */ for (t = 1; t <= len; t++) if (ind[t] == k) break; xassert(1 <= t && t <= len); alfa = val[t]; /* since in the adjacent basis the variable x[j] becomes non-basic, knowing its value in the current basis we can determine its change delta x[j] = new x[j] - old x[j] */ delta_j = (kase < 0 ? floor(x) : ceil(x)) - x; /* and knowing the coefficient alfa we can determine the corresponding change delta x[k] = new x[k] - old x[k], where old x[k] is a value of x[k] in the current basis, and new x[k] is a value of x[k] in the adjacent basis */ delta_k = delta_j / alfa; /* Tomlin noticed that if the variable x[k] is of integer kind, its change cannot be less (eventually) than one in the magnitude */ if (k > m && glp_get_col_kind(mip, k-m) != GLP_CV) { /* x[k] is structural integer variable */ if (fabs(delta_k - floor(delta_k + 0.5)) > 1e-3) { if (delta_k > 0.0) delta_k = ceil(delta_k); /* +3.14 -> +4 */ else delta_k = floor(delta_k); /* -3.14 -> -4 */ } } /* now determine the status and reduced cost of x[k] in the current basis */ if (k <= m) { stat = glp_get_row_stat(mip, k); dk = glp_get_row_dual(mip, k); } else { stat = glp_get_col_stat(mip, k-m); dk = glp_get_col_dual(mip, k-m); } /* if the current basis is dual degenerate, some reduced costs which are close to zero may have wrong sign due to round-off errors, so correct the sign of d[k] */ switch (T->mip->dir) { case GLP_MIN: if (stat == GLP_NL && dk < 0.0 || stat == GLP_NU && dk > 0.0 || stat == GLP_NF) dk = 0.0; break; case GLP_MAX: if (stat == GLP_NL && dk > 0.0 || stat == GLP_NU && dk < 0.0 || stat == GLP_NF) dk = 0.0; break; default: xassert(T != T); } /* now knowing the change of x[k] and its reduced cost d[k] we can compute the corresponding change in the objective function delta Z = new Z - old Z = d[k] * delta x[k]; note that due to Tomlin's modification new Z can be even worse than in the adjacent basis */ delta_z = dk * delta_k; skip: /* new Z is never better than old Z, therefore the change delta Z is always non-negative (in case of minimization) or non-positive (in case of maximization) */ switch (T->mip->dir) { case GLP_MIN: xassert(delta_z >= 0.0); break; case GLP_MAX: xassert(delta_z <= 0.0); break; default: xassert(T != T); } /* save the change in the objective fnction for down- and up-branches, respectively */ if (kase < 0) dz_dn = delta_z; else dz_up = delta_z; } /* thus, in down-branch no integer feasible solution can be better than Z + dz_dn, and in up-branch no integer feasible solution can be better than Z + dz_up, where Z is value of the objective function in the current basis */ /* following the heuristic by Driebeck and Tomlin we choose a column (i.e. structural variable) which provides largest degradation of the objective function in some of branches; besides, we select the branch with smaller degradation to be solved next and keep other branch with larger degradation in the active list hoping to minimize the number of further backtrackings */ if (degrad < fabs(dz_dn) || degrad < fabs(dz_up)) { jj = j; if (fabs(dz_dn) < fabs(dz_up)) { /* select down branch to be solved next */ next = GLP_DN_BRNCH; degrad = fabs(dz_up); } else { /* select up branch to be solved next */ next = GLP_UP_BRNCH; degrad = fabs(dz_dn); } /* save the objective changes for printing */ dd_dn = dz_dn, dd_up = dz_up; /* if down- or up-branch has no feasible solution, we does not need to consider other candidates (in principle, the corresponding branch could be pruned right now) */ if (degrad == DBL_MAX) break; } } /* free working arrays */ xfree(ind); xfree(val); /* something must be chosen */ xassert(1 <= jj && jj <= n); #if 1 /* 02/XI-2009 */ if (degrad < 1e-6 * (1.0 + 0.001 * fabs(mip->obj_val))) { jj = branch_mostf(T, &next); goto done; } #endif if (T->parm->msg_lev >= GLP_MSG_DBG) { xprintf("branch_drtom: column %d chosen to branch on\n", jj); if (fabs(dd_dn) == DBL_MAX) xprintf("branch_drtom: down-branch is infeasible\n"); else xprintf("branch_drtom: down-branch bound is %.9e\n", lpx_get_obj_val(mip) + dd_dn); if (fabs(dd_up) == DBL_MAX) xprintf("branch_drtom: up-branch is infeasible\n"); else xprintf("branch_drtom: up-branch bound is %.9e\n", lpx_get_obj_val(mip) + dd_up); } done: *_next = next; return jj; } /**********************************************************************/ struct csa { /* common storage area */ int *dn_cnt; /* int dn_cnt[1+n]; */ /* dn_cnt[j] is the number of subproblems, whose LP relaxations have been solved and which are down-branches for variable x[j]; dn_cnt[j] = 0 means the down pseudocost is uninitialized */ double *dn_sum; /* double dn_sum[1+n]; */ /* dn_sum[j] is the sum of per unit degradations of the objective over all dn_cnt[j] subproblems */ int *up_cnt; /* int up_cnt[1+n]; */ /* up_cnt[j] is the number of subproblems, whose LP relaxations have been solved and which are up-branches for variable x[j]; up_cnt[j] = 0 means the up pseudocost is uninitialized */ double *up_sum; /* double up_sum[1+n]; */ /* up_sum[j] is the sum of per unit degradations of the objective over all up_cnt[j] subproblems */ }; void *ios_pcost_init(glp_tree *tree) { /* initialize working data used on pseudocost branching */ struct csa *csa; int n = tree->n, j; csa = xmalloc(sizeof(struct csa)); csa->dn_cnt = xcalloc(1+n, sizeof(int)); csa->dn_sum = xcalloc(1+n, sizeof(double)); csa->up_cnt = xcalloc(1+n, sizeof(int)); csa->up_sum = xcalloc(1+n, sizeof(double)); for (j = 1; j <= n; j++) { csa->dn_cnt[j] = csa->up_cnt[j] = 0; csa->dn_sum[j] = csa->up_sum[j] = 0.0; } return csa; } static double eval_degrad(glp_prob *P, int j, double bnd) { /* compute degradation of the objective on fixing x[j] at given value with a limited number of dual simplex iterations */ /* this routine fixes column x[j] at specified value bnd, solves resulting LP, and returns a lower bound to degradation of the objective, degrad >= 0 */ glp_prob *lp; glp_smcp parm; int ret; double degrad; /* the current basis must be optimal */ xassert(glp_get_status(P) == GLP_OPT); /* create a copy of P */ lp = glp_create_prob(); glp_copy_prob(lp, P, 0); /* fix column x[j] at specified value */ glp_set_col_bnds(lp, j, GLP_FX, bnd, bnd); /* try to solve resulting LP */ glp_init_smcp(&parm); parm.msg_lev = GLP_MSG_OFF; parm.meth = GLP_DUAL; parm.it_lim = 30; parm.out_dly = 1000; parm.meth = GLP_DUAL; ret = glp_simplex(lp, &parm); if (ret == 0 || ret == GLP_EITLIM) { if (glp_get_prim_stat(lp) == GLP_NOFEAS) { /* resulting LP has no primal feasible solution */ degrad = DBL_MAX; } else if (glp_get_dual_stat(lp) == GLP_FEAS) { /* resulting basis is optimal or at least dual feasible, so we have the correct lower bound to degradation */ if (P->dir == GLP_MIN) degrad = lp->obj_val - P->obj_val; else if (P->dir == GLP_MAX) degrad = P->obj_val - lp->obj_val; else xassert(P != P); /* degradation cannot be negative by definition */ /* note that the lower bound to degradation may be close to zero even if its exact value is zero due to round-off errors on computing the objective value */ if (degrad < 1e-6 * (1.0 + 0.001 * fabs(P->obj_val))) degrad = 0.0; } else { /* the final basis reported by the simplex solver is dual infeasible, so we cannot determine a non-trivial lower bound to degradation */ degrad = 0.0; } } else { /* the simplex solver failed */ degrad = 0.0; } /* delete the copy of P */ glp_delete_prob(lp); return degrad; } void ios_pcost_update(glp_tree *tree) { /* update history information for pseudocost branching */ /* this routine is called every time when LP relaxation of the current subproblem has been solved to optimality with all lazy and cutting plane constraints included */ int j; double dx, dz, psi; struct csa *csa = tree->pcost; xassert(csa != NULL); xassert(tree->curr != NULL); /* if the current subproblem is the root, skip updating */ if (tree->curr->up == NULL) goto skip; /* determine branching variable x[j], which was used in the parent subproblem to create the current subproblem */ j = tree->curr->up->br_var; xassert(1 <= j && j <= tree->n); /* determine the change dx[j] = new x[j] - old x[j], where new x[j] is a value of x[j] in optimal solution to LP relaxation of the current subproblem, old x[j] is a value of x[j] in optimal solution to LP relaxation of the parent subproblem */ dx = tree->mip->col[j]->prim - tree->curr->up->br_val; xassert(dx != 0.0); /* determine corresponding change dz = new dz - old dz in the objective function value */ dz = tree->mip->obj_val - tree->curr->up->lp_obj; /* determine per unit degradation of the objective function */ psi = fabs(dz / dx); /* update history information */ if (dx < 0.0) { /* the current subproblem is down-branch */ csa->dn_cnt[j]++; csa->dn_sum[j] += psi; } else /* dx > 0.0 */ { /* the current subproblem is up-branch */ csa->up_cnt[j]++; csa->up_sum[j] += psi; } skip: return; } void ios_pcost_free(glp_tree *tree) { /* free working area used on pseudocost branching */ struct csa *csa = tree->pcost; xassert(csa != NULL); xfree(csa->dn_cnt); xfree(csa->dn_sum); xfree(csa->up_cnt); xfree(csa->up_sum); xfree(csa); tree->pcost = NULL; return; } static double eval_psi(glp_tree *T, int j, int brnch) { /* compute estimation of pseudocost of variable x[j] for down- or up-branch */ struct csa *csa = T->pcost; double beta, degrad, psi; xassert(csa != NULL); xassert(1 <= j && j <= T->n); if (brnch == GLP_DN_BRNCH) { /* down-branch */ if (csa->dn_cnt[j] == 0) { /* initialize down pseudocost */ beta = T->mip->col[j]->prim; degrad = eval_degrad(T->mip, j, floor(beta)); if (degrad == DBL_MAX) { psi = DBL_MAX; goto done; } csa->dn_cnt[j] = 1; csa->dn_sum[j] = degrad / (beta - floor(beta)); } psi = csa->dn_sum[j] / (double)csa->dn_cnt[j]; } else if (brnch == GLP_UP_BRNCH) { /* up-branch */ if (csa->up_cnt[j] == 0) { /* initialize up pseudocost */ beta = T->mip->col[j]->prim; degrad = eval_degrad(T->mip, j, ceil(beta)); if (degrad == DBL_MAX) { psi = DBL_MAX; goto done; } csa->up_cnt[j] = 1; csa->up_sum[j] = degrad / (ceil(beta) - beta); } psi = csa->up_sum[j] / (double)csa->up_cnt[j]; } else xassert(brnch != brnch); done: return psi; } static void progress(glp_tree *T) { /* display progress of pseudocost initialization */ struct csa *csa = T->pcost; int j, nv = 0, ni = 0; for (j = 1; j <= T->n; j++) { if (glp_ios_can_branch(T, j)) { nv++; if (csa->dn_cnt[j] > 0 && csa->up_cnt[j] > 0) ni++; } } xprintf("Pseudocosts initialized for %d of %d variables\n", ni, nv); return; } int ios_pcost_branch(glp_tree *T, int *_next) { /* choose branching variable with pseudocost branching */ glp_long t = xtime(); int j, jjj, sel; double beta, psi, d1, d2, d, dmax; /* initialize the working arrays */ if (T->pcost == NULL) T->pcost = ios_pcost_init(T); /* nothing has been chosen so far */ jjj = 0, dmax = -1.0; /* go through the list of branching candidates */ for (j = 1; j <= T->n; j++) { if (!glp_ios_can_branch(T, j)) continue; /* determine primal value of x[j] in optimal solution to LP relaxation of the current subproblem */ beta = T->mip->col[j]->prim; /* estimate pseudocost of x[j] for down-branch */ psi = eval_psi(T, j, GLP_DN_BRNCH); if (psi == DBL_MAX) { /* down-branch has no primal feasible solution */ jjj = j, sel = GLP_DN_BRNCH; goto done; } /* estimate degradation of the objective for down-branch */ d1 = psi * (beta - floor(beta)); /* estimate pseudocost of x[j] for up-branch */ psi = eval_psi(T, j, GLP_UP_BRNCH); if (psi == DBL_MAX) { /* up-branch has no primal feasible solution */ jjj = j, sel = GLP_UP_BRNCH; goto done; } /* estimate degradation of the objective for up-branch */ d2 = psi * (ceil(beta) - beta); /* determine d = max(d1, d2) */ d = (d1 > d2 ? d1 : d2); /* choose x[j] which provides maximal estimated degradation of the objective either in down- or up-branch */ if (dmax < d) { dmax = d; jjj = j; /* continue the search from a subproblem, where degradation is less than in other one */ sel = (d1 <= d2 ? GLP_DN_BRNCH : GLP_UP_BRNCH); } /* display progress of pseudocost initialization */ if (T->parm->msg_lev >= GLP_ON) { if (xdifftime(xtime(), t) >= 10.0) { progress(T); t = xtime(); } } } if (dmax == 0.0) { /* no degradation is indicated; choose a variable having most fractional value */ jjj = branch_mostf(T, &sel); } done: *_next = sel; return jjj; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpapi12.c0000644000175000017500000023514011632352620020227 0ustar csheltoncshelton/* glpapi12.c (basis factorization and simplex tableau routines) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" /*********************************************************************** * NAME * * glp_bf_exists - check if the basis factorization exists * * SYNOPSIS * * int glp_bf_exists(glp_prob *lp); * * RETURNS * * If the basis factorization for the current basis associated with * the specified problem object exists and therefore is available for * computations, the routine glp_bf_exists returns non-zero. Otherwise * the routine returns zero. */ int glp_bf_exists(glp_prob *lp) { int ret; ret = (lp->m == 0 || lp->valid); return ret; } /*********************************************************************** * NAME * * glp_factorize - compute the basis factorization * * SYNOPSIS * * int glp_factorize(glp_prob *lp); * * DESCRIPTION * * The routine glp_factorize computes the basis factorization for the * current basis associated with the specified problem object. * * RETURNS * * 0 The basis factorization has been successfully computed. * * GLP_EBADB * The basis matrix is invalid, i.e. the number of basic (auxiliary * and structural) variables differs from the number of rows in the * problem object. * * GLP_ESING * The basis matrix is singular within the working precision. * * GLP_ECOND * The basis matrix is ill-conditioned. */ static int b_col(void *info, int j, int ind[], double val[]) { glp_prob *lp = info; int m = lp->m; GLPAIJ *aij; int k, len; xassert(1 <= j && j <= m); /* determine the ordinal number of basic auxiliary or structural variable x[k] corresponding to basic variable xB[j] */ k = lp->head[j]; /* build j-th column of the basic matrix, which is k-th column of the scaled augmented matrix (I | -R*A*S) */ if (k <= m) { /* x[k] is auxiliary variable */ len = 1; ind[1] = k; val[1] = 1.0; } else { /* x[k] is structural variable */ len = 0; for (aij = lp->col[k-m]->ptr; aij != NULL; aij = aij->c_next) { len++; ind[len] = aij->row->i; val[len] = - aij->row->rii * aij->val * aij->col->sjj; } } return len; } static void copy_bfcp(glp_prob *lp); int glp_factorize(glp_prob *lp) { int m = lp->m; int n = lp->n; GLPROW **row = lp->row; GLPCOL **col = lp->col; int *head = lp->head; int j, k, stat, ret; /* invalidate the basis factorization */ lp->valid = 0; /* build the basis header */ j = 0; for (k = 1; k <= m+n; k++) { if (k <= m) { stat = row[k]->stat; row[k]->bind = 0; } else { stat = col[k-m]->stat; col[k-m]->bind = 0; } if (stat == GLP_BS) { j++; if (j > m) { /* too many basic variables */ ret = GLP_EBADB; goto fini; } head[j] = k; if (k <= m) row[k]->bind = j; else col[k-m]->bind = j; } } if (j < m) { /* too few basic variables */ ret = GLP_EBADB; goto fini; } /* try to factorize the basis matrix */ if (m > 0) { if (lp->bfd == NULL) { lp->bfd = bfd_create_it(); copy_bfcp(lp); } switch (bfd_factorize(lp->bfd, m, lp->head, b_col, lp)) { case 0: /* ok */ break; case BFD_ESING: /* singular matrix */ ret = GLP_ESING; goto fini; case BFD_ECOND: /* ill-conditioned matrix */ ret = GLP_ECOND; goto fini; default: xassert(lp != lp); } lp->valid = 1; } /* factorization successful */ ret = 0; fini: /* bring the return code to the calling program */ return ret; } /*********************************************************************** * NAME * * glp_bf_updated - check if the basis factorization has been updated * * SYNOPSIS * * int glp_bf_updated(glp_prob *lp); * * RETURNS * * If the basis factorization has been just computed from scratch, the * routine glp_bf_updated returns zero. Otherwise, if the factorization * has been updated one or more times, the routine returns non-zero. */ int glp_bf_updated(glp_prob *lp) { int cnt; if (!(lp->m == 0 || lp->valid)) xerror("glp_bf_update: basis factorization does not exist\n"); #if 0 /* 15/XI-2009 */ cnt = (lp->m == 0 ? 0 : lp->bfd->upd_cnt); #else cnt = (lp->m == 0 ? 0 : bfd_get_count(lp->bfd)); #endif return cnt; } /*********************************************************************** * NAME * * glp_get_bfcp - retrieve basis factorization control parameters * * SYNOPSIS * * void glp_get_bfcp(glp_prob *lp, glp_bfcp *parm); * * DESCRIPTION * * The routine glp_get_bfcp retrieves control parameters, which are * used on computing and updating the basis factorization associated * with the specified problem object. * * Current values of control parameters are stored by the routine in * a glp_bfcp structure, which the parameter parm points to. */ void glp_get_bfcp(glp_prob *lp, glp_bfcp *parm) { glp_bfcp *bfcp = lp->bfcp; if (bfcp == NULL) { parm->type = GLP_BF_FT; parm->lu_size = 0; parm->piv_tol = 0.10; parm->piv_lim = 4; parm->suhl = GLP_ON; parm->eps_tol = 1e-15; parm->max_gro = 1e+10; parm->nfs_max = 100; parm->upd_tol = 1e-6; parm->nrs_max = 100; parm->rs_size = 0; } else memcpy(parm, bfcp, sizeof(glp_bfcp)); return; } /*********************************************************************** * NAME * * glp_set_bfcp - change basis factorization control parameters * * SYNOPSIS * * void glp_set_bfcp(glp_prob *lp, const glp_bfcp *parm); * * DESCRIPTION * * The routine glp_set_bfcp changes control parameters, which are used * by internal GLPK routines in computing and updating the basis * factorization associated with the specified problem object. * * New values of the control parameters should be passed in a structure * glp_bfcp, which the parameter parm points to. * * The parameter parm can be specified as NULL, in which case all * control parameters are reset to their default values. */ #if 0 /* 15/XI-2009 */ static void copy_bfcp(glp_prob *lp) { glp_bfcp _parm, *parm = &_parm; BFD *bfd = lp->bfd; glp_get_bfcp(lp, parm); xassert(bfd != NULL); bfd->type = parm->type; bfd->lu_size = parm->lu_size; bfd->piv_tol = parm->piv_tol; bfd->piv_lim = parm->piv_lim; bfd->suhl = parm->suhl; bfd->eps_tol = parm->eps_tol; bfd->max_gro = parm->max_gro; bfd->nfs_max = parm->nfs_max; bfd->upd_tol = parm->upd_tol; bfd->nrs_max = parm->nrs_max; bfd->rs_size = parm->rs_size; return; } #else static void copy_bfcp(glp_prob *lp) { glp_bfcp _parm, *parm = &_parm; glp_get_bfcp(lp, parm); bfd_set_parm(lp->bfd, parm); return; } #endif void glp_set_bfcp(glp_prob *lp, const glp_bfcp *parm) { glp_bfcp *bfcp = lp->bfcp; if (parm == NULL) { /* reset to default values */ if (bfcp != NULL) xfree(bfcp), lp->bfcp = NULL; } else { /* set to specified values */ if (bfcp == NULL) bfcp = lp->bfcp = xmalloc(sizeof(glp_bfcp)); memcpy(bfcp, parm, sizeof(glp_bfcp)); if (!(bfcp->type == GLP_BF_FT || bfcp->type == GLP_BF_BG || bfcp->type == GLP_BF_GR)) xerror("glp_set_bfcp: type = %d; invalid parameter\n", bfcp->type); if (bfcp->lu_size < 0) xerror("glp_set_bfcp: lu_size = %d; invalid parameter\n", bfcp->lu_size); if (!(0.0 < bfcp->piv_tol && bfcp->piv_tol < 1.0)) xerror("glp_set_bfcp: piv_tol = %g; invalid parameter\n", bfcp->piv_tol); if (bfcp->piv_lim < 1) xerror("glp_set_bfcp: piv_lim = %d; invalid parameter\n", bfcp->piv_lim); if (!(bfcp->suhl == GLP_ON || bfcp->suhl == GLP_OFF)) xerror("glp_set_bfcp: suhl = %d; invalid parameter\n", bfcp->suhl); if (!(0.0 <= bfcp->eps_tol && bfcp->eps_tol <= 1e-6)) xerror("glp_set_bfcp: eps_tol = %g; invalid parameter\n", bfcp->eps_tol); if (bfcp->max_gro < 1.0) xerror("glp_set_bfcp: max_gro = %g; invalid parameter\n", bfcp->max_gro); if (!(1 <= bfcp->nfs_max && bfcp->nfs_max <= 32767)) xerror("glp_set_bfcp: nfs_max = %d; invalid parameter\n", bfcp->nfs_max); if (!(0.0 < bfcp->upd_tol && bfcp->upd_tol < 1.0)) xerror("glp_set_bfcp: upd_tol = %g; invalid parameter\n", bfcp->upd_tol); if (!(1 <= bfcp->nrs_max && bfcp->nrs_max <= 32767)) xerror("glp_set_bfcp: nrs_max = %d; invalid parameter\n", bfcp->nrs_max); if (bfcp->rs_size < 0) xerror("glp_set_bfcp: rs_size = %d; invalid parameter\n", bfcp->nrs_max); if (bfcp->rs_size == 0) bfcp->rs_size = 20 * bfcp->nrs_max; } if (lp->bfd != NULL) copy_bfcp(lp); return; } /*********************************************************************** * NAME * * glp_get_bhead - retrieve the basis header information * * SYNOPSIS * * int glp_get_bhead(glp_prob *lp, int k); * * DESCRIPTION * * The routine glp_get_bhead returns the basis header information for * the current basis associated with the specified problem object. * * RETURNS * * If xB[k], 1 <= k <= m, is i-th auxiliary variable (1 <= i <= m), the * routine returns i. Otherwise, if xB[k] is j-th structural variable * (1 <= j <= n), the routine returns m+j. Here m is the number of rows * and n is the number of columns in the problem object. */ int glp_get_bhead(glp_prob *lp, int k) { if (!(lp->m == 0 || lp->valid)) xerror("glp_get_bhead: basis factorization does not exist\n"); if (!(1 <= k && k <= lp->m)) xerror("glp_get_bhead: k = %d; index out of range\n", k); return lp->head[k]; } /*********************************************************************** * NAME * * glp_get_row_bind - retrieve row index in the basis header * * SYNOPSIS * * int glp_get_row_bind(glp_prob *lp, int i); * * RETURNS * * The routine glp_get_row_bind returns the index k of basic variable * xB[k], 1 <= k <= m, which is i-th auxiliary variable, 1 <= i <= m, * in the current basis associated with the specified problem object, * where m is the number of rows. However, if i-th auxiliary variable * is non-basic, the routine returns zero. */ int glp_get_row_bind(glp_prob *lp, int i) { if (!(lp->m == 0 || lp->valid)) xerror("glp_get_row_bind: basis factorization does not exist\n" ); if (!(1 <= i && i <= lp->m)) xerror("glp_get_row_bind: i = %d; row number out of range\n", i); return lp->row[i]->bind; } /*********************************************************************** * NAME * * glp_get_col_bind - retrieve column index in the basis header * * SYNOPSIS * * int glp_get_col_bind(glp_prob *lp, int j); * * RETURNS * * The routine glp_get_col_bind returns the index k of basic variable * xB[k], 1 <= k <= m, which is j-th structural variable, 1 <= j <= n, * in the current basis associated with the specified problem object, * where m is the number of rows, n is the number of columns. However, * if j-th structural variable is non-basic, the routine returns zero.*/ int glp_get_col_bind(glp_prob *lp, int j) { if (!(lp->m == 0 || lp->valid)) xerror("glp_get_col_bind: basis factorization does not exist\n" ); if (!(1 <= j && j <= lp->n)) xerror("glp_get_col_bind: j = %d; column number out of range\n" , j); return lp->col[j]->bind; } /*********************************************************************** * NAME * * glp_ftran - perform forward transformation (solve system B*x = b) * * SYNOPSIS * * void glp_ftran(glp_prob *lp, double x[]); * * DESCRIPTION * * The routine glp_ftran performs forward transformation, i.e. solves * the system B*x = b, where B is the basis matrix corresponding to the * current basis for the specified problem object, x is the vector of * unknowns to be computed, b is the vector of right-hand sides. * * On entry elements of the vector b should be stored in dense format * in locations x[1], ..., x[m], where m is the number of rows. On exit * the routine stores elements of the vector x in the same locations. * * SCALING/UNSCALING * * Let A~ = (I | -A) is the augmented constraint matrix of the original * (unscaled) problem. In the scaled LP problem instead the matrix A the * scaled matrix A" = R*A*S is actually used, so * * A~" = (I | A") = (I | R*A*S) = (R*I*inv(R) | R*A*S) = * (1) * = R*(I | A)*S~ = R*A~*S~, * * is the scaled augmented constraint matrix, where R and S are diagonal * scaling matrices used to scale rows and columns of the matrix A, and * * S~ = diag(inv(R) | S) (2) * * is an augmented diagonal scaling matrix. * * By definition: * * A~ = (B | N), (3) * * where B is the basic matrix, which consists of basic columns of the * augmented constraint matrix A~, and N is a matrix, which consists of * non-basic columns of A~. From (1) it follows that: * * A~" = (B" | N") = (R*B*SB | R*N*SN), (4) * * where SB and SN are parts of the augmented scaling matrix S~, which * correspond to basic and non-basic variables, respectively. Therefore * * B" = R*B*SB, (5) * * which is the scaled basis matrix. */ void glp_ftran(glp_prob *lp, double x[]) { int m = lp->m; GLPROW **row = lp->row; GLPCOL **col = lp->col; int i, k; /* B*x = b ===> (R*B*SB)*(inv(SB)*x) = R*b ===> B"*x" = b", where b" = R*b, x = SB*x" */ if (!(m == 0 || lp->valid)) xerror("glp_ftran: basis factorization does not exist\n"); /* b" := R*b */ for (i = 1; i <= m; i++) x[i] *= row[i]->rii; /* x" := inv(B")*b" */ if (m > 0) bfd_ftran(lp->bfd, x); /* x := SB*x" */ for (i = 1; i <= m; i++) { k = lp->head[i]; if (k <= m) x[i] /= row[k]->rii; else x[i] *= col[k-m]->sjj; } return; } /*********************************************************************** * NAME * * glp_btran - perform backward transformation (solve system B'*x = b) * * SYNOPSIS * * void glp_btran(glp_prob *lp, double x[]); * * DESCRIPTION * * The routine glp_btran performs backward transformation, i.e. solves * the system B'*x = b, where B' is a matrix transposed to the basis * matrix corresponding to the current basis for the specified problem * problem object, x is the vector of unknowns to be computed, b is the * vector of right-hand sides. * * On entry elements of the vector b should be stored in dense format * in locations x[1], ..., x[m], where m is the number of rows. On exit * the routine stores elements of the vector x in the same locations. * * SCALING/UNSCALING * * See comments to the routine glp_ftran. */ void glp_btran(glp_prob *lp, double x[]) { int m = lp->m; GLPROW **row = lp->row; GLPCOL **col = lp->col; int i, k; /* B'*x = b ===> (SB*B'*R)*(inv(R)*x) = SB*b ===> (B")'*x" = b", where b" = SB*b, x = R*x" */ if (!(m == 0 || lp->valid)) xerror("glp_btran: basis factorization does not exist\n"); /* b" := SB*b */ for (i = 1; i <= m; i++) { k = lp->head[i]; if (k <= m) x[i] /= row[k]->rii; else x[i] *= col[k-m]->sjj; } /* x" := inv[(B")']*b" */ if (m > 0) bfd_btran(lp->bfd, x); /* x := R*x" */ for (i = 1; i <= m; i++) x[i] *= row[i]->rii; return; } /*********************************************************************** * NAME * * glp_warm_up - "warm up" LP basis * * SYNOPSIS * * int glp_warm_up(glp_prob *P); * * DESCRIPTION * * The routine glp_warm_up "warms up" the LP basis for the specified * problem object using current statuses assigned to rows and columns * (that is, to auxiliary and structural variables). * * This operation includes computing factorization of the basis matrix * (if it does not exist), computing primal and dual components of basic * solution, and determining the solution status. * * RETURNS * * 0 The operation has been successfully performed. * * GLP_EBADB * The basis matrix is invalid, i.e. the number of basic (auxiliary * and structural) variables differs from the number of rows in the * problem object. * * GLP_ESING * The basis matrix is singular within the working precision. * * GLP_ECOND * The basis matrix is ill-conditioned. */ int glp_warm_up(glp_prob *P) { GLPROW *row; GLPCOL *col; GLPAIJ *aij; int i, j, type, ret; double eps, temp, *work; /* invalidate basic solution */ P->pbs_stat = P->dbs_stat = GLP_UNDEF; P->obj_val = 0.0; P->some = 0; for (i = 1; i <= P->m; i++) { row = P->row[i]; row->prim = row->dual = 0.0; } for (j = 1; j <= P->n; j++) { col = P->col[j]; col->prim = col->dual = 0.0; } /* compute the basis factorization, if necessary */ if (!glp_bf_exists(P)) { ret = glp_factorize(P); if (ret != 0) goto done; } /* allocate working array */ work = xcalloc(1+P->m, sizeof(double)); /* determine and store values of non-basic variables, compute vector (- N * xN) */ for (i = 1; i <= P->m; i++) work[i] = 0.0; for (i = 1; i <= P->m; i++) { row = P->row[i]; if (row->stat == GLP_BS) continue; else if (row->stat == GLP_NL) row->prim = row->lb; else if (row->stat == GLP_NU) row->prim = row->ub; else if (row->stat == GLP_NF) row->prim = 0.0; else if (row->stat == GLP_NS) row->prim = row->lb; else xassert(row != row); /* N[j] is i-th column of matrix (I|-A) */ work[i] -= row->prim; } for (j = 1; j <= P->n; j++) { col = P->col[j]; if (col->stat == GLP_BS) continue; else if (col->stat == GLP_NL) col->prim = col->lb; else if (col->stat == GLP_NU) col->prim = col->ub; else if (col->stat == GLP_NF) col->prim = 0.0; else if (col->stat == GLP_NS) col->prim = col->lb; else xassert(col != col); /* N[j] is (m+j)-th column of matrix (I|-A) */ if (col->prim != 0.0) { for (aij = col->ptr; aij != NULL; aij = aij->c_next) work[aij->row->i] += aij->val * col->prim; } } /* compute vector of basic variables xB = - inv(B) * N * xN */ glp_ftran(P, work); /* store values of basic variables, check primal feasibility */ P->pbs_stat = GLP_FEAS; for (i = 1; i <= P->m; i++) { row = P->row[i]; if (row->stat != GLP_BS) continue; row->prim = work[row->bind]; type = row->type; if (type == GLP_LO || type == GLP_DB || type == GLP_FX) { eps = 1e-6 + 1e-9 * fabs(row->lb); if (row->prim < row->lb - eps) P->pbs_stat = GLP_INFEAS; } if (type == GLP_UP || type == GLP_DB || type == GLP_FX) { eps = 1e-6 + 1e-9 * fabs(row->ub); if (row->prim > row->ub + eps) P->pbs_stat = GLP_INFEAS; } } for (j = 1; j <= P->n; j++) { col = P->col[j]; if (col->stat != GLP_BS) continue; col->prim = work[col->bind]; type = col->type; if (type == GLP_LO || type == GLP_DB || type == GLP_FX) { eps = 1e-6 + 1e-9 * fabs(col->lb); if (col->prim < col->lb - eps) P->pbs_stat = GLP_INFEAS; } if (type == GLP_UP || type == GLP_DB || type == GLP_FX) { eps = 1e-6 + 1e-9 * fabs(col->ub); if (col->prim > col->ub + eps) P->pbs_stat = GLP_INFEAS; } } /* compute value of the objective function */ P->obj_val = P->c0; for (j = 1; j <= P->n; j++) { col = P->col[j]; P->obj_val += col->coef * col->prim; } /* build vector cB of objective coefficients at basic variables */ for (i = 1; i <= P->m; i++) work[i] = 0.0; for (j = 1; j <= P->n; j++) { col = P->col[j]; if (col->stat == GLP_BS) work[col->bind] = col->coef; } /* compute vector of simplex multipliers pi = inv(B') * cB */ glp_btran(P, work); /* compute and store reduced costs of non-basic variables d[j] = c[j] - N'[j] * pi, check dual feasibility */ P->dbs_stat = GLP_FEAS; for (i = 1; i <= P->m; i++) { row = P->row[i]; if (row->stat == GLP_BS) { row->dual = 0.0; continue; } /* N[j] is i-th column of matrix (I|-A) */ row->dual = - work[i]; type = row->type; temp = (P->dir == GLP_MIN ? + row->dual : - row->dual); if ((type == GLP_FR || type == GLP_LO) && temp < -1e-5 || (type == GLP_FR || type == GLP_UP) && temp > +1e-5) P->dbs_stat = GLP_INFEAS; } for (j = 1; j <= P->n; j++) { col = P->col[j]; if (col->stat == GLP_BS) { col->dual = 0.0; continue; } /* N[j] is (m+j)-th column of matrix (I|-A) */ col->dual = col->coef; for (aij = col->ptr; aij != NULL; aij = aij->c_next) col->dual += aij->val * work[aij->row->i]; type = col->type; temp = (P->dir == GLP_MIN ? + col->dual : - col->dual); if ((type == GLP_FR || type == GLP_LO) && temp < -1e-5 || (type == GLP_FR || type == GLP_UP) && temp > +1e-5) P->dbs_stat = GLP_INFEAS; } /* free working array */ xfree(work); ret = 0; done: return ret; } /*********************************************************************** * NAME * * glp_eval_tab_row - compute row of the simplex tableau * * SYNOPSIS * * int glp_eval_tab_row(glp_prob *lp, int k, int ind[], double val[]); * * DESCRIPTION * * The routine glp_eval_tab_row computes a row of the current simplex * tableau for the basic variable, which is specified by the number k: * if 1 <= k <= m, x[k] is k-th auxiliary variable; if m+1 <= k <= m+n, * x[k] is (k-m)-th structural variable, where m is number of rows, and * n is number of columns. The current basis must be available. * * The routine stores column indices and numerical values of non-zero * elements of the computed row using sparse format to the locations * ind[1], ..., ind[len] and val[1], ..., val[len], respectively, where * 0 <= len <= n is number of non-zeros returned on exit. * * Element indices stored in the array ind have the same sense as the * index k, i.e. indices 1 to m denote auxiliary variables and indices * m+1 to m+n denote structural ones (all these variables are obviously * non-basic by definition). * * The computed row shows how the specified basic variable x[k] = xB[i] * depends on non-basic variables: * * xB[i] = alfa[i,1]*xN[1] + alfa[i,2]*xN[2] + ... + alfa[i,n]*xN[n], * * where alfa[i,j] are elements of the simplex table row, xN[j] are * non-basic (auxiliary and structural) variables. * * RETURNS * * The routine returns number of non-zero elements in the simplex table * row stored in the arrays ind and val. * * BACKGROUND * * The system of equality constraints of the LP problem is: * * xR = A * xS, (1) * * where xR is the vector of auxliary variables, xS is the vector of * structural variables, A is the matrix of constraint coefficients. * * The system (1) can be written in homogenous form as follows: * * A~ * x = 0, (2) * * where A~ = (I | -A) is the augmented constraint matrix (has m rows * and m+n columns), x = (xR | xS) is the vector of all (auxiliary and * structural) variables. * * By definition for the current basis we have: * * A~ = (B | N), (3) * * where B is the basis matrix. Thus, the system (2) can be written as: * * B * xB + N * xN = 0. (4) * * From (4) it follows that: * * xB = A^ * xN, (5) * * where the matrix * * A^ = - inv(B) * N (6) * * is called the simplex table. * * It is understood that i-th row of the simplex table is: * * e * A^ = - e * inv(B) * N, (7) * * where e is a unity vector with e[i] = 1. * * To compute i-th row of the simplex table the routine first computes * i-th row of the inverse: * * rho = inv(B') * e, (8) * * where B' is a matrix transposed to B, and then computes elements of * i-th row of the simplex table as scalar products: * * alfa[i,j] = - rho * N[j] for all j, (9) * * where N[j] is a column of the augmented constraint matrix A~, which * corresponds to some non-basic auxiliary or structural variable. */ int glp_eval_tab_row(glp_prob *lp, int k, int ind[], double val[]) { int m = lp->m; int n = lp->n; int i, t, len, lll, *iii; double alfa, *rho, *vvv; if (!(m == 0 || lp->valid)) xerror("glp_eval_tab_row: basis factorization does not exist\n" ); if (!(1 <= k && k <= m+n)) xerror("glp_eval_tab_row: k = %d; variable number out of range" , k); /* determine xB[i] which corresponds to x[k] */ if (k <= m) i = glp_get_row_bind(lp, k); else i = glp_get_col_bind(lp, k-m); if (i == 0) xerror("glp_eval_tab_row: k = %d; variable must be basic", k); xassert(1 <= i && i <= m); /* allocate working arrays */ rho = xcalloc(1+m, sizeof(double)); iii = xcalloc(1+m, sizeof(int)); vvv = xcalloc(1+m, sizeof(double)); /* compute i-th row of the inverse; see (8) */ for (t = 1; t <= m; t++) rho[t] = 0.0; rho[i] = 1.0; glp_btran(lp, rho); /* compute i-th row of the simplex table */ len = 0; for (k = 1; k <= m+n; k++) { if (k <= m) { /* x[k] is auxiliary variable, so N[k] is a unity column */ if (glp_get_row_stat(lp, k) == GLP_BS) continue; /* compute alfa[i,j]; see (9) */ alfa = - rho[k]; } else { /* x[k] is structural variable, so N[k] is a column of the original constraint matrix A with negative sign */ if (glp_get_col_stat(lp, k-m) == GLP_BS) continue; /* compute alfa[i,j]; see (9) */ lll = glp_get_mat_col(lp, k-m, iii, vvv); alfa = 0.0; for (t = 1; t <= lll; t++) alfa += rho[iii[t]] * vvv[t]; } /* store alfa[i,j] */ if (alfa != 0.0) len++, ind[len] = k, val[len] = alfa; } xassert(len <= n); /* free working arrays */ xfree(rho); xfree(iii); xfree(vvv); /* return to the calling program */ return len; } /*********************************************************************** * NAME * * glp_eval_tab_col - compute column of the simplex tableau * * SYNOPSIS * * int glp_eval_tab_col(glp_prob *lp, int k, int ind[], double val[]); * * DESCRIPTION * * The routine glp_eval_tab_col computes a column of the current simplex * table for the non-basic variable, which is specified by the number k: * if 1 <= k <= m, x[k] is k-th auxiliary variable; if m+1 <= k <= m+n, * x[k] is (k-m)-th structural variable, where m is number of rows, and * n is number of columns. The current basis must be available. * * The routine stores row indices and numerical values of non-zero * elements of the computed column using sparse format to the locations * ind[1], ..., ind[len] and val[1], ..., val[len] respectively, where * 0 <= len <= m is number of non-zeros returned on exit. * * Element indices stored in the array ind have the same sense as the * index k, i.e. indices 1 to m denote auxiliary variables and indices * m+1 to m+n denote structural ones (all these variables are obviously * basic by the definition). * * The computed column shows how basic variables depend on the specified * non-basic variable x[k] = xN[j]: * * xB[1] = ... + alfa[1,j]*xN[j] + ... * xB[2] = ... + alfa[2,j]*xN[j] + ... * . . . . . . * xB[m] = ... + alfa[m,j]*xN[j] + ... * * where alfa[i,j] are elements of the simplex table column, xB[i] are * basic (auxiliary and structural) variables. * * RETURNS * * The routine returns number of non-zero elements in the simplex table * column stored in the arrays ind and val. * * BACKGROUND * * As it was explained in comments to the routine glp_eval_tab_row (see * above) the simplex table is the following matrix: * * A^ = - inv(B) * N. (1) * * Therefore j-th column of the simplex table is: * * A^ * e = - inv(B) * N * e = - inv(B) * N[j], (2) * * where e is a unity vector with e[j] = 1, B is the basis matrix, N[j] * is a column of the augmented constraint matrix A~, which corresponds * to the given non-basic auxiliary or structural variable. */ int glp_eval_tab_col(glp_prob *lp, int k, int ind[], double val[]) { int m = lp->m; int n = lp->n; int t, len, stat; double *col; if (!(m == 0 || lp->valid)) xerror("glp_eval_tab_col: basis factorization does not exist\n" ); if (!(1 <= k && k <= m+n)) xerror("glp_eval_tab_col: k = %d; variable number out of range" , k); if (k <= m) stat = glp_get_row_stat(lp, k); else stat = glp_get_col_stat(lp, k-m); if (stat == GLP_BS) xerror("glp_eval_tab_col: k = %d; variable must be non-basic", k); /* obtain column N[k] with negative sign */ col = xcalloc(1+m, sizeof(double)); for (t = 1; t <= m; t++) col[t] = 0.0; if (k <= m) { /* x[k] is auxiliary variable, so N[k] is a unity column */ col[k] = -1.0; } else { /* x[k] is structural variable, so N[k] is a column of the original constraint matrix A with negative sign */ len = glp_get_mat_col(lp, k-m, ind, val); for (t = 1; t <= len; t++) col[ind[t]] = val[t]; } /* compute column of the simplex table, which corresponds to the specified non-basic variable x[k] */ glp_ftran(lp, col); len = 0; for (t = 1; t <= m; t++) { if (col[t] != 0.0) { len++; ind[len] = glp_get_bhead(lp, t); val[len] = col[t]; } } xfree(col); /* return to the calling program */ return len; } /*********************************************************************** * NAME * * glp_transform_row - transform explicitly specified row * * SYNOPSIS * * int glp_transform_row(glp_prob *P, int len, int ind[], double val[]); * * DESCRIPTION * * The routine glp_transform_row performs the same operation as the * routine glp_eval_tab_row with exception that the row to be * transformed is specified explicitly as a sparse vector. * * The explicitly specified row may be thought as a linear form: * * x = a[1]*x[m+1] + a[2]*x[m+2] + ... + a[n]*x[m+n], (1) * * where x is an auxiliary variable for this row, a[j] are coefficients * of the linear form, x[m+j] are structural variables. * * On entry column indices and numerical values of non-zero elements of * the row should be stored in locations ind[1], ..., ind[len] and * val[1], ..., val[len], where len is the number of non-zero elements. * * This routine uses the system of equality constraints and the current * basis in order to express the auxiliary variable x in (1) through the * current non-basic variables (as if the transformed row were added to * the problem object and its auxiliary variable were basic), i.e. the * resultant row has the form: * * x = alfa[1]*xN[1] + alfa[2]*xN[2] + ... + alfa[n]*xN[n], (2) * * where xN[j] are non-basic (auxiliary or structural) variables, n is * the number of columns in the LP problem object. * * On exit the routine stores indices and numerical values of non-zero * elements of the resultant row (2) in locations ind[1], ..., ind[len'] * and val[1], ..., val[len'], where 0 <= len' <= n is the number of * non-zero elements in the resultant row returned by the routine. Note * that indices (numbers) of non-basic variables stored in the array ind * correspond to original ordinal numbers of variables: indices 1 to m * mean auxiliary variables and indices m+1 to m+n mean structural ones. * * RETURNS * * The routine returns len', which is the number of non-zero elements in * the resultant row stored in the arrays ind and val. * * BACKGROUND * * The explicitly specified row (1) is transformed in the same way as it * were the objective function row. * * From (1) it follows that: * * x = aB * xB + aN * xN, (3) * * where xB is the vector of basic variables, xN is the vector of * non-basic variables. * * The simplex table, which corresponds to the current basis, is: * * xB = [-inv(B) * N] * xN. (4) * * Therefore substituting xB from (4) to (3) we have: * * x = aB * [-inv(B) * N] * xN + aN * xN = * (5) * = rho * (-N) * xN + aN * xN = alfa * xN, * * where: * * rho = inv(B') * aB, (6) * * and * * alfa = aN + rho * (-N) (7) * * is the resultant row computed by the routine. */ int glp_transform_row(glp_prob *P, int len, int ind[], double val[]) { int i, j, k, m, n, t, lll, *iii; double alfa, *a, *aB, *rho, *vvv; if (!glp_bf_exists(P)) xerror("glp_transform_row: basis factorization does not exist " "\n"); m = glp_get_num_rows(P); n = glp_get_num_cols(P); /* unpack the row to be transformed to the array a */ a = xcalloc(1+n, sizeof(double)); for (j = 1; j <= n; j++) a[j] = 0.0; if (!(0 <= len && len <= n)) xerror("glp_transform_row: len = %d; invalid row length\n", len); for (t = 1; t <= len; t++) { j = ind[t]; if (!(1 <= j && j <= n)) xerror("glp_transform_row: ind[%d] = %d; column index out o" "f range\n", t, j); if (val[t] == 0.0) xerror("glp_transform_row: val[%d] = 0; zero coefficient no" "t allowed\n", t); if (a[j] != 0.0) xerror("glp_transform_row: ind[%d] = %d; duplicate column i" "ndices not allowed\n", t, j); a[j] = val[t]; } /* construct the vector aB */ aB = xcalloc(1+m, sizeof(double)); for (i = 1; i <= m; i++) { k = glp_get_bhead(P, i); /* xB[i] is k-th original variable */ xassert(1 <= k && k <= m+n); aB[i] = (k <= m ? 0.0 : a[k-m]); } /* solve the system B'*rho = aB to compute the vector rho */ rho = aB, glp_btran(P, rho); /* compute coefficients at non-basic auxiliary variables */ len = 0; for (i = 1; i <= m; i++) { if (glp_get_row_stat(P, i) != GLP_BS) { alfa = - rho[i]; if (alfa != 0.0) { len++; ind[len] = i; val[len] = alfa; } } } /* compute coefficients at non-basic structural variables */ iii = xcalloc(1+m, sizeof(int)); vvv = xcalloc(1+m, sizeof(double)); for (j = 1; j <= n; j++) { if (glp_get_col_stat(P, j) != GLP_BS) { alfa = a[j]; lll = glp_get_mat_col(P, j, iii, vvv); for (t = 1; t <= lll; t++) alfa += vvv[t] * rho[iii[t]]; if (alfa != 0.0) { len++; ind[len] = m+j; val[len] = alfa; } } } xassert(len <= n); xfree(iii); xfree(vvv); xfree(aB); xfree(a); return len; } /*********************************************************************** * NAME * * glp_transform_col - transform explicitly specified column * * SYNOPSIS * * int glp_transform_col(glp_prob *P, int len, int ind[], double val[]); * * DESCRIPTION * * The routine glp_transform_col performs the same operation as the * routine glp_eval_tab_col with exception that the column to be * transformed is specified explicitly as a sparse vector. * * The explicitly specified column may be thought as if it were added * to the original system of equality constraints: * * x[1] = a[1,1]*x[m+1] + ... + a[1,n]*x[m+n] + a[1]*x * x[2] = a[2,1]*x[m+1] + ... + a[2,n]*x[m+n] + a[2]*x (1) * . . . . . . . . . . . . . . . * x[m] = a[m,1]*x[m+1] + ... + a[m,n]*x[m+n] + a[m]*x * * where x[i] are auxiliary variables, x[m+j] are structural variables, * x is a structural variable for the explicitly specified column, a[i] * are constraint coefficients for x. * * On entry row indices and numerical values of non-zero elements of * the column should be stored in locations ind[1], ..., ind[len] and * val[1], ..., val[len], where len is the number of non-zero elements. * * This routine uses the system of equality constraints and the current * basis in order to express the current basic variables through the * structural variable x in (1) (as if the transformed column were added * to the problem object and the variable x were non-basic), i.e. the * resultant column has the form: * * xB[1] = ... + alfa[1]*x * xB[2] = ... + alfa[2]*x (2) * . . . . . . * xB[m] = ... + alfa[m]*x * * where xB are basic (auxiliary and structural) variables, m is the * number of rows in the problem object. * * On exit the routine stores indices and numerical values of non-zero * elements of the resultant column (2) in locations ind[1], ..., * ind[len'] and val[1], ..., val[len'], where 0 <= len' <= m is the * number of non-zero element in the resultant column returned by the * routine. Note that indices (numbers) of basic variables stored in * the array ind correspond to original ordinal numbers of variables: * indices 1 to m mean auxiliary variables and indices m+1 to m+n mean * structural ones. * * RETURNS * * The routine returns len', which is the number of non-zero elements * in the resultant column stored in the arrays ind and val. * * BACKGROUND * * The explicitly specified column (1) is transformed in the same way * as any other column of the constraint matrix using the formula: * * alfa = inv(B) * a, (3) * * where alfa is the resultant column computed by the routine. */ int glp_transform_col(glp_prob *P, int len, int ind[], double val[]) { int i, m, t; double *a, *alfa; if (!glp_bf_exists(P)) xerror("glp_transform_col: basis factorization does not exist " "\n"); m = glp_get_num_rows(P); /* unpack the column to be transformed to the array a */ a = xcalloc(1+m, sizeof(double)); for (i = 1; i <= m; i++) a[i] = 0.0; if (!(0 <= len && len <= m)) xerror("glp_transform_col: len = %d; invalid column length\n", len); for (t = 1; t <= len; t++) { i = ind[t]; if (!(1 <= i && i <= m)) xerror("glp_transform_col: ind[%d] = %d; row index out of r" "ange\n", t, i); if (val[t] == 0.0) xerror("glp_transform_col: val[%d] = 0; zero coefficient no" "t allowed\n", t); if (a[i] != 0.0) xerror("glp_transform_col: ind[%d] = %d; duplicate row indi" "ces not allowed\n", t, i); a[i] = val[t]; } /* solve the system B*a = alfa to compute the vector alfa */ alfa = a, glp_ftran(P, alfa); /* store resultant coefficients */ len = 0; for (i = 1; i <= m; i++) { if (alfa[i] != 0.0) { len++; ind[len] = glp_get_bhead(P, i); val[len] = alfa[i]; } } xfree(a); return len; } /*********************************************************************** * NAME * * glp_prim_rtest - perform primal ratio test * * SYNOPSIS * * int glp_prim_rtest(glp_prob *P, int len, const int ind[], * const double val[], int dir, double eps); * * DESCRIPTION * * The routine glp_prim_rtest performs the primal ratio test using an * explicitly specified column of the simplex table. * * The current basic solution associated with the LP problem object * must be primal feasible. * * The explicitly specified column of the simplex table shows how the * basic variables xB depend on some non-basic variable x (which is not * necessarily presented in the problem object): * * xB[1] = ... + alfa[1] * x + ... * xB[2] = ... + alfa[2] * x + ... (*) * . . . . . . . . * xB[m] = ... + alfa[m] * x + ... * * The column (*) is specifed on entry to the routine using the sparse * format. Ordinal numbers of basic variables xB[i] should be placed in * locations ind[1], ..., ind[len], where ordinal number 1 to m denote * auxiliary variables, and ordinal numbers m+1 to m+n denote structural * variables. The corresponding non-zero coefficients alfa[i] should be * placed in locations val[1], ..., val[len]. The arrays ind and val are * not changed on exit. * * The parameter dir specifies direction in which the variable x changes * on entering the basis: +1 means increasing, -1 means decreasing. * * The parameter eps is an absolute tolerance (small positive number) * used by the routine to skip small alfa[j] of the row (*). * * The routine determines which basic variable (among specified in * ind[1], ..., ind[len]) should leave the basis in order to keep primal * feasibility. * * RETURNS * * The routine glp_prim_rtest returns the index piv in the arrays ind * and val corresponding to the pivot element chosen, 1 <= piv <= len. * If the adjacent basic solution is primal unbounded and therefore the * choice cannot be made, the routine returns zero. * * COMMENTS * * If the non-basic variable x is presented in the LP problem object, * the column (*) can be computed with the routine glp_eval_tab_col; * otherwise it can be computed with the routine glp_transform_col. */ int glp_prim_rtest(glp_prob *P, int len, const int ind[], const double val[], int dir, double eps) { int k, m, n, piv, t, type, stat; double alfa, big, beta, lb, ub, temp, teta; if (glp_get_prim_stat(P) != GLP_FEAS) xerror("glp_prim_rtest: basic solution is not primal feasible " "\n"); if (!(dir == +1 || dir == -1)) xerror("glp_prim_rtest: dir = %d; invalid parameter\n", dir); if (!(0.0 < eps && eps < 1.0)) xerror("glp_prim_rtest: eps = %g; invalid parameter\n", eps); m = glp_get_num_rows(P); n = glp_get_num_cols(P); /* initial settings */ piv = 0, teta = DBL_MAX, big = 0.0; /* walk through the entries of the specified column */ for (t = 1; t <= len; t++) { /* get the ordinal number of basic variable */ k = ind[t]; if (!(1 <= k && k <= m+n)) xerror("glp_prim_rtest: ind[%d] = %d; variable number out o" "f range\n", t, k); /* determine type, bounds, status and primal value of basic variable xB[i] = x[k] in the current basic solution */ if (k <= m) { type = glp_get_row_type(P, k); lb = glp_get_row_lb(P, k); ub = glp_get_row_ub(P, k); stat = glp_get_row_stat(P, k); beta = glp_get_row_prim(P, k); } else { type = glp_get_col_type(P, k-m); lb = glp_get_col_lb(P, k-m); ub = glp_get_col_ub(P, k-m); stat = glp_get_col_stat(P, k-m); beta = glp_get_col_prim(P, k-m); } if (stat != GLP_BS) xerror("glp_prim_rtest: ind[%d] = %d; non-basic variable no" "t allowed\n", t, k); /* determine influence coefficient at basic variable xB[i] in the explicitly specified column and turn to the case of increasing the variable x in order to simplify the program logic */ alfa = (dir > 0 ? + val[t] : - val[t]); /* analyze main cases */ if (type == GLP_FR) { /* xB[i] is free variable */ continue; } else if (type == GLP_LO) lo: { /* xB[i] has an lower bound */ if (alfa > - eps) continue; temp = (lb - beta) / alfa; } else if (type == GLP_UP) up: { /* xB[i] has an upper bound */ if (alfa < + eps) continue; temp = (ub - beta) / alfa; } else if (type == GLP_DB) { /* xB[i] has both lower and upper bounds */ if (alfa < 0.0) goto lo; else goto up; } else if (type == GLP_FX) { /* xB[i] is fixed variable */ if (- eps < alfa && alfa < + eps) continue; temp = 0.0; } else xassert(type != type); /* if the value of the variable xB[i] violates its lower or upper bound (slightly, because the current basis is assumed to be primal feasible), temp is negative; we can think this happens due to round-off errors and the value is exactly on the bound; this allows replacing temp by zero */ if (temp < 0.0) temp = 0.0; /* apply the minimal ratio test */ if (teta > temp || teta == temp && big < fabs(alfa)) piv = t, teta = temp, big = fabs(alfa); } /* return index of the pivot element chosen */ return piv; } /*********************************************************************** * NAME * * glp_dual_rtest - perform dual ratio test * * SYNOPSIS * * int glp_dual_rtest(glp_prob *P, int len, const int ind[], * const double val[], int dir, double eps); * * DESCRIPTION * * The routine glp_dual_rtest performs the dual ratio test using an * explicitly specified row of the simplex table. * * The current basic solution associated with the LP problem object * must be dual feasible. * * The explicitly specified row of the simplex table is a linear form * that shows how some basic variable x (which is not necessarily * presented in the problem object) depends on non-basic variables xN: * * x = alfa[1] * xN[1] + alfa[2] * xN[2] + ... + alfa[n] * xN[n]. (*) * * The row (*) is specified on entry to the routine using the sparse * format. Ordinal numbers of non-basic variables xN[j] should be placed * in locations ind[1], ..., ind[len], where ordinal numbers 1 to m * denote auxiliary variables, and ordinal numbers m+1 to m+n denote * structural variables. The corresponding non-zero coefficients alfa[j] * should be placed in locations val[1], ..., val[len]. The arrays ind * and val are not changed on exit. * * The parameter dir specifies direction in which the variable x changes * on leaving the basis: +1 means that x goes to its lower bound, and -1 * means that x goes to its upper bound. * * The parameter eps is an absolute tolerance (small positive number) * used by the routine to skip small alfa[j] of the row (*). * * The routine determines which non-basic variable (among specified in * ind[1], ..., ind[len]) should enter the basis in order to keep dual * feasibility. * * RETURNS * * The routine glp_dual_rtest returns the index piv in the arrays ind * and val corresponding to the pivot element chosen, 1 <= piv <= len. * If the adjacent basic solution is dual unbounded and therefore the * choice cannot be made, the routine returns zero. * * COMMENTS * * If the basic variable x is presented in the LP problem object, the * row (*) can be computed with the routine glp_eval_tab_row; otherwise * it can be computed with the routine glp_transform_row. */ int glp_dual_rtest(glp_prob *P, int len, const int ind[], const double val[], int dir, double eps) { int k, m, n, piv, t, stat; double alfa, big, cost, obj, temp, teta; if (glp_get_dual_stat(P) != GLP_FEAS) xerror("glp_dual_rtest: basic solution is not dual feasible\n") ; if (!(dir == +1 || dir == -1)) xerror("glp_dual_rtest: dir = %d; invalid parameter\n", dir); if (!(0.0 < eps && eps < 1.0)) xerror("glp_dual_rtest: eps = %g; invalid parameter\n", eps); m = glp_get_num_rows(P); n = glp_get_num_cols(P); /* take into account optimization direction */ obj = (glp_get_obj_dir(P) == GLP_MIN ? +1.0 : -1.0); /* initial settings */ piv = 0, teta = DBL_MAX, big = 0.0; /* walk through the entries of the specified row */ for (t = 1; t <= len; t++) { /* get ordinal number of non-basic variable */ k = ind[t]; if (!(1 <= k && k <= m+n)) xerror("glp_dual_rtest: ind[%d] = %d; variable number out o" "f range\n", t, k); /* determine status and reduced cost of non-basic variable x[k] = xN[j] in the current basic solution */ if (k <= m) { stat = glp_get_row_stat(P, k); cost = glp_get_row_dual(P, k); } else { stat = glp_get_col_stat(P, k-m); cost = glp_get_col_dual(P, k-m); } if (stat == GLP_BS) xerror("glp_dual_rtest: ind[%d] = %d; basic variable not al" "lowed\n", t, k); /* determine influence coefficient at non-basic variable xN[j] in the explicitly specified row and turn to the case of increasing the variable x in order to simplify the program logic */ alfa = (dir > 0 ? + val[t] : - val[t]); /* analyze main cases */ if (stat == GLP_NL) { /* xN[j] is on its lower bound */ if (alfa < + eps) continue; temp = (obj * cost) / alfa; } else if (stat == GLP_NU) { /* xN[j] is on its upper bound */ if (alfa > - eps) continue; temp = (obj * cost) / alfa; } else if (stat == GLP_NF) { /* xN[j] is non-basic free variable */ if (- eps < alfa && alfa < + eps) continue; temp = 0.0; } else if (stat == GLP_NS) { /* xN[j] is non-basic fixed variable */ continue; } else xassert(stat != stat); /* if the reduced cost of the variable xN[j] violates its zero bound (slightly, because the current basis is assumed to be dual feasible), temp is negative; we can think this happens due to round-off errors and the reduced cost is exact zero; this allows replacing temp by zero */ if (temp < 0.0) temp = 0.0; /* apply the minimal ratio test */ if (teta > temp || teta == temp && big < fabs(alfa)) piv = t, teta = temp, big = fabs(alfa); } /* return index of the pivot element chosen */ return piv; } /*********************************************************************** * NAME * * glp_analyze_row - simulate one iteration of dual simplex method * * SYNOPSIS * * int glp_analyze_row(glp_prob *P, int len, const int ind[], * const double val[], int type, double rhs, double eps, int *piv, * double *x, double *dx, double *y, double *dy, double *dz); * * DESCRIPTION * * Let the current basis be optimal or dual feasible, and there be * specified a row (constraint), which is violated by the current basic * solution. The routine glp_analyze_row simulates one iteration of the * dual simplex method to determine some information on the adjacent * basis (see below), where the specified row becomes active constraint * (i.e. its auxiliary variable becomes non-basic). * * The current basic solution associated with the problem object passed * to the routine must be dual feasible, and its primal components must * be defined. * * The row to be analyzed must be previously transformed either with * the routine glp_eval_tab_row (if the row is in the problem object) * or with the routine glp_transform_row (if the row is external, i.e. * not in the problem object). This is needed to express the row only * through (auxiliary and structural) variables, which are non-basic in * the current basis: * * y = alfa[1] * xN[1] + alfa[2] * xN[2] + ... + alfa[n] * xN[n], * * where y is an auxiliary variable of the row, alfa[j] is an influence * coefficient, xN[j] is a non-basic variable. * * The row is passed to the routine in sparse format. Ordinal numbers * of non-basic variables are stored in locations ind[1], ..., ind[len], * where numbers 1 to m denote auxiliary variables while numbers m+1 to * m+n denote structural variables. Corresponding non-zero coefficients * alfa[j] are stored in locations val[1], ..., val[len]. The arrays * ind and val are ot changed on exit. * * The parameters type and rhs specify the row type and its right-hand * side as follows: * * type = GLP_LO: y = sum alfa[j] * xN[j] >= rhs * * type = GLP_UP: y = sum alfa[j] * xN[j] <= rhs * * The parameter eps is an absolute tolerance (small positive number) * used by the routine to skip small coefficients alfa[j] on performing * the dual ratio test. * * If the operation was successful, the routine stores the following * information to corresponding location (if some parameter is NULL, * its value is not stored): * * piv index in the array ind and val, 1 <= piv <= len, determining * the non-basic variable, which would enter the adjacent basis; * * x value of the non-basic variable in the current basis; * * dx difference between values of the non-basic variable in the * adjacent and current bases, dx = x.new - x.old; * * y value of the row (i.e. of its auxiliary variable) in the * current basis; * * dy difference between values of the row in the adjacent and * current bases, dy = y.new - y.old; * * dz difference between values of the objective function in the * adjacent and current bases, dz = z.new - z.old. Note that in * case of minimization dz >= 0, and in case of maximization * dz <= 0, i.e. in the adjacent basis the objective function * always gets worse (degrades). */ int _glp_analyze_row(glp_prob *P, int len, const int ind[], const double val[], int type, double rhs, double eps, int *_piv, double *_x, double *_dx, double *_y, double *_dy, double *_dz) { int t, k, dir, piv, ret = 0; double x, dx, y, dy, dz; if (P->pbs_stat == GLP_UNDEF) xerror("glp_analyze_row: primal basic solution components are " "undefined\n"); if (P->dbs_stat != GLP_FEAS) xerror("glp_analyze_row: basic solution is not dual feasible\n" ); /* compute the row value y = sum alfa[j] * xN[j] in the current basis */ if (!(0 <= len && len <= P->n)) xerror("glp_analyze_row: len = %d; invalid row length\n", len); y = 0.0; for (t = 1; t <= len; t++) { /* determine value of x[k] = xN[j] in the current basis */ k = ind[t]; if (!(1 <= k && k <= P->m+P->n)) xerror("glp_analyze_row: ind[%d] = %d; row/column index out" " of range\n", t, k); if (k <= P->m) { /* x[k] is auxiliary variable */ if (P->row[k]->stat == GLP_BS) xerror("glp_analyze_row: ind[%d] = %d; basic auxiliary v" "ariable is not allowed\n", t, k); x = P->row[k]->prim; } else { /* x[k] is structural variable */ if (P->col[k-P->m]->stat == GLP_BS) xerror("glp_analyze_row: ind[%d] = %d; basic structural " "variable is not allowed\n", t, k); x = P->col[k-P->m]->prim; } y += val[t] * x; } /* check if the row is primal infeasible in the current basis, i.e. the constraint is violated at the current point */ if (type == GLP_LO) { if (y >= rhs) { /* the constraint is not violated */ ret = 1; goto done; } /* in the adjacent basis y goes to its lower bound */ dir = +1; } else if (type == GLP_UP) { if (y <= rhs) { /* the constraint is not violated */ ret = 1; goto done; } /* in the adjacent basis y goes to its upper bound */ dir = -1; } else xerror("glp_analyze_row: type = %d; invalid parameter\n", type); /* compute dy = y.new - y.old */ dy = rhs - y; /* perform dual ratio test to determine which non-basic variable should enter the adjacent basis to keep it dual feasible */ piv = glp_dual_rtest(P, len, ind, val, dir, eps); if (piv == 0) { /* no dual feasible adjacent basis exists */ ret = 2; goto done; } /* non-basic variable x[k] = xN[j] should enter the basis */ k = ind[piv]; xassert(1 <= k && k <= P->m+P->n); /* determine its value in the current basis */ if (k <= P->m) x = P->row[k]->prim; else x = P->col[k-P->m]->prim; /* compute dx = x.new - x.old = dy / alfa[j] */ xassert(val[piv] != 0.0); dx = dy / val[piv]; /* compute dz = z.new - z.old = d[j] * dx, where d[j] is reduced cost of xN[j] in the current basis */ if (k <= P->m) dz = P->row[k]->dual * dx; else dz = P->col[k-P->m]->dual * dx; /* store the analysis results */ if (_piv != NULL) *_piv = piv; if (_x != NULL) *_x = x; if (_dx != NULL) *_dx = dx; if (_y != NULL) *_y = y; if (_dy != NULL) *_dy = dy; if (_dz != NULL) *_dz = dz; done: return ret; } #if 0 int main(void) { /* example program for the routine glp_analyze_row */ glp_prob *P; glp_smcp parm; int i, k, len, piv, ret, ind[1+100]; double rhs, x, dx, y, dy, dz, val[1+100]; P = glp_create_prob(); /* read plan.mps (see glpk/examples) */ ret = glp_read_mps(P, GLP_MPS_DECK, NULL, "plan.mps"); glp_assert(ret == 0); /* and solve it to optimality */ ret = glp_simplex(P, NULL); glp_assert(ret == 0); glp_assert(glp_get_status(P) == GLP_OPT); /* the optimal objective value is 296.217 */ /* we would like to know what happens if we would add a new row (constraint) to plan.mps: .01 * bin1 + .01 * bin2 + .02 * bin4 + .02 * bin5 <= 12 */ /* first, we specify this new row */ glp_create_index(P); len = 0; ind[++len] = glp_find_col(P, "BIN1"), val[len] = .01; ind[++len] = glp_find_col(P, "BIN2"), val[len] = .01; ind[++len] = glp_find_col(P, "BIN4"), val[len] = .02; ind[++len] = glp_find_col(P, "BIN5"), val[len] = .02; rhs = 12; /* then we can compute value of the row (i.e. of its auxiliary variable) in the current basis to see if the constraint is violated */ y = 0.0; for (k = 1; k <= len; k++) y += val[k] * glp_get_col_prim(P, ind[k]); glp_printf("y = %g\n", y); /* this prints y = 15.1372, so the constraint is violated, since we require that y <= rhs = 12 */ /* now we transform the row to express it only through non-basic (auxiliary and artificial) variables */ len = glp_transform_row(P, len, ind, val); /* finally, we simulate one step of the dual simplex method to obtain necessary information for the adjacent basis */ ret = _glp_analyze_row(P, len, ind, val, GLP_UP, rhs, 1e-9, &piv, &x, &dx, &y, &dy, &dz); glp_assert(ret == 0); glp_printf("k = %d, x = %g; dx = %g; y = %g; dy = %g; dz = %g\n", ind[piv], x, dx, y, dy, dz); /* this prints dz = 5.64418 and means that in the adjacent basis the objective function would be 296.217 + 5.64418 = 301.861 */ /* now we actually include the row into the problem object; note that the arrays ind and val are clobbered, so we need to build them once again */ len = 0; ind[++len] = glp_find_col(P, "BIN1"), val[len] = .01; ind[++len] = glp_find_col(P, "BIN2"), val[len] = .01; ind[++len] = glp_find_col(P, "BIN4"), val[len] = .02; ind[++len] = glp_find_col(P, "BIN5"), val[len] = .02; rhs = 12; i = glp_add_rows(P, 1); glp_set_row_bnds(P, i, GLP_UP, 0, rhs); glp_set_mat_row(P, i, len, ind, val); /* and perform one dual simplex iteration */ glp_init_smcp(&parm); parm.meth = GLP_DUAL; parm.it_lim = 1; glp_simplex(P, &parm); /* the current objective value is 301.861 */ return 0; } #endif /*********************************************************************** * NAME * * glp_analyze_bound - analyze active bound of non-basic variable * * SYNOPSIS * * void glp_analyze_bound(glp_prob *P, int k, double *limit1, int *var1, * double *limit2, int *var2); * * DESCRIPTION * * The routine glp_analyze_bound analyzes the effect of varying the * active bound of specified non-basic variable. * * The non-basic variable is specified by the parameter k, where * 1 <= k <= m means auxiliary variable of corresponding row while * m+1 <= k <= m+n means structural variable (column). * * Note that the current basic solution must be optimal, and the basis * factorization must exist. * * Results of the analysis have the following meaning. * * value1 is the minimal value of the active bound, at which the basis * still remains primal feasible and thus optimal. -DBL_MAX means that * the active bound has no lower limit. * * var1 is the ordinal number of an auxiliary (1 to m) or structural * (m+1 to n) basic variable, which reaches its bound first and thereby * limits further decreasing the active bound being analyzed. * if value1 = -DBL_MAX, var1 is set to 0. * * value2 is the maximal value of the active bound, at which the basis * still remains primal feasible and thus optimal. +DBL_MAX means that * the active bound has no upper limit. * * var2 is the ordinal number of an auxiliary (1 to m) or structural * (m+1 to n) basic variable, which reaches its bound first and thereby * limits further increasing the active bound being analyzed. * if value2 = +DBL_MAX, var2 is set to 0. */ void glp_analyze_bound(glp_prob *P, int k, double *value1, int *var1, double *value2, int *var2) { GLPROW *row; GLPCOL *col; int m, n, stat, kase, p, len, piv, *ind; double x, new_x, ll, uu, xx, delta, *val; /* sanity checks */ if (P == NULL || P->magic != GLP_PROB_MAGIC) xerror("glp_analyze_bound: P = %p; invalid problem object\n", P); m = P->m, n = P->n; if (!(P->pbs_stat == GLP_FEAS && P->dbs_stat == GLP_FEAS)) xerror("glp_analyze_bound: optimal basic solution required\n"); if (!(m == 0 || P->valid)) xerror("glp_analyze_bound: basis factorization required\n"); if (!(1 <= k && k <= m+n)) xerror("glp_analyze_bound: k = %d; variable number out of rang" "e\n", k); /* retrieve information about the specified non-basic variable x[k] whose active bound is to be analyzed */ if (k <= m) { row = P->row[k]; stat = row->stat; x = row->prim; } else { col = P->col[k-m]; stat = col->stat; x = col->prim; } if (stat == GLP_BS) xerror("glp_analyze_bound: k = %d; basic variable not allowed " "\n", k); /* allocate working arrays */ ind = xcalloc(1+m, sizeof(int)); val = xcalloc(1+m, sizeof(double)); /* compute column of the simplex table corresponding to the non-basic variable x[k] */ len = glp_eval_tab_col(P, k, ind, val); xassert(0 <= len && len <= m); /* perform analysis */ for (kase = -1; kase <= +1; kase += 2) { /* kase < 0 means active bound of x[k] is decreasing; kase > 0 means active bound of x[k] is increasing */ /* use the primal ratio test to determine some basic variable x[p] which reaches its bound first */ piv = glp_prim_rtest(P, len, ind, val, kase, 1e-9); if (piv == 0) { /* nothing limits changing the active bound of x[k] */ p = 0; new_x = (kase < 0 ? -DBL_MAX : +DBL_MAX); goto store; } /* basic variable x[p] limits changing the active bound of x[k]; determine its value in the current basis */ xassert(1 <= piv && piv <= len); p = ind[piv]; if (p <= m) { row = P->row[p]; ll = glp_get_row_lb(P, row->i); uu = glp_get_row_ub(P, row->i); stat = row->stat; xx = row->prim; } else { col = P->col[p-m]; ll = glp_get_col_lb(P, col->j); uu = glp_get_col_ub(P, col->j); stat = col->stat; xx = col->prim; } xassert(stat == GLP_BS); /* determine delta x[p] = bound of x[p] - value of x[p] */ if (kase < 0 && val[piv] > 0.0 || kase > 0 && val[piv] < 0.0) { /* delta x[p] < 0, so x[p] goes toward its lower bound */ xassert(ll != -DBL_MAX); delta = ll - xx; } else { /* delta x[p] > 0, so x[p] goes toward its upper bound */ xassert(uu != +DBL_MAX); delta = uu - xx; } /* delta x[p] = alfa[p,k] * delta x[k], so new x[k] = x[k] + delta x[k] = x[k] + delta x[p] / alfa[p,k] is the value of x[k] in the adjacent basis */ xassert(val[piv] != 0.0); new_x = x + delta / val[piv]; store: /* store analysis results */ if (kase < 0) { if (value1 != NULL) *value1 = new_x; if (var1 != NULL) *var1 = p; } else { if (value2 != NULL) *value2 = new_x; if (var2 != NULL) *var2 = p; } } /* free working arrays */ xfree(ind); xfree(val); return; } /*********************************************************************** * NAME * * glp_analyze_coef - analyze objective coefficient at basic variable * * SYNOPSIS * * void glp_analyze_coef(glp_prob *P, int k, double *coef1, int *var1, * double *value1, double *coef2, int *var2, double *value2); * * DESCRIPTION * * The routine glp_analyze_coef analyzes the effect of varying the * objective coefficient at specified basic variable. * * The basic variable is specified by the parameter k, where * 1 <= k <= m means auxiliary variable of corresponding row while * m+1 <= k <= m+n means structural variable (column). * * Note that the current basic solution must be optimal, and the basis * factorization must exist. * * Results of the analysis have the following meaning. * * coef1 is the minimal value of the objective coefficient, at which * the basis still remains dual feasible and thus optimal. -DBL_MAX * means that the objective coefficient has no lower limit. * * var1 is the ordinal number of an auxiliary (1 to m) or structural * (m+1 to n) non-basic variable, whose reduced cost reaches its zero * bound first and thereby limits further decreasing the objective * coefficient being analyzed. If coef1 = -DBL_MAX, var1 is set to 0. * * value1 is value of the basic variable being analyzed in an adjacent * basis, which is defined as follows. Let the objective coefficient * reaches its minimal value (coef1) and continues decreasing. Then the * reduced cost of the limiting non-basic variable (var1) becomes dual * infeasible and the current basis becomes non-optimal that forces the * limiting non-basic variable to enter the basis replacing there some * basic variable that leaves the basis to keep primal feasibility. * Should note that on determining the adjacent basis current bounds * of the basic variable being analyzed are ignored as if it were free * (unbounded) variable, so it cannot leave the basis. It may happen * that no dual feasible adjacent basis exists, in which case value1 is * set to -DBL_MAX or +DBL_MAX. * * coef2 is the maximal value of the objective coefficient, at which * the basis still remains dual feasible and thus optimal. +DBL_MAX * means that the objective coefficient has no upper limit. * * var2 is the ordinal number of an auxiliary (1 to m) or structural * (m+1 to n) non-basic variable, whose reduced cost reaches its zero * bound first and thereby limits further increasing the objective * coefficient being analyzed. If coef2 = +DBL_MAX, var2 is set to 0. * * value2 is value of the basic variable being analyzed in an adjacent * basis, which is defined exactly in the same way as value1 above with * exception that now the objective coefficient is increasing. */ void glp_analyze_coef(glp_prob *P, int k, double *coef1, int *var1, double *value1, double *coef2, int *var2, double *value2) { GLPROW *row; GLPCOL *col; int m, n, type, stat, kase, p, q, dir, clen, cpiv, rlen, rpiv, *cind, *rind; double lb, ub, coef, x, lim_coef, new_x, d, delta, ll, uu, xx, *rval, *cval; /* sanity checks */ if (P == NULL || P->magic != GLP_PROB_MAGIC) xerror("glp_analyze_coef: P = %p; invalid problem object\n", P); m = P->m, n = P->n; if (!(P->pbs_stat == GLP_FEAS && P->dbs_stat == GLP_FEAS)) xerror("glp_analyze_coef: optimal basic solution required\n"); if (!(m == 0 || P->valid)) xerror("glp_analyze_coef: basis factorization required\n"); if (!(1 <= k && k <= m+n)) xerror("glp_analyze_coef: k = %d; variable number out of range" "\n", k); /* retrieve information about the specified basic variable x[k] whose objective coefficient c[k] is to be analyzed */ if (k <= m) { row = P->row[k]; type = row->type; lb = row->lb; ub = row->ub; coef = 0.0; stat = row->stat; x = row->prim; } else { col = P->col[k-m]; type = col->type; lb = col->lb; ub = col->ub; coef = col->coef; stat = col->stat; x = col->prim; } if (stat != GLP_BS) xerror("glp_analyze_coef: k = %d; non-basic variable not allow" "ed\n", k); /* allocate working arrays */ cind = xcalloc(1+m, sizeof(int)); cval = xcalloc(1+m, sizeof(double)); rind = xcalloc(1+n, sizeof(int)); rval = xcalloc(1+n, sizeof(double)); /* compute row of the simplex table corresponding to the basic variable x[k] */ rlen = glp_eval_tab_row(P, k, rind, rval); xassert(0 <= rlen && rlen <= n); /* perform analysis */ for (kase = -1; kase <= +1; kase += 2) { /* kase < 0 means objective coefficient c[k] is decreasing; kase > 0 means objective coefficient c[k] is increasing */ /* note that decreasing c[k] is equivalent to increasing dual variable lambda[k] and vice versa; we need to correctly set the dir flag as required by the routine glp_dual_rtest */ if (P->dir == GLP_MIN) dir = - kase; else if (P->dir == GLP_MAX) dir = + kase; else xassert(P != P); /* use the dual ratio test to determine non-basic variable x[q] whose reduced cost d[q] reaches zero bound first */ rpiv = glp_dual_rtest(P, rlen, rind, rval, dir, 1e-9); if (rpiv == 0) { /* nothing limits changing c[k] */ lim_coef = (kase < 0 ? -DBL_MAX : +DBL_MAX); q = 0; /* x[k] keeps its current value */ new_x = x; goto store; } /* non-basic variable x[q] limits changing coefficient c[k]; determine its status and reduced cost d[k] in the current basis */ xassert(1 <= rpiv && rpiv <= rlen); q = rind[rpiv]; xassert(1 <= q && q <= m+n); if (q <= m) { row = P->row[q]; stat = row->stat; d = row->dual; } else { col = P->col[q-m]; stat = col->stat; d = col->dual; } /* note that delta d[q] = new d[q] - d[q] = - d[q], because new d[q] = 0; delta d[q] = alfa[k,q] * delta c[k], so delta c[k] = delta d[q] / alfa[k,q] = - d[q] / alfa[k,q] */ xassert(rval[rpiv] != 0.0); delta = - d / rval[rpiv]; /* compute new c[k] = c[k] + delta c[k], which is the limiting value of the objective coefficient c[k] */ lim_coef = coef + delta; /* let c[k] continue decreasing/increasing that makes d[q] dual infeasible and forces x[q] to enter the basis; to perform the primal ratio test we need to know in which direction x[q] changes on entering the basis; we determine that analyzing the sign of delta d[q] (see above), since d[q] may be close to zero having wrong sign */ /* let, for simplicity, the problem is minimization */ if (kase < 0 && rval[rpiv] > 0.0 || kase > 0 && rval[rpiv] < 0.0) { /* delta d[q] < 0, so d[q] being non-negative will become negative, so x[q] will increase */ dir = +1; } else { /* delta d[q] > 0, so d[q] being non-positive will become positive, so x[q] will decrease */ dir = -1; } /* if the problem is maximization, correct the direction */ if (P->dir == GLP_MAX) dir = - dir; /* check that we didn't make a silly mistake */ if (dir > 0) xassert(stat == GLP_NL || stat == GLP_NF); else xassert(stat == GLP_NU || stat == GLP_NF); /* compute column of the simplex table corresponding to the non-basic variable x[q] */ clen = glp_eval_tab_col(P, q, cind, cval); /* make x[k] temporarily free (unbounded) */ if (k <= m) { row = P->row[k]; row->type = GLP_FR; row->lb = row->ub = 0.0; } else { col = P->col[k-m]; col->type = GLP_FR; col->lb = col->ub = 0.0; } /* use the primal ratio test to determine some basic variable which leaves the basis */ cpiv = glp_prim_rtest(P, clen, cind, cval, dir, 1e-9); /* restore original bounds of the basic variable x[k] */ if (k <= m) { row = P->row[k]; row->type = type; row->lb = lb, row->ub = ub; } else { col = P->col[k-m]; col->type = type; col->lb = lb, col->ub = ub; } if (cpiv == 0) { /* non-basic variable x[q] can change unlimitedly */ if (dir < 0 && rval[rpiv] > 0.0 || dir > 0 && rval[rpiv] < 0.0) { /* delta x[k] = alfa[k,q] * delta x[q] < 0 */ new_x = -DBL_MAX; } else { /* delta x[k] = alfa[k,q] * delta x[q] > 0 */ new_x = +DBL_MAX; } goto store; } /* some basic variable x[p] limits changing non-basic variable x[q] in the adjacent basis */ xassert(1 <= cpiv && cpiv <= clen); p = cind[cpiv]; xassert(1 <= p && p <= m+n); xassert(p != k); if (p <= m) { row = P->row[p]; xassert(row->stat == GLP_BS); ll = glp_get_row_lb(P, row->i); uu = glp_get_row_ub(P, row->i); xx = row->prim; } else { col = P->col[p-m]; xassert(col->stat == GLP_BS); ll = glp_get_col_lb(P, col->j); uu = glp_get_col_ub(P, col->j); xx = col->prim; } /* determine delta x[p] = new x[p] - x[p] */ if (dir < 0 && cval[cpiv] > 0.0 || dir > 0 && cval[cpiv] < 0.0) { /* delta x[p] < 0, so x[p] goes toward its lower bound */ xassert(ll != -DBL_MAX); delta = ll - xx; } else { /* delta x[p] > 0, so x[p] goes toward its upper bound */ xassert(uu != +DBL_MAX); delta = uu - xx; } /* compute new x[k] = x[k] + alfa[k,q] * delta x[q], where delta x[q] = delta x[p] / alfa[p,q] */ xassert(cval[cpiv] != 0.0); new_x = x + (rval[rpiv] / cval[cpiv]) * delta; store: /* store analysis results */ if (kase < 0) { if (coef1 != NULL) *coef1 = lim_coef; if (var1 != NULL) *var1 = q; if (value1 != NULL) *value1 = new_x; } else { if (coef2 != NULL) *coef2 = lim_coef; if (var2 != NULL) *var2 = q; if (value2 != NULL) *value2 = new_x; } } /* free working arrays */ xfree(cind); xfree(cval); xfree(rind); xfree(rval); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpios08.c0000644000175000017500000006721011632352620020256 0ustar csheltoncshelton/* glpios08.c (clique cut generator) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpios.h" static double get_row_lb(LPX *lp, int i) { /* this routine returns lower bound of row i or -DBL_MAX if the row has no lower bound */ double lb; switch (lpx_get_row_type(lp, i)) { case LPX_FR: case LPX_UP: lb = -DBL_MAX; break; case LPX_LO: case LPX_DB: case LPX_FX: lb = lpx_get_row_lb(lp, i); break; default: xassert(lp != lp); } return lb; } static double get_row_ub(LPX *lp, int i) { /* this routine returns upper bound of row i or +DBL_MAX if the row has no upper bound */ double ub; switch (lpx_get_row_type(lp, i)) { case LPX_FR: case LPX_LO: ub = +DBL_MAX; break; case LPX_UP: case LPX_DB: case LPX_FX: ub = lpx_get_row_ub(lp, i); break; default: xassert(lp != lp); } return ub; } static double get_col_lb(LPX *lp, int j) { /* this routine returns lower bound of column j or -DBL_MAX if the column has no lower bound */ double lb; switch (lpx_get_col_type(lp, j)) { case LPX_FR: case LPX_UP: lb = -DBL_MAX; break; case LPX_LO: case LPX_DB: case LPX_FX: lb = lpx_get_col_lb(lp, j); break; default: xassert(lp != lp); } return lb; } static double get_col_ub(LPX *lp, int j) { /* this routine returns upper bound of column j or +DBL_MAX if the column has no upper bound */ double ub; switch (lpx_get_col_type(lp, j)) { case LPX_FR: case LPX_LO: ub = +DBL_MAX; break; case LPX_UP: case LPX_DB: case LPX_FX: ub = lpx_get_col_ub(lp, j); break; default: xassert(lp != lp); } return ub; } static int is_binary(LPX *lp, int j) { /* this routine checks if variable x[j] is binary */ return lpx_get_col_kind(lp, j) == LPX_IV && lpx_get_col_type(lp, j) == LPX_DB && lpx_get_col_lb(lp, j) == 0.0 && lpx_get_col_ub(lp, j) == 1.0; } static double eval_lf_min(LPX *lp, int len, int ind[], double val[]) { /* this routine computes the minimum of a specified linear form sum a[j]*x[j] j using the formula: min = sum a[j]*lb[j] + sum a[j]*ub[j], j in J+ j in J- where J+ = {j: a[j] > 0}, J- = {j: a[j] < 0}, lb[j] and ub[j] are lower and upper bound of variable x[j], resp. */ int j, t; double lb, ub, sum; sum = 0.0; for (t = 1; t <= len; t++) { j = ind[t]; if (val[t] > 0.0) { lb = get_col_lb(lp, j); if (lb == -DBL_MAX) { sum = -DBL_MAX; break; } sum += val[t] * lb; } else if (val[t] < 0.0) { ub = get_col_ub(lp, j); if (ub == +DBL_MAX) { sum = -DBL_MAX; break; } sum += val[t] * ub; } else xassert(val != val); } return sum; } static double eval_lf_max(LPX *lp, int len, int ind[], double val[]) { /* this routine computes the maximum of a specified linear form sum a[j]*x[j] j using the formula: max = sum a[j]*ub[j] + sum a[j]*lb[j], j in J+ j in J- where J+ = {j: a[j] > 0}, J- = {j: a[j] < 0}, lb[j] and ub[j] are lower and upper bound of variable x[j], resp. */ int j, t; double lb, ub, sum; sum = 0.0; for (t = 1; t <= len; t++) { j = ind[t]; if (val[t] > 0.0) { ub = get_col_ub(lp, j); if (ub == +DBL_MAX) { sum = +DBL_MAX; break; } sum += val[t] * ub; } else if (val[t] < 0.0) { lb = get_col_lb(lp, j); if (lb == -DBL_MAX) { sum = +DBL_MAX; break; } sum += val[t] * lb; } else xassert(val != val); } return sum; } /*---------------------------------------------------------------------- -- probing - determine logical relation between binary variables. -- -- This routine tentatively sets a binary variable to 0 and then to 1 -- and examines whether another binary variable is caused to be fixed. -- -- The examination is based only on one row (constraint), which is the -- following: -- -- L <= sum a[j]*x[j] <= U. (1) -- j -- -- Let x[p] be a probing variable, x[q] be an examined variable. Then -- (1) can be written as: -- -- L <= sum a[j]*x[j] + a[p]*x[p] + a[q]*x[q] <= U, (2) -- j in J' -- -- where J' = {j: j != p and j != q}. -- -- Let -- -- L' = L - a[p]*x[p], (3) -- -- U' = U - a[p]*x[p], (4) -- -- where x[p] is assumed to be fixed at 0 or 1. So (2) can be rewritten -- as follows: -- -- L' <= sum a[j]*x[j] + a[q]*x[q] <= U', (5) -- j in J' -- -- from where we have: -- -- L' - sum a[j]*x[j] <= a[q]*x[q] <= U' - sum a[j]*x[j]. (6) -- j in J' j in J' -- -- Thus, -- -- min a[q]*x[q] = L' - MAX, (7) -- -- max a[q]*x[q] = U' - MIN, (8) -- -- where -- -- MIN = min sum a[j]*x[j], (9) -- j in J' -- -- MAX = max sum a[j]*x[j]. (10) -- j in J' -- -- Formulae (7) and (8) allows determining implied lower and upper -- bounds of x[q]. -- -- Parameters len, val, L and U specify the constraint (1). -- -- Parameters lf_min and lf_max specify implied lower and upper bounds -- of the linear form (1). It is assumed that these bounds are computed -- with the routines eval_lf_min and eval_lf_max (see above). -- -- Parameter p specifies the probing variable x[p], which is set to 0 -- (if set is 0) or to 1 (if set is 1). -- -- Parameter q specifies the examined variable x[q]. -- -- On exit the routine returns one of the following codes: -- -- 0 - there is no logical relation between x[p] and x[q]; -- 1 - x[q] can take only on value 0; -- 2 - x[q] can take only on value 1. */ static int probing(int len, double val[], double L, double U, double lf_min, double lf_max, int p, int set, int q) { double temp; xassert(1 <= p && p < q && q <= len); /* compute L' (3) */ if (L != -DBL_MAX && set) L -= val[p]; /* compute U' (4) */ if (U != +DBL_MAX && set) U -= val[p]; /* compute MIN (9) */ if (lf_min != -DBL_MAX) { if (val[p] < 0.0) lf_min -= val[p]; if (val[q] < 0.0) lf_min -= val[q]; } /* compute MAX (10) */ if (lf_max != +DBL_MAX) { if (val[p] > 0.0) lf_max -= val[p]; if (val[q] > 0.0) lf_max -= val[q]; } /* compute implied lower bound of x[q]; see (7), (8) */ if (val[q] > 0.0) { if (L == -DBL_MAX || lf_max == +DBL_MAX) temp = -DBL_MAX; else temp = (L - lf_max) / val[q]; } else { if (U == +DBL_MAX || lf_min == -DBL_MAX) temp = -DBL_MAX; else temp = (U - lf_min) / val[q]; } if (temp > 0.001) return 2; /* compute implied upper bound of x[q]; see (7), (8) */ if (val[q] > 0.0) { if (U == +DBL_MAX || lf_min == -DBL_MAX) temp = +DBL_MAX; else temp = (U - lf_min) / val[q]; } else { if (L == -DBL_MAX || lf_max == +DBL_MAX) temp = +DBL_MAX; else temp = (L - lf_max) / val[q]; } if (temp < 0.999) return 1; /* there is no logical relation between x[p] and x[q] */ return 0; } struct COG { /* conflict graph; it represents logical relations between binary variables and has a vertex for each binary variable and its complement, and an edge between two vertices when at most one of the variables represented by the vertices can equal one in an optimal solution */ int n; /* number of variables */ int nb; /* number of binary variables represented in the graph (note that not all binary variables can be represented); vertices which correspond to binary variables have numbers 1, ..., nb while vertices which correspond to complements of binary variables have numbers nb+1, ..., nb+nb */ int ne; /* number of edges in the graph */ int *vert; /* int vert[1+n]; */ /* if x[j] is a binary variable represented in the graph, vert[j] is the vertex number corresponding to x[j]; otherwise vert[j] is zero */ int *orig; /* int list[1:nb]; */ /* if vert[j] = k > 0, then orig[k] = j */ unsigned char *a; /* adjacency matrix of the graph having 2*nb rows and columns; only strict lower triangle is stored in dense packed form */ }; /*---------------------------------------------------------------------- -- lpx_create_cog - create the conflict graph. -- -- SYNOPSIS -- -- #include "glplpx.h" -- void *lpx_create_cog(LPX *lp); -- -- DESCRIPTION -- -- The routine lpx_create_cog creates the conflict graph for a given -- problem instance. -- -- RETURNS -- -- If the graph has been created, the routine returns a pointer to it. -- Otherwise the routine returns NULL. */ #define MAX_NB 4000 #define MAX_ROW_LEN 500 static void lpx_add_cog_edge(void *_cog, int i, int j); static void *lpx_create_cog(LPX *lp) { struct COG *cog = NULL; int m, n, nb, i, j, p, q, len, *ind, *vert, *orig; double L, U, lf_min, lf_max, *val; xprintf("Creating the conflict graph...\n"); m = lpx_get_num_rows(lp); n = lpx_get_num_cols(lp); /* determine which binary variables should be included in the conflict graph */ nb = 0; vert = xcalloc(1+n, sizeof(int)); for (j = 1; j <= n; j++) vert[j] = 0; orig = xcalloc(1+n, sizeof(int)); ind = xcalloc(1+n, sizeof(int)); val = xcalloc(1+n, sizeof(double)); for (i = 1; i <= m; i++) { L = get_row_lb(lp, i); U = get_row_ub(lp, i); if (L == -DBL_MAX && U == +DBL_MAX) continue; len = lpx_get_mat_row(lp, i, ind, val); if (len > MAX_ROW_LEN) continue; lf_min = eval_lf_min(lp, len, ind, val); lf_max = eval_lf_max(lp, len, ind, val); for (p = 1; p <= len; p++) { if (!is_binary(lp, ind[p])) continue; for (q = p+1; q <= len; q++) { if (!is_binary(lp, ind[q])) continue; if (probing(len, val, L, U, lf_min, lf_max, p, 0, q) || probing(len, val, L, U, lf_min, lf_max, p, 1, q)) { /* there is a logical relation */ /* include the first variable in the graph */ j = ind[p]; if (vert[j] == 0) nb++, vert[j] = nb, orig[nb] = j; /* incude the second variable in the graph */ j = ind[q]; if (vert[j] == 0) nb++, vert[j] = nb, orig[nb] = j; } } } } /* if the graph is either empty or has too many vertices, do not create it */ if (nb == 0 || nb > MAX_NB) { xprintf("The conflict graph is either empty or too big\n"); xfree(vert); xfree(orig); goto done; } /* create the conflict graph */ cog = xmalloc(sizeof(struct COG)); cog->n = n; cog->nb = nb; cog->ne = 0; cog->vert = vert; cog->orig = orig; len = nb + nb; /* number of vertices */ len = (len * (len - 1)) / 2; /* number of entries in triangle */ len = (len + (CHAR_BIT - 1)) / CHAR_BIT; /* bytes needed */ cog->a = xmalloc(len); memset(cog->a, 0, len); for (j = 1; j <= nb; j++) { /* add edge between variable and its complement */ lpx_add_cog_edge(cog, +orig[j], -orig[j]); } for (i = 1; i <= m; i++) { L = get_row_lb(lp, i); U = get_row_ub(lp, i); if (L == -DBL_MAX && U == +DBL_MAX) continue; len = lpx_get_mat_row(lp, i, ind, val); if (len > MAX_ROW_LEN) continue; lf_min = eval_lf_min(lp, len, ind, val); lf_max = eval_lf_max(lp, len, ind, val); for (p = 1; p <= len; p++) { if (!is_binary(lp, ind[p])) continue; for (q = p+1; q <= len; q++) { if (!is_binary(lp, ind[q])) continue; /* set x[p] to 0 and examine x[q] */ switch (probing(len, val, L, U, lf_min, lf_max, p, 0, q)) { case 0: /* no logical relation */ break; case 1: /* x[p] = 0 implies x[q] = 0 */ lpx_add_cog_edge(cog, -ind[p], +ind[q]); break; case 2: /* x[p] = 0 implies x[q] = 1 */ lpx_add_cog_edge(cog, -ind[p], -ind[q]); break; default: xassert(lp != lp); } /* set x[p] to 1 and examine x[q] */ switch (probing(len, val, L, U, lf_min, lf_max, p, 1, q)) { case 0: /* no logical relation */ break; case 1: /* x[p] = 1 implies x[q] = 0 */ lpx_add_cog_edge(cog, +ind[p], +ind[q]); break; case 2: /* x[p] = 1 implies x[q] = 1 */ lpx_add_cog_edge(cog, +ind[p], -ind[q]); break; default: xassert(lp != lp); } } } } xprintf("The conflict graph has 2*%d vertices and %d edges\n", cog->nb, cog->ne); done: xfree(ind); xfree(val); return cog; } /*---------------------------------------------------------------------- -- lpx_add_cog_edge - add edge to the conflict graph. -- -- SYNOPSIS -- -- #include "glplpx.h" -- void lpx_add_cog_edge(void *cog, int i, int j); -- -- DESCRIPTION -- -- The routine lpx_add_cog_edge adds an edge to the conflict graph. -- The edge connects x[i] (if i > 0) or its complement (if i < 0) and -- x[j] (if j > 0) or its complement (if j < 0), where i and j are -- original ordinal numbers of corresponding variables. */ static void lpx_add_cog_edge(void *_cog, int i, int j) { struct COG *cog = _cog; int k; xassert(i != j); /* determine indices of corresponding vertices */ if (i > 0) { xassert(1 <= i && i <= cog->n); i = cog->vert[i]; xassert(i != 0); } else { i = -i; xassert(1 <= i && i <= cog->n); i = cog->vert[i]; xassert(i != 0); i += cog->nb; } if (j > 0) { xassert(1 <= j && j <= cog->n); j = cog->vert[j]; xassert(j != 0); } else { j = -j; xassert(1 <= j && j <= cog->n); j = cog->vert[j]; xassert(j != 0); j += cog->nb; } /* only lower triangle is stored, so we need i > j */ if (i < j) k = i, i = j, j = k; k = ((i - 1) * (i - 2)) / 2 + (j - 1); cog->a[k / CHAR_BIT] |= (unsigned char)(1 << ((CHAR_BIT - 1) - k % CHAR_BIT)); cog->ne++; return; } /*---------------------------------------------------------------------- -- MAXIMUM WEIGHT CLIQUE -- -- Two subroutines sub() and wclique() below are intended to find a -- maximum weight clique in a given undirected graph. These subroutines -- are slightly modified version of the program WCLIQUE developed by -- Patric Ostergard and based -- on ideas from the article "P. R. J. Ostergard, A new algorithm for -- the maximum-weight clique problem, submitted for publication", which -- in turn is a generalization of the algorithm for unweighted graphs -- presented in "P. R. J. Ostergard, A fast algorithm for the maximum -- clique problem, submitted for publication". -- -- USED WITH PERMISSION OF THE AUTHOR OF THE ORIGINAL CODE. */ struct dsa { /* dynamic storage area */ int n; /* number of vertices */ int *wt; /* int wt[0:n-1]; */ /* weights */ unsigned char *a; /* adjacency matrix (packed lower triangle without main diag.) */ int record; /* weight of best clique */ int rec_level; /* number of vertices in best clique */ int *rec; /* int rec[0:n-1]; */ /* best clique so far */ int *clique; /* int clique[0:n-1]; */ /* table for pruning */ int *set; /* int set[0:n-1]; */ /* current clique */ }; #define n (dsa->n) #define wt (dsa->wt) #define a (dsa->a) #define record (dsa->record) #define rec_level (dsa->rec_level) #define rec (dsa->rec) #define clique (dsa->clique) #define set (dsa->set) #if 0 static int is_edge(struct dsa *dsa, int i, int j) { /* if there is arc (i,j), the routine returns true; otherwise false; 0 <= i, j < n */ int k; xassert(0 <= i && i < n); xassert(0 <= j && j < n); if (i == j) return 0; if (i < j) k = i, i = j, j = k; k = (i * (i - 1)) / 2 + j; return a[k / CHAR_BIT] & (unsigned char)(1 << ((CHAR_BIT - 1) - k % CHAR_BIT)); } #else #define is_edge(dsa, i, j) ((i) == (j) ? 0 : \ (i) > (j) ? is_edge1(i, j) : is_edge1(j, i)) #define is_edge1(i, j) is_edge2(((i) * ((i) - 1)) / 2 + (j)) #define is_edge2(k) (a[(k) / CHAR_BIT] & \ (unsigned char)(1 << ((CHAR_BIT - 1) - (k) % CHAR_BIT))) #endif static void sub(struct dsa *dsa, int ct, int table[], int level, int weight, int l_weight) { int i, j, k, curr_weight, left_weight, *p1, *p2, *newtable; newtable = xcalloc(n, sizeof(int)); if (ct <= 0) { /* 0 or 1 elements left; include these */ if (ct == 0) { set[level++] = table[0]; weight += l_weight; } if (weight > record) { record = weight; rec_level = level; for (i = 0; i < level; i++) rec[i] = set[i]; } goto done; } for (i = ct; i >= 0; i--) { if ((level == 0) && (i < ct)) goto done; k = table[i]; if ((level > 0) && (clique[k] <= (record - weight))) goto done; /* prune */ set[level] = k; curr_weight = weight + wt[k]; l_weight -= wt[k]; if (l_weight <= (record - curr_weight)) goto done; /* prune */ p1 = newtable; p2 = table; left_weight = 0; while (p2 < table + i) { j = *p2++; if (is_edge(dsa, j, k)) { *p1++ = j; left_weight += wt[j]; } } if (left_weight <= (record - curr_weight)) continue; sub(dsa, p1 - newtable - 1, newtable, level + 1, curr_weight, left_weight); } done: xfree(newtable); return; } static int wclique(int _n, int w[], unsigned char _a[], int sol[]) { struct dsa _dsa, *dsa = &_dsa; int i, j, p, max_wt, max_nwt, wth, *used, *nwt, *pos; glp_long timer; n = _n; wt = &w[1]; a = _a; record = 0; rec_level = 0; rec = &sol[1]; clique = xcalloc(n, sizeof(int)); set = xcalloc(n, sizeof(int)); used = xcalloc(n, sizeof(int)); nwt = xcalloc(n, sizeof(int)); pos = xcalloc(n, sizeof(int)); /* start timer */ timer = xtime(); /* order vertices */ for (i = 0; i < n; i++) { nwt[i] = 0; for (j = 0; j < n; j++) if (is_edge(dsa, i, j)) nwt[i] += wt[j]; } for (i = 0; i < n; i++) used[i] = 0; for (i = n-1; i >= 0; i--) { max_wt = -1; max_nwt = -1; for (j = 0; j < n; j++) { if ((!used[j]) && ((wt[j] > max_wt) || (wt[j] == max_wt && nwt[j] > max_nwt))) { max_wt = wt[j]; max_nwt = nwt[j]; p = j; } } pos[i] = p; used[p] = 1; for (j = 0; j < n; j++) if ((!used[j]) && (j != p) && (is_edge(dsa, p, j))) nwt[j] -= wt[p]; } /* main routine */ wth = 0; for (i = 0; i < n; i++) { wth += wt[pos[i]]; sub(dsa, i, pos, 0, 0, wth); clique[pos[i]] = record; #if 0 if (utime() >= timer + 5.0) #else if (xdifftime(xtime(), timer) >= 5.0 - 0.001) #endif { /* print current record and reset timer */ xprintf("level = %d (%d); best = %d\n", i+1, n, record); #if 0 timer = utime(); #else timer = xtime(); #endif } } xfree(clique); xfree(set); xfree(used); xfree(nwt); xfree(pos); /* return the solution found */ for (i = 1; i <= rec_level; i++) sol[i]++; return rec_level; } #undef n #undef wt #undef a #undef record #undef rec_level #undef rec #undef clique #undef set /*---------------------------------------------------------------------- -- lpx_clique_cut - generate cluque cut. -- -- SYNOPSIS -- -- #include "glplpx.h" -- int lpx_clique_cut(LPX *lp, void *cog, int ind[], double val[]); -- -- DESCRIPTION -- -- The routine lpx_clique_cut generates a clique cut using the conflict -- graph specified by the parameter cog. -- -- If a violated clique cut has been found, it has the following form: -- -- sum{j in J} a[j]*x[j] <= b. -- -- Variable indices j in J are stored in elements ind[1], ..., ind[len] -- while corresponding constraint coefficients are stored in elements -- val[1], ..., val[len], where len is returned on exit. The right-hand -- side b is stored in element val[0]. -- -- RETURNS -- -- If the cutting plane has been successfully generated, the routine -- returns 1 <= len <= n, which is the number of non-zero coefficients -- in the inequality constraint. Otherwise, the routine returns zero. */ static int lpx_clique_cut(LPX *lp, void *_cog, int ind[], double val[]) { struct COG *cog = _cog; int n = lpx_get_num_cols(lp); int j, t, v, card, temp, len = 0, *w, *sol; double x, sum, b, *vec; /* allocate working arrays */ w = xcalloc(1 + 2 * cog->nb, sizeof(int)); sol = xcalloc(1 + 2 * cog->nb, sizeof(int)); vec = xcalloc(1+n, sizeof(double)); /* assign weights to vertices of the conflict graph */ for (t = 1; t <= cog->nb; t++) { j = cog->orig[t]; x = lpx_get_col_prim(lp, j); temp = (int)(100.0 * x + 0.5); if (temp < 0) temp = 0; if (temp > 100) temp = 100; w[t] = temp; w[cog->nb + t] = 100 - temp; } /* find a clique of maximum weight */ card = wclique(2 * cog->nb, w, cog->a, sol); /* compute the clique weight for unscaled values */ sum = 0.0; for ( t = 1; t <= card; t++) { v = sol[t]; xassert(1 <= v && v <= 2 * cog->nb); if (v <= cog->nb) { /* vertex v corresponds to binary variable x[j] */ j = cog->orig[v]; x = lpx_get_col_prim(lp, j); sum += x; } else { /* vertex v corresponds to the complement of x[j] */ j = cog->orig[v - cog->nb]; x = lpx_get_col_prim(lp, j); sum += 1.0 - x; } } /* if the sum of binary variables and their complements in the clique greater than 1, the clique cut is violated */ if (sum >= 1.01) { /* construct the inquality */ for (j = 1; j <= n; j++) vec[j] = 0; b = 1.0; for (t = 1; t <= card; t++) { v = sol[t]; if (v <= cog->nb) { /* vertex v corresponds to binary variable x[j] */ j = cog->orig[v]; xassert(1 <= j && j <= n); vec[j] += 1.0; } else { /* vertex v corresponds to the complement of x[j] */ j = cog->orig[v - cog->nb]; xassert(1 <= j && j <= n); vec[j] -= 1.0; b -= 1.0; } } xassert(len == 0); for (j = 1; j <= n; j++) { if (vec[j] != 0.0) { len++; ind[len] = j, val[len] = vec[j]; } } ind[0] = 0, val[0] = b; } /* free working arrays */ xfree(w); xfree(sol); xfree(vec); /* return to the calling program */ return len; } /*---------------------------------------------------------------------- -- lpx_delete_cog - delete the conflict graph. -- -- SYNOPSIS -- -- #include "glplpx.h" -- void lpx_delete_cog(void *cog); -- -- DESCRIPTION -- -- The routine lpx_delete_cog deletes the conflict graph, which the -- parameter cog points to, freeing all the memory allocated to this -- object. */ static void lpx_delete_cog(void *_cog) { struct COG *cog = _cog; xfree(cog->vert); xfree(cog->orig); xfree(cog->a); xfree(cog); } /**********************************************************************/ void *ios_clq_init(glp_tree *tree) { /* initialize clique cut generator */ glp_prob *mip = tree->mip; xassert(mip != NULL); return lpx_create_cog(mip); } /*********************************************************************** * NAME * * ios_clq_gen - generate clique cuts * * SYNOPSIS * * #include "glpios.h" * void ios_clq_gen(glp_tree *tree, void *gen); * * DESCRIPTION * * The routine ios_clq_gen generates clique cuts for the current point * and adds them to the clique pool. */ void ios_clq_gen(glp_tree *tree, void *gen) { int n = lpx_get_num_cols(tree->mip); int len, *ind; double *val; xassert(gen != NULL); ind = xcalloc(1+n, sizeof(int)); val = xcalloc(1+n, sizeof(double)); len = lpx_clique_cut(tree->mip, gen, ind, val); if (len > 0) { /* xprintf("len = %d\n", len); */ glp_ios_add_row(tree, NULL, GLP_RF_CLQ, 0, len, ind, val, GLP_UP, val[0]); } xfree(ind); xfree(val); return; } /**********************************************************************/ void ios_clq_term(void *gen) { /* terminate clique cut generator */ xassert(gen != NULL); lpx_delete_cog(gen); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpqmd.h0000644000175000017500000000431311632352620020075 0ustar csheltoncshelton/* glpqmd.h (quotient minimum degree algorithm) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPQMD_H #define GLPQMD_H #define genqmd _glp_qmd_genqmd void genqmd(int *neqns, int xadj[], int adjncy[], int perm[], int invp[], int deg[], int marker[], int rchset[], int nbrhd[], int qsize[], int qlink[], int *nofsub); /* GENeral Quotient Minimum Degree algorithm */ #define qmdrch _glp_qmd_qmdrch void qmdrch(int *root, int xadj[], int adjncy[], int deg[], int marker[], int *rchsze, int rchset[], int *nhdsze, int nbrhd[]); /* Quotient MD ReaCHable set */ #define qmdqt _glp_qmd_qmdqt void qmdqt(int *root, int xadj[], int adjncy[], int marker[], int *rchsze, int rchset[], int nbrhd[]); /* Quotient MD Quotient graph Transformation */ #define qmdupd _glp_qmd_qmdupd void qmdupd(int xadj[], int adjncy[], int *nlist, int list[], int deg[], int qsize[], int qlink[], int marker[], int rchset[], int nbrhd[]); /* Quotient MD UPDate */ #define qmdmrg _glp_qmd_qmdmrg void qmdmrg(int xadj[], int adjncy[], int deg[], int qsize[], int qlink[], int marker[], int *deg0, int *nhdsze, int nbrhd[], int rchset[], int ovrlp[]); /* Quotient MD MeRGe */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpssx01.c0000644000175000017500000006616211632352620020277 0ustar csheltoncshelton/* glpssx01.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpenv.h" #include "glpssx.h" #define xfault xerror /*---------------------------------------------------------------------- // ssx_create - create simplex solver workspace. // // This routine creates the workspace used by simplex solver routines, // and returns a pointer to it. // // Parameters m, n, and nnz specify, respectively, the number of rows, // columns, and non-zero constraint coefficients. // // This routine only allocates the memory for the workspace components, // so the workspace needs to be saturated by data. */ SSX *ssx_create(int m, int n, int nnz) { SSX *ssx; int i, j, k; if (m < 1) xfault("ssx_create: m = %d; invalid number of rows\n", m); if (n < 1) xfault("ssx_create: n = %d; invalid number of columns\n", n); if (nnz < 0) xfault("ssx_create: nnz = %d; invalid number of non-zero const" "raint coefficients\n", nnz); ssx = xmalloc(sizeof(SSX)); ssx->m = m; ssx->n = n; ssx->type = xcalloc(1+m+n, sizeof(int)); ssx->lb = xcalloc(1+m+n, sizeof(mpq_t)); for (k = 1; k <= m+n; k++) mpq_init(ssx->lb[k]); ssx->ub = xcalloc(1+m+n, sizeof(mpq_t)); for (k = 1; k <= m+n; k++) mpq_init(ssx->ub[k]); ssx->coef = xcalloc(1+m+n, sizeof(mpq_t)); for (k = 0; k <= m+n; k++) mpq_init(ssx->coef[k]); ssx->A_ptr = xcalloc(1+n+1, sizeof(int)); ssx->A_ptr[n+1] = nnz+1; ssx->A_ind = xcalloc(1+nnz, sizeof(int)); ssx->A_val = xcalloc(1+nnz, sizeof(mpq_t)); for (k = 1; k <= nnz; k++) mpq_init(ssx->A_val[k]); ssx->stat = xcalloc(1+m+n, sizeof(int)); ssx->Q_row = xcalloc(1+m+n, sizeof(int)); ssx->Q_col = xcalloc(1+m+n, sizeof(int)); ssx->binv = bfx_create_binv(); ssx->bbar = xcalloc(1+m, sizeof(mpq_t)); for (i = 0; i <= m; i++) mpq_init(ssx->bbar[i]); ssx->pi = xcalloc(1+m, sizeof(mpq_t)); for (i = 1; i <= m; i++) mpq_init(ssx->pi[i]); ssx->cbar = xcalloc(1+n, sizeof(mpq_t)); for (j = 1; j <= n; j++) mpq_init(ssx->cbar[j]); ssx->rho = xcalloc(1+m, sizeof(mpq_t)); for (i = 1; i <= m; i++) mpq_init(ssx->rho[i]); ssx->ap = xcalloc(1+n, sizeof(mpq_t)); for (j = 1; j <= n; j++) mpq_init(ssx->ap[j]); ssx->aq = xcalloc(1+m, sizeof(mpq_t)); for (i = 1; i <= m; i++) mpq_init(ssx->aq[i]); mpq_init(ssx->delta); return ssx; } /*---------------------------------------------------------------------- // ssx_factorize - factorize the current basis matrix. // // This routine computes factorization of the current basis matrix B // and returns the singularity flag. If the matrix B is non-singular, // the flag is zero, otherwise non-zero. */ static int basis_col(void *info, int j, int ind[], mpq_t val[]) { /* this auxiliary routine provides row indices and numeric values of non-zero elements in j-th column of the matrix B */ SSX *ssx = info; int m = ssx->m; int n = ssx->n; int *A_ptr = ssx->A_ptr; int *A_ind = ssx->A_ind; mpq_t *A_val = ssx->A_val; int *Q_col = ssx->Q_col; int k, len, ptr; xassert(1 <= j && j <= m); k = Q_col[j]; /* x[k] = xB[j] */ xassert(1 <= k && k <= m+n); /* j-th column of the matrix B is k-th column of the augmented constraint matrix (I | -A) */ if (k <= m) { /* it is a column of the unity matrix I */ len = 1, ind[1] = k, mpq_set_si(val[1], 1, 1); } else { /* it is a column of the original constraint matrix -A */ len = 0; for (ptr = A_ptr[k-m]; ptr < A_ptr[k-m+1]; ptr++) { len++; ind[len] = A_ind[ptr]; mpq_neg(val[len], A_val[ptr]); } } return len; } int ssx_factorize(SSX *ssx) { int ret; ret = bfx_factorize(ssx->binv, ssx->m, basis_col, ssx); return ret; } /*---------------------------------------------------------------------- // ssx_get_xNj - determine value of non-basic variable. // // This routine determines the value of non-basic variable xN[j] in the // current basic solution defined as follows: // // 0, if xN[j] is free variable // lN[j], if xN[j] is on its lower bound // uN[j], if xN[j] is on its upper bound // lN[j] = uN[j], if xN[j] is fixed variable // // where lN[j] and uN[j] are lower and upper bounds of xN[j]. */ void ssx_get_xNj(SSX *ssx, int j, mpq_t x) { int m = ssx->m; int n = ssx->n; mpq_t *lb = ssx->lb; mpq_t *ub = ssx->ub; int *stat = ssx->stat; int *Q_col = ssx->Q_col; int k; xassert(1 <= j && j <= n); k = Q_col[m+j]; /* x[k] = xN[j] */ xassert(1 <= k && k <= m+n); switch (stat[k]) { case SSX_NL: /* xN[j] is on its lower bound */ mpq_set(x, lb[k]); break; case SSX_NU: /* xN[j] is on its upper bound */ mpq_set(x, ub[k]); break; case SSX_NF: /* xN[j] is free variable */ mpq_set_si(x, 0, 1); break; case SSX_NS: /* xN[j] is fixed variable */ mpq_set(x, lb[k]); break; default: xassert(stat != stat); } return; } /*---------------------------------------------------------------------- // ssx_eval_bbar - compute values of basic variables. // // This routine computes values of basic variables xB in the current // basic solution as follows: // // beta = - inv(B) * N * xN, // // where B is the basis matrix, N is the matrix of non-basic columns, // xN is a vector of current values of non-basic variables. */ void ssx_eval_bbar(SSX *ssx) { int m = ssx->m; int n = ssx->n; mpq_t *coef = ssx->coef; int *A_ptr = ssx->A_ptr; int *A_ind = ssx->A_ind; mpq_t *A_val = ssx->A_val; int *Q_col = ssx->Q_col; mpq_t *bbar = ssx->bbar; int i, j, k, ptr; mpq_t x, temp; mpq_init(x); mpq_init(temp); /* bbar := 0 */ for (i = 1; i <= m; i++) mpq_set_si(bbar[i], 0, 1); /* bbar := - N * xN = - N[1] * xN[1] - ... - N[n] * xN[n] */ for (j = 1; j <= n; j++) { ssx_get_xNj(ssx, j, x); if (mpq_sgn(x) == 0) continue; k = Q_col[m+j]; /* x[k] = xN[j] */ if (k <= m) { /* N[j] is a column of the unity matrix I */ mpq_sub(bbar[k], bbar[k], x); } else { /* N[j] is a column of the original constraint matrix -A */ for (ptr = A_ptr[k-m]; ptr < A_ptr[k-m+1]; ptr++) { mpq_mul(temp, A_val[ptr], x); mpq_add(bbar[A_ind[ptr]], bbar[A_ind[ptr]], temp); } } } /* bbar := inv(B) * bbar */ bfx_ftran(ssx->binv, bbar, 0); #if 1 /* compute value of the objective function */ /* bbar[0] := c[0] */ mpq_set(bbar[0], coef[0]); /* bbar[0] := bbar[0] + sum{i in B} cB[i] * xB[i] */ for (i = 1; i <= m; i++) { k = Q_col[i]; /* x[k] = xB[i] */ if (mpq_sgn(coef[k]) == 0) continue; mpq_mul(temp, coef[k], bbar[i]); mpq_add(bbar[0], bbar[0], temp); } /* bbar[0] := bbar[0] + sum{j in N} cN[j] * xN[j] */ for (j = 1; j <= n; j++) { k = Q_col[m+j]; /* x[k] = xN[j] */ if (mpq_sgn(coef[k]) == 0) continue; ssx_get_xNj(ssx, j, x); mpq_mul(temp, coef[k], x); mpq_add(bbar[0], bbar[0], temp); } #endif mpq_clear(x); mpq_clear(temp); return; } /*---------------------------------------------------------------------- // ssx_eval_pi - compute values of simplex multipliers. // // This routine computes values of simplex multipliers (shadow prices) // pi in the current basic solution as follows: // // pi = inv(B') * cB, // // where B' is a matrix transposed to the basis matrix B, cB is a vector // of objective coefficients at basic variables xB. */ void ssx_eval_pi(SSX *ssx) { int m = ssx->m; mpq_t *coef = ssx->coef; int *Q_col = ssx->Q_col; mpq_t *pi = ssx->pi; int i; /* pi := cB */ for (i = 1; i <= m; i++) mpq_set(pi[i], coef[Q_col[i]]); /* pi := inv(B') * cB */ bfx_btran(ssx->binv, pi); return; } /*---------------------------------------------------------------------- // ssx_eval_dj - compute reduced cost of non-basic variable. // // This routine computes reduced cost d[j] of non-basic variable xN[j] // in the current basic solution as follows: // // d[j] = cN[j] - N[j] * pi, // // where cN[j] is an objective coefficient at xN[j], N[j] is a column // of the augmented constraint matrix (I | -A) corresponding to xN[j], // pi is the vector of simplex multipliers (shadow prices). */ void ssx_eval_dj(SSX *ssx, int j, mpq_t dj) { int m = ssx->m; int n = ssx->n; mpq_t *coef = ssx->coef; int *A_ptr = ssx->A_ptr; int *A_ind = ssx->A_ind; mpq_t *A_val = ssx->A_val; int *Q_col = ssx->Q_col; mpq_t *pi = ssx->pi; int k, ptr, end; mpq_t temp; mpq_init(temp); xassert(1 <= j && j <= n); k = Q_col[m+j]; /* x[k] = xN[j] */ xassert(1 <= k && k <= m+n); /* j-th column of the matrix N is k-th column of the augmented constraint matrix (I | -A) */ if (k <= m) { /* it is a column of the unity matrix I */ mpq_sub(dj, coef[k], pi[k]); } else { /* it is a column of the original constraint matrix -A */ mpq_set(dj, coef[k]); for (ptr = A_ptr[k-m], end = A_ptr[k-m+1]; ptr < end; ptr++) { mpq_mul(temp, A_val[ptr], pi[A_ind[ptr]]); mpq_add(dj, dj, temp); } } mpq_clear(temp); return; } /*---------------------------------------------------------------------- // ssx_eval_cbar - compute reduced costs of all non-basic variables. // // This routine computes the vector of reduced costs pi in the current // basic solution for all non-basic variables, including fixed ones. */ void ssx_eval_cbar(SSX *ssx) { int n = ssx->n; mpq_t *cbar = ssx->cbar; int j; for (j = 1; j <= n; j++) ssx_eval_dj(ssx, j, cbar[j]); return; } /*---------------------------------------------------------------------- // ssx_eval_rho - compute p-th row of the inverse. // // This routine computes p-th row of the matrix inv(B), where B is the // current basis matrix. // // p-th row of the inverse is computed using the following formula: // // rho = inv(B') * e[p], // // where B' is a matrix transposed to B, e[p] is a unity vector, which // contains one in p-th position. */ void ssx_eval_rho(SSX *ssx) { int m = ssx->m; int p = ssx->p; mpq_t *rho = ssx->rho; int i; xassert(1 <= p && p <= m); /* rho := 0 */ for (i = 1; i <= m; i++) mpq_set_si(rho[i], 0, 1); /* rho := e[p] */ mpq_set_si(rho[p], 1, 1); /* rho := inv(B') * rho */ bfx_btran(ssx->binv, rho); return; } /*---------------------------------------------------------------------- // ssx_eval_row - compute pivot row of the simplex table. // // This routine computes p-th (pivot) row of the current simplex table // A~ = - inv(B) * N using the following formula: // // A~[p] = - N' * inv(B') * e[p] = - N' * rho[p], // // where N' is a matrix transposed to the matrix N, rho[p] is p-th row // of the inverse inv(B). */ void ssx_eval_row(SSX *ssx) { int m = ssx->m; int n = ssx->n; int *A_ptr = ssx->A_ptr; int *A_ind = ssx->A_ind; mpq_t *A_val = ssx->A_val; int *Q_col = ssx->Q_col; mpq_t *rho = ssx->rho; mpq_t *ap = ssx->ap; int j, k, ptr; mpq_t temp; mpq_init(temp); for (j = 1; j <= n; j++) { /* ap[j] := - N'[j] * rho (inner product) */ k = Q_col[m+j]; /* x[k] = xN[j] */ if (k <= m) mpq_neg(ap[j], rho[k]); else { mpq_set_si(ap[j], 0, 1); for (ptr = A_ptr[k-m]; ptr < A_ptr[k-m+1]; ptr++) { mpq_mul(temp, A_val[ptr], rho[A_ind[ptr]]); mpq_add(ap[j], ap[j], temp); } } } mpq_clear(temp); return; } /*---------------------------------------------------------------------- // ssx_eval_col - compute pivot column of the simplex table. // // This routine computes q-th (pivot) column of the current simplex // table A~ = - inv(B) * N using the following formula: // // A~[q] = - inv(B) * N[q], // // where N[q] is q-th column of the matrix N corresponding to chosen // non-basic variable xN[q]. */ void ssx_eval_col(SSX *ssx) { int m = ssx->m; int n = ssx->n; int *A_ptr = ssx->A_ptr; int *A_ind = ssx->A_ind; mpq_t *A_val = ssx->A_val; int *Q_col = ssx->Q_col; int q = ssx->q; mpq_t *aq = ssx->aq; int i, k, ptr; xassert(1 <= q && q <= n); /* aq := 0 */ for (i = 1; i <= m; i++) mpq_set_si(aq[i], 0, 1); /* aq := N[q] */ k = Q_col[m+q]; /* x[k] = xN[q] */ if (k <= m) { /* N[q] is a column of the unity matrix I */ mpq_set_si(aq[k], 1, 1); } else { /* N[q] is a column of the original constraint matrix -A */ for (ptr = A_ptr[k-m]; ptr < A_ptr[k-m+1]; ptr++) mpq_neg(aq[A_ind[ptr]], A_val[ptr]); } /* aq := inv(B) * aq */ bfx_ftran(ssx->binv, aq, 1); /* aq := - aq */ for (i = 1; i <= m; i++) mpq_neg(aq[i], aq[i]); return; } /*---------------------------------------------------------------------- // ssx_chuzc - choose pivot column. // // This routine chooses non-basic variable xN[q] whose reduced cost // indicates possible improving of the objective function to enter it // in the basis. // // Currently the standard (textbook) pricing is used, i.e. that // non-basic variable is preferred which has greatest reduced cost (in // magnitude). // // If xN[q] has been chosen, the routine stores its number q and also // sets the flag q_dir that indicates direction in which xN[q] has to // change (+1 means increasing, -1 means decreasing). // // If the choice cannot be made, because the current basic solution is // dual feasible, the routine sets the number q to 0. */ void ssx_chuzc(SSX *ssx) { int m = ssx->m; int n = ssx->n; int dir = (ssx->dir == SSX_MIN ? +1 : -1); int *Q_col = ssx->Q_col; int *stat = ssx->stat; mpq_t *cbar = ssx->cbar; int j, k, s, q, q_dir; double best, temp; /* nothing is chosen so far */ q = 0, q_dir = 0, best = 0.0; /* look through the list of non-basic variables */ for (j = 1; j <= n; j++) { k = Q_col[m+j]; /* x[k] = xN[j] */ s = dir * mpq_sgn(cbar[j]); if ((stat[k] == SSX_NF || stat[k] == SSX_NL) && s < 0 || (stat[k] == SSX_NF || stat[k] == SSX_NU) && s > 0) { /* reduced cost of xN[j] indicates possible improving of the objective function */ temp = fabs(mpq_get_d(cbar[j])); xassert(temp != 0.0); if (q == 0 || best < temp) q = j, q_dir = - s, best = temp; } } ssx->q = q, ssx->q_dir = q_dir; return; } /*---------------------------------------------------------------------- // ssx_chuzr - choose pivot row. // // This routine looks through elements of q-th column of the simplex // table and chooses basic variable xB[p] which should leave the basis. // // The choice is based on the standard (textbook) ratio test. // // If xB[p] has been chosen, the routine stores its number p and also // sets its non-basic status p_stat which should be assigned to xB[p] // when it has left the basis and become xN[q]. // // Special case p < 0 means that xN[q] is double-bounded variable and // it reaches its opposite bound before any basic variable does that, // so the current basis remains unchanged. // // If the choice cannot be made, because xN[q] can infinitely change in // the feasible direction, the routine sets the number p to 0. */ void ssx_chuzr(SSX *ssx) { int m = ssx->m; int n = ssx->n; int *type = ssx->type; mpq_t *lb = ssx->lb; mpq_t *ub = ssx->ub; int *Q_col = ssx->Q_col; mpq_t *bbar = ssx->bbar; int q = ssx->q; mpq_t *aq = ssx->aq; int q_dir = ssx->q_dir; int i, k, s, t, p, p_stat; mpq_t teta, temp; mpq_init(teta); mpq_init(temp); xassert(1 <= q && q <= n); xassert(q_dir == +1 || q_dir == -1); /* nothing is chosen so far */ p = 0, p_stat = 0; /* look through the list of basic variables */ for (i = 1; i <= m; i++) { s = q_dir * mpq_sgn(aq[i]); if (s < 0) { /* xB[i] decreases */ k = Q_col[i]; /* x[k] = xB[i] */ t = type[k]; if (t == SSX_LO || t == SSX_DB || t == SSX_FX) { /* xB[i] has finite lower bound */ mpq_sub(temp, bbar[i], lb[k]); mpq_div(temp, temp, aq[i]); mpq_abs(temp, temp); if (p == 0 || mpq_cmp(teta, temp) > 0) { p = i; p_stat = (t == SSX_FX ? SSX_NS : SSX_NL); mpq_set(teta, temp); } } } else if (s > 0) { /* xB[i] increases */ k = Q_col[i]; /* x[k] = xB[i] */ t = type[k]; if (t == SSX_UP || t == SSX_DB || t == SSX_FX) { /* xB[i] has finite upper bound */ mpq_sub(temp, bbar[i], ub[k]); mpq_div(temp, temp, aq[i]); mpq_abs(temp, temp); if (p == 0 || mpq_cmp(teta, temp) > 0) { p = i; p_stat = (t == SSX_FX ? SSX_NS : SSX_NU); mpq_set(teta, temp); } } } /* if something has been chosen and the ratio test indicates exact degeneracy, the search can be finished */ if (p != 0 && mpq_sgn(teta) == 0) break; } /* if xN[q] is double-bounded, check if it can reach its opposite bound before any basic variable */ k = Q_col[m+q]; /* x[k] = xN[q] */ if (type[k] == SSX_DB) { mpq_sub(temp, ub[k], lb[k]); if (p == 0 || mpq_cmp(teta, temp) > 0) { p = -1; p_stat = -1; mpq_set(teta, temp); } } ssx->p = p; ssx->p_stat = p_stat; /* if xB[p] has been chosen, determine its actual change in the adjacent basis (it has the same sign as q_dir) */ if (p != 0) { xassert(mpq_sgn(teta) >= 0); if (q_dir > 0) mpq_set(ssx->delta, teta); else mpq_neg(ssx->delta, teta); } mpq_clear(teta); mpq_clear(temp); return; } /*---------------------------------------------------------------------- // ssx_update_bbar - update values of basic variables. // // This routine recomputes the current values of basic variables for // the adjacent basis. // // The simplex table for the current basis is the following: // // xB[i] = sum{j in 1..n} alfa[i,j] * xN[q], i = 1,...,m // // therefore // // delta xB[i] = alfa[i,q] * delta xN[q], i = 1,...,m // // where delta xN[q] = xN.new[q] - xN[q] is the change of xN[q] in the // adjacent basis, and delta xB[i] = xB.new[i] - xB[i] is the change of // xB[i]. This gives formulae for recomputing values of xB[i]: // // xB.new[p] = xN[q] + delta xN[q] // // (because xN[q] becomes xB[p] in the adjacent basis), and // // xB.new[i] = xB[i] + alfa[i,q] * delta xN[q], i != p // // for other basic variables. */ void ssx_update_bbar(SSX *ssx) { int m = ssx->m; int n = ssx->n; mpq_t *bbar = ssx->bbar; mpq_t *cbar = ssx->cbar; int p = ssx->p; int q = ssx->q; mpq_t *aq = ssx->aq; int i; mpq_t temp; mpq_init(temp); xassert(1 <= q && q <= n); if (p < 0) { /* xN[q] is double-bounded and goes to its opposite bound */ /* nop */; } else { /* xN[q] becomes xB[p] in the adjacent basis */ /* xB.new[p] = xN[q] + delta xN[q] */ xassert(1 <= p && p <= m); ssx_get_xNj(ssx, q, temp); mpq_add(bbar[p], temp, ssx->delta); } /* update values of other basic variables depending on xN[q] */ for (i = 1; i <= m; i++) { if (i == p) continue; /* xB.new[i] = xB[i] + alfa[i,q] * delta xN[q] */ if (mpq_sgn(aq[i]) == 0) continue; mpq_mul(temp, aq[i], ssx->delta); mpq_add(bbar[i], bbar[i], temp); } #if 1 /* update value of the objective function */ /* z.new = z + d[q] * delta xN[q] */ mpq_mul(temp, cbar[q], ssx->delta); mpq_add(bbar[0], bbar[0], temp); #endif mpq_clear(temp); return; } /*---------------------------------------------------------------------- -- ssx_update_pi - update simplex multipliers. -- -- This routine recomputes the vector of simplex multipliers for the -- adjacent basis. */ void ssx_update_pi(SSX *ssx) { int m = ssx->m; int n = ssx->n; mpq_t *pi = ssx->pi; mpq_t *cbar = ssx->cbar; int p = ssx->p; int q = ssx->q; mpq_t *aq = ssx->aq; mpq_t *rho = ssx->rho; int i; mpq_t new_dq, temp; mpq_init(new_dq); mpq_init(temp); xassert(1 <= p && p <= m); xassert(1 <= q && q <= n); /* compute d[q] in the adjacent basis */ mpq_div(new_dq, cbar[q], aq[p]); /* update the vector of simplex multipliers */ for (i = 1; i <= m; i++) { if (mpq_sgn(rho[i]) == 0) continue; mpq_mul(temp, new_dq, rho[i]); mpq_sub(pi[i], pi[i], temp); } mpq_clear(new_dq); mpq_clear(temp); return; } /*---------------------------------------------------------------------- // ssx_update_cbar - update reduced costs of non-basic variables. // // This routine recomputes the vector of reduced costs of non-basic // variables for the adjacent basis. */ void ssx_update_cbar(SSX *ssx) { int m = ssx->m; int n = ssx->n; mpq_t *cbar = ssx->cbar; int p = ssx->p; int q = ssx->q; mpq_t *ap = ssx->ap; int j; mpq_t temp; mpq_init(temp); xassert(1 <= p && p <= m); xassert(1 <= q && q <= n); /* compute d[q] in the adjacent basis */ /* d.new[q] = d[q] / alfa[p,q] */ mpq_div(cbar[q], cbar[q], ap[q]); /* update reduced costs of other non-basic variables */ for (j = 1; j <= n; j++) { if (j == q) continue; /* d.new[j] = d[j] - (alfa[p,j] / alfa[p,q]) * d[q] */ if (mpq_sgn(ap[j]) == 0) continue; mpq_mul(temp, ap[j], cbar[q]); mpq_sub(cbar[j], cbar[j], temp); } mpq_clear(temp); return; } /*---------------------------------------------------------------------- // ssx_change_basis - change current basis to adjacent one. // // This routine changes the current basis to the adjacent one swapping // basic variable xB[p] and non-basic variable xN[q]. */ void ssx_change_basis(SSX *ssx) { int m = ssx->m; int n = ssx->n; int *type = ssx->type; int *stat = ssx->stat; int *Q_row = ssx->Q_row; int *Q_col = ssx->Q_col; int p = ssx->p; int q = ssx->q; int p_stat = ssx->p_stat; int k, kp, kq; if (p < 0) { /* special case: xN[q] goes to its opposite bound */ xassert(1 <= q && q <= n); k = Q_col[m+q]; /* x[k] = xN[q] */ xassert(type[k] == SSX_DB); switch (stat[k]) { case SSX_NL: stat[k] = SSX_NU; break; case SSX_NU: stat[k] = SSX_NL; break; default: xassert(stat != stat); } } else { /* xB[p] leaves the basis, xN[q] enters the basis */ xassert(1 <= p && p <= m); xassert(1 <= q && q <= n); kp = Q_col[p]; /* x[kp] = xB[p] */ kq = Q_col[m+q]; /* x[kq] = xN[q] */ /* check non-basic status of xB[p] which becomes xN[q] */ switch (type[kp]) { case SSX_FR: xassert(p_stat == SSX_NF); break; case SSX_LO: xassert(p_stat == SSX_NL); break; case SSX_UP: xassert(p_stat == SSX_NU); break; case SSX_DB: xassert(p_stat == SSX_NL || p_stat == SSX_NU); break; case SSX_FX: xassert(p_stat == SSX_NS); break; default: xassert(type != type); } /* swap xB[p] and xN[q] */ stat[kp] = (char)p_stat, stat[kq] = SSX_BS; Q_row[kp] = m+q, Q_row[kq] = p; Q_col[p] = kq, Q_col[m+q] = kp; /* update factorization of the basis matrix */ if (bfx_update(ssx->binv, p)) { if (ssx_factorize(ssx)) xassert(("Internal error: basis matrix is singular", 0)); } } return; } /*---------------------------------------------------------------------- // ssx_delete - delete simplex solver workspace. // // This routine deletes the simplex solver workspace freeing all the // memory allocated to this object. */ void ssx_delete(SSX *ssx) { int m = ssx->m; int n = ssx->n; int nnz = ssx->A_ptr[n+1]-1; int i, j, k; xfree(ssx->type); for (k = 1; k <= m+n; k++) mpq_clear(ssx->lb[k]); xfree(ssx->lb); for (k = 1; k <= m+n; k++) mpq_clear(ssx->ub[k]); xfree(ssx->ub); for (k = 0; k <= m+n; k++) mpq_clear(ssx->coef[k]); xfree(ssx->coef); xfree(ssx->A_ptr); xfree(ssx->A_ind); for (k = 1; k <= nnz; k++) mpq_clear(ssx->A_val[k]); xfree(ssx->A_val); xfree(ssx->stat); xfree(ssx->Q_row); xfree(ssx->Q_col); bfx_delete_binv(ssx->binv); for (i = 0; i <= m; i++) mpq_clear(ssx->bbar[i]); xfree(ssx->bbar); for (i = 1; i <= m; i++) mpq_clear(ssx->pi[i]); xfree(ssx->pi); for (j = 1; j <= n; j++) mpq_clear(ssx->cbar[j]); xfree(ssx->cbar); for (i = 1; i <= m; i++) mpq_clear(ssx->rho[i]); xfree(ssx->rho); for (j = 1; j <= n; j++) mpq_clear(ssx->ap[j]); xfree(ssx->ap); for (i = 1; i <= m; i++) mpq_clear(ssx->aq[i]); xfree(ssx->aq); mpq_clear(ssx->delta); xfree(ssx); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpnet03.c0000644000175000017500000006040511632352620020244 0ustar csheltoncshelton/* glpnet03.c (Klingman's network problem generator) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * This code is the result of translation of the Fortran program NETGEN * developed by Dr. Darwin Klingman, which is publically available from * NETLIB at . * * The translation was made by Andrew Makhorin . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" /*********************************************************************** * NAME * * glp_netgen - Klingman's network problem generator * * SYNOPSIS * * int glp_netgen(glp_graph *G, int v_rhs, int a_cap, int a_cost, * const int parm[1+15]); * * DESCRIPTION * * The routine glp_netgen is a network problem generator developed by * Dr. Darwin Klingman. It can create capacitated and uncapacitated * minimum cost flow (or transshipment), transportation, and assignment * problems. * * The parameter G specifies the graph object, to which the generated * problem data have to be stored. Note that on entry the graph object * is erased with the routine glp_erase_graph. * * The parameter v_rhs specifies an offset of the field of type double * in the vertex data block, to which the routine stores the supply or * demand value. If v_rhs < 0, the value is not stored. * * The parameter a_cap specifies an offset of the field of type double * in the arc data block, to which the routine stores the arc capacity. * If a_cap < 0, the capacity is not stored. * * The parameter a_cost specifies an offset of the field of type double * in the arc data block, to which the routine stores the per-unit cost * if the arc flow. If a_cost < 0, the cost is not stored. * * The array parm contains description of the network to be generated: * * parm[0] not used * parm[1] (iseed) 8-digit positive random number seed * parm[2] (nprob) 8-digit problem id number * parm[3] (nodes) total number of nodes * parm[4] (nsorc) total number of source nodes (including * transshipment nodes) * parm[5] (nsink) total number of sink nodes (including * transshipment nodes) * parm[6] (iarcs) number of arcs * parm[7] (mincst) minimum cost for arcs * parm[8] (maxcst) maximum cost for arcs * parm[9] (itsup) total supply * parm[10] (ntsorc) number of transshipment source nodes * parm[11] (ntsink) number of transshipment sink nodes * parm[12] (iphic) percentage of skeleton arcs to be given * the maximum cost * parm[13] (ipcap) percentage of arcs to be capacitated * parm[14] (mincap) minimum upper bound for capacitated arcs * parm[15] (maxcap) maximum upper bound for capacitated arcs * * The routine generates a transportation problem if: * * nsorc + nsink = nodes, ntsorc = 0, and ntsink = 0. * * The routine generates an assignment problem if the requirements for * a transportation problem are met and: * * nsorc = nsink and itsup = nsorc. * * RETURNS * * If the instance was successfully generated, the routine glp_netgen * returns zero; otherwise, if specified parameters are inconsistent, * the routine returns a non-zero error code. * * REFERENCES * * D.Klingman, A.Napier, and J.Stutz. NETGEN: A program for generating * large scale capacitated assignment, transportation, and minimum cost * flow networks. Management Science 20 (1974), 814-20. */ struct csa { /* common storage area */ glp_graph *G; int v_rhs, a_cap, a_cost; int nodes, iarcs, mincst, maxcst, itsup, nsorc, nsink, nonsor, nfsink, narcs, nsort, nftsor, ipcap, mincap, maxcap, ktl, nodlft, *ipred, *ihead, *itail, *iflag, *isup, *lsinks, mult, modul, i15, i16, jran; }; #define G (csa->G) #define v_rhs (csa->v_rhs) #define a_cap (csa->a_cap) #define a_cost (csa->a_cost) #define nodes (csa->nodes) #define iarcs (csa->iarcs) #define mincst (csa->mincst) #define maxcst (csa->maxcst) #define itsup (csa->itsup) #define nsorc (csa->nsorc) #define nsink (csa->nsink) #define nonsor (csa->nonsor) #define nfsink (csa->nfsink) #define narcs (csa->narcs) #define nsort (csa->nsort) #define nftsor (csa->nftsor) #define ipcap (csa->ipcap) #define mincap (csa->mincap) #define maxcap (csa->maxcap) #define ktl (csa->ktl) #define nodlft (csa->nodlft) #if 0 /* spent a day to find out this bug */ #define ist (csa->ist) #else #define ist (ipred[0]) #endif #define ipred (csa->ipred) #define ihead (csa->ihead) #define itail (csa->itail) #define iflag (csa->iflag) #define isup (csa->isup) #define lsinks (csa->lsinks) #define mult (csa->mult) #define modul (csa->modul) #define i15 (csa->i15) #define i16 (csa->i16) #define jran (csa->jran) static void cresup(struct csa *csa); static void chain(struct csa *csa, int lpick, int lsorc); static void chnarc(struct csa *csa, int lsorc); static void sort(struct csa *csa); static void pickj(struct csa *csa, int it); static void assign(struct csa *csa); static void setran(struct csa *csa, int iseed); static int iran(struct csa *csa, int ilow, int ihigh); int glp_netgen(glp_graph *G_, int _v_rhs, int _a_cap, int _a_cost, const int parm[1+15]) { struct csa _csa, *csa = &_csa; int iseed, nprob, ntsorc, ntsink, iphic, i, nskel, nltr, ltsink, ntrans, npsink, nftr, npsorc, ntravl, ntrrem, lsorc, lpick, nsksr, nsrchn, j, item, l, ks, k, ksp, li, n, ii, it, ih, icap, jcap, icost, jcost, ret; G = G_; v_rhs = _v_rhs; a_cap = _a_cap; a_cost = _a_cost; if (G != NULL) { if (v_rhs >= 0 && v_rhs > G->v_size - (int)sizeof(double)) xerror("glp_netgen: v_rhs = %d; invalid offset\n", v_rhs); if (a_cap >= 0 && a_cap > G->a_size - (int)sizeof(double)) xerror("glp_netgen: a_cap = %d; invalid offset\n", a_cap); if (a_cost >= 0 && a_cost > G->a_size - (int)sizeof(double)) xerror("glp_netgen: a_cost = %d; invalid offset\n", a_cost); } /* Input the user's random number seed and fix it if non-positive. */ iseed = parm[1]; nprob = parm[2]; if (iseed <= 0) iseed = 13502460; setran(csa, iseed); /* Input the user's problem characteristics. */ nodes = parm[3]; nsorc = parm[4]; nsink = parm[5]; iarcs = parm[6]; mincst = parm[7]; maxcst = parm[8]; itsup = parm[9]; ntsorc = parm[10]; ntsink = parm[11]; iphic = parm[12]; ipcap = parm[13]; mincap = parm[14]; maxcap = parm[15]; /* Check the size of the problem. */ if (!(10 <= nodes && nodes <= 100000)) { ret = 1; goto done; } /* Check user supplied parameters for consistency. */ if (!(nsorc >= 0 && nsink >= 0 && nsorc + nsink <= nodes)) { ret = 2; goto done; } if (iarcs < 0) { ret = 3; goto done; } if (mincst > maxcst) { ret = 4; goto done; } if (itsup < 0) { ret = 5; goto done; } if (!(0 <= ntsorc && ntsorc <= nsorc)) { ret = 6; goto done; } if (!(0 <= ntsink && ntsink <= nsink)) { ret = 7; goto done; } if (!(0 <= iphic && iphic <= 100)) { ret = 8; goto done; } if (!(0 <= ipcap && ipcap <= 100)) { ret = 9; goto done; } if (mincap > maxcap) { ret = 10; goto done; } /* Initailize the graph object. */ if (G != NULL) { glp_erase_graph(G, G->v_size, G->a_size); glp_add_vertices(G, nodes); if (v_rhs >= 0) { double zero = 0.0; for (i = 1; i <= nodes; i++) { glp_vertex *v = G->v[i]; memcpy((char *)v->data + v_rhs, &zero, sizeof(double)); } } } /* Allocate working arrays. */ ipred = xcalloc(1+nodes, sizeof(int)); ihead = xcalloc(1+nodes, sizeof(int)); itail = xcalloc(1+nodes, sizeof(int)); iflag = xcalloc(1+nodes, sizeof(int)); isup = xcalloc(1+nodes, sizeof(int)); lsinks = xcalloc(1+nodes, sizeof(int)); /* Print the problem documentation records. */ if (G == NULL) { xprintf("BEGIN\n"); xprintf("NETGEN PROBLEM%8d%10s%10d NODES AND%10d ARCS\n", nprob, "", nodes, iarcs); xprintf("USER:%11d%11d%11d%11d%11d%11d\nDATA:%11d%11d%11d%11d%" "11d%11d\n", iseed, nsorc, nsink, mincst, maxcst, itsup, ntsorc, ntsink, iphic, ipcap, mincap, maxcap); } else glp_set_graph_name(G, "NETGEN"); /* Set various constants used in the program. */ narcs = 0; nskel = 0; nltr = nodes - nsink; ltsink = nltr + ntsink; ntrans = nltr - nsorc; nfsink = nltr + 1; nonsor = nodes - nsorc + ntsorc; npsink = nsink - ntsink; nodlft = nodes - nsink + ntsink; nftr = nsorc + 1; nftsor = nsorc - ntsorc + 1; npsorc = nsorc - ntsorc; /* Randomly distribute the supply among the source nodes. */ if (npsorc + npsink == nodes && npsorc == npsink && itsup == nsorc) { assign(csa); nskel = nsorc; goto L390; } cresup(csa); /* Print the supply records. */ if (G == NULL) { xprintf("SUPPLY\n"); for (i = 1; i <= nsorc; i++) xprintf("%6s%6d%18s%10d\n", "", i, "", isup[i]); xprintf("ARCS\n"); } else { if (v_rhs >= 0) { for (i = 1; i <= nsorc; i++) { double temp = (double)isup[i]; glp_vertex *v = G->v[i]; memcpy((char *)v->data + v_rhs, &temp, sizeof(double)); } } } /* Make the sources point to themselves in ipred array. */ for (i = 1; i <= nsorc; i++) ipred[i] = i; if (ntrans == 0) goto L170; /* Chain the transshipment nodes together in the ipred array. */ ist = nftr; ipred[nltr] = 0; for (i = nftr; i < nltr; i++) ipred[i] = i+1; /* Form even length chains for 60 percent of the transshipments.*/ ntravl = 6 * ntrans / 10; ntrrem = ntrans - ntravl; L140: lsorc = 1; while (ntravl != 0) { lpick = iran(csa, 1, ntravl + ntrrem); ntravl--; chain(csa, lpick, lsorc); if (lsorc == nsorc) goto L140; lsorc++; } /* Add the remaining transshipments to the chains. */ while (ntrrem != 0) { lpick = iran(csa, 1, ntrrem); ntrrem--; lsorc = iran(csa, 1, nsorc); chain(csa, lpick, lsorc); } L170: /* Set all demands equal to zero. */ for (i = nfsink; i <= nodes; i++) ipred[i] = 0; /* The following loop takes one chain at a time (through the use of logic contained in the loop and calls to other routines) and creates the remaining network arcs. */ for (lsorc = 1; lsorc <= nsorc; lsorc++) { chnarc(csa, lsorc); for (i = nfsink; i <= nodes; i++) iflag[i] = 0; /* Choose the number of sinks to be hooked up to the current chain. */ if (ntrans != 0) nsksr = (nsort * 2 * nsink) / ntrans; else nsksr = nsink / nsorc + 1; if (nsksr < 2) nsksr = 2; if (nsksr > nsink) nsksr = nsink; nsrchn = nsort; /* Randomly pick nsksr sinks and put their names in lsinks. */ ktl = nsink; for (j = 1; j <= nsksr; j++) { item = iran(csa, 1, ktl); ktl--; for (l = nfsink; l <= nodes; l++) { if (iflag[l] != 1) { item--; if (item == 0) goto L230; } } break; L230: lsinks[j] = l; iflag[l] = 1; } /* If last source chain, add all sinks with zero demand to lsinks list. */ if (lsorc == nsorc) { for (j = nfsink; j <= nodes; j++) { if (ipred[j] == 0 && iflag[j] != 1) { nsksr++; lsinks[nsksr] = j; iflag[j] = 1; } } } /* Create demands for group of sinks in lsinks. */ ks = isup[lsorc] / nsksr; k = ipred[lsorc]; for (i = 1; i <= nsksr; i++) { nsort++; ksp = iran(csa, 1, ks); j = iran(csa, 1, nsksr); itail[nsort] = k; li = lsinks[i]; ihead[nsort] = li; ipred[li] += ksp; li = lsinks[j]; ipred[li] += ks - ksp; n = iran(csa, 1, nsrchn); k = lsorc; for (ii = 1; ii <= n; ii++) k = ipred[k]; } li = lsinks[1]; ipred[li] += isup[lsorc] - ks * nsksr; nskel += nsort; /* Sort the arcs in the chain from source lsorc using itail as sort key. */ sort(csa); /* Print this part of skeleton and create the arcs for these nodes. */ i = 1; itail[nsort+1] = 0; L300: for (j = nftsor; j <= nodes; j++) iflag[j] = 0; ktl = nonsor - 1; it = itail[i]; iflag[it] = 1; L320: ih = ihead[i]; iflag[ih] = 1; narcs++; ktl--; /* Determine if this skeleton arc should be capacitated. */ icap = itsup; jcap = iran(csa, 1, 100); if (jcap <= ipcap) { icap = isup[lsorc]; if (mincap > icap) icap = mincap; } /* Determine if this skeleton arc should have the maximum cost. */ icost = maxcst; jcost = iran(csa, 1, 100); if (jcost > iphic) icost = iran(csa, mincst, maxcst); if (G == NULL) xprintf("%6s%6d%6d%2s%10d%10d\n", "", it, ih, "", icost, icap); else { glp_arc *a = glp_add_arc(G, it, ih); if (a_cap >= 0) { double temp = (double)icap; memcpy((char *)a->data + a_cap, &temp, sizeof(double)); } if (a_cost >= 0) { double temp = (double)icost; memcpy((char *)a->data + a_cost, &temp, sizeof(double)); } } i++; if (itail[i] == it) goto L320; pickj(csa, it); if (i <= nsort) goto L300; } /* Create arcs from the transshipment sinks. */ if (ntsink != 0) { for (i = nfsink; i <= ltsink; i++) { for (j = nftsor; j <= nodes; j++) iflag[j] = 0; ktl = nonsor - 1; iflag[i] = 1; pickj(csa, i); } } L390: /* Print the demand records and end record. */ if (G == NULL) { xprintf("DEMAND\n"); for (i = nfsink; i <= nodes; i++) xprintf("%6s%6d%18s%10d\n", "", i, "", ipred[i]); xprintf("END\n"); } else { if (v_rhs >= 0) { for (i = nfsink; i <= nodes; i++) { double temp = - (double)ipred[i]; glp_vertex *v = G->v[i]; memcpy((char *)v->data + v_rhs, &temp, sizeof(double)); } } } /* Free working arrays. */ xfree(ipred); xfree(ihead); xfree(itail); xfree(iflag); xfree(isup); xfree(lsinks); /* The instance has been successfully generated. */ ret = 0; done: return ret; } /*********************************************************************** * The routine cresup randomly distributes the total supply among the * source nodes. */ static void cresup(struct csa *csa) { int i, j, ks, ksp; xassert(itsup > nsorc); ks = itsup / nsorc; for (i = 1; i <= nsorc; i++) isup[i] = 0; for (i = 1; i <= nsorc; i++) { ksp = iran(csa, 1, ks); j = iran(csa, 1, nsorc); isup[i] += ksp; isup[j] += ks - ksp; } j = iran(csa, 1, nsorc); isup[j] += itsup - ks * nsorc; return; } /*********************************************************************** * The routine chain adds node lpick to the end of the chain with source * node lsorc. */ static void chain(struct csa *csa, int lpick, int lsorc) { int i, j, k, l, m; k = 0; m = ist; for (i = 1; i <= lpick; i++) { l = k; k = m; m = ipred[k]; } ipred[l] = m; j = ipred[lsorc]; ipred[k] = j; ipred[lsorc] = k; return; } /*********************************************************************** * The routine chnarc puts the arcs in the chain from source lsorc into * the ihead and itail arrays for sorting. */ static void chnarc(struct csa *csa, int lsorc) { int ito, ifrom; nsort = 0; ito = ipred[lsorc]; L10: if (ito == lsorc) return; nsort++; ifrom = ipred[ito]; ihead[nsort] = ito; itail[nsort] = ifrom; ito = ifrom; goto L10; } /*********************************************************************** * The routine sort sorts the nsort arcs in the ihead and itail arrays. * ihead is used as the sort key (i.e. forward star sort order). */ static void sort(struct csa *csa) { int i, j, k, l, m, n, it; n = nsort; m = n; L10: m /= 2; if (m == 0) return; k = n - m; j = 1; L20: i = j; L30: l = i + m; if (itail[i] <= itail[l]) goto L40; it = itail[i]; itail[i] = itail[l]; itail[l] = it; it = ihead[i]; ihead[i] = ihead[l]; ihead[l] = it; i -= m; if (i >= 1) goto L30; L40: j++; if (j <= k) goto L20; goto L10; } /*********************************************************************** * The routine pickj creates a random number of arcs out of node 'it'. * Various parameters are dynamically adjusted in an attempt to ensure * that the generated network has the correct number of arcs. */ static void pickj(struct csa *csa, int it) { int j, k, l, nn, nupbnd, icap, jcap, icost; if ((nodlft - 1) * 2 > iarcs - narcs - 1) { nodlft--; return; } if ((iarcs - narcs + nonsor - ktl - 1) / nodlft - nonsor + 1 >= 0) k = nonsor; else { nupbnd = (iarcs - narcs - nodlft) / nodlft * 2; L40: k = iran(csa, 1, nupbnd); if (nodlft == 1) k = iarcs - narcs; if ((nodlft - 1) * (nonsor - 1) < iarcs - narcs - k) goto L40; } nodlft--; for (j = 1; j <= k; j++) { nn = iran(csa, 1, ktl); ktl--; for (l = nftsor; l <= nodes; l++) { if (iflag[l] != 1) { nn--; if (nn == 0) goto L70; } } return; L70: iflag[l] = 1; icap = itsup; jcap = iran(csa, 1, 100); if (jcap <= ipcap) icap = iran(csa, mincap, maxcap); icost = iran(csa, mincst, maxcst); if (G == NULL) xprintf("%6s%6d%6d%2s%10d%10d\n", "", it, l, "", icost, icap); else { glp_arc *a = glp_add_arc(G, it, l); if (a_cap >= 0) { double temp = (double)icap; memcpy((char *)a->data + a_cap, &temp, sizeof(double)); } if (a_cost >= 0) { double temp = (double)icost; memcpy((char *)a->data + a_cost, &temp, sizeof(double)); } } narcs++; } return; } /*********************************************************************** * The routine assign generate assignment problems. It defines the unit * supplies, builds a skeleton, then calls pickj to create the arcs. */ static void assign(struct csa *csa) { int i, it, nn, l, ll, icost; if (G == NULL) xprintf("SUPPLY\n"); for (i = 1; i <= nsorc; i++) { isup[i] = 1; iflag[i] = 0; if (G == NULL) xprintf("%6s%6d%18s%10d\n", "", i, "", isup[i]); else { if (v_rhs >= 0) { double temp = (double)isup[i]; glp_vertex *v = G->v[i]; memcpy((char *)v->data + v_rhs, &temp, sizeof(double)); } } } if (G == NULL) xprintf("ARCS\n"); for (i = nfsink; i <= nodes; i++) ipred[i] = 1; for (it = 1; it <= nsorc; it++) { for (i = nfsink; i <= nodes; i++) iflag[i] = 0; ktl = nsink - 1; nn = iran(csa, 1, nsink - it + 1); for (l = 1; l <= nsorc; l++) { if (iflag[l] != 1) { nn--; if (nn == 0) break; } } narcs++; ll = nsorc + l; icost = iran(csa, mincst, maxcst); if (G == NULL) xprintf("%6s%6d%6d%2s%10d%10d\n", "", it, ll, "", icost, isup[1]); else { glp_arc *a = glp_add_arc(G, it, ll); if (a_cap >= 0) { double temp = (double)isup[1]; memcpy((char *)a->data + a_cap, &temp, sizeof(double)); } if (a_cost >= 0) { double temp = (double)icost; memcpy((char *)a->data + a_cost, &temp, sizeof(double)); } } iflag[l] = 1; iflag[ll] = 1; pickj(csa, it); } return; } /*********************************************************************** * Portable congruential (uniform) random number generator: * * next_value = ((7**5) * previous_value) modulo ((2**31)-1) * * This generator consists of three routines: * * (1) setran - initializes constants and seed * (2) iran - generates an integer random number * (3) rran - generates a real random number * * The generator requires a machine with at least 32 bits of precision. * The seed (iseed) must be in the range [1,(2**31)-1]. */ static void setran(struct csa *csa, int iseed) { xassert(iseed >= 1); mult = 16807; modul = 2147483647; i15 = 1 << 15; i16 = 1 << 16; jran = iseed; return; } /*********************************************************************** * The routine iran generates an integer random number between ilow and * ihigh. If ilow > ihigh then iran returns ihigh. */ static int iran(struct csa *csa, int ilow, int ihigh) { int ixhi, ixlo, ixalo, leftlo, ixahi, ifulhi, irtlo, iover, irthi, j; ixhi = jran / i16; ixlo = jran - ixhi * i16; ixalo = ixlo * mult; leftlo = ixalo / i16; ixahi = ixhi * mult; ifulhi = ixahi + leftlo; irtlo = ixalo - leftlo * i16; iover = ifulhi / i15; irthi = ifulhi - iover * i15; jran = ((irtlo - modul) + irthi * i16) + iover; if (jran < 0) jran += modul; j = ihigh - ilow + 1; if (j > 0) return jran % j + ilow; else return ihigh; } /**********************************************************************/ #if 0 static int scan(char card[80+1], int pos, int len) { char buf[10+1]; memcpy(buf, &card[pos-1], len); buf[len] = '\0'; return atoi(buf); } int main(void) { int parm[1+15]; char card[80+1]; xassert(fgets(card, sizeof(card), stdin) == card); parm[1] = scan(card, 1, 8); parm[2] = scan(card, 9, 8); xassert(fgets(card, sizeof(card), stdin) == card); parm[3] = scan(card, 1, 5); parm[4] = scan(card, 6, 5); parm[5] = scan(card, 11, 5); parm[6] = scan(card, 16, 5); parm[7] = scan(card, 21, 5); parm[8] = scan(card, 26, 5); parm[9] = scan(card, 31, 10); parm[10] = scan(card, 41, 5); parm[11] = scan(card, 46, 5); parm[12] = scan(card, 51, 5); parm[13] = scan(card, 56, 5); parm[14] = scan(card, 61, 10); parm[15] = scan(card, 71, 10); glp_netgen(NULL, 0, 0, 0, parm); return 0; } #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpnpp01.c0000644000175000017500000007065511632352620020261 0ustar csheltoncshelton/* glpnpp01.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpnpp.h" NPP *npp_create_wksp(void) { /* create LP/MIP preprocessor workspace */ NPP *npp; npp = xmalloc(sizeof(NPP)); npp->orig_dir = 0; npp->orig_m = npp->orig_n = npp->orig_nnz = 0; npp->pool = dmp_create_pool(); npp->name = npp->obj = NULL; npp->c0 = 0.0; npp->nrows = npp->ncols = 0; npp->r_head = npp->r_tail = NULL; npp->c_head = npp->c_tail = NULL; npp->stack = dmp_create_pool(); npp->top = NULL; #if 0 /* 16/XII-2009 */ memset(&npp->count, 0, sizeof(npp->count)); #endif npp->m = npp->n = npp->nnz = 0; npp->row_ref = npp->col_ref = NULL; npp->sol = npp->scaling = 0; npp->p_stat = npp->d_stat = npp->t_stat = npp->i_stat = 0; npp->r_stat = NULL; /*npp->r_prim =*/ npp->r_pi = NULL; npp->c_stat = NULL; npp->c_value = /*npp->c_dual =*/ NULL; return npp; } void npp_insert_row(NPP *npp, NPPROW *row, int where) { /* insert row to the row list */ if (where == 0) { /* insert row to the beginning of the row list */ row->prev = NULL; row->next = npp->r_head; if (row->next == NULL) npp->r_tail = row; else row->next->prev = row; npp->r_head = row; } else { /* insert row to the end of the row list */ row->prev = npp->r_tail; row->next = NULL; if (row->prev == NULL) npp->r_head = row; else row->prev->next = row; npp->r_tail = row; } return; } void npp_remove_row(NPP *npp, NPPROW *row) { /* remove row from the row list */ if (row->prev == NULL) npp->r_head = row->next; else row->prev->next = row->next; if (row->next == NULL) npp->r_tail = row->prev; else row->next->prev = row->prev; return; } void npp_activate_row(NPP *npp, NPPROW *row) { /* make row active */ if (!row->temp) { row->temp = 1; /* move the row to the beginning of the row list */ npp_remove_row(npp, row); npp_insert_row(npp, row, 0); } return; } void npp_deactivate_row(NPP *npp, NPPROW *row) { /* make row inactive */ if (row->temp) { row->temp = 0; /* move the row to the end of the row list */ npp_remove_row(npp, row); npp_insert_row(npp, row, 1); } return; } void npp_insert_col(NPP *npp, NPPCOL *col, int where) { /* insert column to the column list */ if (where == 0) { /* insert column to the beginning of the column list */ col->prev = NULL; col->next = npp->c_head; if (col->next == NULL) npp->c_tail = col; else col->next->prev = col; npp->c_head = col; } else { /* insert column to the end of the column list */ col->prev = npp->c_tail; col->next = NULL; if (col->prev == NULL) npp->c_head = col; else col->prev->next = col; npp->c_tail = col; } return; } void npp_remove_col(NPP *npp, NPPCOL *col) { /* remove column from the column list */ if (col->prev == NULL) npp->c_head = col->next; else col->prev->next = col->next; if (col->next == NULL) npp->c_tail = col->prev; else col->next->prev = col->prev; return; } void npp_activate_col(NPP *npp, NPPCOL *col) { /* make column active */ if (!col->temp) { col->temp = 1; /* move the column to the beginning of the column list */ npp_remove_col(npp, col); npp_insert_col(npp, col, 0); } return; } void npp_deactivate_col(NPP *npp, NPPCOL *col) { /* make column inactive */ if (col->temp) { col->temp = 0; /* move the column to the end of the column list */ npp_remove_col(npp, col); npp_insert_col(npp, col, 1); } return; } NPPROW *npp_add_row(NPP *npp) { /* add new row to the current problem */ NPPROW *row; row = dmp_get_atom(npp->pool, sizeof(NPPROW)); row->i = ++(npp->nrows); row->name = NULL; row->lb = -DBL_MAX, row->ub = +DBL_MAX; row->ptr = NULL; row->temp = 0; npp_insert_row(npp, row, 1); return row; } NPPCOL *npp_add_col(NPP *npp) { /* add new column to the current problem */ NPPCOL *col; col = dmp_get_atom(npp->pool, sizeof(NPPCOL)); col->j = ++(npp->ncols); col->name = NULL; #if 0 col->kind = GLP_CV; #else col->is_int = 0; #endif col->lb = col->ub = col->coef = 0.0; col->ptr = NULL; col->temp = 0; npp_insert_col(npp, col, 1); return col; } NPPAIJ *npp_add_aij(NPP *npp, NPPROW *row, NPPCOL *col, double val) { /* add new element to the constraint matrix */ NPPAIJ *aij; aij = dmp_get_atom(npp->pool, sizeof(NPPAIJ)); aij->row = row; aij->col = col; aij->val = val; aij->r_prev = NULL; aij->r_next = row->ptr; aij->c_prev = NULL; aij->c_next = col->ptr; if (aij->r_next != NULL) aij->r_next->r_prev = aij; if (aij->c_next != NULL) aij->c_next->c_prev = aij; row->ptr = col->ptr = aij; return aij; } int npp_row_nnz(NPP *npp, NPPROW *row) { /* count number of non-zero coefficients in row */ NPPAIJ *aij; int nnz; xassert(npp == npp); nnz = 0; for (aij = row->ptr; aij != NULL; aij = aij->r_next) nnz++; return nnz; } int npp_col_nnz(NPP *npp, NPPCOL *col) { /* count number of non-zero coefficients in column */ NPPAIJ *aij; int nnz; xassert(npp == npp); nnz = 0; for (aij = col->ptr; aij != NULL; aij = aij->c_next) nnz++; return nnz; } void *npp_push_tse(NPP *npp, int (*func)(NPP *npp, void *info), int size) { /* push new entry to the transformation stack */ NPPTSE *tse; tse = dmp_get_atom(npp->stack, sizeof(NPPTSE)); tse->func = func; tse->info = dmp_get_atom(npp->stack, size); tse->link = npp->top; npp->top = tse; return tse->info; } #if 1 /* 23/XII-2009 */ void npp_erase_row(NPP *npp, NPPROW *row) { /* erase row content to make it empty */ NPPAIJ *aij; while (row->ptr != NULL) { aij = row->ptr; row->ptr = aij->r_next; if (aij->c_prev == NULL) aij->col->ptr = aij->c_next; else aij->c_prev->c_next = aij->c_next; if (aij->c_next == NULL) ; else aij->c_next->c_prev = aij->c_prev; dmp_free_atom(npp->pool, aij, sizeof(NPPAIJ)); } return; } #endif void npp_del_row(NPP *npp, NPPROW *row) { /* remove row from the current problem */ #if 0 /* 23/XII-2009 */ NPPAIJ *aij; #endif if (row->name != NULL) dmp_free_atom(npp->pool, row->name, strlen(row->name)+1); #if 0 /* 23/XII-2009 */ while (row->ptr != NULL) { aij = row->ptr; row->ptr = aij->r_next; if (aij->c_prev == NULL) aij->col->ptr = aij->c_next; else aij->c_prev->c_next = aij->c_next; if (aij->c_next == NULL) ; else aij->c_next->c_prev = aij->c_prev; dmp_free_atom(npp->pool, aij, sizeof(NPPAIJ)); } #else npp_erase_row(npp, row); #endif npp_remove_row(npp, row); dmp_free_atom(npp->pool, row, sizeof(NPPROW)); return; } void npp_del_col(NPP *npp, NPPCOL *col) { /* remove column from the current problem */ NPPAIJ *aij; if (col->name != NULL) dmp_free_atom(npp->pool, col->name, strlen(col->name)+1); while (col->ptr != NULL) { aij = col->ptr; col->ptr = aij->c_next; if (aij->r_prev == NULL) aij->row->ptr = aij->r_next; else aij->r_prev->r_next = aij->r_next; if (aij->r_next == NULL) ; else aij->r_next->r_prev = aij->r_prev; dmp_free_atom(npp->pool, aij, sizeof(NPPAIJ)); } npp_remove_col(npp, col); dmp_free_atom(npp->pool, col, sizeof(NPPCOL)); return; } void npp_del_aij(NPP *npp, NPPAIJ *aij) { /* remove element from the constraint matrix */ if (aij->r_prev == NULL) aij->row->ptr = aij->r_next; else aij->r_prev->r_next = aij->r_next; if (aij->r_next == NULL) ; else aij->r_next->r_prev = aij->r_prev; if (aij->c_prev == NULL) aij->col->ptr = aij->c_next; else aij->c_prev->c_next = aij->c_next; if (aij->c_next == NULL) ; else aij->c_next->c_prev = aij->c_prev; dmp_free_atom(npp->pool, aij, sizeof(NPPAIJ)); return; } void npp_load_prob(NPP *npp, glp_prob *orig, int names, int sol, int scaling) { /* load original problem into the preprocessor workspace */ int m = orig->m; int n = orig->n; NPPROW **link; int i, j; double dir; xassert(names == GLP_OFF || names == GLP_ON); xassert(sol == GLP_SOL || sol == GLP_IPT || sol == GLP_MIP); xassert(scaling == GLP_OFF || scaling == GLP_ON); if (sol == GLP_MIP) xassert(!scaling); npp->orig_dir = orig->dir; if (npp->orig_dir == GLP_MIN) dir = +1.0; else if (npp->orig_dir == GLP_MAX) dir = -1.0; else xassert(npp != npp); npp->orig_m = m; npp->orig_n = n; npp->orig_nnz = orig->nnz; if (names && orig->name != NULL) { npp->name = dmp_get_atom(npp->pool, strlen(orig->name)+1); strcpy(npp->name, orig->name); } if (names && orig->obj != NULL) { npp->obj = dmp_get_atom(npp->pool, strlen(orig->obj)+1); strcpy(npp->obj, orig->obj); } npp->c0 = dir * orig->c0; /* load rows */ link = xcalloc(1+m, sizeof(NPPROW *)); for (i = 1; i <= m; i++) { GLPROW *rrr = orig->row[i]; NPPROW *row; link[i] = row = npp_add_row(npp); xassert(row->i == i); if (names && rrr->name != NULL) { row->name = dmp_get_atom(npp->pool, strlen(rrr->name)+1); strcpy(row->name, rrr->name); } if (!scaling) { if (rrr->type == GLP_FR) row->lb = -DBL_MAX, row->ub = +DBL_MAX; else if (rrr->type == GLP_LO) row->lb = rrr->lb, row->ub = +DBL_MAX; else if (rrr->type == GLP_UP) row->lb = -DBL_MAX, row->ub = rrr->ub; else if (rrr->type == GLP_DB) row->lb = rrr->lb, row->ub = rrr->ub; else if (rrr->type == GLP_FX) row->lb = row->ub = rrr->lb; else xassert(rrr != rrr); } else { double rii = rrr->rii; if (rrr->type == GLP_FR) row->lb = -DBL_MAX, row->ub = +DBL_MAX; else if (rrr->type == GLP_LO) row->lb = rrr->lb * rii, row->ub = +DBL_MAX; else if (rrr->type == GLP_UP) row->lb = -DBL_MAX, row->ub = rrr->ub * rii; else if (rrr->type == GLP_DB) row->lb = rrr->lb * rii, row->ub = rrr->ub * rii; else if (rrr->type == GLP_FX) row->lb = row->ub = rrr->lb * rii; else xassert(rrr != rrr); } } /* load columns and constraint coefficients */ for (j = 1; j <= n; j++) { GLPCOL *ccc = orig->col[j]; GLPAIJ *aaa; NPPCOL *col; col = npp_add_col(npp); xassert(col->j == j); if (names && ccc->name != NULL) { col->name = dmp_get_atom(npp->pool, strlen(ccc->name)+1); strcpy(col->name, ccc->name); } if (sol == GLP_MIP) #if 0 col->kind = ccc->kind; #else col->is_int = (char)(ccc->kind == GLP_IV); #endif if (!scaling) { if (ccc->type == GLP_FR) col->lb = -DBL_MAX, col->ub = +DBL_MAX; else if (ccc->type == GLP_LO) col->lb = ccc->lb, col->ub = +DBL_MAX; else if (ccc->type == GLP_UP) col->lb = -DBL_MAX, col->ub = ccc->ub; else if (ccc->type == GLP_DB) col->lb = ccc->lb, col->ub = ccc->ub; else if (ccc->type == GLP_FX) col->lb = col->ub = ccc->lb; else xassert(ccc != ccc); col->coef = dir * ccc->coef; for (aaa = ccc->ptr; aaa != NULL; aaa = aaa->c_next) npp_add_aij(npp, link[aaa->row->i], col, aaa->val); } else { double sjj = ccc->sjj; if (ccc->type == GLP_FR) col->lb = -DBL_MAX, col->ub = +DBL_MAX; else if (ccc->type == GLP_LO) col->lb = ccc->lb / sjj, col->ub = +DBL_MAX; else if (ccc->type == GLP_UP) col->lb = -DBL_MAX, col->ub = ccc->ub / sjj; else if (ccc->type == GLP_DB) col->lb = ccc->lb / sjj, col->ub = ccc->ub / sjj; else if (ccc->type == GLP_FX) col->lb = col->ub = ccc->lb / sjj; else xassert(ccc != ccc); col->coef = dir * ccc->coef * sjj; for (aaa = ccc->ptr; aaa != NULL; aaa = aaa->c_next) npp_add_aij(npp, link[aaa->row->i], col, aaa->row->rii * aaa->val * sjj); } } xfree(link); /* keep solution indicator and scaling option */ npp->sol = sol; npp->scaling = scaling; return; } void npp_build_prob(NPP *npp, glp_prob *prob) { /* build resultant (preprocessed) problem */ NPPROW *row; NPPCOL *col; NPPAIJ *aij; int i, j, type, len, *ind; double dir, *val; glp_erase_prob(prob); glp_set_prob_name(prob, npp->name); glp_set_obj_name(prob, npp->obj); glp_set_obj_dir(prob, npp->orig_dir); if (npp->orig_dir == GLP_MIN) dir = +1.0; else if (npp->orig_dir == GLP_MAX) dir = -1.0; else xassert(npp != npp); glp_set_obj_coef(prob, 0, dir * npp->c0); /* build rows */ for (row = npp->r_head; row != NULL; row = row->next) { row->temp = i = glp_add_rows(prob, 1); glp_set_row_name(prob, i, row->name); if (row->lb == -DBL_MAX && row->ub == +DBL_MAX) type = GLP_FR; else if (row->ub == +DBL_MAX) type = GLP_LO; else if (row->lb == -DBL_MAX) type = GLP_UP; else if (row->lb != row->ub) type = GLP_DB; else type = GLP_FX; glp_set_row_bnds(prob, i, type, row->lb, row->ub); } /* build columns and the constraint matrix */ ind = xcalloc(1+prob->m, sizeof(int)); val = xcalloc(1+prob->m, sizeof(double)); for (col = npp->c_head; col != NULL; col = col->next) { j = glp_add_cols(prob, 1); glp_set_col_name(prob, j, col->name); #if 0 glp_set_col_kind(prob, j, col->kind); #else glp_set_col_kind(prob, j, col->is_int ? GLP_IV : GLP_CV); #endif if (col->lb == -DBL_MAX && col->ub == +DBL_MAX) type = GLP_FR; else if (col->ub == +DBL_MAX) type = GLP_LO; else if (col->lb == -DBL_MAX) type = GLP_UP; else if (col->lb != col->ub) type = GLP_DB; else type = GLP_FX; glp_set_col_bnds(prob, j, type, col->lb, col->ub); glp_set_obj_coef(prob, j, dir * col->coef); len = 0; for (aij = col->ptr; aij != NULL; aij = aij->c_next) { len++; ind[len] = aij->row->temp; val[len] = aij->val; } glp_set_mat_col(prob, j, len, ind, val); } xfree(ind); xfree(val); /* resultant problem has been built */ npp->m = prob->m; npp->n = prob->n; npp->nnz = prob->nnz; npp->row_ref = xcalloc(1+npp->m, sizeof(int)); npp->col_ref = xcalloc(1+npp->n, sizeof(int)); for (row = npp->r_head, i = 0; row != NULL; row = row->next) npp->row_ref[++i] = row->i; for (col = npp->c_head, j = 0; col != NULL; col = col->next) npp->col_ref[++j] = col->j; /* transformed problem segment is no longer needed */ dmp_delete_pool(npp->pool), npp->pool = NULL; npp->name = npp->obj = NULL; npp->c0 = 0.0; npp->r_head = npp->r_tail = NULL; npp->c_head = npp->c_tail = NULL; return; } void npp_postprocess(NPP *npp, glp_prob *prob) { /* postprocess solution from the resultant problem */ GLPROW *row; GLPCOL *col; NPPTSE *tse; int i, j, k; double dir; xassert(npp->orig_dir == prob->dir); if (npp->orig_dir == GLP_MIN) dir = +1.0; else if (npp->orig_dir == GLP_MAX) dir = -1.0; else xassert(npp != npp); xassert(npp->m == prob->m); xassert(npp->n == prob->n); xassert(npp->nnz == prob->nnz); /* copy solution status */ if (npp->sol == GLP_SOL) { npp->p_stat = prob->pbs_stat; npp->d_stat = prob->dbs_stat; } else if (npp->sol == GLP_IPT) npp->t_stat = prob->ipt_stat; else if (npp->sol == GLP_MIP) npp->i_stat = prob->mip_stat; else xassert(npp != npp); /* allocate solution arrays */ if (npp->sol == GLP_SOL) { if (npp->r_stat == NULL) npp->r_stat = xcalloc(1+npp->nrows, sizeof(char)); for (i = 1; i <= npp->nrows; i++) npp->r_stat[i] = 0; if (npp->c_stat == NULL) npp->c_stat = xcalloc(1+npp->ncols, sizeof(char)); for (j = 1; j <= npp->ncols; j++) npp->c_stat[j] = 0; } #if 0 if (npp->r_prim == NULL) npp->r_prim = xcalloc(1+npp->nrows, sizeof(double)); for (i = 1; i <= npp->nrows; i++) npp->r_prim[i] = DBL_MAX; #endif if (npp->c_value == NULL) npp->c_value = xcalloc(1+npp->ncols, sizeof(double)); for (j = 1; j <= npp->ncols; j++) npp->c_value[j] = DBL_MAX; if (npp->sol != GLP_MIP) { if (npp->r_pi == NULL) npp->r_pi = xcalloc(1+npp->nrows, sizeof(double)); for (i = 1; i <= npp->nrows; i++) npp->r_pi[i] = DBL_MAX; #if 0 if (npp->c_dual == NULL) npp->c_dual = xcalloc(1+npp->ncols, sizeof(double)); for (j = 1; j <= npp->ncols; j++) npp->c_dual[j] = DBL_MAX; #endif } /* copy solution components from the resultant problem */ if (npp->sol == GLP_SOL) { for (i = 1; i <= npp->m; i++) { row = prob->row[i]; k = npp->row_ref[i]; npp->r_stat[k] = (char)row->stat; /*npp->r_prim[k] = row->prim;*/ npp->r_pi[k] = dir * row->dual; } for (j = 1; j <= npp->n; j++) { col = prob->col[j]; k = npp->col_ref[j]; npp->c_stat[k] = (char)col->stat; npp->c_value[k] = col->prim; /*npp->c_dual[k] = dir * col->dual;*/ } } else if (npp->sol == GLP_IPT) { for (i = 1; i <= npp->m; i++) { row = prob->row[i]; k = npp->row_ref[i]; /*npp->r_prim[k] = row->pval;*/ npp->r_pi[k] = dir * row->dval; } for (j = 1; j <= npp->n; j++) { col = prob->col[j]; k = npp->col_ref[j]; npp->c_value[k] = col->pval; /*npp->c_dual[k] = dir * col->dval;*/ } } else if (npp->sol == GLP_MIP) { #if 0 for (i = 1; i <= npp->m; i++) { row = prob->row[i]; k = npp->row_ref[i]; /*npp->r_prim[k] = row->mipx;*/ } #endif for (j = 1; j <= npp->n; j++) { col = prob->col[j]; k = npp->col_ref[j]; npp->c_value[k] = col->mipx; } } else xassert(npp != npp); /* perform postprocessing to construct solution to the original problem */ for (tse = npp->top; tse != NULL; tse = tse->link) { xassert(tse->func != NULL); xassert(tse->func(npp, tse->info) == 0); } return; } void npp_unload_sol(NPP *npp, glp_prob *orig) { /* store solution to the original problem */ GLPROW *row; GLPCOL *col; int i, j; double dir; xassert(npp->orig_dir == orig->dir); if (npp->orig_dir == GLP_MIN) dir = +1.0; else if (npp->orig_dir == GLP_MAX) dir = -1.0; else xassert(npp != npp); xassert(npp->orig_m == orig->m); xassert(npp->orig_n == orig->n); xassert(npp->orig_nnz == orig->nnz); if (npp->sol == GLP_SOL) { /* store basic solution */ orig->valid = 0; orig->pbs_stat = npp->p_stat; orig->dbs_stat = npp->d_stat; orig->obj_val = orig->c0; orig->some = 0; for (i = 1; i <= orig->m; i++) { row = orig->row[i]; row->stat = npp->r_stat[i]; if (!npp->scaling) { /*row->prim = npp->r_prim[i];*/ row->dual = dir * npp->r_pi[i]; } else { /*row->prim = npp->r_prim[i] / row->rii;*/ row->dual = dir * npp->r_pi[i] * row->rii; } if (row->stat == GLP_BS) row->dual = 0.0; else if (row->stat == GLP_NL) { xassert(row->type == GLP_LO || row->type == GLP_DB); row->prim = row->lb; } else if (row->stat == GLP_NU) { xassert(row->type == GLP_UP || row->type == GLP_DB); row->prim = row->ub; } else if (row->stat == GLP_NF) { xassert(row->type == GLP_FR); row->prim = 0.0; } else if (row->stat == GLP_NS) { xassert(row->type == GLP_FX); row->prim = row->lb; } else xassert(row != row); } for (j = 1; j <= orig->n; j++) { col = orig->col[j]; col->stat = npp->c_stat[j]; if (!npp->scaling) { col->prim = npp->c_value[j]; /*col->dual = dir * npp->c_dual[j];*/ } else { col->prim = npp->c_value[j] * col->sjj; /*col->dual = dir * npp->c_dual[j] / col->sjj;*/ } if (col->stat == GLP_BS) col->dual = 0.0; #if 1 else if (col->stat == GLP_NL) { xassert(col->type == GLP_LO || col->type == GLP_DB); col->prim = col->lb; } else if (col->stat == GLP_NU) { xassert(col->type == GLP_UP || col->type == GLP_DB); col->prim = col->ub; } else if (col->stat == GLP_NF) { xassert(col->type == GLP_FR); col->prim = 0.0; } else if (col->stat == GLP_NS) { xassert(col->type == GLP_FX); col->prim = col->lb; } else xassert(col != col); #endif orig->obj_val += col->coef * col->prim; } #if 1 /* compute primal values of inactive rows */ for (i = 1; i <= orig->m; i++) { row = orig->row[i]; if (row->stat == GLP_BS) { GLPAIJ *aij; double temp; temp = 0.0; for (aij = row->ptr; aij != NULL; aij = aij->r_next) temp += aij->val * aij->col->prim; row->prim = temp; } } /* compute reduced costs of active columns */ for (j = 1; j <= orig->n; j++) { col = orig->col[j]; if (col->stat != GLP_BS) { GLPAIJ *aij; double temp; temp = col->coef; for (aij = col->ptr; aij != NULL; aij = aij->c_next) temp -= aij->val * aij->row->dual; col->dual = temp; } } #endif } else if (npp->sol == GLP_IPT) { /* store interior-point solution */ orig->ipt_stat = npp->t_stat; orig->ipt_obj = orig->c0; for (i = 1; i <= orig->m; i++) { row = orig->row[i]; if (!npp->scaling) { /*row->pval = npp->r_prim[i];*/ row->dval = dir * npp->r_pi[i]; } else { /*row->pval = npp->r_prim[i] / row->rii;*/ row->dval = dir * npp->r_pi[i] * row->rii; } } for (j = 1; j <= orig->n; j++) { col = orig->col[j]; if (!npp->scaling) { col->pval = npp->c_value[j]; /*col->dval = dir * npp->c_dual[j];*/ } else { col->pval = npp->c_value[j] * col->sjj; /*col->dval = dir * npp->c_dual[j] / col->sjj;*/ } orig->ipt_obj += col->coef * col->pval; } #if 1 /* compute row primal values */ for (i = 1; i <= orig->m; i++) { row = orig->row[i]; { GLPAIJ *aij; double temp; temp = 0.0; for (aij = row->ptr; aij != NULL; aij = aij->r_next) temp += aij->val * aij->col->pval; row->pval = temp; } } /* compute column dual values */ for (j = 1; j <= orig->n; j++) { col = orig->col[j]; { GLPAIJ *aij; double temp; temp = col->coef; for (aij = col->ptr; aij != NULL; aij = aij->c_next) temp -= aij->val * aij->row->dval; col->dval = temp; } } #endif } else if (npp->sol == GLP_MIP) { /* store MIP solution */ xassert(!npp->scaling); orig->mip_stat = npp->i_stat; orig->mip_obj = orig->c0; #if 0 for (i = 1; i <= orig->m; i++) { row = orig->row[i]; /*row->mipx = npp->r_prim[i];*/ } #endif for (j = 1; j <= orig->n; j++) { col = orig->col[j]; col->mipx = npp->c_value[j]; if (col->kind == GLP_IV) xassert(col->mipx == floor(col->mipx)); orig->mip_obj += col->coef * col->mipx; } #if 1 /* compute row primal values */ for (i = 1; i <= orig->m; i++) { row = orig->row[i]; { GLPAIJ *aij; double temp; temp = 0.0; for (aij = row->ptr; aij != NULL; aij = aij->r_next) temp += aij->val * aij->col->mipx; row->mipx = temp; } } #endif } else xassert(npp != npp); return; } void npp_delete_wksp(NPP *npp) { /* delete LP/MIP preprocessor workspace */ if (npp->pool != NULL) dmp_delete_pool(npp->pool); if (npp->stack != NULL) dmp_delete_pool(npp->stack); if (npp->row_ref != NULL) xfree(npp->row_ref); if (npp->col_ref != NULL) xfree(npp->col_ref); if (npp->r_stat != NULL) xfree(npp->r_stat); #if 0 if (npp->r_prim != NULL) xfree(npp->r_prim); #endif if (npp->r_pi != NULL) xfree(npp->r_pi); if (npp->c_stat != NULL) xfree(npp->c_stat); if (npp->c_value != NULL) xfree(npp->c_value); #if 0 if (npp->c_dual != NULL) xfree(npp->c_dual); #endif xfree(npp); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpnet08.c0000644000175000017500000001642611632352620020255 0ustar csheltoncshelton/* glpnet08.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Two subroutines sub() and wclique() below are intended to find a * maximum weight clique in a given undirected graph. These subroutines * are slightly modified version of the program WCLIQUE developed by * Patric Ostergard and based * on ideas from the article "P. R. J. Ostergard, A new algorithm for * the maximum-weight clique problem, submitted for publication", which * in turn is a generalization of the algorithm for unweighted graphs * presented in "P. R. J. Ostergard, A fast algorithm for the maximum * clique problem, submitted for publication". * * USED WITH PERMISSION OF THE AUTHOR OF THE ORIGINAL CODE. * * Changes were made by Andrew Makhorin . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpenv.h" #include "glpnet.h" /*********************************************************************** * NAME * * wclique - find maximum weight clique with Ostergard's algorithm * * SYNOPSIS * * int wclique(int n, const int w[], const unsigned char a[], * int ind[]); * * DESCRIPTION * * The routine wclique finds a maximum weight clique in an undirected * graph with Ostergard's algorithm. * * INPUT PARAMETERS * * n is the number of vertices, n > 0. * * w[i], i = 1,...,n, is a weight of vertex i. * * a[*] is the strict (without main diagonal) lower triangle of the * graph adjacency matrix in packed format. * * OUTPUT PARAMETER * * ind[k], k = 1,...,size, is the number of a vertex included in the * clique found, 1 <= ind[k] <= n, where size is the number of vertices * in the clique returned on exit. * * RETURNS * * The routine returns the clique size, i.e. the number of vertices in * the clique. */ struct csa { /* common storage area */ int n; /* number of vertices */ const int *wt; /* int wt[0:n-1]; */ /* weights */ const unsigned char *a; /* adjacency matrix (packed lower triangle without main diag.) */ int record; /* weight of best clique */ int rec_level; /* number of vertices in best clique */ int *rec; /* int rec[0:n-1]; */ /* best clique so far */ int *clique; /* int clique[0:n-1]; */ /* table for pruning */ int *set; /* int set[0:n-1]; */ /* current clique */ }; #define n (csa->n) #define wt (csa->wt) #define a (csa->a) #define record (csa->record) #define rec_level (csa->rec_level) #define rec (csa->rec) #define clique (csa->clique) #define set (csa->set) #if 0 static int is_edge(struct csa *csa, int i, int j) { /* if there is arc (i,j), the routine returns true; otherwise false; 0 <= i, j < n */ int k; xassert(0 <= i && i < n); xassert(0 <= j && j < n); if (i == j) return 0; if (i < j) k = i, i = j, j = k; k = (i * (i - 1)) / 2 + j; return a[k / CHAR_BIT] & (unsigned char)(1 << ((CHAR_BIT - 1) - k % CHAR_BIT)); } #else #define is_edge(csa, i, j) ((i) == (j) ? 0 : \ (i) > (j) ? is_edge1(i, j) : is_edge1(j, i)) #define is_edge1(i, j) is_edge2(((i) * ((i) - 1)) / 2 + (j)) #define is_edge2(k) (a[(k) / CHAR_BIT] & \ (unsigned char)(1 << ((CHAR_BIT - 1) - (k) % CHAR_BIT))) #endif static void sub(struct csa *csa, int ct, int table[], int level, int weight, int l_weight) { int i, j, k, curr_weight, left_weight, *p1, *p2, *newtable; newtable = xcalloc(n, sizeof(int)); if (ct <= 0) { /* 0 or 1 elements left; include these */ if (ct == 0) { set[level++] = table[0]; weight += l_weight; } if (weight > record) { record = weight; rec_level = level; for (i = 0; i < level; i++) rec[i] = set[i]; } goto done; } for (i = ct; i >= 0; i--) { if ((level == 0) && (i < ct)) goto done; k = table[i]; if ((level > 0) && (clique[k] <= (record - weight))) goto done; /* prune */ set[level] = k; curr_weight = weight + wt[k]; l_weight -= wt[k]; if (l_weight <= (record - curr_weight)) goto done; /* prune */ p1 = newtable; p2 = table; left_weight = 0; while (p2 < table + i) { j = *p2++; if (is_edge(csa, j, k)) { *p1++ = j; left_weight += wt[j]; } } if (left_weight <= (record - curr_weight)) continue; sub(csa, p1 - newtable - 1, newtable, level + 1, curr_weight, left_weight); } done: xfree(newtable); return; } int wclique(int _n, const int w[], const unsigned char _a[], int ind[]) { struct csa _csa, *csa = &_csa; int i, j, p, max_wt, max_nwt, wth, *used, *nwt, *pos; glp_long timer; n = _n; xassert(n > 0); wt = &w[1]; a = _a; record = 0; rec_level = 0; rec = &ind[1]; clique = xcalloc(n, sizeof(int)); set = xcalloc(n, sizeof(int)); used = xcalloc(n, sizeof(int)); nwt = xcalloc(n, sizeof(int)); pos = xcalloc(n, sizeof(int)); /* start timer */ timer = xtime(); /* order vertices */ for (i = 0; i < n; i++) { nwt[i] = 0; for (j = 0; j < n; j++) if (is_edge(csa, i, j)) nwt[i] += wt[j]; } for (i = 0; i < n; i++) used[i] = 0; for (i = n-1; i >= 0; i--) { max_wt = -1; max_nwt = -1; for (j = 0; j < n; j++) { if ((!used[j]) && ((wt[j] > max_wt) || (wt[j] == max_wt && nwt[j] > max_nwt))) { max_wt = wt[j]; max_nwt = nwt[j]; p = j; } } pos[i] = p; used[p] = 1; for (j = 0; j < n; j++) if ((!used[j]) && (j != p) && (is_edge(csa, p, j))) nwt[j] -= wt[p]; } /* main routine */ wth = 0; for (i = 0; i < n; i++) { wth += wt[pos[i]]; sub(csa, i, pos, 0, 0, wth); clique[pos[i]] = record; if (xdifftime(xtime(), timer) >= 5.0 - 0.001) { /* print current record and reset timer */ xprintf("level = %d (%d); best = %d\n", i+1, n, record); timer = xtime(); } } xfree(clique); xfree(set); xfree(used); xfree(nwt); xfree(pos); /* return the solution found */ for (i = 1; i <= rec_level; i++) ind[i]++; return rec_level; } #undef n #undef wt #undef a #undef record #undef rec_level #undef rec #undef clique #undef set /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpscf.c0000644000175000017500000004760111632352620020071 0ustar csheltoncshelton/* glpscf.c (Schur complement factorization) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpenv.h" #include "glpscf.h" #define xfault xerror #define _GLPSCF_DEBUG 0 #define eps 1e-10 /*********************************************************************** * NAME * * scf_create_it - create Schur complement factorization * * SYNOPSIS * * #include "glpscf.h" * SCF *scf_create_it(int n_max); * * DESCRIPTION * * The routine scf_create_it creates the factorization of matrix C, * which initially has no rows and columns. * * The parameter n_max specifies the maximal order of matrix C to be * factorized, 1 <= n_max <= 32767. * * RETURNS * * The routine scf_create_it returns a pointer to the structure SCF, * which defines the factorization. */ SCF *scf_create_it(int n_max) { SCF *scf; #if _GLPSCF_DEBUG xprintf("scf_create_it: warning: debug mode enabled\n"); #endif if (!(1 <= n_max && n_max <= 32767)) xfault("scf_create_it: n_max = %d; invalid parameter\n", n_max); scf = xmalloc(sizeof(SCF)); scf->n_max = n_max; scf->n = 0; scf->f = xcalloc(1 + n_max * n_max, sizeof(double)); scf->u = xcalloc(1 + n_max * (n_max + 1) / 2, sizeof(double)); scf->p = xcalloc(1 + n_max, sizeof(int)); scf->t_opt = SCF_TBG; scf->rank = 0; #if _GLPSCF_DEBUG scf->c = xcalloc(1 + n_max * n_max, sizeof(double)); #else scf->c = NULL; #endif scf->w = xcalloc(1 + n_max, sizeof(double)); return scf; } /*********************************************************************** * The routine f_loc determines location of matrix element F[i,j] in * the one-dimensional array f. */ static int f_loc(SCF *scf, int i, int j) { int n_max = scf->n_max; int n = scf->n; xassert(1 <= i && i <= n); xassert(1 <= j && j <= n); return (i - 1) * n_max + j; } /*********************************************************************** * The routine u_loc determines location of matrix element U[i,j] in * the one-dimensional array u. */ static int u_loc(SCF *scf, int i, int j) { int n_max = scf->n_max; int n = scf->n; xassert(1 <= i && i <= n); xassert(i <= j && j <= n); return (i - 1) * n_max + j - i * (i - 1) / 2; } /*********************************************************************** * The routine bg_transform applies Bartels-Golub version of gaussian * elimination to restore triangular structure of matrix U. * * On entry matrix U has the following structure: * * 1 k n * 1 * * * * * * * * * * * . * * * * * * * * * * . . * * * * * * * * * . . . * * * * * * * * k . . . . * * * * * * * . . . . . * * * * * * . . . . . . * * * * * . . . . . . . * * * * . . . . . . . . * * * n . . . . # # # # # # * * where '#' is a row spike to be eliminated. * * Elements of n-th row are passed separately in locations un[k], ..., * un[n]. On exit the content of the array un is destroyed. * * REFERENCES * * R.H.Bartels, G.H.Golub, "The Simplex Method of Linear Programming * Using LU-decomposition", Comm. ACM, 12, pp. 266-68, 1969. */ static void bg_transform(SCF *scf, int k, double un[]) { int n = scf->n; double *f = scf->f; double *u = scf->u; int j, k1, kj, kk, n1, nj; double t; xassert(1 <= k && k <= n); /* main elimination loop */ for (k = k; k < n; k++) { /* determine location of U[k,k] */ kk = u_loc(scf, k, k); /* determine location of F[k,1] */ k1 = f_loc(scf, k, 1); /* determine location of F[n,1] */ n1 = f_loc(scf, n, 1); /* if |U[k,k]| < |U[n,k]|, interchange k-th and n-th rows to provide |U[k,k]| >= |U[n,k]| */ if (fabs(u[kk]) < fabs(un[k])) { /* interchange k-th and n-th rows of matrix U */ for (j = k, kj = kk; j <= n; j++, kj++) t = u[kj], u[kj] = un[j], un[j] = t; /* interchange k-th and n-th rows of matrix F to keep the main equality F * C = U * P */ for (j = 1, kj = k1, nj = n1; j <= n; j++, kj++, nj++) t = f[kj], f[kj] = f[nj], f[nj] = t; } /* now |U[k,k]| >= |U[n,k]| */ /* if U[k,k] is too small in the magnitude, replace U[k,k] and U[n,k] by exact zero */ if (fabs(u[kk]) < eps) u[kk] = un[k] = 0.0; /* if U[n,k] is already zero, elimination is not needed */ if (un[k] == 0.0) continue; /* compute gaussian multiplier t = U[n,k] / U[k,k] */ t = un[k] / u[kk]; /* apply gaussian elimination to nullify U[n,k] */ /* (n-th row of U) := (n-th row of U) - t * (k-th row of U) */ for (j = k+1, kj = kk+1; j <= n; j++, kj++) un[j] -= t * u[kj]; /* (n-th row of F) := (n-th row of F) - t * (k-th row of F) to keep the main equality F * C = U * P */ for (j = 1, kj = k1, nj = n1; j <= n; j++, kj++, nj++) f[nj] -= t * f[kj]; } /* if U[n,n] is too small in the magnitude, replace it by exact zero */ if (fabs(un[n]) < eps) un[n] = 0.0; /* store U[n,n] in a proper location */ u[u_loc(scf, n, n)] = un[n]; return; } /*********************************************************************** * The routine givens computes the parameters of Givens plane rotation * c = cos(teta) and s = sin(teta) such that: * * ( c -s ) ( a ) ( r ) * ( ) ( ) = ( ) , * ( s c ) ( b ) ( 0 ) * * where a and b are given scalars. * * REFERENCES * * G.H.Golub, C.F.Van Loan, "Matrix Computations", 2nd ed. */ static void givens(double a, double b, double *c, double *s) { double t; if (b == 0.0) (*c) = 1.0, (*s) = 0.0; else if (fabs(a) <= fabs(b)) t = - a / b, (*s) = 1.0 / sqrt(1.0 + t * t), (*c) = (*s) * t; else t = - b / a, (*c) = 1.0 / sqrt(1.0 + t * t), (*s) = (*c) * t; return; } /*---------------------------------------------------------------------- * The routine gr_transform applies Givens plane rotations to restore * triangular structure of matrix U. * * On entry matrix U has the following structure: * * 1 k n * 1 * * * * * * * * * * * . * * * * * * * * * * . . * * * * * * * * * . . . * * * * * * * * k . . . . * * * * * * * . . . . . * * * * * * . . . . . . * * * * * . . . . . . . * * * * . . . . . . . . * * * n . . . . # # # # # # * * where '#' is a row spike to be eliminated. * * Elements of n-th row are passed separately in locations un[k], ..., * un[n]. On exit the content of the array un is destroyed. * * REFERENCES * * R.H.Bartels, G.H.Golub, "The Simplex Method of Linear Programming * Using LU-decomposition", Comm. ACM, 12, pp. 266-68, 1969. */ static void gr_transform(SCF *scf, int k, double un[]) { int n = scf->n; double *f = scf->f; double *u = scf->u; int j, k1, kj, kk, n1, nj; double c, s; xassert(1 <= k && k <= n); /* main elimination loop */ for (k = k; k < n; k++) { /* determine location of U[k,k] */ kk = u_loc(scf, k, k); /* determine location of F[k,1] */ k1 = f_loc(scf, k, 1); /* determine location of F[n,1] */ n1 = f_loc(scf, n, 1); /* if both U[k,k] and U[n,k] are too small in the magnitude, replace them by exact zero */ if (fabs(u[kk]) < eps && fabs(un[k]) < eps) u[kk] = un[k] = 0.0; /* if U[n,k] is already zero, elimination is not needed */ if (un[k] == 0.0) continue; /* compute the parameters of Givens plane rotation */ givens(u[kk], un[k], &c, &s); /* apply Givens rotation to k-th and n-th rows of matrix U */ for (j = k, kj = kk; j <= n; j++, kj++) { double ukj = u[kj], unj = un[j]; u[kj] = c * ukj - s * unj; un[j] = s * ukj + c * unj; } /* apply Givens rotation to k-th and n-th rows of matrix F to keep the main equality F * C = U * P */ for (j = 1, kj = k1, nj = n1; j <= n; j++, kj++, nj++) { double fkj = f[kj], fnj = f[nj]; f[kj] = c * fkj - s * fnj; f[nj] = s * fkj + c * fnj; } } /* if U[n,n] is too small in the magnitude, replace it by exact zero */ if (fabs(un[n]) < eps) un[n] = 0.0; /* store U[n,n] in a proper location */ u[u_loc(scf, n, n)] = un[n]; return; } /*********************************************************************** * The routine transform restores triangular structure of matrix U. * It is a driver to the routines bg_transform and gr_transform (see * comments to these routines above). */ static void transform(SCF *scf, int k, double un[]) { switch (scf->t_opt) { case SCF_TBG: bg_transform(scf, k, un); break; case SCF_TGR: gr_transform(scf, k, un); break; default: xassert(scf != scf); } return; } /*********************************************************************** * The routine estimate_rank estimates the rank of matrix C. * * Since all transformations applied to matrix F are non-singular, * and F is assumed to be well conditioned, from the main equaility * F * C = U * P it follows that rank(C) = rank(U), where rank(U) is * estimated as the number of non-zero diagonal elements of U. */ static int estimate_rank(SCF *scf) { int n_max = scf->n_max; int n = scf->n; double *u = scf->u; int i, ii, inc, rank = 0; for (i = 1, ii = u_loc(scf, i, i), inc = n_max; i <= n; i++, ii += inc, inc--) if (u[ii] != 0.0) rank++; return rank; } #if _GLPSCF_DEBUG /*********************************************************************** * The routine check_error computes the maximal relative error between * left- and right-hand sides of the main equality F * C = U * P. (This * routine is intended only for debugging.) */ static void check_error(SCF *scf, const char *func) { int n = scf->n; double *f = scf->f; double *u = scf->u; int *p = scf->p; double *c = scf->c; int i, j, k; double d, dmax = 0.0, s, t; xassert(c != NULL); for (i = 1; i <= n; i++) { for (j = 1; j <= n; j++) { /* compute element (i,j) of product F * C */ s = 0.0; for (k = 1; k <= n; k++) s += f[f_loc(scf, i, k)] * c[f_loc(scf, k, j)]; /* compute element (i,j) of product U * P */ k = p[j]; t = (i <= k ? u[u_loc(scf, i, k)] : 0.0); /* compute the maximal relative error */ d = fabs(s - t) / (1.0 + fabs(t)); if (dmax < d) dmax = d; } } if (dmax > 1e-8) xprintf("%s: dmax = %g; relative error too large\n", func, dmax); return; } #endif /*********************************************************************** * NAME * * scf_update_exp - update factorization on expanding C * * SYNOPSIS * * #include "glpscf.h" * int scf_update_exp(SCF *scf, const double x[], const double y[], * double z); * * DESCRIPTION * * The routine scf_update_exp updates the factorization of matrix C on * expanding it by adding a new row and column as follows: * * ( C x ) * new C = ( ) * ( y' z ) * * where x[1,...,n] is a new column, y[1,...,n] is a new row, and z is * a new diagonal element. * * If on entry the factorization is empty, the parameters x and y can * be specified as NULL. * * RETURNS * * 0 The factorization has been successfully updated. * * SCF_ESING * The factorization has been successfully updated, however, new * matrix C is singular within working precision. Note that the new * factorization remains valid. * * SCF_ELIMIT * There is not enough room to expand the factorization, because * n = n_max. The factorization remains unchanged. * * ALGORITHM * * We can see that: * * ( F 0 ) ( C x ) ( FC Fx ) ( UP Fx ) * ( ) ( ) = ( ) = ( ) = * ( 0 1 ) ( y' z ) ( y' z ) ( y' z ) * * ( U Fx ) ( P 0 ) * = ( ) ( ), * ( y'P' z ) ( 0 1 ) * * therefore to keep the main equality F * C = U * P we can take: * * ( F 0 ) ( U Fx ) ( P 0 ) * new F = ( ), new U = ( ), new P = ( ), * ( 0 1 ) ( y'P' z ) ( 0 1 ) * * and eliminate the row spike y'P' in the last row of new U to restore * its upper triangular structure. */ int scf_update_exp(SCF *scf, const double x[], const double y[], double z) { int n_max = scf->n_max; int n = scf->n; double *f = scf->f; double *u = scf->u; int *p = scf->p; #if _GLPSCF_DEBUG double *c = scf->c; #endif double *un = scf->w; int i, ij, in, j, k, nj, ret = 0; double t; /* check if the factorization can be expanded */ if (n == n_max) { /* there is not enough room */ ret = SCF_ELIMIT; goto done; } /* increase the order of the factorization */ scf->n = ++n; /* fill new zero column of matrix F */ for (i = 1, in = f_loc(scf, i, n); i < n; i++, in += n_max) f[in] = 0.0; /* fill new zero row of matrix F */ for (j = 1, nj = f_loc(scf, n, j); j < n; j++, nj++) f[nj] = 0.0; /* fill new unity diagonal element of matrix F */ f[f_loc(scf, n, n)] = 1.0; /* compute new column of matrix U, which is (old F) * x */ for (i = 1; i < n; i++) { /* u[i,n] := (i-th row of old F) * x */ t = 0.0; for (j = 1, ij = f_loc(scf, i, 1); j < n; j++, ij++) t += f[ij] * x[j]; u[u_loc(scf, i, n)] = t; } /* compute new (spiked) row of matrix U, which is (old P) * y */ for (j = 1; j < n; j++) un[j] = y[p[j]]; /* store new diagonal element of matrix U, which is z */ un[n] = z; /* expand matrix P */ p[n] = n; #if _GLPSCF_DEBUG /* expand matrix C */ /* fill its new column, which is x */ for (i = 1, in = f_loc(scf, i, n); i < n; i++, in += n_max) c[in] = x[i]; /* fill its new row, which is y */ for (j = 1, nj = f_loc(scf, n, j); j < n; j++, nj++) c[nj] = y[j]; /* fill its new diagonal element, which is z */ c[f_loc(scf, n, n)] = z; #endif /* restore upper triangular structure of matrix U */ for (k = 1; k < n; k++) if (un[k] != 0.0) break; transform(scf, k, un); /* estimate the rank of matrices C and U */ scf->rank = estimate_rank(scf); if (scf->rank != n) ret = SCF_ESING; #if _GLPSCF_DEBUG /* check that the factorization is accurate enough */ check_error(scf, "scf_update_exp"); #endif done: return ret; } /*********************************************************************** * The routine solve solves the system C * x = b. * * From the main equation F * C = U * P it follows that: * * C * x = b => F * C * x = F * b => U * P * x = F * b => * * P * x = inv(U) * F * b => x = P' * inv(U) * F * b. * * On entry the array x contains right-hand side vector b. On exit this * array contains solution vector x. */ static void solve(SCF *scf, double x[]) { int n = scf->n; double *f = scf->f; double *u = scf->u; int *p = scf->p; double *y = scf->w; int i, j, ij; double t; /* y := F * b */ for (i = 1; i <= n; i++) { /* y[i] = (i-th row of F) * b */ t = 0.0; for (j = 1, ij = f_loc(scf, i, 1); j <= n; j++, ij++) t += f[ij] * x[j]; y[i] = t; } /* y := inv(U) * y */ for (i = n; i >= 1; i--) { t = y[i]; for (j = n, ij = u_loc(scf, i, n); j > i; j--, ij--) t -= u[ij] * y[j]; y[i] = t / u[ij]; } /* x := P' * y */ for (i = 1; i <= n; i++) x[p[i]] = y[i]; return; } /*********************************************************************** * The routine tsolve solves the transposed system C' * x = b. * * From the main equation F * C = U * P it follows that: * * C' * F' = P' * U', * * therefore: * * C' * x = b => C' * F' * inv(F') * x = b => * * P' * U' * inv(F') * x = b => U' * inv(F') * x = P * b => * * inv(F') * x = inv(U') * P * b => x = F' * inv(U') * P * b. * * On entry the array x contains right-hand side vector b. On exit this * array contains solution vector x. */ static void tsolve(SCF *scf, double x[]) { int n = scf->n; double *f = scf->f; double *u = scf->u; int *p = scf->p; double *y = scf->w; int i, j, ij; double t; /* y := P * b */ for (i = 1; i <= n; i++) y[i] = x[p[i]]; /* y := inv(U') * y */ for (i = 1; i <= n; i++) { /* compute y[i] */ ij = u_loc(scf, i, i); t = (y[i] /= u[ij]); /* substitute y[i] in other equations */ for (j = i+1, ij++; j <= n; j++, ij++) y[j] -= u[ij] * t; } /* x := F' * y (computed as linear combination of rows of F) */ for (j = 1; j <= n; j++) x[j] = 0.0; for (i = 1; i <= n; i++) { t = y[i]; /* coefficient of linear combination */ for (j = 1, ij = f_loc(scf, i, 1); j <= n; j++, ij++) x[j] += f[ij] * t; } return; } /*********************************************************************** * NAME * * scf_solve_it - solve either system C * x = b or C' * x = b * * SYNOPSIS * * #include "glpscf.h" * void scf_solve_it(SCF *scf, int tr, double x[]); * * DESCRIPTION * * The routine scf_solve_it solves either the system C * x = b (if tr * is zero) or the system C' * x = b, where C' is a matrix transposed * to C (if tr is non-zero). C is assumed to be non-singular. * * On entry the array x should contain the right-hand side vector b in * locations x[1], ..., x[n], where n is the order of matrix C. On exit * the array x contains the solution vector x in the same locations. */ void scf_solve_it(SCF *scf, int tr, double x[]) { if (scf->rank < scf->n) xfault("scf_solve_it: singular matrix\n"); if (!tr) solve(scf, x); else tsolve(scf, x); return; } void scf_reset_it(SCF *scf) { /* reset factorization for empty matrix C */ scf->n = scf->rank = 0; return; } /*********************************************************************** * NAME * * scf_delete_it - delete Schur complement factorization * * SYNOPSIS * * #include "glpscf.h" * void scf_delete_it(SCF *scf); * * DESCRIPTION * * The routine scf_delete_it deletes the specified factorization and * frees all the memory allocated to this object. */ void scf_delete_it(SCF *scf) { xfree(scf->f); xfree(scf->u); xfree(scf->p); #if _GLPSCF_DEBUG xfree(scf->c); #endif xfree(scf->w); xfree(scf); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/minisat/0000755000175000017500000000000011632352620020103 5ustar csheltoncsheltonctbnrle.1.1.1/glpk/glpk-4.47/src/minisat/minisat.c0000644000175000017500000011176711632352620021730 0ustar csheltoncshelton/* minisat.c */ /* Modified by Andrew Makhorin , August 2011 */ /*********************************************************************** * MiniSat -- Copyright (c) 2005, Niklas Sorensson * http://www.cs.chalmers.se/Cs/Research/FormalMethods/MiniSat/ * * Permission is hereby granted, free of charge, to any person * obtaining a copy of this software and associated documentation files * (the "Software"), to deal in the Software without restriction, * including without limitation the rights to use, copy, modify, merge, * publish, distribute, sublicense, and/or sell copies of the Software, * and to permit persons to whom the Software is furnished to do so, * subject to the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS * BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN * ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. ***********************************************************************/ /* Modified to compile with MS Visual Studio 6.0 by Alan Mishchenko */ #include "glpenv.h" #include "minisat.h" #if 1 /* by mao */ static void *ymalloc(int size) { void *ptr; xassert(size > 0); ptr = malloc(size); if (ptr == NULL) xerror("MiniSat: no memory available\n"); return ptr; } static void *yrealloc(void *ptr, int size) { xassert(size > 0); if (ptr == NULL) ptr = malloc(size); else ptr = realloc(ptr, size); if (ptr == NULL) xerror("MiniSat: no memory available\n"); return ptr; } static void yfree(void *ptr) { xassert(ptr != NULL); free(ptr); return; } #define assert xassert #define printf xprintf #define fflush(f) /* nop */ #define malloc ymalloc #define realloc yrealloc #define free yfree #define inline /* empty */ #endif /*====================================================================*/ /* Debug: */ #if 0 #define VERBOSEDEBUG 1 #endif /* For derivation output (verbosity level 2) */ #define L_IND "%-*d" #define L_ind solver_dlevel(s)*3+3,solver_dlevel(s) #define L_LIT "%sx%d" #define L_lit(p) lit_sign(p)?"~":"", (lit_var(p)) #if 0 /* by mao */ /* Just like 'assert()' but expression will be evaluated in the release version as well. */ static inline void check(int expr) { assert(expr); } #endif #if 0 /* by mao */ static void printlits(lit* begin, lit* end) { int i; for (i = 0; i < end - begin; i++) printf(L_LIT" ",L_lit(begin[i])); } #endif /*====================================================================*/ /* Random numbers: */ /* Returns a random float 0 <= x < 1. Seed must never be 0. */ static inline double drand(double* seed) { int q; *seed *= 1389796; q = (int)(*seed / 2147483647); *seed -= (double)q * 2147483647; return *seed / 2147483647; } /* Returns a random integer 0 <= x < size. Seed must never be 0. */ static inline int irand(double* seed, int size) { return (int)(drand(seed) * size); } /*====================================================================*/ /* Predeclarations: */ static void sort(void** array, int size, int(*comp)(const void *, const void *)); /*====================================================================*/ /* Clause datatype + minor functions: */ #if 0 /* by mao; see minisat.h */ struct clause_t { int size_learnt; lit lits[0]; }; #endif #define clause_size(c) ((c)->size_learnt >> 1) #define clause_begin(c) ((c)->lits) #define clause_learnt(c) ((c)->size_learnt & 1) #define clause_activity(c) \ (*((float*)&(c)->lits[(c)->size_learnt>>1])) #define clause_setactivity(c, a) \ (void)(*((float*)&(c)->lits[(c)->size_learnt>>1]) = (a)) /*====================================================================*/ /* Encode literals in clause pointers: */ #define clause_from_lit(l) \ (clause*)((unsigned long)(l) + (unsigned long)(l) + 1) #define clause_is_lit(c) \ ((unsigned long)(c) & 1) #define clause_read_lit(c) \ (lit)((unsigned long)(c) >> 1) /*====================================================================*/ /* Simple helpers: */ #define solver_dlevel(s) \ (int)veci_size(&(s)->trail_lim) #define solver_read_wlist(s, l) \ (vecp *)(&(s)->wlists[l]) static inline void vecp_remove(vecp* v, void* e) { void** ws = vecp_begin(v); int j = 0; for (; ws[j] != e ; j++); assert(j < vecp_size(v)); for (; j < vecp_size(v)-1; j++) ws[j] = ws[j+1]; vecp_resize(v,vecp_size(v)-1); } /*====================================================================*/ /* Variable order functions: */ static inline void order_update(solver* s, int v) { /* updateorder */ int* orderpos = s->orderpos; double* activity = s->activity; int* heap = veci_begin(&s->order); int i = orderpos[v]; int x = heap[i]; int parent = (i - 1) / 2; assert(s->orderpos[v] != -1); while (i != 0 && activity[x] > activity[heap[parent]]){ heap[i] = heap[parent]; orderpos[heap[i]] = i; i = parent; parent = (i - 1) / 2; } heap[i] = x; orderpos[x] = i; } #define order_assigned(s, v) /* nop */ static inline void order_unassigned(solver* s, int v) { /* undoorder */ int* orderpos = s->orderpos; if (orderpos[v] == -1){ orderpos[v] = veci_size(&s->order); veci_push(&s->order,v); order_update(s,v); } } static int order_select(solver* s, float random_var_freq) { /* selectvar */ int* heap; double* activity; int* orderpos; lbool* values = s->assigns; /* Random decision: */ if (drand(&s->random_seed) < random_var_freq){ int next = irand(&s->random_seed,s->size); assert(next >= 0 && next < s->size); if (values[next] == l_Undef) return next; } /* Activity based decision: */ heap = veci_begin(&s->order); activity = s->activity; orderpos = s->orderpos; while (veci_size(&s->order) > 0){ int next = heap[0]; int size = veci_size(&s->order)-1; int x = heap[size]; veci_resize(&s->order,size); orderpos[next] = -1; if (size > 0){ double act = activity[x]; int i = 0; int child = 1; while (child < size){ if (child+1 < size && activity[heap[child]] < activity[heap[child+1]]) child++; assert(child < size); if (act >= activity[heap[child]]) break; heap[i] = heap[child]; orderpos[heap[i]] = i; i = child; child = 2 * child + 1; } heap[i] = x; orderpos[heap[i]] = i; } if (values[next] == l_Undef) return next; } return var_Undef; } /*====================================================================*/ /* Activity functions: */ static inline void act_var_rescale(solver* s) { double* activity = s->activity; int i; for (i = 0; i < s->size; i++) activity[i] *= 1e-100; s->var_inc *= 1e-100; } static inline void act_var_bump(solver* s, int v) { double* activity = s->activity; if ((activity[v] += s->var_inc) > 1e100) act_var_rescale(s); /* printf("bump %d %f\n", v-1, activity[v]); */ if (s->orderpos[v] != -1) order_update(s,v); } static inline void act_var_decay(solver* s) { s->var_inc *= s->var_decay; } static inline void act_clause_rescale(solver* s) { clause** cs = (clause**)vecp_begin(&s->learnts); int i; for (i = 0; i < vecp_size(&s->learnts); i++){ float a = clause_activity(cs[i]); clause_setactivity(cs[i], a * (float)1e-20); } s->cla_inc *= (float)1e-20; } static inline void act_clause_bump(solver* s, clause *c) { float a = clause_activity(c) + s->cla_inc; clause_setactivity(c,a); if (a > 1e20) act_clause_rescale(s); } static inline void act_clause_decay(solver* s) { s->cla_inc *= s->cla_decay; } /*====================================================================*/ /* Clause functions: */ /* pre: size > 1 && no variable occurs twice */ static clause* clause_new(solver* s, lit* begin, lit* end, int learnt) { int size; clause* c; int i; assert(end - begin > 1); assert(learnt >= 0 && learnt < 2); size = end - begin; c = (clause*)malloc(sizeof(clause) + sizeof(lit) * size + learnt * sizeof(float)); c->size_learnt = (size << 1) | learnt; #if 0 /* by mao; meaningless non-portable check */ assert(((unsigned int)c & 1) == 0); #endif for (i = 0; i < size; i++) c->lits[i] = begin[i]; if (learnt) *((float*)&c->lits[size]) = 0.0; assert(begin[0] >= 0); assert(begin[0] < s->size*2); assert(begin[1] >= 0); assert(begin[1] < s->size*2); assert(lit_neg(begin[0]) < s->size*2); assert(lit_neg(begin[1]) < s->size*2); /* vecp_push(solver_read_wlist(s,lit_neg(begin[0])),(void*)c); */ /* vecp_push(solver_read_wlist(s,lit_neg(begin[1])),(void*)c); */ vecp_push(solver_read_wlist(s,lit_neg(begin[0])), (void*)(size > 2 ? c : clause_from_lit(begin[1]))); vecp_push(solver_read_wlist(s,lit_neg(begin[1])), (void*)(size > 2 ? c : clause_from_lit(begin[0]))); return c; } static void clause_remove(solver* s, clause* c) { lit* lits = clause_begin(c); assert(lit_neg(lits[0]) < s->size*2); assert(lit_neg(lits[1]) < s->size*2); /* vecp_remove(solver_read_wlist(s,lit_neg(lits[0])),(void*)c); */ /* vecp_remove(solver_read_wlist(s,lit_neg(lits[1])),(void*)c); */ assert(lits[0] < s->size*2); vecp_remove(solver_read_wlist(s,lit_neg(lits[0])), (void*)(clause_size(c) > 2 ? c : clause_from_lit(lits[1]))); vecp_remove(solver_read_wlist(s,lit_neg(lits[1])), (void*)(clause_size(c) > 2 ? c : clause_from_lit(lits[0]))); if (clause_learnt(c)){ s->stats.learnts--; s->stats.learnts_literals -= clause_size(c); }else{ s->stats.clauses--; s->stats.clauses_literals -= clause_size(c); } free(c); } static lbool clause_simplify(solver* s, clause* c) { lit* lits = clause_begin(c); lbool* values = s->assigns; int i; assert(solver_dlevel(s) == 0); for (i = 0; i < clause_size(c); i++){ lbool sig = !lit_sign(lits[i]); sig += sig - 1; if (values[lit_var(lits[i])] == sig) return l_True; } return l_False; } /*====================================================================*/ /* Minor (solver) functions: */ void solver_setnvars(solver* s,int n) { int var; if (s->cap < n){ while (s->cap < n) s->cap = s->cap*2+1; s->wlists = (vecp*) realloc(s->wlists, sizeof(vecp)*s->cap*2); s->activity = (double*) realloc(s->activity, sizeof(double)*s->cap); s->assigns = (lbool*) realloc(s->assigns, sizeof(lbool)*s->cap); s->orderpos = (int*) realloc(s->orderpos, sizeof(int)*s->cap); s->reasons = (clause**)realloc(s->reasons, sizeof(clause*)*s->cap); s->levels = (int*) realloc(s->levels, sizeof(int)*s->cap); s->tags = (lbool*) realloc(s->tags, sizeof(lbool)*s->cap); s->trail = (lit*) realloc(s->trail, sizeof(lit)*s->cap); } for (var = s->size; var < n; var++){ vecp_new(&s->wlists[2*var]); vecp_new(&s->wlists[2*var+1]); s->activity [var] = 0; s->assigns [var] = l_Undef; s->orderpos [var] = veci_size(&s->order); s->reasons [var] = (clause*)0; s->levels [var] = 0; s->tags [var] = l_Undef; /* does not hold because variables enqueued at top level will not be reinserted in the heap assert(veci_size(&s->order) == var); */ veci_push(&s->order,var); order_update(s, var); } s->size = n > s->size ? n : s->size; } static inline bool enqueue(solver* s, lit l, clause* from) { lbool* values = s->assigns; int v = lit_var(l); lbool val = values[v]; lbool sig; #ifdef VERBOSEDEBUG printf(L_IND"enqueue("L_LIT")\n", L_ind, L_lit(l)); #endif /* lbool */ sig = !lit_sign(l); sig += sig - 1; if (val != l_Undef){ return val == sig; }else{ /* New fact -- store it. */ int* levels; clause** reasons; #ifdef VERBOSEDEBUG printf(L_IND"bind("L_LIT")\n", L_ind, L_lit(l)); #endif /* int* */ levels = s->levels; /* clause** */ reasons = s->reasons; values [v] = sig; levels [v] = solver_dlevel(s); reasons[v] = from; s->trail[s->qtail++] = l; order_assigned(s, v); return true; } } static inline void assume(solver* s, lit l){ assert(s->qtail == s->qhead); assert(s->assigns[lit_var(l)] == l_Undef); #ifdef VERBOSEDEBUG printf(L_IND"assume("L_LIT")\n", L_ind, L_lit(l)); #endif veci_push(&s->trail_lim,s->qtail); enqueue(s,l,(clause*)0); } static inline void solver_canceluntil(solver* s, int level) { lit* trail; lbool* values; clause** reasons; int bound; int c; if (solver_dlevel(s) <= level) return; trail = s->trail; values = s->assigns; reasons = s->reasons; bound = (veci_begin(&s->trail_lim))[level]; for (c = s->qtail-1; c >= bound; c--) { int x = lit_var(trail[c]); values [x] = l_Undef; reasons[x] = (clause*)0; } for (c = s->qhead-1; c >= bound; c--) order_unassigned(s,lit_var(trail[c])); s->qhead = s->qtail = bound; veci_resize(&s->trail_lim,level); } static void solver_record(solver* s, veci* cls) { lit* begin = veci_begin(cls); lit* end = begin + veci_size(cls); clause* c = (veci_size(cls) > 1) ? clause_new(s,begin,end,1) : (clause*)0; enqueue(s,*begin,c); assert(veci_size(cls) > 0); if (c != 0) { vecp_push(&s->learnts,c); act_clause_bump(s,c); s->stats.learnts++; s->stats.learnts_literals += veci_size(cls); } } static double solver_progress(solver* s) { lbool* values = s->assigns; int* levels = s->levels; int i; double progress = 0; double F = 1.0 / s->size; for (i = 0; i < s->size; i++) if (values[i] != l_Undef) progress += pow(F, levels[i]); return progress / s->size; } /*====================================================================*/ /* Major methods: */ static bool solver_lit_removable(solver* s, lit l, int minl) { lbool* tags = s->tags; clause** reasons = s->reasons; int* levels = s->levels; int top = veci_size(&s->tagged); assert(lit_var(l) >= 0 && lit_var(l) < s->size); assert(reasons[lit_var(l)] != 0); veci_resize(&s->stack,0); veci_push(&s->stack,lit_var(l)); while (veci_size(&s->stack) > 0){ clause* c; int v = veci_begin(&s->stack)[veci_size(&s->stack)-1]; assert(v >= 0 && v < s->size); veci_resize(&s->stack,veci_size(&s->stack)-1); assert(reasons[v] != 0); c = reasons[v]; if (clause_is_lit(c)){ int v = lit_var(clause_read_lit(c)); if (tags[v] == l_Undef && levels[v] != 0){ if (reasons[v] != 0 && ((1 << (levels[v] & 31)) & minl)){ veci_push(&s->stack,v); tags[v] = l_True; veci_push(&s->tagged,v); }else{ int* tagged = veci_begin(&s->tagged); int j; for (j = top; j < veci_size(&s->tagged); j++) tags[tagged[j]] = l_Undef; veci_resize(&s->tagged,top); return false; } } }else{ lit* lits = clause_begin(c); int i, j; for (i = 1; i < clause_size(c); i++){ int v = lit_var(lits[i]); if (tags[v] == l_Undef && levels[v] != 0){ if (reasons[v] != 0 && ((1 << (levels[v] & 31)) & minl)){ veci_push(&s->stack,lit_var(lits[i])); tags[v] = l_True; veci_push(&s->tagged,v); }else{ int* tagged = veci_begin(&s->tagged); for (j = top; j < veci_size(&s->tagged); j++) tags[tagged[j]] = l_Undef; veci_resize(&s->tagged,top); return false; } } } } } return true; } static void solver_analyze(solver* s, clause* c, veci* learnt) { lit* trail = s->trail; lbool* tags = s->tags; clause** reasons = s->reasons; int* levels = s->levels; int cnt = 0; lit p = lit_Undef; int ind = s->qtail-1; lit* lits; int i, j, minl; int* tagged; veci_push(learnt,lit_Undef); do{ assert(c != 0); if (clause_is_lit(c)){ lit q = clause_read_lit(c); assert(lit_var(q) >= 0 && lit_var(q) < s->size); if (tags[lit_var(q)] == l_Undef && levels[lit_var(q)] > 0){ tags[lit_var(q)] = l_True; veci_push(&s->tagged,lit_var(q)); act_var_bump(s,lit_var(q)); if (levels[lit_var(q)] == solver_dlevel(s)) cnt++; else veci_push(learnt,q); } }else{ if (clause_learnt(c)) act_clause_bump(s,c); lits = clause_begin(c); /* printlits(lits,lits+clause_size(c)); printf("\n"); */ for (j = (p == lit_Undef ? 0 : 1); j < clause_size(c); j++){ lit q = lits[j]; assert(lit_var(q) >= 0 && lit_var(q) < s->size); if (tags[lit_var(q)] == l_Undef && levels[lit_var(q)] > 0){ tags[lit_var(q)] = l_True; veci_push(&s->tagged,lit_var(q)); act_var_bump(s,lit_var(q)); if (levels[lit_var(q)] == solver_dlevel(s)) cnt++; else veci_push(learnt,q); } } } while (tags[lit_var(trail[ind--])] == l_Undef); p = trail[ind+1]; c = reasons[lit_var(p)]; cnt--; }while (cnt > 0); *veci_begin(learnt) = lit_neg(p); lits = veci_begin(learnt); minl = 0; for (i = 1; i < veci_size(learnt); i++){ int lev = levels[lit_var(lits[i])]; minl |= 1 << (lev & 31); } /* simplify (full) */ for (i = j = 1; i < veci_size(learnt); i++){ if (reasons[lit_var(lits[i])] == 0 || !solver_lit_removable(s,lits[i],minl)) lits[j++] = lits[i]; } /* update size of learnt + statistics */ s->stats.max_literals += veci_size(learnt); veci_resize(learnt,j); s->stats.tot_literals += j; /* clear tags */ tagged = veci_begin(&s->tagged); for (i = 0; i < veci_size(&s->tagged); i++) tags[tagged[i]] = l_Undef; veci_resize(&s->tagged,0); #ifdef DEBUG for (i = 0; i < s->size; i++) assert(tags[i] == l_Undef); #endif #ifdef VERBOSEDEBUG printf(L_IND"Learnt {", L_ind); for (i = 0; i < veci_size(learnt); i++) printf(" "L_LIT, L_lit(lits[i])); #endif if (veci_size(learnt) > 1){ int max_i = 1; int max = levels[lit_var(lits[1])]; lit tmp; for (i = 2; i < veci_size(learnt); i++) if (levels[lit_var(lits[i])] > max){ max = levels[lit_var(lits[i])]; max_i = i; } tmp = lits[1]; lits[1] = lits[max_i]; lits[max_i] = tmp; } #ifdef VERBOSEDEBUG { int lev = veci_size(learnt) > 1 ? levels[lit_var(lits[1])] : 0; printf(" } at level %d\n", lev); } #endif } clause* solver_propagate(solver* s) { lbool* values = s->assigns; clause* confl = (clause*)0; lit* lits; /* printf("solver_propagate\n"); */ while (confl == 0 && s->qtail - s->qhead > 0){ lit p = s->trail[s->qhead++]; vecp* ws = solver_read_wlist(s,p); clause **begin = (clause**)vecp_begin(ws); clause **end = begin + vecp_size(ws); clause **i, **j; s->stats.propagations++; s->simpdb_props--; /* printf("checking lit %d: "L_LIT"\n", veci_size(ws), L_lit(p)); */ for (i = j = begin; i < end; ){ if (clause_is_lit(*i)){ *j++ = *i; if (!enqueue(s,clause_read_lit(*i),clause_from_lit(p))){ confl = s->binary; (clause_begin(confl))[1] = lit_neg(p); (clause_begin(confl))[0] = clause_read_lit(*i++); /* Copy the remaining watches: */ while (i < end) *j++ = *i++; } }else{ lit false_lit; lbool sig; lits = clause_begin(*i); /* Make sure the false literal is data[1]: */ false_lit = lit_neg(p); if (lits[0] == false_lit){ lits[0] = lits[1]; lits[1] = false_lit; } assert(lits[1] == false_lit); /* printf("checking clause: "); printlits(lits, lits+clause_size(*i)); printf("\n"); */ /* If 0th watch is true, then clause is already satisfied. */ sig = !lit_sign(lits[0]); sig += sig - 1; if (values[lit_var(lits[0])] == sig){ *j++ = *i; }else{ /* Look for new watch: */ lit* stop = lits + clause_size(*i); lit* k; for (k = lits + 2; k < stop; k++){ lbool sig = lit_sign(*k); sig += sig - 1; if (values[lit_var(*k)] != sig){ lits[1] = *k; *k = false_lit; vecp_push(solver_read_wlist(s, lit_neg(lits[1])),*i); goto next; } } *j++ = *i; /* Clause is unit under assignment: */ if (!enqueue(s,lits[0], *i)){ confl = *i++; /* Copy the remaining watches: */ while (i < end) *j++ = *i++; } } } next: i++; } s->stats.inspects += j - (clause**)vecp_begin(ws); vecp_resize(ws,j - (clause**)vecp_begin(ws)); } return confl; } static inline int clause_cmp (const void* x, const void* y) { return clause_size((clause*)x) > 2 && (clause_size((clause*)y) == 2 || clause_activity((clause*)x) < clause_activity((clause*)y)) ? -1 : 1; } void solver_reducedb(solver* s) { int i, j; double extra_lim = s->cla_inc / vecp_size(&s->learnts); /* Remove any clause below this activity */ clause** learnts = (clause**)vecp_begin(&s->learnts); clause** reasons = s->reasons; sort(vecp_begin(&s->learnts), vecp_size(&s->learnts), clause_cmp); for (i = j = 0; i < vecp_size(&s->learnts) / 2; i++){ if (clause_size(learnts[i]) > 2 && reasons[lit_var(*clause_begin(learnts[i]))] != learnts[i]) clause_remove(s,learnts[i]); else learnts[j++] = learnts[i]; } for (; i < vecp_size(&s->learnts); i++){ if (clause_size(learnts[i]) > 2 && reasons[lit_var(*clause_begin(learnts[i]))] != learnts[i] && clause_activity(learnts[i]) < extra_lim) clause_remove(s,learnts[i]); else learnts[j++] = learnts[i]; } /* printf("reducedb deleted %d\n", vecp_size(&s->learnts) - j); */ vecp_resize(&s->learnts,j); } static lbool solver_search(solver* s, int nof_conflicts, int nof_learnts) { int* levels = s->levels; double var_decay = 0.95; double clause_decay = 0.999; double random_var_freq = 0.02; int conflictC = 0; veci learnt_clause; assert(s->root_level == solver_dlevel(s)); s->stats.starts++; s->var_decay = (float)(1 / var_decay ); s->cla_decay = (float)(1 / clause_decay); veci_resize(&s->model,0); veci_new(&learnt_clause); for (;;){ clause* confl = solver_propagate(s); if (confl != 0){ /* CONFLICT */ int blevel; #ifdef VERBOSEDEBUG printf(L_IND"**CONFLICT**\n", L_ind); #endif s->stats.conflicts++; conflictC++; if (solver_dlevel(s) == s->root_level){ veci_delete(&learnt_clause); return l_False; } veci_resize(&learnt_clause,0); solver_analyze(s, confl, &learnt_clause); blevel = veci_size(&learnt_clause) > 1 ? levels[lit_var(veci_begin(&learnt_clause)[1])] : s->root_level; blevel = s->root_level > blevel ? s->root_level : blevel; solver_canceluntil(s,blevel); solver_record(s,&learnt_clause); act_var_decay(s); act_clause_decay(s); }else{ /* NO CONFLICT */ int next; if (nof_conflicts >= 0 && conflictC >= nof_conflicts){ /* Reached bound on number of conflicts: */ s->progress_estimate = solver_progress(s); solver_canceluntil(s,s->root_level); veci_delete(&learnt_clause); return l_Undef; } if (solver_dlevel(s) == 0) /* Simplify the set of problem clauses: */ solver_simplify(s); if (nof_learnts >= 0 && vecp_size(&s->learnts) - s->qtail >= nof_learnts) /* Reduce the set of learnt clauses: */ solver_reducedb(s); /* New variable decision: */ s->stats.decisions++; next = order_select(s,(float)random_var_freq); if (next == var_Undef){ /* Model found: */ lbool* values = s->assigns; int i; for (i = 0; i < s->size; i++) veci_push(&s->model,(int)values[i]); solver_canceluntil(s,s->root_level); veci_delete(&learnt_clause); /* veci apa; veci_new(&apa); for (i = 0; i < s->size; i++) veci_push(&apa,(int)(s->model.ptr[i] == l_True ? toLit(i) : lit_neg(toLit(i)))); printf("model: "); printlits((lit*)apa.ptr, (lit*)apa.ptr + veci_size(&apa)); printf("\n"); veci_delete(&apa); */ return l_True; } assume(s,lit_neg(toLit(next))); } } #if 0 /* by mao; unreachable code */ return l_Undef; /* cannot happen */ #endif } /*====================================================================*/ /* External solver functions: */ solver* solver_new(void) { solver* s = (solver*)malloc(sizeof(solver)); /* initialize vectors */ vecp_new(&s->clauses); vecp_new(&s->learnts); veci_new(&s->order); veci_new(&s->trail_lim); veci_new(&s->tagged); veci_new(&s->stack); veci_new(&s->model); /* initialize arrays */ s->wlists = 0; s->activity = 0; s->assigns = 0; s->orderpos = 0; s->reasons = 0; s->levels = 0; s->tags = 0; s->trail = 0; /* initialize other vars */ s->size = 0; s->cap = 0; s->qhead = 0; s->qtail = 0; s->cla_inc = 1; s->cla_decay = 1; s->var_inc = 1; s->var_decay = 1; s->root_level = 0; s->simpdb_assigns = 0; s->simpdb_props = 0; s->random_seed = 91648253; s->progress_estimate = 0; s->binary = (clause*)malloc(sizeof(clause) + sizeof(lit)*2); s->binary->size_learnt = (2 << 1); s->verbosity = 0; s->stats.starts = 0; s->stats.decisions = 0; s->stats.propagations = 0; s->stats.inspects = 0; s->stats.conflicts = 0; s->stats.clauses = 0; s->stats.clauses_literals = 0; s->stats.learnts = 0; s->stats.learnts_literals = 0; s->stats.max_literals = 0; s->stats.tot_literals = 0; return s; } void solver_delete(solver* s) { int i; for (i = 0; i < vecp_size(&s->clauses); i++) free(vecp_begin(&s->clauses)[i]); for (i = 0; i < vecp_size(&s->learnts); i++) free(vecp_begin(&s->learnts)[i]); /* delete vectors */ vecp_delete(&s->clauses); vecp_delete(&s->learnts); veci_delete(&s->order); veci_delete(&s->trail_lim); veci_delete(&s->tagged); veci_delete(&s->stack); veci_delete(&s->model); free(s->binary); /* delete arrays */ if (s->wlists != 0){ int i; for (i = 0; i < s->size*2; i++) vecp_delete(&s->wlists[i]); /* if one is different from null, all are */ free(s->wlists); free(s->activity ); free(s->assigns ); free(s->orderpos ); free(s->reasons ); free(s->levels ); free(s->trail ); free(s->tags ); } free(s); } bool solver_addclause(solver* s, lit* begin, lit* end) { lit *i,*j; int maxvar; lbool* values; lit last; if (begin == end) return false; /* printlits(begin,end); printf("\n"); */ /* insertion sort */ maxvar = lit_var(*begin); for (i = begin + 1; i < end; i++){ lit l = *i; maxvar = lit_var(l) > maxvar ? lit_var(l) : maxvar; for (j = i; j > begin && *(j-1) > l; j--) *j = *(j-1); *j = l; } solver_setnvars(s,maxvar+1); /* printlits(begin,end); printf("\n"); */ values = s->assigns; /* delete duplicates */ last = lit_Undef; for (i = j = begin; i < end; i++){ /* printf("lit: "L_LIT", value = %d\n", L_lit(*i), (lit_sign(*i) ? -values[lit_var(*i)] : values[lit_var(*i)])); */ lbool sig = !lit_sign(*i); sig += sig - 1; if (*i == lit_neg(last) || sig == values[lit_var(*i)]) return true; /* tautology */ else if (*i != last && values[lit_var(*i)] == l_Undef) last = *j++ = *i; } /* printf("final: "); printlits(begin,j); printf("\n"); */ if (j == begin) /* empty clause */ return false; else if (j - begin == 1) /* unit clause */ return enqueue(s,*begin,(clause*)0); /* create new clause */ vecp_push(&s->clauses,clause_new(s,begin,j,0)); s->stats.clauses++; s->stats.clauses_literals += j - begin; return true; } bool solver_simplify(solver* s) { clause** reasons; int type; assert(solver_dlevel(s) == 0); if (solver_propagate(s) != 0) return false; if (s->qhead == s->simpdb_assigns || s->simpdb_props > 0) return true; reasons = s->reasons; for (type = 0; type < 2; type++){ vecp* cs = type ? &s->learnts : &s->clauses; clause** cls = (clause**)vecp_begin(cs); int i, j; for (j = i = 0; i < vecp_size(cs); i++){ if (reasons[lit_var(*clause_begin(cls[i]))] != cls[i] && clause_simplify(s,cls[i]) == l_True) clause_remove(s,cls[i]); else cls[j++] = cls[i]; } vecp_resize(cs,j); } s->simpdb_assigns = s->qhead; /* (shouldn't depend on 'stats' really, but it will do for now) */ s->simpdb_props = (int)(s->stats.clauses_literals + s->stats.learnts_literals); return true; } bool solver_solve(solver* s, lit* begin, lit* end) { double nof_conflicts = 100; double nof_learnts = solver_nclauses(s) / 3; lbool status = l_Undef; lbool* values = s->assigns; lit* i; /* printf("solve: "); printlits(begin, end); printf("\n"); */ for (i = begin; i < end; i++){ switch (lit_sign(*i) ? -values[lit_var(*i)] : values[lit_var(*i)]){ case 1: /* l_True: */ break; case 0: /* l_Undef */ assume(s, *i); if (solver_propagate(s) == NULL) break; /* falltrough */ case -1: /* l_False */ solver_canceluntil(s, 0); return false; } } s->root_level = solver_dlevel(s); if (s->verbosity >= 1){ printf("==================================[MINISAT]============" "=======================\n"); printf("| Conflicts | ORIGINAL | LEARNT " " | Progress |\n"); printf("| | Clauses Literals | Limit Clauses Litera" "ls Lit/Cl | |\n"); printf("=======================================================" "=======================\n"); } while (status == l_Undef){ double Ratio = (s->stats.learnts == 0)? 0.0 : s->stats.learnts_literals / (double)s->stats.learnts; if (s->verbosity >= 1){ printf("| %9.0f | %7.0f %8.0f | %7.0f %7.0f %8.0f %7.1f | %" "6.3f %% |\n", (double)s->stats.conflicts, (double)s->stats.clauses, (double)s->stats.clauses_literals, (double)nof_learnts, (double)s->stats.learnts, (double)s->stats.learnts_literals, Ratio, s->progress_estimate*100); fflush(stdout); } status = solver_search(s,(int)nof_conflicts, (int)nof_learnts); nof_conflicts *= 1.5; nof_learnts *= 1.1; } if (s->verbosity >= 1) printf("=======================================================" "=======================\n"); solver_canceluntil(s,0); return status != l_False; } int solver_nvars(solver* s) { return s->size; } int solver_nclauses(solver* s) { return vecp_size(&s->clauses); } int solver_nconflicts(solver* s) { return (int)s->stats.conflicts; } /*====================================================================*/ /* Sorting functions (sigh): */ static inline void selectionsort(void** array, int size, int(*comp)(const void *, const void *)) { int i, j, best_i; void* tmp; for (i = 0; i < size-1; i++){ best_i = i; for (j = i+1; j < size; j++){ if (comp(array[j], array[best_i]) < 0) best_i = j; } tmp = array[i]; array[i] = array[best_i]; array[best_i] = tmp; } } static void sortrnd(void** array, int size, int(*comp)(const void *, const void *), double* seed) { if (size <= 15) selectionsort(array, size, comp); else{ void* pivot = array[irand(seed, size)]; void* tmp; int i = -1; int j = size; for(;;){ do i++; while(comp(array[i], pivot)<0); do j--; while(comp(pivot, array[j])<0); if (i >= j) break; tmp = array[i]; array[i] = array[j]; array[j] = tmp; } sortrnd(array , i , comp, seed); sortrnd(&array[i], size-i, comp, seed); } } static void sort(void** array, int size, int(*comp)(const void *, const void *)) { double seed = 91648253; sortrnd(array,size,comp,&seed); } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/minisat/minisat.h0000644000175000017500000001617611632352620021733 0ustar csheltoncshelton/* minisat.h */ /* Modified by Andrew Makhorin , August 2011 */ /*********************************************************************** * MiniSat -- Copyright (c) 2005, Niklas Sorensson * http://www.cs.chalmers.se/Cs/Research/FormalMethods/MiniSat/ * * Permission is hereby granted, free of charge, to any person * obtaining a copy of this software and associated documentation files * (the "Software"), to deal in the Software without restriction, * including without limitation the rights to use, copy, modify, merge, * publish, distribute, sublicense, and/or sell copies of the Software, * and to permit persons to whom the Software is furnished to do so, * subject to the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS * BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN * ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. ***********************************************************************/ /* Modified to compile with MS Visual Studio 6.0 by Alan Mishchenko */ #ifndef MINISAT_H #define MINISAT_H /*====================================================================*/ /* Simple types: */ typedef int bool; #define true 1 #define false 0 typedef int lit; #if 0 /* by mao */ typedef char lbool; #else typedef int lbool; #endif #define var_Undef (int)(-1) #define lit_Undef (lit)(-2) #define l_Undef (lbool)0 #define l_True (lbool)1 #define l_False (lbool)(-1) #define toLit(v) (lit)((v) + (v)) #define lit_neg(l) (lit)((l) ^ 1) #define lit_var(l) (int)((l) >> 1) #define lit_sign(l) (int)((l) & 1) /*====================================================================*/ /* Vectors: */ /* vector of 32-bit intergers (added for 64-bit portability) */ typedef struct /* veci_t */ { int size; int cap; int* ptr; } veci; #define veci_new(v) \ { (v)->size = 0; \ (v)->cap = 4; \ (v)->ptr = (int*)malloc(sizeof(int)*(v)->cap); \ } #define veci_delete(v) free((v)->ptr) #define veci_begin(v) ((v)->ptr) #define veci_size(v) ((v)->size) #define veci_resize(v, k) (void)((v)->size = (k)) /* only safe to shrink !! */ #define veci_push(v, e) \ { if ((v)->size == (v)->cap) \ { int newsize = (v)->cap * 2+1; \ (v)->ptr = (int*)realloc((v)->ptr,sizeof(int)*newsize); \ (v)->cap = newsize; \ } \ (v)->ptr[(v)->size++] = (e); \ } /* vector of 32- or 64-bit pointers */ typedef struct /* vecp_t */ { int size; int cap; void** ptr; } vecp; #define vecp_new(v) \ { (v)->size = 0; \ (v)->cap = 4; \ (v)->ptr = (void**)malloc(sizeof(void*)*(v)->cap); \ } #define vecp_delete(v) free((v)->ptr) #define vecp_begin(v) ((v)->ptr) #define vecp_size(v) ((v)->size) #define vecp_resize(v, k) (void)((v)->size = (k)) /* only safe to shrink !! */ #define vecp_push(v, e) \ { if ((v)->size == (v)->cap) \ { int newsize = (v)->cap * 2+1; \ (v)->ptr = (void**)realloc((v)->ptr,sizeof(void*)*newsize); \ (v)->cap = newsize; \ } \ (v)->ptr[(v)->size++] = (e); \ } /*====================================================================*/ /* Solver representation: */ typedef struct /* clause_t */ { int size_learnt; lit lits[1]; } clause; typedef struct /* stats_t */ { double starts, decisions, propagations, inspects, conflicts; double clauses, clauses_literals, learnts, learnts_literals, max_literals, tot_literals; } stats; typedef struct /* solver_t */ { int size; /* nof variables */ int cap; /* size of varmaps */ int qhead; /* Head index of queue. */ int qtail; /* Tail index of queue. */ /* clauses */ vecp clauses; /* List of problem constraints. (contains: clause*) */ vecp learnts; /* List of learnt clauses. (contains: clause*) */ /* activities */ double var_inc; /* Amount to bump next variable with. */ double var_decay; /* INVERSE decay factor for variable activity: stores 1/decay. */ float cla_inc; /* Amount to bump next clause with. */ float cla_decay; /* INVERSE decay factor for clause activity: stores 1/decay. */ vecp* wlists; double* activity; /* A heuristic measurement of the activity of a variable. */ lbool* assigns; /* Current values of variables. */ int* orderpos; /* Index in variable order. */ clause** reasons; int* levels; lit* trail; clause* binary; /* A temporary binary clause */ lbool* tags; veci tagged; /* (contains: var) */ veci stack; /* (contains: var) */ veci order; /* Variable order. (heap) (contains: var) */ veci trail_lim; /* Separator indices for different decision levels in 'trail'. (contains: int) */ veci model; /* If problem is solved, this vector contains the model (contains: lbool). */ int root_level; /* Level of first proper decision. */ int simpdb_assigns;/* Number of top-level assignments at last 'simplifyDB()'. */ int simpdb_props; /* Number of propagations before next 'simplifyDB()'. */ double random_seed; double progress_estimate; int verbosity; /* Verbosity level. 0=silent, 1=some progress report, 2=everything */ stats stats; } solver; /*====================================================================*/ /* Public interface: */ #if 1 /* by mao; to keep namespace clean */ #define solver_new _glp_minisat_new #define solver_delete _glp_minisat_delete #define solver_addclause _glp_minisat_addclause #define solver_simplify _glp_minisat_simplify #define solver_solve _glp_minisat_solve #define solver_nvars _glp_minisat_nvars #define solver_nclauses _glp_minisat_nclauses #define solver_nconflicts _glp_minisat_nconflicts #define solver_setnvars _glp_minisat_setnvars #define solver_propagate _glp_minisat_propagate #define solver_reducedb _glp_minisat_reducedb #endif solver* solver_new(void); void solver_delete(solver* s); bool solver_addclause(solver* s, lit* begin, lit* end); bool solver_simplify(solver* s); bool solver_solve(solver* s, lit* begin, lit* end); int solver_nvars(solver* s); int solver_nclauses(solver* s); int solver_nconflicts(solver* s); void solver_setnvars(solver* s,int n); #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/minisat/LICENSE0000644000175000017500000000206011632352620021106 0ustar csheltoncsheltonMiniSat -- Copyright (c) 2005, Niklas Sorensson Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ctbnrle.1.1.1/glpk/glpk-4.47/src/minisat/README0000644000175000017500000000160711632352620020767 0ustar csheltoncsheltonNOTE: Files in this subdirectory are NOT part of the GLPK package, but are used with GLPK. The original code was modified according to GLPK requirements by Andrew Makhorin . ************************************************************************ MiniSat-C v1.14.1 ======================================== * Fixed some serious bugs. * Tweaked to be Visual Studio friendly (by Alan Mishchenko). This disabled reading of gzipped DIMACS files and signal handling, but none of these features are essential (and easy to re-enable, if wanted). MiniSat-C v1.14 ======================================== Ok, we get it. You hate C++. You hate templates. We agree; C++ is a seriously messed up language. Although we are more pragmatic about the quirks and maldesigns in C++, we sympathize with you. So here is a pure C version of MiniSat, put together by Niklas Sorensson. ctbnrle.1.1.1/glpk/glpk-4.47/src/glpspm.c0000644000175000017500000006050111632352620020107 0ustar csheltoncshelton/* glpspm.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glphbm.h" #include "glprgr.h" #include "glpspm.h" /*********************************************************************** * NAME * * spm_create_mat - create general sparse matrix * * SYNOPSIS * * #include "glpspm.h" * SPM *spm_create_mat(int m, int n); * * DESCRIPTION * * The routine spm_create_mat creates a general sparse matrix having * m rows and n columns. Being created the matrix is zero (empty), i.e. * has no elements. * * RETURNS * * The routine returns a pointer to the matrix created. */ SPM *spm_create_mat(int m, int n) { SPM *A; xassert(0 <= m && m < INT_MAX); xassert(0 <= n && n < INT_MAX); A = xmalloc(sizeof(SPM)); A->m = m; A->n = n; if (m == 0 || n == 0) { A->pool = NULL; A->row = NULL; A->col = NULL; } else { int i, j; A->pool = dmp_create_pool(); A->row = xcalloc(1+m, sizeof(SPME *)); for (i = 1; i <= m; i++) A->row[i] = NULL; A->col = xcalloc(1+n, sizeof(SPME *)); for (j = 1; j <= n; j++) A->col[j] = NULL; } return A; } /*********************************************************************** * NAME * * spm_new_elem - add new element to sparse matrix * * SYNOPSIS * * #include "glpspm.h" * SPME *spm_new_elem(SPM *A, int i, int j, double val); * * DESCRIPTION * * The routine spm_new_elem adds a new element to the specified sparse * matrix. Parameters i, j, and val specify the row number, the column * number, and a numerical value of the element, respectively. * * RETURNS * * The routine returns a pointer to the new element added. */ SPME *spm_new_elem(SPM *A, int i, int j, double val) { SPME *e; xassert(1 <= i && i <= A->m); xassert(1 <= j && j <= A->n); e = dmp_get_atom(A->pool, sizeof(SPME)); e->i = i; e->j = j; e->val = val; e->r_prev = NULL; e->r_next = A->row[i]; if (e->r_next != NULL) e->r_next->r_prev = e; e->c_prev = NULL; e->c_next = A->col[j]; if (e->c_next != NULL) e->c_next->c_prev = e; A->row[i] = A->col[j] = e; return e; } /*********************************************************************** * NAME * * spm_delete_mat - delete general sparse matrix * * SYNOPSIS * * #include "glpspm.h" * void spm_delete_mat(SPM *A); * * DESCRIPTION * * The routine deletes the specified general sparse matrix freeing all * the memory allocated to this object. */ void spm_delete_mat(SPM *A) { /* delete sparse matrix */ if (A->pool != NULL) dmp_delete_pool(A->pool); if (A->row != NULL) xfree(A->row); if (A->col != NULL) xfree(A->col); xfree(A); return; } /*********************************************************************** * NAME * * spm_test_mat_e - create test sparse matrix of E(n,c) class * * SYNOPSIS * * #include "glpspm.h" * SPM *spm_test_mat_e(int n, int c); * * DESCRIPTION * * The routine spm_test_mat_e creates a test sparse matrix of E(n,c) * class as described in the book: Ole 0sterby, Zahari Zlatev. Direct * Methods for Sparse Matrices. Springer-Verlag, 1983. * * Matrix of E(n,c) class is a symmetric positive definite matrix of * the order n. It has the number 4 on its main diagonal and the number * -1 on its four co-diagonals, two of which are neighbour to the main * diagonal and two others are shifted from the main diagonal on the * distance c. * * It is necessary that n >= 3 and 2 <= c <= n-1. * * RETURNS * * The routine returns a pointer to the matrix created. */ SPM *spm_test_mat_e(int n, int c) { SPM *A; int i; xassert(n >= 3 && 2 <= c && c <= n-1); A = spm_create_mat(n, n); for (i = 1; i <= n; i++) spm_new_elem(A, i, i, 4.0); for (i = 1; i <= n-1; i++) { spm_new_elem(A, i, i+1, -1.0); spm_new_elem(A, i+1, i, -1.0); } for (i = 1; i <= n-c; i++) { spm_new_elem(A, i, i+c, -1.0); spm_new_elem(A, i+c, i, -1.0); } return A; } /*********************************************************************** * NAME * * spm_test_mat_d - create test sparse matrix of D(n,c) class * * SYNOPSIS * * #include "glpspm.h" * SPM *spm_test_mat_d(int n, int c); * * DESCRIPTION * * The routine spm_test_mat_d creates a test sparse matrix of D(n,c) * class as described in the book: Ole 0sterby, Zahari Zlatev. Direct * Methods for Sparse Matrices. Springer-Verlag, 1983. * * Matrix of D(n,c) class is a non-singular matrix of the order n. It * has unity main diagonal, three co-diagonals above the main diagonal * on the distance c, which are cyclically continued below the main * diagonal, and a triangle block of the size 10x10 in the upper right * corner. * * It is necessary that n >= 14 and 1 <= c <= n-13. * * RETURNS * * The routine returns a pointer to the matrix created. */ SPM *spm_test_mat_d(int n, int c) { SPM *A; int i, j; xassert(n >= 14 && 1 <= c && c <= n-13); A = spm_create_mat(n, n); for (i = 1; i <= n; i++) spm_new_elem(A, i, i, 1.0); for (i = 1; i <= n-c; i++) spm_new_elem(A, i, i+c, (double)(i+1)); for (i = n-c+1; i <= n; i++) spm_new_elem(A, i, i-n+c, (double)(i+1)); for (i = 1; i <= n-c-1; i++) spm_new_elem(A, i, i+c+1, (double)(-i)); for (i = n-c; i <= n; i++) spm_new_elem(A, i, i-n+c+1, (double)(-i)); for (i = 1; i <= n-c-2; i++) spm_new_elem(A, i, i+c+2, 16.0); for (i = n-c-1; i <= n; i++) spm_new_elem(A, i, i-n+c+2, 16.0); for (j = 1; j <= 10; j++) for (i = 1; i <= 11-j; i++) spm_new_elem(A, i, n-11+i+j, 100.0 * (double)j); return A; } /*********************************************************************** * NAME * * spm_show_mat - write sparse matrix pattern in BMP file format * * SYNOPSIS * * #include "glpspm.h" * int spm_show_mat(const SPM *A, const char *fname); * * DESCRIPTION * * The routine spm_show_mat writes pattern of the specified sparse * matrix in uncompressed BMP file format (Windows bitmap) to a binary * file whose name is specified by the character string fname. * * Each pixel corresponds to one matrix element. The pixel colors have * the following meaning: * * Black structurally zero element * White positive element * Cyan negative element * Green zero element * Red duplicate element * * RETURNS * * If no error occured, the routine returns zero. Otherwise, it prints * an appropriate error message and returns non-zero. */ int spm_show_mat(const SPM *A, const char *fname) { int m = A->m; int n = A->n; int i, j, k, ret; char *map; xprintf("spm_show_mat: writing matrix pattern to `%s'...\n", fname); xassert(1 <= m && m <= 32767); xassert(1 <= n && n <= 32767); map = xmalloc(m * n); memset(map, 0x08, m * n); for (i = 1; i <= m; i++) { SPME *e; for (e = A->row[i]; e != NULL; e = e->r_next) { j = e->j; xassert(1 <= j && j <= n); k = n * (i - 1) + (j - 1); if (map[k] != 0x08) map[k] = 0x0C; else if (e->val > 0.0) map[k] = 0x0F; else if (e->val < 0.0) map[k] = 0x0B; else map[k] = 0x0A; } } ret = rgr_write_bmp16(fname, m, n, map); xfree(map); return ret; } /*********************************************************************** * NAME * * spm_read_hbm - read sparse matrix in Harwell-Boeing format * * SYNOPSIS * * #include "glpspm.h" * SPM *spm_read_hbm(const char *fname); * * DESCRIPTION * * The routine spm_read_hbm reads a sparse matrix in the Harwell-Boeing * format from a text file whose name is the character string fname. * * Detailed description of the Harwell-Boeing format recognised by this * routine can be found in the following report: * * I.S.Duff, R.G.Grimes, J.G.Lewis. User's Guide for the Harwell-Boeing * Sparse Matrix Collection (Release I), TR/PA/92/86, October 1992. * * NOTE * * The routine spm_read_hbm reads the matrix "as is", due to which zero * and/or duplicate elements can appear in the matrix. * * RETURNS * * If no error occured, the routine returns a pointer to the matrix * created. Otherwise, the routine prints an appropriate error message * and returns NULL. */ SPM *spm_read_hbm(const char *fname) { SPM *A = NULL; HBM *hbm; int nrow, ncol, nnzero, i, j, beg, end, ptr, *colptr, *rowind; double val, *values; char *mxtype; hbm = hbm_read_mat(fname); if (hbm == NULL) { xprintf("spm_read_hbm: unable to read matrix\n"); goto fini; } mxtype = hbm->mxtype; nrow = hbm->nrow; ncol = hbm->ncol; nnzero = hbm->nnzero; colptr = hbm->colptr; rowind = hbm->rowind; values = hbm->values; if (!(strcmp(mxtype, "RSA") == 0 || strcmp(mxtype, "PSA") == 0 || strcmp(mxtype, "RUA") == 0 || strcmp(mxtype, "PUA") == 0 || strcmp(mxtype, "RRA") == 0 || strcmp(mxtype, "PRA") == 0)) { xprintf("spm_read_hbm: matrix type `%s' not supported\n", mxtype); goto fini; } A = spm_create_mat(nrow, ncol); if (mxtype[1] == 'S' || mxtype[1] == 'U') xassert(nrow == ncol); for (j = 1; j <= ncol; j++) { beg = colptr[j]; end = colptr[j+1]; xassert(1 <= beg && beg <= end && end <= nnzero + 1); for (ptr = beg; ptr < end; ptr++) { i = rowind[ptr]; xassert(1 <= i && i <= nrow); if (mxtype[0] == 'R') val = values[ptr]; else val = 1.0; spm_new_elem(A, i, j, val); if (mxtype[1] == 'S' && i != j) spm_new_elem(A, j, i, val); } } fini: if (hbm != NULL) hbm_free_mat(hbm); return A; } /*********************************************************************** * NAME * * spm_count_nnz - determine number of non-zeros in sparse matrix * * SYNOPSIS * * #include "glpspm.h" * int spm_count_nnz(const SPM *A); * * RETURNS * * The routine spm_count_nnz returns the number of structural non-zero * elements in the specified sparse matrix. */ int spm_count_nnz(const SPM *A) { SPME *e; int i, nnz = 0; for (i = 1; i <= A->m; i++) for (e = A->row[i]; e != NULL; e = e->r_next) nnz++; return nnz; } /*********************************************************************** * NAME * * spm_drop_zeros - remove zero elements from sparse matrix * * SYNOPSIS * * #include "glpspm.h" * int spm_drop_zeros(SPM *A, double eps); * * DESCRIPTION * * The routine spm_drop_zeros removes all elements from the specified * sparse matrix, whose absolute value is less than eps. * * If the parameter eps is 0, only zero elements are removed from the * matrix. * * RETURNS * * The routine returns the number of elements removed. */ int spm_drop_zeros(SPM *A, double eps) { SPME *e, *next; int i, count = 0; for (i = 1; i <= A->m; i++) { for (e = A->row[i]; e != NULL; e = next) { next = e->r_next; if (e->val == 0.0 || fabs(e->val) < eps) { /* remove element from the row list */ if (e->r_prev == NULL) A->row[e->i] = e->r_next; else e->r_prev->r_next = e->r_next; if (e->r_next == NULL) ; else e->r_next->r_prev = e->r_prev; /* remove element from the column list */ if (e->c_prev == NULL) A->col[e->j] = e->c_next; else e->c_prev->c_next = e->c_next; if (e->c_next == NULL) ; else e->c_next->c_prev = e->c_prev; /* return element to the memory pool */ dmp_free_atom(A->pool, e, sizeof(SPME)); count++; } } } return count; } /*********************************************************************** * NAME * * spm_read_mat - read sparse matrix from text file * * SYNOPSIS * * #include "glpspm.h" * SPM *spm_read_mat(const char *fname); * * DESCRIPTION * * The routine reads a sparse matrix from a text file whose name is * specified by the parameter fname. * * For the file format see description of the routine spm_write_mat. * * RETURNS * * On success the routine returns a pointer to the matrix created, * otherwise NULL. */ #if 1 SPM *spm_read_mat(const char *fname) { xassert(fname != fname); return NULL; } #else SPM *spm_read_mat(const char *fname) { SPM *A = NULL; PDS *pds; jmp_buf jump; int i, j, k, m, n, nnz, fail = 0; double val; xprintf("spm_read_mat: reading matrix from `%s'...\n", fname); pds = pds_open_file(fname); if (pds == NULL) { xprintf("spm_read_mat: unable to open `%s' - %s\n", fname, strerror(errno)); fail = 1; goto done; } if (setjmp(jump)) { fail = 1; goto done; } pds_set_jump(pds, jump); /* number of rows, number of columns, number of non-zeros */ m = pds_scan_int(pds); if (m < 0) pds_error(pds, "invalid number of rows\n"); n = pds_scan_int(pds); if (n < 0) pds_error(pds, "invalid number of columns\n"); nnz = pds_scan_int(pds); if (nnz < 0) pds_error(pds, "invalid number of non-zeros\n"); /* create matrix */ xprintf("spm_read_mat: %d rows, %d columns, %d non-zeros\n", m, n, nnz); A = spm_create_mat(m, n); /* read matrix elements */ for (k = 1; k <= nnz; k++) { /* row index, column index, element value */ i = pds_scan_int(pds); if (!(1 <= i && i <= m)) pds_error(pds, "row index out of range\n"); j = pds_scan_int(pds); if (!(1 <= j && j <= n)) pds_error(pds, "column index out of range\n"); val = pds_scan_num(pds); /* add new element to the matrix */ spm_new_elem(A, i, j, val); } xprintf("spm_read_mat: %d lines were read\n", pds->count); done: if (pds != NULL) pds_close_file(pds); if (fail && A != NULL) spm_delete_mat(A), A = NULL; return A; } #endif /*********************************************************************** * NAME * * spm_write_mat - write sparse matrix to text file * * SYNOPSIS * * #include "glpspm.h" * int spm_write_mat(const SPM *A, const char *fname); * * DESCRIPTION * * The routine spm_write_mat writes the specified sparse matrix to a * text file whose name is specified by the parameter fname. This file * can be read back with the routine spm_read_mat. * * RETURNS * * On success the routine returns zero, otherwise non-zero. * * FILE FORMAT * * The file created by the routine spm_write_mat is a plain text file, * which contains the following information: * * m n nnz * row[1] col[1] val[1] * row[2] col[2] val[2] * . . . * row[nnz] col[nnz] val[nnz] * * where: * m is the number of rows; * n is the number of columns; * nnz is the number of non-zeros; * row[k], k = 1,...,nnz, are row indices; * col[k], k = 1,...,nnz, are column indices; * val[k], k = 1,...,nnz, are element values. */ #if 1 int spm_write_mat(const SPM *A, const char *fname) { xassert(A != A); xassert(fname != fname); return 0; } #else int spm_write_mat(const SPM *A, const char *fname) { FILE *fp; int i, nnz, ret = 0; xprintf("spm_write_mat: writing matrix to `%s'...\n", fname); fp = fopen(fname, "w"); if (fp == NULL) { xprintf("spm_write_mat: unable to create `%s' - %s\n", fname, strerror(errno)); ret = 1; goto done; } /* number of rows, number of columns, number of non-zeros */ nnz = spm_count_nnz(A); fprintf(fp, "%d %d %d\n", A->m, A->n, nnz); /* walk through rows of the matrix */ for (i = 1; i <= A->m; i++) { SPME *e; /* walk through elements of i-th row */ for (e = A->row[i]; e != NULL; e = e->r_next) { /* row index, column index, element value */ fprintf(fp, "%d %d %.*g\n", e->i, e->j, DBL_DIG, e->val); } } fflush(fp); if (ferror(fp)) { xprintf("spm_write_mat: writing error on `%s' - %s\n", fname, strerror(errno)); ret = 1; goto done; } xprintf("spm_write_mat: %d lines were written\n", 1 + nnz); done: if (fp != NULL) fclose(fp); return ret; } #endif /*********************************************************************** * NAME * * spm_transpose - transpose sparse matrix * * SYNOPSIS * * #include "glpspm.h" * SPM *spm_transpose(const SPM *A); * * RETURNS * * The routine computes and returns sparse matrix B, which is a matrix * transposed to sparse matrix A. */ SPM *spm_transpose(const SPM *A) { SPM *B; int i; B = spm_create_mat(A->n, A->m); for (i = 1; i <= A->m; i++) { SPME *e; for (e = A->row[i]; e != NULL; e = e->r_next) spm_new_elem(B, e->j, i, e->val); } return B; } SPM *spm_add_sym(const SPM *A, const SPM *B) { /* add two sparse matrices (symbolic phase) */ SPM *C; int i, j, *flag; xassert(A->m == B->m); xassert(A->n == B->n); /* create resultant matrix */ C = spm_create_mat(A->m, A->n); /* allocate and clear the flag array */ flag = xcalloc(1+C->n, sizeof(int)); for (j = 1; j <= C->n; j++) flag[j] = 0; /* compute pattern of C = A + B */ for (i = 1; i <= C->m; i++) { SPME *e; /* at the beginning i-th row of C is empty */ /* (i-th row of C) := (i-th row of C) union (i-th row of A) */ for (e = A->row[i]; e != NULL; e = e->r_next) { /* (note that i-th row of A may have duplicate elements) */ j = e->j; if (!flag[j]) { spm_new_elem(C, i, j, 0.0); flag[j] = 1; } } /* (i-th row of C) := (i-th row of C) union (i-th row of B) */ for (e = B->row[i]; e != NULL; e = e->r_next) { /* (note that i-th row of B may have duplicate elements) */ j = e->j; if (!flag[j]) { spm_new_elem(C, i, j, 0.0); flag[j] = 1; } } /* reset the flag array */ for (e = C->row[i]; e != NULL; e = e->r_next) flag[e->j] = 0; } /* check and deallocate the flag array */ for (j = 1; j <= C->n; j++) xassert(!flag[j]); xfree(flag); return C; } void spm_add_num(SPM *C, double alfa, const SPM *A, double beta, const SPM *B) { /* add two sparse matrices (numeric phase) */ int i, j; double *work; /* allocate and clear the working array */ work = xcalloc(1+C->n, sizeof(double)); for (j = 1; j <= C->n; j++) work[j] = 0.0; /* compute matrix C = alfa * A + beta * B */ for (i = 1; i <= C->n; i++) { SPME *e; /* work := alfa * (i-th row of A) + beta * (i-th row of B) */ /* (note that A and/or B may have duplicate elements) */ for (e = A->row[i]; e != NULL; e = e->r_next) work[e->j] += alfa * e->val; for (e = B->row[i]; e != NULL; e = e->r_next) work[e->j] += beta * e->val; /* (i-th row of C) := work, work := 0 */ for (e = C->row[i]; e != NULL; e = e->r_next) { j = e->j; e->val = work[j]; work[j] = 0.0; } } /* check and deallocate the working array */ for (j = 1; j <= C->n; j++) xassert(work[j] == 0.0); xfree(work); return; } SPM *spm_add_mat(double alfa, const SPM *A, double beta, const SPM *B) { /* add two sparse matrices (driver routine) */ SPM *C; C = spm_add_sym(A, B); spm_add_num(C, alfa, A, beta, B); return C; } SPM *spm_mul_sym(const SPM *A, const SPM *B) { /* multiply two sparse matrices (symbolic phase) */ int i, j, k, *flag; SPM *C; xassert(A->n == B->m); /* create resultant matrix */ C = spm_create_mat(A->m, B->n); /* allocate and clear the flag array */ flag = xcalloc(1+C->n, sizeof(int)); for (j = 1; j <= C->n; j++) flag[j] = 0; /* compute pattern of C = A * B */ for (i = 1; i <= C->m; i++) { SPME *e, *ee; /* compute pattern of i-th row of C */ for (e = A->row[i]; e != NULL; e = e->r_next) { k = e->j; for (ee = B->row[k]; ee != NULL; ee = ee->r_next) { j = ee->j; /* if a[i,k] != 0 and b[k,j] != 0 then c[i,j] != 0 */ if (!flag[j]) { /* c[i,j] does not exist, so create it */ spm_new_elem(C, i, j, 0.0); flag[j] = 1; } } } /* reset the flag array */ for (e = C->row[i]; e != NULL; e = e->r_next) flag[e->j] = 0; } /* check and deallocate the flag array */ for (j = 1; j <= C->n; j++) xassert(!flag[j]); xfree(flag); return C; } void spm_mul_num(SPM *C, const SPM *A, const SPM *B) { /* multiply two sparse matrices (numeric phase) */ int i, j; double *work; /* allocate and clear the working array */ work = xcalloc(1+A->n, sizeof(double)); for (j = 1; j <= A->n; j++) work[j] = 0.0; /* compute matrix C = A * B */ for (i = 1; i <= C->m; i++) { SPME *e, *ee; double temp; /* work := (i-th row of A) */ /* (note that A may have duplicate elements) */ for (e = A->row[i]; e != NULL; e = e->r_next) work[e->j] += e->val; /* compute i-th row of C */ for (e = C->row[i]; e != NULL; e = e->r_next) { j = e->j; /* c[i,j] := work * (j-th column of B) */ temp = 0.0; for (ee = B->col[j]; ee != NULL; ee = ee->c_next) temp += work[ee->i] * ee->val; e->val = temp; } /* reset the working array */ for (e = A->row[i]; e != NULL; e = e->r_next) work[e->j] = 0.0; } /* check and deallocate the working array */ for (j = 1; j <= A->n; j++) xassert(work[j] == 0.0); xfree(work); return; } SPM *spm_mul_mat(const SPM *A, const SPM *B) { /* multiply two sparse matrices (driver routine) */ SPM *C; C = spm_mul_sym(A, B); spm_mul_num(C, A, B); return C; } PER *spm_create_per(int n) { /* create permutation matrix */ PER *P; int k; xassert(n >= 0); P = xmalloc(sizeof(PER)); P->n = n; P->row = xcalloc(1+n, sizeof(int)); P->col = xcalloc(1+n, sizeof(int)); /* initially it is identity matrix */ for (k = 1; k <= n; k++) P->row[k] = P->col[k] = k; return P; } void spm_check_per(PER *P) { /* check permutation matrix for correctness */ int i, j; xassert(P->n >= 0); for (i = 1; i <= P->n; i++) { j = P->row[i]; xassert(1 <= j && j <= P->n); xassert(P->col[j] == i); } return; } void spm_delete_per(PER *P) { /* delete permutation matrix */ xfree(P->row); xfree(P->col); xfree(P); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glplib01.c0000644000175000017500000002260511632352620020222 0ustar csheltoncshelton/* glplib01.c (bignum arithmetic) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpenv.h" #include "glplib.h" /*********************************************************************** * Two routines below are intended to multiply and divide unsigned * integer numbers of arbitrary precision. * * The routines assume that an unsigned integer number is represented in * the positional numeral system with the base 2^16 = 65536, i.e. each * "digit" of the number is in the range [0, 65535] and represented as * a 16-bit value of the unsigned short type. In other words, a number x * has the following representation: * * n-1 * x = sum d[j] * 65536^j, * j=0 * * where n is the number of places (positions), and d[j] is j-th "digit" * of x, 0 <= d[j] <= 65535. ***********************************************************************/ /*********************************************************************** * NAME * * bigmul - multiply unsigned integer numbers of arbitrary precision * * SYNOPSIS * * #include "glplib.h" * void bigmul(int n, int m, unsigned short x[], unsigned short y[]); * * DESCRIPTION * * The routine bigmul multiplies unsigned integer numbers of arbitrary * precision. * * n is the number of digits of multiplicand, n >= 1; * * m is the number of digits of multiplier, m >= 1; * * x is an array containing digits of the multiplicand in elements * x[m], x[m+1], ..., x[n+m-1]. Contents of x[0], x[1], ..., x[m-1] are * ignored on entry. * * y is an array containing digits of the multiplier in elements y[0], * y[1], ..., y[m-1]. * * On exit digits of the product are stored in elements x[0], x[1], ..., * x[n+m-1]. The array y is not changed. */ void bigmul(int n, int m, unsigned short x[], unsigned short y[]) { int i, j; unsigned int t; xassert(n >= 1); xassert(m >= 1); for (j = 0; j < m; j++) x[j] = 0; for (i = 0; i < n; i++) { if (x[i+m]) { t = 0; for (j = 0; j < m; j++) { t += (unsigned int)x[i+m] * (unsigned int)y[j] + (unsigned int)x[i+j]; x[i+j] = (unsigned short)t; t >>= 16; } x[i+m] = (unsigned short)t; } } return; } /*********************************************************************** * NAME * * bigdiv - divide unsigned integer numbers of arbitrary precision * * SYNOPSIS * * #include "glplib.h" * void bigdiv(int n, int m, unsigned short x[], unsigned short y[]); * * DESCRIPTION * * The routine bigdiv divides one unsigned integer number of arbitrary * precision by another with the algorithm described in [1]. * * n is the difference between the number of digits of dividend and the * number of digits of divisor, n >= 0. * * m is the number of digits of divisor, m >= 1. * * x is an array containing digits of the dividend in elements x[0], * x[1], ..., x[n+m-1]. * * y is an array containing digits of the divisor in elements y[0], * y[1], ..., y[m-1]. The highest digit y[m-1] must be non-zero. * * On exit n+1 digits of the quotient are stored in elements x[m], * x[m+1], ..., x[n+m], and m digits of the remainder are stored in * elements x[0], x[1], ..., x[m-1]. The array y is changed but then * restored. * * REFERENCES * * 1. D. Knuth. The Art of Computer Programming. Vol. 2: Seminumerical * Algorithms. Stanford University, 1969. */ void bigdiv(int n, int m, unsigned short x[], unsigned short y[]) { int i, j; unsigned int t; unsigned short d, q, r; xassert(n >= 0); xassert(m >= 1); xassert(y[m-1] != 0); /* special case when divisor has the only digit */ if (m == 1) { d = 0; for (i = n; i >= 0; i--) { t = ((unsigned int)d << 16) + (unsigned int)x[i]; x[i+1] = (unsigned short)(t / y[0]); d = (unsigned short)(t % y[0]); } x[0] = d; goto done; } /* multiply dividend and divisor by a normalizing coefficient in order to provide the condition y[m-1] >= base / 2 */ d = (unsigned short)(0x10000 / ((unsigned int)y[m-1] + 1)); if (d == 1) x[n+m] = 0; else { t = 0; for (i = 0; i < n+m; i++) { t += (unsigned int)x[i] * (unsigned int)d; x[i] = (unsigned short)t; t >>= 16; } x[n+m] = (unsigned short)t; t = 0; for (j = 0; j < m; j++) { t += (unsigned int)y[j] * (unsigned int)d; y[j] = (unsigned short)t; t >>= 16; } } /* main loop */ for (i = n; i >= 0; i--) { /* estimate and correct the current digit of quotient */ if (x[i+m] < y[m-1]) { t = ((unsigned int)x[i+m] << 16) + (unsigned int)x[i+m-1]; q = (unsigned short)(t / (unsigned int)y[m-1]); r = (unsigned short)(t % (unsigned int)y[m-1]); if (q == 0) goto putq; else goto test; } q = 0; r = x[i+m-1]; decr: q--; /* if q = 0 then q-- = 0xFFFF */ t = (unsigned int)r + (unsigned int)y[m-1]; r = (unsigned short)t; if (t > 0xFFFF) goto msub; test: t = (unsigned int)y[m-2] * (unsigned int)q; if ((unsigned short)(t >> 16) > r) goto decr; if ((unsigned short)(t >> 16) < r) goto msub; if ((unsigned short)t > x[i+m-2]) goto decr; msub: /* now subtract divisor multiplied by the current digit of quotient from the current dividend */ if (q == 0) goto putq; t = 0; for (j = 0; j < m; j++) { t += (unsigned int)y[j] * (unsigned int)q; if (x[i+j] < (unsigned short)t) t += 0x10000; x[i+j] -= (unsigned short)t; t >>= 16; } if (x[i+m] >= (unsigned short)t) goto putq; /* perform correcting addition, because the current digit of quotient is greater by one than its correct value */ q--; t = 0; for (j = 0; j < m; j++) { t += (unsigned int)x[i+j] + (unsigned int)y[j]; x[i+j] = (unsigned short)t; t >>= 16; } putq: /* store the current digit of quotient */ x[i+m] = q; } /* divide divisor and remainder by the normalizing coefficient in order to restore their original values */ if (d > 1) { t = 0; for (i = m-1; i >= 0; i--) { t = (t << 16) + (unsigned int)x[i]; x[i] = (unsigned short)(t / (unsigned int)d); t %= (unsigned int)d; } t = 0; for (j = m-1; j >= 0; j--) { t = (t << 16) + (unsigned int)y[j]; y[j] = (unsigned short)(t / (unsigned int)d); t %= (unsigned int)d; } } done: return; } /**********************************************************************/ #if 0 #include #include #include #include "glprng.h" #define N_MAX 7 /* maximal number of digits in multiplicand */ #define M_MAX 5 /* maximal number of digits in multiplier */ #define N_TEST 1000000 /* number of tests */ int main(void) { RNG *rand; int d, j, n, m, test; unsigned short x[N_MAX], y[M_MAX], z[N_MAX+M_MAX]; rand = rng_create_rand(); for (test = 1; test <= N_TEST; test++) { /* x[0,...,n-1] := multiplicand */ n = 1 + rng_unif_rand(rand, N_MAX-1); assert(1 <= n && n <= N_MAX); for (j = 0; j < n; j++) { d = rng_unif_rand(rand, 65536); assert(0 <= d && d <= 65535); x[j] = (unsigned short)d; } /* y[0,...,m-1] := multiplier */ m = 1 + rng_unif_rand(rand, M_MAX-1); assert(1 <= m && m <= M_MAX); for (j = 0; j < m; j++) { d = rng_unif_rand(rand, 65536); assert(0 <= d && d <= 65535); y[j] = (unsigned short)d; } if (y[m-1] == 0) y[m-1] = 1; /* z[0,...,n+m-1] := x * y */ for (j = 0; j < n; j++) z[m+j] = x[j]; bigmul(n, m, z, y); /* z[0,...,m-1] := z mod y, z[m,...,n+m-1] := z div y */ bigdiv(n, m, z, y); /* z mod y must be 0 */ for (j = 0; j < m; j++) assert(z[j] == 0); /* z div y must be x */ for (j = 0; j < n; j++) assert(z[m+j] == x[j]); } fprintf(stderr, "%d tests successfully passed\n", N_TEST); rng_delete_rand(rand); return 0; } #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpipm.h0000644000175000017500000000246411632352620020106 0ustar csheltoncshelton/* glpipm.h (primal-dual interior-point method) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPIPM_H #define GLPIPM_H #include "glpapi.h" #define ipm_solve _glp_ipm_solve int ipm_solve(glp_prob *P, const glp_iptcp *parm); /* core LP solver based on the interior-point method */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpini01.c0000644000175000017500000005415711632352620020242 0ustar csheltoncshelton/* glpini01.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" /*---------------------------------------------------------------------- -- triang - find maximal triangular part of a rectangular matrix. -- -- *Synopsis* -- -- int triang(int m, int n, -- void *info, int (*mat)(void *info, int k, int ndx[]), -- int rn[], int cn[]); -- -- *Description* -- -- For a given rectangular (sparse) matrix A with m rows and n columns -- the routine triang tries to find such permutation matrices P and Q -- that the first rows and columns of the matrix B = P*A*Q form a lower -- triangular submatrix of as greatest size as possible: -- -- 1 n -- 1 * . . . . . . x x x x x x -- * * . . . . . x x x x x x -- * * * . . . . x x x x x x -- * * * * . . . x x x x x x -- B = P*A*Q = * * * * * . . x x x x x x -- * * * * * * . x x x x x x -- * * * * * * * x x x x x x -- x x x x x x x x x x x x x -- x x x x x x x x x x x x x -- m x x x x x x x x x x x x x -- -- where: '*' - elements of the lower triangular part, '.' - structural -- zeros, 'x' - other (either non-zero or zero) elements. -- -- The parameter info is a transit pointer passed to the formal routine -- mat (see below). -- -- The formal routine mat specifies the given matrix A in both row- and -- column-wise formats. In order to obtain an i-th row of the matrix A -- the routine triang calls the routine mat with the parameter k = +i, -- 1 <= i <= m. In response the routine mat should store column indices -- of (non-zero) elements of the i-th row to the locations ndx[1], ..., -- ndx[len], where len is number of non-zeros in the i-th row returned -- on exit. Analogously, in order to obtain a j-th column of the matrix -- A, the routine mat is called with the parameter k = -j, 1 <= j <= n, -- and should return pattern of the j-th column in the same way as for -- row patterns. Note that the routine mat may be called more than once -- for the same rows and columns. -- -- On exit the routine computes two resultant arrays rn and cn, which -- define the permutation matrices P and Q, respectively. The array rn -- should have at least 1+m locations, where rn[i] = i' (1 <= i <= m) -- means that i-th row of the original matrix A corresponds to i'-th row -- of the matrix B = P*A*Q. Similarly, the array cn should have at least -- 1+n locations, where cn[j] = j' (1 <= j <= n) means that j-th column -- of the matrix A corresponds to j'-th column of the matrix B. -- -- *Returns* -- -- The routine triang returns the size of the lower tringular part of -- the matrix B = P*A*Q (see the figure above). -- -- *Complexity* -- -- The time complexity of the routine triang is O(nnz), where nnz is -- number of non-zeros in the given matrix A. -- -- *Algorithm* -- -- The routine triang starts from the matrix B = P*Q*A, where P and Q -- are unity matrices, so initially B = A. -- -- Before the next iteration B = (B1 | B2 | B3), where B1 is partially -- built a lower triangular submatrix, B2 is the active submatrix, and -- B3 is a submatrix that contains rejected columns. Thus, the current -- matrix B looks like follows (initially k1 = 1 and k2 = n): -- -- 1 k1 k2 n -- 1 x . . . . . . . . . . . . . # # # -- x x . . . . . . . . . . . . # # # -- x x x . . . . . . . . . . # # # # -- x x x x . . . . . . . . . # # # # -- x x x x x . . . . . . . # # # # # -- k1 x x x x x * * * * * * * # # # # # -- x x x x x * * * * * * * # # # # # -- x x x x x * * * * * * * # # # # # -- x x x x x * * * * * * * # # # # # -- m x x x x x * * * * * * * # # # # # -- <--B1---> <----B2-----> <---B3--> -- -- On each iteartion the routine looks for a singleton row, i.e. some -- row that has the only non-zero in the active submatrix B2. If such -- row exists and the corresponding non-zero is b[i,j], where (by the -- definition) k1 <= i <= m and k1 <= j <= k2, the routine permutes -- k1-th and i-th rows and k1-th and j-th columns of the matrix B (in -- order to place the element in the position b[k1,k1]), removes the -- k1-th column from the active submatrix B2, and adds this column to -- the submatrix B1. If no row singletons exist, but B2 is not empty -- yet, the routine chooses a j-th column, which has maximal number of -- non-zeros among other columns of B2, removes this column from B2 and -- adds it to the submatrix B3 in the hope that new row singletons will -- appear in the active submatrix. */ static int triang(int m, int n, void *info, int (*mat)(void *info, int k, int ndx[]), int rn[], int cn[]) { int *ndx; /* int ndx[1+max(m,n)]; */ /* this array is used for querying row and column patterns of the given matrix A (the third parameter to the routine mat) */ int *rs_len; /* int rs_len[1+m]; */ /* rs_len[0] is not used; rs_len[i], 1 <= i <= m, is number of non-zeros in the i-th row of the matrix A, which (non-zeros) belong to the current active submatrix */ int *rs_head; /* int rs_head[1+n]; */ /* rs_head[len], 0 <= len <= n, is the number i of the first row of the matrix A, for which rs_len[i] = len */ int *rs_prev; /* int rs_prev[1+m]; */ /* rs_prev[0] is not used; rs_prev[i], 1 <= i <= m, is a number i' of the previous row of the matrix A, for which rs_len[i] = rs_len[i'] (zero marks the end of this linked list) */ int *rs_next; /* int rs_next[1+m]; */ /* rs_next[0] is not used; rs_next[i], 1 <= i <= m, is a number i' of the next row of the matrix A, for which rs_len[i] = rs_len[i'] (zero marks the end this linked list) */ int cs_head; /* is a number j of the first column of the matrix A, which has maximal number of non-zeros among other columns */ int *cs_prev; /* cs_prev[1+n]; */ /* cs_prev[0] is not used; cs_prev[j], 1 <= j <= n, is a number of the previous column of the matrix A with the same or greater number of non-zeros than in the j-th column (zero marks the end of this linked list) */ int *cs_next; /* cs_next[1+n]; */ /* cs_next[0] is not used; cs_next[j], 1 <= j <= n, is a number of the next column of the matrix A with the same or lesser number of non-zeros than in the j-th column (zero marks the end of this linked list) */ int i, j, ii, jj, k1, k2, len, t, size = 0; int *head, *rn_inv, *cn_inv; if (!(m > 0 && n > 0)) xerror("triang: m = %d; n = %d; invalid dimension\n", m, n); /* allocate working arrays */ ndx = xcalloc(1+(m >= n ? m : n), sizeof(int)); rs_len = xcalloc(1+m, sizeof(int)); rs_head = xcalloc(1+n, sizeof(int)); rs_prev = xcalloc(1+m, sizeof(int)); rs_next = xcalloc(1+m, sizeof(int)); cs_prev = xcalloc(1+n, sizeof(int)); cs_next = xcalloc(1+n, sizeof(int)); /* build linked lists of columns of the matrix A with the same number of non-zeros */ head = rs_len; /* currently rs_len is used as working array */ for (len = 0; len <= m; len ++) head[len] = 0; for (j = 1; j <= n; j++) { /* obtain length of the j-th column */ len = mat(info, -j, ndx); xassert(0 <= len && len <= m); /* include the j-th column in the corresponding linked list */ cs_prev[j] = head[len]; head[len] = j; } /* merge all linked lists of columns in one linked list, where columns are ordered by descending of their lengths */ cs_head = 0; for (len = 0; len <= m; len++) { for (j = head[len]; j != 0; j = cs_prev[j]) { cs_next[j] = cs_head; cs_head = j; } } jj = 0; for (j = cs_head; j != 0; j = cs_next[j]) { cs_prev[j] = jj; jj = j; } /* build initial doubly linked lists of rows of the matrix A with the same number of non-zeros */ for (len = 0; len <= n; len++) rs_head[len] = 0; for (i = 1; i <= m; i++) { /* obtain length of the i-th row */ rs_len[i] = len = mat(info, +i, ndx); xassert(0 <= len && len <= n); /* include the i-th row in the correspondng linked list */ rs_prev[i] = 0; rs_next[i] = rs_head[len]; if (rs_next[i] != 0) rs_prev[rs_next[i]] = i; rs_head[len] = i; } /* initially all rows and columns of the matrix A are active */ for (i = 1; i <= m; i++) rn[i] = 0; for (j = 1; j <= n; j++) cn[j] = 0; /* set initial bounds of the active submatrix */ k1 = 1, k2 = n; /* main loop starts here */ while (k1 <= k2) { i = rs_head[1]; if (i != 0) { /* the i-th row of the matrix A is a row singleton, since it has the only non-zero in the active submatrix */ xassert(rs_len[i] == 1); /* determine the number j of an active column of the matrix A, in which this non-zero is placed */ j = 0; t = mat(info, +i, ndx); xassert(0 <= t && t <= n); for (t = t; t >= 1; t--) { jj = ndx[t]; xassert(1 <= jj && jj <= n); if (cn[jj] == 0) { xassert(j == 0); j = jj; } } xassert(j != 0); /* the singleton is a[i,j]; move a[i,j] to the position b[k1,k1] of the matrix B */ rn[i] = cn[j] = k1; /* shift the left bound of the active submatrix */ k1++; /* increase the size of the lower triangular part */ size++; } else { /* the current active submatrix has no row singletons */ /* remove an active column with maximal number of non-zeros from the active submatrix */ j = cs_head; xassert(j != 0); cn[j] = k2; /* shift the right bound of the active submatrix */ k2--; } /* the j-th column of the matrix A has been removed from the active submatrix */ /* remove the j-th column from the linked list */ if (cs_prev[j] == 0) cs_head = cs_next[j]; else cs_next[cs_prev[j]] = cs_next[j]; if (cs_next[j] == 0) /* nop */; else cs_prev[cs_next[j]] = cs_prev[j]; /* go through non-zeros of the j-th columns and update active lengths of the corresponding rows */ t = mat(info, -j, ndx); xassert(0 <= t && t <= m); for (t = t; t >= 1; t--) { i = ndx[t]; xassert(1 <= i && i <= m); /* the non-zero a[i,j] has left the active submatrix */ len = rs_len[i]; xassert(len >= 1); /* remove the i-th row from the linked list of rows with active length len */ if (rs_prev[i] == 0) rs_head[len] = rs_next[i]; else rs_next[rs_prev[i]] = rs_next[i]; if (rs_next[i] == 0) /* nop */; else rs_prev[rs_next[i]] = rs_prev[i]; /* decrease the active length of the i-th row */ rs_len[i] = --len; /* return the i-th row to the corresponding linked list */ rs_prev[i] = 0; rs_next[i] = rs_head[len]; if (rs_next[i] != 0) rs_prev[rs_next[i]] = i; rs_head[len] = i; } } /* other rows of the matrix A, which are still active, correspond to rows k1, ..., m of the matrix B (in arbitrary order) */ for (i = 1; i <= m; i++) if (rn[i] == 0) rn[i] = k1++; /* but for columns this is not needed, because now the submatrix B2 has no columns */ for (j = 1; j <= n; j++) xassert(cn[j] != 0); /* perform some optional checks */ /* make sure that rn is a permutation of {1, ..., m} and cn is a permutation of {1, ..., n} */ rn_inv = rs_len; /* used as working array */ for (ii = 1; ii <= m; ii++) rn_inv[ii] = 0; for (i = 1; i <= m; i++) { ii = rn[i]; xassert(1 <= ii && ii <= m); xassert(rn_inv[ii] == 0); rn_inv[ii] = i; } cn_inv = rs_head; /* used as working array */ for (jj = 1; jj <= n; jj++) cn_inv[jj] = 0; for (j = 1; j <= n; j++) { jj = cn[j]; xassert(1 <= jj && jj <= n); xassert(cn_inv[jj] == 0); cn_inv[jj] = j; } /* make sure that the matrix B = P*A*Q really has the form, which was declared */ for (ii = 1; ii <= size; ii++) { int diag = 0; i = rn_inv[ii]; t = mat(info, +i, ndx); xassert(0 <= t && t <= n); for (t = t; t >= 1; t--) { j = ndx[t]; xassert(1 <= j && j <= n); jj = cn[j]; if (jj <= size) xassert(jj <= ii); if (jj == ii) { xassert(!diag); diag = 1; } } xassert(diag); } /* free working arrays */ xfree(ndx); xfree(rs_len); xfree(rs_head); xfree(rs_prev); xfree(rs_next); xfree(cs_prev); xfree(cs_next); /* return to the calling program */ return size; } /*---------------------------------------------------------------------- -- adv_basis - construct advanced initial LP basis. -- -- *Synopsis* -- -- #include "glpini.h" -- void adv_basis(glp_prob *lp); -- -- *Description* -- -- The routine adv_basis constructs an advanced initial basis for an LP -- problem object, which the parameter lp points to. -- -- In order to build the initial basis the routine does the following: -- -- 1) includes in the basis all non-fixed auxiliary variables; -- -- 2) includes in the basis as many as possible non-fixed structural -- variables preserving triangular form of the basis matrix; -- -- 3) includes in the basis appropriate (fixed) auxiliary variables -- in order to complete the basis. -- -- As a result the initial basis has minimum of fixed variables and the -- corresponding basis matrix is triangular. */ static int mat(void *info, int k, int ndx[]) { /* this auxiliary routine returns the pattern of a given row or a given column of the augmented constraint matrix A~ = (I|-A), in which columns of fixed variables are implicitly cleared */ LPX *lp = info; int m = lpx_get_num_rows(lp); int n = lpx_get_num_cols(lp); int typx, i, j, lll, len = 0; if (k > 0) { /* the pattern of the i-th row is required */ i = +k; xassert(1 <= i && i <= m); #if 0 /* 22/XII-2003 */ /* if the auxiliary variable x[i] is non-fixed, include its element (placed in the i-th column) in the pattern */ lpx_get_row_bnds(lp, i, &typx, NULL, NULL); if (typx != LPX_FX) ndx[++len] = i; /* include in the pattern elements placed in columns, which correspond to non-fixed structural varables */ i_beg = aa_ptr[i]; i_end = i_beg + aa_len[i] - 1; for (i_ptr = i_beg; i_ptr <= i_end; i_ptr++) { j = m + sv_ndx[i_ptr]; lpx_get_col_bnds(lp, j-m, &typx, NULL, NULL); if (typx != LPX_FX) ndx[++len] = j; } #else lll = lpx_get_mat_row(lp, i, ndx, NULL); for (k = 1; k <= lll; k++) { lpx_get_col_bnds(lp, ndx[k], &typx, NULL, NULL); if (typx != LPX_FX) ndx[++len] = m + ndx[k]; } lpx_get_row_bnds(lp, i, &typx, NULL, NULL); if (typx != LPX_FX) ndx[++len] = i; #endif } else { /* the pattern of the j-th column is required */ j = -k; xassert(1 <= j && j <= m+n); /* if the (auxiliary or structural) variable x[j] is fixed, the pattern of its column is empty */ if (j <= m) lpx_get_row_bnds(lp, j, &typx, NULL, NULL); else lpx_get_col_bnds(lp, j-m, &typx, NULL, NULL); if (typx != LPX_FX) { if (j <= m) { /* x[j] is non-fixed auxiliary variable */ ndx[++len] = j; } else { /* x[j] is non-fixed structural variables */ #if 0 /* 22/XII-2003 */ j_beg = aa_ptr[j]; j_end = j_beg + aa_len[j] - 1; for (j_ptr = j_beg; j_ptr <= j_end; j_ptr++) ndx[++len] = sv_ndx[j_ptr]; #else len = lpx_get_mat_col(lp, j-m, ndx, NULL); #endif } } } /* return the length of the row/column pattern */ return len; } static void adv_basis(glp_prob *lp) { int m = lpx_get_num_rows(lp); int n = lpx_get_num_cols(lp); int i, j, jj, k, size; int *rn, *cn, *rn_inv, *cn_inv; int typx, *tagx = xcalloc(1+m+n, sizeof(int)); double lb, ub; xprintf("Constructing initial basis...\n"); #if 0 /* 13/V-2009 */ if (m == 0) xerror("glp_adv_basis: problem has no rows\n"); if (n == 0) xerror("glp_adv_basis: problem has no columns\n"); #else if (m == 0 || n == 0) { glp_std_basis(lp); return; } #endif /* use the routine triang (see above) to find maximal triangular part of the augmented constraint matrix A~ = (I|-A); in order to prevent columns of fixed variables to be included in the triangular part, such columns are implictly removed from the matrix A~ by the routine adv_mat */ rn = xcalloc(1+m, sizeof(int)); cn = xcalloc(1+m+n, sizeof(int)); size = triang(m, m+n, lp, mat, rn, cn); if (lpx_get_int_parm(lp, LPX_K_MSGLEV) >= 3) xprintf("Size of triangular part = %d\n", size); /* the first size rows and columns of the matrix P*A~*Q (where P and Q are permutation matrices defined by the arrays rn and cn) form a lower triangular matrix; build the arrays (rn_inv and cn_inv), which define the matrices inv(P) and inv(Q) */ rn_inv = xcalloc(1+m, sizeof(int)); cn_inv = xcalloc(1+m+n, sizeof(int)); for (i = 1; i <= m; i++) rn_inv[rn[i]] = i; for (j = 1; j <= m+n; j++) cn_inv[cn[j]] = j; /* include the columns of the matrix A~, which correspond to the first size columns of the matrix P*A~*Q, in the basis */ for (k = 1; k <= m+n; k++) tagx[k] = -1; for (jj = 1; jj <= size; jj++) { j = cn_inv[jj]; /* the j-th column of A~ is the jj-th column of P*A~*Q */ tagx[j] = LPX_BS; } /* if size < m, we need to add appropriate columns of auxiliary variables to the basis */ for (jj = size + 1; jj <= m; jj++) { /* the jj-th column of P*A~*Q should be replaced by the column of the auxiliary variable, for which the only unity element is placed in the position [jj,jj] */ i = rn_inv[jj]; /* the jj-th row of P*A~*Q is the i-th row of A~, but in the i-th row of A~ the unity element belongs to the i-th column of A~; therefore the disired column corresponds to the i-th auxiliary variable (note that this column doesn't belong to the triangular part found by the routine triang) */ xassert(1 <= i && i <= m); xassert(cn[i] > size); tagx[i] = LPX_BS; } /* free working arrays */ xfree(rn); xfree(cn); xfree(rn_inv); xfree(cn_inv); /* build tags of non-basic variables */ for (k = 1; k <= m+n; k++) { if (tagx[k] != LPX_BS) { if (k <= m) lpx_get_row_bnds(lp, k, &typx, &lb, &ub); else lpx_get_col_bnds(lp, k-m, &typx, &lb, &ub); switch (typx) { case LPX_FR: tagx[k] = LPX_NF; break; case LPX_LO: tagx[k] = LPX_NL; break; case LPX_UP: tagx[k] = LPX_NU; break; case LPX_DB: tagx[k] = (fabs(lb) <= fabs(ub) ? LPX_NL : LPX_NU); break; case LPX_FX: tagx[k] = LPX_NS; break; default: xassert(typx != typx); } } } for (k = 1; k <= m+n; k++) { if (k <= m) lpx_set_row_stat(lp, k, tagx[k]); else lpx_set_col_stat(lp, k-m, tagx[k]); } xfree(tagx); return; } /*********************************************************************** * NAME * * glp_adv_basis - construct advanced initial LP basis * * SYNOPSIS * * void glp_adv_basis(glp_prob *lp, int flags); * * DESCRIPTION * * The routine glp_adv_basis constructs an advanced initial basis for * the specified problem object. * * The parameter flags is reserved for use in the future and must be * specified as zero. */ void glp_adv_basis(glp_prob *lp, int flags) { if (flags != 0) xerror("glp_adv_basis: flags = %d; invalid flags\n", flags); if (lp->m == 0 || lp->n == 0) glp_std_basis(lp); else adv_basis(lp); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpmpl02.c0000644000175000017500000013073011632352620020244 0ustar csheltoncshelton/* glpmpl02.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #define _GLPSTD_STDIO #include "glpenv.h" #include "glpmpl.h" /**********************************************************************/ /* * * PROCESSING DATA SECTION * * */ /**********************************************************************/ /*---------------------------------------------------------------------- -- create_slice - create slice. -- -- This routine creates a slice, which initially has no components. */ SLICE *create_slice(MPL *mpl) { SLICE *slice; xassert(mpl == mpl); slice = NULL; return slice; } /*---------------------------------------------------------------------- -- expand_slice - append new component to slice. -- -- This routine expands slice appending to it either a given symbol or -- null component, which becomes the last component of the slice. */ SLICE *expand_slice ( MPL *mpl, SLICE *slice, /* destroyed */ SYMBOL *sym /* destroyed */ ) { SLICE *tail, *temp; /* create a new component */ tail = dmp_get_atom(mpl->tuples, sizeof(SLICE)); tail->sym = sym; tail->next = NULL; /* and append it to the component list */ if (slice == NULL) slice = tail; else { for (temp = slice; temp->next != NULL; temp = temp->next); temp->next = tail; } return slice; } /*---------------------------------------------------------------------- -- slice_dimen - determine dimension of slice. -- -- This routine returns dimension of slice, which is number of all its -- components including null ones. */ int slice_dimen ( MPL *mpl, SLICE *slice /* not changed */ ) { SLICE *temp; int dim; xassert(mpl == mpl); dim = 0; for (temp = slice; temp != NULL; temp = temp->next) dim++; return dim; } /*---------------------------------------------------------------------- -- slice_arity - determine arity of slice. -- -- This routine returns arity of slice, i.e. number of null components -- (indicated by asterisks) in the slice. */ int slice_arity ( MPL *mpl, SLICE *slice /* not changed */ ) { SLICE *temp; int arity; xassert(mpl == mpl); arity = 0; for (temp = slice; temp != NULL; temp = temp->next) if (temp->sym == NULL) arity++; return arity; } /*---------------------------------------------------------------------- -- fake_slice - create fake slice of all asterisks. -- -- This routine creates a fake slice of given dimension, which contains -- asterisks in all components. Zero dimension is allowed. */ SLICE *fake_slice(MPL *mpl, int dim) { SLICE *slice; slice = create_slice(mpl); while (dim-- > 0) slice = expand_slice(mpl, slice, NULL); return slice; } /*---------------------------------------------------------------------- -- delete_slice - delete slice. -- -- This routine deletes specified slice. */ void delete_slice ( MPL *mpl, SLICE *slice /* destroyed */ ) { SLICE *temp; while (slice != NULL) { temp = slice; slice = temp->next; if (temp->sym != NULL) delete_symbol(mpl, temp->sym); xassert(sizeof(SLICE) == sizeof(TUPLE)); dmp_free_atom(mpl->tuples, temp, sizeof(TUPLE)); } return; } /*---------------------------------------------------------------------- -- is_number - check if current token is number. -- -- If the current token is a number, this routine returns non-zero. -- Otherwise zero is returned. */ int is_number(MPL *mpl) { return mpl->token == T_NUMBER; } /*---------------------------------------------------------------------- -- is_symbol - check if current token is symbol. -- -- If the current token is suitable to be a symbol, the routine returns -- non-zero. Otherwise zero is returned. */ int is_symbol(MPL *mpl) { return mpl->token == T_NUMBER || mpl->token == T_SYMBOL || mpl->token == T_STRING; } /*---------------------------------------------------------------------- -- is_literal - check if current token is given symbolic literal. -- -- If the current token is given symbolic literal, this routine returns -- non-zero. Otherwise zero is returned. -- -- This routine is used on processing the data section in the same way -- as the routine is_keyword on processing the model section. */ int is_literal(MPL *mpl, char *literal) { return is_symbol(mpl) && strcmp(mpl->image, literal) == 0; } /*---------------------------------------------------------------------- -- read_number - read number. -- -- This routine reads the current token, which must be a number, and -- returns its numeric value. */ double read_number(MPL *mpl) { double num; xassert(is_number(mpl)); num = mpl->value; get_token(mpl /* */); return num; } /*---------------------------------------------------------------------- -- read_symbol - read symbol. -- -- This routine reads the current token, which must be a symbol, and -- returns its symbolic value. */ SYMBOL *read_symbol(MPL *mpl) { SYMBOL *sym; xassert(is_symbol(mpl)); if (is_number(mpl)) sym = create_symbol_num(mpl, mpl->value); else sym = create_symbol_str(mpl, create_string(mpl, mpl->image)); get_token(mpl /* */); return sym; } /*---------------------------------------------------------------------- -- read_slice - read slice. -- -- This routine reads slice using the syntax: -- -- ::= [ ] -- ::= ( ) -- ::= -- ::= , -- ::= -- ::= * -- -- The bracketed form of slice is used for members of multi-dimensional -- objects while the parenthesized form is used for elemental sets. */ SLICE *read_slice ( MPL *mpl, char *name, /* not changed */ int dim ) { SLICE *slice; int close; xassert(name != NULL); switch (mpl->token) { case T_LBRACKET: close = T_RBRACKET; break; case T_LEFT: xassert(dim > 0); close = T_RIGHT; break; default: xassert(mpl != mpl); } if (dim == 0) error(mpl, "%s cannot be subscripted", name); get_token(mpl /* ( | [ */); /* read slice components */ slice = create_slice(mpl); for (;;) { /* the current token must be a symbol or asterisk */ if (is_symbol(mpl)) slice = expand_slice(mpl, slice, read_symbol(mpl)); else if (mpl->token == T_ASTERISK) { slice = expand_slice(mpl, slice, NULL); get_token(mpl /* * */); } else error(mpl, "number, symbol, or asterisk missing where expec" "ted"); /* check a token that follows the symbol */ if (mpl->token == T_COMMA) get_token(mpl /* , */); else if (mpl->token == close) break; else error(mpl, "syntax error in slice"); } /* number of slice components must be the same as the appropriate dimension */ if (slice_dimen(mpl, slice) != dim) { switch (close) { case T_RBRACKET: error(mpl, "%s must have %d subscript%s, not %d", name, dim, dim == 1 ? "" : "s", slice_dimen(mpl, slice)); break; case T_RIGHT: error(mpl, "%s has dimension %d, not %d", name, dim, slice_dimen(mpl, slice)); break; default: xassert(close != close); } } get_token(mpl /* ) | ] */); return slice; } /*---------------------------------------------------------------------- -- select_set - select set to saturate it with elemental sets. -- -- This routine selects set to saturate it with elemental sets provided -- in the data section. */ SET *select_set ( MPL *mpl, char *name /* not changed */ ) { SET *set; AVLNODE *node; xassert(name != NULL); node = avl_find_node(mpl->tree, name); if (node == NULL || avl_get_node_type(node) != A_SET) error(mpl, "%s not a set", name); set = (SET *)avl_get_node_link(node); if (set->assign != NULL || set->gadget != NULL) error(mpl, "%s needs no data", name); set->data = 1; return set; } /*---------------------------------------------------------------------- -- simple_format - read set data block in simple format. -- -- This routine reads set data block using the syntax: -- -- ::= , , ... , -- -- where are used to construct a complete n-tuple, which is -- included in elemental set assigned to the set member. Commae between -- symbols are optional and may be omitted anywhere. -- -- Number of components in the slice must be the same as dimension of -- n-tuples in elemental sets assigned to the set members. To construct -- complete n-tuple the routine replaces null positions in the slice by -- corresponding . -- -- If the slice contains at least one null position, the current token -- must be symbol. Otherwise, the routine reads no symbols to construct -- the n-tuple, so the current token is not checked. */ void simple_format ( MPL *mpl, SET *set, /* not changed */ MEMBER *memb, /* modified */ SLICE *slice /* not changed */ ) { TUPLE *tuple; SLICE *temp; SYMBOL *sym, *with = NULL; xassert(set != NULL); xassert(memb != NULL); xassert(slice != NULL); xassert(set->dimen == slice_dimen(mpl, slice)); xassert(memb->value.set->dim == set->dimen); if (slice_arity(mpl, slice) > 0) xassert(is_symbol(mpl)); /* read symbols and construct complete n-tuple */ tuple = create_tuple(mpl); for (temp = slice; temp != NULL; temp = temp->next) { if (temp->sym == NULL) { /* substitution is needed; read symbol */ if (!is_symbol(mpl)) { int lack = slice_arity(mpl, temp); /* with cannot be null due to assertion above */ xassert(with != NULL); if (lack == 1) error(mpl, "one item missing in data group beginning " "with %s", format_symbol(mpl, with)); else error(mpl, "%d items missing in data group beginning " "with %s", lack, format_symbol(mpl, with)); } sym = read_symbol(mpl); if (with == NULL) with = sym; } else { /* copy symbol from the slice */ sym = copy_symbol(mpl, temp->sym); } /* append the symbol to the n-tuple */ tuple = expand_tuple(mpl, tuple, sym); /* skip optional comma *between* */ if (temp->next != NULL && mpl->token == T_COMMA) get_token(mpl /* , */); } /* add constructed n-tuple to elemental set */ check_then_add(mpl, memb->value.set, tuple); return; } /*---------------------------------------------------------------------- -- matrix_format - read set data block in matrix format. -- -- This routine reads set data block using the syntax: -- -- ::= ... := -- +/- +/- ... +/- -- +/- +/- ... +/- -- . . . . . . . . . . . -- +/- +/- ... +/- -- -- where are symbols that denote rows of the matrix, -- are symbols that denote columns of the matrix, "+" and "-" indicate -- whether corresponding n-tuple needs to be included in the elemental -- set or not, respectively. -- -- Number of the slice components must be the same as dimension of the -- elemental set. The slice must have two null positions. To construct -- complete n-tuple for particular element of the matrix the routine -- replaces first null position of the slice by the corresponding -- (or , if the flag tr is on) and second null position by the -- corresponding (or by , if the flag tr is on). */ void matrix_format ( MPL *mpl, SET *set, /* not changed */ MEMBER *memb, /* modified */ SLICE *slice, /* not changed */ int tr ) { SLICE *list, *col, *temp; TUPLE *tuple; SYMBOL *row; xassert(set != NULL); xassert(memb != NULL); xassert(slice != NULL); xassert(set->dimen == slice_dimen(mpl, slice)); xassert(memb->value.set->dim == set->dimen); xassert(slice_arity(mpl, slice) == 2); /* read the matrix heading that contains column symbols (there may be no columns at all) */ list = create_slice(mpl); while (mpl->token != T_ASSIGN) { /* read column symbol and append it to the column list */ if (!is_symbol(mpl)) error(mpl, "number, symbol, or := missing where expected"); list = expand_slice(mpl, list, read_symbol(mpl)); } get_token(mpl /* := */); /* read zero or more rows that contain matrix data */ while (is_symbol(mpl)) { /* read row symbol (if the matrix has no columns, row symbols are just ignored) */ row = read_symbol(mpl); /* read the matrix row accordingly to the column list */ for (col = list; col != NULL; col = col->next) { int which = 0; /* check indicator */ if (is_literal(mpl, "+")) ; else if (is_literal(mpl, "-")) { get_token(mpl /* - */); continue; } else { int lack = slice_dimen(mpl, col); if (lack == 1) error(mpl, "one item missing in data group beginning " "with %s", format_symbol(mpl, row)); else error(mpl, "%d items missing in data group beginning " "with %s", lack, format_symbol(mpl, row)); } /* construct complete n-tuple */ tuple = create_tuple(mpl); for (temp = slice; temp != NULL; temp = temp->next) { if (temp->sym == NULL) { /* substitution is needed */ switch (++which) { case 1: /* substitute in the first null position */ tuple = expand_tuple(mpl, tuple, copy_symbol(mpl, tr ? col->sym : row)); break; case 2: /* substitute in the second null position */ tuple = expand_tuple(mpl, tuple, copy_symbol(mpl, tr ? row : col->sym)); break; default: xassert(which != which); } } else { /* copy symbol from the slice */ tuple = expand_tuple(mpl, tuple, copy_symbol(mpl, temp->sym)); } } xassert(which == 2); /* add constructed n-tuple to elemental set */ check_then_add(mpl, memb->value.set, tuple); get_token(mpl /* + */); } /* delete the row symbol */ delete_symbol(mpl, row); } /* delete the column list */ delete_slice(mpl, list); return; } /*---------------------------------------------------------------------- -- set_data - read set data. -- -- This routine reads set data using the syntax: -- -- ::= set ; -- ::= set [ ] ; -- ::= -- ::= -- ::= , := -- ::= , ( ) -- ::= , -- ::= , : -- ::= , (tr) -- ::= , (tr) : -- -- Commae in are optional and may be omitted anywhere. */ void set_data(MPL *mpl) { SET *set; TUPLE *tuple; MEMBER *memb; SLICE *slice; int tr = 0; xassert(is_literal(mpl, "set")); get_token(mpl /* set */); /* symbolic name of set must follows the keyword 'set' */ if (!is_symbol(mpl)) error(mpl, "set name missing where expected"); /* select the set to saturate it with data */ set = select_set(mpl, mpl->image); get_token(mpl /* */); /* read optional subscript list, which identifies member of the set to be read */ tuple = create_tuple(mpl); if (mpl->token == T_LBRACKET) { /* subscript list is specified */ if (set->dim == 0) error(mpl, "%s cannot be subscripted", set->name); get_token(mpl /* [ */); /* read symbols and construct subscript list */ for (;;) { if (!is_symbol(mpl)) error(mpl, "number or symbol missing where expected"); tuple = expand_tuple(mpl, tuple, read_symbol(mpl)); if (mpl->token == T_COMMA) get_token(mpl /* , */); else if (mpl->token == T_RBRACKET) break; else error(mpl, "syntax error in subscript list"); } if (set->dim != tuple_dimen(mpl, tuple)) error(mpl, "%s must have %d subscript%s rather than %d", set->name, set->dim, set->dim == 1 ? "" : "s", tuple_dimen(mpl, tuple)); get_token(mpl /* ] */); } else { /* subscript list is not specified */ if (set->dim != 0) error(mpl, "%s must be subscripted", set->name); } /* there must be no member with the same subscript list */ if (find_member(mpl, set->array, tuple) != NULL) error(mpl, "%s%s already defined", set->name, format_tuple(mpl, '[', tuple)); /* add new member to the set and assign it empty elemental set */ memb = add_member(mpl, set->array, tuple); memb->value.set = create_elemset(mpl, set->dimen); /* create an initial fake slice of all asterisks */ slice = fake_slice(mpl, set->dimen); /* read zero or more data assignments */ for (;;) { /* skip optional comma */ if (mpl->token == T_COMMA) get_token(mpl /* , */); /* process assignment element */ if (mpl->token == T_ASSIGN) { /* assignment ligature is non-significant element */ get_token(mpl /* := */); } else if (mpl->token == T_LEFT) { /* left parenthesis begins either new slice or "transpose" indicator */ int is_tr; get_token(mpl /* ( */); is_tr = is_literal(mpl, "tr"); unget_token(mpl /* ( */); if (is_tr) goto left; /* delete the current slice and read new one */ delete_slice(mpl, slice); slice = read_slice(mpl, set->name, set->dimen); /* each new slice resets the "transpose" indicator */ tr = 0; /* if the new slice is 0-ary, formally there is one 0-tuple (in the simple format) that follows it */ if (slice_arity(mpl, slice) == 0) simple_format(mpl, set, memb, slice); } else if (is_symbol(mpl)) { /* number or symbol begins data in the simple format */ simple_format(mpl, set, memb, slice); } else if (mpl->token == T_COLON) { /* colon begins data in the matrix format */ if (slice_arity(mpl, slice) != 2) err1: error(mpl, "slice currently used must specify 2 asterisk" "s, not %d", slice_arity(mpl, slice)); get_token(mpl /* : */); /* read elemental set data in the matrix format */ matrix_format(mpl, set, memb, slice, tr); } else if (mpl->token == T_LEFT) left: { /* left parenthesis begins the "transpose" indicator, which is followed by data in the matrix format */ get_token(mpl /* ( */); if (!is_literal(mpl, "tr")) err2: error(mpl, "transpose indicator (tr) incomplete"); if (slice_arity(mpl, slice) != 2) goto err1; get_token(mpl /* tr */); if (mpl->token != T_RIGHT) goto err2; get_token(mpl /* ) */); /* in this case the colon is optional */ if (mpl->token == T_COLON) get_token(mpl /* : */); /* set the "transpose" indicator */ tr = 1; /* read elemental set data in the matrix format */ matrix_format(mpl, set, memb, slice, tr); } else if (mpl->token == T_SEMICOLON) { /* semicolon terminates the data block */ get_token(mpl /* ; */); break; } else error(mpl, "syntax error in set data block"); } /* delete the current slice */ delete_slice(mpl, slice); return; } /*---------------------------------------------------------------------- -- select_parameter - select parameter to saturate it with data. -- -- This routine selects parameter to saturate it with data provided in -- the data section. */ PARAMETER *select_parameter ( MPL *mpl, char *name /* not changed */ ) { PARAMETER *par; AVLNODE *node; xassert(name != NULL); node = avl_find_node(mpl->tree, name); if (node == NULL || avl_get_node_type(node) != A_PARAMETER) error(mpl, "%s not a parameter", name); par = (PARAMETER *)avl_get_node_link(node); if (par->assign != NULL) error(mpl, "%s needs no data", name); if (par->data) error(mpl, "%s already provided with data", name); par->data = 1; return par; } /*---------------------------------------------------------------------- -- set_default - set default parameter value. -- -- This routine sets default value for specified parameter. */ void set_default ( MPL *mpl, PARAMETER *par, /* not changed */ SYMBOL *altval /* destroyed */ ) { xassert(par != NULL); xassert(altval != NULL); if (par->option != NULL) error(mpl, "default value for %s already specified in model se" "ction", par->name); xassert(par->defval == NULL); par->defval = altval; return; } /*---------------------------------------------------------------------- -- read_value - read value and assign it to parameter member. -- -- This routine reads numeric or symbolic value from the input stream -- and assigns to new parameter member specified by its n-tuple, which -- (the member) is created and added to the parameter array. */ MEMBER *read_value ( MPL *mpl, PARAMETER *par, /* not changed */ TUPLE *tuple /* destroyed */ ) { MEMBER *memb; xassert(par != NULL); xassert(is_symbol(mpl)); /* there must be no member with the same n-tuple */ if (find_member(mpl, par->array, tuple) != NULL) error(mpl, "%s%s already defined", par->name, format_tuple(mpl, '[', tuple)); /* create new parameter member with given n-tuple */ memb = add_member(mpl, par->array, tuple); /* read value and assigns it to the new parameter member */ switch (par->type) { case A_NUMERIC: case A_INTEGER: case A_BINARY: if (!is_number(mpl)) error(mpl, "%s requires numeric data", par->name); memb->value.num = read_number(mpl); break; case A_SYMBOLIC: memb->value.sym = read_symbol(mpl); break; default: xassert(par != par); } return memb; } /*---------------------------------------------------------------------- -- plain_format - read parameter data block in plain format. -- -- This routine reads parameter data block using the syntax: -- -- ::= , , ... , , -- -- where are used to determine a complete subscript list for -- parameter member, is a numeric or symbolic value assigned to -- the parameter member. Commae between data items are optional and may -- be omitted anywhere. -- -- Number of components in the slice must be the same as dimension of -- the parameter. To construct the complete subscript list the routine -- replaces null positions in the slice by corresponding . */ void plain_format ( MPL *mpl, PARAMETER *par, /* not changed */ SLICE *slice /* not changed */ ) { TUPLE *tuple; SLICE *temp; SYMBOL *sym, *with = NULL; xassert(par != NULL); xassert(par->dim == slice_dimen(mpl, slice)); xassert(is_symbol(mpl)); /* read symbols and construct complete subscript list */ tuple = create_tuple(mpl); for (temp = slice; temp != NULL; temp = temp->next) { if (temp->sym == NULL) { /* substitution is needed; read symbol */ if (!is_symbol(mpl)) { int lack = slice_arity(mpl, temp) + 1; xassert(with != NULL); xassert(lack > 1); error(mpl, "%d items missing in data group beginning wit" "h %s", lack, format_symbol(mpl, with)); } sym = read_symbol(mpl); if (with == NULL) with = sym; } else { /* copy symbol from the slice */ sym = copy_symbol(mpl, temp->sym); } /* append the symbol to the subscript list */ tuple = expand_tuple(mpl, tuple, sym); /* skip optional comma */ if (mpl->token == T_COMMA) get_token(mpl /* , */); } /* read value and assign it to new parameter member */ if (!is_symbol(mpl)) { xassert(with != NULL); error(mpl, "one item missing in data group beginning with %s", format_symbol(mpl, with)); } read_value(mpl, par, tuple); return; } /*---------------------------------------------------------------------- -- tabular_format - read parameter data block in tabular format. -- -- This routine reads parameter data block using the syntax: -- -- ::= ... := -- ... -- ... -- . . . . . . . . . . . -- ... -- -- where are symbols that denote rows of the table, -- are symbols that denote columns of the table, are numeric -- or symbolic values assigned to the corresponding parameter members. -- If is specified as single point, no value is provided. -- -- Number of components in the slice must be the same as dimension of -- the parameter. The slice must have two null positions. To construct -- complete subscript list for particular the routine replaces -- the first null position of the slice by the corresponding (or -- , if the flag tr is on) and the second null position by the -- corresponding (or by , if the flag tr is on). */ void tabular_format ( MPL *mpl, PARAMETER *par, /* not changed */ SLICE *slice, /* not changed */ int tr ) { SLICE *list, *col, *temp; TUPLE *tuple; SYMBOL *row; xassert(par != NULL); xassert(par->dim == slice_dimen(mpl, slice)); xassert(slice_arity(mpl, slice) == 2); /* read the table heading that contains column symbols (the table may have no columns) */ list = create_slice(mpl); while (mpl->token != T_ASSIGN) { /* read column symbol and append it to the column list */ if (!is_symbol(mpl)) error(mpl, "number, symbol, or := missing where expected"); list = expand_slice(mpl, list, read_symbol(mpl)); } get_token(mpl /* := */); /* read zero or more rows that contain tabular data */ while (is_symbol(mpl)) { /* read row symbol (if the table has no columns, these symbols are just ignored) */ row = read_symbol(mpl); /* read values accordingly to the column list */ for (col = list; col != NULL; col = col->next) { int which = 0; /* if the token is single point, no value is provided */ if (is_literal(mpl, ".")) { get_token(mpl /* . */); continue; } /* construct complete subscript list */ tuple = create_tuple(mpl); for (temp = slice; temp != NULL; temp = temp->next) { if (temp->sym == NULL) { /* substitution is needed */ switch (++which) { case 1: /* substitute in the first null position */ tuple = expand_tuple(mpl, tuple, copy_symbol(mpl, tr ? col->sym : row)); break; case 2: /* substitute in the second null position */ tuple = expand_tuple(mpl, tuple, copy_symbol(mpl, tr ? row : col->sym)); break; default: xassert(which != which); } } else { /* copy symbol from the slice */ tuple = expand_tuple(mpl, tuple, copy_symbol(mpl, temp->sym)); } } xassert(which == 2); /* read value and assign it to new parameter member */ if (!is_symbol(mpl)) { int lack = slice_dimen(mpl, col); if (lack == 1) error(mpl, "one item missing in data group beginning " "with %s", format_symbol(mpl, row)); else error(mpl, "%d items missing in data group beginning " "with %s", lack, format_symbol(mpl, row)); } read_value(mpl, par, tuple); } /* delete the row symbol */ delete_symbol(mpl, row); } /* delete the column list */ delete_slice(mpl, list); return; } /*---------------------------------------------------------------------- -- tabbing_format - read parameter data block in tabbing format. -- -- This routine reads parameter data block using the syntax: -- -- ::= , ... , , := , -- , ... , , , ... , , -- , ... , , , ... , , -- . . . . . . . . . . . . . . . . . -- , ... , , , ... , -- ::= -- ::= : -- -- where are names of parameters (all the parameters must be -- subscripted and have identical dimensions), are symbols -- used to define subscripts of parameter members, are numeric -- or symbolic values assigned to the corresponding parameter members. -- Optional may specify a simple set, in which case n-tuples -- built of for each row of the data table (i.e. subscripts -- of parameter members) are added to the specified set. Commae between -- data items are optional and may be omitted anywhere. -- -- If the parameter altval is not NULL, it specifies a default value -- provided for all the parameters specified in the data block. */ void tabbing_format ( MPL *mpl, SYMBOL *altval /* not changed */ ) { SET *set = NULL; PARAMETER *par; SLICE *list, *col; TUPLE *tuple; int next_token, j, dim = 0; char *last_name = NULL; /* read the optional */ if (is_symbol(mpl)) { get_token(mpl /* */); next_token = mpl->token; unget_token(mpl /* */); if (next_token == T_COLON) { /* select the set to saturate it with data */ set = select_set(mpl, mpl->image); /* the set must be simple (i.e. not set of sets) */ if (set->dim != 0) error(mpl, "%s must be a simple set", set->name); /* and must not be defined yet */ if (set->array->head != NULL) error(mpl, "%s already defined", set->name); /* add new (the only) member to the set and assign it empty elemental set */ add_member(mpl, set->array, NULL)->value.set = create_elemset(mpl, set->dimen); last_name = set->name, dim = set->dimen; get_token(mpl /* */); xassert(mpl->token == T_COLON); get_token(mpl /* : */); } } /* read the table heading that contains parameter names */ list = create_slice(mpl); while (mpl->token != T_ASSIGN) { /* there must be symbolic name of parameter */ if (!is_symbol(mpl)) error(mpl, "parameter name or := missing where expected"); /* select the parameter to saturate it with data */ par = select_parameter(mpl, mpl->image); /* the parameter must be subscripted */ if (par->dim == 0) error(mpl, "%s not a subscripted parameter", mpl->image); /* the set (if specified) and all the parameters in the data block must have identical dimension */ if (dim != 0 && par->dim != dim) { xassert(last_name != NULL); error(mpl, "%s has dimension %d while %s has dimension %d", last_name, dim, par->name, par->dim); } /* set default value for the parameter (if specified) */ if (altval != NULL) set_default(mpl, par, copy_symbol(mpl, altval)); /* append the parameter to the column list */ list = expand_slice(mpl, list, (SYMBOL *)par); last_name = par->name, dim = par->dim; get_token(mpl /* */); /* skip optional comma */ if (mpl->token == T_COMMA) get_token(mpl /* , */); } if (slice_dimen(mpl, list) == 0) error(mpl, "at least one parameter name required"); get_token(mpl /* := */); /* skip optional comma */ if (mpl->token == T_COMMA) get_token(mpl /* , */); /* read rows that contain tabbing data */ while (is_symbol(mpl)) { /* read subscript list */ tuple = create_tuple(mpl); for (j = 1; j <= dim; j++) { /* read j-th subscript */ if (!is_symbol(mpl)) { int lack = slice_dimen(mpl, list) + dim - j + 1; xassert(tuple != NULL); xassert(lack > 1); error(mpl, "%d items missing in data group beginning wit" "h %s", lack, format_symbol(mpl, tuple->sym)); } /* read and append j-th subscript to the n-tuple */ tuple = expand_tuple(mpl, tuple, read_symbol(mpl)); /* skip optional comma *between* */ if (j < dim && mpl->token == T_COMMA) get_token(mpl /* , */); } /* if the set is specified, add to it new n-tuple, which is a copy of the subscript list just read */ if (set != NULL) check_then_add(mpl, set->array->head->value.set, copy_tuple(mpl, tuple)); /* skip optional comma between and */ if (mpl->token == T_COMMA) get_token(mpl /* , */); /* read values accordingly to the column list */ for (col = list; col != NULL; col = col->next) { /* if the token is single point, no value is provided */ if (is_literal(mpl, ".")) { get_token(mpl /* . */); continue; } /* read value and assign it to new parameter member */ if (!is_symbol(mpl)) { int lack = slice_dimen(mpl, col); xassert(tuple != NULL); if (lack == 1) error(mpl, "one item missing in data group beginning " "with %s", format_symbol(mpl, tuple->sym)); else error(mpl, "%d items missing in data group beginning " "with %s", lack, format_symbol(mpl, tuple->sym)); } read_value(mpl, (PARAMETER *)col->sym, copy_tuple(mpl, tuple)); /* skip optional comma preceding the next value */ if (col->next != NULL && mpl->token == T_COMMA) get_token(mpl /* , */); } /* delete the original subscript list */ delete_tuple(mpl, tuple); /* skip optional comma (only if there is next data group) */ if (mpl->token == T_COMMA) { get_token(mpl /* , */); if (!is_symbol(mpl)) unget_token(mpl /* , */); } } /* delete the column list (it contains parameters, not symbols, so nullify it before) */ for (col = list; col != NULL; col = col->next) col->sym = NULL; delete_slice(mpl, list); return; } /*---------------------------------------------------------------------- -- parameter_data - read parameter data. -- -- This routine reads parameter data using the syntax: -- -- ::= param : ; -- ::= param -- ; -- ::= -- ::= -- ::= default -- ::= -- ::= , := -- ::= , [ ] -- ::= , -- ::= , : -- ::= , (tr) -- ::= , (tr) : -- -- Commae in are optional and may be omitted anywhere. */ void parameter_data(MPL *mpl) { PARAMETER *par; SYMBOL *altval = NULL; SLICE *slice; int tr = 0; xassert(is_literal(mpl, "param")); get_token(mpl /* param */); /* read optional default value */ if (is_literal(mpl, "default")) { get_token(mpl /* default */); if (!is_symbol(mpl)) error(mpl, "default value missing where expected"); altval = read_symbol(mpl); /* if the default value follows the keyword 'param', the next token must be only the colon */ if (mpl->token != T_COLON) error(mpl, "colon missing where expected"); } /* being used after the keyword 'param' or the optional default value the colon begins data in the tabbing format */ if (mpl->token == T_COLON) { get_token(mpl /* : */); /* skip optional comma */ if (mpl->token == T_COMMA) get_token(mpl /* , */); /* read parameter data in the tabbing format */ tabbing_format(mpl, altval); /* on reading data in the tabbing format the default value is always copied, so delete the original symbol */ if (altval != NULL) delete_symbol(mpl, altval); /* the next token must be only semicolon */ if (mpl->token != T_SEMICOLON) error(mpl, "symbol, number, or semicolon missing where expe" "cted"); get_token(mpl /* ; */); goto done; } /* in other cases there must be symbolic name of parameter, which follows the keyword 'param' */ if (!is_symbol(mpl)) error(mpl, "parameter name missing where expected"); /* select the parameter to saturate it with data */ par = select_parameter(mpl, mpl->image); get_token(mpl /* */); /* read optional default value */ if (is_literal(mpl, "default")) { get_token(mpl /* default */); if (!is_symbol(mpl)) error(mpl, "default value missing where expected"); altval = read_symbol(mpl); /* set default value for the parameter */ set_default(mpl, par, altval); } /* create initial fake slice of all asterisks */ slice = fake_slice(mpl, par->dim); /* read zero or more data assignments */ for (;;) { /* skip optional comma */ if (mpl->token == T_COMMA) get_token(mpl /* , */); /* process current assignment */ if (mpl->token == T_ASSIGN) { /* assignment ligature is non-significant element */ get_token(mpl /* := */); } else if (mpl->token == T_LBRACKET) { /* left bracket begins new slice; delete the current slice and read new one */ delete_slice(mpl, slice); slice = read_slice(mpl, par->name, par->dim); /* each new slice resets the "transpose" indicator */ tr = 0; } else if (is_symbol(mpl)) { /* number or symbol begins data in the plain format */ plain_format(mpl, par, slice); } else if (mpl->token == T_COLON) { /* colon begins data in the tabular format */ if (par->dim == 0) err1: error(mpl, "%s not a subscripted parameter", par->name); if (slice_arity(mpl, slice) != 2) err2: error(mpl, "slice currently used must specify 2 asterisk" "s, not %d", slice_arity(mpl, slice)); get_token(mpl /* : */); /* read parameter data in the tabular format */ tabular_format(mpl, par, slice, tr); } else if (mpl->token == T_LEFT) { /* left parenthesis begins the "transpose" indicator, which is followed by data in the tabular format */ get_token(mpl /* ( */); if (!is_literal(mpl, "tr")) err3: error(mpl, "transpose indicator (tr) incomplete"); if (par->dim == 0) goto err1; if (slice_arity(mpl, slice) != 2) goto err2; get_token(mpl /* tr */); if (mpl->token != T_RIGHT) goto err3; get_token(mpl /* ) */); /* in this case the colon is optional */ if (mpl->token == T_COLON) get_token(mpl /* : */); /* set the "transpose" indicator */ tr = 1; /* read parameter data in the tabular format */ tabular_format(mpl, par, slice, tr); } else if (mpl->token == T_SEMICOLON) { /* semicolon terminates the data block */ get_token(mpl /* ; */); break; } else error(mpl, "syntax error in parameter data block"); } /* delete the current slice */ delete_slice(mpl, slice); done: return; } /*---------------------------------------------------------------------- -- data_section - read data section. -- -- This routine reads data section using the syntax: -- -- ::= -- ::= ; -- ::= -- ::= -- -- Reading data section is terminated by either the keyword 'end' or -- the end of file. */ void data_section(MPL *mpl) { while (!(mpl->token == T_EOF || is_literal(mpl, "end"))) { if (is_literal(mpl, "set")) set_data(mpl); else if (is_literal(mpl, "param")) parameter_data(mpl); else error(mpl, "syntax error in data section"); } return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpbfd.c0000644000175000017500000003455211632352620020052 0ustar csheltoncshelton/* glpbfd.c (LP basis factorization driver) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ typedef struct BFD BFD; #define GLPBFD_PRIVATE #include "glpapi.h" #include "glpfhv.h" #include "glplpf.h" /* CAUTION: DO NOT CHANGE THE LIMIT BELOW */ #define M_MAX 100000000 /* = 100*10^6 */ /* maximal order of the basis matrix */ struct BFD { /* LP basis factorization */ int valid; /* factorization is valid only if this flag is set */ int type; /* factorization type: GLP_BF_FT - LUF + Forrest-Tomlin GLP_BF_BG - LUF + Schur compl. + Bartels-Golub GLP_BF_GR - LUF + Schur compl. + Givens rotation */ FHV *fhv; /* LP basis factorization (GLP_BF_FT) */ LPF *lpf; /* LP basis factorization (GLP_BF_BG, GLP_BF_GR) */ int lu_size; /* luf.sv_size */ double piv_tol; /* luf.piv_tol */ int piv_lim; /* luf.piv_lim */ int suhl; /* luf.suhl */ double eps_tol; /* luf.eps_tol */ double max_gro; /* luf.max_gro */ int nfs_max; /* fhv.hh_max */ double upd_tol; /* fhv.upd_tol */ int nrs_max; /* lpf.n_max */ int rs_size; /* lpf.v_size */ /* internal control parameters */ int upd_lim; /* the factorization update limit */ int upd_cnt; /* the factorization update count */ }; /*********************************************************************** * NAME * * bfd_create_it - create LP basis factorization * * SYNOPSIS * * #include "glpbfd.h" * BFD *bfd_create_it(void); * * DESCRIPTION * * The routine bfd_create_it creates a program object, which represents * a factorization of LP basis. * * RETURNS * * The routine bfd_create_it returns a pointer to the object created. */ BFD *bfd_create_it(void) { BFD *bfd; bfd = xmalloc(sizeof(BFD)); bfd->valid = 0; bfd->type = GLP_BF_FT; bfd->fhv = NULL; bfd->lpf = NULL; bfd->lu_size = 0; bfd->piv_tol = 0.10; bfd->piv_lim = 4; bfd->suhl = 1; bfd->eps_tol = 1e-15; bfd->max_gro = 1e+10; bfd->nfs_max = 100; bfd->upd_tol = 1e-6; bfd->nrs_max = 100; bfd->rs_size = 1000; bfd->upd_lim = -1; bfd->upd_cnt = 0; return bfd; } /**********************************************************************/ void bfd_set_parm(BFD *bfd, const void *_parm) { /* change LP basis factorization control parameters */ const glp_bfcp *parm = _parm; xassert(bfd != NULL); bfd->type = parm->type; bfd->lu_size = parm->lu_size; bfd->piv_tol = parm->piv_tol; bfd->piv_lim = parm->piv_lim; bfd->suhl = parm->suhl; bfd->eps_tol = parm->eps_tol; bfd->max_gro = parm->max_gro; bfd->nfs_max = parm->nfs_max; bfd->upd_tol = parm->upd_tol; bfd->nrs_max = parm->nrs_max; bfd->rs_size = parm->rs_size; return; } /*********************************************************************** * NAME * * bfd_factorize - compute LP basis factorization * * SYNOPSIS * * #include "glpbfd.h" * int bfd_factorize(BFD *bfd, int m, int bh[], int (*col)(void *info, * int j, int ind[], double val[]), void *info); * * DESCRIPTION * * The routine bfd_factorize computes the factorization of the basis * matrix B specified by the routine col. * * The parameter bfd specified the basis factorization data structure * created with the routine bfd_create_it. * * The parameter m specifies the order of B, m > 0. * * The array bh specifies the basis header: bh[j], 1 <= j <= m, is the * number of j-th column of B in some original matrix. The array bh is * optional and can be specified as NULL. * * The formal routine col specifies the matrix B to be factorized. To * obtain j-th column of A the routine bfd_factorize calls the routine * col with the parameter j (1 <= j <= n). In response the routine col * should store row indices and numerical values of non-zero elements * of j-th column of B to locations ind[1,...,len] and val[1,...,len], * respectively, where len is the number of non-zeros in j-th column * returned on exit. Neither zero nor duplicate elements are allowed. * * The parameter info is a transit pointer passed to the routine col. * * RETURNS * * 0 The factorization has been successfully computed. * * BFD_ESING * The specified matrix is singular within the working precision. * * BFD_ECOND * The specified matrix is ill-conditioned. * * For more details see comments to the routine luf_factorize. */ int bfd_factorize(BFD *bfd, int m, const int bh[], int (*col) (void *info, int j, int ind[], double val[]), void *info) { LUF *luf; int nov, ret; xassert(bfd != NULL); xassert(1 <= m && m <= M_MAX); /* invalidate the factorization */ bfd->valid = 0; /* create the factorization, if necessary */ nov = 0; switch (bfd->type) { case GLP_BF_FT: if (bfd->lpf != NULL) lpf_delete_it(bfd->lpf), bfd->lpf = NULL; if (bfd->fhv == NULL) bfd->fhv = fhv_create_it(), nov = 1; break; case GLP_BF_BG: case GLP_BF_GR: if (bfd->fhv != NULL) fhv_delete_it(bfd->fhv), bfd->fhv = NULL; if (bfd->lpf == NULL) bfd->lpf = lpf_create_it(), nov = 1; break; default: xassert(bfd != bfd); } /* set control parameters specific to LUF */ if (bfd->fhv != NULL) luf = bfd->fhv->luf; else if (bfd->lpf != NULL) luf = bfd->lpf->luf; else xassert(bfd != bfd); if (nov) luf->new_sva = bfd->lu_size; luf->piv_tol = bfd->piv_tol; luf->piv_lim = bfd->piv_lim; luf->suhl = bfd->suhl; luf->eps_tol = bfd->eps_tol; luf->max_gro = bfd->max_gro; /* set control parameters specific to FHV */ if (bfd->fhv != NULL) { if (nov) bfd->fhv->hh_max = bfd->nfs_max; bfd->fhv->upd_tol = bfd->upd_tol; } /* set control parameters specific to LPF */ if (bfd->lpf != NULL) { if (nov) bfd->lpf->n_max = bfd->nrs_max; if (nov) bfd->lpf->v_size = bfd->rs_size; } /* try to factorize the basis matrix */ if (bfd->fhv != NULL) { switch (fhv_factorize(bfd->fhv, m, col, info)) { case 0: break; case FHV_ESING: ret = BFD_ESING; goto done; case FHV_ECOND: ret = BFD_ECOND; goto done; default: xassert(bfd != bfd); } } else if (bfd->lpf != NULL) { switch (lpf_factorize(bfd->lpf, m, bh, col, info)) { case 0: /* set the Schur complement update type */ switch (bfd->type) { case GLP_BF_BG: /* Bartels-Golub update */ bfd->lpf->scf->t_opt = SCF_TBG; break; case GLP_BF_GR: /* Givens rotation update */ bfd->lpf->scf->t_opt = SCF_TGR; break; default: xassert(bfd != bfd); } break; case LPF_ESING: ret = BFD_ESING; goto done; case LPF_ECOND: ret = BFD_ECOND; goto done; default: xassert(bfd != bfd); } } else xassert(bfd != bfd); /* the basis matrix has been successfully factorized */ bfd->valid = 1; bfd->upd_cnt = 0; ret = 0; done: /* return to the calling program */ return ret; } /*********************************************************************** * NAME * * bfd_ftran - perform forward transformation (solve system B*x = b) * * SYNOPSIS * * #include "glpbfd.h" * void bfd_ftran(BFD *bfd, double x[]); * * DESCRIPTION * * The routine bfd_ftran performs forward transformation, i.e. solves * the system B*x = b, where B is the basis matrix, x is the vector of * unknowns to be computed, b is the vector of right-hand sides. * * On entry elements of the vector b should be stored in dense format * in locations x[1], ..., x[m], where m is the number of rows. On exit * the routine stores elements of the vector x in the same locations. */ void bfd_ftran(BFD *bfd, double x[]) { xassert(bfd != NULL); xassert(bfd->valid); if (bfd->fhv != NULL) fhv_ftran(bfd->fhv, x); else if (bfd->lpf != NULL) lpf_ftran(bfd->lpf, x); else xassert(bfd != bfd); return; } /*********************************************************************** * NAME * * bfd_btran - perform backward transformation (solve system B'*x = b) * * SYNOPSIS * * #include "glpbfd.h" * void bfd_btran(BFD *bfd, double x[]); * * DESCRIPTION * * The routine bfd_btran performs backward transformation, i.e. solves * the system B'*x = b, where B' is a matrix transposed to the basis * matrix B, x is the vector of unknowns to be computed, b is the vector * of right-hand sides. * * On entry elements of the vector b should be stored in dense format * in locations x[1], ..., x[m], where m is the number of rows. On exit * the routine stores elements of the vector x in the same locations. */ void bfd_btran(BFD *bfd, double x[]) { xassert(bfd != NULL); xassert(bfd->valid); if (bfd->fhv != NULL) fhv_btran(bfd->fhv, x); else if (bfd->lpf != NULL) lpf_btran(bfd->lpf, x); else xassert(bfd != bfd); return; } /*********************************************************************** * NAME * * bfd_update_it - update LP basis factorization * * SYNOPSIS * * #include "glpbfd.h" * int bfd_update_it(BFD *bfd, int j, int bh, int len, const int ind[], * const double val[]); * * DESCRIPTION * * The routine bfd_update_it updates the factorization of the basis * matrix B after replacing its j-th column by a new vector. * * The parameter j specifies the number of column of B, which has been * replaced, 1 <= j <= m, where m is the order of B. * * The parameter bh specifies the basis header entry for the new column * of B, which is the number of the new column in some original matrix. * This parameter is optional and can be specified as 0. * * Row indices and numerical values of non-zero elements of the new * column of B should be placed in locations ind[1], ..., ind[len] and * val[1], ..., val[len], resp., where len is the number of non-zeros * in the column. Neither zero nor duplicate elements are allowed. * * RETURNS * * 0 The factorization has been successfully updated. * * BFD_ESING * New basis matrix is singular within the working precision. * * BFD_ECHECK * The factorization is inaccurate. * * BFD_ELIMIT * Factorization update limit has been reached. * * BFD_EROOM * Overflow of the sparse vector area. * * In case of non-zero return code the factorization becomes invalid. * It should not be used until it has been recomputed with the routine * bfd_factorize. */ int bfd_update_it(BFD *bfd, int j, int bh, int len, const int ind[], const double val[]) { int ret; xassert(bfd != NULL); xassert(bfd->valid); /* try to update the factorization */ if (bfd->fhv != NULL) { switch (fhv_update_it(bfd->fhv, j, len, ind, val)) { case 0: break; case FHV_ESING: bfd->valid = 0; ret = BFD_ESING; goto done; case FHV_ECHECK: bfd->valid = 0; ret = BFD_ECHECK; goto done; case FHV_ELIMIT: bfd->valid = 0; ret = BFD_ELIMIT; goto done; case FHV_EROOM: bfd->valid = 0; ret = BFD_EROOM; goto done; default: xassert(bfd != bfd); } } else if (bfd->lpf != NULL) { switch (lpf_update_it(bfd->lpf, j, bh, len, ind, val)) { case 0: break; case LPF_ESING: bfd->valid = 0; ret = BFD_ESING; goto done; case LPF_ELIMIT: bfd->valid = 0; ret = BFD_ELIMIT; goto done; default: xassert(bfd != bfd); } } else xassert(bfd != bfd); /* the factorization has been successfully updated */ /* increase the update count */ bfd->upd_cnt++; ret = 0; done: /* return to the calling program */ return ret; } /**********************************************************************/ int bfd_get_count(BFD *bfd) { /* determine factorization update count */ xassert(bfd != NULL); xassert(bfd->valid); return bfd->upd_cnt; } /*********************************************************************** * NAME * * bfd_delete_it - delete LP basis factorization * * SYNOPSIS * * #include "glpbfd.h" * void bfd_delete_it(BFD *bfd); * * DESCRIPTION * * The routine bfd_delete_it deletes LP basis factorization specified * by the parameter fhv and frees all memory allocated to this program * object. */ void bfd_delete_it(BFD *bfd) { xassert(bfd != NULL); if (bfd->fhv != NULL) fhv_delete_it(bfd->fhv); if (bfd->lpf != NULL) lpf_delete_it(bfd->lpf); xfree(bfd); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpenv01.c0000644000175000017500000001640011632352620020240 0ustar csheltoncshelton/* glpenv01.c (environment initialization/termination) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" /*********************************************************************** * NAME * * glp_init_env - initialize GLPK environment * * SYNOPSIS * * int glp_init_env(void); * * DESCRIPTION * * The routine glp_init_env initializes the GLPK environment. Normally * the application program does not need to call this routine, because * it is called automatically on the first call to any API routine. * * RETURNS * * The routine glp_init_env returns one of the following codes: * * 0 - initialization successful; * 1 - environment has been already initialized; * 2 - initialization failed (insufficient memory); * 3 - initialization failed (unsupported programming model). */ int glp_init_env(void) { ENV *env; int ok; /* check if the programming model is supported */ ok = (CHAR_BIT == 8 && sizeof(char) == 1 && sizeof(short) == 2 && sizeof(int) == 4 && (sizeof(void *) == 4 || sizeof(void *) == 8)); if (!ok) return 3; /* check if the environment is already initialized */ if (tls_get_ptr() != NULL) return 1; /* allocate and initialize the environment block */ env = malloc(sizeof(ENV)); if (env == NULL) return 2; env->magic = ENV_MAGIC; sprintf(env->version, "%d.%d", GLP_MAJOR_VERSION, GLP_MINOR_VERSION); env->term_buf = malloc(TERM_BUF_SIZE); if (env->term_buf == NULL) { free(env); return 2; } env->term_out = GLP_ON; env->term_hook = NULL; env->term_info = NULL; env->tee_file = NULL; env->err_file = ""; env->err_line = 0; env->err_hook = NULL; env->err_info = NULL; env->mem_limit.hi = 0x7FFFFFFF, env->mem_limit.lo = 0xFFFFFFFF; env->mem_ptr = NULL; env->mem_count = env->mem_cpeak = 0; env->mem_total = env->mem_tpeak = xlset(0); env->file_ptr = NULL; env->ioerr_msg = malloc(IOERR_MSG_SIZE); if (env->ioerr_msg == NULL) { free(env->term_buf); free(env); return 2; } strcpy(env->ioerr_msg, "No error"); env->h_odbc = env->h_mysql = NULL; /* save pointer to the environment block */ tls_set_ptr(env); /* initialization successful */ return 0; } /*********************************************************************** * NAME * * get_env_ptr - retrieve pointer to environment block * * SYNOPSIS * * #include "glpenv.h" * ENV *get_env_ptr(void); * * DESCRIPTION * * The routine get_env_ptr retrieves and returns a pointer to the GLPK * environment block. * * If the GLPK environment has not been initialized yet, the routine * performs initialization. If initialization fails, the routine prints * an error message to stderr and terminates the program. * * RETURNS * * The routine returns a pointer to the environment block. */ ENV *get_env_ptr(void) { ENV *env = tls_get_ptr(); /* check if the environment has been initialized */ if (env == NULL) { /* not initialized yet; perform initialization */ if (glp_init_env() != 0) { /* initialization failed; display an error message */ fprintf(stderr, "GLPK initialization failed\n"); fflush(stderr); /* and abnormally terminate the program */ abort(); } /* initialization successful; retrieve the pointer */ env = tls_get_ptr(); } /* check if the environment block is valid */ if (env->magic != ENV_MAGIC) { fprintf(stderr, "Invalid GLPK environment\n"); fflush(stderr); abort(); } return env; } /*********************************************************************** * NAME * * glp_version - determine library version * * SYNOPSIS * * const char *glp_version(void); * * RETURNS * * The routine glp_version returns a pointer to a null-terminated * character string, which specifies the version of the GLPK library in * the form "X.Y", where X is the major version number, and Y is the * minor version number, for example, "4.16". */ const char *glp_version(void) { ENV *env = get_env_ptr(); return env->version; } /*********************************************************************** * NAME * * glp_free_env - free GLPK environment * * SYNOPSIS * * int glp_free_env(void); * * DESCRIPTION * * The routine glp_free_env frees all resources used by GLPK routines * (memory blocks, etc.) which are currently still in use. * * Normally the application program does not need to call this routine, * because GLPK routines always free all unused resources. However, if * the application program even has deleted all problem objects, there * will be several memory blocks still allocated for the library needs. * For some reasons the application program may want GLPK to free this * memory, in which case it should call glp_free_env. * * Note that a call to glp_free_env invalidates all problem objects as * if no GLPK routine were called. * * RETURNS * * 0 - termination successful; * 1 - environment is inactive (was not initialized). */ int glp_free_env(void) { ENV *env = tls_get_ptr(); MEM *desc; /* check if the environment is active */ if (env == NULL) return 1; /* check if the environment block is valid */ if (env->magic != ENV_MAGIC) { fprintf(stderr, "Invalid GLPK environment\n"); fflush(stderr); abort(); } /* close handles to shared libraries */ if (env->h_odbc != NULL) xdlclose(env->h_odbc); if (env->h_mysql != NULL) xdlclose(env->h_mysql); /* close streams which are still open */ while (env->file_ptr != NULL) xfclose(env->file_ptr); /* free memory blocks which are still allocated */ while (env->mem_ptr != NULL) { desc = env->mem_ptr; env->mem_ptr = desc->next; free(desc); } /* invalidate the environment block */ env->magic = -1; /* free memory allocated to the environment block */ free(env->term_buf); free(env->ioerr_msg); free(env); /* reset a pointer to the environment block */ tls_set_ptr(NULL); /* termination successful */ return 0; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpenv05.c0000644000175000017500000001613611632352620020252 0ustar csheltoncshelton/* glpenv05.c (memory allocation) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" /* some processors need data to be properly aligned; the macro align_datasize enlarges the specified size of a data item to provide a proper alignment of immediately following data */ #define align_datasize(size) ((((size) + 15) / 16) * 16) /* 16 bytes is sufficient in both 32- and 64-bit environments (8 bytes is not sufficient in 64-bit environment due to jmp_buf) */ /*********************************************************************** * NAME * * glp_malloc - allocate memory block * * SYNOPSIS * * void *glp_malloc(int size); * * DESCRIPTION * * The routine glp_malloc allocates a memory block of size bytes long. * * Note that being allocated the memory block contains arbitrary data * (not binary zeros). * * RETURNS * * The routine glp_malloc returns a pointer to the allocated block. * To free this block the routine glp_free (not free!) must be used. */ void *glp_malloc(int size) { ENV *env = get_env_ptr(); MEM *desc; int size_of_desc = align_datasize(sizeof(MEM)); if (size < 1 || size > INT_MAX - size_of_desc) xerror("glp_malloc: size = %d; invalid parameter\n", size); size += size_of_desc; if (xlcmp(xlset(size), xlsub(env->mem_limit, env->mem_total)) > 0) xerror("glp_malloc: memory limit exceeded\n"); if (env->mem_count == INT_MAX) xerror("glp_malloc: too many memory blocks allocated\n"); desc = malloc(size); if (desc == NULL) xerror("glp_malloc: no memory available\n"); memset(desc, '?', size); desc->flag = MEM_MAGIC; desc->size = size; desc->prev = NULL; desc->next = env->mem_ptr; if (desc->next != NULL) desc->next->prev = desc; env->mem_ptr = desc; env->mem_count++; if (env->mem_cpeak < env->mem_count) env->mem_cpeak = env->mem_count; env->mem_total = xladd(env->mem_total, xlset(size)); if (xlcmp(env->mem_tpeak, env->mem_total) < 0) env->mem_tpeak = env->mem_total; return (void *)((char *)desc + size_of_desc); } /*********************************************************************** * NAME * * glp_calloc - allocate memory block * * SYNOPSIS * * void *glp_calloc(int n, int size); * * DESCRIPTION * * The routine glp_calloc allocates a memory block of (n*size) bytes * long. * * Note that being allocated the memory block contains arbitrary data * (not binary zeros). * * RETURNS * * The routine glp_calloc returns a pointer to the allocated block. * To free this block the routine glp_free (not free!) must be used. */ void *glp_calloc(int n, int size) { if (n < 1) xerror("glp_calloc: n = %d; invalid parameter\n", n); if (size < 1) xerror("glp_calloc: size = %d; invalid parameter\n", size); if (n > INT_MAX / size) xerror("glp_calloc: n = %d; size = %d; array too big\n", n, size); return xmalloc(n * size); } /*********************************************************************** * NAME * * glp_free - free memory block * * SYNOPSIS * * void glp_free(void *ptr); * * DESCRIPTION * * The routine glp_free frees a memory block pointed to by ptr, which * was previuosly allocated by the routine glp_malloc or glp_calloc. */ void glp_free(void *ptr) { ENV *env = get_env_ptr(); MEM *desc; int size_of_desc = align_datasize(sizeof(MEM)); if (ptr == NULL) xerror("glp_free: ptr = %p; null pointer\n", ptr); desc = (void *)((char *)ptr - size_of_desc); if (desc->flag != MEM_MAGIC) xerror("glp_free: ptr = %p; invalid pointer\n", ptr); if (env->mem_count == 0 || xlcmp(env->mem_total, xlset(desc->size)) < 0) xerror("glp_free: memory allocation error\n"); if (desc->prev == NULL) env->mem_ptr = desc->next; else desc->prev->next = desc->next; if (desc->next == NULL) ; else desc->next->prev = desc->prev; env->mem_count--; env->mem_total = xlsub(env->mem_total, xlset(desc->size)); memset(desc, '?', size_of_desc); free(desc); return; } /*********************************************************************** * NAME * * glp_mem_limit - set memory usage limit * * SYNOPSIS * * void glp_mem_limit(int limit); * * DESCRIPTION * * The routine glp_mem_limit limits the amount of memory available for * dynamic allocation (in GLPK routines) to limit megabytes. */ void glp_mem_limit(int limit) { ENV *env = get_env_ptr(); if (limit < 0) xerror("glp_mem_limit: limit = %d; invalid parameter\n", limit); env->mem_limit = xlmul(xlset(limit), xlset(1 << 20)); return; } /*********************************************************************** * NAME * * glp_mem_usage - get memory usage information * * SYNOPSIS * * void glp_mem_usage(int *count, int *cpeak, glp_long *total, * glp_long *tpeak); * * DESCRIPTION * * The routine glp_mem_usage reports some information about utilization * of the memory by GLPK routines. Information is stored to locations * specified by corresponding parameters (see below). Any parameter can * be specified as NULL, in which case corresponding information is not * stored. * * *count is the number of the memory blocks currently allocated by the * routines xmalloc and xcalloc (one call to xmalloc or xcalloc results * in allocating one memory block). * * *cpeak is the peak value of *count reached since the initialization * of the GLPK library environment. * * *total is the total amount, in bytes, of the memory blocks currently * allocated by the routines xmalloc and xcalloc. * * *tpeak is the peak value of *total reached since the initialization * of the GLPK library envirionment. */ void glp_mem_usage(int *count, int *cpeak, glp_long *total, glp_long *tpeak) { ENV *env = get_env_ptr(); if (count != NULL) *count = env->mem_count; if (cpeak != NULL) *cpeak = env->mem_cpeak; if (total != NULL) *total = env->mem_total; if (tpeak != NULL) *tpeak = env->mem_tpeak; return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpapi03.c0000644000175000017500000001216511632352620020227 0ustar csheltoncshelton/* glpapi03.c (row and column searching routines) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" /*********************************************************************** * NAME * * glp_create_index - create the name index * * SYNOPSIS * * void glp_create_index(glp_prob *lp); * * DESCRIPTION * * The routine glp_create_index creates the name index for the * specified problem object. The name index is an auxiliary data * structure, which is intended to quickly (i.e. for logarithmic time) * find rows and columns by their names. * * This routine can be called at any time. If the name index already * exists, the routine does nothing. */ void glp_create_index(glp_prob *lp) { GLPROW *row; GLPCOL *col; int i, j; /* create row name index */ if (lp->r_tree == NULL) { lp->r_tree = avl_create_tree(avl_strcmp, NULL); for (i = 1; i <= lp->m; i++) { row = lp->row[i]; xassert(row->node == NULL); if (row->name != NULL) { row->node = avl_insert_node(lp->r_tree, row->name); avl_set_node_link(row->node, row); } } } /* create column name index */ if (lp->c_tree == NULL) { lp->c_tree = avl_create_tree(avl_strcmp, NULL); for (j = 1; j <= lp->n; j++) { col = lp->col[j]; xassert(col->node == NULL); if (col->name != NULL) { col->node = avl_insert_node(lp->c_tree, col->name); avl_set_node_link(col->node, col); } } } return; } /*********************************************************************** * NAME * * glp_find_row - find row by its name * * SYNOPSIS * * int glp_find_row(glp_prob *lp, const char *name); * * RETURNS * * The routine glp_find_row returns the ordinal number of a row, * which is assigned (by the routine glp_set_row_name) the specified * symbolic name. If no such row exists, the routine returns 0. */ int glp_find_row(glp_prob *lp, const char *name) { AVLNODE *node; int i = 0; if (lp->r_tree == NULL) xerror("glp_find_row: row name index does not exist\n"); if (!(name == NULL || name[0] == '\0' || strlen(name) > 255)) { node = avl_find_node(lp->r_tree, name); if (node != NULL) i = ((GLPROW *)avl_get_node_link(node))->i; } return i; } /*********************************************************************** * NAME * * glp_find_col - find column by its name * * SYNOPSIS * * int glp_find_col(glp_prob *lp, const char *name); * * RETURNS * * The routine glp_find_col returns the ordinal number of a column, * which is assigned (by the routine glp_set_col_name) the specified * symbolic name. If no such column exists, the routine returns 0. */ int glp_find_col(glp_prob *lp, const char *name) { AVLNODE *node; int j = 0; if (lp->c_tree == NULL) xerror("glp_find_col: column name index does not exist\n"); if (!(name == NULL || name[0] == '\0' || strlen(name) > 255)) { node = avl_find_node(lp->c_tree, name); if (node != NULL) j = ((GLPCOL *)avl_get_node_link(node))->j; } return j; } /*********************************************************************** * NAME * * glp_delete_index - delete the name index * * SYNOPSIS * * void glp_delete_index(glp_prob *lp); * * DESCRIPTION * * The routine glp_delete_index deletes the name index previously * created by the routine glp_create_index and frees the memory * allocated to this auxiliary data structure. * * This routine can be called at any time. If the name index does not * exist, the routine does nothing. */ void glp_delete_index(glp_prob *lp) { int i, j; /* delete row name index */ if (lp->r_tree != NULL) { for (i = 1; i <= lp->m; i++) lp->row[i]->node = NULL; avl_delete_tree(lp->r_tree), lp->r_tree = NULL; } /* delete column name index */ if (lp->c_tree != NULL) { for (j = 1; j <= lp->n; j++) lp->col[j]->node = NULL; avl_delete_tree(lp->c_tree), lp->c_tree = NULL; } return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpdmp.c0000644000175000017500000001713511632352620020075 0ustar csheltoncshelton/* glpdmp.c (dynamic memory pool) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpdmp.h" #if 1 /* 29/VIII-2008 */ /* some processors need data to be properly aligned; the macro align_datasize enlarges the specified size of a data item to provide a proper alignment of immediately following data */ #define align_datasize(size) ((((size) + 7) / 8) * 8) /* 8 bytes is sufficient in both 32- and 64-bit environments */ #endif #ifdef GLP_DEBUG struct info { DMP *pool; int size; }; #endif /*********************************************************************** * NAME * * dmp_create_pool - create dynamic memory pool * * SYNOPSIS * * #include "glpdmp.h" * DMP *dmp_create_pool(void); * * DESCRIPTION * * The routine dmp_create_pool creates a dynamic memory pool. * * RETURNS * * The routine returns a pointer to the memory pool created. */ DMP *dmp_create_pool(void) { DMP *pool; int k; #ifdef GLP_DEBUG xprintf("dmp_create_pool: warning: debug mode enabled\n"); #endif pool = xmalloc(sizeof(DMP)); #if 0 pool->size = 0; #endif for (k = 0; k <= 31; k++) pool->avail[k] = NULL; pool->block = NULL; pool->used = DMP_BLK_SIZE; pool->count.lo = pool->count.hi = 0; return pool; } /*********************************************************************** * NAME * * dmp_get_atom - get free atom from dynamic memory pool * * SYNOPSIS * * #include "glpdmp.h" * void *dmp_get_atom(DMP *pool, int size); * * DESCRIPTION * * The routine dmp_get_atom obtains a free atom (memory block) from the * specified memory pool. * * The parameter size is the atom size, in bytes, 1 <= size <= 256. * * Note that the free atom contains arbitrary data, not binary zeros. * * RETURNS * * The routine returns a pointer to the free atom obtained. */ void *dmp_get_atom(DMP *pool, int size) { void *atom; int k; #ifdef GLP_DEBUG int orig_size = size; #endif if (!(1 <= size && size <= 256)) xerror("dmp_get_atom: size = %d; invalid atom size\n", size); #if 0 if (!(pool->size == 0 || pool->size == size)) xerror("dmp_get_atom: size = %d; wrong atom size\n", size); #endif /* adjust the size to provide the proper data alignment */ size = align_datasize(size); #ifdef GLP_DEBUG size += align_datasize(sizeof(struct info)); #endif /* adjust the size to make it multiple of 8 bytes, if needed */ size = ((size + 7) / 8) * 8; /* determine the corresponding list of free cells */ k = size / 8 - 1; xassert(0 <= k && k <= 31); /* obtain a free atom */ if (pool->avail[k] == NULL) { /* the list of free cells is empty */ if (pool->used + size > DMP_BLK_SIZE) { /* allocate a new memory block */ void *block = xmalloc(DMP_BLK_SIZE); *(void **)block = pool->block; pool->block = block; pool->used = align_datasize(sizeof(void *)); } /* place the atom in the current memory block */ atom = (char *)pool->block + pool->used; pool->used += size; } else { /* obtain the atom from the list of free cells */ atom = pool->avail[k]; pool->avail[k] = *(void **)atom; } memset(atom, '?', size); /* increase the number of atoms which are currently in use */ pool->count.lo++; if (pool->count.lo == 0) pool->count.hi++; #ifdef GLP_DEBUG ((struct info *)atom)->pool = pool; ((struct info *)atom)->size = orig_size; atom = (char *)atom + align_datasize(sizeof(struct info)); #endif return atom; } /*********************************************************************** * NAME * * dmp_free_atom - return atom to dynamic memory pool * * SYNOPSIS * * #include "glpdmp.h" * void dmp_free_atom(DMP *pool, void *atom, int size); * * DESCRIPTION * * The routine dmp_free_atom returns the specified atom (memory block) * to the specified memory pool, making it free. * * The parameter size is the atom size, in bytes, 1 <= size <= 256. * * Note that the atom can be returned only to the pool, from which it * was obtained, and its size must be exactly the same as on obtaining * it from the pool. */ void dmp_free_atom(DMP *pool, void *atom, int size) { int k; if (!(1 <= size && size <= 256)) xerror("dmp_free_atom: size = %d; invalid atom size\n", size); #if 0 if (!(pool->size == 0 || pool->size == size)) xerror("dmp_free_atom: size = %d; wrong atom size\n", size); #endif if (pool->count.lo == 0 && pool->count.hi == 0) xerror("dmp_free_atom: pool allocation error\n"); #ifdef GLP_DEBUG atom = (char *)atom - align_datasize(sizeof(struct info)); xassert(((struct info *)atom)->pool == pool); xassert(((struct info *)atom)->size == size); #endif /* adjust the size to provide the proper data alignment */ size = align_datasize(size); #ifdef GLP_DEBUG size += align_datasize(sizeof(struct info)); #endif /* adjust the size to make it multiple of 8 bytes, if needed */ size = ((size + 7) / 8) * 8; /* determine the corresponding list of free cells */ k = size / 8 - 1; xassert(0 <= k && k <= 31); /* return the atom to the list of free cells */ *(void **)atom = pool->avail[k]; pool->avail[k] = atom; /* decrease the number of atoms which are currently in use */ pool->count.lo--; if (pool->count.lo == 0xFFFFFFFF) pool->count.hi--; return; } /*********************************************************************** * NAME * * dmp_in_use - determine how many atoms are still in use * * SYNOPSIS * * #include "glpdmp.h" * glp_long dmp_in_use(DMP *pool); * * DESCRIPTION * * The routine dmp_in_use determines how many atoms allocated from the * specified memory pool with the routine dmp_get_atom are still in use, * i.e. not returned to the pool with the routine dmp_free_atom. * * RETURNS * * The routine returns the number of atoms which are still in use. */ glp_long dmp_in_use(DMP *pool) { return pool->count; } /*********************************************************************** * NAME * * dmp_delete_pool - delete dynamic memory pool * * SYNOPSIS * * #include "glpdmp.h" * void dmp_delete_pool(DMP *pool); * * DESCRIPTION * * The routine dmp_delete_pool deletes the specified dynamic memory * pool and frees all the memory allocated to this object. */ void dmp_delete_pool(DMP *pool) { while (pool->block != NULL) { void *block = pool->block; pool->block = *(void **)block; xfree(block); } xfree(pool); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpios01.c0000644000175000017500000015013211632352620020243 0ustar csheltoncshelton/* glpios01.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpios.h" /*********************************************************************** * NAME * * ios_create_tree - create branch-and-bound tree * * SYNOPSIS * * #include "glpios.h" * glp_tree *ios_create_tree(glp_prob *mip, const glp_iocp *parm); * * DESCRIPTION * * The routine ios_create_tree creates the branch-and-bound tree. * * Being created the tree consists of the only root subproblem whose * reference number is 1. Note that initially the root subproblem is in * frozen state and therefore needs to be revived. * * RETURNS * * The routine returns a pointer to the tree created. */ static IOSNPD *new_node(glp_tree *tree, IOSNPD *parent); glp_tree *ios_create_tree(glp_prob *mip, const glp_iocp *parm) { int m = mip->m; int n = mip->n; glp_tree *tree; int i, j; xassert(mip->tree == NULL); mip->tree = tree = xmalloc(sizeof(glp_tree)); tree->pool = dmp_create_pool(); tree->n = n; /* save original problem components */ tree->orig_m = m; tree->orig_type = xcalloc(1+m+n, sizeof(char)); tree->orig_lb = xcalloc(1+m+n, sizeof(double)); tree->orig_ub = xcalloc(1+m+n, sizeof(double)); tree->orig_stat = xcalloc(1+m+n, sizeof(char)); tree->orig_prim = xcalloc(1+m+n, sizeof(double)); tree->orig_dual = xcalloc(1+m+n, sizeof(double)); for (i = 1; i <= m; i++) { GLPROW *row = mip->row[i]; tree->orig_type[i] = (char)row->type; tree->orig_lb[i] = row->lb; tree->orig_ub[i] = row->ub; tree->orig_stat[i] = (char)row->stat; tree->orig_prim[i] = row->prim; tree->orig_dual[i] = row->dual; } for (j = 1; j <= n; j++) { GLPCOL *col = mip->col[j]; tree->orig_type[m+j] = (char)col->type; tree->orig_lb[m+j] = col->lb; tree->orig_ub[m+j] = col->ub; tree->orig_stat[m+j] = (char)col->stat; tree->orig_prim[m+j] = col->prim; tree->orig_dual[m+j] = col->dual; } tree->orig_obj = mip->obj_val; /* initialize the branch-and-bound tree */ tree->nslots = 0; tree->avail = 0; tree->slot = NULL; tree->head = tree->tail = NULL; tree->a_cnt = tree->n_cnt = tree->t_cnt = 0; /* the root subproblem is not solved yet, so its final components are unknown so far */ tree->root_m = 0; tree->root_type = NULL; tree->root_lb = tree->root_ub = NULL; tree->root_stat = NULL; /* the current subproblem does not exist yet */ tree->curr = NULL; tree->mip = mip; /*tree->solved = 0;*/ tree->non_int = xcalloc(1+n, sizeof(char)); memset(&tree->non_int[1], 0, n); /* arrays to save parent subproblem components will be allocated later */ tree->pred_m = tree->pred_max = 0; tree->pred_type = NULL; tree->pred_lb = tree->pred_ub = NULL; tree->pred_stat = NULL; /* cut generator */ tree->local = ios_create_pool(tree); /*tree->first_attempt = 1;*/ /*tree->max_added_cuts = 0;*/ /*tree->min_eff = 0.0;*/ /*tree->miss = 0;*/ /*tree->just_selected = 0;*/ tree->mir_gen = NULL; tree->clq_gen = NULL; /*tree->round = 0;*/ #if 0 /* create the conflict graph */ tree->n_ref = xcalloc(1+n, sizeof(int)); memset(&tree->n_ref[1], 0, n * sizeof(int)); tree->c_ref = xcalloc(1+n, sizeof(int)); memset(&tree->c_ref[1], 0, n * sizeof(int)); tree->g = scg_create_graph(0); tree->j_ref = xcalloc(1+tree->g->n_max, sizeof(int)); #endif /* pseudocost branching */ tree->pcost = NULL; tree->iwrk = xcalloc(1+n, sizeof(int)); tree->dwrk = xcalloc(1+n, sizeof(double)); /* initialize control parameters */ tree->parm = parm; tree->tm_beg = xtime(); tree->tm_lag = xlset(0); tree->sol_cnt = 0; /* initialize advanced solver interface */ tree->reason = 0; tree->reopt = 0; tree->reinv = 0; tree->br_var = 0; tree->br_sel = 0; tree->child = 0; tree->next_p = 0; /*tree->btrack = NULL;*/ tree->stop = 0; /* create the root subproblem, which initially is identical to the original MIP */ new_node(tree, NULL); return tree; } /*********************************************************************** * NAME * * ios_revive_node - revive specified subproblem * * SYNOPSIS * * #include "glpios.h" * void ios_revive_node(glp_tree *tree, int p); * * DESCRIPTION * * The routine ios_revive_node revives the specified subproblem, whose * reference number is p, and thereby makes it the current subproblem. * Note that the specified subproblem must be active. Besides, if the * current subproblem already exists, it must be frozen before reviving * another subproblem. */ void ios_revive_node(glp_tree *tree, int p) { glp_prob *mip = tree->mip; IOSNPD *node, *root; /* obtain pointer to the specified subproblem */ xassert(1 <= p && p <= tree->nslots); node = tree->slot[p].node; xassert(node != NULL); /* the specified subproblem must be active */ xassert(node->count == 0); /* the current subproblem must not exist */ xassert(tree->curr == NULL); /* the specified subproblem becomes current */ tree->curr = node; /*tree->solved = 0;*/ /* obtain pointer to the root subproblem */ root = tree->slot[1].node; xassert(root != NULL); /* at this point problem object components correspond to the root subproblem, so if the root subproblem should be revived, there is nothing more to do */ if (node == root) goto done; xassert(mip->m == tree->root_m); /* build path from the root to the current node */ node->temp = NULL; for (node = node; node != NULL; node = node->up) { if (node->up == NULL) xassert(node == root); else node->up->temp = node; } /* go down from the root to the current node and make necessary changes to restore components of the current subproblem */ for (node = root; node != NULL; node = node->temp) { int m = mip->m; int n = mip->n; /* if the current node is reached, the problem object at this point corresponds to its parent, so save attributes of rows and columns for the parent subproblem */ if (node->temp == NULL) { int i, j; tree->pred_m = m; /* allocate/reallocate arrays, if necessary */ if (tree->pred_max < m + n) { int new_size = m + n + 100; if (tree->pred_type != NULL) xfree(tree->pred_type); if (tree->pred_lb != NULL) xfree(tree->pred_lb); if (tree->pred_ub != NULL) xfree(tree->pred_ub); if (tree->pred_stat != NULL) xfree(tree->pred_stat); tree->pred_max = new_size; tree->pred_type = xcalloc(1+new_size, sizeof(char)); tree->pred_lb = xcalloc(1+new_size, sizeof(double)); tree->pred_ub = xcalloc(1+new_size, sizeof(double)); tree->pred_stat = xcalloc(1+new_size, sizeof(char)); } /* save row attributes */ for (i = 1; i <= m; i++) { GLPROW *row = mip->row[i]; tree->pred_type[i] = (char)row->type; tree->pred_lb[i] = row->lb; tree->pred_ub[i] = row->ub; tree->pred_stat[i] = (char)row->stat; } /* save column attributes */ for (j = 1; j <= n; j++) { GLPCOL *col = mip->col[j]; tree->pred_type[mip->m+j] = (char)col->type; tree->pred_lb[mip->m+j] = col->lb; tree->pred_ub[mip->m+j] = col->ub; tree->pred_stat[mip->m+j] = (char)col->stat; } } /* change bounds of rows and columns */ { IOSBND *b; for (b = node->b_ptr; b != NULL; b = b->next) { if (b->k <= m) glp_set_row_bnds(mip, b->k, b->type, b->lb, b->ub); else glp_set_col_bnds(mip, b->k-m, b->type, b->lb, b->ub); } } /* change statuses of rows and columns */ { IOSTAT *s; for (s = node->s_ptr; s != NULL; s = s->next) { if (s->k <= m) glp_set_row_stat(mip, s->k, s->stat); else glp_set_col_stat(mip, s->k-m, s->stat); } } /* add new rows */ if (node->r_ptr != NULL) { IOSROW *r; IOSAIJ *a; int i, len, *ind; double *val; ind = xcalloc(1+n, sizeof(int)); val = xcalloc(1+n, sizeof(double)); for (r = node->r_ptr; r != NULL; r = r->next) { i = glp_add_rows(mip, 1); glp_set_row_name(mip, i, r->name); #if 1 /* 20/IX-2008 */ xassert(mip->row[i]->level == 0); mip->row[i]->level = node->level; mip->row[i]->origin = r->origin; mip->row[i]->klass = r->klass; #endif glp_set_row_bnds(mip, i, r->type, r->lb, r->ub); len = 0; for (a = r->ptr; a != NULL; a = a->next) len++, ind[len] = a->j, val[len] = a->val; glp_set_mat_row(mip, i, len, ind, val); glp_set_rii(mip, i, r->rii); glp_set_row_stat(mip, i, r->stat); } xfree(ind); xfree(val); } #if 0 /* add new edges to the conflict graph */ /* add new cliques to the conflict graph */ /* (not implemented yet) */ xassert(node->own_nn == 0); xassert(node->own_nc == 0); xassert(node->e_ptr == NULL); #endif } /* the specified subproblem has been revived */ node = tree->curr; /* delete its bound change list */ while (node->b_ptr != NULL) { IOSBND *b; b = node->b_ptr; node->b_ptr = b->next; dmp_free_atom(tree->pool, b, sizeof(IOSBND)); } /* delete its status change list */ while (node->s_ptr != NULL) { IOSTAT *s; s = node->s_ptr; node->s_ptr = s->next; dmp_free_atom(tree->pool, s, sizeof(IOSTAT)); } #if 1 /* 20/XI-2009 */ /* delete its row addition list (additional rows may appear, for example, due to branching on GUB constraints */ while (node->r_ptr != NULL) { IOSROW *r; r = node->r_ptr; node->r_ptr = r->next; xassert(r->name == NULL); while (r->ptr != NULL) { IOSAIJ *a; a = r->ptr; r->ptr = a->next; dmp_free_atom(tree->pool, a, sizeof(IOSAIJ)); } dmp_free_atom(tree->pool, r, sizeof(IOSROW)); } #endif done: return; } /*********************************************************************** * NAME * * ios_freeze_node - freeze current subproblem * * SYNOPSIS * * #include "glpios.h" * void ios_freeze_node(glp_tree *tree); * * DESCRIPTION * * The routine ios_freeze_node freezes the current subproblem. */ void ios_freeze_node(glp_tree *tree) { glp_prob *mip = tree->mip; int m = mip->m; int n = mip->n; IOSNPD *node; /* obtain pointer to the current subproblem */ node = tree->curr; xassert(node != NULL); if (node->up == NULL) { /* freeze the root subproblem */ int k; xassert(node->p == 1); xassert(tree->root_m == 0); xassert(tree->root_type == NULL); xassert(tree->root_lb == NULL); xassert(tree->root_ub == NULL); xassert(tree->root_stat == NULL); tree->root_m = m; tree->root_type = xcalloc(1+m+n, sizeof(char)); tree->root_lb = xcalloc(1+m+n, sizeof(double)); tree->root_ub = xcalloc(1+m+n, sizeof(double)); tree->root_stat = xcalloc(1+m+n, sizeof(char)); for (k = 1; k <= m+n; k++) { if (k <= m) { GLPROW *row = mip->row[k]; tree->root_type[k] = (char)row->type; tree->root_lb[k] = row->lb; tree->root_ub[k] = row->ub; tree->root_stat[k] = (char)row->stat; } else { GLPCOL *col = mip->col[k-m]; tree->root_type[k] = (char)col->type; tree->root_lb[k] = col->lb; tree->root_ub[k] = col->ub; tree->root_stat[k] = (char)col->stat; } } } else { /* freeze non-root subproblem */ int root_m = tree->root_m; int pred_m = tree->pred_m; int i, j, k; xassert(pred_m <= m); /* build change lists for rows and columns which exist in the parent subproblem */ xassert(node->b_ptr == NULL); xassert(node->s_ptr == NULL); for (k = 1; k <= pred_m + n; k++) { int pred_type, pred_stat, type, stat; double pred_lb, pred_ub, lb, ub; /* determine attributes in the parent subproblem */ pred_type = tree->pred_type[k]; pred_lb = tree->pred_lb[k]; pred_ub = tree->pred_ub[k]; pred_stat = tree->pred_stat[k]; /* determine attributes in the current subproblem */ if (k <= pred_m) { GLPROW *row = mip->row[k]; type = row->type; lb = row->lb; ub = row->ub; stat = row->stat; } else { GLPCOL *col = mip->col[k - pred_m]; type = col->type; lb = col->lb; ub = col->ub; stat = col->stat; } /* save type and bounds of a row/column, if changed */ if (!(pred_type == type && pred_lb == lb && pred_ub == ub)) { IOSBND *b; b = dmp_get_atom(tree->pool, sizeof(IOSBND)); b->k = k; b->type = (unsigned char)type; b->lb = lb; b->ub = ub; b->next = node->b_ptr; node->b_ptr = b; } /* save status of a row/column, if changed */ if (pred_stat != stat) { IOSTAT *s; s = dmp_get_atom(tree->pool, sizeof(IOSTAT)); s->k = k; s->stat = (unsigned char)stat; s->next = node->s_ptr; node->s_ptr = s; } } /* save new rows added to the current subproblem */ xassert(node->r_ptr == NULL); if (pred_m < m) { int i, len, *ind; double *val; ind = xcalloc(1+n, sizeof(int)); val = xcalloc(1+n, sizeof(double)); for (i = m; i > pred_m; i--) { GLPROW *row = mip->row[i]; IOSROW *r; const char *name; r = dmp_get_atom(tree->pool, sizeof(IOSROW)); name = glp_get_row_name(mip, i); if (name == NULL) r->name = NULL; else { r->name = dmp_get_atom(tree->pool, strlen(name)+1); strcpy(r->name, name); } #if 1 /* 20/IX-2008 */ r->origin = row->origin; r->klass = row->klass; #endif r->type = (unsigned char)row->type; r->lb = row->lb; r->ub = row->ub; r->ptr = NULL; len = glp_get_mat_row(mip, i, ind, val); for (k = 1; k <= len; k++) { IOSAIJ *a; a = dmp_get_atom(tree->pool, sizeof(IOSAIJ)); a->j = ind[k]; a->val = val[k]; a->next = r->ptr; r->ptr = a; } r->rii = row->rii; r->stat = (unsigned char)row->stat; r->next = node->r_ptr; node->r_ptr = r; } xfree(ind); xfree(val); } /* remove all rows missing in the root subproblem */ if (m != root_m) { int nrs, *num; nrs = m - root_m; xassert(nrs > 0); num = xcalloc(1+nrs, sizeof(int)); for (i = 1; i <= nrs; i++) num[i] = root_m + i; glp_del_rows(mip, nrs, num); xfree(num); } m = mip->m; /* and restore attributes of all rows and columns for the root subproblem */ xassert(m == root_m); for (i = 1; i <= m; i++) { glp_set_row_bnds(mip, i, tree->root_type[i], tree->root_lb[i], tree->root_ub[i]); glp_set_row_stat(mip, i, tree->root_stat[i]); } for (j = 1; j <= n; j++) { glp_set_col_bnds(mip, j, tree->root_type[m+j], tree->root_lb[m+j], tree->root_ub[m+j]); glp_set_col_stat(mip, j, tree->root_stat[m+j]); } #if 1 /* remove all edges and cliques missing in the conflict graph for the root subproblem */ /* (not implemented yet) */ #endif } /* the current subproblem has been frozen */ tree->curr = NULL; return; } /*********************************************************************** * NAME * * ios_clone_node - clone specified subproblem * * SYNOPSIS * * #include "glpios.h" * void ios_clone_node(glp_tree *tree, int p, int nnn, int ref[]); * * DESCRIPTION * * The routine ios_clone_node clones the specified subproblem, whose * reference number is p, creating its nnn exact copies. Note that the * specified subproblem must be active and must be in the frozen state * (i.e. it must not be the current subproblem). * * Each clone, an exact copy of the specified subproblem, becomes a new * active subproblem added to the end of the active list. After cloning * the specified subproblem becomes inactive. * * The reference numbers of clone subproblems are stored to locations * ref[1], ..., ref[nnn]. */ static int get_slot(glp_tree *tree) { int p; /* if no free slots are available, increase the room */ if (tree->avail == 0) { int nslots = tree->nslots; IOSLOT *save = tree->slot; if (nslots == 0) tree->nslots = 20; else { tree->nslots = nslots + nslots; xassert(tree->nslots > nslots); } tree->slot = xcalloc(1+tree->nslots, sizeof(IOSLOT)); if (save != NULL) { memcpy(&tree->slot[1], &save[1], nslots * sizeof(IOSLOT)); xfree(save); } /* push more free slots into the stack */ for (p = tree->nslots; p > nslots; p--) { tree->slot[p].node = NULL; tree->slot[p].next = tree->avail; tree->avail = p; } } /* pull a free slot from the stack */ p = tree->avail; tree->avail = tree->slot[p].next; xassert(tree->slot[p].node == NULL); tree->slot[p].next = 0; return p; } static IOSNPD *new_node(glp_tree *tree, IOSNPD *parent) { IOSNPD *node; int p; /* pull a free slot for the new node */ p = get_slot(tree); /* create descriptor of the new subproblem */ node = dmp_get_atom(tree->pool, sizeof(IOSNPD)); tree->slot[p].node = node; node->p = p; node->up = parent; node->level = (parent == NULL ? 0 : parent->level + 1); node->count = 0; node->b_ptr = NULL; node->s_ptr = NULL; node->r_ptr = NULL; node->solved = 0; #if 0 node->own_nn = node->own_nc = 0; node->e_ptr = NULL; #endif #if 1 /* 04/X-2008 */ node->lp_obj = (parent == NULL ? (tree->mip->dir == GLP_MIN ? -DBL_MAX : +DBL_MAX) : parent->lp_obj); #endif node->bound = (parent == NULL ? (tree->mip->dir == GLP_MIN ? -DBL_MAX : +DBL_MAX) : parent->bound); node->br_var = 0; node->br_val = 0.0; node->ii_cnt = 0; node->ii_sum = 0.0; #if 1 /* 30/XI-2009 */ node->changed = 0; #endif if (tree->parm->cb_size == 0) node->data = NULL; else { node->data = dmp_get_atom(tree->pool, tree->parm->cb_size); memset(node->data, 0, tree->parm->cb_size); } node->temp = NULL; node->prev = tree->tail; node->next = NULL; /* add the new subproblem to the end of the active list */ if (tree->head == NULL) tree->head = node; else tree->tail->next = node; tree->tail = node; tree->a_cnt++; tree->n_cnt++; tree->t_cnt++; /* increase the number of child subproblems */ if (parent == NULL) xassert(p == 1); else parent->count++; return node; } void ios_clone_node(glp_tree *tree, int p, int nnn, int ref[]) { IOSNPD *node; int k; /* obtain pointer to the subproblem to be cloned */ xassert(1 <= p && p <= tree->nslots); node = tree->slot[p].node; xassert(node != NULL); /* the specified subproblem must be active */ xassert(node->count == 0); /* and must be in the frozen state */ xassert(tree->curr != node); /* remove the specified subproblem from the active list, because it becomes inactive */ if (node->prev == NULL) tree->head = node->next; else node->prev->next = node->next; if (node->next == NULL) tree->tail = node->prev; else node->next->prev = node->prev; node->prev = node->next = NULL; tree->a_cnt--; /* create clone subproblems */ xassert(nnn > 0); for (k = 1; k <= nnn; k++) ref[k] = new_node(tree, node)->p; return; } /*********************************************************************** * NAME * * ios_delete_node - delete specified subproblem * * SYNOPSIS * * #include "glpios.h" * void ios_delete_node(glp_tree *tree, int p); * * DESCRIPTION * * The routine ios_delete_node deletes the specified subproblem, whose * reference number is p. The subproblem must be active and must be in * the frozen state (i.e. it must not be the current subproblem). * * Note that deletion is performed recursively, i.e. if a subproblem to * be deleted is the only child of its parent, the parent subproblem is * also deleted, etc. */ void ios_delete_node(glp_tree *tree, int p) { IOSNPD *node, *temp; /* obtain pointer to the subproblem to be deleted */ xassert(1 <= p && p <= tree->nslots); node = tree->slot[p].node; xassert(node != NULL); /* the specified subproblem must be active */ xassert(node->count == 0); /* and must be in the frozen state */ xassert(tree->curr != node); /* remove the specified subproblem from the active list, because it is gone from the tree */ if (node->prev == NULL) tree->head = node->next; else node->prev->next = node->next; if (node->next == NULL) tree->tail = node->prev; else node->next->prev = node->prev; node->prev = node->next = NULL; tree->a_cnt--; loop: /* recursive deletion starts here */ /* delete the bound change list */ { IOSBND *b; while (node->b_ptr != NULL) { b = node->b_ptr; node->b_ptr = b->next; dmp_free_atom(tree->pool, b, sizeof(IOSBND)); } } /* delete the status change list */ { IOSTAT *s; while (node->s_ptr != NULL) { s = node->s_ptr; node->s_ptr = s->next; dmp_free_atom(tree->pool, s, sizeof(IOSTAT)); } } /* delete the row addition list */ while (node->r_ptr != NULL) { IOSROW *r; r = node->r_ptr; if (r->name != NULL) dmp_free_atom(tree->pool, r->name, strlen(r->name)+1); while (r->ptr != NULL) { IOSAIJ *a; a = r->ptr; r->ptr = a->next; dmp_free_atom(tree->pool, a, sizeof(IOSAIJ)); } node->r_ptr = r->next; dmp_free_atom(tree->pool, r, sizeof(IOSROW)); } #if 0 /* delete the edge addition list */ /* delete the clique addition list */ /* (not implemented yet) */ xassert(node->own_nn == 0); xassert(node->own_nc == 0); xassert(node->e_ptr == NULL); #endif /* free application-specific data */ if (tree->parm->cb_size == 0) xassert(node->data == NULL); else dmp_free_atom(tree->pool, node->data, tree->parm->cb_size); /* free the corresponding node slot */ p = node->p; xassert(tree->slot[p].node == node); tree->slot[p].node = NULL; tree->slot[p].next = tree->avail; tree->avail = p; /* save pointer to the parent subproblem */ temp = node->up; /* delete the subproblem descriptor */ dmp_free_atom(tree->pool, node, sizeof(IOSNPD)); tree->n_cnt--; /* take pointer to the parent subproblem */ node = temp; if (node != NULL) { /* the parent subproblem exists; decrease the number of its child subproblems */ xassert(node->count > 0); node->count--; /* if now the parent subproblem has no childs, it also must be deleted */ if (node->count == 0) goto loop; } return; } /*********************************************************************** * NAME * * ios_delete_tree - delete branch-and-bound tree * * SYNOPSIS * * #include "glpios.h" * void ios_delete_tree(glp_tree *tree); * * DESCRIPTION * * The routine ios_delete_tree deletes the branch-and-bound tree, which * the parameter tree points to, and frees all the memory allocated to * this program object. * * On exit components of the problem object are restored to correspond * to the original MIP passed to the routine ios_create_tree. */ void ios_delete_tree(glp_tree *tree) { glp_prob *mip = tree->mip; int i, j; int m = mip->m; int n = mip->n; xassert(mip->tree == tree); /* remove all additional rows */ if (m != tree->orig_m) { int nrs, *num; nrs = m - tree->orig_m; xassert(nrs > 0); num = xcalloc(1+nrs, sizeof(int)); for (i = 1; i <= nrs; i++) num[i] = tree->orig_m + i; glp_del_rows(mip, nrs, num); xfree(num); } m = tree->orig_m; /* restore original attributes of rows and columns */ xassert(m == tree->orig_m); xassert(n == tree->n); for (i = 1; i <= m; i++) { glp_set_row_bnds(mip, i, tree->orig_type[i], tree->orig_lb[i], tree->orig_ub[i]); glp_set_row_stat(mip, i, tree->orig_stat[i]); mip->row[i]->prim = tree->orig_prim[i]; mip->row[i]->dual = tree->orig_dual[i]; } for (j = 1; j <= n; j++) { glp_set_col_bnds(mip, j, tree->orig_type[m+j], tree->orig_lb[m+j], tree->orig_ub[m+j]); glp_set_col_stat(mip, j, tree->orig_stat[m+j]); mip->col[j]->prim = tree->orig_prim[m+j]; mip->col[j]->dual = tree->orig_dual[m+j]; } mip->pbs_stat = mip->dbs_stat = GLP_FEAS; mip->obj_val = tree->orig_obj; /* delete the branch-and-bound tree */ xassert(tree->local != NULL); ios_delete_pool(tree, tree->local); dmp_delete_pool(tree->pool); xfree(tree->orig_type); xfree(tree->orig_lb); xfree(tree->orig_ub); xfree(tree->orig_stat); xfree(tree->orig_prim); xfree(tree->orig_dual); xfree(tree->slot); if (tree->root_type != NULL) xfree(tree->root_type); if (tree->root_lb != NULL) xfree(tree->root_lb); if (tree->root_ub != NULL) xfree(tree->root_ub); if (tree->root_stat != NULL) xfree(tree->root_stat); xfree(tree->non_int); #if 0 xfree(tree->n_ref); xfree(tree->c_ref); xfree(tree->j_ref); #endif if (tree->pcost != NULL) ios_pcost_free(tree); xfree(tree->iwrk); xfree(tree->dwrk); #if 0 scg_delete_graph(tree->g); #endif if (tree->pred_type != NULL) xfree(tree->pred_type); if (tree->pred_lb != NULL) xfree(tree->pred_lb); if (tree->pred_ub != NULL) xfree(tree->pred_ub); if (tree->pred_stat != NULL) xfree(tree->pred_stat); #if 0 xassert(tree->cut_gen == NULL); #endif xassert(tree->mir_gen == NULL); xassert(tree->clq_gen == NULL); xfree(tree); mip->tree = NULL; return; } /*********************************************************************** * NAME * * ios_eval_degrad - estimate obj. degrad. for down- and up-branches * * SYNOPSIS * * #include "glpios.h" * void ios_eval_degrad(glp_tree *tree, int j, double *dn, double *up); * * DESCRIPTION * * Given optimal basis to LP relaxation of the current subproblem the * routine ios_eval_degrad performs the dual ratio test to compute the * objective values in the adjacent basis for down- and up-branches, * which are stored in locations *dn and *up, assuming that x[j] is a * variable chosen to branch upon. */ void ios_eval_degrad(glp_tree *tree, int j, double *dn, double *up) { glp_prob *mip = tree->mip; int m = mip->m, n = mip->n; int len, kase, k, t, stat; double alfa, beta, gamma, delta, dz; int *ind = tree->iwrk; double *val = tree->dwrk; /* current basis must be optimal */ xassert(glp_get_status(mip) == GLP_OPT); /* basis factorization must exist */ xassert(glp_bf_exists(mip)); /* obtain (fractional) value of x[j] in optimal basic solution to LP relaxation of the current subproblem */ xassert(1 <= j && j <= n); beta = mip->col[j]->prim; /* since the value of x[j] is fractional, it is basic; compute corresponding row of the simplex table */ len = lpx_eval_tab_row(mip, m+j, ind, val); /* kase < 0 means down-branch; kase > 0 means up-branch */ for (kase = -1; kase <= +1; kase += 2) { /* for down-branch we introduce new upper bound floor(beta) for x[j]; similarly, for up-branch we introduce new lower bound ceil(beta) for x[j]; in the current basis this new upper/lower bound is violated, so in the adjacent basis x[j] will leave the basis and go to its new upper/lower bound; we need to know which non-basic variable x[k] should enter the basis to keep dual feasibility */ #if 0 /* 23/XI-2009 */ k = lpx_dual_ratio_test(mip, len, ind, val, kase, 1e-7); #else k = lpx_dual_ratio_test(mip, len, ind, val, kase, 1e-9); #endif /* if no variable has been chosen, current basis being primal infeasible due to the new upper/lower bound of x[j] is dual unbounded, therefore, LP relaxation to corresponding branch has no primal feasible solution */ if (k == 0) { if (mip->dir == GLP_MIN) { if (kase < 0) *dn = +DBL_MAX; else *up = +DBL_MAX; } else if (mip->dir == GLP_MAX) { if (kase < 0) *dn = -DBL_MAX; else *up = -DBL_MAX; } else xassert(mip != mip); continue; } xassert(1 <= k && k <= m+n); /* row of the simplex table corresponding to specified basic variable x[j] is the following: x[j] = ... + alfa * x[k] + ... ; we need to know influence coefficient, alfa, at non-basic variable x[k] chosen with the dual ratio test */ for (t = 1; t <= len; t++) if (ind[t] == k) break; xassert(1 <= t && t <= len); alfa = val[t]; /* determine status and reduced cost of variable x[k] */ if (k <= m) { stat = mip->row[k]->stat; gamma = mip->row[k]->dual; } else { stat = mip->col[k-m]->stat; gamma = mip->col[k-m]->dual; } /* x[k] cannot be basic or fixed non-basic */ xassert(stat == GLP_NL || stat == GLP_NU || stat == GLP_NF); /* if the current basis is dual degenerative, some reduced costs, which are close to zero, may have wrong sign due to round-off errors, so correct the sign of gamma */ if (mip->dir == GLP_MIN) { if (stat == GLP_NL && gamma < 0.0 || stat == GLP_NU && gamma > 0.0 || stat == GLP_NF) gamma = 0.0; } else if (mip->dir == GLP_MAX) { if (stat == GLP_NL && gamma > 0.0 || stat == GLP_NU && gamma < 0.0 || stat == GLP_NF) gamma = 0.0; } else xassert(mip != mip); /* determine the change of x[j] in the adjacent basis: delta x[j] = new x[j] - old x[j] */ delta = (kase < 0 ? floor(beta) : ceil(beta)) - beta; /* compute the change of x[k] in the adjacent basis: delta x[k] = new x[k] - old x[k] = delta x[j] / alfa */ delta /= alfa; /* compute the change of the objective in the adjacent basis: delta z = new z - old z = gamma * delta x[k] */ dz = gamma * delta; if (mip->dir == GLP_MIN) xassert(dz >= 0.0); else if (mip->dir == GLP_MAX) xassert(dz <= 0.0); else xassert(mip != mip); /* compute the new objective value in the adjacent basis: new z = old z + delta z */ if (kase < 0) *dn = mip->obj_val + dz; else *up = mip->obj_val + dz; } /*xprintf("obj = %g; dn = %g; up = %g\n", mip->obj_val, *dn, *up);*/ return; } /*********************************************************************** * NAME * * ios_round_bound - improve local bound by rounding * * SYNOPSIS * * #include "glpios.h" * double ios_round_bound(glp_tree *tree, double bound); * * RETURNS * * For the given local bound for any integer feasible solution to the * current subproblem the routine ios_round_bound returns an improved * local bound for the same integer feasible solution. * * BACKGROUND * * Let the current subproblem has the following objective function: * * z = sum c[j] * x[j] + s >= b, (1) * j in J * * where J = {j: c[j] is non-zero and integer, x[j] is integer}, s is * the sum of terms corresponding to fixed variables, b is an initial * local bound (minimization). * * From (1) it follows that: * * d * sum (c[j] / d) * x[j] + s >= b, (2) * j in J * * or, equivalently, * * sum (c[j] / d) * x[j] >= (b - s) / d = h, (3) * j in J * * where d = gcd(c[j]). Since the left-hand side of (3) is integer, * h = (b - s) / d can be rounded up to the nearest integer: * * h' = ceil(h) = (b' - s) / d, (4) * * that gives an rounded, improved local bound: * * b' = d * h' + s. (5) * * In case of maximization '>=' in (1) should be replaced by '<=' that * leads to the following formula: * * h' = floor(h) = (b' - s) / d, (6) * * which should used in the same way as (4). * * NOTE: If b is a valid local bound for a child of the current * subproblem, b' is also valid for that child subproblem. */ double ios_round_bound(glp_tree *tree, double bound) { glp_prob *mip = tree->mip; int n = mip->n; int d, j, nn, *c = tree->iwrk; double s, h; /* determine c[j] and compute s */ nn = 0, s = mip->c0, d = 0; for (j = 1; j <= n; j++) { GLPCOL *col = mip->col[j]; if (col->coef == 0.0) continue; if (col->type == GLP_FX) { /* fixed variable */ s += col->coef * col->prim; } else { /* non-fixed variable */ if (col->kind != GLP_IV) goto skip; if (col->coef != floor(col->coef)) goto skip; if (fabs(col->coef) <= (double)INT_MAX) c[++nn] = (int)fabs(col->coef); else d = 1; } } /* compute d = gcd(c[1],...c[nn]) */ if (d == 0) { if (nn == 0) goto skip; d = gcdn(nn, c); } xassert(d > 0); /* compute new local bound */ if (mip->dir == GLP_MIN) { if (bound != +DBL_MAX) { h = (bound - s) / (double)d; if (h >= floor(h) + 0.001) { /* round up */ h = ceil(h); /*xprintf("d = %d; old = %g; ", d, bound);*/ bound = (double)d * h + s; /*xprintf("new = %g\n", bound);*/ } } } else if (mip->dir == GLP_MAX) { if (bound != -DBL_MAX) { h = (bound - s) / (double)d; if (h <= ceil(h) - 0.001) { /* round down */ h = floor(h); bound = (double)d * h + s; } } } else xassert(mip != mip); skip: return bound; } /*********************************************************************** * NAME * * ios_is_hopeful - check if subproblem is hopeful * * SYNOPSIS * * #include "glpios.h" * int ios_is_hopeful(glp_tree *tree, double bound); * * DESCRIPTION * * Given the local bound of a subproblem the routine ios_is_hopeful * checks if the subproblem can have an integer optimal solution which * is better than the best one currently known. * * RETURNS * * If the subproblem can have a better integer optimal solution, the * routine returns non-zero; otherwise, if the corresponding branch can * be pruned, the routine returns zero. */ int ios_is_hopeful(glp_tree *tree, double bound) { glp_prob *mip = tree->mip; int ret = 1; double eps; if (mip->mip_stat == GLP_FEAS) { eps = tree->parm->tol_obj * (1.0 + fabs(mip->mip_obj)); switch (mip->dir) { case GLP_MIN: if (bound >= mip->mip_obj - eps) ret = 0; break; case GLP_MAX: if (bound <= mip->mip_obj + eps) ret = 0; break; default: xassert(mip != mip); } } else { switch (mip->dir) { case GLP_MIN: if (bound == +DBL_MAX) ret = 0; break; case GLP_MAX: if (bound == -DBL_MAX) ret = 0; break; default: xassert(mip != mip); } } return ret; } /*********************************************************************** * NAME * * ios_best_node - find active node with best local bound * * SYNOPSIS * * #include "glpios.h" * int ios_best_node(glp_tree *tree); * * DESCRIPTION * * The routine ios_best_node finds an active node whose local bound is * best among other active nodes. * * It is understood that the integer optimal solution of the original * mip problem cannot be better than the best bound, so the best bound * is an lower (minimization) or upper (maximization) global bound for * the original problem. * * RETURNS * * The routine ios_best_node returns the subproblem reference number * for the best node. However, if the tree is empty, it returns zero. */ int ios_best_node(glp_tree *tree) { IOSNPD *node, *best = NULL; switch (tree->mip->dir) { case GLP_MIN: /* minimization */ for (node = tree->head; node != NULL; node = node->next) if (best == NULL || best->bound > node->bound) best = node; break; case GLP_MAX: /* maximization */ for (node = tree->head; node != NULL; node = node->next) if (best == NULL || best->bound < node->bound) best = node; break; default: xassert(tree != tree); } return best == NULL ? 0 : best->p; } /*********************************************************************** * NAME * * ios_relative_gap - compute relative mip gap * * SYNOPSIS * * #include "glpios.h" * double ios_relative_gap(glp_tree *tree); * * DESCRIPTION * * The routine ios_relative_gap computes the relative mip gap using the * formula: * * gap = |best_mip - best_bnd| / (|best_mip| + DBL_EPSILON), * * where best_mip is the best integer feasible solution found so far, * best_bnd is the best (global) bound. If no integer feasible solution * has been found yet, rel_gap is set to DBL_MAX. * * RETURNS * * The routine ios_relative_gap returns the relative mip gap. */ double ios_relative_gap(glp_tree *tree) { glp_prob *mip = tree->mip; int p; double best_mip, best_bnd, gap; if (mip->mip_stat == GLP_FEAS) { best_mip = mip->mip_obj; p = ios_best_node(tree); if (p == 0) { /* the tree is empty */ gap = 0.0; } else { best_bnd = tree->slot[p].node->bound; gap = fabs(best_mip - best_bnd) / (fabs(best_mip) + DBL_EPSILON); } } else { /* no integer feasible solution has been found yet */ gap = DBL_MAX; } return gap; } /*********************************************************************** * NAME * * ios_solve_node - solve LP relaxation of current subproblem * * SYNOPSIS * * #include "glpios.h" * int ios_solve_node(glp_tree *tree); * * DESCRIPTION * * The routine ios_solve_node re-optimizes LP relaxation of the current * subproblem using the dual simplex method. * * RETURNS * * The routine returns the code which is reported by glp_simplex. */ int ios_solve_node(glp_tree *tree) { glp_prob *mip = tree->mip; glp_smcp parm; int ret; /* the current subproblem must exist */ xassert(tree->curr != NULL); /* set some control parameters */ glp_init_smcp(&parm); switch (tree->parm->msg_lev) { case GLP_MSG_OFF: parm.msg_lev = GLP_MSG_OFF; break; case GLP_MSG_ERR: parm.msg_lev = GLP_MSG_ERR; break; case GLP_MSG_ON: case GLP_MSG_ALL: parm.msg_lev = GLP_MSG_ON; break; case GLP_MSG_DBG: parm.msg_lev = GLP_MSG_ALL; break; default: xassert(tree != tree); } parm.meth = GLP_DUALP; if (tree->parm->msg_lev < GLP_MSG_DBG) parm.out_dly = tree->parm->out_dly; else parm.out_dly = 0; /* if the incumbent objective value is already known, use it to prematurely terminate the dual simplex search */ if (mip->mip_stat == GLP_FEAS) { switch (tree->mip->dir) { case GLP_MIN: parm.obj_ul = mip->mip_obj; break; case GLP_MAX: parm.obj_ll = mip->mip_obj; break; default: xassert(mip != mip); } } /* try to solve/re-optimize the LP relaxation */ ret = glp_simplex(mip, &parm); tree->curr->solved++; #if 0 xprintf("ret = %d; status = %d; pbs = %d; dbs = %d; some = %d\n", ret, glp_get_status(mip), mip->pbs_stat, mip->dbs_stat, mip->some); lpx_print_sol(mip, "sol"); #endif return ret; } /**********************************************************************/ IOSPOOL *ios_create_pool(glp_tree *tree) { /* create cut pool */ IOSPOOL *pool; #if 0 pool = dmp_get_atom(tree->pool, sizeof(IOSPOOL)); #else xassert(tree == tree); pool = xmalloc(sizeof(IOSPOOL)); #endif pool->size = 0; pool->head = pool->tail = NULL; pool->ord = 0, pool->curr = NULL; return pool; } int ios_add_row(glp_tree *tree, IOSPOOL *pool, const char *name, int klass, int flags, int len, const int ind[], const double val[], int type, double rhs) { /* add row (constraint) to the cut pool */ IOSCUT *cut; IOSAIJ *aij; int k; xassert(pool != NULL); cut = dmp_get_atom(tree->pool, sizeof(IOSCUT)); if (name == NULL || name[0] == '\0') cut->name = NULL; else { for (k = 0; name[k] != '\0'; k++) { if (k == 256) xerror("glp_ios_add_row: cut name too long\n"); if (iscntrl((unsigned char)name[k])) xerror("glp_ios_add_row: cut name contains invalid chara" "cter(s)\n"); } cut->name = dmp_get_atom(tree->pool, strlen(name)+1); strcpy(cut->name, name); } if (!(0 <= klass && klass <= 255)) xerror("glp_ios_add_row: klass = %d; invalid cut class\n", klass); cut->klass = (unsigned char)klass; if (flags != 0) xerror("glp_ios_add_row: flags = %d; invalid cut flags\n", flags); cut->ptr = NULL; if (!(0 <= len && len <= tree->n)) xerror("glp_ios_add_row: len = %d; invalid cut length\n", len); for (k = 1; k <= len; k++) { aij = dmp_get_atom(tree->pool, sizeof(IOSAIJ)); if (!(1 <= ind[k] && ind[k] <= tree->n)) xerror("glp_ios_add_row: ind[%d] = %d; column index out of " "range\n", k, ind[k]); aij->j = ind[k]; aij->val = val[k]; aij->next = cut->ptr; cut->ptr = aij; } if (!(type == GLP_LO || type == GLP_UP || type == GLP_FX)) xerror("glp_ios_add_row: type = %d; invalid cut type\n", type); cut->type = (unsigned char)type; cut->rhs = rhs; cut->prev = pool->tail; cut->next = NULL; if (cut->prev == NULL) pool->head = cut; else cut->prev->next = cut; pool->tail = cut; pool->size++; return pool->size; } IOSCUT *ios_find_row(IOSPOOL *pool, int i) { /* find row (constraint) in the cut pool */ /* (smart linear search) */ xassert(pool != NULL); xassert(1 <= i && i <= pool->size); if (pool->ord == 0) { xassert(pool->curr == NULL); pool->ord = 1; pool->curr = pool->head; } xassert(pool->curr != NULL); if (i < pool->ord) { if (i < pool->ord - i) { pool->ord = 1; pool->curr = pool->head; while (pool->ord != i) { pool->ord++; xassert(pool->curr != NULL); pool->curr = pool->curr->next; } } else { while (pool->ord != i) { pool->ord--; xassert(pool->curr != NULL); pool->curr = pool->curr->prev; } } } else if (i > pool->ord) { if (i - pool->ord < pool->size - i) { while (pool->ord != i) { pool->ord++; xassert(pool->curr != NULL); pool->curr = pool->curr->next; } } else { pool->ord = pool->size; pool->curr = pool->tail; while (pool->ord != i) { pool->ord--; xassert(pool->curr != NULL); pool->curr = pool->curr->prev; } } } xassert(pool->ord == i); xassert(pool->curr != NULL); return pool->curr; } void ios_del_row(glp_tree *tree, IOSPOOL *pool, int i) { /* remove row (constraint) from the cut pool */ IOSCUT *cut; IOSAIJ *aij; xassert(pool != NULL); if (!(1 <= i && i <= pool->size)) xerror("glp_ios_del_row: i = %d; cut number out of range\n", i); cut = ios_find_row(pool, i); xassert(pool->curr == cut); if (cut->next != NULL) pool->curr = cut->next; else if (cut->prev != NULL) pool->ord--, pool->curr = cut->prev; else pool->ord = 0, pool->curr = NULL; if (cut->name != NULL) dmp_free_atom(tree->pool, cut->name, strlen(cut->name)+1); if (cut->prev == NULL) { xassert(pool->head == cut); pool->head = cut->next; } else { xassert(cut->prev->next == cut); cut->prev->next = cut->next; } if (cut->next == NULL) { xassert(pool->tail == cut); pool->tail = cut->prev; } else { xassert(cut->next->prev == cut); cut->next->prev = cut->prev; } while (cut->ptr != NULL) { aij = cut->ptr; cut->ptr = aij->next; dmp_free_atom(tree->pool, aij, sizeof(IOSAIJ)); } dmp_free_atom(tree->pool, cut, sizeof(IOSCUT)); pool->size--; return; } void ios_clear_pool(glp_tree *tree, IOSPOOL *pool) { /* remove all rows (constraints) from the cut pool */ xassert(pool != NULL); while (pool->head != NULL) { IOSCUT *cut = pool->head; pool->head = cut->next; if (cut->name != NULL) dmp_free_atom(tree->pool, cut->name, strlen(cut->name)+1); while (cut->ptr != NULL) { IOSAIJ *aij = cut->ptr; cut->ptr = aij->next; dmp_free_atom(tree->pool, aij, sizeof(IOSAIJ)); } dmp_free_atom(tree->pool, cut, sizeof(IOSCUT)); } pool->size = 0; pool->head = pool->tail = NULL; pool->ord = 0, pool->curr = NULL; return; } void ios_delete_pool(glp_tree *tree, IOSPOOL *pool) { /* delete cut pool */ xassert(pool != NULL); ios_clear_pool(tree, pool); xfree(pool); return; } /**********************************************************************/ #if 0 static int refer_to_node(glp_tree *tree, int j) { /* determine node number corresponding to binary variable x[j] or its complement */ glp_prob *mip = tree->mip; int n = mip->n; int *ref; if (j > 0) ref = tree->n_ref; else ref = tree->c_ref, j = - j; xassert(1 <= j && j <= n); if (ref[j] == 0) { /* new node is needed */ SCG *g = tree->g; int n_max = g->n_max; ref[j] = scg_add_nodes(g, 1); if (g->n_max > n_max) { int *save = tree->j_ref; tree->j_ref = xcalloc(1+g->n_max, sizeof(int)); memcpy(&tree->j_ref[1], &save[1], g->n * sizeof(int)); xfree(save); } xassert(ref[j] == g->n); tree->j_ref[ref[j]] = j; xassert(tree->curr != NULL); if (tree->curr->level > 0) tree->curr->own_nn++; } return ref[j]; } #endif #if 0 void ios_add_edge(glp_tree *tree, int j1, int j2) { /* add new edge to the conflict graph */ glp_prob *mip = tree->mip; int n = mip->n; SCGRIB *e; int first, i1, i2; xassert(-n <= j1 && j1 <= +n && j1 != 0); xassert(-n <= j2 && j2 <= +n && j2 != 0); xassert(j1 != j2); /* determine number of the first node, which was added for the current subproblem */ xassert(tree->curr != NULL); first = tree->g->n - tree->curr->own_nn + 1; /* determine node numbers for both endpoints */ i1 = refer_to_node(tree, j1); i2 = refer_to_node(tree, j2); /* add edge (i1,i2) to the conflict graph */ e = scg_add_edge(tree->g, i1, i2); /* if the current subproblem is not the root and both endpoints were created on some previous levels, save the edge */ if (tree->curr->level > 0 && i1 < first && i2 < first) { IOSRIB *rib; rib = dmp_get_atom(tree->pool, sizeof(IOSRIB)); rib->j1 = j1; rib->j2 = j2; rib->e = e; rib->next = tree->curr->e_ptr; tree->curr->e_ptr = rib; } return; } #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glplux.h0000644000175000017500000002123011632352620020121 0ustar csheltoncshelton/* glplux.h (LU-factorization, bignum arithmetic) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPLUX_H #define GLPLUX_H #include "glpdmp.h" #include "glpgmp.h" /*---------------------------------------------------------------------- // The structure LUX defines LU-factorization of a square matrix A, // which is the following quartet: // // [A] = (F, V, P, Q), (1) // // where F and V are such matrices that // // A = F * V, (2) // // and P and Q are such permutation matrices that the matrix // // L = P * F * inv(P) (3) // // is lower triangular with unity diagonal, and the matrix // // U = P * V * Q (4) // // is upper triangular. All the matrices have the order n. // // The matrices F and V are stored in row/column-wise sparse format as // row and column linked lists of non-zero elements. Unity elements on // the main diagonal of the matrix F are not stored. Pivot elements of // the matrix V (that correspond to diagonal elements of the matrix U) // are also missing from the row and column lists and stored separately // in an ordinary array. // // The permutation matrices P and Q are stored as ordinary arrays using // both row- and column-like formats. // // The matrices L and U being completely defined by the matrices F, V, // P, and Q are not stored explicitly. // // It is easy to show that the factorization (1)-(3) is some version of // LU-factorization. Indeed, from (3) and (4) it follows that: // // F = inv(P) * L * P, // // V = inv(P) * U * inv(Q), // // and substitution into (2) gives: // // A = F * V = inv(P) * L * U * inv(Q). // // For more details see the program documentation. */ typedef struct LUX LUX; typedef struct LUXELM LUXELM; typedef struct LUXWKA LUXWKA; struct LUX { /* LU-factorization of a square matrix */ int n; /* the order of matrices A, F, V, P, Q */ DMP *pool; /* memory pool for elements of matrices F and V */ LUXELM **F_row; /* LUXELM *F_row[1+n]; */ /* F_row[0] is not used; F_row[i], 1 <= i <= n, is a pointer to the list of elements in i-th row of matrix F (diagonal elements are not stored) */ LUXELM **F_col; /* LUXELM *F_col[1+n]; */ /* F_col[0] is not used; F_col[j], 1 <= j <= n, is a pointer to the list of elements in j-th column of matrix F (diagonal elements are not stored) */ mpq_t *V_piv; /* mpq_t V_piv[1+n]; */ /* V_piv[0] is not used; V_piv[p], 1 <= p <= n, is a pivot element v[p,q] corresponding to a diagonal element u[k,k] of matrix U = P*V*Q (used on k-th elimination step, k = 1, 2, ..., n) */ LUXELM **V_row; /* LUXELM *V_row[1+n]; */ /* V_row[0] is not used; V_row[i], 1 <= i <= n, is a pointer to the list of elements in i-th row of matrix V (except pivot elements) */ LUXELM **V_col; /* LUXELM *V_col[1+n]; */ /* V_col[0] is not used; V_col[j], 1 <= j <= n, is a pointer to the list of elements in j-th column of matrix V (except pivot elements) */ int *P_row; /* int P_row[1+n]; */ /* P_row[0] is not used; P_row[i] = j means that p[i,j] = 1, where p[i,j] is an element of permutation matrix P */ int *P_col; /* int P_col[1+n]; */ /* P_col[0] is not used; P_col[j] = i means that p[i,j] = 1, where p[i,j] is an element of permutation matrix P */ /* if i-th row or column of matrix F is i'-th row or column of matrix L = P*F*inv(P), or if i-th row of matrix V is i'-th row of matrix U = P*V*Q, then P_row[i'] = i and P_col[i] = i' */ int *Q_row; /* int Q_row[1+n]; */ /* Q_row[0] is not used; Q_row[i] = j means that q[i,j] = 1, where q[i,j] is an element of permutation matrix Q */ int *Q_col; /* int Q_col[1+n]; */ /* Q_col[0] is not used; Q_col[j] = i means that q[i,j] = 1, where q[i,j] is an element of permutation matrix Q */ /* if j-th column of matrix V is j'-th column of matrix U = P*V*Q, then Q_row[j] = j' and Q_col[j'] = j */ int rank; /* the (exact) rank of matrices A and V */ }; struct LUXELM { /* element of matrix F or V */ int i; /* row index, 1 <= i <= m */ int j; /* column index, 1 <= j <= n */ mpq_t val; /* numeric (non-zero) element value */ LUXELM *r_prev; /* pointer to previous element in the same row */ LUXELM *r_next; /* pointer to next element in the same row */ LUXELM *c_prev; /* pointer to previous element in the same column */ LUXELM *c_next; /* pointer to next element in the same column */ }; struct LUXWKA { /* working area (used only during factorization) */ /* in order to efficiently implement Markowitz strategy and Duff search technique there are two families {R[0], R[1], ..., R[n]} and {C[0], C[1], ..., C[n]}; member R[k] is a set of active rows of matrix V having k non-zeros, and member C[k] is a set of active columns of matrix V having k non-zeros (in the active submatrix); each set R[k] and C[k] is implemented as a separate doubly linked list */ int *R_len; /* int R_len[1+n]; */ /* R_len[0] is not used; R_len[i], 1 <= i <= n, is the number of non-zero elements in i-th row of matrix V (that is the length of i-th row) */ int *R_head; /* int R_head[1+n]; */ /* R_head[k], 0 <= k <= n, is the number of a first row, which is active and whose length is k */ int *R_prev; /* int R_prev[1+n]; */ /* R_prev[0] is not used; R_prev[i], 1 <= i <= n, is the number of a previous row, which is active and has the same length as i-th row */ int *R_next; /* int R_next[1+n]; */ /* R_prev[0] is not used; R_prev[i], 1 <= i <= n, is the number of a next row, which is active and has the same length as i-th row */ int *C_len; /* int C_len[1+n]; */ /* C_len[0] is not used; C_len[j], 1 <= j <= n, is the number of non-zero elements in j-th column of the active submatrix of matrix V (that is the length of j-th column in the active submatrix) */ int *C_head; /* int C_head[1+n]; */ /* C_head[k], 0 <= k <= n, is the number of a first column, which is active and whose length is k */ int *C_prev; /* int C_prev[1+n]; */ /* C_prev[0] is not used; C_prev[j], 1 <= j <= n, is the number of a previous column, which is active and has the same length as j-th column */ int *C_next; /* int C_next[1+n]; */ /* C_next[0] is not used; C_next[j], 1 <= j <= n, is the number of a next column, which is active and has the same length as j-th column */ }; #define lux_create _glp_lux_create #define lux_decomp _glp_lux_decomp #define lux_f_solve _glp_lux_f_solve #define lux_v_solve _glp_lux_v_solve #define lux_solve _glp_lux_solve #define lux_delete _glp_lux_delete LUX *lux_create(int n); /* create LU-factorization */ int lux_decomp(LUX *lux, int (*col)(void *info, int j, int ind[], mpq_t val[]), void *info); /* compute LU-factorization */ void lux_f_solve(LUX *lux, int tr, mpq_t x[]); /* solve system F*x = b or F'*x = b */ void lux_v_solve(LUX *lux, int tr, mpq_t x[]); /* solve system V*x = b or V'*x = b */ void lux_solve(LUX *lux, int tr, mpq_t x[]); /* solve system A*x = b or A'*x = b */ void lux_delete(LUX *lux); /* delete LU-factorization */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glprng02.c0000644000175000017500000000441511632352620020242 0ustar csheltoncshelton/* glprng02.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpenv.h" #include "glprng.h" #define xfault xerror /*********************************************************************** * NAME * * rng_unif_01 - obtain pseudo-random number in the range [0, 1] * * SYNOPSIS * * #include "glprng.h" * double rng_unif_01(RNG *rand); * * RETURNS * * The routine rng_unif_01 returns a next pseudo-random number which is * uniformly distributed in the range [0, 1]. */ double rng_unif_01(RNG *rand) { double x; x = (double)rng_next_rand(rand) / 2147483647.0; xassert(0.0 <= x && x <= 1.0); return x; } /*********************************************************************** * NAME * * rng_uniform - obtain pseudo-random number in the range [a, b] * * SYNOPSIS * * #include "glprng.h" * double rng_uniform(RNG *rand, double a, double b); * * RETURNS * * The routine rng_uniform returns a next pseudo-random number which is * uniformly distributed in the range [a, b]. */ double rng_uniform(RNG *rand, double a, double b) { double x; if (a >= b) xfault("rng_uniform: a = %g, b = %g; invalid range\n", a, b); x = rng_unif_01(rand); x = a * (1.0 - x) + b * x; xassert(a <= x && x <= b); return x; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpmps.c0000644000175000017500000013325511632352620020116 0ustar csheltoncshelton/* glpmps.c (MPS format routines) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" /*********************************************************************** * NAME * * glp_init_mpscp - initialize MPS format control parameters * * SYNOPSIS * * void glp_init_mpscp(glp_mpscp *parm); * * DESCRIPTION * * The routine glp_init_mpscp initializes control parameters, which are * used by the MPS input/output routines glp_read_mps and glp_write_mps, * with default values. * * Default values of the control parameters are stored in the glp_mpscp * structure, which the parameter parm points to. */ void glp_init_mpscp(glp_mpscp *parm) { parm->blank = '\0'; parm->obj_name = NULL; parm->tol_mps = 1e-12; return; } static void check_parm(const char *func, const glp_mpscp *parm) { /* check control parameters */ if (!(0x00 <= parm->blank && parm->blank <= 0xFF) || !(parm->blank == '\0' || isprint(parm->blank))) xerror("%s: blank = 0x%02X; invalid parameter\n", func, parm->blank); if (!(parm->obj_name == NULL || strlen(parm->obj_name) <= 255)) xerror("%s: obj_name = \"%.12s...\"; parameter too long\n", func, parm->obj_name); if (!(0.0 <= parm->tol_mps && parm->tol_mps < 1.0)) xerror("%s: tol_mps = %g; invalid parameter\n", func, parm->tol_mps); return; } /*********************************************************************** * NAME * * glp_read_mps - read problem data in MPS format * * SYNOPSIS * * int glp_read_mps(glp_prob *P, int fmt, const glp_mpscp *parm, * const char *fname); * * DESCRIPTION * * The routine glp_read_mps reads problem data in MPS format from a * text file. * * The parameter fmt specifies the version of MPS format: * * GLP_MPS_DECK - fixed (ancient) MPS format; * GLP_MPS_FILE - free (modern) MPS format. * * The parameter parm is a pointer to the structure glp_mpscp, which * specifies control parameters used by the routine. If parm is NULL, * the routine uses default settings. * * The character string fname specifies a name of the text file to be * read. * * Note that before reading data the current content of the problem * object is completely erased with the routine glp_erase_prob. * * RETURNS * * If the operation was successful, the routine glp_read_mps returns * zero. Otherwise, it prints an error message and returns non-zero. */ struct csa { /* common storage area */ glp_prob *P; /* pointer to problem object */ int deck; /* MPS format (0 - free, 1 - fixed) */ const glp_mpscp *parm; /* pointer to control parameters */ const char *fname; /* name of input MPS file */ XFILE *fp; /* stream assigned to input MPS file */ jmp_buf jump; /* label for go to in case of error */ int recno; /* current record (card) number */ int recpos; /* current record (card) position */ int c; /* current character */ int fldno; /* current field number */ char field[255+1]; /* current field content */ int w80; /* warning 'record must not be longer than 80 chars' issued */ int wef; /* warning 'extra fields detected beyond field 6' issued */ int obj_row; /* objective row number */ void *work1, *work2, *work3; /* working arrays */ }; static void error(struct csa *csa, const char *fmt, ...) { /* print error message and terminate processing */ va_list arg; xprintf("%s:%d: ", csa->fname, csa->recno); va_start(arg, fmt); xvprintf(fmt, arg); va_end(arg); longjmp(csa->jump, 1); /* no return */ } static void warning(struct csa *csa, const char *fmt, ...) { /* print warning message and continue processing */ va_list arg; xprintf("%s:%d: warning: ", csa->fname, csa->recno); va_start(arg, fmt); xvprintf(fmt, arg); va_end(arg); return; } static void read_char(struct csa *csa) { /* read next character */ int c; if (csa->c == '\n') csa->recno++, csa->recpos = 0; csa->recpos++; read: c = xfgetc(csa->fp); if (c < 0) { if (xferror(csa->fp)) error(csa, "read error - %s\n", xerrmsg()); else if (csa->c == '\n') error(csa, "unexpected end of file\n"); else { warning(csa, "missing final end of line\n"); c = '\n'; } } else if (c == '\n') ; else if (csa->c == '\r') { c = '\r'; goto badc; } else if (csa->deck && c == '\r') { csa->c = '\r'; goto read; } else if (c == ' ') ; else if (isspace(c)) { if (csa->deck) badc: error(csa, "in fixed MPS format white-space character 0x%02" "X is not allowed\n", c); c = ' '; } else if (iscntrl(c)) error(csa, "invalid control character 0x%02X\n", c); if (csa->deck && csa->recpos == 81 && c != '\n' && csa->w80 < 1) { warning(csa, "in fixed MPS format record must not be longer th" "an 80 characters\n"); csa->w80++; } csa->c = c; return; } static int indicator(struct csa *csa, int name) { /* skip comment records and read possible indicator record */ int ret; /* reset current field number */ csa->fldno = 0; loop: /* read the very first character of the next record */ xassert(csa->c == '\n'); read_char(csa); if (csa->c == ' ' || csa->c == '\n') { /* data record */ ret = 0; } else if (csa->c == '*') { /* comment record */ while (csa->c != '\n') read_char(csa); goto loop; } else { /* indicator record */ int len = 0; while (csa->c != ' ' && csa->c != '\n' && len < 12) { csa->field[len++] = (char)csa->c; read_char(csa); } csa->field[len] = '\0'; if (!(strcmp(csa->field, "NAME") == 0 || strcmp(csa->field, "ROWS") == 0 || strcmp(csa->field, "COLUMNS") == 0 || strcmp(csa->field, "RHS") == 0 || strcmp(csa->field, "RANGES") == 0 || strcmp(csa->field, "BOUNDS") == 0 || strcmp(csa->field, "ENDATA") == 0)) error(csa, "invalid indicator record\n"); if (!name) { while (csa->c != '\n') read_char(csa); } ret = 1; } return ret; } static void read_field(struct csa *csa) { /* read next field of the current data record */ csa->fldno++; if (csa->deck) { /* fixed MPS format */ int beg, end, pos; /* determine predefined field positions */ if (csa->fldno == 1) beg = 2, end = 3; else if (csa->fldno == 2) beg = 5, end = 12; else if (csa->fldno == 3) beg = 15, end = 22; else if (csa->fldno == 4) beg = 25, end = 36; else if (csa->fldno == 5) beg = 40, end = 47; else if (csa->fldno == 6) beg = 50, end = 61; else xassert(csa != csa); /* skip blanks preceding the current field */ if (csa->c != '\n') { pos = csa->recpos; while (csa->recpos < beg) { if (csa->c == ' ') ; else if (csa->c == '\n') break; else error(csa, "in fixed MPS format positions %d-%d must " "be blank\n", pos, beg-1); read_char(csa); } } /* skip possible comment beginning in the field 3 or 5 */ if ((csa->fldno == 3 || csa->fldno == 5) && csa->c == '$') { while (csa->c != '\n') read_char(csa); } /* read the current field */ for (pos = beg; pos <= end; pos++) { if (csa->c == '\n') break; csa->field[pos-beg] = (char)csa->c; read_char(csa); } csa->field[pos-beg] = '\0'; strtrim(csa->field); /* skip blanks following the last field */ if (csa->fldno == 6 && csa->c != '\n') { while (csa->recpos <= 72) { if (csa->c == ' ') ; else if (csa->c == '\n') break; else error(csa, "in fixed MPS format positions 62-72 must " "be blank\n"); read_char(csa); } while (csa->c != '\n') read_char(csa); } } else { /* free MPS format */ int len; /* skip blanks preceding the current field */ while (csa->c == ' ') read_char(csa); /* skip possible comment */ if (csa->c == '$') { while (csa->c != '\n') read_char(csa); } /* read the current field */ len = 0; while (!(csa->c == ' ' || csa->c == '\n')) { if (len == 255) error(csa, "length of field %d exceeds 255 characters\n", csa->fldno++); csa->field[len++] = (char)csa->c; read_char(csa); } csa->field[len] = '\0'; /* skip anything following the last field (any extra fields are considered to be comments) */ if (csa->fldno == 6) { while (csa->c == ' ') read_char(csa); if (csa->c != '$' && csa->c != '\n' && csa->wef < 1) { warning(csa, "some extra field(s) detected beyond field " "6; field(s) ignored\n"); csa->wef++; } while (csa->c != '\n') read_char(csa); } } return; } static void patch_name(struct csa *csa, char *name) { /* process embedded blanks in symbolic name */ int blank = csa->parm->blank; if (blank == '\0') { /* remove emedded blanks */ strspx(name); } else { /* replace embedded blanks by specified character */ for (; *name != '\0'; name++) if (*name == ' ') *name = (char)blank; } return; } static double read_number(struct csa *csa) { /* read next field and convert it to floating-point number */ double x; char *s; /* read next field */ read_field(csa); xassert(csa->fldno == 4 || csa->fldno == 6); if (csa->field[0] == '\0') error(csa, "missing numeric value in field %d\n", csa->fldno); /* skip initial spaces of the field */ for (s = csa->field; *s == ' '; s++); /* perform conversion */ if (str2num(s, &x) != 0) error(csa, "cannot convert `%s' to floating-point number\n", s); return x; } static void skip_field(struct csa *csa) { /* read and skip next field (assumed to be blank) */ read_field(csa); if (csa->field[0] != '\0') error(csa, "field %d must be blank\n", csa->fldno); return; } static void read_name(struct csa *csa) { /* read NAME indicator record */ if (!(indicator(csa, 1) && strcmp(csa->field, "NAME") == 0)) error(csa, "missing NAME indicator record\n"); /* this indicator record looks like a data record; simulate that fields 1 and 2 were read */ csa->fldno = 2; /* field 3: model name */ read_field(csa), patch_name(csa, csa->field); if (csa->field[0] == '\0') warning(csa, "missing model name in field 3\n"); else glp_set_prob_name(csa->P, csa->field); /* skip anything following field 3 */ while (csa->c != '\n') read_char(csa); return; } static void read_rows(struct csa *csa) { /* read ROWS section */ int i, type; loop: if (indicator(csa, 0)) goto done; /* field 1: row type */ read_field(csa), strspx(csa->field); if (strcmp(csa->field, "N") == 0) type = GLP_FR; else if (strcmp(csa->field, "G") == 0) type = GLP_LO; else if (strcmp(csa->field, "L") == 0) type = GLP_UP; else if (strcmp(csa->field, "E") == 0) type = GLP_FX; else if (csa->field[0] == '\0') error(csa, "missing row type in field 1\n"); else error(csa, "invalid row type in field 1\n"); /* field 2: row name */ read_field(csa), patch_name(csa, csa->field); if (csa->field[0] == '\0') error(csa, "missing row name in field 2\n"); if (glp_find_row(csa->P, csa->field) != 0) error(csa, "row `%s' multiply specified\n", csa->field); i = glp_add_rows(csa->P, 1); glp_set_row_name(csa->P, i, csa->field); glp_set_row_bnds(csa->P, i, type, 0.0, 0.0); /* fields 3, 4, 5, and 6 must be blank */ skip_field(csa); skip_field(csa); skip_field(csa); skip_field(csa); goto loop; done: return; } static void read_columns(struct csa *csa) { /* read COLUMNS section */ int i, j, f, len, kind = GLP_CV, *ind; double aij, *val; char name[255+1], *flag; /* allocate working arrays */ csa->work1 = ind = xcalloc(1+csa->P->m, sizeof(int)); csa->work2 = val = xcalloc(1+csa->P->m, sizeof(double)); csa->work3 = flag = xcalloc(1+csa->P->m, sizeof(char)); memset(&flag[1], 0, csa->P->m); /* no current column exists */ j = 0, len = 0; loop: if (indicator(csa, 0)) goto done; /* field 1 must be blank */ if (csa->deck) { read_field(csa); if (csa->field[0] != '\0') error(csa, "field 1 must be blank\n"); } else csa->fldno++; /* field 2: column or kind name */ read_field(csa), patch_name(csa, csa->field); strcpy(name, csa->field); /* field 3: row name or keyword 'MARKER' */ read_field(csa), patch_name(csa, csa->field); if (strcmp(csa->field, "'MARKER'") == 0) { /* process kind data record */ /* field 4 must be blank */ if (csa->deck) { read_field(csa); if (csa->field[0] != '\0') error(csa, "field 4 must be blank\n"); } else csa->fldno++; /* field 5: keyword 'INTORG' or 'INTEND' */ read_field(csa), patch_name(csa, csa->field); if (strcmp(csa->field, "'INTORG'") == 0) kind = GLP_IV; else if (strcmp(csa->field, "'INTEND'") == 0) kind = GLP_CV; else if (csa->field[0] == '\0') error(csa, "missing keyword in field 5\n"); else error(csa, "invalid keyword in field 5\n"); /* field 6 must be blank */ skip_field(csa); goto loop; } /* process column name specified in field 2 */ if (name[0] == '\0') { /* the same column as in previous data record */ if (j == 0) error(csa, "missing column name in field 2\n"); } else if (j != 0 && strcmp(name, csa->P->col[j]->name) == 0) { /* the same column as in previous data record */ xassert(j != 0); } else { /* store the current column */ if (j != 0) { glp_set_mat_col(csa->P, j, len, ind, val); while (len > 0) flag[ind[len--]] = 0; } /* create new column */ if (glp_find_col(csa->P, name) != 0) error(csa, "column `%s' multiply specified\n", name); j = glp_add_cols(csa->P, 1); glp_set_col_name(csa->P, j, name); glp_set_col_kind(csa->P, j, kind); if (kind == GLP_CV) glp_set_col_bnds(csa->P, j, GLP_LO, 0.0, 0.0); else if (kind == GLP_IV) glp_set_col_bnds(csa->P, j, GLP_DB, 0.0, 1.0); else xassert(kind != kind); } /* process fields 3-4 and 5-6 */ for (f = 3; f <= 5; f += 2) { /* field 3 or 5: row name */ if (f == 3) { if (csa->field[0] == '\0') error(csa, "missing row name in field 3\n"); } else { read_field(csa), patch_name(csa, csa->field); if (csa->field[0] == '\0') { /* if field 5 is blank, field 6 also must be blank */ skip_field(csa); continue; } } i = glp_find_row(csa->P, csa->field); if (i == 0) error(csa, "row `%s' not found\n", csa->field); if (flag[i]) error(csa, "duplicate coefficient in row `%s'\n", csa->field); /* field 4 or 6: coefficient value */ aij = read_number(csa); if (fabs(aij) < csa->parm->tol_mps) aij = 0.0; len++, ind[len] = i, val[len] = aij, flag[i] = 1; } goto loop; done: /* store the last column */ if (j != 0) glp_set_mat_col(csa->P, j, len, ind, val); /* free working arrays */ xfree(ind); xfree(val); xfree(flag); csa->work1 = csa->work2 = csa->work3 = NULL; return; } static void read_rhs(struct csa *csa) { /* read RHS section */ int i, f, v, type; double rhs; char name[255+1], *flag; /* allocate working array */ csa->work3 = flag = xcalloc(1+csa->P->m, sizeof(char)); memset(&flag[1], 0, csa->P->m); /* no current RHS vector exists */ v = 0; loop: if (indicator(csa, 0)) goto done; /* field 1 must be blank */ if (csa->deck) { read_field(csa); if (csa->field[0] != '\0') error(csa, "field 1 must be blank\n"); } else csa->fldno++; /* field 2: RHS vector name */ read_field(csa), patch_name(csa, csa->field); if (csa->field[0] == '\0') { /* the same RHS vector as in previous data record */ if (v == 0) { warning(csa, "missing RHS vector name in field 2\n"); goto blnk; } } else if (v != 0 && strcmp(csa->field, name) == 0) { /* the same RHS vector as in previous data record */ xassert(v != 0); } else blnk: { /* new RHS vector */ if (v != 0) error(csa, "multiple RHS vectors not supported\n"); v++; strcpy(name, csa->field); } /* process fields 3-4 and 5-6 */ for (f = 3; f <= 5; f += 2) { /* field 3 or 5: row name */ read_field(csa), patch_name(csa, csa->field); if (csa->field[0] == '\0') { if (f == 3) error(csa, "missing row name in field 3\n"); else { /* if field 5 is blank, field 6 also must be blank */ skip_field(csa); continue; } } i = glp_find_row(csa->P, csa->field); if (i == 0) error(csa, "row `%s' not found\n", csa->field); if (flag[i]) error(csa, "duplicate right-hand side for row `%s'\n", csa->field); /* field 4 or 6: right-hand side value */ rhs = read_number(csa); if (fabs(rhs) < csa->parm->tol_mps) rhs = 0.0; type = csa->P->row[i]->type; if (type == GLP_FR) { if (i == csa->obj_row) glp_set_obj_coef(csa->P, 0, rhs); else if (rhs != 0.0) warning(csa, "non-zero right-hand side for free row `%s'" " ignored\n", csa->P->row[i]->name); } else glp_set_row_bnds(csa->P, i, type, rhs, rhs); flag[i] = 1; } goto loop; done: /* free working array */ xfree(flag); csa->work3 = NULL; return; } static void read_ranges(struct csa *csa) { /* read RANGES section */ int i, f, v, type; double rhs, rng; char name[255+1], *flag; /* allocate working array */ csa->work3 = flag = xcalloc(1+csa->P->m, sizeof(char)); memset(&flag[1], 0, csa->P->m); /* no current RANGES vector exists */ v = 0; loop: if (indicator(csa, 0)) goto done; /* field 1 must be blank */ if (csa->deck) { read_field(csa); if (csa->field[0] != '\0') error(csa, "field 1 must be blank\n"); } else csa->fldno++; /* field 2: RANGES vector name */ read_field(csa), patch_name(csa, csa->field); if (csa->field[0] == '\0') { /* the same RANGES vector as in previous data record */ if (v == 0) { warning(csa, "missing RANGES vector name in field 2\n"); goto blnk; } } else if (v != 0 && strcmp(csa->field, name) == 0) { /* the same RANGES vector as in previous data record */ xassert(v != 0); } else blnk: { /* new RANGES vector */ if (v != 0) error(csa, "multiple RANGES vectors not supported\n"); v++; strcpy(name, csa->field); } /* process fields 3-4 and 5-6 */ for (f = 3; f <= 5; f += 2) { /* field 3 or 5: row name */ read_field(csa), patch_name(csa, csa->field); if (csa->field[0] == '\0') { if (f == 3) error(csa, "missing row name in field 3\n"); else { /* if field 5 is blank, field 6 also must be blank */ skip_field(csa); continue; } } i = glp_find_row(csa->P, csa->field); if (i == 0) error(csa, "row `%s' not found\n", csa->field); if (flag[i]) error(csa, "duplicate range for row `%s'\n", csa->field); /* field 4 or 6: range value */ rng = read_number(csa); if (fabs(rng) < csa->parm->tol_mps) rng = 0.0; type = csa->P->row[i]->type; if (type == GLP_FR) warning(csa, "range for free row `%s' ignored\n", csa->P->row[i]->name); else if (type == GLP_LO) { rhs = csa->P->row[i]->lb; glp_set_row_bnds(csa->P, i, rhs == 0.0 ? GLP_FX : GLP_DB, rhs, rhs + fabs(rng)); } else if (type == GLP_UP) { rhs = csa->P->row[i]->ub; glp_set_row_bnds(csa->P, i, rhs == 0.0 ? GLP_FX : GLP_DB, rhs - fabs(rng), rhs); } else if (type == GLP_FX) { rhs = csa->P->row[i]->lb; if (rng > 0.0) glp_set_row_bnds(csa->P, i, GLP_DB, rhs, rhs + rng); else if (rng < 0.0) glp_set_row_bnds(csa->P, i, GLP_DB, rhs + rng, rhs); } else xassert(type != type); flag[i] = 1; } goto loop; done: /* free working array */ xfree(flag); csa->work3 = NULL; return; } static void read_bounds(struct csa *csa) { /* read BOUNDS section */ GLPCOL *col; int j, v, mask, data; double bnd, lb, ub; char type[2+1], name[255+1], *flag; /* allocate working array */ csa->work3 = flag = xcalloc(1+csa->P->n, sizeof(char)); memset(&flag[1], 0, csa->P->n); /* no current BOUNDS vector exists */ v = 0; loop: if (indicator(csa, 0)) goto done; /* field 1: bound type */ read_field(csa); if (strcmp(csa->field, "LO") == 0) mask = 0x01, data = 1; else if (strcmp(csa->field, "UP") == 0) mask = 0x10, data = 1; else if (strcmp(csa->field, "FX") == 0) mask = 0x11, data = 1; else if (strcmp(csa->field, "FR") == 0) mask = 0x11, data = 0; else if (strcmp(csa->field, "MI") == 0) mask = 0x01, data = 0; else if (strcmp(csa->field, "PL") == 0) mask = 0x10, data = 0; else if (strcmp(csa->field, "LI") == 0) mask = 0x01, data = 1; else if (strcmp(csa->field, "UI") == 0) mask = 0x10, data = 1; else if (strcmp(csa->field, "BV") == 0) mask = 0x11, data = 0; else if (csa->field[0] == '\0') error(csa, "missing bound type in field 1\n"); else error(csa, "invalid bound type in field 1\n"); strcpy(type, csa->field); /* field 2: BOUNDS vector name */ read_field(csa), patch_name(csa, csa->field); if (csa->field[0] == '\0') { /* the same BOUNDS vector as in previous data record */ if (v == 0) { warning(csa, "missing BOUNDS vector name in field 2\n"); goto blnk; } } else if (v != 0 && strcmp(csa->field, name) == 0) { /* the same BOUNDS vector as in previous data record */ xassert(v != 0); } else blnk: { /* new BOUNDS vector */ if (v != 0) error(csa, "multiple BOUNDS vectors not supported\n"); v++; strcpy(name, csa->field); } /* field 3: column name */ read_field(csa), patch_name(csa, csa->field); if (csa->field[0] == '\0') error(csa, "missing column name in field 3\n"); j = glp_find_col(csa->P, csa->field); if (j == 0) error(csa, "column `%s' not found\n", csa->field); if ((flag[j] & mask) == 0x01) error(csa, "duplicate lower bound for column `%s'\n", csa->field); if ((flag[j] & mask) == 0x10) error(csa, "duplicate upper bound for column `%s'\n", csa->field); xassert((flag[j] & mask) == 0x00); /* field 4: bound value */ if (data) { bnd = read_number(csa); if (fabs(bnd) < csa->parm->tol_mps) bnd = 0.0; } else read_field(csa), bnd = 0.0; /* get current column bounds */ col = csa->P->col[j]; if (col->type == GLP_FR) lb = -DBL_MAX, ub = +DBL_MAX; else if (col->type == GLP_LO) lb = col->lb, ub = +DBL_MAX; else if (col->type == GLP_UP) lb = -DBL_MAX, ub = col->ub; else if (col->type == GLP_DB) lb = col->lb, ub = col->ub; else if (col->type == GLP_FX) lb = ub = col->lb; else xassert(col != col); /* change column bounds */ if (strcmp(type, "LO") == 0) lb = bnd; else if (strcmp(type, "UP") == 0) ub = bnd; else if (strcmp(type, "FX") == 0) lb = ub = bnd; else if (strcmp(type, "FR") == 0) lb = -DBL_MAX, ub = +DBL_MAX; else if (strcmp(type, "MI") == 0) lb = -DBL_MAX; else if (strcmp(type, "PL") == 0) ub = +DBL_MAX; else if (strcmp(type, "LI") == 0) { glp_set_col_kind(csa->P, j, GLP_IV); lb = ceil(bnd); } else if (strcmp(type, "UI") == 0) { glp_set_col_kind(csa->P, j, GLP_IV); ub = floor(bnd); } else if (strcmp(type, "BV") == 0) { glp_set_col_kind(csa->P, j, GLP_IV); lb = 0.0, ub = 1.0; } else xassert(type != type); /* set new column bounds */ if (lb == -DBL_MAX && ub == +DBL_MAX) glp_set_col_bnds(csa->P, j, GLP_FR, lb, ub); else if (ub == +DBL_MAX) glp_set_col_bnds(csa->P, j, GLP_LO, lb, ub); else if (lb == -DBL_MAX) glp_set_col_bnds(csa->P, j, GLP_UP, lb, ub); else if (lb != ub) glp_set_col_bnds(csa->P, j, GLP_DB, lb, ub); else glp_set_col_bnds(csa->P, j, GLP_FX, lb, ub); flag[j] |= (char)mask; /* fields 5 and 6 must be blank */ skip_field(csa); skip_field(csa); goto loop; done: /* free working array */ xfree(flag); csa->work3 = NULL; return; } int glp_read_mps(glp_prob *P, int fmt, const glp_mpscp *parm, const char *fname) { /* read problem data in MPS format */ glp_mpscp _parm; struct csa _csa, *csa = &_csa; int ret; xprintf("Reading problem data from `%s'...\n", fname); if (!(fmt == GLP_MPS_DECK || fmt == GLP_MPS_FILE)) xerror("glp_read_mps: fmt = %d; invalid parameter\n", fmt); if (parm == NULL) glp_init_mpscp(&_parm), parm = &_parm; /* check control parameters */ check_parm("glp_read_mps", parm); /* initialize common storage area */ csa->P = P; csa->deck = (fmt == GLP_MPS_DECK); csa->parm = parm; csa->fname = fname; csa->fp = NULL; if (setjmp(csa->jump)) { ret = 1; goto done; } csa->recno = csa->recpos = 0; csa->c = '\n'; csa->fldno = 0; csa->field[0] = '\0'; csa->w80 = csa->wef = 0; csa->obj_row = 0; csa->work1 = csa->work2 = csa->work3 = NULL; /* erase problem object */ glp_erase_prob(P); glp_create_index(P); /* open input MPS file */ csa->fp = xfopen(fname, "r"); if (csa->fp == NULL) { xprintf("Unable to open `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } /* read NAME indicator record */ read_name(csa); if (P->name != NULL) xprintf("Problem: %s\n", P->name); /* read ROWS section */ if (!(indicator(csa, 0) && strcmp(csa->field, "ROWS") == 0)) error(csa, "missing ROWS indicator record\n"); read_rows(csa); /* determine objective row */ if (parm->obj_name == NULL || parm->obj_name[0] == '\0') { /* use the first row of N type */ int i; for (i = 1; i <= P->m; i++) { if (P->row[i]->type == GLP_FR) { csa->obj_row = i; break; } } if (csa->obj_row == 0) warning(csa, "unable to determine objective row\n"); } else { /* use a row with specified name */ int i; for (i = 1; i <= P->m; i++) { xassert(P->row[i]->name != NULL); if (strcmp(parm->obj_name, P->row[i]->name) == 0) { csa->obj_row = i; break; } } if (csa->obj_row == 0) error(csa, "objective row `%s' not found\n", parm->obj_name); } if (csa->obj_row != 0) { glp_set_obj_name(P, P->row[csa->obj_row]->name); xprintf("Objective: %s\n", P->obj); } /* read COLUMNS section */ if (strcmp(csa->field, "COLUMNS") != 0) error(csa, "missing COLUMNS indicator record\n"); read_columns(csa); /* set objective coefficients */ if (csa->obj_row != 0) { GLPAIJ *aij; for (aij = P->row[csa->obj_row]->ptr; aij != NULL; aij = aij->r_next) glp_set_obj_coef(P, aij->col->j, aij->val); } /* read optional RHS section */ if (strcmp(csa->field, "RHS") == 0) read_rhs(csa); /* read optional RANGES section */ if (strcmp(csa->field, "RANGES") == 0) read_ranges(csa); /* read optional BOUNDS section */ if (strcmp(csa->field, "BOUNDS") == 0) read_bounds(csa); /* read ENDATA indicator record */ if (strcmp(csa->field, "ENDATA") != 0) error(csa, "invalid use of %s indicator record\n", csa->field); /* print some statistics */ xprintf("%d row%s, %d column%s, %d non-zero%s\n", P->m, P->m == 1 ? "" : "s", P->n, P->n == 1 ? "" : "s", P->nnz, P->nnz == 1 ? "" : "s"); if (glp_get_num_int(P) > 0) { int ni = glp_get_num_int(P); int nb = glp_get_num_bin(P); if (ni == 1) { if (nb == 0) xprintf("One variable is integer\n"); else xprintf("One variable is binary\n"); } else { xprintf("%d integer variables, ", ni); if (nb == 0) xprintf("none"); else if (nb == 1) xprintf("one"); else if (nb == ni) xprintf("all"); else xprintf("%d", nb); xprintf(" of which %s binary\n", nb == 1 ? "is" : "are"); } } xprintf("%d records were read\n", csa->recno); /* problem data has been successfully read */ glp_delete_index(P); glp_sort_matrix(P); ret = 0; done: if (csa->fp != NULL) xfclose(csa->fp); if (csa->work1 != NULL) xfree(csa->work1); if (csa->work2 != NULL) xfree(csa->work2); if (csa->work3 != NULL) xfree(csa->work3); if (ret != 0) glp_erase_prob(P); return ret; } /*********************************************************************** * NAME * * glp_write_mps - write problem data in MPS format * * SYNOPSIS * * int glp_write_mps(glp_prob *P, int fmt, const glp_mpscp *parm, * const char *fname); * * DESCRIPTION * * The routine glp_write_mps writes problem data in MPS format to a * text file. * * The parameter fmt specifies the version of MPS format: * * GLP_MPS_DECK - fixed (ancient) MPS format; * GLP_MPS_FILE - free (modern) MPS format. * * The parameter parm is a pointer to the structure glp_mpscp, which * specifies control parameters used by the routine. If parm is NULL, * the routine uses default settings. * * The character string fname specifies a name of the text file to be * written. * * RETURNS * * If the operation was successful, the routine glp_read_mps returns * zero. Otherwise, it prints an error message and returns non-zero. */ #define csa csa1 struct csa { /* common storage area */ glp_prob *P; /* pointer to problem object */ int deck; /* MPS format (0 - free, 1 - fixed) */ const glp_mpscp *parm; /* pointer to control parameters */ char field[255+1]; /* field buffer */ }; static char *mps_name(struct csa *csa) { /* make problem name */ char *f; if (csa->P->name == NULL) csa->field[0] = '\0'; else if (csa->deck) { strncpy(csa->field, csa->P->name, 8); csa->field[8] = '\0'; } else strcpy(csa->field, csa->P->name); for (f = csa->field; *f != '\0'; f++) if (*f == ' ') *f = '_'; return csa->field; } static char *row_name(struct csa *csa, int i) { /* make i-th row name */ char *f; xassert(0 <= i && i <= csa->P->m); if (i == 0 || csa->P->row[i]->name == NULL || csa->deck && strlen(csa->P->row[i]->name) > 8) sprintf(csa->field, "R%07d", i); else { strcpy(csa->field, csa->P->row[i]->name); for (f = csa->field; *f != '\0'; f++) if (*f == ' ') *f = '_'; } return csa->field; } static char *col_name(struct csa *csa, int j) { /* make j-th column name */ char *f; xassert(1 <= j && j <= csa->P->n); if (csa->P->col[j]->name == NULL || csa->deck && strlen(csa->P->col[j]->name) > 8) sprintf(csa->field, "C%07d", j); else { strcpy(csa->field, csa->P->col[j]->name); for (f = csa->field; *f != '\0'; f++) if (*f == ' ') *f = '_'; } return csa->field; } static char *mps_numb(struct csa *csa, double val) { /* format floating-point number */ int dig; char *exp; for (dig = 12; dig >= 6; dig--) { if (val != 0.0 && fabs(val) < 0.002) sprintf(csa->field, "%.*E", dig-1, val); else sprintf(csa->field, "%.*G", dig, val); exp = strchr(csa->field, 'E'); if (exp != NULL) sprintf(exp+1, "%d", atoi(exp+1)); if (strlen(csa->field) <= 12) break; } xassert(strlen(csa->field) <= 12); return csa->field; } int glp_write_mps(glp_prob *P, int fmt, const glp_mpscp *parm, const char *fname) { /* write problem data in MPS format */ glp_mpscp _parm; struct csa _csa, *csa = &_csa; XFILE *fp; int out_obj, one_col = 0, empty = 0; int i, j, recno, marker, count, gap, ret; xprintf("Writing problem data to `%s'...\n", fname); if (!(fmt == GLP_MPS_DECK || fmt == GLP_MPS_FILE)) xerror("glp_write_mps: fmt = %d; invalid parameter\n", fmt); if (parm == NULL) glp_init_mpscp(&_parm), parm = &_parm; /* check control parameters */ check_parm("glp_write_mps", parm); /* initialize common storage area */ csa->P = P; csa->deck = (fmt == GLP_MPS_DECK); csa->parm = parm; /* create output MPS file */ fp = xfopen(fname, "w"), recno = 0; if (fp == NULL) { xprintf("Unable to create `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } /* write comment records */ xfprintf(fp, "* %-*s%s\n", P->name == NULL ? 1 : 12, "Problem:", P->name == NULL ? "" : P->name), recno++; xfprintf(fp, "* %-12s%s\n", "Class:", glp_get_num_int(P) == 0 ? "LP" : "MIP"), recno++; xfprintf(fp, "* %-12s%d\n", "Rows:", P->m), recno++; if (glp_get_num_int(P) == 0) xfprintf(fp, "* %-12s%d\n", "Columns:", P->n), recno++; else xfprintf(fp, "* %-12s%d (%d integer, %d binary)\n", "Columns:", P->n, glp_get_num_int(P), glp_get_num_bin(P)), recno++; xfprintf(fp, "* %-12s%d\n", "Non-zeros:", P->nnz), recno++; xfprintf(fp, "* %-12s%s\n", "Format:", csa->deck ? "Fixed MPS" : "Free MPS"), recno++; xfprintf(fp, "*\n", recno++); /* write NAME indicator record */ xfprintf(fp, "NAME%*s%s\n", P->name == NULL ? 0 : csa->deck ? 10 : 1, "", mps_name(csa)), recno++; #if 1 /* determine whether to write the objective row */ out_obj = 1; for (i = 1; i <= P->m; i++) { if (P->row[i]->type == GLP_FR) { out_obj = 0; break; } } #endif /* write ROWS section */ xfprintf(fp, "ROWS\n"), recno++; for (i = (out_obj ? 0 : 1); i <= P->m; i++) { int type; type = (i == 0 ? GLP_FR : P->row[i]->type); if (type == GLP_FR) type = 'N'; else if (type == GLP_LO) type = 'G'; else if (type == GLP_UP) type = 'L'; else if (type == GLP_DB || type == GLP_FX) type = 'E'; else xassert(type != type); xfprintf(fp, " %c%*s%s\n", type, csa->deck ? 2 : 1, "", row_name(csa, i)), recno++; } /* write COLUMNS section */ xfprintf(fp, "COLUMNS\n"), recno++; marker = 0; for (j = 1; j <= P->n; j++) { GLPAIJ cj, *aij; int kind; kind = P->col[j]->kind; if (kind == GLP_CV) { if (marker % 2 == 1) { /* close current integer block */ marker++; xfprintf(fp, "%*sM%07d%*s'MARKER'%*s'INTEND'\n", csa->deck ? 4 : 1, "", marker, csa->deck ? 2 : 1, "", csa->deck ? 17 : 1, ""), recno++; } } else if (kind == GLP_IV) { if (marker % 2 == 0) { /* open new integer block */ marker++; xfprintf(fp, "%*sM%07d%*s'MARKER'%*s'INTORG'\n", csa->deck ? 4 : 1, "", marker, csa->deck ? 2 : 1, "", csa->deck ? 17 : 1, ""), recno++; } } else xassert(kind != kind); if (out_obj && P->col[j]->coef != 0.0) { /* make fake objective coefficient */ aij = &cj; aij->row = NULL; aij->val = P->col[j]->coef; aij->c_next = P->col[j]->ptr; } else aij = P->col[j]->ptr; #if 1 /* FIXME */ if (aij == NULL) { /* empty column */ empty++; xfprintf(fp, "%*s%-*s", csa->deck ? 4 : 1, "", csa->deck ? 8 : 1, col_name(csa, j)); /* we need a row */ xassert(P->m > 0); xfprintf(fp, "%*s%-*s", csa->deck ? 2 : 1, "", csa->deck ? 8 : 1, row_name(csa, 1)); xfprintf(fp, "%*s0%*s$ empty column\n", csa->deck ? 13 : 1, "", csa->deck ? 3 : 1, ""), recno++; } #endif count = 0; for (aij = aij; aij != NULL; aij = aij->c_next) { if (one_col || count % 2 == 0) xfprintf(fp, "%*s%-*s", csa->deck ? 4 : 1, "", csa->deck ? 8 : 1, col_name(csa, j)); gap = (one_col || count % 2 == 0 ? 2 : 3); xfprintf(fp, "%*s%-*s", csa->deck ? gap : 1, "", csa->deck ? 8 : 1, row_name(csa, aij->row == NULL ? 0 : aij->row->i)); xfprintf(fp, "%*s%*s", csa->deck ? 2 : 1, "", csa->deck ? 12 : 1, mps_numb(csa, aij->val)), count++; if (one_col || count % 2 == 0) xfprintf(fp, "\n"), recno++; } if (!(one_col || count % 2 == 0)) xfprintf(fp, "\n"), recno++; } if (marker % 2 == 1) { /* close last integer block */ marker++; xfprintf(fp, "%*sM%07d%*s'MARKER'%*s'INTEND'\n", csa->deck ? 4 : 1, "", marker, csa->deck ? 2 : 1, "", csa->deck ? 17 : 1, ""), recno++; } #if 1 if (empty > 0) xprintf("Warning: problem has %d empty column(s)\n", empty); #endif /* write RHS section */ xfprintf(fp, "RHS\n"), recno++; count = 0; for (i = (out_obj ? 0 : 1); i <= P->m; i++) { int type; double rhs; if (i == 0) rhs = P->c0; else { type = P->row[i]->type; if (type == GLP_FR) rhs = 0.0; else if (type == GLP_LO) rhs = P->row[i]->lb; else if (type == GLP_UP) rhs = P->row[i]->ub; else if (type == GLP_DB || type == GLP_FX) rhs = P->row[i]->lb; else xassert(type != type); } if (rhs != 0.0) { if (one_col || count % 2 == 0) xfprintf(fp, "%*s%-*s", csa->deck ? 4 : 1, "", csa->deck ? 8 : 1, "RHS1"); gap = (one_col || count % 2 == 0 ? 2 : 3); xfprintf(fp, "%*s%-*s", csa->deck ? gap : 1, "", csa->deck ? 8 : 1, row_name(csa, i)); xfprintf(fp, "%*s%*s", csa->deck ? 2 : 1, "", csa->deck ? 12 : 1, mps_numb(csa, rhs)), count++; if (one_col || count % 2 == 0) xfprintf(fp, "\n"), recno++; } } if (!(one_col || count % 2 == 0)) xfprintf(fp, "\n"), recno++; /* write RANGES section */ for (i = P->m; i >= 1; i--) if (P->row[i]->type == GLP_DB) break; if (i == 0) goto bnds; xfprintf(fp, "RANGES\n"), recno++; count = 0; for (i = 1; i <= P->m; i++) { if (P->row[i]->type == GLP_DB) { if (one_col || count % 2 == 0) xfprintf(fp, "%*s%-*s", csa->deck ? 4 : 1, "", csa->deck ? 8 : 1, "RNG1"); gap = (one_col || count % 2 == 0 ? 2 : 3); xfprintf(fp, "%*s%-*s", csa->deck ? gap : 1, "", csa->deck ? 8 : 1, row_name(csa, i)); xfprintf(fp, "%*s%*s", csa->deck ? 2 : 1, "", csa->deck ? 12 : 1, mps_numb(csa, P->row[i]->ub - P->row[i]->lb)), count++; if (one_col || count % 2 == 0) xfprintf(fp, "\n"), recno++; } } if (!(one_col || count % 2 == 0)) xfprintf(fp, "\n"), recno++; bnds: /* write BOUNDS section */ for (j = P->n; j >= 1; j--) if (!(P->col[j]->type == GLP_LO && P->col[j]->lb == 0.0)) break; if (j == 0) goto endt; xfprintf(fp, "BOUNDS\n"), recno++; for (j = 1; j <= P->n; j++) { int type, data[2]; double bnd[2]; char *spec[2]; spec[0] = spec[1] = NULL; type = P->col[j]->type; if (type == GLP_FR) spec[0] = "FR", data[0] = 0; else if (type == GLP_LO) { if (P->col[j]->lb != 0.0) spec[0] = "LO", data[0] = 1, bnd[0] = P->col[j]->lb; if (P->col[j]->kind == GLP_IV) spec[1] = "PL", data[1] = 0; } else if (type == GLP_UP) { spec[0] = "MI", data[0] = 0; spec[1] = "UP", data[1] = 1, bnd[1] = P->col[j]->ub; } else if (type == GLP_DB) { if (P->col[j]->lb != 0.0) spec[0] = "LO", data[0] = 1, bnd[0] = P->col[j]->lb; spec[1] = "UP", data[1] = 1, bnd[1] = P->col[j]->ub; } else if (type == GLP_FX) spec[0] = "FX", data[0] = 1, bnd[0] = P->col[j]->lb; else xassert(type != type); for (i = 0; i <= 1; i++) { if (spec[i] != NULL) { xfprintf(fp, " %s %-*s%*s%-*s", spec[i], csa->deck ? 8 : 1, "BND1", csa->deck ? 2 : 1, "", csa->deck ? 8 : 1, col_name(csa, j)); if (data[i]) xfprintf(fp, "%*s%*s", csa->deck ? 2 : 1, "", csa->deck ? 12 : 1, mps_numb(csa, bnd[i])); xfprintf(fp, "\n"), recno++; } } } endt: /* write ENDATA indicator record */ xfprintf(fp, "ENDATA\n"), recno++; xfflush(fp); if (xferror(fp)) { xprintf("Write error on `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } /* problem data has been successfully written */ xprintf("%d records were written\n", recno); ret = 0; done: if (fp != NULL) xfclose(fp); return ret; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpbfx.c0000644000175000017500000000514111632352620020066 0ustar csheltoncshelton/* glpbfx.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ typedef struct BFX BFX; #define GLPBFX_DEFINED #include "glpbfx.h" #include "glpenv.h" #include "glplux.h" struct BFX { int valid; LUX *lux; }; BFX *bfx_create_binv(void) { /* create factorization of the basis matrix */ BFX *bfx; bfx = xmalloc(sizeof(BFX)); bfx->valid = 0; bfx->lux = NULL; return bfx; } int bfx_factorize(BFX *binv, int m, int (*col)(void *info, int j, int ind[], mpq_t val[]), void *info) { /* compute factorization of the basis matrix */ int ret; xassert(m > 0); if (binv->lux != NULL && binv->lux->n != m) { lux_delete(binv->lux); binv->lux = NULL; } if (binv->lux == NULL) binv->lux = lux_create(m); ret = lux_decomp(binv->lux, col, info); binv->valid = (ret == 0); return ret; } void bfx_ftran(BFX *binv, mpq_t x[], int save) { /* perform forward transformation (FTRAN) */ xassert(binv->valid); lux_solve(binv->lux, 0, x); xassert(save == save); return; } void bfx_btran(BFX *binv, mpq_t x[]) { /* perform backward transformation (BTRAN) */ xassert(binv->valid); lux_solve(binv->lux, 1, x); return; } int bfx_update(BFX *binv, int j) { /* update factorization of the basis matrix */ xassert(binv->valid); xassert(1 <= j && j <= binv->lux->n); return 1; } void bfx_delete_binv(BFX *binv) { /* delete factorization of the basis matrix */ if (binv->lux != NULL) lux_delete(binv->lux); xfree(binv); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpmat.h0000644000175000017500000001646611632352620020111 0ustar csheltoncshelton/* glpmat.h (linear algebra routines) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPMAT_H #define GLPMAT_H /*********************************************************************** * FULL-VECTOR STORAGE * * For a sparse vector x having n elements, ne of which are non-zero, * the full-vector storage format uses two arrays x_ind and x_vec, which * are set up as follows: * * x_ind is an integer array of length [1+ne]. Location x_ind[0] is * not used, and locations x_ind[1], ..., x_ind[ne] contain indices of * non-zero elements in vector x. * * x_vec is a floating-point array of length [1+n]. Location x_vec[0] * is not used, and locations x_vec[1], ..., x_vec[n] contain numeric * values of ALL elements in vector x, including its zero elements. * * Let, for example, the following sparse vector x be given: * * (0, 1, 0, 0, 2, 3, 0, 4) * * Then the arrays are: * * x_ind = { X; 2, 5, 6, 8 } * * x_vec = { X; 0, 1, 0, 0, 2, 3, 0, 4 } * * COMPRESSED-VECTOR STORAGE * * For a sparse vector x having n elements, ne of which are non-zero, * the compressed-vector storage format uses two arrays x_ind and x_vec, * which are set up as follows: * * x_ind is an integer array of length [1+ne]. Location x_ind[0] is * not used, and locations x_ind[1], ..., x_ind[ne] contain indices of * non-zero elements in vector x. * * x_vec is a floating-point array of length [1+ne]. Location x_vec[0] * is not used, and locations x_vec[1], ..., x_vec[ne] contain numeric * values of corresponding non-zero elements in vector x. * * Let, for example, the following sparse vector x be given: * * (0, 1, 0, 0, 2, 3, 0, 4) * * Then the arrays are: * * x_ind = { X; 2, 5, 6, 8 } * * x_vec = { X; 1, 2, 3, 4 } * * STORAGE-BY-ROWS * * For a sparse matrix A, which has m rows, n columns, and ne non-zero * elements the storage-by-rows format uses three arrays A_ptr, A_ind, * and A_val, which are set up as follows: * * A_ptr is an integer array of length [1+m+1] also called "row pointer * array". It contains the relative starting positions of each row of A * in the arrays A_ind and A_val, i.e. element A_ptr[i], 1 <= i <= m, * indicates where row i begins in the arrays A_ind and A_val. If all * elements in row i are zero, then A_ptr[i] = A_ptr[i+1]. Location * A_ptr[0] is not used, location A_ptr[1] must contain 1, and location * A_ptr[m+1] must contain ne+1 that indicates the position after the * last element in the arrays A_ind and A_val. * * A_ind is an integer array of length [1+ne]. Location A_ind[0] is not * used, and locations A_ind[1], ..., A_ind[ne] contain column indices * of (non-zero) elements in matrix A. * * A_val is a floating-point array of length [1+ne]. Location A_val[0] * is not used, and locations A_val[1], ..., A_val[ne] contain numeric * values of non-zero elements in matrix A. * * Non-zero elements of matrix A are stored contiguously, and the rows * of matrix A are stored consecutively from 1 to m in the arrays A_ind * and A_val. The elements in each row of A may be stored in any order * in A_ind and A_val. Note that elements with duplicate column indices * are not allowed. * * Let, for example, the following sparse matrix A be given: * * | 11 . 13 . . . | * | 21 22 . 24 . . | * | . 32 33 . . . | * | . . 43 44 . 46 | * | . . . . . . | * | 61 62 . . . 66 | * * Then the arrays are: * * A_ptr = { X; 1, 3, 6, 8, 11, 11; 14 } * * A_ind = { X; 1, 3; 4, 2, 1; 2, 3; 4, 3, 6; 1, 2, 6 } * * A_val = { X; 11, 13; 24, 22, 21; 32, 33; 44, 43, 46; 61, 62, 66 } * * PERMUTATION MATRICES * * Let P be a permutation matrix of the order n. It is represented as * an integer array P_per of length [1+n+n] as follows: if p[i,j] = 1, * then P_per[i] = j and P_per[n+j] = i. Location P_per[0] is not used. * * Let A' = P*A. If i-th row of A corresponds to i'-th row of A', then * P_per[i'] = i and P_per[n+i] = i'. * * References: * * 1. Gustavson F.G. Some basic techniques for solving sparse systems of * linear equations. In Rose and Willoughby (1972), pp. 41-52. * * 2. Basic Linear Algebra Subprograms Technical (BLAST) Forum Standard. * University of Tennessee (2001). */ #define check_fvs _glp_mat_check_fvs int check_fvs(int n, int nnz, int ind[], double vec[]); /* check sparse vector in full-vector storage format */ #define check_pattern _glp_mat_check_pattern int check_pattern(int m, int n, int A_ptr[], int A_ind[]); /* check pattern of sparse matrix */ #define transpose _glp_mat_transpose void transpose(int m, int n, int A_ptr[], int A_ind[], double A_val[], int AT_ptr[], int AT_ind[], double AT_val[]); /* transpose sparse matrix */ #define adat_symbolic _glp_mat_adat_symbolic int *adat_symbolic(int m, int n, int P_per[], int A_ptr[], int A_ind[], int S_ptr[]); /* compute S = P*A*D*A'*P' (symbolic phase) */ #define adat_numeric _glp_mat_adat_numeric void adat_numeric(int m, int n, int P_per[], int A_ptr[], int A_ind[], double A_val[], double D_diag[], int S_ptr[], int S_ind[], double S_val[], double S_diag[]); /* compute S = P*A*D*A'*P' (numeric phase) */ #define min_degree _glp_mat_min_degree void min_degree(int n, int A_ptr[], int A_ind[], int P_per[]); /* minimum degree ordering */ #define amd_order1 _glp_mat_amd_order1 void amd_order1(int n, int A_ptr[], int A_ind[], int P_per[]); /* approximate minimum degree ordering (AMD) */ #define symamd_ord _glp_mat_symamd_ord void symamd_ord(int n, int A_ptr[], int A_ind[], int P_per[]); /* approximate minimum degree ordering (SYMAMD) */ #define chol_symbolic _glp_mat_chol_symbolic int *chol_symbolic(int n, int A_ptr[], int A_ind[], int U_ptr[]); /* compute Cholesky factorization (symbolic phase) */ #define chol_numeric _glp_mat_chol_numeric int chol_numeric(int n, int A_ptr[], int A_ind[], double A_val[], double A_diag[], int U_ptr[], int U_ind[], double U_val[], double U_diag[]); /* compute Cholesky factorization (numeric phase) */ #define u_solve _glp_mat_u_solve void u_solve(int n, int U_ptr[], int U_ind[], double U_val[], double U_diag[], double x[]); /* solve upper triangular system U*x = b */ #define ut_solve _glp_mat_ut_solve void ut_solve(int n, int U_ptr[], int U_ind[], double U_val[], double U_diag[], double x[]); /* solve lower triangular system U'*x = b */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glphbm.c0000644000175000017500000004557511632352620020074 0ustar csheltoncshelton/* glphbm.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #define _GLPSTD_ERRNO #define _GLPSTD_STDIO #include "glphbm.h" #include "glpenv.h" /*********************************************************************** * NAME * * hbm_read_mat - read sparse matrix in Harwell-Boeing format * * SYNOPSIS * * #include "glphbm.h" * HBM *hbm_read_mat(const char *fname); * * DESCRIPTION * * The routine hbm_read_mat reads a sparse matrix in the Harwell-Boeing * format from a text file whose name is the character string fname. * * Detailed description of the Harwell-Boeing format recognised by this * routine is given in the following report: * * I.S.Duff, R.G.Grimes, J.G.Lewis. User's Guide for the Harwell-Boeing * Sparse Matrix Collection (Release I), TR/PA/92/86, October 1992. * * RETURNS * * If no error occured, the routine hbm_read_mat returns a pointer to * a data structure containing the matrix. In case of error the routine * prints an appropriate error message and returns NULL. */ struct dsa { /* working area used by routine hbm_read_mat */ const char *fname; /* name of input text file */ FILE *fp; /* stream assigned to input text file */ int seqn; /* card sequential number */ char card[80+1]; /* card image buffer */ int fmt_p; /* scale factor */ int fmt_k; /* iterator */ int fmt_f; /* format code */ int fmt_w; /* field width */ int fmt_d; /* number of decimal places after point */ }; /*********************************************************************** * read_card - read next data card * * This routine reads the next 80-column card from the input text file * and stores its image into the character string card. If the card was * read successfully, the routine returns zero, otherwise non-zero. */ static int read_card(struct dsa *dsa) { int k, c; dsa->seqn++; memset(dsa->card, ' ', 80), dsa->card[80] = '\0'; k = 0; for (;;) { c = fgetc(dsa->fp); if (ferror(dsa->fp)) { xprintf("%s:%d: read error - %s\n", dsa->fname, dsa->seqn, strerror(errno)); return 1; } if (feof(dsa->fp)) { if (k == 0) xprintf("%s:%d: unexpected EOF\n", dsa->fname, dsa->seqn); else xprintf("%s:%d: missing final LF\n", dsa->fname, dsa->seqn); return 1; } if (c == '\r') continue; if (c == '\n') break; if (iscntrl(c)) { xprintf("%s:%d: invalid control character 0x%02X\n", dsa->fname, dsa->seqn, c); return 1; } if (k == 80) { xprintf("%s:%d: card image too long\n", dsa->fname, dsa->seqn); return 1; } dsa->card[k++] = (char)c; } return 0; } /*********************************************************************** * scan_int - scan integer value from the current card * * This routine scans an integer value from the current card, where fld * is the name of the field, pos is the position of the field, width is * the width of the field, val points to a location to which the scanned * value should be stored. If the value was scanned successfully, the * routine returns zero, otherwise non-zero. */ static int scan_int(struct dsa *dsa, char *fld, int pos, int width, int *val) { char str[80+1]; xassert(1 <= width && width <= 80); memcpy(str, dsa->card + pos, width), str[width] = '\0'; if (str2int(strspx(str), val)) { xprintf("%s:%d: field `%s' contains invalid value `%s'\n", dsa->fname, dsa->seqn, fld, str); return 1; } return 0; } /*********************************************************************** * parse_fmt - parse Fortran format specification * * This routine parses the Fortran format specification represented as * character string which fmt points to and stores format elements into * appropriate static locations. Should note that not all valid Fortran * format specifications may be recognised. If the format specification * was recognised, the routine returns zero, otherwise non-zero. */ static int parse_fmt(struct dsa *dsa, char *fmt) { int k, s, val; char str[80+1]; /* first character should be left parenthesis */ if (fmt[0] != '(') fail: { xprintf("hbm_read_mat: format `%s' not recognised\n", fmt); return 1; } k = 1; /* optional scale factor */ dsa->fmt_p = 0; if (isdigit((unsigned char)fmt[k])) { s = 0; while (isdigit((unsigned char)fmt[k])) { if (s == 80) goto fail; str[s++] = fmt[k++]; } str[s] = '\0'; if (str2int(str, &val)) goto fail; if (toupper((unsigned char)fmt[k]) != 'P') goto iter; dsa->fmt_p = val, k++; if (!(0 <= dsa->fmt_p && dsa->fmt_p <= 255)) goto fail; /* optional comma may follow scale factor */ if (fmt[k] == ',') k++; } /* optional iterator */ dsa->fmt_k = 1; if (isdigit((unsigned char)fmt[k])) { s = 0; while (isdigit((unsigned char)fmt[k])) { if (s == 80) goto fail; str[s++] = fmt[k++]; } str[s] = '\0'; if (str2int(str, &val)) goto fail; iter: dsa->fmt_k = val; if (!(1 <= dsa->fmt_k && dsa->fmt_k <= 255)) goto fail; } /* format code */ dsa->fmt_f = toupper((unsigned char)fmt[k++]); if (!(dsa->fmt_f == 'D' || dsa->fmt_f == 'E' || dsa->fmt_f == 'F' || dsa->fmt_f == 'G' || dsa->fmt_f == 'I')) goto fail; /* field width */ if (!isdigit((unsigned char)fmt[k])) goto fail; s = 0; while (isdigit((unsigned char)fmt[k])) { if (s == 80) goto fail; str[s++] = fmt[k++]; } str[s] = '\0'; if (str2int(str, &dsa->fmt_w)) goto fail; if (!(1 <= dsa->fmt_w && dsa->fmt_w <= 255)) goto fail; /* optional number of decimal places after point */ dsa->fmt_d = 0; if (fmt[k] == '.') { k++; if (!isdigit((unsigned char)fmt[k])) goto fail; s = 0; while (isdigit((unsigned char)fmt[k])) { if (s == 80) goto fail; str[s++] = fmt[k++]; } str[s] = '\0'; if (str2int(str, &dsa->fmt_d)) goto fail; if (!(0 <= dsa->fmt_d && dsa->fmt_d <= 255)) goto fail; } /* last character should be right parenthesis */ if (!(fmt[k] == ')' && fmt[k+1] == '\0')) goto fail; return 0; } /*********************************************************************** * read_int_array - read array of integer type * * This routine reads an integer array from the input text file, where * name is array name, fmt is Fortran format specification that controls * reading, n is number of array elements, val is array of integer type. * If the array was read successful, the routine returns zero, otherwise * non-zero. */ static int read_int_array(struct dsa *dsa, char *name, char *fmt, int n, int val[]) { int k, pos; char str[80+1]; if (parse_fmt(dsa, fmt)) return 1; if (!(dsa->fmt_f == 'I' && dsa->fmt_w <= 80 && dsa->fmt_k * dsa->fmt_w <= 80)) { xprintf( "%s:%d: can't read array `%s' - invalid format `%s'\n", dsa->fname, dsa->seqn, name, fmt); return 1; } for (k = 1, pos = INT_MAX; k <= n; k++, pos++) { if (pos >= dsa->fmt_k) { if (read_card(dsa)) return 1; pos = 0; } memcpy(str, dsa->card + dsa->fmt_w * pos, dsa->fmt_w); str[dsa->fmt_w] = '\0'; strspx(str); if (str2int(str, &val[k])) { xprintf( "%s:%d: can't read array `%s' - invalid value `%s'\n", dsa->fname, dsa->seqn, name, str); return 1; } } return 0; } /*********************************************************************** * read_real_array - read array of real type * * This routine reads a real array from the input text file, where name * is array name, fmt is Fortran format specification that controls * reading, n is number of array elements, val is array of real type. * If the array was read successful, the routine returns zero, otherwise * non-zero. */ static int read_real_array(struct dsa *dsa, char *name, char *fmt, int n, double val[]) { int k, pos; char str[80+1], *ptr; if (parse_fmt(dsa, fmt)) return 1; if (!(dsa->fmt_f != 'I' && dsa->fmt_w <= 80 && dsa->fmt_k * dsa->fmt_w <= 80)) { xprintf( "%s:%d: can't read array `%s' - invalid format `%s'\n", dsa->fname, dsa->seqn, name, fmt); return 1; } for (k = 1, pos = INT_MAX; k <= n; k++, pos++) { if (pos >= dsa->fmt_k) { if (read_card(dsa)) return 1; pos = 0; } memcpy(str, dsa->card + dsa->fmt_w * pos, dsa->fmt_w); str[dsa->fmt_w] = '\0'; strspx(str); if (strchr(str, '.') == NULL && strcmp(str, "0")) { xprintf("%s(%d): can't read array `%s' - value `%s' has no " "decimal point\n", dsa->fname, dsa->seqn, name, str); return 1; } /* sometimes lower case letters appear */ for (ptr = str; *ptr; ptr++) *ptr = (char)toupper((unsigned char)*ptr); ptr = strchr(str, 'D'); if (ptr != NULL) *ptr = 'E'; /* value may appear with decimal exponent but without letters E or D (for example, -123.456-012), so missing letter should be inserted */ ptr = strchr(str+1, '+'); if (ptr == NULL) ptr = strchr(str+1, '-'); if (ptr != NULL && *(ptr-1) != 'E') { xassert(strlen(str) < 80); memmove(ptr+1, ptr, strlen(ptr)+1); *ptr = 'E'; } if (str2num(str, &val[k])) { xprintf( "%s:%d: can't read array `%s' - invalid value `%s'\n", dsa->fname, dsa->seqn, name, str); return 1; } } return 0; } HBM *hbm_read_mat(const char *fname) { struct dsa _dsa, *dsa = &_dsa; HBM *hbm = NULL; dsa->fname = fname; xprintf("hbm_read_mat: reading matrix from `%s'...\n", dsa->fname); dsa->fp = fopen(dsa->fname, "r"); if (dsa->fp == NULL) { xprintf("hbm_read_mat: unable to open `%s' - %s\n", dsa->fname, strerror(errno)); goto fail; } dsa->seqn = 0; hbm = xmalloc(sizeof(HBM)); memset(hbm, 0, sizeof(HBM)); /* read the first heading card */ if (read_card(dsa)) goto fail; memcpy(hbm->title, dsa->card, 72), hbm->title[72] = '\0'; strtrim(hbm->title); xprintf("%s\n", hbm->title); memcpy(hbm->key, dsa->card+72, 8), hbm->key[8] = '\0'; strspx(hbm->key); xprintf("key = %s\n", hbm->key); /* read the second heading card */ if (read_card(dsa)) goto fail; if (scan_int(dsa, "totcrd", 0, 14, &hbm->totcrd)) goto fail; if (scan_int(dsa, "ptrcrd", 14, 14, &hbm->ptrcrd)) goto fail; if (scan_int(dsa, "indcrd", 28, 14, &hbm->indcrd)) goto fail; if (scan_int(dsa, "valcrd", 42, 14, &hbm->valcrd)) goto fail; if (scan_int(dsa, "rhscrd", 56, 14, &hbm->rhscrd)) goto fail; xprintf("totcrd = %d; ptrcrd = %d; indcrd = %d; valcrd = %d; rhsc" "rd = %d\n", hbm->totcrd, hbm->ptrcrd, hbm->indcrd, hbm->valcrd, hbm->rhscrd); /* read the third heading card */ if (read_card(dsa)) goto fail; memcpy(hbm->mxtype, dsa->card, 3), hbm->mxtype[3] = '\0'; if (strchr("RCP", hbm->mxtype[0]) == NULL || strchr("SUHZR", hbm->mxtype[1]) == NULL || strchr("AE", hbm->mxtype[2]) == NULL) { xprintf("%s:%d: matrix type `%s' not recognised\n", dsa->fname, dsa->seqn, hbm->mxtype); goto fail; } if (scan_int(dsa, "nrow", 14, 14, &hbm->nrow)) goto fail; if (scan_int(dsa, "ncol", 28, 14, &hbm->ncol)) goto fail; if (scan_int(dsa, "nnzero", 42, 14, &hbm->nnzero)) goto fail; if (scan_int(dsa, "neltvl", 56, 14, &hbm->neltvl)) goto fail; xprintf("mxtype = %s; nrow = %d; ncol = %d; nnzero = %d; neltvl =" " %d\n", hbm->mxtype, hbm->nrow, hbm->ncol, hbm->nnzero, hbm->neltvl); /* read the fourth heading card */ if (read_card(dsa)) goto fail; memcpy(hbm->ptrfmt, dsa->card, 16), hbm->ptrfmt[16] = '\0'; strspx(hbm->ptrfmt); memcpy(hbm->indfmt, dsa->card+16, 16), hbm->indfmt[16] = '\0'; strspx(hbm->indfmt); memcpy(hbm->valfmt, dsa->card+32, 20), hbm->valfmt[20] = '\0'; strspx(hbm->valfmt); memcpy(hbm->rhsfmt, dsa->card+52, 20), hbm->rhsfmt[20] = '\0'; strspx(hbm->rhsfmt); xprintf("ptrfmt = %s; indfmt = %s; valfmt = %s; rhsfmt = %s\n", hbm->ptrfmt, hbm->indfmt, hbm->valfmt, hbm->rhsfmt); /* read the fifth heading card (optional) */ if (hbm->rhscrd <= 0) { strcpy(hbm->rhstyp, "???"); hbm->nrhs = 0; hbm->nrhsix = 0; } else { if (read_card(dsa)) goto fail; memcpy(hbm->rhstyp, dsa->card, 3), hbm->rhstyp[3] = '\0'; if (scan_int(dsa, "nrhs", 14, 14, &hbm->nrhs)) goto fail; if (scan_int(dsa, "nrhsix", 28, 14, &hbm->nrhsix)) goto fail; xprintf("rhstyp = `%s'; nrhs = %d; nrhsix = %d\n", hbm->rhstyp, hbm->nrhs, hbm->nrhsix); } /* read matrix structure */ hbm->colptr = xcalloc(1+hbm->ncol+1, sizeof(int)); if (read_int_array(dsa, "colptr", hbm->ptrfmt, hbm->ncol+1, hbm->colptr)) goto fail; hbm->rowind = xcalloc(1+hbm->nnzero, sizeof(int)); if (read_int_array(dsa, "rowind", hbm->indfmt, hbm->nnzero, hbm->rowind)) goto fail; /* read matrix values */ if (hbm->valcrd <= 0) goto done; if (hbm->mxtype[2] == 'A') { /* assembled matrix */ hbm->values = xcalloc(1+hbm->nnzero, sizeof(double)); if (read_real_array(dsa, "values", hbm->valfmt, hbm->nnzero, hbm->values)) goto fail; } else { /* elemental (unassembled) matrix */ hbm->values = xcalloc(1+hbm->neltvl, sizeof(double)); if (read_real_array(dsa, "values", hbm->valfmt, hbm->neltvl, hbm->values)) goto fail; } /* read right-hand sides */ if (hbm->nrhs <= 0) goto done; if (hbm->rhstyp[0] == 'F') { /* dense format */ hbm->nrhsvl = hbm->nrow * hbm->nrhs; hbm->rhsval = xcalloc(1+hbm->nrhsvl, sizeof(double)); if (read_real_array(dsa, "rhsval", hbm->rhsfmt, hbm->nrhsvl, hbm->rhsval)) goto fail; } else if (hbm->rhstyp[0] == 'M' && hbm->mxtype[2] == 'A') { /* sparse format */ /* read pointers */ hbm->rhsptr = xcalloc(1+hbm->nrhs+1, sizeof(int)); if (read_int_array(dsa, "rhsptr", hbm->ptrfmt, hbm->nrhs+1, hbm->rhsptr)) goto fail; /* read sparsity pattern */ hbm->rhsind = xcalloc(1+hbm->nrhsix, sizeof(int)); if (read_int_array(dsa, "rhsind", hbm->indfmt, hbm->nrhsix, hbm->rhsind)) goto fail; /* read values */ hbm->rhsval = xcalloc(1+hbm->nrhsix, sizeof(double)); if (read_real_array(dsa, "rhsval", hbm->rhsfmt, hbm->nrhsix, hbm->rhsval)) goto fail; } else if (hbm->rhstyp[0] == 'M' && hbm->mxtype[2] == 'E') { /* elemental format */ hbm->rhsval = xcalloc(1+hbm->nrhsvl, sizeof(double)); if (read_real_array(dsa, "rhsval", hbm->rhsfmt, hbm->nrhsvl, hbm->rhsval)) goto fail; } else { xprintf("%s:%d: right-hand side type `%c' not recognised\n", dsa->fname, dsa->seqn, hbm->rhstyp[0]); goto fail; } /* read starting guesses */ if (hbm->rhstyp[1] == 'G') { hbm->nguess = hbm->nrow * hbm->nrhs; hbm->sguess = xcalloc(1+hbm->nguess, sizeof(double)); if (read_real_array(dsa, "sguess", hbm->rhsfmt, hbm->nguess, hbm->sguess)) goto fail; } /* read solution vectors */ if (hbm->rhstyp[2] == 'X') { hbm->nexact = hbm->nrow * hbm->nrhs; hbm->xexact = xcalloc(1+hbm->nexact, sizeof(double)); if (read_real_array(dsa, "xexact", hbm->rhsfmt, hbm->nexact, hbm->xexact)) goto fail; } done: /* reading has been completed */ xprintf("hbm_read_mat: %d cards were read\n", dsa->seqn); fclose(dsa->fp); return hbm; fail: /* something wrong in Danish kingdom */ if (hbm != NULL) { if (hbm->colptr != NULL) xfree(hbm->colptr); if (hbm->rowind != NULL) xfree(hbm->rowind); if (hbm->rhsptr != NULL) xfree(hbm->rhsptr); if (hbm->rhsind != NULL) xfree(hbm->rhsind); if (hbm->values != NULL) xfree(hbm->values); if (hbm->rhsval != NULL) xfree(hbm->rhsval); if (hbm->sguess != NULL) xfree(hbm->sguess); if (hbm->xexact != NULL) xfree(hbm->xexact); xfree(hbm); } if (dsa->fp != NULL) fclose(dsa->fp); return NULL; } /*********************************************************************** * NAME * * hbm_free_mat - free sparse matrix in Harwell-Boeing format * * SYNOPSIS * * #include "glphbm.h" * void hbm_free_mat(HBM *hbm); * * DESCRIPTION * * The hbm_free_mat routine frees all the memory allocated to the data * structure containing a sparse matrix in the Harwell-Boeing format. */ void hbm_free_mat(HBM *hbm) { if (hbm->colptr != NULL) xfree(hbm->colptr); if (hbm->rowind != NULL) xfree(hbm->rowind); if (hbm->rhsptr != NULL) xfree(hbm->rhsptr); if (hbm->rhsind != NULL) xfree(hbm->rhsind); if (hbm->values != NULL) xfree(hbm->values); if (hbm->rhsval != NULL) xfree(hbm->rhsval); if (hbm->sguess != NULL) xfree(hbm->sguess); if (hbm->xexact != NULL) xfree(hbm->xexact); xfree(hbm); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpspx.h0000644000175000017500000000266211632352620020133 0ustar csheltoncshelton/* glpspx.h (core simplex solvers) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPSPX_H #define GLPSPX_H #include "glpapi.h" #define spx_primal _glp_spx_primal int spx_primal(glp_prob *lp, const glp_smcp *parm); /* core LP solver based on the primal simplex method */ #define spx_dual _glp_spx_dual int spx_dual(glp_prob *lp, const glp_smcp *parm); /* core LP solver based on the dual simplex method */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glprng01.c0000644000175000017500000001413311632352620020237 0ustar csheltoncshelton/* glprng01.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * This code is a modified version of the module GB_FLIP, a portable * pseudo-random number generator. The original version of GB_FLIP is * a part of The Stanford GraphBase developed by Donald E. Knuth (see * http://www-cs-staff.stanford.edu/~knuth/sgb.html). * * Note that all changes concern only external names, so this modified * version produces exactly the same results as the original version. * * Changes were made by Andrew Makhorin . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpenv.h" #include "glprng.h" #if 0 int A[56] = { -1 }; #else #define A (rand->A) #endif /* pseudo-random values */ #if 0 int *fptr = A; #else #define fptr (rand->fptr) #endif /* the next A value to be exported */ #define mod_diff(x, y) (((x) - (y)) & 0x7FFFFFFF) /* difference modulo 2^31 */ static int flip_cycle(RNG *rand) { /* this is an auxiliary routine to do 55 more steps of the basic recurrence, at high speed, and to reset fptr */ int *ii, *jj; for (ii = &A[1], jj = &A[32]; jj <= &A[55]; ii++, jj++) *ii = mod_diff(*ii, *jj); for (jj = &A[1]; ii <= &A[55]; ii++, jj++) *ii = mod_diff(*ii, *jj); fptr = &A[54]; return A[55]; } /*********************************************************************** * NAME * * rng_create_rand - create pseudo-random number generator * * SYNOPSIS * * #include "glprng.h" * RNG *rng_create_rand(void); * * DESCRIPTION * * The routine rng_create_rand creates and initializes a pseudo-random * number generator. * * RETURNS * * The routine returns a pointer to the generator created. */ RNG *rng_create_rand(void) { RNG *rand; int i; rand = xmalloc(sizeof(RNG)); A[0] = -1; for (i = 1; i <= 55; i++) A[i] = 0; fptr = A; rng_init_rand(rand, 1); return rand; } /*********************************************************************** * NAME * * rng_init_rand - initialize pseudo-random number generator * * SYNOPSIS * * #include "glprng.h" * void rng_init_rand(RNG *rand, int seed); * * DESCRIPTION * * The routine rng_init_rand initializes the pseudo-random number * generator. The parameter seed may be any integer number. Note that * on creating the generator this routine is called with the parameter * seed equal to 1. */ void rng_init_rand(RNG *rand, int seed) { int i; int prev = seed, next = 1; seed = prev = mod_diff(prev, 0); A[55] = prev; for (i = 21; i; i = (i + 21) % 55) { A[i] = next; next = mod_diff(prev, next); if (seed & 1) seed = 0x40000000 + (seed >> 1); else seed >>= 1; next = mod_diff(next, seed); prev = A[i]; } flip_cycle(rand); flip_cycle(rand); flip_cycle(rand); flip_cycle(rand); flip_cycle(rand); return; } /*********************************************************************** * NAME * * rng_next_rand - obtain pseudo-random integer in the range [0, 2^31-1] * * SYNOPSIS * * #include "glprng.h" * int rng_next_rand(RNG *rand); * * RETURNS * * The routine rng_next_rand returns a next pseudo-random integer which * is uniformly distributed between 0 and 2^31-1, inclusive. The period * length of the generated numbers is 2^85 - 2^30. The low order bits of * the generated numbers are just as random as the high-order bits. */ int rng_next_rand(RNG *rand) { return *fptr >= 0 ? *fptr-- : flip_cycle(rand); } /*********************************************************************** * NAME * * rng_unif_rand - obtain pseudo-random integer in the range [0, m-1] * * SYNOPSIS * * #include "glprng.h" * int rng_unif_rand(RNG *rand, int m); * * RETURNS * * The routine rng_unif_rand returns a next pseudo-random integer which * is uniformly distributed between 0 and m-1, inclusive, where m is any * positive integer less than 2^31. */ #define two_to_the_31 ((unsigned int)0x80000000) int rng_unif_rand(RNG *rand, int m) { unsigned int t = two_to_the_31 - (two_to_the_31 % m); int r; xassert(m > 0); do { r = rng_next_rand(rand); } while (t <= (unsigned int)r); return r % m; } /*********************************************************************** * NAME * * rng_delete_rand - delete pseudo-random number generator * * SYNOPSIS * * #include "glprng.h" * void rng_delete_rand(RNG *rand); * * DESCRIPTION * * The routine rng_delete_rand frees all the memory allocated to the * specified pseudo-random number generator. */ void rng_delete_rand(RNG *rand) { xfree(rand); return; } /**********************************************************************/ #if 0 /* To be sure that this modified version produces the same results as the original version, run this validation program. */ int main(void) { RNG *rand; int j; rand = rng_create_rand(); rng_init_rand(rand, -314159); if (rng_next_rand(rand) != 119318998) { fprintf(stderr, "Failure on the first try!\n"); return -1; } for (j = 1; j <= 133; j++) rng_next_rand(rand); if (rng_unif_rand(rand, 0x55555555) != 748103812) { fprintf(stderr, "Failure on the second try!\n"); return -2; } fprintf(stderr, "OK, the random-number generator routines seem to" " work!\n"); rng_delete_rand(rand); return 0; } #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpenv03.c0000644000175000017500000001474711632352620020256 0ustar csheltoncshelton/* glpenv03.c (terminal output) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" /*********************************************************************** * NAME * * glp_printf - write formatted output to terminal * * SYNOPSIS * * void glp_printf(const char *fmt, ...); * * DESCRIPTION * * The routine glp_printf uses the format control string fmt to format * its parameters and writes the formatted output to the terminal. */ void glp_printf(const char *fmt, ...) { va_list arg; va_start(arg, fmt); xvprintf(fmt, arg); va_end(arg); return; } /*********************************************************************** * NAME * * glp_vprintf - write formatted output to terminal * * SYNOPSIS * * void glp_vprintf(const char *fmt, va_list arg); * * DESCRIPTION * * The routine glp_vprintf uses the format control string fmt to format * its parameters specified by the list arg and writes the formatted * output to the terminal. */ void glp_vprintf(const char *fmt, va_list arg) { ENV *env = get_env_ptr(); /* if terminal output is disabled, do nothing */ if (!env->term_out) goto skip; /* format the output */ vsprintf(env->term_buf, fmt, arg); /* pass the output to the user-defined routine */ if (env->term_hook != NULL) { if (env->term_hook(env->term_info, env->term_buf) != 0) goto skip; } /* send the output to the terminal */ fputs(env->term_buf, stdout); fflush(stdout); /* copy the output to the text file */ if (env->tee_file != NULL) { fputs(env->term_buf, env->tee_file); fflush(env->tee_file); } skip: return; } /*********************************************************************** * NAME * * glp_term_out - enable/disable terminal output * * SYNOPSIS * * int glp_term_out(int flag); * * DESCRIPTION * * Depending on the parameter flag the routine glp_term_out enables or * disables terminal output performed by glpk routines: * * GLP_ON - enable terminal output; * GLP_OFF - disable terminal output. * * RETURNS * * The routine glp_term_out returns the previous value of the terminal * output flag. */ int glp_term_out(int flag) { ENV *env = get_env_ptr(); int old = env->term_out; if (!(flag == GLP_ON || flag == GLP_OFF)) xerror("glp_term_out: flag = %d; invalid value\n", flag); env->term_out = flag; return old; } /*********************************************************************** * NAME * * glp_term_hook - install hook to intercept terminal output * * SYNOPSIS * * void glp_term_hook(int (*func)(void *info, const char *s), * void *info); * * DESCRIPTION * * The routine glp_term_hook installs a user-defined hook routine to * intercept all terminal output performed by glpk routines. * * This feature can be used to redirect the terminal output to other * destination, for example to a file or a text window. * * The parameter func specifies the user-defined hook routine. It is * called from an internal printing routine, which passes to it two * parameters: info and s. The parameter info is a transit pointer, * specified in the corresponding call to the routine glp_term_hook; * it may be used to pass some information to the hook routine. The * parameter s is a pointer to the null terminated character string, * which is intended to be written to the terminal. If the hook routine * returns zero, the printing routine writes the string s to the * terminal in a usual way; otherwise, if the hook routine returns * non-zero, no terminal output is performed. * * To uninstall the hook routine the parameters func and info should be * specified as NULL. */ void glp_term_hook(int (*func)(void *info, const char *s), void *info) { ENV *env = get_env_ptr(); if (func == NULL) { env->term_hook = NULL; env->term_info = NULL; } else { env->term_hook = func; env->term_info = info; } return; } /*********************************************************************** * NAME * * glp_open_tee - start copying terminal output to text file * * SYNOPSIS * * int glp_open_tee(const char *fname); * * DESCRIPTION * * The routine glp_open_tee starts copying all the terminal output to * an output text file, whose name is specified by the character string * fname. * * RETURNS * * 0 - operation successful * 1 - copying terminal output is already active * 2 - unable to create output file */ int glp_open_tee(const char *fname) { ENV *env = get_env_ptr(); if (env->tee_file != NULL) { /* copying terminal output is already active */ return 1; } env->tee_file = fopen(fname, "w"); if (env->tee_file == NULL) { /* unable to create output file */ return 2; } return 0; } /*********************************************************************** * NAME * * glp_close_tee - stop copying terminal output to text file * * SYNOPSIS * * int glp_close_tee(void); * * DESCRIPTION * * The routine glp_close_tee stops copying the terminal output to the * output text file previously open by the routine glp_open_tee closing * that file. * * RETURNS * * 0 - operation successful * 1 - copying terminal output was not started */ int glp_close_tee(void) { ENV *env = get_env_ptr(); if (env->tee_file == NULL) { /* copying terminal output was not started */ return 1; } fclose(env->tee_file); env->tee_file = NULL; return 0; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpavl.h0000644000175000017500000001036411632352620020101 0ustar csheltoncshelton/* glpavl.h (binary search tree) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPAVL_H #define GLPAVL_H #include "glpdmp.h" typedef struct AVL AVL; typedef struct AVLNODE AVLNODE; struct AVL { /* AVL tree (Adelson-Velsky & Landis binary search tree) */ DMP *pool; /* memory pool for allocating nodes */ AVLNODE *root; /* pointer to the root node */ int (*fcmp)(void *info, const void *key1, const void *key2); /* application-defined key comparison routine */ void *info; /* transit pointer passed to the routine fcmp */ int size; /* the tree size (the total number of nodes) */ int height; /* the tree height */ }; struct AVLNODE { /* node of AVL tree */ const void *key; /* pointer to the node key (data structure for representing keys is supplied by the application) */ int rank; /* node rank = relative position of the node in its own subtree = the number of nodes in the left subtree plus one */ int type; /* reserved for the application specific information */ void *link; /* reserved for the application specific information */ AVLNODE *up; /* pointer to the parent node */ short int flag; /* node flag: 0 - this node is the left child of its parent (or this node is the root of the tree and has no parent) 1 - this node is the right child of its parent */ short int bal; /* node balance = the difference between heights of the right and left subtrees: -1 - the left subtree is higher than the right one; 0 - the left and right subtrees have the same height; +1 - the left subtree is lower than the right one */ AVLNODE *left; /* pointer to the root of the left subtree */ AVLNODE *right; /* pointer to the root of the right subtree */ }; #define avl_create_tree _glp_avl_create_tree AVL *avl_create_tree(int (*fcmp)(void *info, const void *key1, const void *key2), void *info); /* create AVL tree */ #define avl_strcmp _glp_avl_strcmp int avl_strcmp(void *info, const void *key1, const void *key2); /* compare character string keys */ #define avl_insert_node _glp_avl_insert_node AVLNODE *avl_insert_node(AVL *tree, const void *key); /* insert new node into AVL tree */ #define avl_set_node_type _glp_avl_set_node_type void avl_set_node_type(AVLNODE *node, int type); /* assign the type field of specified node */ #define avl_set_node_link _glp_avl_set_node_link void avl_set_node_link(AVLNODE *node, void *link); /* assign the link field of specified node */ #define avl_find_node _glp_avl_find_node AVLNODE *avl_find_node(AVL *tree, const void *key); /* find node in AVL tree */ #define avl_get_node_type _glp_avl_get_node_type int avl_get_node_type(AVLNODE *node); /* retrieve the type field of specified node */ #define avl_get_node_link _glp_avl_get_node_link void *avl_get_node_link(AVLNODE *node); /* retrieve the link field of specified node */ #define avl_delete_node _glp_avl_delete_node void avl_delete_node(AVL *tree, AVLNODE *node); /* delete specified node from AVL tree */ #define avl_delete_tree _glp_avl_delete_tree void avl_delete_tree(AVL *tree); /* delete AVL tree */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpsdf.c0000644000175000017500000001577111632352620020075 0ustar csheltoncshelton/* glpsdf.c (plain data file reading routines) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #define GLPSDF_H #define GLP_DATA_DEFINED typedef struct glp_data glp_data; #include "glpapi.h" struct glp_data { /* plain data file */ char *fname; /* name of data file */ XFILE *fp; /* stream assigned to data file */ void *jump; /* jmp_buf jump; */ /* label for go to in case of error */ int count; /* line count */ int c; /* current character of XEOF */ char item[255+1]; /* current data item */ }; static void next_char(glp_data *data); glp_data *glp_sdf_open_file(const char *fname) { /* open plain data file */ glp_data *data = NULL; XFILE *fp; jmp_buf jump; fp = xfopen(fname, "r"); if (fp == NULL) { xprintf("Unable to open `%s' - %s\n", fname, xerrmsg()); goto done; } data = xmalloc(sizeof(glp_data)); data->fname = xmalloc(strlen(fname)+1); strcpy(data->fname, fname); data->fp = fp; data->jump = NULL; data->count = 0; data->c = '\n'; data->item[0] = '\0'; /* read the very first character */ if (setjmp(jump)) { glp_sdf_close_file(data); data = NULL; goto done; } data->jump = jump; next_char(data); data->jump = NULL; done: return data; } void glp_sdf_set_jump(glp_data *data, void *jump) { /* set up error handling */ data->jump = jump; return; } void glp_sdf_error(glp_data *data, const char *fmt, ...) { /* print error message */ va_list arg; xprintf("%s:%d: ", data->fname, data->count); va_start(arg, fmt); xvprintf(fmt, arg); va_end(arg); if (data->jump == NULL) xerror(""); else longjmp(data->jump, 1); /* no return */ } void glp_sdf_warning(glp_data *data, const char *fmt, ...) { /* print warning message */ va_list arg; xprintf("%s:%d: warning: ", data->fname, data->count); va_start(arg, fmt); xvprintf(fmt, arg); va_end(arg); return; } static void next_char(glp_data *data) { /* read next character */ int c; if (data->c == XEOF) glp_sdf_error(data, "unexpected end of file\n"); else if (data->c == '\n') data->count++; c = xfgetc(data->fp); if (c < 0) { if (xferror(data->fp)) glp_sdf_error(data, "read error - %s\n", xerrmsg()); else if (data->c == '\n') c = XEOF; else { glp_sdf_warning(data, "missing final end of line\n"); c = '\n'; } } else if (c == '\n') ; else if (isspace(c)) c = ' '; else if (iscntrl(c)) glp_sdf_error(data, "invalid control character 0x%02X\n", c); data->c = c; return; } static void skip_pad(glp_data *data) { /* skip uninteresting characters and comments */ loop: while (data->c == ' ' || data->c == '\n') next_char(data); if (data->c == '/') { next_char(data); if (data->c != '*') glp_sdf_error(data, "invalid use of slash\n"); next_char(data); for (;;) { if (data->c == '*') { next_char(data); if (data->c == '/') { next_char(data); break; } } next_char(data); } goto loop; } return; } static void next_item(glp_data *data) { /* read next item */ int len; skip_pad(data); len = 0; while (!(data->c == ' ' || data->c == '\n')) { data->item[len++] = (char)data->c; if (len == sizeof(data->item)) glp_sdf_error(data, "data item `%.31s...' too long\n", data->item); next_char(data); } data->item[len] = '\0'; return; } int glp_sdf_read_int(glp_data *data) { /* read integer number */ int x; next_item(data); switch (str2int(data->item, &x)) { case 0: break; case 1: glp_sdf_error(data, "integer `%s' out of range\n", data->item); case 2: glp_sdf_error(data, "cannot convert `%s' to integer\n", data->item); default: xassert(data != data); } return x; } double glp_sdf_read_num(glp_data *data) { /* read floating-point number */ double x; next_item(data); switch (str2num(data->item, &x)) { case 0: break; case 1: glp_sdf_error(data, "number `%s' out of range\n", data->item); case 2: glp_sdf_error(data, "cannot convert `%s' to number\n", data->item); default: xassert(data != data); } return x; } const char *glp_sdf_read_item(glp_data *data) { /* read data item */ next_item(data); return data->item; } const char *glp_sdf_read_text(glp_data *data) { /* read text until end of line */ int c, len = 0; for (;;) { c = data->c; next_char(data); if (c == ' ') { /* ignore initial spaces */ if (len == 0) continue; /* and multiple ones */ if (data->item[len-1] == ' ') continue; } else if (c == '\n') { /* remove trailing space */ if (len > 0 && data->item[len-1] == ' ') len--; /* and stop reading */ break; } /* add current character to the buffer */ data->item[len++] = (char)c; if (len == sizeof(data->item)) glp_sdf_error(data, "line too long\n", data->item); } data->item[len] = '\0'; return data->item; } int glp_sdf_line(glp_data *data) { /* determine current line number */ return data->count; } void glp_sdf_close_file(glp_data *data) { /* close plain data file */ xfclose(data->fp); xfree(data->fname); xfree(data); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpenv04.c0000644000175000017500000000751611632352620020253 0ustar csheltoncshelton/* glpenv04.c (error handling) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" /*********************************************************************** * NAME * * glp_error - display error message and terminate execution * * SYNOPSIS * * void glp_error(const char *fmt, ...); * * DESCRIPTION * * The routine glp_error (implemented as a macro) formats its * parameters using the format control string fmt, writes the formatted * message to the terminal, and abnormally terminates the program. */ static void error(const char *fmt, ...) { ENV *env = get_env_ptr(); va_list arg; env->term_out = GLP_ON; va_start(arg, fmt); xvprintf(fmt, arg); va_end(arg); xprintf("Error detected in file %s at line %d\n", env->err_file, env->err_line); if (env->err_hook != NULL) env->err_hook(env->err_info); abort(); exit(EXIT_FAILURE); /* no return */ } _glp_error glp_error_(const char *file, int line) { ENV *env = get_env_ptr(); env->err_file = file; env->err_line = line; return error; } /*********************************************************************** * NAME * * glp_assert - check for logical condition * * SYNOPSIS * * #include "glplib.h" * void glp_assert(int expr); * * DESCRIPTION * * The routine glp_assert (implemented as a macro) checks for a logical * condition specified by the parameter expr. If the condition is false * (i.e. the value of expr is zero), the routine writes a message to * the terminal and abnormally terminates the program. */ void glp_assert_(const char *expr, const char *file, int line) { glp_error_(file, line)("Assertion failed: %s\n", expr); /* no return */ } /*********************************************************************** * NAME * * glp_error_hook - install hook to intercept abnormal termination * * SYNOPSIS * * void glp_error_hook(void (*func)(void *info), void *info); * * DESCRIPTION * * The routine glp_error_hook installs a user-defined hook routine to * intercept abnormal termination. * * The parameter func specifies the user-defined hook routine. It is * called from the routine glp_error before the latter calls the abort * function to abnormally terminate the application program because of * fatal error. The parameter info is a transit pointer, specified in * the corresponding call to the routine glp_error_hook; it may be used * to pass some information to the hook routine. * * To uninstall the hook routine the parameters func and info should be * specified as NULL. */ void glp_error_hook(void (*func)(void *info), void *info) { ENV *env = get_env_ptr(); if (func == NULL) { env->err_hook = NULL; env->err_info = NULL; } else { env->err_hook = func; env->err_info = info; } return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpnet06.c0000644000175000017500000003061411632352620020246 0ustar csheltoncshelton/* glpnet06.c (out-of-kilter algorithm) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpenv.h" #include "glpnet.h" /*********************************************************************** * NAME * * okalg - out-of-kilter algorithm * * SYNOPSIS * * #include "glpnet.h" * int okalg(int nv, int na, const int tail[], const int head[], * const int low[], const int cap[], const int cost[], int x[], * int pi[]); * * DESCRIPTION * * The routine okalg implements the out-of-kilter algorithm to find a * minimal-cost circulation in the specified flow network. * * INPUT PARAMETERS * * nv is the number of nodes, nv >= 0. * * na is the number of arcs, na >= 0. * * tail[a], a = 1,...,na, is the index of tail node of arc a. * * head[a], a = 1,...,na, is the index of head node of arc a. * * low[a], a = 1,...,na, is an lower bound to the flow through arc a. * * cap[a], a = 1,...,na, is an upper bound to the flow through arc a, * which is the capacity of the arc. * * cost[a], a = 1,...,na, is a per-unit cost of the flow through arc a. * * NOTES * * 1. Multiple arcs are allowed, but self-loops are not allowed. * * 2. It is required that 0 <= low[a] <= cap[a] for all arcs. * * 3. Arc costs may have any sign. * * OUTPUT PARAMETERS * * x[a], a = 1,...,na, is optimal value of the flow through arc a. * * pi[i], i = 1,...,nv, is Lagrange multiplier for flow conservation * equality constraint corresponding to node i (the node potential). * * RETURNS * * 0 optimal circulation found; * * 1 there is no feasible circulation; * * 2 integer overflow occured; * * 3 optimality test failed (logic error). * * REFERENCES * * L.R.Ford, Jr., and D.R.Fulkerson, "Flows in Networks," The RAND * Corp., Report R-375-PR (August 1962), Chap. III "Minimal Cost Flow * Problems," pp.113-26. */ static int overflow(int u, int v) { /* check for integer overflow on computing u + v */ if (u > 0 && v > 0 && u + v < 0) return 1; if (u < 0 && v < 0 && u + v > 0) return 1; return 0; } int okalg(int nv, int na, const int tail[], const int head[], const int low[], const int cap[], const int cost[], int x[], int pi[]) { int a, aok, delta, i, j, k, lambda, pos1, pos2, s, t, temp, ret, *ptr, *arc, *link, *list; /* sanity checks */ xassert(nv >= 0); xassert(na >= 0); for (a = 1; a <= na; a++) { i = tail[a], j = head[a]; xassert(1 <= i && i <= nv); xassert(1 <= j && j <= nv); xassert(i != j); xassert(0 <= low[a] && low[a] <= cap[a]); } /* allocate working arrays */ ptr = xcalloc(1+nv+1, sizeof(int)); arc = xcalloc(1+na+na, sizeof(int)); link = xcalloc(1+nv, sizeof(int)); list = xcalloc(1+nv, sizeof(int)); /* ptr[i] := (degree of node i) */ for (i = 1; i <= nv; i++) ptr[i] = 0; for (a = 1; a <= na; a++) { ptr[tail[a]]++; ptr[head[a]]++; } /* initialize arc pointers */ ptr[1]++; for (i = 1; i < nv; i++) ptr[i+1] += ptr[i]; ptr[nv+1] = ptr[nv]; /* build arc lists */ for (a = 1; a <= na; a++) { arc[--ptr[tail[a]]] = a; arc[--ptr[head[a]]] = a; } xassert(ptr[1] == 1); xassert(ptr[nv+1] == na+na+1); /* now the indices of arcs incident to node i are stored in locations arc[ptr[i]], arc[ptr[i]+1], ..., arc[ptr[i+1]-1] */ /* initialize arc flows and node potentials */ for (a = 1; a <= na; a++) x[a] = 0; for (i = 1; i <= nv; i++) pi[i] = 0; loop: /* main loop starts here */ /* find out-of-kilter arc */ aok = 0; for (a = 1; a <= na; a++) { i = tail[a], j = head[a]; if (overflow(cost[a], pi[i] - pi[j])) { ret = 2; goto done; } lambda = cost[a] + (pi[i] - pi[j]); if (x[a] < low[a] || lambda < 0 && x[a] < cap[a]) { /* arc a = i->j is out of kilter, and we need to increase the flow through this arc */ aok = a, s = j, t = i; break; } if (x[a] > cap[a] || lambda > 0 && x[a] > low[a]) { /* arc a = i->j is out of kilter, and we need to decrease the flow through this arc */ aok = a, s = i, t = j; break; } } if (aok == 0) { /* all arcs are in kilter */ /* check for feasibility */ for (a = 1; a <= na; a++) { if (!(low[a] <= x[a] && x[a] <= cap[a])) { ret = 3; goto done; } } for (i = 1; i <= nv; i++) { temp = 0; for (k = ptr[i]; k < ptr[i+1]; k++) { a = arc[k]; if (tail[a] == i) { /* a is outgoing arc */ temp += x[a]; } else if (head[a] == i) { /* a is incoming arc */ temp -= x[a]; } else xassert(a != a); } if (temp != 0) { ret = 3; goto done; } } /* check for optimality */ for (a = 1; a <= na; a++) { i = tail[a], j = head[a]; lambda = cost[a] + (pi[i] - pi[j]); if (lambda > 0 && x[a] != low[a] || lambda < 0 && x[a] != cap[a]) { ret = 3; goto done; } } /* current circulation is optimal */ ret = 0; goto done; } /* now we need to find a cycle (t, a, s, ..., t), which allows increasing the flow along it, where a is the out-of-kilter arc just found */ /* link[i] = 0 means that node i is not labelled yet; link[i] = a means that arc a immediately precedes node i */ /* initially only node s is labelled */ for (i = 1; i <= nv; i++) link[i] = 0; link[s] = aok, list[1] = s, pos1 = pos2 = 1; /* breadth first search */ while (pos1 <= pos2) { /* dequeue node i */ i = list[pos1++]; /* consider all arcs incident to node i */ for (k = ptr[i]; k < ptr[i+1]; k++) { a = arc[k]; if (tail[a] == i) { /* a = i->j is a forward arc from s to t */ j = head[a]; /* if node j has been labelled, skip the arc */ if (link[j] != 0) continue; /* if the arc does not allow increasing the flow through it, skip the arc */ if (x[a] >= cap[a]) continue; if (overflow(cost[a], pi[i] - pi[j])) { ret = 2; goto done; } lambda = cost[a] + (pi[i] - pi[j]); if (lambda > 0 && x[a] >= low[a]) continue; } else if (head[a] == i) { /* a = i<-j is a backward arc from s to t */ j = tail[a]; /* if node j has been labelled, skip the arc */ if (link[j] != 0) continue; /* if the arc does not allow decreasing the flow through it, skip the arc */ if (x[a] <= low[a]) continue; if (overflow(cost[a], pi[j] - pi[i])) { ret = 2; goto done; } lambda = cost[a] + (pi[j] - pi[i]); if (lambda < 0 && x[a] <= cap[a]) continue; } else xassert(a != a); /* label node j and enqueue it */ link[j] = a, list[++pos2] = j; /* check for breakthrough */ if (j == t) goto brkt; } } /* NONBREAKTHROUGH */ /* consider all arcs, whose one endpoint is labelled and other is not, and determine maximal change of node potentials */ delta = 0; for (a = 1; a <= na; a++) { i = tail[a], j = head[a]; if (link[i] != 0 && link[j] == 0) { /* a = i->j, where node i is labelled, node j is not */ if (overflow(cost[a], pi[i] - pi[j])) { ret = 2; goto done; } lambda = cost[a] + (pi[i] - pi[j]); if (x[a] <= cap[a] && lambda > 0) if (delta == 0 || delta > + lambda) delta = + lambda; } else if (link[i] == 0 && link[j] != 0) { /* a = j<-i, where node j is labelled, node i is not */ if (overflow(cost[a], pi[i] - pi[j])) { ret = 2; goto done; } lambda = cost[a] + (pi[i] - pi[j]); if (x[a] >= low[a] && lambda < 0) if (delta == 0 || delta > - lambda) delta = - lambda; } } if (delta == 0) { /* there is no feasible circulation */ ret = 1; goto done; } /* increase potentials of all unlabelled nodes */ for (i = 1; i <= nv; i++) { if (link[i] == 0) { if (overflow(pi[i], delta)) { ret = 2; goto done; } pi[i] += delta; } } goto loop; brkt: /* BREAKTHROUGH */ /* walk through arcs of the cycle (t, a, s, ..., t) found in the reverse order and determine maximal change of the flow */ delta = 0; for (j = t;; j = i) { /* arc a immediately precedes node j in the cycle */ a = link[j]; if (head[a] == j) { /* a = i->j is a forward arc of the cycle */ i = tail[a]; lambda = cost[a] + (pi[i] - pi[j]); if (lambda > 0 && x[a] < low[a]) { /* x[a] may be increased until its lower bound */ temp = low[a] - x[a]; } else if (lambda <= 0 && x[a] < cap[a]) { /* x[a] may be increased until its upper bound */ temp = cap[a] - x[a]; } else xassert(a != a); } else if (tail[a] == j) { /* a = i<-j is a backward arc of the cycle */ i = head[a]; lambda = cost[a] + (pi[j] - pi[i]); if (lambda < 0 && x[a] > cap[a]) { /* x[a] may be decreased until its upper bound */ temp = x[a] - cap[a]; } else if (lambda >= 0 && x[a] > low[a]) { /* x[a] may be decreased until its lower bound */ temp = x[a] - low[a]; } else xassert(a != a); } else xassert(a != a); if (delta == 0 || delta > temp) delta = temp; /* check for end of the cycle */ if (i == t) break; } xassert(delta > 0); /* increase the flow along the cycle */ for (j = t;; j = i) { /* arc a immediately precedes node j in the cycle */ a = link[j]; if (head[a] == j) { /* a = i->j is a forward arc of the cycle */ i = tail[a]; /* overflow cannot occur */ x[a] += delta; } else if (tail[a] == j) { /* a = i<-j is a backward arc of the cycle */ i = head[a]; /* overflow cannot occur */ x[a] -= delta; } else xassert(a != a); /* check for end of the cycle */ if (i == t) break; } goto loop; done: /* free working arrays */ xfree(ptr); xfree(arc); xfree(link); xfree(list); return ret; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glplib.h0000644000175000017500000000765711632352620020100 0ustar csheltoncshelton/* glplib.h (miscellaneous library routines) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPLIB_H #define GLPLIB_H #define bigmul _glp_lib_bigmul void bigmul(int n, int m, unsigned short x[], unsigned short y[]); /* multiply unsigned integer numbers of arbitrary precision */ #define bigdiv _glp_lib_bigdiv void bigdiv(int n, int m, unsigned short x[], unsigned short y[]); /* divide unsigned integer numbers of arbitrary precision */ #ifndef GLP_LONG_DEFINED #define GLP_LONG_DEFINED typedef struct { int lo, hi; } glp_long; /* long integer data type */ #endif typedef struct { glp_long quot, rem; } glp_ldiv; /* result of long integer division */ #define xlset _glp_lib_xlset glp_long xlset(int x); /* expand integer to long integer */ #define xlneg _glp_lib_xlneg glp_long xlneg(glp_long x); /* negate long integer */ #define xladd _glp_lib_xladd glp_long xladd(glp_long x, glp_long y); /* add long integers */ #define xlsub _glp_lib_xlsub glp_long xlsub(glp_long x, glp_long y); /* subtract long integers */ #define xlcmp _glp_lib_xlcmp int xlcmp(glp_long x, glp_long y); /* compare long integers */ #define xlmul _glp_lib_xlmul glp_long xlmul(glp_long x, glp_long y); /* multiply long integers */ #define xldiv _glp_lib_xldiv glp_ldiv xldiv(glp_long x, glp_long y); /* divide long integers */ #define xltod _glp_lib_xltod double xltod(glp_long x); /* convert long integer to double */ #define xltoa _glp_lib_xltoa char *xltoa(glp_long x, char *s); /* convert long integer to character string */ #define str2int _glp_lib_str2int int str2int(const char *str, int *val); /* convert character string to value of int type */ #define str2num _glp_lib_str2num int str2num(const char *str, double *val); /* convert character string to value of double type */ #define strspx _glp_lib_strspx char *strspx(char *str); /* remove all spaces from character string */ #define strtrim _glp_lib_strtrim char *strtrim(char *str); /* remove trailing spaces from character string */ #define strrev _glp_lib_strrev char *strrev(char *s); /* reverse character string */ #define gcd _glp_lib_gcd int gcd(int x, int y); /* find greatest common divisor of two integers */ #define gcdn _glp_lib_gcdn int gcdn(int n, int x[]); /* find greatest common divisor of n integers */ #define lcm _glp_lib_lcm int lcm(int x, int y); /* find least common multiple of two integers */ #define lcmn _glp_lib_lcmn int lcmn(int n, int x[]); /* find least common multiple of n integers */ #define round2n _glp_lib_round2n double round2n(double x); /* round floating-point number to nearest power of two */ #define fp2rat _glp_lib_fp2rat int fp2rat(double x, double eps, double *p, double *q); /* convert floating-point number to rational number */ #define jday _glp_lib_jday int jday(int d, int m, int y); /* convert calendar date to Julian day number */ #define jdate _glp_lib_jdate int jdate(int j, int *d, int *m, int *y); /* convert Julian day number to calendar date */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpapi11.c0000644000175000017500000012676411632352620020241 0ustar csheltoncshelton/* glpapi11.c (utility routines) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" int glp_print_sol(glp_prob *P, const char *fname) { /* write basic solution in printable format */ XFILE *fp; GLPROW *row; GLPCOL *col; int i, j, t, ae_ind, re_ind, ret; double ae_max, re_max; xprintf("Writing basic solution to `%s'...\n", fname); fp = xfopen(fname, "w"); if (fp == NULL) { xprintf("Unable to create `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xfprintf(fp, "%-12s%s\n", "Problem:", P->name == NULL ? "" : P->name); xfprintf(fp, "%-12s%d\n", "Rows:", P->m); xfprintf(fp, "%-12s%d\n", "Columns:", P->n); xfprintf(fp, "%-12s%d\n", "Non-zeros:", P->nnz); t = glp_get_status(P); xfprintf(fp, "%-12s%s\n", "Status:", t == GLP_OPT ? "OPTIMAL" : t == GLP_FEAS ? "FEASIBLE" : t == GLP_INFEAS ? "INFEASIBLE (INTERMEDIATE)" : t == GLP_NOFEAS ? "INFEASIBLE (FINAL)" : t == GLP_UNBND ? "UNBOUNDED" : t == GLP_UNDEF ? "UNDEFINED" : "???"); xfprintf(fp, "%-12s%s%s%.10g (%s)\n", "Objective:", P->obj == NULL ? "" : P->obj, P->obj == NULL ? "" : " = ", P->obj_val, P->dir == GLP_MIN ? "MINimum" : P->dir == GLP_MAX ? "MAXimum" : "???"); xfprintf(fp, "\n"); xfprintf(fp, " No. Row name St Activity Lower bound " " Upper bound Marginal\n"); xfprintf(fp, "------ ------------ -- ------------- ------------- " "------------- -------------\n"); for (i = 1; i <= P->m; i++) { row = P->row[i]; xfprintf(fp, "%6d ", i); if (row->name == NULL || strlen(row->name) <= 12) xfprintf(fp, "%-12s ", row->name == NULL ? "" : row->name); else xfprintf(fp, "%s\n%20s", row->name, ""); xfprintf(fp, "%s ", row->stat == GLP_BS ? "B " : row->stat == GLP_NL ? "NL" : row->stat == GLP_NU ? "NU" : row->stat == GLP_NF ? "NF" : row->stat == GLP_NS ? "NS" : "??"); xfprintf(fp, "%13.6g ", fabs(row->prim) <= 1e-9 ? 0.0 : row->prim); if (row->type == GLP_LO || row->type == GLP_DB || row->type == GLP_FX) xfprintf(fp, "%13.6g ", row->lb); else xfprintf(fp, "%13s ", ""); if (row->type == GLP_UP || row->type == GLP_DB) xfprintf(fp, "%13.6g ", row->ub); else xfprintf(fp, "%13s ", row->type == GLP_FX ? "=" : ""); if (row->stat != GLP_BS) { if (fabs(row->dual) <= 1e-9) xfprintf(fp, "%13s", "< eps"); else xfprintf(fp, "%13.6g ", row->dual); } xfprintf(fp, "\n"); } xfprintf(fp, "\n"); xfprintf(fp, " No. Column name St Activity Lower bound " " Upper bound Marginal\n"); xfprintf(fp, "------ ------------ -- ------------- ------------- " "------------- -------------\n"); for (j = 1; j <= P->n; j++) { col = P->col[j]; xfprintf(fp, "%6d ", j); if (col->name == NULL || strlen(col->name) <= 12) xfprintf(fp, "%-12s ", col->name == NULL ? "" : col->name); else xfprintf(fp, "%s\n%20s", col->name, ""); xfprintf(fp, "%s ", col->stat == GLP_BS ? "B " : col->stat == GLP_NL ? "NL" : col->stat == GLP_NU ? "NU" : col->stat == GLP_NF ? "NF" : col->stat == GLP_NS ? "NS" : "??"); xfprintf(fp, "%13.6g ", fabs(col->prim) <= 1e-9 ? 0.0 : col->prim); if (col->type == GLP_LO || col->type == GLP_DB || col->type == GLP_FX) xfprintf(fp, "%13.6g ", col->lb); else xfprintf(fp, "%13s ", ""); if (col->type == GLP_UP || col->type == GLP_DB) xfprintf(fp, "%13.6g ", col->ub); else xfprintf(fp, "%13s ", col->type == GLP_FX ? "=" : ""); if (col->stat != GLP_BS) { if (fabs(col->dual) <= 1e-9) xfprintf(fp, "%13s", "< eps"); else xfprintf(fp, "%13.6g ", col->dual); } xfprintf(fp, "\n"); } xfprintf(fp, "\n"); xfprintf(fp, "Karush-Kuhn-Tucker optimality conditions:\n"); xfprintf(fp, "\n"); _glp_check_kkt(P, GLP_SOL, GLP_KKT_PE, &ae_max, &ae_ind, &re_max, &re_ind); xfprintf(fp, "KKT.PE: max.abs.err = %.2e on row %d\n", ae_max, ae_ind); xfprintf(fp, " max.rel.err = %.2e on row %d\n", re_max, re_ind); xfprintf(fp, "%8s%s\n", "", re_max <= 1e-9 ? "High quality" : re_max <= 1e-6 ? "Medium quality" : re_max <= 1e-3 ? "Low quality" : "PRIMAL SOLUTION IS WRONG"); xfprintf(fp, "\n"); _glp_check_kkt(P, GLP_SOL, GLP_KKT_PB, &ae_max, &ae_ind, &re_max, &re_ind); xfprintf(fp, "KKT.PB: max.abs.err = %.2e on %s %d\n", ae_max, ae_ind <= P->m ? "row" : "column", ae_ind <= P->m ? ae_ind : ae_ind - P->m); xfprintf(fp, " max.rel.err = %.2e on %s %d\n", re_max, re_ind <= P->m ? "row" : "column", re_ind <= P->m ? re_ind : re_ind - P->m); xfprintf(fp, "%8s%s\n", "", re_max <= 1e-9 ? "High quality" : re_max <= 1e-6 ? "Medium quality" : re_max <= 1e-3 ? "Low quality" : "PRIMAL SOLUTION IS INFEASIBL" "E"); xfprintf(fp, "\n"); _glp_check_kkt(P, GLP_SOL, GLP_KKT_DE, &ae_max, &ae_ind, &re_max, &re_ind); xfprintf(fp, "KKT.DE: max.abs.err = %.2e on column %d\n", ae_max, ae_ind == 0 ? 0 : ae_ind - P->m); xfprintf(fp, " max.rel.err = %.2e on column %d\n", re_max, re_ind == 0 ? 0 : re_ind - P->m); xfprintf(fp, "%8s%s\n", "", re_max <= 1e-9 ? "High quality" : re_max <= 1e-6 ? "Medium quality" : re_max <= 1e-3 ? "Low quality" : "DUAL SOLUTION IS WRONG"); xfprintf(fp, "\n"); _glp_check_kkt(P, GLP_SOL, GLP_KKT_DB, &ae_max, &ae_ind, &re_max, &re_ind); xfprintf(fp, "KKT.DB: max.abs.err = %.2e on %s %d\n", ae_max, ae_ind <= P->m ? "row" : "column", ae_ind <= P->m ? ae_ind : ae_ind - P->m); xfprintf(fp, " max.rel.err = %.2e on %s %d\n", re_max, re_ind <= P->m ? "row" : "column", re_ind <= P->m ? re_ind : re_ind - P->m); xfprintf(fp, "%8s%s\n", "", re_max <= 1e-9 ? "High quality" : re_max <= 1e-6 ? "Medium quality" : re_max <= 1e-3 ? "Low quality" : "DUAL SOLUTION IS INFEASIBLE") ; xfprintf(fp, "\n"); xfprintf(fp, "End of output\n"); xfflush(fp); if (xferror(fp)) { xprintf("Write error on `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } ret = 0; done: if (fp != NULL) xfclose(fp); return ret; } /*********************************************************************** * NAME * * glp_read_sol - read basic solution from text file * * SYNOPSIS * * int glp_read_sol(glp_prob *lp, const char *fname); * * DESCRIPTION * * The routine glp_read_sol reads basic solution from a text file whose * name is specified by the parameter fname into the problem object. * * For the file format see description of the routine glp_write_sol. * * RETURNS * * On success the routine returns zero, otherwise non-zero. */ int glp_read_sol(glp_prob *lp, const char *fname) { glp_data *data; jmp_buf jump; int i, j, k, ret = 0; xprintf("Reading basic solution from `%s'...\n", fname); data = glp_sdf_open_file(fname); if (data == NULL) { ret = 1; goto done; } if (setjmp(jump)) { ret = 1; goto done; } glp_sdf_set_jump(data, jump); /* number of rows, number of columns */ k = glp_sdf_read_int(data); if (k != lp->m) glp_sdf_error(data, "wrong number of rows\n"); k = glp_sdf_read_int(data); if (k != lp->n) glp_sdf_error(data, "wrong number of columns\n"); /* primal status, dual status, objective value */ k = glp_sdf_read_int(data); if (!(k == GLP_UNDEF || k == GLP_FEAS || k == GLP_INFEAS || k == GLP_NOFEAS)) glp_sdf_error(data, "invalid primal status\n"); lp->pbs_stat = k; k = glp_sdf_read_int(data); if (!(k == GLP_UNDEF || k == GLP_FEAS || k == GLP_INFEAS || k == GLP_NOFEAS)) glp_sdf_error(data, "invalid dual status\n"); lp->dbs_stat = k; lp->obj_val = glp_sdf_read_num(data); /* rows (auxiliary variables) */ for (i = 1; i <= lp->m; i++) { GLPROW *row = lp->row[i]; /* status, primal value, dual value */ k = glp_sdf_read_int(data); if (!(k == GLP_BS || k == GLP_NL || k == GLP_NU || k == GLP_NF || k == GLP_NS)) glp_sdf_error(data, "invalid row status\n"); glp_set_row_stat(lp, i, k); row->prim = glp_sdf_read_num(data); row->dual = glp_sdf_read_num(data); } /* columns (structural variables) */ for (j = 1; j <= lp->n; j++) { GLPCOL *col = lp->col[j]; /* status, primal value, dual value */ k = glp_sdf_read_int(data); if (!(k == GLP_BS || k == GLP_NL || k == GLP_NU || k == GLP_NF || k == GLP_NS)) glp_sdf_error(data, "invalid column status\n"); glp_set_col_stat(lp, j, k); col->prim = glp_sdf_read_num(data); col->dual = glp_sdf_read_num(data); } xprintf("%d lines were read\n", glp_sdf_line(data)); done: if (ret) lp->pbs_stat = lp->dbs_stat = GLP_UNDEF; if (data != NULL) glp_sdf_close_file(data); return ret; } /*********************************************************************** * NAME * * glp_write_sol - write basic solution to text file * * SYNOPSIS * * int glp_write_sol(glp_prob *lp, const char *fname); * * DESCRIPTION * * The routine glp_write_sol writes the current basic solution to a * text file whose name is specified by the parameter fname. This file * can be read back with the routine glp_read_sol. * * RETURNS * * On success the routine returns zero, otherwise non-zero. * * FILE FORMAT * * The file created by the routine glp_write_sol is a plain text file, * which contains the following information: * * m n * p_stat d_stat obj_val * r_stat[1] r_prim[1] r_dual[1] * . . . * r_stat[m] r_prim[m] r_dual[m] * c_stat[1] c_prim[1] c_dual[1] * . . . * c_stat[n] c_prim[n] c_dual[n] * * where: * m is the number of rows (auxiliary variables); * n is the number of columns (structural variables); * p_stat is the primal status of the basic solution (GLP_UNDEF = 1, * GLP_FEAS = 2, GLP_INFEAS = 3, or GLP_NOFEAS = 4); * d_stat is the dual status of the basic solution (GLP_UNDEF = 1, * GLP_FEAS = 2, GLP_INFEAS = 3, or GLP_NOFEAS = 4); * obj_val is the objective value; * r_stat[i], i = 1,...,m, is the status of i-th row (GLP_BS = 1, * GLP_NL = 2, GLP_NU = 3, GLP_NF = 4, or GLP_NS = 5); * r_prim[i], i = 1,...,m, is the primal value of i-th row; * r_dual[i], i = 1,...,m, is the dual value of i-th row; * c_stat[j], j = 1,...,n, is the status of j-th column (GLP_BS = 1, * GLP_NL = 2, GLP_NU = 3, GLP_NF = 4, or GLP_NS = 5); * c_prim[j], j = 1,...,n, is the primal value of j-th column; * c_dual[j], j = 1,...,n, is the dual value of j-th column. */ int glp_write_sol(glp_prob *lp, const char *fname) { XFILE *fp; int i, j, ret = 0; xprintf("Writing basic solution to `%s'...\n", fname); fp = xfopen(fname, "w"); if (fp == NULL) { xprintf("Unable to create `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } /* number of rows, number of columns */ xfprintf(fp, "%d %d\n", lp->m, lp->n); /* primal status, dual status, objective value */ xfprintf(fp, "%d %d %.*g\n", lp->pbs_stat, lp->dbs_stat, DBL_DIG, lp->obj_val); /* rows (auxiliary variables) */ for (i = 1; i <= lp->m; i++) { GLPROW *row = lp->row[i]; /* status, primal value, dual value */ xfprintf(fp, "%d %.*g %.*g\n", row->stat, DBL_DIG, row->prim, DBL_DIG, row->dual); } /* columns (structural variables) */ for (j = 1; j <= lp->n; j++) { GLPCOL *col = lp->col[j]; /* status, primal value, dual value */ xfprintf(fp, "%d %.*g %.*g\n", col->stat, DBL_DIG, col->prim, DBL_DIG, col->dual); } xfflush(fp); if (xferror(fp)) { xprintf("Write error on `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xprintf("%d lines were written\n", 2 + lp->m + lp->n); done: if (fp != NULL) xfclose(fp); return ret; } /**********************************************************************/ static char *format(char buf[13+1], double x) { /* format floating-point number in MPS/360-like style */ if (x == -DBL_MAX) strcpy(buf, " -Inf"); else if (x == +DBL_MAX) strcpy(buf, " +Inf"); else if (fabs(x) <= 999999.99998) { sprintf(buf, "%13.5f", x); #if 1 if (strcmp(buf, " 0.00000") == 0 || strcmp(buf, " -0.00000") == 0) strcpy(buf, " . "); else if (memcmp(buf, " 0.", 8) == 0) memcpy(buf, " .", 8); else if (memcmp(buf, " -0.", 8) == 0) memcpy(buf, " -.", 8); #endif } else sprintf(buf, "%13.6g", x); return buf; } int glp_print_ranges(glp_prob *P, int len, const int list[], int flags, const char *fname) { /* print sensitivity analysis report */ XFILE *fp = NULL; GLPROW *row; GLPCOL *col; int m, n, pass, k, t, numb, type, stat, var1, var2, count, page, ret; double lb, ub, slack, coef, prim, dual, value1, value2, coef1, coef2, obj1, obj2; const char *name, *limit; char buf[13+1]; /* sanity checks */ if (P == NULL || P->magic != GLP_PROB_MAGIC) xerror("glp_print_ranges: P = %p; invalid problem object\n", P); m = P->m, n = P->n; if (len < 0) xerror("glp_print_ranges: len = %d; invalid list length\n", len); if (len > 0) { if (list == NULL) xerror("glp_print_ranges: list = %p: invalid parameter\n", list); for (t = 1; t <= len; t++) { k = list[t]; if (!(1 <= k && k <= m+n)) xerror("glp_print_ranges: list[%d] = %d; row/column numb" "er out of range\n", t, k); } } if (flags != 0) xerror("glp_print_ranges: flags = %d; invalid parameter\n", flags); if (fname == NULL) xerror("glp_print_ranges: fname = %p; invalid parameter\n", fname); if (glp_get_status(P) != GLP_OPT) { xprintf("glp_print_ranges: optimal basic solution required\n"); ret = 1; goto done; } if (!glp_bf_exists(P)) { xprintf("glp_print_ranges: basis factorization required\n"); ret = 2; goto done; } /* start reporting */ xprintf("Write sensitivity analysis report to `%s'...\n", fname); fp = xfopen(fname, "w"); if (fp == NULL) { xprintf("Unable to create `%s' - %s\n", fname, xerrmsg()); ret = 3; goto done; } page = count = 0; for (pass = 1; pass <= 2; pass++) for (t = 1; t <= (len == 0 ? m+n : len); t++) { if (t == 1) count = 0; k = (len == 0 ? t : list[t]); if (pass == 1 && k > m || pass == 2 && k <= m) continue; if (count == 0) { xfprintf(fp, "GLPK %-4s - SENSITIVITY ANALYSIS REPORT%73sPa" "ge%4d\n", glp_version(), "", ++page); xfprintf(fp, "\n"); xfprintf(fp, "%-12s%s\n", "Problem:", P->name == NULL ? "" : P->name); xfprintf(fp, "%-12s%s%s%.10g (%s)\n", "Objective:", P->obj == NULL ? "" : P->obj, P->obj == NULL ? "" : " = ", P->obj_val, P->dir == GLP_MIN ? "MINimum" : P->dir == GLP_MAX ? "MAXimum" : "???"); xfprintf(fp, "\n"); xfprintf(fp, "%6s %-12s %2s %13s %13s %13s %13s %13s %13s " "%s\n", "No.", pass == 1 ? "Row name" : "Column name", "St", "Activity", pass == 1 ? "Slack" : "Obj coef", "Lower bound", "Activity", "Obj coef", "Obj value at", "Limiting"); xfprintf(fp, "%6s %-12s %2s %13s %13s %13s %13s %13s %13s " "%s\n", "", "", "", "", "Marginal", "Upper bound", "range", "range", "break point", "variable"); xfprintf(fp, "------ ------------ -- ------------- --------" "----- ------------- ------------- ------------- ------" "------- ------------\n"); } if (pass == 1) { numb = k; xassert(1 <= numb && numb <= m); row = P->row[numb]; name = row->name; type = row->type; lb = glp_get_row_lb(P, numb); ub = glp_get_row_ub(P, numb); coef = 0.0; stat = row->stat; prim = row->prim; if (type == GLP_FR) slack = - prim; else if (type == GLP_LO) slack = lb - prim; else if (type == GLP_UP || type == GLP_DB || type == GLP_FX) slack = ub - prim; dual = row->dual; } else { numb = k - m; xassert(1 <= numb && numb <= n); col = P->col[numb]; name = col->name; lb = glp_get_col_lb(P, numb); ub = glp_get_col_ub(P, numb); coef = col->coef; stat = col->stat; prim = col->prim; slack = 0.0; dual = col->dual; } if (stat != GLP_BS) { glp_analyze_bound(P, k, &value1, &var1, &value2, &var2); if (stat == GLP_NF) coef1 = coef2 = coef; else if (stat == GLP_NS) coef1 = -DBL_MAX, coef2 = +DBL_MAX; else if (stat == GLP_NL && P->dir == GLP_MIN || stat == GLP_NU && P->dir == GLP_MAX) coef1 = coef - dual, coef2 = +DBL_MAX; else coef1 = -DBL_MAX, coef2 = coef - dual; if (value1 == -DBL_MAX) { if (dual < -1e-9) obj1 = +DBL_MAX; else if (dual > +1e-9) obj1 = -DBL_MAX; else obj1 = P->obj_val; } else obj1 = P->obj_val + dual * (value1 - prim); if (value2 == +DBL_MAX) { if (dual < -1e-9) obj2 = -DBL_MAX; else if (dual > +1e-9) obj2 = +DBL_MAX; else obj2 = P->obj_val; } else obj2 = P->obj_val + dual * (value2 - prim); } else { glp_analyze_coef(P, k, &coef1, &var1, &value1, &coef2, &var2, &value2); if (coef1 == -DBL_MAX) { if (prim < -1e-9) obj1 = +DBL_MAX; else if (prim > +1e-9) obj1 = -DBL_MAX; else obj1 = P->obj_val; } else obj1 = P->obj_val + (coef1 - coef) * prim; if (coef2 == +DBL_MAX) { if (prim < -1e-9) obj2 = -DBL_MAX; else if (prim > +1e-9) obj2 = +DBL_MAX; else obj2 = P->obj_val; } else obj2 = P->obj_val + (coef2 - coef) * prim; } /*** first line ***/ /* row/column number */ xfprintf(fp, "%6d", numb); /* row/column name */ xfprintf(fp, " %-12.12s", name == NULL ? "" : name); if (name != NULL && strlen(name) > 12) xfprintf(fp, "%s\n%6s %12s", name+12, "", ""); /* row/column status */ xfprintf(fp, " %2s", stat == GLP_BS ? "BS" : stat == GLP_NL ? "NL" : stat == GLP_NU ? "NU" : stat == GLP_NF ? "NF" : stat == GLP_NS ? "NS" : "??"); /* row/column activity */ xfprintf(fp, " %s", format(buf, prim)); /* row slack, column objective coefficient */ xfprintf(fp, " %s", format(buf, k <= m ? slack : coef)); /* row/column lower bound */ xfprintf(fp, " %s", format(buf, lb)); /* row/column activity range */ xfprintf(fp, " %s", format(buf, value1)); /* row/column objective coefficient range */ xfprintf(fp, " %s", format(buf, coef1)); /* objective value at break point */ xfprintf(fp, " %s", format(buf, obj1)); /* limiting variable name */ if (var1 != 0) { if (var1 <= m) limit = glp_get_row_name(P, var1); else limit = glp_get_col_name(P, var1 - m); if (limit != NULL) xfprintf(fp, " %s", limit); } xfprintf(fp, "\n"); /*** second line ***/ xfprintf(fp, "%6s %-12s %2s %13s", "", "", "", ""); /* row/column reduced cost */ xfprintf(fp, " %s", format(buf, dual)); /* row/column upper bound */ xfprintf(fp, " %s", format(buf, ub)); /* row/column activity range */ xfprintf(fp, " %s", format(buf, value2)); /* row/column objective coefficient range */ xfprintf(fp, " %s", format(buf, coef2)); /* objective value at break point */ xfprintf(fp, " %s", format(buf, obj2)); /* limiting variable name */ if (var2 != 0) { if (var2 <= m) limit = glp_get_row_name(P, var2); else limit = glp_get_col_name(P, var2 - m); if (limit != NULL) xfprintf(fp, " %s", limit); } xfprintf(fp, "\n"); xfprintf(fp, "\n"); /* print 10 items per page */ count = (count + 1) % 10; } xfprintf(fp, "End of report\n"); xfflush(fp); if (xferror(fp)) { xprintf("Write error on `%s' - %s\n", fname, xerrmsg()); ret = 4; goto done; } ret = 0; done: if (fp != NULL) xfclose(fp); return ret; } /**********************************************************************/ int glp_print_ipt(glp_prob *P, const char *fname) { /* write interior-point solution in printable format */ XFILE *fp; GLPROW *row; GLPCOL *col; int i, j, t, ae_ind, re_ind, ret; double ae_max, re_max; xprintf("Writing interior-point solution to `%s'...\n", fname); fp = xfopen(fname, "w"); if (fp == NULL) { xprintf("Unable to create `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xfprintf(fp, "%-12s%s\n", "Problem:", P->name == NULL ? "" : P->name); xfprintf(fp, "%-12s%d\n", "Rows:", P->m); xfprintf(fp, "%-12s%d\n", "Columns:", P->n); xfprintf(fp, "%-12s%d\n", "Non-zeros:", P->nnz); t = glp_ipt_status(P); xfprintf(fp, "%-12s%s\n", "Status:", t == GLP_OPT ? "OPTIMAL" : t == GLP_UNDEF ? "UNDEFINED" : t == GLP_INFEAS ? "INFEASIBLE (INTERMEDIATE)" : t == GLP_NOFEAS ? "INFEASIBLE (FINAL)" : "???"); xfprintf(fp, "%-12s%s%s%.10g (%s)\n", "Objective:", P->obj == NULL ? "" : P->obj, P->obj == NULL ? "" : " = ", P->ipt_obj, P->dir == GLP_MIN ? "MINimum" : P->dir == GLP_MAX ? "MAXimum" : "???"); xfprintf(fp, "\n"); xfprintf(fp, " No. Row name Activity Lower bound " " Upper bound Marginal\n"); xfprintf(fp, "------ ------------ ------------- ------------- " "------------- -------------\n"); for (i = 1; i <= P->m; i++) { row = P->row[i]; xfprintf(fp, "%6d ", i); if (row->name == NULL || strlen(row->name) <= 12) xfprintf(fp, "%-12s ", row->name == NULL ? "" : row->name); else xfprintf(fp, "%s\n%20s", row->name, ""); xfprintf(fp, "%3s", ""); xfprintf(fp, "%13.6g ", fabs(row->pval) <= 1e-9 ? 0.0 : row->pval); if (row->type == GLP_LO || row->type == GLP_DB || row->type == GLP_FX) xfprintf(fp, "%13.6g ", row->lb); else xfprintf(fp, "%13s ", ""); if (row->type == GLP_UP || row->type == GLP_DB) xfprintf(fp, "%13.6g ", row->ub); else xfprintf(fp, "%13s ", row->type == GLP_FX ? "=" : ""); if (fabs(row->dval) <= 1e-9) xfprintf(fp, "%13s", "< eps"); else xfprintf(fp, "%13.6g ", row->dval); xfprintf(fp, "\n"); } xfprintf(fp, "\n"); xfprintf(fp, " No. Column name Activity Lower bound " " Upper bound Marginal\n"); xfprintf(fp, "------ ------------ ------------- ------------- " "------------- -------------\n"); for (j = 1; j <= P->n; j++) { col = P->col[j]; xfprintf(fp, "%6d ", j); if (col->name == NULL || strlen(col->name) <= 12) xfprintf(fp, "%-12s ", col->name == NULL ? "" : col->name); else xfprintf(fp, "%s\n%20s", col->name, ""); xfprintf(fp, "%3s", ""); xfprintf(fp, "%13.6g ", fabs(col->pval) <= 1e-9 ? 0.0 : col->pval); if (col->type == GLP_LO || col->type == GLP_DB || col->type == GLP_FX) xfprintf(fp, "%13.6g ", col->lb); else xfprintf(fp, "%13s ", ""); if (col->type == GLP_UP || col->type == GLP_DB) xfprintf(fp, "%13.6g ", col->ub); else xfprintf(fp, "%13s ", col->type == GLP_FX ? "=" : ""); if (fabs(col->dval) <= 1e-9) xfprintf(fp, "%13s", "< eps"); else xfprintf(fp, "%13.6g ", col->dval); xfprintf(fp, "\n"); } xfprintf(fp, "\n"); xfprintf(fp, "Karush-Kuhn-Tucker optimality conditions:\n"); xfprintf(fp, "\n"); _glp_check_kkt(P, GLP_IPT, GLP_KKT_PE, &ae_max, &ae_ind, &re_max, &re_ind); xfprintf(fp, "KKT.PE: max.abs.err = %.2e on row %d\n", ae_max, ae_ind); xfprintf(fp, " max.rel.err = %.2e on row %d\n", re_max, re_ind); xfprintf(fp, "%8s%s\n", "", re_max <= 1e-9 ? "High quality" : re_max <= 1e-6 ? "Medium quality" : re_max <= 1e-3 ? "Low quality" : "PRIMAL SOLUTION IS WRONG"); xfprintf(fp, "\n"); _glp_check_kkt(P, GLP_IPT, GLP_KKT_PB, &ae_max, &ae_ind, &re_max, &re_ind); xfprintf(fp, "KKT.PB: max.abs.err = %.2e on %s %d\n", ae_max, ae_ind <= P->m ? "row" : "column", ae_ind <= P->m ? ae_ind : ae_ind - P->m); xfprintf(fp, " max.rel.err = %.2e on %s %d\n", re_max, re_ind <= P->m ? "row" : "column", re_ind <= P->m ? re_ind : re_ind - P->m); xfprintf(fp, "%8s%s\n", "", re_max <= 1e-9 ? "High quality" : re_max <= 1e-6 ? "Medium quality" : re_max <= 1e-3 ? "Low quality" : "PRIMAL SOLUTION IS INFEASIBL" "E"); xfprintf(fp, "\n"); _glp_check_kkt(P, GLP_IPT, GLP_KKT_DE, &ae_max, &ae_ind, &re_max, &re_ind); xfprintf(fp, "KKT.DE: max.abs.err = %.2e on column %d\n", ae_max, ae_ind == 0 ? 0 : ae_ind - P->m); xfprintf(fp, " max.rel.err = %.2e on column %d\n", re_max, re_ind == 0 ? 0 : re_ind - P->m); xfprintf(fp, "%8s%s\n", "", re_max <= 1e-9 ? "High quality" : re_max <= 1e-6 ? "Medium quality" : re_max <= 1e-3 ? "Low quality" : "DUAL SOLUTION IS WRONG"); xfprintf(fp, "\n"); _glp_check_kkt(P, GLP_IPT, GLP_KKT_DB, &ae_max, &ae_ind, &re_max, &re_ind); xfprintf(fp, "KKT.DB: max.abs.err = %.2e on %s %d\n", ae_max, ae_ind <= P->m ? "row" : "column", ae_ind <= P->m ? ae_ind : ae_ind - P->m); xfprintf(fp, " max.rel.err = %.2e on %s %d\n", re_max, re_ind <= P->m ? "row" : "column", re_ind <= P->m ? re_ind : re_ind - P->m); xfprintf(fp, "%8s%s\n", "", re_max <= 1e-9 ? "High quality" : re_max <= 1e-6 ? "Medium quality" : re_max <= 1e-3 ? "Low quality" : "DUAL SOLUTION IS INFEASIBLE") ; xfprintf(fp, "\n"); xfprintf(fp, "End of output\n"); xfflush(fp); if (xferror(fp)) { xprintf("Write error on `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } ret = 0; done: if (fp != NULL) xfclose(fp); return ret; } /*********************************************************************** * NAME * * glp_read_ipt - read interior-point solution from text file * * SYNOPSIS * * int glp_read_ipt(glp_prob *lp, const char *fname); * * DESCRIPTION * * The routine glp_read_ipt reads interior-point solution from a text * file whose name is specified by the parameter fname into the problem * object. * * For the file format see description of the routine glp_write_ipt. * * RETURNS * * On success the routine returns zero, otherwise non-zero. */ int glp_read_ipt(glp_prob *lp, const char *fname) { glp_data *data; jmp_buf jump; int i, j, k, ret = 0; xprintf("Reading interior-point solution from `%s'...\n", fname); data = glp_sdf_open_file(fname); if (data == NULL) { ret = 1; goto done; } if (setjmp(jump)) { ret = 1; goto done; } glp_sdf_set_jump(data, jump); /* number of rows, number of columns */ k = glp_sdf_read_int(data); if (k != lp->m) glp_sdf_error(data, "wrong number of rows\n"); k = glp_sdf_read_int(data); if (k != lp->n) glp_sdf_error(data, "wrong number of columns\n"); /* solution status, objective value */ k = glp_sdf_read_int(data); if (!(k == GLP_UNDEF || k == GLP_OPT)) glp_sdf_error(data, "invalid solution status\n"); lp->ipt_stat = k; lp->ipt_obj = glp_sdf_read_num(data); /* rows (auxiliary variables) */ for (i = 1; i <= lp->m; i++) { GLPROW *row = lp->row[i]; /* primal value, dual value */ row->pval = glp_sdf_read_num(data); row->dval = glp_sdf_read_num(data); } /* columns (structural variables) */ for (j = 1; j <= lp->n; j++) { GLPCOL *col = lp->col[j]; /* primal value, dual value */ col->pval = glp_sdf_read_num(data); col->dval = glp_sdf_read_num(data); } xprintf("%d lines were read\n", glp_sdf_line(data)); done: if (ret) lp->ipt_stat = GLP_UNDEF; if (data != NULL) glp_sdf_close_file(data); return ret; } /*********************************************************************** * NAME * * glp_write_ipt - write interior-point solution to text file * * SYNOPSIS * * int glp_write_ipt(glp_prob *lp, const char *fname); * * DESCRIPTION * * The routine glp_write_ipt writes the current interior-point solution * to a text file whose name is specified by the parameter fname. This * file can be read back with the routine glp_read_ipt. * * RETURNS * * On success the routine returns zero, otherwise non-zero. * * FILE FORMAT * * The file created by the routine glp_write_ipt is a plain text file, * which contains the following information: * * m n * stat obj_val * r_prim[1] r_dual[1] * . . . * r_prim[m] r_dual[m] * c_prim[1] c_dual[1] * . . . * c_prim[n] c_dual[n] * * where: * m is the number of rows (auxiliary variables); * n is the number of columns (structural variables); * stat is the solution status (GLP_UNDEF = 1 or GLP_OPT = 5); * obj_val is the objective value; * r_prim[i], i = 1,...,m, is the primal value of i-th row; * r_dual[i], i = 1,...,m, is the dual value of i-th row; * c_prim[j], j = 1,...,n, is the primal value of j-th column; * c_dual[j], j = 1,...,n, is the dual value of j-th column. */ int glp_write_ipt(glp_prob *lp, const char *fname) { XFILE *fp; int i, j, ret = 0; xprintf("Writing interior-point solution to `%s'...\n", fname); fp = xfopen(fname, "w"); if (fp == NULL) { xprintf("Unable to create `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } /* number of rows, number of columns */ xfprintf(fp, "%d %d\n", lp->m, lp->n); /* solution status, objective value */ xfprintf(fp, "%d %.*g\n", lp->ipt_stat, DBL_DIG, lp->ipt_obj); /* rows (auxiliary variables) */ for (i = 1; i <= lp->m; i++) { GLPROW *row = lp->row[i]; /* primal value, dual value */ xfprintf(fp, "%.*g %.*g\n", DBL_DIG, row->pval, DBL_DIG, row->dval); } /* columns (structural variables) */ for (j = 1; j <= lp->n; j++) { GLPCOL *col = lp->col[j]; /* primal value, dual value */ xfprintf(fp, "%.*g %.*g\n", DBL_DIG, col->pval, DBL_DIG, col->dval); } xfflush(fp); if (xferror(fp)) { xprintf("Write error on `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xprintf("%d lines were written\n", 2 + lp->m + lp->n); done: if (fp != NULL) xfclose(fp); return ret; } /**********************************************************************/ int glp_print_mip(glp_prob *P, const char *fname) { /* write MIP solution in printable format */ XFILE *fp; GLPROW *row; GLPCOL *col; int i, j, t, ae_ind, re_ind, ret; double ae_max, re_max; xprintf("Writing MIP solution to `%s'...\n", fname); fp = xfopen(fname, "w"); if (fp == NULL) { xprintf("Unable to create `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xfprintf(fp, "%-12s%s\n", "Problem:", P->name == NULL ? "" : P->name); xfprintf(fp, "%-12s%d\n", "Rows:", P->m); xfprintf(fp, "%-12s%d (%d integer, %d binary)\n", "Columns:", P->n, glp_get_num_int(P), glp_get_num_bin(P)); xfprintf(fp, "%-12s%d\n", "Non-zeros:", P->nnz); t = glp_mip_status(P); xfprintf(fp, "%-12s%s\n", "Status:", t == GLP_OPT ? "INTEGER OPTIMAL" : t == GLP_FEAS ? "INTEGER NON-OPTIMAL" : t == GLP_NOFEAS ? "INTEGER EMPTY" : t == GLP_UNDEF ? "INTEGER UNDEFINED" : "???"); xfprintf(fp, "%-12s%s%s%.10g (%s)\n", "Objective:", P->obj == NULL ? "" : P->obj, P->obj == NULL ? "" : " = ", P->mip_obj, P->dir == GLP_MIN ? "MINimum" : P->dir == GLP_MAX ? "MAXimum" : "???"); xfprintf(fp, "\n"); xfprintf(fp, " No. Row name Activity Lower bound " " Upper bound\n"); xfprintf(fp, "------ ------------ ------------- ------------- " "-------------\n"); for (i = 1; i <= P->m; i++) { row = P->row[i]; xfprintf(fp, "%6d ", i); if (row->name == NULL || strlen(row->name) <= 12) xfprintf(fp, "%-12s ", row->name == NULL ? "" : row->name); else xfprintf(fp, "%s\n%20s", row->name, ""); xfprintf(fp, "%3s", ""); xfprintf(fp, "%13.6g ", fabs(row->mipx) <= 1e-9 ? 0.0 : row->mipx); if (row->type == GLP_LO || row->type == GLP_DB || row->type == GLP_FX) xfprintf(fp, "%13.6g ", row->lb); else xfprintf(fp, "%13s ", ""); if (row->type == GLP_UP || row->type == GLP_DB) xfprintf(fp, "%13.6g ", row->ub); else xfprintf(fp, "%13s ", row->type == GLP_FX ? "=" : ""); xfprintf(fp, "\n"); } xfprintf(fp, "\n"); xfprintf(fp, " No. Column name Activity Lower bound " " Upper bound\n"); xfprintf(fp, "------ ------------ ------------- ------------- " "-------------\n"); for (j = 1; j <= P->n; j++) { col = P->col[j]; xfprintf(fp, "%6d ", j); if (col->name == NULL || strlen(col->name) <= 12) xfprintf(fp, "%-12s ", col->name == NULL ? "" : col->name); else xfprintf(fp, "%s\n%20s", col->name, ""); xfprintf(fp, "%s ", col->kind == GLP_CV ? " " : col->kind == GLP_IV ? "*" : "?"); xfprintf(fp, "%13.6g ", fabs(col->mipx) <= 1e-9 ? 0.0 : col->mipx); if (col->type == GLP_LO || col->type == GLP_DB || col->type == GLP_FX) xfprintf(fp, "%13.6g ", col->lb); else xfprintf(fp, "%13s ", ""); if (col->type == GLP_UP || col->type == GLP_DB) xfprintf(fp, "%13.6g ", col->ub); else xfprintf(fp, "%13s ", col->type == GLP_FX ? "=" : ""); xfprintf(fp, "\n"); } xfprintf(fp, "\n"); xfprintf(fp, "Integer feasibility conditions:\n"); xfprintf(fp, "\n"); _glp_check_kkt(P, GLP_MIP, GLP_KKT_PE, &ae_max, &ae_ind, &re_max, &re_ind); xfprintf(fp, "KKT.PE: max.abs.err = %.2e on row %d\n", ae_max, ae_ind); xfprintf(fp, " max.rel.err = %.2e on row %d\n", re_max, re_ind); xfprintf(fp, "%8s%s\n", "", re_max <= 1e-9 ? "High quality" : re_max <= 1e-6 ? "Medium quality" : re_max <= 1e-3 ? "Low quality" : "SOLUTION IS WRONG"); xfprintf(fp, "\n"); _glp_check_kkt(P, GLP_MIP, GLP_KKT_PB, &ae_max, &ae_ind, &re_max, &re_ind); xfprintf(fp, "KKT.PB: max.abs.err = %.2e on %s %d\n", ae_max, ae_ind <= P->m ? "row" : "column", ae_ind <= P->m ? ae_ind : ae_ind - P->m); xfprintf(fp, " max.rel.err = %.2e on %s %d\n", re_max, re_ind <= P->m ? "row" : "column", re_ind <= P->m ? re_ind : re_ind - P->m); xfprintf(fp, "%8s%s\n", "", re_max <= 1e-9 ? "High quality" : re_max <= 1e-6 ? "Medium quality" : re_max <= 1e-3 ? "Low quality" : "SOLUTION IS INFEASIBLE"); xfprintf(fp, "\n"); xfprintf(fp, "End of output\n"); xfflush(fp); if (xferror(fp)) { xprintf("Write error on `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } ret = 0; done: if (fp != NULL) xfclose(fp); return ret; } /*********************************************************************** * NAME * * glp_read_mip - read MIP solution from text file * * SYNOPSIS * * int glp_read_mip(glp_prob *mip, const char *fname); * * DESCRIPTION * * The routine glp_read_mip reads MIP solution from a text file whose * name is specified by the parameter fname into the problem object. * * For the file format see description of the routine glp_write_mip. * * RETURNS * * On success the routine returns zero, otherwise non-zero. */ int glp_read_mip(glp_prob *mip, const char *fname) { glp_data *data; jmp_buf jump; int i, j, k, ret = 0; xprintf("Reading MIP solution from `%s'...\n", fname); data = glp_sdf_open_file(fname); if (data == NULL) { ret = 1; goto done; } if (setjmp(jump)) { ret = 1; goto done; } glp_sdf_set_jump(data, jump); /* number of rows, number of columns */ k = glp_sdf_read_int(data); if (k != mip->m) glp_sdf_error(data, "wrong number of rows\n"); k = glp_sdf_read_int(data); if (k != mip->n) glp_sdf_error(data, "wrong number of columns\n"); /* solution status, objective value */ k = glp_sdf_read_int(data); if (!(k == GLP_UNDEF || k == GLP_OPT || k == GLP_FEAS || k == GLP_NOFEAS)) glp_sdf_error(data, "invalid solution status\n"); mip->mip_stat = k; mip->mip_obj = glp_sdf_read_num(data); /* rows (auxiliary variables) */ for (i = 1; i <= mip->m; i++) { GLPROW *row = mip->row[i]; row->mipx = glp_sdf_read_num(data); } /* columns (structural variables) */ for (j = 1; j <= mip->n; j++) { GLPCOL *col = mip->col[j]; col->mipx = glp_sdf_read_num(data); if (col->kind == GLP_IV && col->mipx != floor(col->mipx)) glp_sdf_error(data, "non-integer column value"); } xprintf("%d lines were read\n", glp_sdf_line(data)); done: if (ret) mip->mip_stat = GLP_UNDEF; if (data != NULL) glp_sdf_close_file(data); return ret; } /*********************************************************************** * NAME * * glp_write_mip - write MIP solution to text file * * SYNOPSIS * * int glp_write_mip(glp_prob *mip, const char *fname); * * DESCRIPTION * * The routine glp_write_mip writes the current MIP solution to a text * file whose name is specified by the parameter fname. This file can * be read back with the routine glp_read_mip. * * RETURNS * * On success the routine returns zero, otherwise non-zero. * * FILE FORMAT * * The file created by the routine glp_write_sol is a plain text file, * which contains the following information: * * m n * stat obj_val * r_val[1] * . . . * r_val[m] * c_val[1] * . . . * c_val[n] * * where: * m is the number of rows (auxiliary variables); * n is the number of columns (structural variables); * stat is the solution status (GLP_UNDEF = 1, GLP_FEAS = 2, * GLP_NOFEAS = 4, or GLP_OPT = 5); * obj_val is the objective value; * r_val[i], i = 1,...,m, is the value of i-th row; * c_val[j], j = 1,...,n, is the value of j-th column. */ int glp_write_mip(glp_prob *mip, const char *fname) { XFILE *fp; int i, j, ret = 0; xprintf("Writing MIP solution to `%s'...\n", fname); fp = xfopen(fname, "w"); if (fp == NULL) { xprintf("Unable to create `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } /* number of rows, number of columns */ xfprintf(fp, "%d %d\n", mip->m, mip->n); /* solution status, objective value */ xfprintf(fp, "%d %.*g\n", mip->mip_stat, DBL_DIG, mip->mip_obj); /* rows (auxiliary variables) */ for (i = 1; i <= mip->m; i++) xfprintf(fp, "%.*g\n", DBL_DIG, mip->row[i]->mipx); /* columns (structural variables) */ for (j = 1; j <= mip->n; j++) xfprintf(fp, "%.*g\n", DBL_DIG, mip->col[j]->mipx); xfflush(fp); if (xferror(fp)) { xprintf("Write error on `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xprintf("%d lines were written\n", 2 + mip->m + mip->n); done: if (fp != NULL) xfclose(fp); return ret; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpapi04.c0000644000175000017500000001103211632352620020220 0ustar csheltoncshelton/* glpapi04.c (problem scaling routines) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" /*********************************************************************** * NAME * * glp_set_rii - set (change) row scale factor * * SYNOPSIS * * void glp_set_rii(glp_prob *lp, int i, double rii); * * DESCRIPTION * * The routine glp_set_rii sets (changes) the scale factor r[i,i] for * i-th row of the specified problem object. */ void glp_set_rii(glp_prob *lp, int i, double rii) { if (!(1 <= i && i <= lp->m)) xerror("glp_set_rii: i = %d; row number out of range\n", i); if (rii <= 0.0) xerror("glp_set_rii: i = %d; rii = %g; invalid scale factor\n", i, rii); if (lp->valid && lp->row[i]->rii != rii) { GLPAIJ *aij; for (aij = lp->row[i]->ptr; aij != NULL; aij = aij->r_next) { if (aij->col->stat == GLP_BS) { /* invalidate the basis factorization */ lp->valid = 0; break; } } } lp->row[i]->rii = rii; return; } /*********************************************************************** * NAME * * glp_set sjj - set (change) column scale factor * * SYNOPSIS * * void glp_set_sjj(glp_prob *lp, int j, double sjj); * * DESCRIPTION * * The routine glp_set_sjj sets (changes) the scale factor s[j,j] for * j-th column of the specified problem object. */ void glp_set_sjj(glp_prob *lp, int j, double sjj) { if (!(1 <= j && j <= lp->n)) xerror("glp_set_sjj: j = %d; column number out of range\n", j); if (sjj <= 0.0) xerror("glp_set_sjj: j = %d; sjj = %g; invalid scale factor\n", j, sjj); if (lp->valid && lp->col[j]->sjj != sjj && lp->col[j]->stat == GLP_BS) { /* invalidate the basis factorization */ lp->valid = 0; } lp->col[j]->sjj = sjj; return; } /*********************************************************************** * NAME * * glp_get_rii - retrieve row scale factor * * SYNOPSIS * * double glp_get_rii(glp_prob *lp, int i); * * RETURNS * * The routine glp_get_rii returns current scale factor r[i,i] for i-th * row of the specified problem object. */ double glp_get_rii(glp_prob *lp, int i) { if (!(1 <= i && i <= lp->m)) xerror("glp_get_rii: i = %d; row number out of range\n", i); return lp->row[i]->rii; } /*********************************************************************** * NAME * * glp_get_sjj - retrieve column scale factor * * SYNOPSIS * * double glp_get_sjj(glp_prob *lp, int j); * * RETURNS * * The routine glp_get_sjj returns current scale factor s[j,j] for j-th * column of the specified problem object. */ double glp_get_sjj(glp_prob *lp, int j) { if (!(1 <= j && j <= lp->n)) xerror("glp_get_sjj: j = %d; column number out of range\n", j); return lp->col[j]->sjj; } /*********************************************************************** * NAME * * glp_unscale_prob - unscale problem data * * SYNOPSIS * * void glp_unscale_prob(glp_prob *lp); * * DESCRIPTION * * The routine glp_unscale_prob performs unscaling of problem data for * the specified problem object. * * "Unscaling" means replacing the current scaling matrices R and S by * unity matrices that cancels the scaling effect. */ void glp_unscale_prob(glp_prob *lp) { int m = glp_get_num_rows(lp); int n = glp_get_num_cols(lp); int i, j; for (i = 1; i <= m; i++) glp_set_rii(lp, i, 1.0); for (j = 1; j <= n; j++) glp_set_sjj(lp, j, 1.0); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glprgr.h0000644000175000017500000000245311632352620020111 0ustar csheltoncshelton/* glprgr.h (raster graphics) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPRGR_H #define GLPRGR_H #define rgr_write_bmp16 _glp_rgr_write_bmp16 int rgr_write_bmp16(const char *fname, int m, int n, const char map[]); /* write 16-color raster image in BMP file format */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glplpx01.c0000644000175000017500000013756511632352620020273 0ustar csheltoncshelton/* glplpx01.c (obsolete API routines) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" struct LPXCPS { /* control parameters and statistics */ int msg_lev; /* level of messages output by the solver: 0 - no output 1 - error messages only 2 - normal output 3 - full output (includes informational messages) */ int scale; /* scaling option: 0 - no scaling 1 - equilibration scaling 2 - geometric mean scaling 3 - geometric mean scaling, then equilibration scaling */ int dual; /* dual simplex option: 0 - use primal simplex 1 - use dual simplex */ int price; /* pricing option (for both primal and dual simplex): 0 - textbook pricing 1 - steepest edge pricing */ double relax; /* relaxation parameter used in the ratio test; if it is zero, the textbook ratio test is used; if it is non-zero (should be positive), Harris' two-pass ratio test is used; in the latter case on the first pass basic variables (in the case of primal simplex) or reduced costs of non-basic variables (in the case of dual simplex) are allowed to slightly violate their bounds, but not more than (relax * tol_bnd) or (relax * tol_dj) (thus, relax is a percentage of tol_bnd or tol_dj) */ double tol_bnd; /* relative tolerance used to check if the current basic solution is primal feasible */ double tol_dj; /* absolute tolerance used to check if the current basic solution is dual feasible */ double tol_piv; /* relative tolerance used to choose eligible pivotal elements of the simplex table in the ratio test */ int round; /* solution rounding option: 0 - report all computed values and reduced costs "as is" 1 - if possible (allowed by the tolerances), replace computed values and reduced costs which are close to zero by exact zeros */ double obj_ll; /* lower limit of the objective function; if on the phase II the objective function reaches this limit and continues decreasing, the solver stops the search */ double obj_ul; /* upper limit of the objective function; if on the phase II the objective function reaches this limit and continues increasing, the solver stops the search */ int it_lim; /* simplex iterations limit; if this value is positive, it is decreased by one each time when one simplex iteration has been performed, and reaching zero value signals the solver to stop the search; negative value means no iterations limit */ double tm_lim; /* searching time limit, in seconds; if this value is positive, it is decreased each time when one simplex iteration has been performed by the amount of time spent for the iteration, and reaching zero value signals the solver to stop the search; negative value means no time limit */ int out_frq; /* output frequency, in iterations; this parameter specifies how frequently the solver sends information about the solution to the standard output */ double out_dly; /* output delay, in seconds; this parameter specifies how long the solver should delay sending information about the solution to the standard output; zero value means no delay */ int branch; /* MIP */ /* branching heuristic: 0 - branch on first variable 1 - branch on last variable 2 - branch using heuristic by Driebeck and Tomlin 3 - branch on most fractional variable */ int btrack; /* MIP */ /* backtracking heuristic: 0 - select most recent node (depth first search) 1 - select earliest node (breadth first search) 2 - select node using the best projection heuristic 3 - select node with best local bound */ double tol_int; /* MIP */ /* absolute tolerance used to check if the current basic solution is integer feasible */ double tol_obj; /* MIP */ /* relative tolerance used to check if the value of the objective function is not better than in the best known integer feasible solution */ int mps_info; /* lpx_write_mps */ /* if this flag is set, the routine lpx_write_mps outputs several comment cards that contains some information about the problem; otherwise the routine outputs no comment cards */ int mps_obj; /* lpx_write_mps */ /* this parameter tells the routine lpx_write_mps how to output the objective function row: 0 - never output objective function row 1 - always output objective function row 2 - output objective function row if and only if the problem has no free rows */ int mps_orig; /* lpx_write_mps */ /* if this flag is set, the routine lpx_write_mps uses original row and column symbolic names; otherwise the routine generates plain names using ordinal numbers of rows and columns */ int mps_wide; /* lpx_write_mps */ /* if this flag is set, the routine lpx_write_mps uses all data fields; otherwise the routine keeps fields 5 and 6 empty */ int mps_free; /* lpx_write_mps */ /* if this flag is set, the routine lpx_write_mps omits column and vector names everytime if possible (free style); otherwise the routine never omits these names (pedantic style) */ int mps_skip; /* lpx_write_mps */ /* if this flag is set, the routine lpx_write_mps skips empty columns (i.e. which has no constraint coefficients); otherwise the routine outputs all columns */ int lpt_orig; /* lpx_write_lpt */ /* if this flag is set, the routine lpx_write_lpt uses original row and column symbolic names; otherwise the routine generates plain names using ordinal numbers of rows and columns */ int presol; /* lpx_simplex */ /* LP presolver option: 0 - do not use LP presolver 1 - use LP presolver */ int binarize; /* lpx_intopt */ /* if this flag is set, the routine lpx_intopt replaces integer columns by binary ones */ int use_cuts; /* lpx_intopt */ /* if this flag is set, the routine lpx_intopt tries generating cutting planes: LPX_C_COVER - mixed cover cuts LPX_C_CLIQUE - clique cuts LPX_C_GOMORY - Gomory's mixed integer cuts LPX_C_ALL - all cuts */ double mip_gap; /* MIP */ /* relative MIP gap tolerance */ }; LPX *lpx_create_prob(void) { /* create problem object */ return glp_create_prob(); } void lpx_set_prob_name(LPX *lp, const char *name) { /* assign (change) problem name */ glp_set_prob_name(lp, name); return; } void lpx_set_obj_name(LPX *lp, const char *name) { /* assign (change) objective function name */ glp_set_obj_name(lp, name); return; } void lpx_set_obj_dir(LPX *lp, int dir) { /* set (change) optimization direction flag */ glp_set_obj_dir(lp, dir - LPX_MIN + GLP_MIN); return; } int lpx_add_rows(LPX *lp, int nrs) { /* add new rows to problem object */ return glp_add_rows(lp, nrs); } int lpx_add_cols(LPX *lp, int ncs) { /* add new columns to problem object */ return glp_add_cols(lp, ncs); } void lpx_set_row_name(LPX *lp, int i, const char *name) { /* assign (change) row name */ glp_set_row_name(lp, i, name); return; } void lpx_set_col_name(LPX *lp, int j, const char *name) { /* assign (change) column name */ glp_set_col_name(lp, j, name); return; } void lpx_set_row_bnds(LPX *lp, int i, int type, double lb, double ub) { /* set (change) row bounds */ glp_set_row_bnds(lp, i, type - LPX_FR + GLP_FR, lb, ub); return; } void lpx_set_col_bnds(LPX *lp, int j, int type, double lb, double ub) { /* set (change) column bounds */ glp_set_col_bnds(lp, j, type - LPX_FR + GLP_FR, lb, ub); return; } void lpx_set_obj_coef(glp_prob *lp, int j, double coef) { /* set (change) obj. coefficient or constant term */ glp_set_obj_coef(lp, j, coef); return; } void lpx_set_mat_row(LPX *lp, int i, int len, const int ind[], const double val[]) { /* set (replace) row of the constraint matrix */ glp_set_mat_row(lp, i, len, ind, val); return; } void lpx_set_mat_col(LPX *lp, int j, int len, const int ind[], const double val[]) { /* set (replace) column of the constraint matrix */ glp_set_mat_col(lp, j, len, ind, val); return; } void lpx_load_matrix(LPX *lp, int ne, const int ia[], const int ja[], const double ar[]) { /* load (replace) the whole constraint matrix */ glp_load_matrix(lp, ne, ia, ja, ar); return; } void lpx_del_rows(LPX *lp, int nrs, const int num[]) { /* delete specified rows from problem object */ glp_del_rows(lp, nrs, num); return; } void lpx_del_cols(LPX *lp, int ncs, const int num[]) { /* delete specified columns from problem object */ glp_del_cols(lp, ncs, num); return; } void lpx_delete_prob(LPX *lp) { /* delete problem object */ glp_delete_prob(lp); return; } const char *lpx_get_prob_name(LPX *lp) { /* retrieve problem name */ return glp_get_prob_name(lp); } const char *lpx_get_obj_name(LPX *lp) { /* retrieve objective function name */ return glp_get_obj_name(lp); } int lpx_get_obj_dir(LPX *lp) { /* retrieve optimization direction flag */ return glp_get_obj_dir(lp) - GLP_MIN + LPX_MIN; } int lpx_get_num_rows(LPX *lp) { /* retrieve number of rows */ return glp_get_num_rows(lp); } int lpx_get_num_cols(LPX *lp) { /* retrieve number of columns */ return glp_get_num_cols(lp); } const char *lpx_get_row_name(LPX *lp, int i) { /* retrieve row name */ return glp_get_row_name(lp, i); } const char *lpx_get_col_name(LPX *lp, int j) { /* retrieve column name */ return glp_get_col_name(lp, j); } int lpx_get_row_type(LPX *lp, int i) { /* retrieve row type */ return glp_get_row_type(lp, i) - GLP_FR + LPX_FR; } double lpx_get_row_lb(glp_prob *lp, int i) { /* retrieve row lower bound */ double lb; lb = glp_get_row_lb(lp, i); if (lb == -DBL_MAX) lb = 0.0; return lb; } double lpx_get_row_ub(glp_prob *lp, int i) { /* retrieve row upper bound */ double ub; ub = glp_get_row_ub(lp, i); if (ub == +DBL_MAX) ub = 0.0; return ub; } void lpx_get_row_bnds(glp_prob *lp, int i, int *typx, double *lb, double *ub) { /* retrieve row bounds */ if (typx != NULL) *typx = lpx_get_row_type(lp, i); if (lb != NULL) *lb = lpx_get_row_lb(lp, i); if (ub != NULL) *ub = lpx_get_row_ub(lp, i); return; } int lpx_get_col_type(LPX *lp, int j) { /* retrieve column type */ return glp_get_col_type(lp, j) - GLP_FR + LPX_FR; } double lpx_get_col_lb(glp_prob *lp, int j) { /* retrieve column lower bound */ double lb; lb = glp_get_col_lb(lp, j); if (lb == -DBL_MAX) lb = 0.0; return lb; } double lpx_get_col_ub(glp_prob *lp, int j) { /* retrieve column upper bound */ double ub; ub = glp_get_col_ub(lp, j); if (ub == +DBL_MAX) ub = 0.0; return ub; } void lpx_get_col_bnds(glp_prob *lp, int j, int *typx, double *lb, double *ub) { /* retrieve column bounds */ if (typx != NULL) *typx = lpx_get_col_type(lp, j); if (lb != NULL) *lb = lpx_get_col_lb(lp, j); if (ub != NULL) *ub = lpx_get_col_ub(lp, j); return; } double lpx_get_obj_coef(LPX *lp, int j) { /* retrieve obj. coefficient or constant term */ return glp_get_obj_coef(lp, j); } int lpx_get_num_nz(LPX *lp) { /* retrieve number of constraint coefficients */ return glp_get_num_nz(lp); } int lpx_get_mat_row(LPX *lp, int i, int ind[], double val[]) { /* retrieve row of the constraint matrix */ return glp_get_mat_row(lp, i, ind, val); } int lpx_get_mat_col(LPX *lp, int j, int ind[], double val[]) { /* retrieve column of the constraint matrix */ return glp_get_mat_col(lp, j, ind, val); } void lpx_create_index(LPX *lp) { /* create the name index */ glp_create_index(lp); return; } int lpx_find_row(LPX *lp, const char *name) { /* find row by its name */ return glp_find_row(lp, name); } int lpx_find_col(LPX *lp, const char *name) { /* find column by its name */ return glp_find_col(lp, name); } void lpx_delete_index(LPX *lp) { /* delete the name index */ glp_delete_index(lp); return; } void lpx_scale_prob(LPX *lp) { /* scale problem data */ switch (lpx_get_int_parm(lp, LPX_K_SCALE)) { case 0: /* no scaling */ glp_unscale_prob(lp); break; case 1: /* equilibration scaling */ glp_scale_prob(lp, GLP_SF_EQ); break; case 2: /* geometric mean scaling */ glp_scale_prob(lp, GLP_SF_GM); break; case 3: /* geometric mean scaling, then equilibration scaling */ glp_scale_prob(lp, GLP_SF_GM | GLP_SF_EQ); break; default: xassert(lp != lp); } return; } void lpx_unscale_prob(LPX *lp) { /* unscale problem data */ glp_unscale_prob(lp); return; } void lpx_set_row_stat(LPX *lp, int i, int stat) { /* set (change) row status */ glp_set_row_stat(lp, i, stat - LPX_BS + GLP_BS); return; } void lpx_set_col_stat(LPX *lp, int j, int stat) { /* set (change) column status */ glp_set_col_stat(lp, j, stat - LPX_BS + GLP_BS); return; } void lpx_std_basis(LPX *lp) { /* construct standard initial LP basis */ glp_std_basis(lp); return; } void lpx_adv_basis(LPX *lp) { /* construct advanced initial LP basis */ glp_adv_basis(lp, 0); return; } void lpx_cpx_basis(LPX *lp) { /* construct Bixby's initial LP basis */ glp_cpx_basis(lp); return; } static void fill_smcp(LPX *lp, glp_smcp *parm) { glp_init_smcp(parm); switch (lpx_get_int_parm(lp, LPX_K_MSGLEV)) { case 0: parm->msg_lev = GLP_MSG_OFF; break; case 1: parm->msg_lev = GLP_MSG_ERR; break; case 2: parm->msg_lev = GLP_MSG_ON; break; case 3: parm->msg_lev = GLP_MSG_ALL; break; default: xassert(lp != lp); } switch (lpx_get_int_parm(lp, LPX_K_DUAL)) { case 0: parm->meth = GLP_PRIMAL; break; case 1: parm->meth = GLP_DUAL; break; default: xassert(lp != lp); } switch (lpx_get_int_parm(lp, LPX_K_PRICE)) { case 0: parm->pricing = GLP_PT_STD; break; case 1: parm->pricing = GLP_PT_PSE; break; default: xassert(lp != lp); } if (lpx_get_real_parm(lp, LPX_K_RELAX) == 0.0) parm->r_test = GLP_RT_STD; else parm->r_test = GLP_RT_HAR; parm->tol_bnd = lpx_get_real_parm(lp, LPX_K_TOLBND); parm->tol_dj = lpx_get_real_parm(lp, LPX_K_TOLDJ); parm->tol_piv = lpx_get_real_parm(lp, LPX_K_TOLPIV); parm->obj_ll = lpx_get_real_parm(lp, LPX_K_OBJLL); parm->obj_ul = lpx_get_real_parm(lp, LPX_K_OBJUL); if (lpx_get_int_parm(lp, LPX_K_ITLIM) < 0) parm->it_lim = INT_MAX; else parm->it_lim = lpx_get_int_parm(lp, LPX_K_ITLIM); if (lpx_get_real_parm(lp, LPX_K_TMLIM) < 0.0) parm->tm_lim = INT_MAX; else parm->tm_lim = (int)(1000.0 * lpx_get_real_parm(lp, LPX_K_TMLIM)); parm->out_frq = lpx_get_int_parm(lp, LPX_K_OUTFRQ); parm->out_dly = (int)(1000.0 * lpx_get_real_parm(lp, LPX_K_OUTDLY)); switch (lpx_get_int_parm(lp, LPX_K_PRESOL)) { case 0: parm->presolve = GLP_OFF; break; case 1: parm->presolve = GLP_ON; break; default: xassert(lp != lp); } return; } int lpx_simplex(LPX *lp) { /* easy-to-use driver to the simplex method */ glp_smcp parm; int ret; fill_smcp(lp, &parm); ret = glp_simplex(lp, &parm); switch (ret) { case 0: ret = LPX_E_OK; break; case GLP_EBADB: case GLP_ESING: case GLP_ECOND: case GLP_EBOUND: ret = LPX_E_FAULT; break; case GLP_EFAIL: ret = LPX_E_SING; break; case GLP_EOBJLL: ret = LPX_E_OBJLL; break; case GLP_EOBJUL: ret = LPX_E_OBJUL; break; case GLP_EITLIM: ret = LPX_E_ITLIM; break; case GLP_ETMLIM: ret = LPX_E_TMLIM; break; case GLP_ENOPFS: ret = LPX_E_NOPFS; break; case GLP_ENODFS: ret = LPX_E_NODFS; break; default: xassert(ret != ret); } return ret; } int lpx_exact(LPX *lp) { /* easy-to-use driver to the exact simplex method */ glp_smcp parm; int ret; fill_smcp(lp, &parm); ret = glp_exact(lp, &parm); switch (ret) { case 0: ret = LPX_E_OK; break; case GLP_EBADB: case GLP_ESING: case GLP_EBOUND: case GLP_EFAIL: ret = LPX_E_FAULT; break; case GLP_EITLIM: ret = LPX_E_ITLIM; break; case GLP_ETMLIM: ret = LPX_E_TMLIM; break; default: xassert(ret != ret); } return ret; } int lpx_get_status(glp_prob *lp) { /* retrieve generic status of basic solution */ int status; switch (glp_get_status(lp)) { case GLP_OPT: status = LPX_OPT; break; case GLP_FEAS: status = LPX_FEAS; break; case GLP_INFEAS: status = LPX_INFEAS; break; case GLP_NOFEAS: status = LPX_NOFEAS; break; case GLP_UNBND: status = LPX_UNBND; break; case GLP_UNDEF: status = LPX_UNDEF; break; default: xassert(lp != lp); } return status; } int lpx_get_prim_stat(glp_prob *lp) { /* retrieve status of primal basic solution */ return glp_get_prim_stat(lp) - GLP_UNDEF + LPX_P_UNDEF; } int lpx_get_dual_stat(glp_prob *lp) { /* retrieve status of dual basic solution */ return glp_get_dual_stat(lp) - GLP_UNDEF + LPX_D_UNDEF; } double lpx_get_obj_val(LPX *lp) { /* retrieve objective value (basic solution) */ return glp_get_obj_val(lp); } int lpx_get_row_stat(LPX *lp, int i) { /* retrieve row status (basic solution) */ return glp_get_row_stat(lp, i) - GLP_BS + LPX_BS; } double lpx_get_row_prim(LPX *lp, int i) { /* retrieve row primal value (basic solution) */ return glp_get_row_prim(lp, i); } double lpx_get_row_dual(LPX *lp, int i) { /* retrieve row dual value (basic solution) */ return glp_get_row_dual(lp, i); } void lpx_get_row_info(glp_prob *lp, int i, int *tagx, double *vx, double *dx) { /* obtain row solution information */ if (tagx != NULL) *tagx = lpx_get_row_stat(lp, i); if (vx != NULL) *vx = lpx_get_row_prim(lp, i); if (dx != NULL) *dx = lpx_get_row_dual(lp, i); return; } int lpx_get_col_stat(LPX *lp, int j) { /* retrieve column status (basic solution) */ return glp_get_col_stat(lp, j) - GLP_BS + LPX_BS; } double lpx_get_col_prim(LPX *lp, int j) { /* retrieve column primal value (basic solution) */ return glp_get_col_prim(lp, j); } double lpx_get_col_dual(glp_prob *lp, int j) { /* retrieve column dual value (basic solution) */ return glp_get_col_dual(lp, j); } void lpx_get_col_info(glp_prob *lp, int j, int *tagx, double *vx, double *dx) { /* obtain column solution information */ if (tagx != NULL) *tagx = lpx_get_col_stat(lp, j); if (vx != NULL) *vx = lpx_get_col_prim(lp, j); if (dx != NULL) *dx = lpx_get_col_dual(lp, j); return; } int lpx_get_ray_info(LPX *lp) { /* determine what causes primal unboundness */ return glp_get_unbnd_ray(lp); } void lpx_check_kkt(LPX *lp, int scaled, LPXKKT *kkt) { /* check Karush-Kuhn-Tucker conditions */ int ae_ind, re_ind; double ae_max, re_max; xassert(scaled == scaled); _glp_check_kkt(lp, GLP_SOL, GLP_KKT_PE, &ae_max, &ae_ind, &re_max, &re_ind); kkt->pe_ae_max = ae_max; kkt->pe_ae_row = ae_ind; kkt->pe_re_max = re_max; kkt->pe_re_row = re_ind; if (re_max <= 1e-9) kkt->pe_quality = 'H'; else if (re_max <= 1e-6) kkt->pe_quality = 'M'; else if (re_max <= 1e-3) kkt->pe_quality = 'L'; else kkt->pe_quality = '?'; _glp_check_kkt(lp, GLP_SOL, GLP_KKT_PB, &ae_max, &ae_ind, &re_max, &re_ind); kkt->pb_ae_max = ae_max; kkt->pb_ae_ind = ae_ind; kkt->pb_re_max = re_max; kkt->pb_re_ind = re_ind; if (re_max <= 1e-9) kkt->pb_quality = 'H'; else if (re_max <= 1e-6) kkt->pb_quality = 'M'; else if (re_max <= 1e-3) kkt->pb_quality = 'L'; else kkt->pb_quality = '?'; _glp_check_kkt(lp, GLP_SOL, GLP_KKT_DE, &ae_max, &ae_ind, &re_max, &re_ind); kkt->de_ae_max = ae_max; if (ae_ind == 0) kkt->de_ae_col = 0; else kkt->de_ae_col = ae_ind - lp->m; kkt->de_re_max = re_max; if (re_ind == 0) kkt->de_re_col = 0; else kkt->de_re_col = ae_ind - lp->m; if (re_max <= 1e-9) kkt->de_quality = 'H'; else if (re_max <= 1e-6) kkt->de_quality = 'M'; else if (re_max <= 1e-3) kkt->de_quality = 'L'; else kkt->de_quality = '?'; _glp_check_kkt(lp, GLP_SOL, GLP_KKT_DB, &ae_max, &ae_ind, &re_max, &re_ind); kkt->db_ae_max = ae_max; kkt->db_ae_ind = ae_ind; kkt->db_re_max = re_max; kkt->db_re_ind = re_ind; if (re_max <= 1e-9) kkt->db_quality = 'H'; else if (re_max <= 1e-6) kkt->db_quality = 'M'; else if (re_max <= 1e-3) kkt->db_quality = 'L'; else kkt->db_quality = '?'; kkt->cs_ae_max = 0.0, kkt->cs_ae_ind = 0; kkt->cs_re_max = 0.0, kkt->cs_re_ind = 0; kkt->cs_quality = 'H'; return; } int lpx_warm_up(LPX *lp) { /* "warm up" LP basis */ int ret; ret = glp_warm_up(lp); if (ret == 0) ret = LPX_E_OK; else if (ret == GLP_EBADB) ret = LPX_E_BADB; else if (ret == GLP_ESING) ret = LPX_E_SING; else if (ret == GLP_ECOND) ret = LPX_E_SING; else xassert(ret != ret); return ret; } int lpx_eval_tab_row(LPX *lp, int k, int ind[], double val[]) { /* compute row of the simplex tableau */ return glp_eval_tab_row(lp, k, ind, val); } int lpx_eval_tab_col(LPX *lp, int k, int ind[], double val[]) { /* compute column of the simplex tableau */ return glp_eval_tab_col(lp, k, ind, val); } int lpx_transform_row(LPX *lp, int len, int ind[], double val[]) { /* transform explicitly specified row */ return glp_transform_row(lp, len, ind, val); } int lpx_transform_col(LPX *lp, int len, int ind[], double val[]) { /* transform explicitly specified column */ return glp_transform_col(lp, len, ind, val); } int lpx_prim_ratio_test(LPX *lp, int len, const int ind[], const double val[], int how, double tol) { /* perform primal ratio test */ int piv; piv = glp_prim_rtest(lp, len, ind, val, how, tol); xassert(0 <= piv && piv <= len); return piv == 0 ? 0 : ind[piv]; } int lpx_dual_ratio_test(LPX *lp, int len, const int ind[], const double val[], int how, double tol) { /* perform dual ratio test */ int piv; piv = glp_dual_rtest(lp, len, ind, val, how, tol); xassert(0 <= piv && piv <= len); return piv == 0 ? 0 : ind[piv]; } int lpx_interior(LPX *lp) { /* easy-to-use driver to the interior-point method */ int ret; ret = glp_interior(lp, NULL); switch (ret) { case 0: ret = LPX_E_OK; break; case GLP_EFAIL: ret = LPX_E_FAULT; break; case GLP_ENOFEAS: ret = LPX_E_NOFEAS; break; case GLP_ENOCVG: ret = LPX_E_NOCONV; break; case GLP_EITLIM: ret = LPX_E_ITLIM; break; case GLP_EINSTAB: ret = LPX_E_INSTAB; break; default: xassert(ret != ret); } return ret; } int lpx_ipt_status(glp_prob *lp) { /* retrieve status of interior-point solution */ int status; switch (glp_ipt_status(lp)) { case GLP_UNDEF: status = LPX_T_UNDEF; break; case GLP_OPT: status = LPX_T_OPT; break; default: xassert(lp != lp); } return status; } double lpx_ipt_obj_val(LPX *lp) { /* retrieve objective value (interior point) */ return glp_ipt_obj_val(lp); } double lpx_ipt_row_prim(LPX *lp, int i) { /* retrieve row primal value (interior point) */ return glp_ipt_row_prim(lp, i); } double lpx_ipt_row_dual(LPX *lp, int i) { /* retrieve row dual value (interior point) */ return glp_ipt_row_dual(lp, i); } double lpx_ipt_col_prim(LPX *lp, int j) { /* retrieve column primal value (interior point) */ return glp_ipt_col_prim(lp, j); } double lpx_ipt_col_dual(LPX *lp, int j) { /* retrieve column dual value (interior point) */ return glp_ipt_col_dual(lp, j); } void lpx_set_class(LPX *lp, int klass) { /* set problem class */ xassert(lp == lp); if (!(klass == LPX_LP || klass == LPX_MIP)) xerror("lpx_set_class: invalid problem class\n"); return; } int lpx_get_class(LPX *lp) { /* determine problem klass */ return glp_get_num_int(lp) == 0 ? LPX_LP : LPX_MIP; } void lpx_set_col_kind(LPX *lp, int j, int kind) { /* set (change) column kind */ glp_set_col_kind(lp, j, kind - LPX_CV + GLP_CV); return; } int lpx_get_col_kind(LPX *lp, int j) { /* retrieve column kind */ return glp_get_col_kind(lp, j) == GLP_CV ? LPX_CV : LPX_IV; } int lpx_get_num_int(LPX *lp) { /* retrieve number of integer columns */ return glp_get_num_int(lp); } int lpx_get_num_bin(LPX *lp) { /* retrieve number of binary columns */ return glp_get_num_bin(lp); } static int solve_mip(LPX *lp, int presolve) { glp_iocp parm; int ret; glp_init_iocp(&parm); switch (lpx_get_int_parm(lp, LPX_K_MSGLEV)) { case 0: parm.msg_lev = GLP_MSG_OFF; break; case 1: parm.msg_lev = GLP_MSG_ERR; break; case 2: parm.msg_lev = GLP_MSG_ON; break; case 3: parm.msg_lev = GLP_MSG_ALL; break; default: xassert(lp != lp); } switch (lpx_get_int_parm(lp, LPX_K_BRANCH)) { case 0: parm.br_tech = GLP_BR_FFV; break; case 1: parm.br_tech = GLP_BR_LFV; break; case 2: parm.br_tech = GLP_BR_DTH; break; case 3: parm.br_tech = GLP_BR_MFV; break; default: xassert(lp != lp); } switch (lpx_get_int_parm(lp, LPX_K_BTRACK)) { case 0: parm.bt_tech = GLP_BT_DFS; break; case 1: parm.bt_tech = GLP_BT_BFS; break; case 2: parm.bt_tech = GLP_BT_BPH; break; case 3: parm.bt_tech = GLP_BT_BLB; break; default: xassert(lp != lp); } parm.tol_int = lpx_get_real_parm(lp, LPX_K_TOLINT); parm.tol_obj = lpx_get_real_parm(lp, LPX_K_TOLOBJ); if (lpx_get_real_parm(lp, LPX_K_TMLIM) < 0.0 || lpx_get_real_parm(lp, LPX_K_TMLIM) > 1e6) parm.tm_lim = INT_MAX; else parm.tm_lim = (int)(1000.0 * lpx_get_real_parm(lp, LPX_K_TMLIM)); parm.mip_gap = lpx_get_real_parm(lp, LPX_K_MIPGAP); if (lpx_get_int_parm(lp, LPX_K_USECUTS) & LPX_C_GOMORY) parm.gmi_cuts = GLP_ON; else parm.gmi_cuts = GLP_OFF; if (lpx_get_int_parm(lp, LPX_K_USECUTS) & LPX_C_MIR) parm.mir_cuts = GLP_ON; else parm.mir_cuts = GLP_OFF; if (lpx_get_int_parm(lp, LPX_K_USECUTS) & LPX_C_COVER) parm.cov_cuts = GLP_ON; else parm.cov_cuts = GLP_OFF; if (lpx_get_int_parm(lp, LPX_K_USECUTS) & LPX_C_CLIQUE) parm.clq_cuts = GLP_ON; else parm.clq_cuts = GLP_OFF; parm.presolve = presolve; if (lpx_get_int_parm(lp, LPX_K_BINARIZE)) parm.binarize = GLP_ON; ret = glp_intopt(lp, &parm); switch (ret) { case 0: ret = LPX_E_OK; break; case GLP_ENOPFS: ret = LPX_E_NOPFS; break; case GLP_ENODFS: ret = LPX_E_NODFS; break; case GLP_EBOUND: case GLP_EROOT: ret = LPX_E_FAULT; break; case GLP_EFAIL: ret = LPX_E_SING; break; case GLP_EMIPGAP: ret = LPX_E_MIPGAP; break; case GLP_ETMLIM: ret = LPX_E_TMLIM; break; default: xassert(ret != ret); } return ret; } int lpx_integer(LPX *lp) { /* easy-to-use driver to the branch-and-bound method */ return solve_mip(lp, GLP_OFF); } int lpx_intopt(LPX *lp) { /* easy-to-use driver to the branch-and-bound method */ return solve_mip(lp, GLP_ON); } int lpx_mip_status(glp_prob *lp) { /* retrieve status of MIP solution */ int status; switch (glp_mip_status(lp)) { case GLP_UNDEF: status = LPX_I_UNDEF; break; case GLP_OPT: status = LPX_I_OPT; break; case GLP_FEAS: status = LPX_I_FEAS; break; case GLP_NOFEAS: status = LPX_I_NOFEAS; break; default: xassert(lp != lp); } return status; } double lpx_mip_obj_val(LPX *lp) { /* retrieve objective value (MIP solution) */ return glp_mip_obj_val(lp); } double lpx_mip_row_val(LPX *lp, int i) { /* retrieve row value (MIP solution) */ return glp_mip_row_val(lp, i); } double lpx_mip_col_val(LPX *lp, int j) { /* retrieve column value (MIP solution) */ return glp_mip_col_val(lp, j); } void lpx_check_int(LPX *lp, LPXKKT *kkt) { /* check integer feasibility conditions */ int ae_ind, re_ind; double ae_max, re_max; _glp_check_kkt(lp, GLP_MIP, GLP_KKT_PE, &ae_max, &ae_ind, &re_max, &re_ind); kkt->pe_ae_max = ae_max; kkt->pe_ae_row = ae_ind; kkt->pe_re_max = re_max; kkt->pe_re_row = re_ind; if (re_max <= 1e-9) kkt->pe_quality = 'H'; else if (re_max <= 1e-6) kkt->pe_quality = 'M'; else if (re_max <= 1e-3) kkt->pe_quality = 'L'; else kkt->pe_quality = '?'; _glp_check_kkt(lp, GLP_MIP, GLP_KKT_PB, &ae_max, &ae_ind, &re_max, &re_ind); kkt->pb_ae_max = ae_max; kkt->pb_ae_ind = ae_ind; kkt->pb_re_max = re_max; kkt->pb_re_ind = re_ind; if (re_max <= 1e-9) kkt->pb_quality = 'H'; else if (re_max <= 1e-6) kkt->pb_quality = 'M'; else if (re_max <= 1e-3) kkt->pb_quality = 'L'; else kkt->pb_quality = '?'; return; } #if 1 /* 17/XI-2009 */ static void reset_parms(LPX *lp) { /* reset control parameters to default values */ struct LPXCPS *cps = lp->parms; xassert(cps != NULL); cps->msg_lev = 3; cps->scale = 1; cps->dual = 0; cps->price = 1; cps->relax = 0.07; cps->tol_bnd = 1e-7; cps->tol_dj = 1e-7; cps->tol_piv = 1e-9; cps->round = 0; cps->obj_ll = -DBL_MAX; cps->obj_ul = +DBL_MAX; cps->it_lim = -1; #if 0 /* 02/XII-2010 */ lp->it_cnt = 0; #endif cps->tm_lim = -1.0; cps->out_frq = 200; cps->out_dly = 0.0; cps->branch = 2; cps->btrack = 3; cps->tol_int = 1e-5; cps->tol_obj = 1e-7; cps->mps_info = 1; cps->mps_obj = 2; cps->mps_orig = 0; cps->mps_wide = 1; cps->mps_free = 0; cps->mps_skip = 0; cps->lpt_orig = 0; cps->presol = 0; cps->binarize = 0; cps->use_cuts = 0; cps->mip_gap = 0.0; return; } #endif #if 1 /* 17/XI-2009 */ static struct LPXCPS *access_parms(LPX *lp) { /* allocate and initialize control parameters, if necessary */ if (lp->parms == NULL) { lp->parms = xmalloc(sizeof(struct LPXCPS)); reset_parms(lp); } return lp->parms; } #endif #if 1 /* 17/XI-2009 */ void lpx_reset_parms(LPX *lp) { /* reset control parameters to default values */ access_parms(lp); reset_parms(lp); return; } #endif void lpx_set_int_parm(LPX *lp, int parm, int val) { /* set (change) integer control parameter */ #if 0 /* 17/XI-2009 */ struct LPXCPS *cps = lp->cps; #else struct LPXCPS *cps = access_parms(lp); #endif switch (parm) { case LPX_K_MSGLEV: if (!(0 <= val && val <= 3)) xerror("lpx_set_int_parm: MSGLEV = %d; invalid value\n", val); cps->msg_lev = val; break; case LPX_K_SCALE: if (!(0 <= val && val <= 3)) xerror("lpx_set_int_parm: SCALE = %d; invalid value\n", val); cps->scale = val; break; case LPX_K_DUAL: if (!(val == 0 || val == 1)) xerror("lpx_set_int_parm: DUAL = %d; invalid value\n", val); cps->dual = val; break; case LPX_K_PRICE: if (!(val == 0 || val == 1)) xerror("lpx_set_int_parm: PRICE = %d; invalid value\n", val); cps->price = val; break; case LPX_K_ROUND: if (!(val == 0 || val == 1)) xerror("lpx_set_int_parm: ROUND = %d; invalid value\n", val); cps->round = val; break; case LPX_K_ITLIM: cps->it_lim = val; break; case LPX_K_ITCNT: lp->it_cnt = val; break; case LPX_K_OUTFRQ: if (!(val > 0)) xerror("lpx_set_int_parm: OUTFRQ = %d; invalid value\n", val); cps->out_frq = val; break; case LPX_K_BRANCH: if (!(val == 0 || val == 1 || val == 2 || val == 3)) xerror("lpx_set_int_parm: BRANCH = %d; invalid value\n", val); cps->branch = val; break; case LPX_K_BTRACK: if (!(val == 0 || val == 1 || val == 2 || val == 3)) xerror("lpx_set_int_parm: BTRACK = %d; invalid value\n", val); cps->btrack = val; break; case LPX_K_MPSINFO: if (!(val == 0 || val == 1)) xerror("lpx_set_int_parm: MPSINFO = %d; invalid value\n", val); cps->mps_info = val; break; case LPX_K_MPSOBJ: if (!(val == 0 || val == 1 || val == 2)) xerror("lpx_set_int_parm: MPSOBJ = %d; invalid value\n", val); cps->mps_obj = val; break; case LPX_K_MPSORIG: if (!(val == 0 || val == 1)) xerror("lpx_set_int_parm: MPSORIG = %d; invalid value\n", val); cps->mps_orig = val; break; case LPX_K_MPSWIDE: if (!(val == 0 || val == 1)) xerror("lpx_set_int_parm: MPSWIDE = %d; invalid value\n", val); cps->mps_wide = val; break; case LPX_K_MPSFREE: if (!(val == 0 || val == 1)) xerror("lpx_set_int_parm: MPSFREE = %d; invalid value\n", val); cps->mps_free = val; break; case LPX_K_MPSSKIP: if (!(val == 0 || val == 1)) xerror("lpx_set_int_parm: MPSSKIP = %d; invalid value\n", val); cps->mps_skip = val; break; case LPX_K_LPTORIG: if (!(val == 0 || val == 1)) xerror("lpx_set_int_parm: LPTORIG = %d; invalid value\n", val); cps->lpt_orig = val; break; case LPX_K_PRESOL: if (!(val == 0 || val == 1)) xerror("lpx_set_int_parm: PRESOL = %d; invalid value\n", val); cps->presol = val; break; case LPX_K_BINARIZE: if (!(val == 0 || val == 1)) xerror("lpx_set_int_parm: BINARIZE = %d; invalid value\n" , val); cps->binarize = val; break; case LPX_K_USECUTS: if (val & ~LPX_C_ALL) xerror("lpx_set_int_parm: USECUTS = 0x%X; invalid value\n", val); cps->use_cuts = val; break; case LPX_K_BFTYPE: #if 0 if (!(1 <= val && val <= 3)) xerror("lpx_set_int_parm: BFTYPE = %d; invalid value\n", val); cps->bf_type = val; #else { glp_bfcp parm; glp_get_bfcp(lp, &parm); switch (val) { case 1: parm.type = GLP_BF_FT; break; case 2: parm.type = GLP_BF_BG; break; case 3: parm.type = GLP_BF_GR; break; default: xerror("lpx_set_int_parm: BFTYPE = %d; invalid val" "ue\n", val); } glp_set_bfcp(lp, &parm); } #endif break; default: xerror("lpx_set_int_parm: parm = %d; invalid parameter\n", parm); } return; } int lpx_get_int_parm(LPX *lp, int parm) { /* query integer control parameter */ #if 0 /* 17/XI-2009 */ struct LPXCPS *cps = lp->cps; #else struct LPXCPS *cps = access_parms(lp); #endif int val = 0; switch (parm) { case LPX_K_MSGLEV: val = cps->msg_lev; break; case LPX_K_SCALE: val = cps->scale; break; case LPX_K_DUAL: val = cps->dual; break; case LPX_K_PRICE: val = cps->price; break; case LPX_K_ROUND: val = cps->round; break; case LPX_K_ITLIM: val = cps->it_lim; break; case LPX_K_ITCNT: val = lp->it_cnt; break; case LPX_K_OUTFRQ: val = cps->out_frq; break; case LPX_K_BRANCH: val = cps->branch; break; case LPX_K_BTRACK: val = cps->btrack; break; case LPX_K_MPSINFO: val = cps->mps_info; break; case LPX_K_MPSOBJ: val = cps->mps_obj; break; case LPX_K_MPSORIG: val = cps->mps_orig; break; case LPX_K_MPSWIDE: val = cps->mps_wide; break; case LPX_K_MPSFREE: val = cps->mps_free; break; case LPX_K_MPSSKIP: val = cps->mps_skip; break; case LPX_K_LPTORIG: val = cps->lpt_orig; break; case LPX_K_PRESOL: val = cps->presol; break; case LPX_K_BINARIZE: val = cps->binarize; break; case LPX_K_USECUTS: val = cps->use_cuts; break; case LPX_K_BFTYPE: #if 0 val = cps->bf_type; break; #else { glp_bfcp parm; glp_get_bfcp(lp, &parm); switch (parm.type) { case GLP_BF_FT: val = 1; break; case GLP_BF_BG: val = 2; break; case GLP_BF_GR: val = 3; break; default: xassert(lp != lp); } } break; #endif default: xerror("lpx_get_int_parm: parm = %d; invalid parameter\n", parm); } return val; } void lpx_set_real_parm(LPX *lp, int parm, double val) { /* set (change) real control parameter */ #if 0 /* 17/XI-2009 */ struct LPXCPS *cps = lp->cps; #else struct LPXCPS *cps = access_parms(lp); #endif switch (parm) { case LPX_K_RELAX: if (!(0.0 <= val && val <= 1.0)) xerror("lpx_set_real_parm: RELAX = %g; invalid value\n", val); cps->relax = val; break; case LPX_K_TOLBND: if (!(DBL_EPSILON <= val && val <= 0.001)) xerror("lpx_set_real_parm: TOLBND = %g; invalid value\n", val); #if 0 if (cps->tol_bnd > val) { /* invalidate the basic solution */ lp->p_stat = LPX_P_UNDEF; lp->d_stat = LPX_D_UNDEF; } #endif cps->tol_bnd = val; break; case LPX_K_TOLDJ: if (!(DBL_EPSILON <= val && val <= 0.001)) xerror("lpx_set_real_parm: TOLDJ = %g; invalid value\n", val); #if 0 if (cps->tol_dj > val) { /* invalidate the basic solution */ lp->p_stat = LPX_P_UNDEF; lp->d_stat = LPX_D_UNDEF; } #endif cps->tol_dj = val; break; case LPX_K_TOLPIV: if (!(DBL_EPSILON <= val && val <= 0.001)) xerror("lpx_set_real_parm: TOLPIV = %g; invalid value\n", val); cps->tol_piv = val; break; case LPX_K_OBJLL: cps->obj_ll = val; break; case LPX_K_OBJUL: cps->obj_ul = val; break; case LPX_K_TMLIM: cps->tm_lim = val; break; case LPX_K_OUTDLY: cps->out_dly = val; break; case LPX_K_TOLINT: if (!(DBL_EPSILON <= val && val <= 0.001)) xerror("lpx_set_real_parm: TOLINT = %g; invalid value\n", val); cps->tol_int = val; break; case LPX_K_TOLOBJ: if (!(DBL_EPSILON <= val && val <= 0.001)) xerror("lpx_set_real_parm: TOLOBJ = %g; invalid value\n", val); cps->tol_obj = val; break; case LPX_K_MIPGAP: if (val < 0.0) xerror("lpx_set_real_parm: MIPGAP = %g; invalid value\n", val); cps->mip_gap = val; break; default: xerror("lpx_set_real_parm: parm = %d; invalid parameter\n", parm); } return; } double lpx_get_real_parm(LPX *lp, int parm) { /* query real control parameter */ #if 0 /* 17/XI-2009 */ struct LPXCPS *cps = lp->cps; #else struct LPXCPS *cps = access_parms(lp); #endif double val = 0.0; switch (parm) { case LPX_K_RELAX: val = cps->relax; break; case LPX_K_TOLBND: val = cps->tol_bnd; break; case LPX_K_TOLDJ: val = cps->tol_dj; break; case LPX_K_TOLPIV: val = cps->tol_piv; break; case LPX_K_OBJLL: val = cps->obj_ll; break; case LPX_K_OBJUL: val = cps->obj_ul; break; case LPX_K_TMLIM: val = cps->tm_lim; break; case LPX_K_OUTDLY: val = cps->out_dly; break; case LPX_K_TOLINT: val = cps->tol_int; break; case LPX_K_TOLOBJ: val = cps->tol_obj; break; case LPX_K_MIPGAP: val = cps->mip_gap; break; default: xerror("lpx_get_real_parm: parm = %d; invalid parameter\n", parm); } return val; } LPX *lpx_read_mps(const char *fname) { /* read problem data in fixed MPS format */ LPX *lp = lpx_create_prob(); if (glp_read_mps(lp, GLP_MPS_DECK, NULL, fname)) lpx_delete_prob(lp), lp = NULL; return lp; } int lpx_write_mps(LPX *lp, const char *fname) { /* write problem data in fixed MPS format */ return glp_write_mps(lp, GLP_MPS_DECK, NULL, fname); } int lpx_read_bas(LPX *lp, const char *fname) { /* read LP basis in fixed MPS format */ #if 0 /* 13/IV-2009 */ return read_bas(lp, fname); #else xassert(lp == lp); xassert(fname == fname); xerror("lpx_read_bas: operation not supported\n"); return 0; #endif } int lpx_write_bas(LPX *lp, const char *fname) { /* write LP basis in fixed MPS format */ #if 0 /* 13/IV-2009 */ return write_bas(lp, fname); #else xassert(lp == lp); xassert(fname == fname); xerror("lpx_write_bas: operation not supported\n"); return 0; #endif } LPX *lpx_read_freemps(const char *fname) { /* read problem data in free MPS format */ LPX *lp = lpx_create_prob(); if (glp_read_mps(lp, GLP_MPS_FILE, NULL, fname)) lpx_delete_prob(lp), lp = NULL; return lp; } int lpx_write_freemps(LPX *lp, const char *fname) { /* write problem data in free MPS format */ return glp_write_mps(lp, GLP_MPS_FILE, NULL, fname); } LPX *lpx_read_cpxlp(const char *fname) { /* read problem data in CPLEX LP format */ LPX *lp; lp = lpx_create_prob(); if (glp_read_lp(lp, NULL, fname)) lpx_delete_prob(lp), lp = NULL; return lp; } int lpx_write_cpxlp(LPX *lp, const char *fname) { /* write problem data in CPLEX LP format */ return glp_write_lp(lp, NULL, fname); } LPX *lpx_read_model(const char *model, const char *data, const char *output) { /* read LP/MIP model written in GNU MathProg language */ LPX *lp = NULL; glp_tran *tran; /* allocate the translator workspace */ tran = glp_mpl_alloc_wksp(); /* read model section and optional data section */ if (glp_mpl_read_model(tran, model, data != NULL)) goto done; /* read separate data section, if required */ if (data != NULL) if (glp_mpl_read_data(tran, data)) goto done; /* generate the model */ if (glp_mpl_generate(tran, output)) goto done; /* build the problem instance from the model */ lp = glp_create_prob(); glp_mpl_build_prob(tran, lp); done: /* free the translator workspace */ glp_mpl_free_wksp(tran); /* bring the problem object to the calling program */ return lp; } int lpx_print_prob(LPX *lp, const char *fname) { /* write problem data in plain text format */ return glp_write_lp(lp, NULL, fname); } int lpx_print_sol(LPX *lp, const char *fname) { /* write LP problem solution in printable format */ return glp_print_sol(lp, fname); } int lpx_print_sens_bnds(LPX *lp, const char *fname) { /* write bounds sensitivity information */ if (glp_get_status(lp) == GLP_OPT && !glp_bf_exists(lp)) glp_factorize(lp); return glp_print_ranges(lp, 0, NULL, 0, fname); } int lpx_print_ips(LPX *lp, const char *fname) { /* write interior point solution in printable format */ return glp_print_ipt(lp, fname); } int lpx_print_mip(LPX *lp, const char *fname) { /* write MIP problem solution in printable format */ return glp_print_mip(lp, fname); } int lpx_is_b_avail(glp_prob *lp) { /* check if LP basis is available */ return glp_bf_exists(lp); } int lpx_main(int argc, const char *argv[]) { /* stand-alone LP/MIP solver */ return glp_main(argc, argv); } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpfhv.h0000644000175000017500000001523111632352620020100 0ustar csheltoncshelton/* glpfhv.h (LP basis factorization, FHV eta file version) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPFHV_H #define GLPFHV_H #include "glpluf.h" /*********************************************************************** * The structure FHV defines the factorization of the basis mxm-matrix * B, where m is the number of rows in corresponding problem instance. * * This factorization is the following sextet: * * [B] = (F, H, V, P0, P, Q), (1) * * where F, H, and V are such matrices that * * B = F * H * V, (2) * * and P0, P, and Q are such permutation matrices that the matrix * * L = P0 * F * inv(P0) (3) * * is lower triangular with unity diagonal, and the matrix * * U = P * V * Q (4) * * is upper triangular. All the matrices have the same order m, which * is the order of the basis matrix B. * * The matrices F, V, P, and Q are stored in the structure LUF (see the * module GLPLUF), which is a member of the structure FHV. * * The matrix H is stored in the form of eta file using row-like format * as follows: * * H = H[1] * H[2] * ... * H[nfs], (5) * * where H[k], k = 1, 2, ..., nfs, is a row-like factor, which differs * from the unity matrix only by one row, nfs is current number of row- * like factors. After the factorization has been built for some given * basis matrix B the matrix H has no factors and thus it is the unity * matrix. Then each time when the factorization is recomputed for an * adjacent basis matrix, the next factor H[k], k = 1, 2, ... is built * and added to the end of the eta file H. * * Being sparse vectors non-trivial rows of the factors H[k] are stored * in the right part of the sparse vector area (SVA) in the same manner * as rows and columns of the matrix F. * * For more details see the program documentation. */ typedef struct FHV FHV; struct FHV { /* LP basis factorization */ int m_max; /* maximal value of m (increased automatically, if necessary) */ int m; /* the order of matrices B, F, H, V, P0, P, Q */ int valid; /* the factorization is valid only if this flag is set */ LUF *luf; /* LU-factorization (contains the matrices F, V, P, Q) */ /*--------------------------------------------------------------*/ /* matrix H in the form of eta file */ int hh_max; /* maximal number of row-like factors (which limits the number of updates of the factorization) */ int hh_nfs; /* current number of row-like factors (0 <= hh_nfs <= hh_max) */ int *hh_ind; /* int hh_ind[1+hh_max]; */ /* hh_ind[k], k = 1, ..., nfs, is the number of a non-trivial row of factor H[k] */ int *hh_ptr; /* int hh_ptr[1+hh_max]; */ /* hh_ptr[k], k = 1, ..., nfs, is a pointer to the first element of the non-trivial row of factor H[k] in the SVA */ int *hh_len; /* int hh_len[1+hh_max]; */ /* hh_len[k], k = 1, ..., nfs, is the number of non-zero elements in the non-trivial row of factor H[k] */ /*--------------------------------------------------------------*/ /* matrix P0 */ int *p0_row; /* int p0_row[1+m_max]; */ /* p0_row[i] = j means that p0[i,j] = 1 */ int *p0_col; /* int p0_col[1+m_max]; */ /* p0_col[j] = i means that p0[i,j] = 1 */ /* if i-th row or column of the matrix F corresponds to i'-th row or column of the matrix L = P0*F*inv(P0), then p0_row[i'] = i and p0_col[i] = i' */ /*--------------------------------------------------------------*/ /* working arrays */ int *cc_ind; /* int cc_ind[1+m_max]; */ /* integer working array */ double *cc_val; /* double cc_val[1+m_max]; */ /* floating-point working array */ /*--------------------------------------------------------------*/ /* control parameters */ double upd_tol; /* update tolerance; if after updating the factorization absolute value of some diagonal element u[k,k] of matrix U = P*V*Q is less than upd_tol * max(|u[k,*]|, |u[*,k]|), the factorization is considered as inaccurate */ /*--------------------------------------------------------------*/ /* some statistics */ int nnz_h; /* current number of non-zeros in all factors of matrix H */ }; /* return codes: */ #define FHV_ESING 1 /* singular matrix */ #define FHV_ECOND 2 /* ill-conditioned matrix */ #define FHV_ECHECK 3 /* insufficient accuracy */ #define FHV_ELIMIT 4 /* update limit reached */ #define FHV_EROOM 5 /* SVA overflow */ #define fhv_create_it _glp_fhv_create_it FHV *fhv_create_it(void); /* create LP basis factorization */ #define fhv_factorize _glp_fhv_factorize int fhv_factorize(FHV *fhv, int m, int (*col)(void *info, int j, int ind[], double val[]), void *info); /* compute LP basis factorization */ #define fhv_h_solve _glp_fhv_h_solve void fhv_h_solve(FHV *fhv, int tr, double x[]); /* solve system H*x = b or H'*x = b */ #define fhv_ftran _glp_fhv_ftran void fhv_ftran(FHV *fhv, double x[]); /* perform forward transformation (solve system B*x = b) */ #define fhv_btran _glp_fhv_btran void fhv_btran(FHV *fhv, double x[]); /* perform backward transformation (solve system B'*x = b) */ #define fhv_update_it _glp_fhv_update_it int fhv_update_it(FHV *fhv, int j, int len, const int ind[], const double val[]); /* update LP basis factorization */ #define fhv_delete_it _glp_fhv_delete_it void fhv_delete_it(FHV *fhv); /* delete LP basis factorization */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpipm.c0000644000175000017500000011411511632352620020076 0ustar csheltoncshelton/* glpipm.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpipm.h" #include "glpmat.h" #define ITER_MAX 100 /* maximal number of iterations */ struct csa { /* common storage area */ /*--------------------------------------------------------------*/ /* LP data */ int m; /* number of rows (equality constraints) */ int n; /* number of columns (structural variables) */ int *A_ptr; /* int A_ptr[1+m+1]; */ int *A_ind; /* int A_ind[A_ptr[m+1]]; */ double *A_val; /* double A_val[A_ptr[m+1]]; */ /* mxn-matrix A in storage-by-rows format */ double *b; /* double b[1+m]; */ /* m-vector b of right-hand sides */ double *c; /* double c[1+n]; */ /* n-vector c of objective coefficients; c[0] is constant term of the objective function */ /*--------------------------------------------------------------*/ /* LP solution */ double *x; /* double x[1+n]; */ double *y; /* double y[1+m]; */ double *z; /* double z[1+n]; */ /* current point in primal-dual space; the best point on exit */ /*--------------------------------------------------------------*/ /* control parameters */ const glp_iptcp *parm; /*--------------------------------------------------------------*/ /* working arrays and variables */ double *D; /* double D[1+n]; */ /* diagonal nxn-matrix D = X*inv(Z), where X = diag(x[j]) and Z = diag(z[j]) */ int *P; /* int P[1+m+m]; */ /* permutation mxm-matrix P used to minimize fill-in in Cholesky factorization */ int *S_ptr; /* int S_ptr[1+m+1]; */ int *S_ind; /* int S_ind[S_ptr[m+1]]; */ double *S_val; /* double S_val[S_ptr[m+1]]; */ double *S_diag; /* double S_diag[1+m]; */ /* symmetric mxm-matrix S = P*A*D*A'*P' whose upper triangular part without diagonal elements is stored in S_ptr, S_ind, and S_val in storage-by-rows format, diagonal elements are stored in S_diag */ int *U_ptr; /* int U_ptr[1+m+1]; */ int *U_ind; /* int U_ind[U_ptr[m+1]]; */ double *U_val; /* double U_val[U_ptr[m+1]]; */ double *U_diag; /* double U_diag[1+m]; */ /* upper triangular mxm-matrix U defining Cholesky factorization S = U'*U; its non-diagonal elements are stored in U_ptr, U_ind, U_val in storage-by-rows format, diagonal elements are stored in U_diag */ int iter; /* iteration number (0, 1, 2, ...); iter = 0 corresponds to the initial point */ double obj; /* current value of the objective function */ double rpi; /* relative primal infeasibility rpi = ||A*x-b||/(1+||b||) */ double rdi; /* relative dual infeasibility rdi = ||A'*y+z-c||/(1+||c||) */ double gap; /* primal-dual gap = |c'*x-b'*y|/(1+|c'*x|) which is a relative difference between primal and dual objective functions */ double phi; /* merit function phi = ||A*x-b||/max(1,||b||) + + ||A'*y+z-c||/max(1,||c||) + + |c'*x-b'*y|/max(1,||b||,||c||) */ double mu; /* duality measure mu = x'*z/n (used as barrier parameter) */ double rmu; /* rmu = max(||A*x-b||,||A'*y+z-c||)/mu */ double rmu0; /* the initial value of rmu on iteration 0 */ double *phi_min; /* double phi_min[1+ITER_MAX]; */ /* phi_min[k] = min(phi[k]), where phi[k] is the value of phi on k-th iteration, 0 <= k <= iter */ int best_iter; /* iteration number, on which the value of phi reached its best (minimal) value */ double *best_x; /* double best_x[1+n]; */ double *best_y; /* double best_y[1+m]; */ double *best_z; /* double best_z[1+n]; */ /* best point (in the sense of the merit function phi) which has been reached on iteration iter_best */ double best_obj; /* objective value at the best point */ double *dx_aff; /* double dx_aff[1+n]; */ double *dy_aff; /* double dy_aff[1+m]; */ double *dz_aff; /* double dz_aff[1+n]; */ /* affine scaling direction */ double alfa_aff_p, alfa_aff_d; /* maximal primal and dual stepsizes in affine scaling direction, on which x and z are still non-negative */ double mu_aff; /* duality measure mu_aff = x_aff'*z_aff/n in the boundary point x_aff' = x+alfa_aff_p*dx_aff, z_aff' = z+alfa_aff_d*dz_aff */ double sigma; /* Mehrotra's heuristic parameter (0 <= sigma <= 1) */ double *dx_cc; /* double dx_cc[1+n]; */ double *dy_cc; /* double dy_cc[1+m]; */ double *dz_cc; /* double dz_cc[1+n]; */ /* centering corrector direction */ double *dx; /* double dx[1+n]; */ double *dy; /* double dy[1+m]; */ double *dz; /* double dz[1+n]; */ /* final combined direction dx = dx_aff+dx_cc, dy = dy_aff+dy_cc, dz = dz_aff+dz_cc */ double alfa_max_p; double alfa_max_d; /* maximal primal and dual stepsizes in combined direction, on which x and z are still non-negative */ }; /*********************************************************************** * initialize - allocate and initialize common storage area * * This routine allocates and initializes the common storage area (CSA) * used by interior-point method routines. */ static void initialize(struct csa *csa) { int m = csa->m; int n = csa->n; int i; if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("Matrix A has %d non-zeros\n", csa->A_ptr[m+1]-1); csa->D = xcalloc(1+n, sizeof(double)); /* P := I */ csa->P = xcalloc(1+m+m, sizeof(int)); for (i = 1; i <= m; i++) csa->P[i] = csa->P[m+i] = i; /* S := A*A', symbolically */ csa->S_ptr = xcalloc(1+m+1, sizeof(int)); csa->S_ind = adat_symbolic(m, n, csa->P, csa->A_ptr, csa->A_ind, csa->S_ptr); if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("Matrix S = A*A' has %d non-zeros (upper triangle)\n", csa->S_ptr[m+1]-1 + m); /* determine P using specified ordering algorithm */ if (csa->parm->ord_alg == GLP_ORD_NONE) { if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("Original ordering is being used\n"); for (i = 1; i <= m; i++) csa->P[i] = csa->P[m+i] = i; } else if (csa->parm->ord_alg == GLP_ORD_QMD) { if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("Minimum degree ordering (QMD)...\n"); min_degree(m, csa->S_ptr, csa->S_ind, csa->P); } else if (csa->parm->ord_alg == GLP_ORD_AMD) { if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("Approximate minimum degree ordering (AMD)...\n"); amd_order1(m, csa->S_ptr, csa->S_ind, csa->P); } else if (csa->parm->ord_alg == GLP_ORD_SYMAMD) { if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("Approximate minimum degree ordering (SYMAMD)...\n") ; symamd_ord(m, csa->S_ptr, csa->S_ind, csa->P); } else xassert(csa != csa); /* S := P*A*A'*P', symbolically */ xfree(csa->S_ind); csa->S_ind = adat_symbolic(m, n, csa->P, csa->A_ptr, csa->A_ind, csa->S_ptr); csa->S_val = xcalloc(csa->S_ptr[m+1], sizeof(double)); csa->S_diag = xcalloc(1+m, sizeof(double)); /* compute Cholesky factorization S = U'*U, symbolically */ if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("Computing Cholesky factorization S = L*L'...\n"); csa->U_ptr = xcalloc(1+m+1, sizeof(int)); csa->U_ind = chol_symbolic(m, csa->S_ptr, csa->S_ind, csa->U_ptr); if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("Matrix L has %d non-zeros\n", csa->U_ptr[m+1]-1 + m); csa->U_val = xcalloc(csa->U_ptr[m+1], sizeof(double)); csa->U_diag = xcalloc(1+m, sizeof(double)); csa->iter = 0; csa->obj = 0.0; csa->rpi = 0.0; csa->rdi = 0.0; csa->gap = 0.0; csa->phi = 0.0; csa->mu = 0.0; csa->rmu = 0.0; csa->rmu0 = 0.0; csa->phi_min = xcalloc(1+ITER_MAX, sizeof(double)); csa->best_iter = 0; csa->best_x = xcalloc(1+n, sizeof(double)); csa->best_y = xcalloc(1+m, sizeof(double)); csa->best_z = xcalloc(1+n, sizeof(double)); csa->best_obj = 0.0; csa->dx_aff = xcalloc(1+n, sizeof(double)); csa->dy_aff = xcalloc(1+m, sizeof(double)); csa->dz_aff = xcalloc(1+n, sizeof(double)); csa->alfa_aff_p = 0.0; csa->alfa_aff_d = 0.0; csa->mu_aff = 0.0; csa->sigma = 0.0; csa->dx_cc = xcalloc(1+n, sizeof(double)); csa->dy_cc = xcalloc(1+m, sizeof(double)); csa->dz_cc = xcalloc(1+n, sizeof(double)); csa->dx = csa->dx_aff; csa->dy = csa->dy_aff; csa->dz = csa->dz_aff; csa->alfa_max_p = 0.0; csa->alfa_max_d = 0.0; return; } /*********************************************************************** * A_by_vec - compute y = A*x * * This routine computes matrix-vector product y = A*x, where A is the * constraint matrix. */ static void A_by_vec(struct csa *csa, double x[], double y[]) { /* compute y = A*x */ int m = csa->m; int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; double *A_val = csa->A_val; int i, t, beg, end; double temp; for (i = 1; i <= m; i++) { temp = 0.0; beg = A_ptr[i], end = A_ptr[i+1]; for (t = beg; t < end; t++) temp += A_val[t] * x[A_ind[t]]; y[i] = temp; } return; } /*********************************************************************** * AT_by_vec - compute y = A'*x * * This routine computes matrix-vector product y = A'*x, where A' is a * matrix transposed to the constraint matrix A. */ static void AT_by_vec(struct csa *csa, double x[], double y[]) { /* compute y = A'*x, where A' is transposed to A */ int m = csa->m; int n = csa->n; int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; double *A_val = csa->A_val; int i, j, t, beg, end; double temp; for (j = 1; j <= n; j++) y[j] = 0.0; for (i = 1; i <= m; i++) { temp = x[i]; if (temp == 0.0) continue; beg = A_ptr[i], end = A_ptr[i+1]; for (t = beg; t < end; t++) y[A_ind[t]] += A_val[t] * temp; } return; } /*********************************************************************** * decomp_NE - numeric factorization of matrix S = P*A*D*A'*P' * * This routine implements numeric phase of Cholesky factorization of * the matrix S = P*A*D*A'*P', which is a permuted matrix of the normal * equation system. Matrix D is assumed to be already computed. */ static void decomp_NE(struct csa *csa) { adat_numeric(csa->m, csa->n, csa->P, csa->A_ptr, csa->A_ind, csa->A_val, csa->D, csa->S_ptr, csa->S_ind, csa->S_val, csa->S_diag); chol_numeric(csa->m, csa->S_ptr, csa->S_ind, csa->S_val, csa->S_diag, csa->U_ptr, csa->U_ind, csa->U_val, csa->U_diag); return; } /*********************************************************************** * solve_NE - solve normal equation system * * This routine solves the normal equation system: * * A*D*A'*y = h. * * It is assumed that the matrix A*D*A' has been previously factorized * by the routine decomp_NE. * * On entry the array y contains the vector of right-hand sides h. On * exit this array contains the computed vector of unknowns y. * * Once the vector y has been computed the routine checks for numeric * stability. If the residual vector: * * r = A*D*A'*y - h * * is relatively small, the routine returns zero, otherwise non-zero is * returned. */ static int solve_NE(struct csa *csa, double y[]) { int m = csa->m; int n = csa->n; int *P = csa->P; int i, j, ret = 0; double *h, *r, *w; /* save vector of right-hand sides h */ h = xcalloc(1+m, sizeof(double)); for (i = 1; i <= m; i++) h[i] = y[i]; /* solve normal equation system (A*D*A')*y = h */ /* since S = P*A*D*A'*P' = U'*U, then A*D*A' = P'*U'*U*P, so we have inv(A*D*A') = P'*inv(U)*inv(U')*P */ /* w := P*h */ w = xcalloc(1+m, sizeof(double)); for (i = 1; i <= m; i++) w[i] = y[P[i]]; /* w := inv(U')*w */ ut_solve(m, csa->U_ptr, csa->U_ind, csa->U_val, csa->U_diag, w); /* w := inv(U)*w */ u_solve(m, csa->U_ptr, csa->U_ind, csa->U_val, csa->U_diag, w); /* y := P'*w */ for (i = 1; i <= m; i++) y[i] = w[P[m+i]]; xfree(w); /* compute residual vector r = A*D*A'*y - h */ r = xcalloc(1+m, sizeof(double)); /* w := A'*y */ w = xcalloc(1+n, sizeof(double)); AT_by_vec(csa, y, w); /* w := D*w */ for (j = 1; j <= n; j++) w[j] *= csa->D[j]; /* r := A*w */ A_by_vec(csa, w, r); xfree(w); /* r := r - h */ for (i = 1; i <= m; i++) r[i] -= h[i]; /* check for numeric stability */ for (i = 1; i <= m; i++) { if (fabs(r[i]) / (1.0 + fabs(h[i])) > 1e-4) { ret = 1; break; } } xfree(h); xfree(r); return ret; } /*********************************************************************** * solve_NS - solve Newtonian system * * This routine solves the Newtonian system: * * A*dx = p * * A'*dy + dz = q * * Z*dx + X*dz = r * * where X = diag(x[j]), Z = diag(z[j]), by reducing it to the normal * equation system: * * (A*inv(Z)*X*A')*dy = A*inv(Z)*(X*q-r)+p * * (it is assumed that the matrix A*inv(Z)*X*A' has been factorized by * the routine decomp_NE). * * Once vector dy has been computed the routine computes vectors dx and * dz as follows: * * dx = inv(Z)*(X*(A'*dy-q)+r) * * dz = inv(X)*(r-Z*dx) * * The routine solve_NS returns the same code which was reported by the * routine solve_NE (see above). */ static int solve_NS(struct csa *csa, double p[], double q[], double r[], double dx[], double dy[], double dz[]) { int m = csa->m; int n = csa->n; double *x = csa->x; double *z = csa->z; int i, j, ret; double *w = dx; /* compute the vector of right-hand sides A*inv(Z)*(X*q-r)+p for the normal equation system */ for (j = 1; j <= n; j++) w[j] = (x[j] * q[j] - r[j]) / z[j]; A_by_vec(csa, w, dy); for (i = 1; i <= m; i++) dy[i] += p[i]; /* solve the normal equation system to compute vector dy */ ret = solve_NE(csa, dy); /* compute vectors dx and dz */ AT_by_vec(csa, dy, dx); for (j = 1; j <= n; j++) { dx[j] = (x[j] * (dx[j] - q[j]) + r[j]) / z[j]; dz[j] = (r[j] - z[j] * dx[j]) / x[j]; } return ret; } /*********************************************************************** * initial_point - choose initial point using Mehrotra's heuristic * * This routine chooses a starting point using a heuristic proposed in * the paper: * * S. Mehrotra. On the implementation of a primal-dual interior point * method. SIAM J. on Optim., 2(4), pp. 575-601, 1992. * * The starting point x in the primal space is chosen as a solution of * the following least squares problem: * * minimize ||x|| * * subject to A*x = b * * which can be computed explicitly as follows: * * x = A'*inv(A*A')*b * * Similarly, the starting point (y, z) in the dual space is chosen as * a solution of the following least squares problem: * * minimize ||z|| * * subject to A'*y + z = c * * which can be computed explicitly as follows: * * y = inv(A*A')*A*c * * z = c - A'*y * * However, some components of the vectors x and z may be non-positive * or close to zero, so the routine uses a Mehrotra's heuristic to find * a more appropriate starting point. */ static void initial_point(struct csa *csa) { int m = csa->m; int n = csa->n; double *b = csa->b; double *c = csa->c; double *x = csa->x; double *y = csa->y; double *z = csa->z; double *D = csa->D; int i, j; double dp, dd, ex, ez, xz; /* factorize A*A' */ for (j = 1; j <= n; j++) D[j] = 1.0; decomp_NE(csa); /* x~ = A'*inv(A*A')*b */ for (i = 1; i <= m; i++) y[i] = b[i]; solve_NE(csa, y); AT_by_vec(csa, y, x); /* y~ = inv(A*A')*A*c */ A_by_vec(csa, c, y); solve_NE(csa, y); /* z~ = c - A'*y~ */ AT_by_vec(csa, y,z); for (j = 1; j <= n; j++) z[j] = c[j] - z[j]; /* use Mehrotra's heuristic in order to choose more appropriate starting point with positive components of vectors x and z */ dp = dd = 0.0; for (j = 1; j <= n; j++) { if (dp < -1.5 * x[j]) dp = -1.5 * x[j]; if (dd < -1.5 * z[j]) dd = -1.5 * z[j]; } /* note that b = 0 involves x = 0, and c = 0 involves y = 0 and z = 0, so we need to be careful */ if (dp == 0.0) dp = 1.5; if (dd == 0.0) dd = 1.5; ex = ez = xz = 0.0; for (j = 1; j <= n; j++) { ex += (x[j] + dp); ez += (z[j] + dd); xz += (x[j] + dp) * (z[j] + dd); } dp += 0.5 * (xz / ez); dd += 0.5 * (xz / ex); for (j = 1; j <= n; j++) { x[j] += dp; z[j] += dd; xassert(x[j] > 0.0 && z[j] > 0.0); } return; } /*********************************************************************** * basic_info - perform basic computations at the current point * * This routine computes the following quantities at the current point: * * 1) value of the objective function: * * F = c'*x + c[0] * * 2) relative primal infeasibility: * * rpi = ||A*x-b|| / (1+||b||) * * 3) relative dual infeasibility: * * rdi = ||A'*y+z-c|| / (1+||c||) * * 4) primal-dual gap (relative difference between the primal and the * dual objective function values): * * gap = |c'*x-b'*y| / (1+|c'*x|) * * 5) merit function: * * phi = ||A*x-b|| / max(1,||b||) + ||A'*y+z-c|| / max(1,||c||) + * * + |c'*x-b'*y| / max(1,||b||,||c||) * * 6) duality measure: * * mu = x'*z / n * * 7) the ratio of infeasibility to mu: * * rmu = max(||A*x-b||,||A'*y+z-c||) / mu * * where ||*|| denotes euclidian norm, *' denotes transposition. */ static void basic_info(struct csa *csa) { int m = csa->m; int n = csa->n; double *b = csa->b; double *c = csa->c; double *x = csa->x; double *y = csa->y; double *z = csa->z; int i, j; double norm1, bnorm, norm2, cnorm, cx, by, *work, temp; /* compute value of the objective function */ temp = c[0]; for (j = 1; j <= n; j++) temp += c[j] * x[j]; csa->obj = temp; /* norm1 = ||A*x-b|| */ work = xcalloc(1+m, sizeof(double)); A_by_vec(csa, x, work); norm1 = 0.0; for (i = 1; i <= m; i++) norm1 += (work[i] - b[i]) * (work[i] - b[i]); norm1 = sqrt(norm1); xfree(work); /* bnorm = ||b|| */ bnorm = 0.0; for (i = 1; i <= m; i++) bnorm += b[i] * b[i]; bnorm = sqrt(bnorm); /* compute relative primal infeasibility */ csa->rpi = norm1 / (1.0 + bnorm); /* norm2 = ||A'*y+z-c|| */ work = xcalloc(1+n, sizeof(double)); AT_by_vec(csa, y, work); norm2 = 0.0; for (j = 1; j <= n; j++) norm2 += (work[j] + z[j] - c[j]) * (work[j] + z[j] - c[j]); norm2 = sqrt(norm2); xfree(work); /* cnorm = ||c|| */ cnorm = 0.0; for (j = 1; j <= n; j++) cnorm += c[j] * c[j]; cnorm = sqrt(cnorm); /* compute relative dual infeasibility */ csa->rdi = norm2 / (1.0 + cnorm); /* by = b'*y */ by = 0.0; for (i = 1; i <= m; i++) by += b[i] * y[i]; /* cx = c'*x */ cx = 0.0; for (j = 1; j <= n; j++) cx += c[j] * x[j]; /* compute primal-dual gap */ csa->gap = fabs(cx - by) / (1.0 + fabs(cx)); /* compute merit function */ csa->phi = 0.0; csa->phi += norm1 / (bnorm > 1.0 ? bnorm : 1.0); csa->phi += norm2 / (cnorm > 1.0 ? cnorm : 1.0); temp = 1.0; if (temp < bnorm) temp = bnorm; if (temp < cnorm) temp = cnorm; csa->phi += fabs(cx - by) / temp; /* compute duality measure */ temp = 0.0; for (j = 1; j <= n; j++) temp += x[j] * z[j]; csa->mu = temp / (double)n; /* compute the ratio of infeasibility to mu */ csa->rmu = (norm1 > norm2 ? norm1 : norm2) / csa->mu; return; } /*********************************************************************** * make_step - compute next point using Mehrotra's technique * * This routine computes the next point using the predictor-corrector * technique proposed in the paper: * * S. Mehrotra. On the implementation of a primal-dual interior point * method. SIAM J. on Optim., 2(4), pp. 575-601, 1992. * * At first, the routine computes so called affine scaling (predictor) * direction (dx_aff,dy_aff,dz_aff) which is a solution of the system: * * A*dx_aff = b - A*x * * A'*dy_aff + dz_aff = c - A'*y - z * * Z*dx_aff + X*dz_aff = - X*Z*e * * where (x,y,z) is the current point, X = diag(x[j]), Z = diag(z[j]), * e = (1,...,1)'. * * Then, the routine computes the centering parameter sigma, using the * following Mehrotra's heuristic: * * alfa_aff_p = inf{0 <= alfa <= 1 | x+alfa*dx_aff >= 0} * * alfa_aff_d = inf{0 <= alfa <= 1 | z+alfa*dz_aff >= 0} * * mu_aff = (x+alfa_aff_p*dx_aff)'*(z+alfa_aff_d*dz_aff)/n * * sigma = (mu_aff/mu)^3 * * where alfa_aff_p is the maximal stepsize along the affine scaling * direction in the primal space, alfa_aff_d is the maximal stepsize * along the same direction in the dual space. * * After determining sigma the routine computes so called centering * (corrector) direction (dx_cc,dy_cc,dz_cc) which is the solution of * the system: * * A*dx_cc = 0 * * A'*dy_cc + dz_cc = 0 * * Z*dx_cc + X*dz_cc = sigma*mu*e - X*Z*e * * Finally, the routine computes the combined direction * * (dx,dy,dz) = (dx_aff,dy_aff,dz_aff) + (dx_cc,dy_cc,dz_cc) * * and determines maximal primal and dual stepsizes along the combined * direction: * * alfa_max_p = inf{0 <= alfa <= 1 | x+alfa*dx >= 0} * * alfa_max_d = inf{0 <= alfa <= 1 | z+alfa*dz >= 0} * * In order to prevent the next point to be too close to the boundary * of the positive ortant, the routine decreases maximal stepsizes: * * alfa_p = gamma_p * alfa_max_p * * alfa_d = gamma_d * alfa_max_d * * where gamma_p and gamma_d are scaling factors, and computes the next * point: * * x_new = x + alfa_p * dx * * y_new = y + alfa_d * dy * * z_new = z + alfa_d * dz * * which becomes the current point on the next iteration. */ static int make_step(struct csa *csa) { int m = csa->m; int n = csa->n; double *b = csa->b; double *c = csa->c; double *x = csa->x; double *y = csa->y; double *z = csa->z; double *dx_aff = csa->dx_aff; double *dy_aff = csa->dy_aff; double *dz_aff = csa->dz_aff; double *dx_cc = csa->dx_cc; double *dy_cc = csa->dy_cc; double *dz_cc = csa->dz_cc; double *dx = csa->dx; double *dy = csa->dy; double *dz = csa->dz; int i, j, ret = 0; double temp, gamma_p, gamma_d, *p, *q, *r; /* allocate working arrays */ p = xcalloc(1+m, sizeof(double)); q = xcalloc(1+n, sizeof(double)); r = xcalloc(1+n, sizeof(double)); /* p = b - A*x */ A_by_vec(csa, x, p); for (i = 1; i <= m; i++) p[i] = b[i] - p[i]; /* q = c - A'*y - z */ AT_by_vec(csa, y,q); for (j = 1; j <= n; j++) q[j] = c[j] - q[j] - z[j]; /* r = - X * Z * e */ for (j = 1; j <= n; j++) r[j] = - x[j] * z[j]; /* solve the first Newtonian system */ if (solve_NS(csa, p, q, r, dx_aff, dy_aff, dz_aff)) { ret = 1; goto done; } /* alfa_aff_p = inf{0 <= alfa <= 1 | x + alfa*dx_aff >= 0} */ /* alfa_aff_d = inf{0 <= alfa <= 1 | z + alfa*dz_aff >= 0} */ csa->alfa_aff_p = csa->alfa_aff_d = 1.0; for (j = 1; j <= n; j++) { if (dx_aff[j] < 0.0) { temp = - x[j] / dx_aff[j]; if (csa->alfa_aff_p > temp) csa->alfa_aff_p = temp; } if (dz_aff[j] < 0.0) { temp = - z[j] / dz_aff[j]; if (csa->alfa_aff_d > temp) csa->alfa_aff_d = temp; } } /* mu_aff = (x+alfa_aff_p*dx_aff)' * (z+alfa_aff_d*dz_aff) / n */ temp = 0.0; for (j = 1; j <= n; j++) temp += (x[j] + csa->alfa_aff_p * dx_aff[j]) * (z[j] + csa->alfa_aff_d * dz_aff[j]); csa->mu_aff = temp / (double)n; /* sigma = (mu_aff/mu)^3 */ temp = csa->mu_aff / csa->mu; csa->sigma = temp * temp * temp; /* p = 0 */ for (i = 1; i <= m; i++) p[i] = 0.0; /* q = 0 */ for (j = 1; j <= n; j++) q[j] = 0.0; /* r = sigma * mu * e - X * Z * e */ for (j = 1; j <= n; j++) r[j] = csa->sigma * csa->mu - dx_aff[j] * dz_aff[j]; /* solve the second Newtonian system with the same coefficients but with altered right-hand sides */ if (solve_NS(csa, p, q, r, dx_cc, dy_cc, dz_cc)) { ret = 1; goto done; } /* (dx,dy,dz) = (dx_aff,dy_aff,dz_aff) + (dx_cc,dy_cc,dz_cc) */ for (j = 1; j <= n; j++) dx[j] = dx_aff[j] + dx_cc[j]; for (i = 1; i <= m; i++) dy[i] = dy_aff[i] + dy_cc[i]; for (j = 1; j <= n; j++) dz[j] = dz_aff[j] + dz_cc[j]; /* alfa_max_p = inf{0 <= alfa <= 1 | x + alfa*dx >= 0} */ /* alfa_max_d = inf{0 <= alfa <= 1 | z + alfa*dz >= 0} */ csa->alfa_max_p = csa->alfa_max_d = 1.0; for (j = 1; j <= n; j++) { if (dx[j] < 0.0) { temp = - x[j] / dx[j]; if (csa->alfa_max_p > temp) csa->alfa_max_p = temp; } if (dz[j] < 0.0) { temp = - z[j] / dz[j]; if (csa->alfa_max_d > temp) csa->alfa_max_d = temp; } } /* determine scale factors (not implemented yet) */ gamma_p = 0.90; gamma_d = 0.90; /* compute the next point */ for (j = 1; j <= n; j++) { x[j] += gamma_p * csa->alfa_max_p * dx[j]; xassert(x[j] > 0.0); } for (i = 1; i <= m; i++) y[i] += gamma_d * csa->alfa_max_d * dy[i]; for (j = 1; j <= n; j++) { z[j] += gamma_d * csa->alfa_max_d * dz[j]; xassert(z[j] > 0.0); } done: /* free working arrays */ xfree(p); xfree(q); xfree(r); return ret; } /*********************************************************************** * terminate - deallocate common storage area * * This routine frees all memory allocated to the common storage area * used by interior-point method routines. */ static void terminate(struct csa *csa) { xfree(csa->D); xfree(csa->P); xfree(csa->S_ptr); xfree(csa->S_ind); xfree(csa->S_val); xfree(csa->S_diag); xfree(csa->U_ptr); xfree(csa->U_ind); xfree(csa->U_val); xfree(csa->U_diag); xfree(csa->phi_min); xfree(csa->best_x); xfree(csa->best_y); xfree(csa->best_z); xfree(csa->dx_aff); xfree(csa->dy_aff); xfree(csa->dz_aff); xfree(csa->dx_cc); xfree(csa->dy_cc); xfree(csa->dz_cc); return; } /*********************************************************************** * ipm_main - main interior-point method routine * * This is a main routine of the primal-dual interior-point method. * * The routine ipm_main returns one of the following codes: * * 0 - optimal solution found; * 1 - problem has no feasible (primal or dual) solution; * 2 - no convergence; * 3 - iteration limit exceeded; * 4 - numeric instability on solving Newtonian system. * * In case of non-zero return code the routine returns the best point, * which has been reached during optimization. */ static int ipm_main(struct csa *csa) { int m = csa->m; int n = csa->n; int i, j, status; double temp; /* choose initial point using Mehrotra's heuristic */ if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("Guessing initial point...\n"); initial_point(csa); /* main loop starts here */ if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("Optimization begins...\n"); for (;;) { /* perform basic computations at the current point */ basic_info(csa); /* save initial value of rmu */ if (csa->iter == 0) csa->rmu0 = csa->rmu; /* accumulate values of min(phi[k]) and save the best point */ xassert(csa->iter <= ITER_MAX); if (csa->iter == 0 || csa->phi_min[csa->iter-1] > csa->phi) { csa->phi_min[csa->iter] = csa->phi; csa->best_iter = csa->iter; for (j = 1; j <= n; j++) csa->best_x[j] = csa->x[j]; for (i = 1; i <= m; i++) csa->best_y[i] = csa->y[i]; for (j = 1; j <= n; j++) csa->best_z[j] = csa->z[j]; csa->best_obj = csa->obj; } else csa->phi_min[csa->iter] = csa->phi_min[csa->iter-1]; /* display information at the current point */ if (csa->parm->msg_lev >= GLP_MSG_ON) xprintf("%3d: obj = %17.9e; rpi = %8.1e; rdi = %8.1e; gap =" " %8.1e\n", csa->iter, csa->obj, csa->rpi, csa->rdi, csa->gap); /* check if the current point is optimal */ if (csa->rpi < 1e-8 && csa->rdi < 1e-8 && csa->gap < 1e-8) { if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("OPTIMAL SOLUTION FOUND\n"); status = 0; break; } /* check if the problem has no feasible solution */ temp = 1e5 * csa->phi_min[csa->iter]; if (temp < 1e-8) temp = 1e-8; if (csa->phi >= temp) { if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("PROBLEM HAS NO FEASIBLE PRIMAL/DUAL SOLUTION\n") ; status = 1; break; } /* check for very slow convergence or divergence */ if (((csa->rpi >= 1e-8 || csa->rdi >= 1e-8) && csa->rmu / csa->rmu0 >= 1e6) || (csa->iter >= 30 && csa->phi_min[csa->iter] >= 0.5 * csa->phi_min[csa->iter - 30])) { if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("NO CONVERGENCE; SEARCH TERMINATED\n"); status = 2; break; } /* check for maximal number of iterations */ if (csa->iter == ITER_MAX) { if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("ITERATION LIMIT EXCEEDED; SEARCH TERMINATED\n"); status = 3; break; } /* start the next iteration */ csa->iter++; /* factorize normal equation system */ for (j = 1; j <= n; j++) csa->D[j] = csa->x[j] / csa->z[j]; decomp_NE(csa); /* compute the next point using Mehrotra's predictor-corrector technique */ if (make_step(csa)) { if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("NUMERIC INSTABILITY; SEARCH TERMINATED\n"); status = 4; break; } } /* restore the best point */ if (status != 0) { for (j = 1; j <= n; j++) csa->x[j] = csa->best_x[j]; for (i = 1; i <= m; i++) csa->y[i] = csa->best_y[i]; for (j = 1; j <= n; j++) csa->z[j] = csa->best_z[j]; if (csa->parm->msg_lev >= GLP_MSG_ALL) xprintf("Best point %17.9e was reached on iteration %d\n", csa->best_obj, csa->best_iter); } /* return to the calling program */ return status; } /*********************************************************************** * NAME * * ipm_solve - core LP solver based on the interior-point method * * SYNOPSIS * * #include "glpipm.h" * int ipm_solve(glp_prob *P, const glp_iptcp *parm); * * DESCRIPTION * * The routine ipm_solve is a core LP solver based on the primal-dual * interior-point method. * * The routine assumes the following standard formulation of LP problem * to be solved: * * minimize * * F = c[0] + c[1]*x[1] + c[2]*x[2] + ... + c[n]*x[n] * * subject to linear constraints * * a[1,1]*x[1] + a[1,2]*x[2] + ... + a[1,n]*x[n] = b[1] * * a[2,1]*x[1] + a[2,2]*x[2] + ... + a[2,n]*x[n] = b[2] * * . . . . . . * * a[m,1]*x[1] + a[m,2]*x[2] + ... + a[m,n]*x[n] = b[m] * * and non-negative variables * * x[1] >= 0, x[2] >= 0, ..., x[n] >= 0 * * where: * F is the objective function; * x[1], ..., x[n] are (structural) variables; * c[0] is a constant term of the objective function; * c[1], ..., c[n] are objective coefficients; * a[1,1], ..., a[m,n] are constraint coefficients; * b[1], ..., b[n] are right-hand sides. * * The solution is three vectors x, y, and z, which are stored by the * routine in the arrays x, y, and z, respectively. These vectors * correspond to the best primal-dual point found during optimization. * They are approximate solution of the following system (which is the * Karush-Kuhn-Tucker optimality conditions): * * A*x = b (primal feasibility condition) * * A'*y + z = c (dual feasibility condition) * * x'*z = 0 (primal-dual complementarity condition) * * x >= 0, z >= 0 (non-negativity condition) * * where: * x[1], ..., x[n] are primal (structural) variables; * y[1], ..., y[m] are dual variables (Lagrange multipliers) for * equality constraints; * z[1], ..., z[n] are dual variables (Lagrange multipliers) for * non-negativity constraints. * * RETURNS * * 0 LP has been successfully solved. * * GLP_ENOCVG * No convergence. * * GLP_EITLIM * Iteration limit exceeded. * * GLP_EINSTAB * Numeric instability on solving Newtonian system. * * In case of non-zero return code the routine returns the best point, * which has been reached during optimization. */ int ipm_solve(glp_prob *P, const glp_iptcp *parm) { struct csa _dsa, *csa = &_dsa; int m = P->m; int n = P->n; int nnz = P->nnz; GLPROW *row; GLPCOL *col; GLPAIJ *aij; int i, j, loc, ret, *A_ind, *A_ptr; double dir, *A_val, *b, *c, *x, *y, *z; xassert(m > 0); xassert(n > 0); /* allocate working arrays */ A_ptr = xcalloc(1+m+1, sizeof(int)); A_ind = xcalloc(1+nnz, sizeof(int)); A_val = xcalloc(1+nnz, sizeof(double)); b = xcalloc(1+m, sizeof(double)); c = xcalloc(1+n, sizeof(double)); x = xcalloc(1+n, sizeof(double)); y = xcalloc(1+m, sizeof(double)); z = xcalloc(1+n, sizeof(double)); /* prepare rows and constraint coefficients */ loc = 1; for (i = 1; i <= m; i++) { row = P->row[i]; xassert(row->type == GLP_FX); b[i] = row->lb * row->rii; A_ptr[i] = loc; for (aij = row->ptr; aij != NULL; aij = aij->r_next) { A_ind[loc] = aij->col->j; A_val[loc] = row->rii * aij->val * aij->col->sjj; loc++; } } A_ptr[m+1] = loc; xassert(loc-1 == nnz); /* prepare columns and objective coefficients */ if (P->dir == GLP_MIN) dir = +1.0; else if (P->dir == GLP_MAX) dir = -1.0; else xassert(P != P); c[0] = dir * P->c0; for (j = 1; j <= n; j++) { col = P->col[j]; xassert(col->type == GLP_LO && col->lb == 0.0); c[j] = dir * col->coef * col->sjj; } /* allocate and initialize the common storage area */ csa->m = m; csa->n = n; csa->A_ptr = A_ptr; csa->A_ind = A_ind; csa->A_val = A_val; csa->b = b; csa->c = c; csa->x = x; csa->y = y; csa->z = z; csa->parm = parm; initialize(csa); /* solve LP with the interior-point method */ ret = ipm_main(csa); /* deallocate the common storage area */ terminate(csa); /* determine solution status */ if (ret == 0) { /* optimal solution found */ P->ipt_stat = GLP_OPT; ret = 0; } else if (ret == 1) { /* problem has no feasible (primal or dual) solution */ P->ipt_stat = GLP_NOFEAS; ret = 0; } else if (ret == 2) { /* no convergence */ P->ipt_stat = GLP_INFEAS; ret = GLP_ENOCVG; } else if (ret == 3) { /* iteration limit exceeded */ P->ipt_stat = GLP_INFEAS; ret = GLP_EITLIM; } else if (ret == 4) { /* numeric instability on solving Newtonian system */ P->ipt_stat = GLP_INFEAS; ret = GLP_EINSTAB; } else xassert(ret != ret); /* store row solution components */ for (i = 1; i <= m; i++) { row = P->row[i]; row->pval = row->lb; row->dval = dir * y[i] * row->rii; } /* store column solution components */ P->ipt_obj = P->c0; for (j = 1; j <= n; j++) { col = P->col[j]; col->pval = x[j] * col->sjj; col->dval = dir * z[j] / col->sjj; P->ipt_obj += col->coef * col->pval; } /* free working arrays */ xfree(A_ptr); xfree(A_ind); xfree(A_val); xfree(b); xfree(c); xfree(x); xfree(y); xfree(z); return ret; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpsql.h0000644000175000017500000000415111632352620020113 0ustar csheltoncshelton/* glpsql.h */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Author: Heinrich Schuchardt . * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPSQL_H #define GLPSQL_H #define db_iodbc_open _glp_db_iodbc_open void *db_iodbc_open(TABDCA *dca, int mode); /* open iODBC database connection */ #define db_iodbc_read _glp_db_iodbc_read int db_iodbc_read(TABDCA *dca, void *link); /* read data from iODBC */ #define db_iodbc_write _glp_db_iodbc_write int db_iodbc_write(TABDCA *dca, void *link); /* write data to iODBC */ #define db_iodbc_close _glp_db_iodbc_close int db_iodbc_close(TABDCA *dca, void *link); /* close iODBC database connection */ #define db_mysql_open _glp_db_mysql_open void *db_mysql_open(TABDCA *dca, int mode); /* open MySQL database connection */ #define db_mysql_read _glp_db_mysql_read int db_mysql_read(TABDCA *dca, void *link); /* read data from MySQL */ #define db_mysql_write _glp_db_mysql_write int db_mysql_write(TABDCA *dca, void *link); /* write data to MySQL */ #define db_mysql_close _glp_db_mysql_close int db_mysql_close(TABDCA *dca, void *link); /* close MySQL database connection */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpapi20.c0000644000175000017500000002302011632352620020216 0ustar csheltoncshelton/* glpapi20.c */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpnpp.h" int glp_intfeas1(glp_prob *P, int use_bound, int obj_bound) { /* solve integer feasibility problem */ NPP *npp = NULL; glp_prob *mip = NULL; int *obj_ind = NULL; double *obj_val = NULL; int obj_row = 0; int i, j, k, obj_len, temp, ret; /* check the problem object */ if (P == NULL || P->magic != GLP_PROB_MAGIC) xerror("glp_intfeas1: P = %p; invalid problem object\n", P); if (P->tree != NULL) xerror("glp_intfeas1: operation not allowed\n"); /* integer solution is currently undefined */ P->mip_stat = GLP_UNDEF; P->mip_obj = 0.0; /* check columns (variables) */ for (j = 1; j <= P->n; j++) { GLPCOL *col = P->col[j]; #if 0 /* currently binarization is not yet implemented */ if (!(col->kind == GLP_IV || col->type == GLP_FX)) { xprintf("glp_intfeas1: column %d: non-integer non-fixed var" "iable not allowed\n", j); #else if (!((col->kind == GLP_IV && col->lb == 0.0 && col->ub == 1.0) || col->type == GLP_FX)) { xprintf("glp_intfeas1: column %d: non-binary non-fixed vari" "able not allowed\n", j); #endif ret = GLP_EDATA; goto done; } temp = (int)col->lb; if ((double)temp != col->lb) { if (col->type == GLP_FX) xprintf("glp_intfeas1: column %d: fixed value %g is non-" "integer or out of range\n", j, col->lb); else xprintf("glp_intfeas1: column %d: lower bound %g is non-" "integer or out of range\n", j, col->lb); ret = GLP_EDATA; goto done; } temp = (int)col->ub; if ((double)temp != col->ub) { xprintf("glp_intfeas1: column %d: upper bound %g is non-int" "eger or out of range\n", j, col->ub); ret = GLP_EDATA; goto done; } if (col->type == GLP_DB && col->lb > col->ub) { xprintf("glp_intfeas1: column %d: lower bound %g is greater" " than upper bound %g\n", j, col->lb, col->ub); ret = GLP_EBOUND; goto done; } } /* check rows (constraints) */ for (i = 1; i <= P->m; i++) { GLPROW *row = P->row[i]; GLPAIJ *aij; for (aij = row->ptr; aij != NULL; aij = aij->r_next) { temp = (int)aij->val; if ((double)temp != aij->val) { xprintf("glp_intfeas1: row = %d, column %d: constraint c" "oefficient %g is non-integer or out of range\n", i, aij->col->j, aij->val); ret = GLP_EDATA; goto done; } } temp = (int)row->lb; if ((double)temp != row->lb) { if (row->type == GLP_FX) xprintf("glp_intfeas1: row = %d: fixed value %g is non-i" "nteger or out of range\n", i, row->lb); else xprintf("glp_intfeas1: row = %d: lower bound %g is non-i" "nteger or out of range\n", i, row->lb); ret = GLP_EDATA; goto done; } temp = (int)row->ub; if ((double)temp != row->ub) { xprintf("glp_intfeas1: row = %d: upper bound %g is non-inte" "ger or out of range\n", i, row->ub); ret = GLP_EDATA; goto done; } if (row->type == GLP_DB && row->lb > row->ub) { xprintf("glp_intfeas1: row %d: lower bound %g is greater th" "an upper bound %g\n", i, row->lb, row->ub); ret = GLP_EBOUND; goto done; } } /* check the objective function */ temp = (int)P->c0; if ((double)temp != P->c0) { xprintf("glp_intfeas1: objective constant term %g is non-integ" "er or out of range\n", P->c0); ret = GLP_EDATA; goto done; } for (j = 1; j <= P->n; j++) { temp = (int)P->col[j]->coef; if ((double)temp != P->col[j]->coef) { xprintf("glp_intfeas1: column %d: objective coefficient is " "non-integer or out of range\n", j, P->col[j]->coef); ret = GLP_EDATA; goto done; } } /* save the objective function and set it to zero */ obj_ind = xcalloc(1+P->n, sizeof(int)); obj_val = xcalloc(1+P->n, sizeof(double)); obj_len = 0; obj_ind[0] = 0; obj_val[0] = P->c0; P->c0 = 0.0; for (j = 1; j <= P->n; j++) { if (P->col[j]->coef != 0.0) { obj_len++; obj_ind[obj_len] = j; obj_val[obj_len] = P->col[j]->coef; P->col[j]->coef = 0.0; } } /* add inequality to bound the objective function, if required */ if (!use_bound) xprintf("Will search for ANY feasible solution\n"); else { xprintf("Will search only for solution not worse than %d\n", obj_bound); obj_row = glp_add_rows(P, 1); glp_set_mat_row(P, obj_row, obj_len, obj_ind, obj_val); if (P->dir == GLP_MIN) glp_set_row_bnds(P, obj_row, GLP_UP, 0.0, (double)obj_bound - obj_val[0]); else if (P->dir == GLP_MAX) glp_set_row_bnds(P, obj_row, GLP_LO, (double)obj_bound - obj_val[0], 0.0); else xassert(P != P); } /* create preprocessor workspace */ xprintf("Translating to CNF-SAT...\n"); xprintf("Original problem has %d row%s, %d column%s, and %d non-z" "ero%s\n", P->m, P->m == 1 ? "" : "s", P->n, P->n == 1 ? "" : "s", P->nnz, P->nnz == 1 ? "" : "s"); npp = npp_create_wksp(); /* load the original problem into the preprocessor workspace */ npp_load_prob(npp, P, GLP_OFF, GLP_MIP, GLP_OFF); /* perform translation to SAT-CNF problem instance */ ret = npp_sat_encode_prob(npp); if (ret == 0) ; else if (ret == GLP_ENOPFS) xprintf("PROBLEM HAS NO INTEGER FEASIBLE SOLUTION\n"); else if (ret == GLP_ERANGE) xprintf("glp_intfeas1: translation to SAT-CNF failed because o" "f integer overflow\n"); else xassert(ret != ret); if (ret != 0) goto done; /* build SAT-CNF problem instance and try to solve it */ mip = glp_create_prob(); npp_build_prob(npp, mip); ret = glp_minisat1(mip); /* only integer feasible solution can be postprocessed */ if (!(mip->mip_stat == GLP_OPT || mip->mip_stat == GLP_FEAS)) { P->mip_stat = mip->mip_stat; goto done; } /* postprocess the solution found */ npp_postprocess(npp, mip); /* the transformed problem is no longer needed */ glp_delete_prob(mip), mip = NULL; /* store solution to the original problem object */ npp_unload_sol(npp, P); /* change the solution status to 'integer feasible' */ P->mip_stat = GLP_FEAS; /* check integer feasibility */ for (i = 1; i <= P->m; i++) { GLPROW *row; GLPAIJ *aij; double sum; row = P->row[i]; sum = 0.0; for (aij = row->ptr; aij != NULL; aij = aij->r_next) sum += aij->val * aij->col->mipx; xassert(sum == row->mipx); if (row->type == GLP_LO || row->type == GLP_DB || row->type == GLP_FX) xassert(sum >= row->lb); if (row->type == GLP_UP || row->type == GLP_DB || row->type == GLP_FX) xassert(sum <= row->ub); } /* compute value of the original objective function */ P->mip_obj = obj_val[0]; for (k = 1; k <= obj_len; k++) P->mip_obj += obj_val[k] * P->col[obj_ind[k]]->mipx; xprintf("Objective value = %17.9e\n", P->mip_obj); done: /* delete the transformed problem, if it exists */ if (mip != NULL) glp_delete_prob(mip); /* delete the preprocessor workspace, if it exists */ if (npp != NULL) npp_delete_wksp(npp); /* remove inequality used to bound the objective function */ if (obj_row > 0) { int ind[1+1]; ind[1] = obj_row; glp_del_rows(P, 1, ind); } /* restore the original objective function */ if (obj_ind != NULL) { P->c0 = obj_val[0]; for (k = 1; k <= obj_len; k++) P->col[obj_ind[k]]->coef = obj_val[k]; xfree(obj_ind); xfree(obj_val); } return ret; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpdmx.c0000644000175000017500000016076611632352620020116 0ustar csheltoncshelton/* glpdmx.c (reading/writing data in DIMACS format) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #define _GLPSTD_STDIO #include "glpapi.h" struct csa { /* common storage area */ jmp_buf jump; /* label for go to in case of error */ const char *fname; /* name of input text file */ XFILE *fp; /* stream assigned to input text file */ int count; /* line count */ int c; /* current character */ char field[255+1]; /* data field */ int empty; /* warning 'empty line ignored' was printed */ int nonint; /* warning 'non-integer data detected' was printed */ }; static void error(struct csa *csa, const char *fmt, ...) { /* print error message and terminate processing */ va_list arg; xprintf("%s:%d: error: ", csa->fname, csa->count); va_start(arg, fmt); xvprintf(fmt, arg); va_end(arg); xprintf("\n"); longjmp(csa->jump, 1); /* no return */ } static void warning(struct csa *csa, const char *fmt, ...) { /* print warning message and continue processing */ va_list arg; xprintf("%s:%d: warning: ", csa->fname, csa->count); va_start(arg, fmt); xvprintf(fmt, arg); va_end(arg); xprintf("\n"); return; } static void read_char(struct csa *csa) { /* read character from input text file */ int c; if (csa->c == '\n') csa->count++; c = xfgetc(csa->fp); if (c < 0) { if (xferror(csa->fp)) error(csa, "read error - %s", xerrmsg()); else if (csa->c == '\n') error(csa, "unexpected end of file"); else { warning(csa, "missing final end of line"); c = '\n'; } } else if (c == '\n') ; else if (isspace(c)) c = ' '; else if (iscntrl(c)) error(csa, "invalid control character 0x%02X", c); csa->c = c; return; } static void read_designator(struct csa *csa) { /* read one-character line designator */ xassert(csa->c == '\n'); read_char(csa); for (;;) { /* skip preceding white-space characters */ while (csa->c == ' ') read_char(csa); if (csa->c == '\n') { /* ignore empty line */ if (!csa->empty) { warning(csa, "empty line ignored"); csa->empty = 1; } read_char(csa); } else if (csa->c == 'c') { /* skip comment line */ while (csa->c != '\n') read_char(csa); read_char(csa); } else { /* hmm... looks like a line designator */ csa->field[0] = (char)csa->c, csa->field[1] = '\0'; /* check that it is followed by a white-space character */ read_char(csa); if (!(csa->c == ' ' || csa->c == '\n')) error(csa, "line designator missing or invalid"); break; } } return; } static void read_field(struct csa *csa) { /* read data field */ int len = 0; /* skip preceding white-space characters */ while (csa->c == ' ') read_char(csa); /* scan data field */ if (csa->c == '\n') error(csa, "unexpected end of line"); while (!(csa->c == ' ' || csa->c == '\n')) { if (len == sizeof(csa->field)-1) error(csa, "data field `%.15s...' too long", csa->field); csa->field[len++] = (char)csa->c; read_char(csa); } csa->field[len] = '\0'; return; } static void end_of_line(struct csa *csa) { /* skip white-space characters until end of line */ while (csa->c == ' ') read_char(csa); if (csa->c != '\n') error(csa, "too many data fields specified"); return; } static void check_int(struct csa *csa, double num) { /* print a warning if non-integer data are detected */ if (!csa->nonint && num != floor(num)) { warning(csa, "non-integer data detected"); csa->nonint = 1; } return; } /*********************************************************************** * NAME * * glp_read_mincost - read min-cost flow problem data in DIMACS format * * SYNOPSIS * * int glp_read_mincost(glp_graph *G, int v_rhs, int a_low, int a_cap, * int a_cost, const char *fname); * * DESCRIPTION * * The routine glp_read_mincost reads minimum cost flow problem data in * DIMACS format from a text file. * * RETURNS * * If the operation was successful, the routine returns zero. Otherwise * it prints an error message and returns non-zero. */ int glp_read_mincost(glp_graph *G, int v_rhs, int a_low, int a_cap, int a_cost, const char *fname) { struct csa _csa, *csa = &_csa; glp_vertex *v; glp_arc *a; int i, j, k, nv, na, ret = 0; double rhs, low, cap, cost; char *flag = NULL; if (v_rhs >= 0 && v_rhs > G->v_size - (int)sizeof(double)) xerror("glp_read_mincost: v_rhs = %d; invalid offset\n", v_rhs); if (a_low >= 0 && a_low > G->a_size - (int)sizeof(double)) xerror("glp_read_mincost: a_low = %d; invalid offset\n", a_low); if (a_cap >= 0 && a_cap > G->a_size - (int)sizeof(double)) xerror("glp_read_mincost: a_cap = %d; invalid offset\n", a_cap); if (a_cost >= 0 && a_cost > G->a_size - (int)sizeof(double)) xerror("glp_read_mincost: a_cost = %d; invalid offset\n", a_cost); glp_erase_graph(G, G->v_size, G->a_size); if (setjmp(csa->jump)) { ret = 1; goto done; } csa->fname = fname; csa->fp = NULL; csa->count = 0; csa->c = '\n'; csa->field[0] = '\0'; csa->empty = csa->nonint = 0; xprintf("Reading min-cost flow problem data from `%s'...\n", fname); csa->fp = xfopen(fname, "r"); if (csa->fp == NULL) { xprintf("Unable to open `%s' - %s\n", fname, xerrmsg()); longjmp(csa->jump, 1); } /* read problem line */ read_designator(csa); if (strcmp(csa->field, "p") != 0) error(csa, "problem line missing or invalid"); read_field(csa); if (strcmp(csa->field, "min") != 0) error(csa, "wrong problem designator; `min' expected"); read_field(csa); if (!(str2int(csa->field, &nv) == 0 && nv >= 0)) error(csa, "number of nodes missing or invalid"); read_field(csa); if (!(str2int(csa->field, &na) == 0 && na >= 0)) error(csa, "number of arcs missing or invalid"); xprintf("Flow network has %d node%s and %d arc%s\n", nv, nv == 1 ? "" : "s", na, na == 1 ? "" : "s"); if (nv > 0) glp_add_vertices(G, nv); end_of_line(csa); /* read node descriptor lines */ flag = xcalloc(1+nv, sizeof(char)); memset(&flag[1], 0, nv * sizeof(char)); if (v_rhs >= 0) { rhs = 0.0; for (i = 1; i <= nv; i++) { v = G->v[i]; memcpy((char *)v->data + v_rhs, &rhs, sizeof(double)); } } for (;;) { read_designator(csa); if (strcmp(csa->field, "n") != 0) break; read_field(csa); if (str2int(csa->field, &i) != 0) error(csa, "node number missing or invalid"); if (!(1 <= i && i <= nv)) error(csa, "node number %d out of range", i); if (flag[i]) error(csa, "duplicate descriptor of node %d", i); read_field(csa); if (str2num(csa->field, &rhs) != 0) error(csa, "node supply/demand missing or invalid"); check_int(csa, rhs); if (v_rhs >= 0) { v = G->v[i]; memcpy((char *)v->data + v_rhs, &rhs, sizeof(double)); } flag[i] = 1; end_of_line(csa); } xfree(flag), flag = NULL; /* read arc descriptor lines */ for (k = 1; k <= na; k++) { if (k > 1) read_designator(csa); if (strcmp(csa->field, "a") != 0) error(csa, "wrong line designator; `a' expected"); read_field(csa); if (str2int(csa->field, &i) != 0) error(csa, "starting node number missing or invalid"); if (!(1 <= i && i <= nv)) error(csa, "starting node number %d out of range", i); read_field(csa); if (str2int(csa->field, &j) != 0) error(csa, "ending node number missing or invalid"); if (!(1 <= j && j <= nv)) error(csa, "ending node number %d out of range", j); read_field(csa); if (!(str2num(csa->field, &low) == 0 && low >= 0.0)) error(csa, "lower bound of arc flow missing or invalid"); check_int(csa, low); read_field(csa); if (!(str2num(csa->field, &cap) == 0 && cap >= low)) error(csa, "upper bound of arc flow missing or invalid"); check_int(csa, cap); read_field(csa); if (str2num(csa->field, &cost) != 0) error(csa, "per-unit cost of arc flow missing or invalid"); check_int(csa, cost); a = glp_add_arc(G, i, j); if (a_low >= 0) memcpy((char *)a->data + a_low, &low, sizeof(double)); if (a_cap >= 0) memcpy((char *)a->data + a_cap, &cap, sizeof(double)); if (a_cost >= 0) memcpy((char *)a->data + a_cost, &cost, sizeof(double)); end_of_line(csa); } xprintf("%d lines were read\n", csa->count); done: if (ret) glp_erase_graph(G, G->v_size, G->a_size); if (csa->fp != NULL) xfclose(csa->fp); if (flag != NULL) xfree(flag); return ret; } /*********************************************************************** * NAME * * glp_write_mincost - write min-cost flow problem data in DIMACS format * * SYNOPSIS * * int glp_write_mincost(glp_graph *G, int v_rhs, int a_low, int a_cap, * int a_cost, const char *fname); * * DESCRIPTION * * The routine glp_write_mincost writes minimum cost flow problem data * in DIMACS format to a text file. * * RETURNS * * If the operation was successful, the routine returns zero. Otherwise * it prints an error message and returns non-zero. */ int glp_write_mincost(glp_graph *G, int v_rhs, int a_low, int a_cap, int a_cost, const char *fname) { XFILE *fp; glp_vertex *v; glp_arc *a; int i, count = 0, ret; double rhs, low, cap, cost; if (v_rhs >= 0 && v_rhs > G->v_size - (int)sizeof(double)) xerror("glp_write_mincost: v_rhs = %d; invalid offset\n", v_rhs); if (a_low >= 0 && a_low > G->a_size - (int)sizeof(double)) xerror("glp_write_mincost: a_low = %d; invalid offset\n", a_low); if (a_cap >= 0 && a_cap > G->a_size - (int)sizeof(double)) xerror("glp_write_mincost: a_cap = %d; invalid offset\n", a_cap); if (a_cost >= 0 && a_cost > G->a_size - (int)sizeof(double)) xerror("glp_write_mincost: a_cost = %d; invalid offset\n", a_cost); xprintf("Writing min-cost flow problem data to `%s'...\n", fname); fp = xfopen(fname, "w"); if (fp == NULL) { xprintf("Unable to create `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xfprintf(fp, "c %s\n", G->name == NULL ? "unknown" : G->name), count++; xfprintf(fp, "p min %d %d\n", G->nv, G->na), count++; if (v_rhs >= 0) { for (i = 1; i <= G->nv; i++) { v = G->v[i]; memcpy(&rhs, (char *)v->data + v_rhs, sizeof(double)); if (rhs != 0.0) xfprintf(fp, "n %d %.*g\n", i, DBL_DIG, rhs), count++; } } for (i = 1; i <= G->nv; i++) { v = G->v[i]; for (a = v->out; a != NULL; a = a->t_next) { if (a_low >= 0) memcpy(&low, (char *)a->data + a_low, sizeof(double)); else low = 0.0; if (a_cap >= 0) memcpy(&cap, (char *)a->data + a_cap, sizeof(double)); else cap = 1.0; if (a_cost >= 0) memcpy(&cost, (char *)a->data + a_cost, sizeof(double)); else cost = 0.0; xfprintf(fp, "a %d %d %.*g %.*g %.*g\n", a->tail->i, a->head->i, DBL_DIG, low, DBL_DIG, cap, DBL_DIG, cost), count++; } } xfprintf(fp, "c eof\n"), count++; xfflush(fp); if (xferror(fp)) { xprintf("Write error on `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xprintf("%d lines were written\n", count); ret = 0; done: if (fp != NULL) xfclose(fp); return ret; } /*********************************************************************** * NAME * * glp_read_maxflow - read maximum flow problem data in DIMACS format * * SYNOPSIS * * int glp_read_maxflow(glp_graph *G, int *s, int *t, int a_cap, * const char *fname); * * DESCRIPTION * * The routine glp_read_maxflow reads maximum flow problem data in * DIMACS format from a text file. * * RETURNS * * If the operation was successful, the routine returns zero. Otherwise * it prints an error message and returns non-zero. */ int glp_read_maxflow(glp_graph *G, int *_s, int *_t, int a_cap, const char *fname) { struct csa _csa, *csa = &_csa; glp_arc *a; int i, j, k, s, t, nv, na, ret = 0; double cap; if (a_cap >= 0 && a_cap > G->a_size - (int)sizeof(double)) xerror("glp_read_maxflow: a_cap = %d; invalid offset\n", a_cap); glp_erase_graph(G, G->v_size, G->a_size); if (setjmp(csa->jump)) { ret = 1; goto done; } csa->fname = fname; csa->fp = NULL; csa->count = 0; csa->c = '\n'; csa->field[0] = '\0'; csa->empty = csa->nonint = 0; xprintf("Reading maximum flow problem data from `%s'...\n", fname); csa->fp = xfopen(fname, "r"); if (csa->fp == NULL) { xprintf("Unable to open `%s' - %s\n", fname, xerrmsg()); longjmp(csa->jump, 1); } /* read problem line */ read_designator(csa); if (strcmp(csa->field, "p") != 0) error(csa, "problem line missing or invalid"); read_field(csa); if (strcmp(csa->field, "max") != 0) error(csa, "wrong problem designator; `max' expected"); read_field(csa); if (!(str2int(csa->field, &nv) == 0 && nv >= 2)) error(csa, "number of nodes missing or invalid"); read_field(csa); if (!(str2int(csa->field, &na) == 0 && na >= 0)) error(csa, "number of arcs missing or invalid"); xprintf("Flow network has %d node%s and %d arc%s\n", nv, nv == 1 ? "" : "s", na, na == 1 ? "" : "s"); if (nv > 0) glp_add_vertices(G, nv); end_of_line(csa); /* read node descriptor lines */ s = t = 0; for (;;) { read_designator(csa); if (strcmp(csa->field, "n") != 0) break; read_field(csa); if (str2int(csa->field, &i) != 0) error(csa, "node number missing or invalid"); if (!(1 <= i && i <= nv)) error(csa, "node number %d out of range", i); read_field(csa); if (strcmp(csa->field, "s") == 0) { if (s > 0) error(csa, "only one source node allowed"); s = i; } else if (strcmp(csa->field, "t") == 0) { if (t > 0) error(csa, "only one sink node allowed"); t = i; } else error(csa, "wrong node designator; `s' or `t' expected"); if (s > 0 && s == t) error(csa, "source and sink nodes must be distinct"); end_of_line(csa); } if (s == 0) error(csa, "source node descriptor missing\n"); if (t == 0) error(csa, "sink node descriptor missing\n"); if (_s != NULL) *_s = s; if (_t != NULL) *_t = t; /* read arc descriptor lines */ for (k = 1; k <= na; k++) { if (k > 1) read_designator(csa); if (strcmp(csa->field, "a") != 0) error(csa, "wrong line designator; `a' expected"); read_field(csa); if (str2int(csa->field, &i) != 0) error(csa, "starting node number missing or invalid"); if (!(1 <= i && i <= nv)) error(csa, "starting node number %d out of range", i); read_field(csa); if (str2int(csa->field, &j) != 0) error(csa, "ending node number missing or invalid"); if (!(1 <= j && j <= nv)) error(csa, "ending node number %d out of range", j); read_field(csa); if (!(str2num(csa->field, &cap) == 0 && cap >= 0.0)) error(csa, "arc capacity missing or invalid"); check_int(csa, cap); a = glp_add_arc(G, i, j); if (a_cap >= 0) memcpy((char *)a->data + a_cap, &cap, sizeof(double)); end_of_line(csa); } xprintf("%d lines were read\n", csa->count); done: if (ret) glp_erase_graph(G, G->v_size, G->a_size); if (csa->fp != NULL) xfclose(csa->fp); return ret; } /*********************************************************************** * NAME * * glp_write_maxflow - write maximum flow problem data in DIMACS format * * SYNOPSIS * * int glp_write_maxflow(glp_graph *G, int s, int t, int a_cap, * const char *fname); * * DESCRIPTION * * The routine glp_write_maxflow writes maximum flow problem data in * DIMACS format to a text file. * * RETURNS * * If the operation was successful, the routine returns zero. Otherwise * it prints an error message and returns non-zero. */ int glp_write_maxflow(glp_graph *G, int s, int t, int a_cap, const char *fname) { XFILE *fp; glp_vertex *v; glp_arc *a; int i, count = 0, ret; double cap; if (!(1 <= s && s <= G->nv)) xerror("glp_write_maxflow: s = %d; source node number out of r" "ange\n", s); if (!(1 <= t && t <= G->nv)) xerror("glp_write_maxflow: t = %d: sink node number out of ran" "ge\n", t); if (a_cap >= 0 && a_cap > G->a_size - (int)sizeof(double)) xerror("glp_write_mincost: a_cap = %d; invalid offset\n", a_cap); xprintf("Writing maximum flow problem data to `%s'...\n", fname); fp = xfopen(fname, "w"); if (fp == NULL) { xprintf("Unable to create `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xfprintf(fp, "c %s\n", G->name == NULL ? "unknown" : G->name), count++; xfprintf(fp, "p max %d %d\n", G->nv, G->na), count++; xfprintf(fp, "n %d s\n", s), count++; xfprintf(fp, "n %d t\n", t), count++; for (i = 1; i <= G->nv; i++) { v = G->v[i]; for (a = v->out; a != NULL; a = a->t_next) { if (a_cap >= 0) memcpy(&cap, (char *)a->data + a_cap, sizeof(double)); else cap = 1.0; xfprintf(fp, "a %d %d %.*g\n", a->tail->i, a->head->i, DBL_DIG, cap), count++; } } xfprintf(fp, "c eof\n"), count++; xfflush(fp); if (xferror(fp)) { xprintf("Write error on `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xprintf("%d lines were written\n", count); ret = 0; done: if (fp != NULL) xfclose(fp); return ret; } /*********************************************************************** * NAME * * glp_read_asnprob - read assignment problem data in DIMACS format * * SYNOPSIS * * int glp_read_asnprob(glp_graph *G, int v_set, int a_cost, * const char *fname); * * DESCRIPTION * * The routine glp_read_asnprob reads assignment problem data in DIMACS * format from a text file. * * RETURNS * * If the operation was successful, the routine returns zero. Otherwise * it prints an error message and returns non-zero. */ int glp_read_asnprob(glp_graph *G, int v_set, int a_cost, const char *fname) { struct csa _csa, *csa = &_csa; glp_vertex *v; glp_arc *a; int nv, na, n1, i, j, k, ret = 0; double cost; char *flag = NULL; if (v_set >= 0 && v_set > G->v_size - (int)sizeof(int)) xerror("glp_read_asnprob: v_set = %d; invalid offset\n", v_set); if (a_cost >= 0 && a_cost > G->a_size - (int)sizeof(double)) xerror("glp_read_asnprob: a_cost = %d; invalid offset\n", a_cost); glp_erase_graph(G, G->v_size, G->a_size); if (setjmp(csa->jump)) { ret = 1; goto done; } csa->fname = fname; csa->fp = NULL; csa->count = 0; csa->c = '\n'; csa->field[0] = '\0'; csa->empty = csa->nonint = 0; xprintf("Reading assignment problem data from `%s'...\n", fname); csa->fp = xfopen(fname, "r"); if (csa->fp == NULL) { xprintf("Unable to open `%s' - %s\n", fname, xerrmsg()); longjmp(csa->jump, 1); } /* read problem line */ read_designator(csa); if (strcmp(csa->field, "p") != 0) error(csa, "problem line missing or invalid"); read_field(csa); if (strcmp(csa->field, "asn") != 0) error(csa, "wrong problem designator; `asn' expected"); read_field(csa); if (!(str2int(csa->field, &nv) == 0 && nv >= 0)) error(csa, "number of nodes missing or invalid"); read_field(csa); if (!(str2int(csa->field, &na) == 0 && na >= 0)) error(csa, "number of arcs missing or invalid"); if (nv > 0) glp_add_vertices(G, nv); end_of_line(csa); /* read node descriptor lines */ flag = xcalloc(1+nv, sizeof(char)); memset(&flag[1], 0, nv * sizeof(char)); n1 = 0; for (;;) { read_designator(csa); if (strcmp(csa->field, "n") != 0) break; read_field(csa); if (str2int(csa->field, &i) != 0) error(csa, "node number missing or invalid"); if (!(1 <= i && i <= nv)) error(csa, "node number %d out of range", i); if (flag[i]) error(csa, "duplicate descriptor of node %d", i); flag[i] = 1, n1++; end_of_line(csa); } xprintf( "Assignment problem has %d + %d = %d node%s and %d arc%s\n", n1, nv - n1, nv, nv == 1 ? "" : "s", na, na == 1 ? "" : "s"); if (v_set >= 0) { for (i = 1; i <= nv; i++) { v = G->v[i]; k = (flag[i] ? 0 : 1); memcpy((char *)v->data + v_set, &k, sizeof(int)); } } /* read arc descriptor lines */ for (k = 1; k <= na; k++) { if (k > 1) read_designator(csa); if (strcmp(csa->field, "a") != 0) error(csa, "wrong line designator; `a' expected"); read_field(csa); if (str2int(csa->field, &i) != 0) error(csa, "starting node number missing or invalid"); if (!(1 <= i && i <= nv)) error(csa, "starting node number %d out of range", i); if (!flag[i]) error(csa, "node %d cannot be a starting node", i); read_field(csa); if (str2int(csa->field, &j) != 0) error(csa, "ending node number missing or invalid"); if (!(1 <= j && j <= nv)) error(csa, "ending node number %d out of range", j); if (flag[j]) error(csa, "node %d cannot be an ending node", j); read_field(csa); if (str2num(csa->field, &cost) != 0) error(csa, "arc cost missing or invalid"); check_int(csa, cost); a = glp_add_arc(G, i, j); if (a_cost >= 0) memcpy((char *)a->data + a_cost, &cost, sizeof(double)); end_of_line(csa); } xprintf("%d lines were read\n", csa->count); done: if (ret) glp_erase_graph(G, G->v_size, G->a_size); if (csa->fp != NULL) xfclose(csa->fp); if (flag != NULL) xfree(flag); return ret; } /*********************************************************************** * NAME * * glp_write_asnprob - write assignment problem data in DIMACS format * * SYNOPSIS * * int glp_write_asnprob(glp_graph *G, int v_set, int a_cost, * const char *fname); * * DESCRIPTION * * The routine glp_write_asnprob writes assignment problem data in * DIMACS format to a text file. * * RETURNS * * If the operation was successful, the routine returns zero. Otherwise * it prints an error message and returns non-zero. */ int glp_write_asnprob(glp_graph *G, int v_set, int a_cost, const char *fname) { XFILE *fp; glp_vertex *v; glp_arc *a; int i, k, count = 0, ret; double cost; if (v_set >= 0 && v_set > G->v_size - (int)sizeof(int)) xerror("glp_write_asnprob: v_set = %d; invalid offset\n", v_set); if (a_cost >= 0 && a_cost > G->a_size - (int)sizeof(double)) xerror("glp_write_asnprob: a_cost = %d; invalid offset\n", a_cost); xprintf("Writing assignment problem data to `%s'...\n", fname); fp = xfopen(fname, "w"); if (fp == NULL) { xprintf("Unable to create `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xfprintf(fp, "c %s\n", G->name == NULL ? "unknown" : G->name), count++; xfprintf(fp, "p asn %d %d\n", G->nv, G->na), count++; for (i = 1; i <= G->nv; i++) { v = G->v[i]; if (v_set >= 0) memcpy(&k, (char *)v->data + v_set, sizeof(int)); else k = (v->out != NULL ? 0 : 1); if (k == 0) xfprintf(fp, "n %d\n", i), count++; } for (i = 1; i <= G->nv; i++) { v = G->v[i]; for (a = v->out; a != NULL; a = a->t_next) { if (a_cost >= 0) memcpy(&cost, (char *)a->data + a_cost, sizeof(double)); else cost = 1.0; xfprintf(fp, "a %d %d %.*g\n", a->tail->i, a->head->i, DBL_DIG, cost), count++; } } xfprintf(fp, "c eof\n"), count++; xfflush(fp); if (xferror(fp)) { xprintf("Write error on `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xprintf("%d lines were written\n", count); ret = 0; done: if (fp != NULL) xfclose(fp); return ret; } /*********************************************************************** * NAME * * glp_read_ccdata - read graph in DIMACS clique/coloring format * * SYNOPSIS * * int glp_read_ccdata(glp_graph *G, int v_wgt, const char *fname); * * DESCRIPTION * * The routine glp_read_ccdata reads an (undirected) graph in DIMACS * clique/coloring format from a text file. * * RETURNS * * If the operation was successful, the routine returns zero. Otherwise * it prints an error message and returns non-zero. */ int glp_read_ccdata(glp_graph *G, int v_wgt, const char *fname) { struct csa _csa, *csa = &_csa; glp_vertex *v; int i, j, k, nv, ne, ret = 0; double w; char *flag = NULL; if (v_wgt >= 0 && v_wgt > G->v_size - (int)sizeof(double)) xerror("glp_read_ccdata: v_wgt = %d; invalid offset\n", v_wgt); glp_erase_graph(G, G->v_size, G->a_size); if (setjmp(csa->jump)) { ret = 1; goto done; } csa->fname = fname; csa->fp = NULL; csa->count = 0; csa->c = '\n'; csa->field[0] = '\0'; csa->empty = csa->nonint = 0; xprintf("Reading graph from `%s'...\n", fname); csa->fp = xfopen(fname, "r"); if (csa->fp == NULL) { xprintf("Unable to open `%s' - %s\n", fname, xerrmsg()); longjmp(csa->jump, 1); } /* read problem line */ read_designator(csa); if (strcmp(csa->field, "p") != 0) error(csa, "problem line missing or invalid"); read_field(csa); if (strcmp(csa->field, "edge") != 0) error(csa, "wrong problem designator; `edge' expected"); read_field(csa); if (!(str2int(csa->field, &nv) == 0 && nv >= 0)) error(csa, "number of vertices missing or invalid"); read_field(csa); if (!(str2int(csa->field, &ne) == 0 && ne >= 0)) error(csa, "number of edges missing or invalid"); xprintf("Graph has %d vert%s and %d edge%s\n", nv, nv == 1 ? "ex" : "ices", ne, ne == 1 ? "" : "s"); if (nv > 0) glp_add_vertices(G, nv); end_of_line(csa); /* read node descriptor lines */ flag = xcalloc(1+nv, sizeof(char)); memset(&flag[1], 0, nv * sizeof(char)); if (v_wgt >= 0) { w = 1.0; for (i = 1; i <= nv; i++) { v = G->v[i]; memcpy((char *)v->data + v_wgt, &w, sizeof(double)); } } for (;;) { read_designator(csa); if (strcmp(csa->field, "n") != 0) break; read_field(csa); if (str2int(csa->field, &i) != 0) error(csa, "vertex number missing or invalid"); if (!(1 <= i && i <= nv)) error(csa, "vertex number %d out of range", i); if (flag[i]) error(csa, "duplicate descriptor of vertex %d", i); read_field(csa); if (str2num(csa->field, &w) != 0) error(csa, "vertex weight missing or invalid"); check_int(csa, w); if (v_wgt >= 0) { v = G->v[i]; memcpy((char *)v->data + v_wgt, &w, sizeof(double)); } flag[i] = 1; end_of_line(csa); } xfree(flag), flag = NULL; /* read edge descriptor lines */ for (k = 1; k <= ne; k++) { if (k > 1) read_designator(csa); if (strcmp(csa->field, "e") != 0) error(csa, "wrong line designator; `e' expected"); read_field(csa); if (str2int(csa->field, &i) != 0) error(csa, "first vertex number missing or invalid"); if (!(1 <= i && i <= nv)) error(csa, "first vertex number %d out of range", i); read_field(csa); if (str2int(csa->field, &j) != 0) error(csa, "second vertex number missing or invalid"); if (!(1 <= j && j <= nv)) error(csa, "second vertex number %d out of range", j); glp_add_arc(G, i, j); end_of_line(csa); } xprintf("%d lines were read\n", csa->count); done: if (ret) glp_erase_graph(G, G->v_size, G->a_size); if (csa->fp != NULL) xfclose(csa->fp); if (flag != NULL) xfree(flag); return ret; } /*********************************************************************** * NAME * * glp_write_ccdata - write graph in DIMACS clique/coloring format * * SYNOPSIS * * int glp_write_ccdata(glp_graph *G, int v_wgt, const char *fname); * * DESCRIPTION * * The routine glp_write_ccdata writes the specified graph in DIMACS * clique/coloring format to a text file. * * RETURNS * * If the operation was successful, the routine returns zero. Otherwise * it prints an error message and returns non-zero. */ int glp_write_ccdata(glp_graph *G, int v_wgt, const char *fname) { XFILE *fp; glp_vertex *v; glp_arc *e; int i, count = 0, ret; double w; if (v_wgt >= 0 && v_wgt > G->v_size - (int)sizeof(double)) xerror("glp_write_ccdata: v_wgt = %d; invalid offset\n", v_wgt); xprintf("Writing graph to `%s'\n", fname); fp = xfopen(fname, "w"); if (fp == NULL) { xprintf("Unable to create `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xfprintf(fp, "c %s\n", G->name == NULL ? "unknown" : G->name), count++; xfprintf(fp, "p edge %d %d\n", G->nv, G->na), count++; if (v_wgt >= 0) { for (i = 1; i <= G->nv; i++) { v = G->v[i]; memcpy(&w, (char *)v->data + v_wgt, sizeof(double)); if (w != 1.0) xfprintf(fp, "n %d %.*g\n", i, DBL_DIG, w), count++; } } for (i = 1; i <= G->nv; i++) { v = G->v[i]; for (e = v->out; e != NULL; e = e->t_next) xfprintf(fp, "e %d %d\n", e->tail->i, e->head->i), count++; } xfprintf(fp, "c eof\n"), count++; xfflush(fp); if (xferror(fp)) { xprintf("Write error on `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xprintf("%d lines were written\n", count); ret = 0; done: if (fp != NULL) xfclose(fp); return ret; } /*********************************************************************** * NAME * * glp_read_prob - read problem data in GLPK format * * SYNOPSIS * * int glp_read_prob(glp_prob *P, int flags, const char *fname); * * The routine glp_read_prob reads problem data in GLPK LP/MIP format * from a text file. * * RETURNS * * If the operation was successful, the routine returns zero. Otherwise * it prints an error message and returns non-zero. */ int glp_read_prob(glp_prob *P, int flags, const char *fname) { struct csa _csa, *csa = &_csa; int mip, m, n, nnz, ne, i, j, k, type, kind, ret, *ln = NULL, *ia = NULL, *ja = NULL; double lb, ub, temp, *ar = NULL; char *rf = NULL, *cf = NULL; if (P == NULL || P->magic != GLP_PROB_MAGIC) xerror("glp_read_prob: P = %p; invalid problem object\n", P); if (flags != 0) xerror("glp_read_prob: flags = %d; invalid parameter\n", flags); if (fname == NULL) xerror("glp_read_prob: fname = %d; invalid parameter\n", fname); glp_erase_prob(P); if (setjmp(csa->jump)) { ret = 1; goto done; } csa->fname = fname; csa->fp = NULL; csa->count = 0; csa->c = '\n'; csa->field[0] = '\0'; csa->empty = csa->nonint = 0; xprintf("Reading problem data from `%s'...\n", fname); csa->fp = xfopen(fname, "r"); if (csa->fp == NULL) { xprintf("Unable to open `%s' - %s\n", fname, xerrmsg()); longjmp(csa->jump, 1); } /* read problem line */ read_designator(csa); if (strcmp(csa->field, "p") != 0) error(csa, "problem line missing or invalid"); read_field(csa); if (strcmp(csa->field, "lp") == 0) mip = 0; else if (strcmp(csa->field, "mip") == 0) mip = 1; else error(csa, "wrong problem designator; `lp' or `mip' expected\n" ); read_field(csa); if (strcmp(csa->field, "min") == 0) glp_set_obj_dir(P, GLP_MIN); else if (strcmp(csa->field, "max") == 0) glp_set_obj_dir(P, GLP_MAX); else error(csa, "objective sense missing or invalid"); read_field(csa); if (!(str2int(csa->field, &m) == 0 && m >= 0)) error(csa, "number of rows missing or invalid"); read_field(csa); if (!(str2int(csa->field, &n) == 0 && n >= 0)) error(csa, "number of columns missing or invalid"); read_field(csa); if (!(str2int(csa->field, &nnz) == 0 && nnz >= 0)) error(csa, "number of constraint coefficients missing or inval" "id"); if (m > 0) { glp_add_rows(P, m); for (i = 1; i <= m; i++) glp_set_row_bnds(P, i, GLP_FX, 0.0, 0.0); } if (n > 0) { glp_add_cols(P, n); for (j = 1; j <= n; j++) { if (!mip) glp_set_col_bnds(P, j, GLP_LO, 0.0, 0.0); else glp_set_col_kind(P, j, GLP_BV); } } end_of_line(csa); /* allocate working arrays */ rf = xcalloc(1+m, sizeof(char)); memset(rf, 0, 1+m); cf = xcalloc(1+n, sizeof(char)); memset(cf, 0, 1+n); ln = xcalloc(1+nnz, sizeof(int)); ia = xcalloc(1+nnz, sizeof(int)); ja = xcalloc(1+nnz, sizeof(int)); ar = xcalloc(1+nnz, sizeof(double)); /* read descriptor lines */ ne = 0; for (;;) { read_designator(csa); if (strcmp(csa->field, "i") == 0) { /* row descriptor */ read_field(csa); if (str2int(csa->field, &i) != 0) error(csa, "row number missing or invalid"); if (!(1 <= i && i <= m)) error(csa, "row number out of range"); read_field(csa); if (strcmp(csa->field, "f") == 0) type = GLP_FR; else if (strcmp(csa->field, "l") == 0) type = GLP_LO; else if (strcmp(csa->field, "u") == 0) type = GLP_UP; else if (strcmp(csa->field, "d") == 0) type = GLP_DB; else if (strcmp(csa->field, "s") == 0) type = GLP_FX; else error(csa, "row type missing or invalid"); if (type == GLP_LO || type == GLP_DB || type == GLP_FX) { read_field(csa); if (str2num(csa->field, &lb) != 0) error(csa, "row lower bound/fixed value missing or in" "valid"); } else lb = 0.0; if (type == GLP_UP || type == GLP_DB) { read_field(csa); if (str2num(csa->field, &ub) != 0) error(csa, "row upper bound missing or invalid"); } else ub = 0.0; if (rf[i] & 0x01) error(csa, "duplicate row descriptor"); glp_set_row_bnds(P, i, type, lb, ub), rf[i] |= 0x01; } else if (strcmp(csa->field, "j") == 0) { /* column descriptor */ read_field(csa); if (str2int(csa->field, &j) != 0) error(csa, "column number missing or invalid"); if (!(1 <= j && j <= n)) error(csa, "column number out of range"); if (!mip) kind = GLP_CV; else { read_field(csa); if (strcmp(csa->field, "c") == 0) kind = GLP_CV; else if (strcmp(csa->field, "i") == 0) kind = GLP_IV; else if (strcmp(csa->field, "b") == 0) { kind = GLP_IV; type = GLP_DB, lb = 0.0, ub = 1.0; goto skip; } else error(csa, "column kind missing or invalid"); } read_field(csa); if (strcmp(csa->field, "f") == 0) type = GLP_FR; else if (strcmp(csa->field, "l") == 0) type = GLP_LO; else if (strcmp(csa->field, "u") == 0) type = GLP_UP; else if (strcmp(csa->field, "d") == 0) type = GLP_DB; else if (strcmp(csa->field, "s") == 0) type = GLP_FX; else error(csa, "column type missing or invalid"); if (type == GLP_LO || type == GLP_DB || type == GLP_FX) { read_field(csa); if (str2num(csa->field, &lb) != 0) error(csa, "column lower bound/fixed value missing or" " invalid"); } else lb = 0.0; if (type == GLP_UP || type == GLP_DB) { read_field(csa); if (str2num(csa->field, &ub) != 0) error(csa, "column upper bound missing or invalid"); } else ub = 0.0; skip: if (cf[j] & 0x01) error(csa, "duplicate column descriptor"); glp_set_col_kind(P, j, kind); glp_set_col_bnds(P, j, type, lb, ub), cf[j] |= 0x01; } else if (strcmp(csa->field, "a") == 0) { /* coefficient descriptor */ read_field(csa); if (str2int(csa->field, &i) != 0) error(csa, "row number missing or invalid"); if (!(0 <= i && i <= m)) error(csa, "row number out of range"); read_field(csa); if (str2int(csa->field, &j) != 0) error(csa, "column number missing or invalid"); if (!((i == 0 ? 0 : 1) <= j && j <= n)) error(csa, "column number out of range"); read_field(csa); if (i == 0) { if (str2num(csa->field, &temp) != 0) error(csa, "objective %s missing or invalid", j == 0 ? "constant term" : "coefficient"); if (cf[j] & 0x10) error(csa, "duplicate objective %s", j == 0 ? "constant term" : "coefficient"); glp_set_obj_coef(P, j, temp), cf[j] |= 0x10; } else { if (str2num(csa->field, &temp) != 0) error(csa, "constraint coefficient missing or invalid" ); if (ne == nnz) error(csa, "too many constraint coefficient descripto" "rs"); ln[++ne] = csa->count; ia[ne] = i, ja[ne] = j, ar[ne] = temp; } } else if (strcmp(csa->field, "n") == 0) { /* symbolic name descriptor */ read_field(csa); if (strcmp(csa->field, "p") == 0) { /* problem name */ read_field(csa); if (P->name != NULL) error(csa, "duplicate problem name"); glp_set_prob_name(P, csa->field); } else if (strcmp(csa->field, "z") == 0) { /* objective name */ read_field(csa); if (P->obj != NULL) error(csa, "duplicate objective name"); glp_set_obj_name(P, csa->field); } else if (strcmp(csa->field, "i") == 0) { /* row name */ read_field(csa); if (str2int(csa->field, &i) != 0) error(csa, "row number missing or invalid"); if (!(1 <= i && i <= m)) error(csa, "row number out of range"); read_field(csa); if (P->row[i]->name != NULL) error(csa, "duplicate row name"); glp_set_row_name(P, i, csa->field); } else if (strcmp(csa->field, "j") == 0) { /* column name */ read_field(csa); if (str2int(csa->field, &j) != 0) error(csa, "column number missing or invalid"); if (!(1 <= j && j <= n)) error(csa, "column number out of range"); read_field(csa); if (P->col[j]->name != NULL) error(csa, "duplicate column name"); glp_set_col_name(P, j, csa->field); } else error(csa, "object designator missing or invalid"); } else if (strcmp(csa->field, "e") == 0) break; else error(csa, "line designator missing or invalid"); end_of_line(csa); } if (ne < nnz) error(csa, "too few constraint coefficient descriptors"); xassert(ne == nnz); k = glp_check_dup(m, n, ne, ia, ja); xassert(0 <= k && k <= nnz); if (k > 0) { csa->count = ln[k]; error(csa, "duplicate constraint coefficient"); } glp_load_matrix(P, ne, ia, ja, ar); /* print some statistics */ if (P->name != NULL) xprintf("Problem: %s\n", P->name); if (P->obj != NULL) xprintf("Objective: %s\n", P->obj); xprintf("%d row%s, %d column%s, %d non-zero%s\n", m, m == 1 ? "" : "s", n, n == 1 ? "" : "s", nnz, nnz == 1 ? "" : "s"); if (glp_get_num_int(P) > 0) { int ni = glp_get_num_int(P); int nb = glp_get_num_bin(P); if (ni == 1) { if (nb == 0) xprintf("One variable is integer\n"); else xprintf("One variable is binary\n"); } else { xprintf("%d integer variables, ", ni); if (nb == 0) xprintf("none"); else if (nb == 1) xprintf("one"); else if (nb == ni) xprintf("all"); else xprintf("%d", nb); xprintf(" of which %s binary\n", nb == 1 ? "is" : "are"); } } xprintf("%d lines were read\n", csa->count); /* problem data has been successfully read */ glp_sort_matrix(P); ret = 0; done: if (csa->fp != NULL) xfclose(csa->fp); if (rf != NULL) xfree(rf); if (cf != NULL) xfree(cf); if (ln != NULL) xfree(ln); if (ia != NULL) xfree(ia); if (ja != NULL) xfree(ja); if (ar != NULL) xfree(ar); if (ret) glp_erase_prob(P); return ret; } /*********************************************************************** * NAME * * glp_write_prob - write problem data in GLPK format * * SYNOPSIS * * int glp_write_prob(glp_prob *P, int flags, const char *fname); * * The routine glp_write_prob writes problem data in GLPK LP/MIP format * to a text file. * * RETURNS * * If the operation was successful, the routine returns zero. Otherwise * it prints an error message and returns non-zero. */ int glp_write_prob(glp_prob *P, int flags, const char *fname) { XFILE *fp; GLPROW *row; GLPCOL *col; GLPAIJ *aij; int mip, i, j, count, ret; if (P == NULL || P->magic != GLP_PROB_MAGIC) xerror("glp_write_prob: P = %p; invalid problem object\n", P); if (flags != 0) xerror("glp_write_prob: flags = %d; invalid parameter\n", flags); if (fname == NULL) xerror("glp_write_prob: fname = %d; invalid parameter\n", fname); xprintf("Writing problem data to `%s'...\n", fname); fp = xfopen(fname, "w"), count = 0; if (fp == NULL) { xprintf("Unable to create `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } /* write problem line */ mip = (glp_get_num_int(P) > 0); xfprintf(fp, "p %s %s %d %d %d\n", !mip ? "lp" : "mip", P->dir == GLP_MIN ? "min" : P->dir == GLP_MAX ? "max" : "???", P->m, P->n, P->nnz), count++; if (P->name != NULL) xfprintf(fp, "n p %s\n", P->name), count++; if (P->obj != NULL) xfprintf(fp, "n z %s\n", P->obj), count++; /* write row descriptors */ for (i = 1; i <= P->m; i++) { row = P->row[i]; if (row->type == GLP_FX && row->lb == 0.0) goto skip1; xfprintf(fp, "i %d ", i), count++; if (row->type == GLP_FR) xfprintf(fp, "f\n"); else if (row->type == GLP_LO) xfprintf(fp, "l %.*g\n", DBL_DIG, row->lb); else if (row->type == GLP_UP) xfprintf(fp, "u %.*g\n", DBL_DIG, row->ub); else if (row->type == GLP_DB) xfprintf(fp, "d %.*g %.*g\n", DBL_DIG, row->lb, DBL_DIG, row->ub); else if (row->type == GLP_FX) xfprintf(fp, "s %.*g\n", DBL_DIG, row->lb); else xassert(row != row); skip1: if (row->name != NULL) xfprintf(fp, "n i %d %s\n", i, row->name), count++; } /* write column descriptors */ for (j = 1; j <= P->n; j++) { col = P->col[j]; if (!mip && col->type == GLP_LO && col->lb == 0.0) goto skip2; if (mip && col->kind == GLP_IV && col->type == GLP_DB && col->lb == 0.0 && col->ub == 1.0) goto skip2; xfprintf(fp, "j %d ", j), count++; if (mip) { if (col->kind == GLP_CV) xfprintf(fp, "c "); else if (col->kind == GLP_IV) xfprintf(fp, "i "); else xassert(col != col); } if (col->type == GLP_FR) xfprintf(fp, "f\n"); else if (col->type == GLP_LO) xfprintf(fp, "l %.*g\n", DBL_DIG, col->lb); else if (col->type == GLP_UP) xfprintf(fp, "u %.*g\n", DBL_DIG, col->ub); else if (col->type == GLP_DB) xfprintf(fp, "d %.*g %.*g\n", DBL_DIG, col->lb, DBL_DIG, col->ub); else if (col->type == GLP_FX) xfprintf(fp, "s %.*g\n", DBL_DIG, col->lb); else xassert(col != col); skip2: if (col->name != NULL) xfprintf(fp, "n j %d %s\n", j, col->name), count++; } /* write objective coefficient descriptors */ if (P->c0 != 0.0) xfprintf(fp, "a 0 0 %.*g\n", DBL_DIG, P->c0), count++; for (j = 1; j <= P->n; j++) { col = P->col[j]; if (col->coef != 0.0) xfprintf(fp, "a 0 %d %.*g\n", j, DBL_DIG, col->coef), count++; } /* write constraint coefficient descriptors */ for (i = 1; i <= P->m; i++) { row = P->row[i]; for (aij = row->ptr; aij != NULL; aij = aij->r_next) xfprintf(fp, "a %d %d %.*g\n", i, aij->col->j, DBL_DIG, aij->val), count++; } /* write end line */ xfprintf(fp, "e o f\n"), count++; xfflush(fp); if (xferror(fp)) { xprintf("Write error on `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xprintf("%d lines were written\n", count); ret = 0; done: if (fp != NULL) xfclose(fp); return ret; } /**********************************************************************/ int glp_read_cnfsat(glp_prob *P, const char *fname) { /* read CNF-SAT problem data in DIMACS format */ struct csa _csa, *csa = &_csa; int m, n, i, j, len, neg, rhs, ret = 0, *ind = NULL; double *val = NULL; char *map = NULL; if (P == NULL || P->magic != GLP_PROB_MAGIC) xerror("glp_read_cnfsat: P = %p; invalid problem object\n", P); if (fname == NULL) xerror("glp_read_cnfsat: fname = %p; invalid parameter\n", fname); glp_erase_prob(P); if (setjmp(csa->jump)) { ret = 1; goto done; } csa->fname = fname; csa->fp = NULL; csa->count = 0; csa->c = '\n'; csa->field[0] = '\0'; csa->empty = csa->nonint = 0; xprintf("Reading CNF-SAT problem data from `%s'...\n", fname); csa->fp = xfopen(fname, "r"); if (csa->fp == NULL) { xprintf("Unable to open `%s' - %s\n", fname, xerrmsg()); longjmp(csa->jump, 1); } /* read problem line */ read_designator(csa); if (strcmp(csa->field, "p") != 0) error(csa, "problem line missing or invalid"); read_field(csa); if (strcmp(csa->field, "cnf") != 0) error(csa, "wrong problem designator; `cnf' expected\n"); read_field(csa); if (!(str2int(csa->field, &n) == 0 && n >= 0)) error(csa, "number of variables missing or invalid\n"); read_field(csa); if (!(str2int(csa->field, &m) == 0 && m >= 0)) error(csa, "number of clauses missing or invalid\n"); xprintf("Instance has %d variable%s and %d clause%s\n", n, n == 1 ? "" : "s", m, m == 1 ? "" : "s"); end_of_line(csa); if (m > 0) glp_add_rows(P, m); if (n > 0) { glp_add_cols(P, n); for (j = 1; j <= n; j++) glp_set_col_kind(P, j, GLP_BV); } /* allocate working arrays */ ind = xcalloc(1+n, sizeof(int)); val = xcalloc(1+n, sizeof(double)); map = xcalloc(1+n, sizeof(char)); for (j = 1; j <= n; j++) map[j] = 0; /* read clauses */ for (i = 1; i <= m; i++) { /* read i-th clause */ len = 0, rhs = 1; for (;;) { /* skip white-space characters */ while (csa->c == ' ' || csa->c == '\n') read_char(csa); /* read term */ read_field(csa); if (str2int(csa->field, &j) != 0) error(csa, "variable number missing or invalid\n"); if (j > 0) neg = 0; else if (j < 0) neg = 1, j = -j, rhs--; else break; if (!(1 <= j && j <= n)) error(csa, "variable number out of range\n"); if (map[j]) error(csa, "duplicate variable number\n"); len++, ind[len] = j, val[len] = (neg ? -1.0 : +1.0); map[j] = 1; } glp_set_row_bnds(P, i, GLP_LO, (double)rhs, 0.0); glp_set_mat_row(P, i, len, ind, val); while (len > 0) map[ind[len--]] = 0; } xprintf("%d lines were read\n", csa->count); /* problem data has been successfully read */ glp_sort_matrix(P); done: if (csa->fp != NULL) xfclose(csa->fp); if (ind != NULL) xfree(ind); if (val != NULL) xfree(val); if (map != NULL) xfree(map); if (ret) glp_erase_prob(P); return ret; } /**********************************************************************/ int glp_check_cnfsat(glp_prob *P) { /* check for CNF-SAT problem instance */ int m = P->m; int n = P->n; GLPROW *row; GLPCOL *col; GLPAIJ *aij; int i, j, neg; if (P == NULL || P->magic != GLP_PROB_MAGIC) xerror("glp_check_cnfsat: P = %p; invalid problem object\n", P); /* check columns */ for (j = 1; j <= n; j++) { col = P->col[j]; /* the variable should be binary */ if (!(col->kind == GLP_IV && col->type == GLP_DB && col->lb == 0.0 && col->ub == 1.0)) return 1; } /* objective function should be zero */ if (P->c0 != 0.0) return 2; for (j = 1; j <= n; j++) { col = P->col[j]; if (col->coef != 0.0) return 3; } /* check rows */ for (i = 1; i <= m; i++) { row = P->row[i]; /* the row should be of ">=" type */ if (row->type != GLP_LO) return 4; /* check constraint coefficients */ neg = 0; for (aij = row->ptr; aij != NULL; aij = aij->r_next) { /* the constraint coefficient should be +1 or -1 */ if (aij->val == +1.0) ; else if (aij->val == -1.0) neg++; else return 5; } /* the right-hand side should be (1 - neg), where neg is the number of negative constraint coefficients in the row */ if (row->lb != (double)(1 - neg)) return 6; } /* congratulations; this is CNF-SAT */ return 0; } /**********************************************************************/ int glp_write_cnfsat(glp_prob *P, const char *fname) { /* write CNF-SAT problem data in DIMACS format */ XFILE *fp = NULL; GLPAIJ *aij; int i, j, len, count = 0, ret; char s[50]; if (P == NULL || P->magic != GLP_PROB_MAGIC) xerror("glp_write_cnfsat: P = %p; invalid problem object\n", P); if (glp_check_cnfsat(P) != 0) { xprintf("glp_write_cnfsat: problem object does not encode CNF-" "SAT instance\n"); ret = 1; goto done; } xprintf("Writing CNF-SAT problem data to `%s'...\n", fname); fp = xfopen(fname, "w"); if (fp == NULL) { xprintf("Unable to create `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xfprintf(fp, "c %s\n", P->name == NULL ? "unknown" : P->name), count++; xfprintf(fp, "p cnf %d %d\n", P->n, P->m), count++; for (i = 1; i <= P->m; i++) { len = 0; for (aij = P->row[i]->ptr; aij != NULL; aij = aij->r_next) { j = aij->col->j; if (aij->val < 0.0) j = -j; sprintf(s, "%d", j); if (len > 0 && len + 1 + strlen(s) > 72) xfprintf(fp, "\n"), count++, len = 0; xfprintf(fp, "%s%s", len == 0 ? "" : " ", s); if (len > 0) len++; len += strlen(s); } if (len > 0 && len + 1 + 1 > 72) xfprintf(fp, "\n"), count++, len = 0; xfprintf(fp, "%s0\n", len == 0 ? "" : " "), count++; } xfprintf(fp, "c eof\n"), count++; xfflush(fp); if (xferror(fp)) { xprintf("Write error on `%s' - %s\n", fname, xerrmsg()); ret = 1; goto done; } xprintf("%d lines were written\n", count); ret = 0; done: if (fp != NULL) xfclose(fp); return ret; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/0000755000175000017500000000000011632352620017200 5ustar csheltoncsheltonctbnrle.1.1.1/glpk/glpk-4.47/src/amd/amd_preprocess.c0000644000175000017500000001017711632352620022360 0ustar csheltoncshelton/* ========================================================================= */ /* === AMD_preprocess ====================================================== */ /* ========================================================================= */ /* ------------------------------------------------------------------------- */ /* AMD, Copyright (c) Timothy A. Davis, */ /* Patrick R. Amestoy, and Iain S. Duff. See ../README.txt for License. */ /* email: davis at cise.ufl.edu CISE Department, Univ. of Florida. */ /* web: http://www.cise.ufl.edu/research/sparse/amd */ /* ------------------------------------------------------------------------- */ /* Sorts, removes duplicate entries, and transposes from the nonzero pattern of * a column-form matrix A, to obtain the matrix R. The input matrix can have * duplicate entries and/or unsorted columns (AMD_valid (n,Ap,Ai) must not be * AMD_INVALID). * * This input condition is NOT checked. This routine is not user-callable. */ #include "amd_internal.h" /* ========================================================================= */ /* === AMD_preprocess ====================================================== */ /* ========================================================================= */ /* AMD_preprocess does not check its input for errors or allocate workspace. * On input, the condition (AMD_valid (n,n,Ap,Ai) != AMD_INVALID) must hold. */ GLOBAL void AMD_preprocess ( Int n, /* input matrix: A is n-by-n */ const Int Ap [ ], /* size n+1 */ const Int Ai [ ], /* size nz = Ap [n] */ /* output matrix R: */ Int Rp [ ], /* size n+1 */ Int Ri [ ], /* size nz (or less, if duplicates present) */ Int W [ ], /* workspace of size n */ Int Flag [ ] /* workspace of size n */ ) { /* --------------------------------------------------------------------- */ /* local variables */ /* --------------------------------------------------------------------- */ Int i, j, p, p2 ; ASSERT (AMD_valid (n, n, Ap, Ai) != AMD_INVALID) ; /* --------------------------------------------------------------------- */ /* count the entries in each row of A (excluding duplicates) */ /* --------------------------------------------------------------------- */ for (i = 0 ; i < n ; i++) { W [i] = 0 ; /* # of nonzeros in row i (excl duplicates) */ Flag [i] = EMPTY ; /* Flag [i] = j if i appears in column j */ } for (j = 0 ; j < n ; j++) { p2 = Ap [j+1] ; for (p = Ap [j] ; p < p2 ; p++) { i = Ai [p] ; if (Flag [i] != j) { /* row index i has not yet appeared in column j */ W [i]++ ; /* one more entry in row i */ Flag [i] = j ; /* flag row index i as appearing in col j*/ } } } /* --------------------------------------------------------------------- */ /* compute the row pointers for R */ /* --------------------------------------------------------------------- */ Rp [0] = 0 ; for (i = 0 ; i < n ; i++) { Rp [i+1] = Rp [i] + W [i] ; } for (i = 0 ; i < n ; i++) { W [i] = Rp [i] ; Flag [i] = EMPTY ; } /* --------------------------------------------------------------------- */ /* construct the row form matrix R */ /* --------------------------------------------------------------------- */ /* R = row form of pattern of A */ for (j = 0 ; j < n ; j++) { p2 = Ap [j+1] ; for (p = Ap [j] ; p < p2 ; p++) { i = Ai [p] ; if (Flag [i] != j) { /* row index i has not yet appeared in column j */ Ri [W [i]++] = j ; /* put col j in row i */ Flag [i] = j ; /* flag row index i as appearing in col j*/ } } } #ifndef NDEBUG ASSERT (AMD_valid (n, n, Rp, Ri) == AMD_OK) ; for (j = 0 ; j < n ; j++) { ASSERT (W [j] == Rp [j+1]) ; } #endif } ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/amd_dump.c0000644000175000017500000001400111632352620021126 0ustar csheltoncshelton/* ========================================================================= */ /* === AMD_dump ============================================================ */ /* ========================================================================= */ /* ------------------------------------------------------------------------- */ /* AMD, Copyright (c) Timothy A. Davis, */ /* Patrick R. Amestoy, and Iain S. Duff. See ../README.txt for License. */ /* email: davis at cise.ufl.edu CISE Department, Univ. of Florida. */ /* web: http://www.cise.ufl.edu/research/sparse/amd */ /* ------------------------------------------------------------------------- */ /* Debugging routines for AMD. Not used if NDEBUG is not defined at compile- * time (the default). See comments in amd_internal.h on how to enable * debugging. Not user-callable. */ #include "amd_internal.h" #ifndef NDEBUG /* This global variable is present only when debugging */ GLOBAL Int AMD_debug = -999 ; /* default is no debug printing */ /* ========================================================================= */ /* === AMD_debug_init ====================================================== */ /* ========================================================================= */ /* Sets the debug print level, by reading the file debug.amd (if it exists) */ GLOBAL void AMD_debug_init ( char *s ) { FILE *f ; f = fopen ("debug.amd", "r") ; if (f == (FILE *) NULL) { AMD_debug = -999 ; } else { fscanf (f, ID, &AMD_debug) ; fclose (f) ; } if (AMD_debug >= 0) { printf ("%s: AMD_debug_init, D= "ID"\n", s, AMD_debug) ; } } /* ========================================================================= */ /* === AMD_dump ============================================================ */ /* ========================================================================= */ /* Dump AMD's data structure, except for the hash buckets. This routine * cannot be called when the hash buckets are non-empty. */ GLOBAL void AMD_dump ( Int n, /* A is n-by-n */ Int Pe [ ], /* pe [0..n-1]: index in iw of start of row i */ Int Iw [ ], /* workspace of size iwlen, iwlen [0..pfree-1] * holds the matrix on input */ Int Len [ ], /* len [0..n-1]: length for row i */ Int iwlen, /* length of iw */ Int pfree, /* iw [pfree ... iwlen-1] is empty on input */ Int Nv [ ], /* nv [0..n-1] */ Int Next [ ], /* next [0..n-1] */ Int Last [ ], /* last [0..n-1] */ Int Head [ ], /* head [0..n-1] */ Int Elen [ ], /* size n */ Int Degree [ ], /* size n */ Int W [ ], /* size n */ Int nel ) { Int i, pe, elen, nv, len, e, p, k, j, deg, w, cnt, ilast ; if (AMD_debug < 0) return ; ASSERT (pfree <= iwlen) ; AMD_DEBUG3 (("\nAMD dump, pfree: "ID"\n", pfree)) ; for (i = 0 ; i < n ; i++) { pe = Pe [i] ; elen = Elen [i] ; nv = Nv [i] ; len = Len [i] ; w = W [i] ; if (elen >= EMPTY) { if (nv == 0) { AMD_DEBUG3 (("\nI "ID": nonprincipal: ", i)) ; ASSERT (elen == EMPTY) ; if (pe == EMPTY) { AMD_DEBUG3 ((" dense node\n")) ; ASSERT (w == 1) ; } else { ASSERT (pe < EMPTY) ; AMD_DEBUG3 ((" i "ID" -> parent "ID"\n", i, FLIP (Pe[i]))); } } else { AMD_DEBUG3 (("\nI "ID": active principal supervariable:\n",i)); AMD_DEBUG3 ((" nv(i): "ID" Flag: %d\n", nv, (nv < 0))) ; ASSERT (elen >= 0) ; ASSERT (nv > 0 && pe >= 0) ; p = pe ; AMD_DEBUG3 ((" e/s: ")) ; if (elen == 0) AMD_DEBUG3 ((" : ")) ; ASSERT (pe + len <= pfree) ; for (k = 0 ; k < len ; k++) { j = Iw [p] ; AMD_DEBUG3 ((" "ID"", j)) ; ASSERT (j >= 0 && j < n) ; if (k == elen-1) AMD_DEBUG3 ((" : ")) ; p++ ; } AMD_DEBUG3 (("\n")) ; } } else { e = i ; if (w == 0) { AMD_DEBUG3 (("\nE "ID": absorbed element: w "ID"\n", e, w)) ; ASSERT (nv > 0 && pe < 0) ; AMD_DEBUG3 ((" e "ID" -> parent "ID"\n", e, FLIP (Pe [e]))) ; } else { AMD_DEBUG3 (("\nE "ID": unabsorbed element: w "ID"\n", e, w)) ; ASSERT (nv > 0 && pe >= 0) ; p = pe ; AMD_DEBUG3 ((" : ")) ; ASSERT (pe + len <= pfree) ; for (k = 0 ; k < len ; k++) { j = Iw [p] ; AMD_DEBUG3 ((" "ID"", j)) ; ASSERT (j >= 0 && j < n) ; p++ ; } AMD_DEBUG3 (("\n")) ; } } } /* this routine cannot be called when the hash buckets are non-empty */ AMD_DEBUG3 (("\nDegree lists:\n")) ; if (nel >= 0) { cnt = 0 ; for (deg = 0 ; deg < n ; deg++) { if (Head [deg] == EMPTY) continue ; ilast = EMPTY ; AMD_DEBUG3 ((ID": \n", deg)) ; for (i = Head [deg] ; i != EMPTY ; i = Next [i]) { AMD_DEBUG3 ((" "ID" : next "ID" last "ID" deg "ID"\n", i, Next [i], Last [i], Degree [i])) ; ASSERT (i >= 0 && i < n && ilast == Last [i] && deg == Degree [i]) ; cnt += Nv [i] ; ilast = i ; } AMD_DEBUG3 (("\n")) ; } ASSERT (cnt == n - nel) ; } } #endif ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/amd_defaults.c0000644000175000017500000000257311632352620022003 0ustar csheltoncshelton/* ========================================================================= */ /* === AMD_defaults ======================================================== */ /* ========================================================================= */ /* ------------------------------------------------------------------------- */ /* AMD, Copyright (c) Timothy A. Davis, */ /* Patrick R. Amestoy, and Iain S. Duff. See ../README.txt for License. */ /* email: davis at cise.ufl.edu CISE Department, Univ. of Florida. */ /* web: http://www.cise.ufl.edu/research/sparse/amd */ /* ------------------------------------------------------------------------- */ /* User-callable. Sets default control parameters for AMD. See amd.h * for details. */ #include "amd_internal.h" /* ========================================================================= */ /* === AMD defaults ======================================================== */ /* ========================================================================= */ GLOBAL void AMD_defaults ( double Control [ ] ) { Int i ; if (Control != (double *) NULL) { for (i = 0 ; i < AMD_CONTROL ; i++) { Control [i] = 0 ; } Control [AMD_DENSE] = AMD_DEFAULT_DENSE ; Control [AMD_AGGRESSIVE] = AMD_DEFAULT_AGGRESSIVE ; } } ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/amd_info.c0000644000175000017500000001065611632352620021130 0ustar csheltoncshelton/* ========================================================================= */ /* === AMD_info ============================================================ */ /* ========================================================================= */ /* ------------------------------------------------------------------------- */ /* AMD, Copyright (c) Timothy A. Davis, */ /* Patrick R. Amestoy, and Iain S. Duff. See ../README.txt for License. */ /* email: davis at cise.ufl.edu CISE Department, Univ. of Florida. */ /* web: http://www.cise.ufl.edu/research/sparse/amd */ /* ------------------------------------------------------------------------- */ /* User-callable. Prints the output statistics for AMD. See amd.h * for details. If the Info array is not present, nothing is printed. */ #include "amd_internal.h" #define PRI(format,x) { if (x >= 0) { PRINTF ((format, x)) ; }} GLOBAL void AMD_info ( double Info [ ] ) { double n, ndiv, nmultsubs_ldl, nmultsubs_lu, lnz, lnzd ; PRINTF (("\nAMD version %d.%d.%d, %s, results:\n", AMD_MAIN_VERSION, AMD_SUB_VERSION, AMD_SUBSUB_VERSION, AMD_DATE)) ; if (!Info) { return ; } n = Info [AMD_N] ; ndiv = Info [AMD_NDIV] ; nmultsubs_ldl = Info [AMD_NMULTSUBS_LDL] ; nmultsubs_lu = Info [AMD_NMULTSUBS_LU] ; lnz = Info [AMD_LNZ] ; lnzd = (n >= 0 && lnz >= 0) ? (n + lnz) : (-1) ; /* AMD return status */ PRINTF ((" status: ")) ; if (Info [AMD_STATUS] == AMD_OK) { PRINTF (("OK\n")) ; } else if (Info [AMD_STATUS] == AMD_OUT_OF_MEMORY) { PRINTF (("out of memory\n")) ; } else if (Info [AMD_STATUS] == AMD_INVALID) { PRINTF (("invalid matrix\n")) ; } else if (Info [AMD_STATUS] == AMD_OK_BUT_JUMBLED) { PRINTF (("OK, but jumbled\n")) ; } else { PRINTF (("unknown\n")) ; } /* statistics about the input matrix */ PRI (" n, dimension of A: %.20g\n", n); PRI (" nz, number of nonzeros in A: %.20g\n", Info [AMD_NZ]) ; PRI (" symmetry of A: %.4f\n", Info [AMD_SYMMETRY]) ; PRI (" number of nonzeros on diagonal: %.20g\n", Info [AMD_NZDIAG]) ; PRI (" nonzeros in pattern of A+A' (excl. diagonal): %.20g\n", Info [AMD_NZ_A_PLUS_AT]) ; PRI (" # dense rows/columns of A+A': %.20g\n", Info [AMD_NDENSE]) ; /* statistics about AMD's behavior */ PRI (" memory used, in bytes: %.20g\n", Info [AMD_MEMORY]) ; PRI (" # of memory compactions: %.20g\n", Info [AMD_NCMPA]) ; /* statistics about the ordering quality */ PRINTF (("\n" " The following approximate statistics are for a subsequent\n" " factorization of A(P,P) + A(P,P)'. They are slight upper\n" " bounds if there are no dense rows/columns in A+A', and become\n" " looser if dense rows/columns exist.\n\n")) ; PRI (" nonzeros in L (excluding diagonal): %.20g\n", lnz) ; PRI (" nonzeros in L (including diagonal): %.20g\n", lnzd) ; PRI (" # divide operations for LDL' or LU: %.20g\n", ndiv) ; PRI (" # multiply-subtract operations for LDL': %.20g\n", nmultsubs_ldl) ; PRI (" # multiply-subtract operations for LU: %.20g\n", nmultsubs_lu) ; PRI (" max nz. in any column of L (incl. diagonal): %.20g\n", Info [AMD_DMAX]) ; /* total flop counts for various factorizations */ if (n >= 0 && ndiv >= 0 && nmultsubs_ldl >= 0 && nmultsubs_lu >= 0) { PRINTF (("\n" " chol flop count for real A, sqrt counted as 1 flop: %.20g\n" " LDL' flop count for real A: %.20g\n" " LDL' flop count for complex A: %.20g\n" " LU flop count for real A (with no pivoting): %.20g\n" " LU flop count for complex A (with no pivoting): %.20g\n\n", n + ndiv + 2*nmultsubs_ldl, ndiv + 2*nmultsubs_ldl, 9*ndiv + 8*nmultsubs_ldl, ndiv + 2*nmultsubs_lu, 9*ndiv + 8*nmultsubs_lu)) ; } } ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/amd_2.c0000644000175000017500000023050111632352620020327 0ustar csheltoncshelton/* ========================================================================= */ /* === AMD_2 =============================================================== */ /* ========================================================================= */ /* ------------------------------------------------------------------------- */ /* AMD, Copyright (c) Timothy A. Davis, */ /* Patrick R. Amestoy, and Iain S. Duff. See ../README.txt for License. */ /* email: davis at cise.ufl.edu CISE Department, Univ. of Florida. */ /* web: http://www.cise.ufl.edu/research/sparse/amd */ /* ------------------------------------------------------------------------- */ /* AMD_2: performs the AMD ordering on a symmetric sparse matrix A, followed * by a postordering (via depth-first search) of the assembly tree using the * AMD_postorder routine. */ #include "amd_internal.h" /* ========================================================================= */ /* === clear_flag ========================================================== */ /* ========================================================================= */ static Int clear_flag (Int wflg, Int wbig, Int W [ ], Int n) { Int x ; if (wflg < 2 || wflg >= wbig) { for (x = 0 ; x < n ; x++) { if (W [x] != 0) W [x] = 1 ; } wflg = 2 ; } /* at this point, W [0..n-1] < wflg holds */ return (wflg) ; } /* ========================================================================= */ /* === AMD_2 =============================================================== */ /* ========================================================================= */ GLOBAL void AMD_2 ( Int n, /* A is n-by-n, where n > 0 */ Int Pe [ ], /* Pe [0..n-1]: index in Iw of row i on input */ Int Iw [ ], /* workspace of size iwlen. Iw [0..pfree-1] * holds the matrix on input */ Int Len [ ], /* Len [0..n-1]: length for row/column i on input */ Int iwlen, /* length of Iw. iwlen >= pfree + n */ Int pfree, /* Iw [pfree ... iwlen-1] is empty on input */ /* 7 size-n workspaces, not defined on input: */ Int Nv [ ], /* the size of each supernode on output */ Int Next [ ], /* the output inverse permutation */ Int Last [ ], /* the output permutation */ Int Head [ ], Int Elen [ ], /* the size columns of L for each supernode */ Int Degree [ ], Int W [ ], /* control parameters and output statistics */ double Control [ ], /* array of size AMD_CONTROL */ double Info [ ] /* array of size AMD_INFO */ ) { /* * Given a representation of the nonzero pattern of a symmetric matrix, A, * (excluding the diagonal) perform an approximate minimum (UMFPACK/MA38-style) * degree ordering to compute a pivot order such that the introduction of * nonzeros (fill-in) in the Cholesky factors A = LL' is kept low. At each * step, the pivot selected is the one with the minimum UMFAPACK/MA38-style * upper-bound on the external degree. This routine can optionally perform * aggresive absorption (as done by MC47B in the Harwell Subroutine * Library). * * The approximate degree algorithm implemented here is the symmetric analog of * the degree update algorithm in MA38 and UMFPACK (the Unsymmetric-pattern * MultiFrontal PACKage, both by Davis and Duff). The routine is based on the * MA27 minimum degree ordering algorithm by Iain Duff and John Reid. * * This routine is a translation of the original AMDBAR and MC47B routines, * in Fortran, with the following modifications: * * (1) dense rows/columns are removed prior to ordering the matrix, and placed * last in the output order. The presence of a dense row/column can * increase the ordering time by up to O(n^2), unless they are removed * prior to ordering. * * (2) the minimum degree ordering is followed by a postordering (depth-first * search) of the assembly tree. Note that mass elimination (discussed * below) combined with the approximate degree update can lead to the mass * elimination of nodes with lower exact degree than the current pivot * element. No additional fill-in is caused in the representation of the * Schur complement. The mass-eliminated nodes merge with the current * pivot element. They are ordered prior to the current pivot element. * Because they can have lower exact degree than the current element, the * merger of two or more of these nodes in the current pivot element can * lead to a single element that is not a "fundamental supernode". The * diagonal block can have zeros in it. Thus, the assembly tree used here * is not guaranteed to be the precise supernodal elemination tree (with * "funadmental" supernodes), and the postordering performed by this * routine is not guaranteed to be a precise postordering of the * elimination tree. * * (3) input parameters are added, to control aggressive absorption and the * detection of "dense" rows/columns of A. * * (4) additional statistical information is returned, such as the number of * nonzeros in L, and the flop counts for subsequent LDL' and LU * factorizations. These are slight upper bounds, because of the mass * elimination issue discussed above. * * (5) additional routines are added to interface this routine to MATLAB * to provide a simple C-callable user-interface, to check inputs for * errors, compute the symmetry of the pattern of A and the number of * nonzeros in each row/column of A+A', to compute the pattern of A+A', * to perform the assembly tree postordering, and to provide debugging * ouput. Many of these functions are also provided by the Fortran * Harwell Subroutine Library routine MC47A. * * (6) both int and UF_long versions are provided. In the descriptions below * and integer is and int or UF_long depending on which version is * being used. ********************************************************************** ***** CAUTION: ARGUMENTS ARE NOT CHECKED FOR ERRORS ON INPUT. ****** ********************************************************************** ** If you want error checking, a more versatile input format, and a ** ** simpler user interface, use amd_order or amd_l_order instead. ** ** This routine is not meant to be user-callable. ** ********************************************************************** * ---------------------------------------------------------------------------- * References: * ---------------------------------------------------------------------------- * * [1] Timothy A. Davis and Iain Duff, "An unsymmetric-pattern multifrontal * method for sparse LU factorization", SIAM J. Matrix Analysis and * Applications, vol. 18, no. 1, pp. 140-158. Discusses UMFPACK / MA38, * which first introduced the approximate minimum degree used by this * routine. * * [2] Patrick Amestoy, Timothy A. Davis, and Iain S. Duff, "An approximate * minimum degree ordering algorithm," SIAM J. Matrix Analysis and * Applications, vol. 17, no. 4, pp. 886-905, 1996. Discusses AMDBAR and * MC47B, which are the Fortran versions of this routine. * * [3] Alan George and Joseph Liu, "The evolution of the minimum degree * ordering algorithm," SIAM Review, vol. 31, no. 1, pp. 1-19, 1989. * We list below the features mentioned in that paper that this code * includes: * * mass elimination: * Yes. MA27 relied on supervariable detection for mass elimination. * * indistinguishable nodes: * Yes (we call these "supervariables"). This was also in the MA27 * code - although we modified the method of detecting them (the * previous hash was the true degree, which we no longer keep track * of). A supervariable is a set of rows with identical nonzero * pattern. All variables in a supervariable are eliminated together. * Each supervariable has as its numerical name that of one of its * variables (its principal variable). * * quotient graph representation: * Yes. We use the term "element" for the cliques formed during * elimination. This was also in the MA27 code. The algorithm can * operate in place, but it will work more efficiently if given some * "elbow room." * * element absorption: * Yes. This was also in the MA27 code. * * external degree: * Yes. The MA27 code was based on the true degree. * * incomplete degree update and multiple elimination: * No. This was not in MA27, either. Our method of degree update * within MC47B is element-based, not variable-based. It is thus * not well-suited for use with incomplete degree update or multiple * elimination. * * Authors, and Copyright (C) 2004 by: * Timothy A. Davis, Patrick Amestoy, Iain S. Duff, John K. Reid. * * Acknowledgements: This work (and the UMFPACK package) was supported by the * National Science Foundation (ASC-9111263, DMS-9223088, and CCR-0203270). * The UMFPACK/MA38 approximate degree update algorithm, the unsymmetric analog * which forms the basis of AMD, was developed while Tim Davis was supported by * CERFACS (Toulouse, France) in a post-doctoral position. This C version, and * the etree postorder, were written while Tim Davis was on sabbatical at * Stanford University and Lawrence Berkeley National Laboratory. * ---------------------------------------------------------------------------- * INPUT ARGUMENTS (unaltered): * ---------------------------------------------------------------------------- * n: The matrix order. Restriction: n >= 1. * * iwlen: The size of the Iw array. On input, the matrix is stored in * Iw [0..pfree-1]. However, Iw [0..iwlen-1] should be slightly larger * than what is required to hold the matrix, at least iwlen >= pfree + n. * Otherwise, excessive compressions will take place. The recommended * value of iwlen is 1.2 * pfree + n, which is the value used in the * user-callable interface to this routine (amd_order.c). The algorithm * will not run at all if iwlen < pfree. Restriction: iwlen >= pfree + n. * Note that this is slightly more restrictive than the actual minimum * (iwlen >= pfree), but AMD_2 will be very slow with no elbow room. * Thus, this routine enforces a bare minimum elbow room of size n. * * pfree: On input the tail end of the array, Iw [pfree..iwlen-1], is empty, * and the matrix is stored in Iw [0..pfree-1]. During execution, * additional data is placed in Iw, and pfree is modified so that * Iw [pfree..iwlen-1] is always the unused part of Iw. * * Control: A double array of size AMD_CONTROL containing input parameters * that affect how the ordering is computed. If NULL, then default * settings are used. * * Control [AMD_DENSE] is used to determine whether or not a given input * row is "dense". A row is "dense" if the number of entries in the row * exceeds Control [AMD_DENSE] times sqrt (n), except that rows with 16 or * fewer entries are never considered "dense". To turn off the detection * of dense rows, set Control [AMD_DENSE] to a negative number, or to a * number larger than sqrt (n). The default value of Control [AMD_DENSE] * is AMD_DEFAULT_DENSE, which is defined in amd.h as 10. * * Control [AMD_AGGRESSIVE] is used to determine whether or not aggressive * absorption is to be performed. If nonzero, then aggressive absorption * is performed (this is the default). * ---------------------------------------------------------------------------- * INPUT/OUPUT ARGUMENTS: * ---------------------------------------------------------------------------- * * Pe: An integer array of size n. On input, Pe [i] is the index in Iw of * the start of row i. Pe [i] is ignored if row i has no off-diagonal * entries. Thus Pe [i] must be in the range 0 to pfree-1 for non-empty * rows. * * During execution, it is used for both supervariables and elements: * * Principal supervariable i: index into Iw of the description of * supervariable i. A supervariable represents one or more rows of * the matrix with identical nonzero pattern. In this case, * Pe [i] >= 0. * * Non-principal supervariable i: if i has been absorbed into another * supervariable j, then Pe [i] = FLIP (j), where FLIP (j) is defined * as (-(j)-2). Row j has the same pattern as row i. Note that j * might later be absorbed into another supervariable j2, in which * case Pe [i] is still FLIP (j), and Pe [j] = FLIP (j2) which is * < EMPTY, where EMPTY is defined as (-1) in amd_internal.h. * * Unabsorbed element e: the index into Iw of the description of element * e, if e has not yet been absorbed by a subsequent element. Element * e is created when the supervariable of the same name is selected as * the pivot. In this case, Pe [i] >= 0. * * Absorbed element e: if element e is absorbed into element e2, then * Pe [e] = FLIP (e2). This occurs when the pattern of e (which we * refer to as Le) is found to be a subset of the pattern of e2 (that * is, Le2). In this case, Pe [i] < EMPTY. If element e is "null" * (it has no nonzeros outside its pivot block), then Pe [e] = EMPTY, * and e is the root of an assembly subtree (or the whole tree if * there is just one such root). * * Dense variable i: if i is "dense", then Pe [i] = EMPTY. * * On output, Pe holds the assembly tree/forest, which implicitly * represents a pivot order with identical fill-in as the actual order * (via a depth-first search of the tree), as follows. If Nv [i] > 0, * then i represents a node in the assembly tree, and the parent of i is * Pe [i], or EMPTY if i is a root. If Nv [i] = 0, then (i, Pe [i]) * represents an edge in a subtree, the root of which is a node in the * assembly tree. Note that i refers to a row/column in the original * matrix, not the permuted matrix. * * Info: A double array of size AMD_INFO. If present, (that is, not NULL), * then statistics about the ordering are returned in the Info array. * See amd.h for a description. * ---------------------------------------------------------------------------- * INPUT/MODIFIED (undefined on output): * ---------------------------------------------------------------------------- * * Len: An integer array of size n. On input, Len [i] holds the number of * entries in row i of the matrix, excluding the diagonal. The contents * of Len are undefined on output. * * Iw: An integer array of size iwlen. On input, Iw [0..pfree-1] holds the * description of each row i in the matrix. The matrix must be symmetric, * and both upper and lower triangular parts must be present. The * diagonal must not be present. Row i is held as follows: * * Len [i]: the length of the row i data structure in the Iw array. * Iw [Pe [i] ... Pe [i] + Len [i] - 1]: * the list of column indices for nonzeros in row i (simple * supervariables), excluding the diagonal. All supervariables * start with one row/column each (supervariable i is just row i). * If Len [i] is zero on input, then Pe [i] is ignored on input. * * Note that the rows need not be in any particular order, and there * may be empty space between the rows. * * During execution, the supervariable i experiences fill-in. This is * represented by placing in i a list of the elements that cause fill-in * in supervariable i: * * Len [i]: the length of supervariable i in the Iw array. * Iw [Pe [i] ... Pe [i] + Elen [i] - 1]: * the list of elements that contain i. This list is kept short * by removing absorbed elements. * Iw [Pe [i] + Elen [i] ... Pe [i] + Len [i] - 1]: * the list of supervariables in i. This list is kept short by * removing nonprincipal variables, and any entry j that is also * contained in at least one of the elements (j in Le) in the list * for i (e in row i). * * When supervariable i is selected as pivot, we create an element e of * the same name (e=i): * * Len [e]: the length of element e in the Iw array. * Iw [Pe [e] ... Pe [e] + Len [e] - 1]: * the list of supervariables in element e. * * An element represents the fill-in that occurs when supervariable i is * selected as pivot (which represents the selection of row i and all * non-principal variables whose principal variable is i). We use the * term Le to denote the set of all supervariables in element e. Absorbed * supervariables and elements are pruned from these lists when * computationally convenient. * * CAUTION: THE INPUT MATRIX IS OVERWRITTEN DURING COMPUTATION. * The contents of Iw are undefined on output. * ---------------------------------------------------------------------------- * OUTPUT (need not be set on input): * ---------------------------------------------------------------------------- * * Nv: An integer array of size n. During execution, ABS (Nv [i]) is equal to * the number of rows that are represented by the principal supervariable * i. If i is a nonprincipal or dense variable, then Nv [i] = 0. * Initially, Nv [i] = 1 for all i. Nv [i] < 0 signifies that i is a * principal variable in the pattern Lme of the current pivot element me. * After element me is constructed, Nv [i] is set back to a positive * value. * * On output, Nv [i] holds the number of pivots represented by super * row/column i of the original matrix, or Nv [i] = 0 for non-principal * rows/columns. Note that i refers to a row/column in the original * matrix, not the permuted matrix. * * Elen: An integer array of size n. See the description of Iw above. At the * start of execution, Elen [i] is set to zero for all rows i. During * execution, Elen [i] is the number of elements in the list for * supervariable i. When e becomes an element, Elen [e] = FLIP (esize) is * set, where esize is the size of the element (the number of pivots, plus * the number of nonpivotal entries). Thus Elen [e] < EMPTY. * Elen (i) = EMPTY set when variable i becomes nonprincipal. * * For variables, Elen (i) >= EMPTY holds until just before the * postordering and permutation vectors are computed. For elements, * Elen [e] < EMPTY holds. * * On output, Elen [i] is the degree of the row/column in the Cholesky * factorization of the permuted matrix, corresponding to the original row * i, if i is a super row/column. It is equal to EMPTY if i is * non-principal. Note that i refers to a row/column in the original * matrix, not the permuted matrix. * * Note that the contents of Elen on output differ from the Fortran * version (Elen holds the inverse permutation in the Fortran version, * which is instead returned in the Next array in this C version, * described below). * * Last: In a degree list, Last [i] is the supervariable preceding i, or EMPTY * if i is the head of the list. In a hash bucket, Last [i] is the hash * key for i. * * Last [Head [hash]] is also used as the head of a hash bucket if * Head [hash] contains a degree list (see the description of Head, * below). * * On output, Last [0..n-1] holds the permutation. That is, if * i = Last [k], then row i is the kth pivot row (where k ranges from 0 to * n-1). Row Last [k] of A is the kth row in the permuted matrix, PAP'. * * Next: Next [i] is the supervariable following i in a link list, or EMPTY if * i is the last in the list. Used for two kinds of lists: degree lists * and hash buckets (a supervariable can be in only one kind of list at a * time). * * On output Next [0..n-1] holds the inverse permutation. That is, if * k = Next [i], then row i is the kth pivot row. Row i of A appears as * the (Next[i])-th row in the permuted matrix, PAP'. * * Note that the contents of Next on output differ from the Fortran * version (Next is undefined on output in the Fortran version). * ---------------------------------------------------------------------------- * LOCAL WORKSPACE (not input or output - used only during execution): * ---------------------------------------------------------------------------- * * Degree: An integer array of size n. If i is a supervariable, then * Degree [i] holds the current approximation of the external degree of * row i (an upper bound). The external degree is the number of nonzeros * in row i, minus ABS (Nv [i]), the diagonal part. The bound is equal to * the exact external degree if Elen [i] is less than or equal to two. * * We also use the term "external degree" for elements e to refer to * |Le \ Lme|. If e is an element, then Degree [e] is |Le|, which is the * degree of the off-diagonal part of the element e (not including the * diagonal part). * * Head: An integer array of size n. Head is used for degree lists. * Head [deg] is the first supervariable in a degree list. All * supervariables i in a degree list Head [deg] have the same approximate * degree, namely, deg = Degree [i]. If the list Head [deg] is empty then * Head [deg] = EMPTY. * * During supervariable detection Head [hash] also serves as a pointer to * a hash bucket. If Head [hash] >= 0, there is a degree list of degree * hash. The hash bucket head pointer is Last [Head [hash]]. If * Head [hash] = EMPTY, then the degree list and hash bucket are both * empty. If Head [hash] < EMPTY, then the degree list is empty, and * FLIP (Head [hash]) is the head of the hash bucket. After supervariable * detection is complete, all hash buckets are empty, and the * (Last [Head [hash]] = EMPTY) condition is restored for the non-empty * degree lists. * * W: An integer array of size n. The flag array W determines the status of * elements and variables, and the external degree of elements. * * for elements: * if W [e] = 0, then the element e is absorbed. * if W [e] >= wflg, then W [e] - wflg is the size of the set * |Le \ Lme|, in terms of nonzeros (the sum of ABS (Nv [i]) for * each principal variable i that is both in the pattern of * element e and NOT in the pattern of the current pivot element, * me). * if wflg > W [e] > 0, then e is not absorbed and has not yet been * seen in the scan of the element lists in the computation of * |Le\Lme| in Scan 1 below. * * for variables: * during supervariable detection, if W [j] != wflg then j is * not in the pattern of variable i. * * The W array is initialized by setting W [i] = 1 for all i, and by * setting wflg = 2. It is reinitialized if wflg becomes too large (to * ensure that wflg+n does not cause integer overflow). * ---------------------------------------------------------------------------- * LOCAL INTEGERS: * ---------------------------------------------------------------------------- */ Int deg, degme, dext, lemax, e, elenme, eln, i, ilast, inext, j, jlast, jnext, k, knt1, knt2, knt3, lenj, ln, me, mindeg, nel, nleft, nvi, nvj, nvpiv, slenme, wbig, we, wflg, wnvi, ok, ndense, ncmpa, dense, aggressive ; unsigned Int hash ; /* unsigned, so that hash % n is well defined.*/ /* * deg: the degree of a variable or element * degme: size, |Lme|, of the current element, me (= Degree [me]) * dext: external degree, |Le \ Lme|, of some element e * lemax: largest |Le| seen so far (called dmax in Fortran version) * e: an element * elenme: the length, Elen [me], of element list of pivotal variable * eln: the length, Elen [...], of an element list * hash: the computed value of the hash function * i: a supervariable * ilast: the entry in a link list preceding i * inext: the entry in a link list following i * j: a supervariable * jlast: the entry in a link list preceding j * jnext: the entry in a link list, or path, following j * k: the pivot order of an element or variable * knt1: loop counter used during element construction * knt2: loop counter used during element construction * knt3: loop counter used during compression * lenj: Len [j] * ln: length of a supervariable list * me: current supervariable being eliminated, and the current * element created by eliminating that supervariable * mindeg: current minimum degree * nel: number of pivots selected so far * nleft: n - nel, the number of nonpivotal rows/columns remaining * nvi: the number of variables in a supervariable i (= Nv [i]) * nvj: the number of variables in a supervariable j (= Nv [j]) * nvpiv: number of pivots in current element * slenme: number of variables in variable list of pivotal variable * wbig: = INT_MAX - n for the int version, UF_long_max - n for the * UF_long version. wflg is not allowed to be >= wbig. * we: W [e] * wflg: used for flagging the W array. See description of Iw. * wnvi: wflg - Nv [i] * x: either a supervariable or an element * * ok: true if supervariable j can be absorbed into i * ndense: number of "dense" rows/columns * dense: rows/columns with initial degree > dense are considered "dense" * aggressive: true if aggressive absorption is being performed * ncmpa: number of garbage collections * ---------------------------------------------------------------------------- * LOCAL DOUBLES, used for statistical output only (except for alpha): * ---------------------------------------------------------------------------- */ double f, r, ndiv, s, nms_lu, nms_ldl, dmax, alpha, lnz, lnzme ; /* * f: nvpiv * r: degme + nvpiv * ndiv: number of divisions for LU or LDL' factorizations * s: number of multiply-subtract pairs for LU factorization, for the * current element me * nms_lu number of multiply-subtract pairs for LU factorization * nms_ldl number of multiply-subtract pairs for LDL' factorization * dmax: the largest number of entries in any column of L, including the * diagonal * alpha: "dense" degree ratio * lnz: the number of nonzeros in L (excluding the diagonal) * lnzme: the number of nonzeros in L (excl. the diagonal) for the * current element me * ---------------------------------------------------------------------------- * LOCAL "POINTERS" (indices into the Iw array) * ---------------------------------------------------------------------------- */ Int p, p1, p2, p3, p4, pdst, pend, pj, pme, pme1, pme2, pn, psrc ; /* * Any parameter (Pe [...] or pfree) or local variable starting with "p" (for * Pointer) is an index into Iw, and all indices into Iw use variables starting * with "p." The only exception to this rule is the iwlen input argument. * * p: pointer into lots of things * p1: Pe [i] for some variable i (start of element list) * p2: Pe [i] + Elen [i] - 1 for some variable i * p3: index of first supervariable in clean list * p4: * pdst: destination pointer, for compression * pend: end of memory to compress * pj: pointer into an element or variable * pme: pointer into the current element (pme1...pme2) * pme1: the current element, me, is stored in Iw [pme1...pme2] * pme2: the end of the current element * pn: pointer into a "clean" variable, also used to compress * psrc: source pointer, for compression */ /* ========================================================================= */ /* INITIALIZATIONS */ /* ========================================================================= */ /* Note that this restriction on iwlen is slightly more restrictive than * what is actually required in AMD_2. AMD_2 can operate with no elbow * room at all, but it will be slow. For better performance, at least * size-n elbow room is enforced. */ ASSERT (iwlen >= pfree + n) ; ASSERT (n > 0) ; /* initialize output statistics */ lnz = 0 ; ndiv = 0 ; nms_lu = 0 ; nms_ldl = 0 ; dmax = 1 ; me = EMPTY ; mindeg = 0 ; ncmpa = 0 ; nel = 0 ; lemax = 0 ; /* get control parameters */ if (Control != (double *) NULL) { alpha = Control [AMD_DENSE] ; aggressive = (Control [AMD_AGGRESSIVE] != 0) ; } else { alpha = AMD_DEFAULT_DENSE ; aggressive = AMD_DEFAULT_AGGRESSIVE ; } /* Note: if alpha is NaN, this is undefined: */ if (alpha < 0) { /* only remove completely dense rows/columns */ dense = n-2 ; } else { dense = alpha * sqrt ((double) n) ; } dense = MAX (16, dense) ; dense = MIN (n, dense) ; AMD_DEBUG1 (("\n\nAMD (debug), alpha %g, aggr. "ID"\n", alpha, aggressive)) ; for (i = 0 ; i < n ; i++) { Last [i] = EMPTY ; Head [i] = EMPTY ; Next [i] = EMPTY ; /* if separate Hhead array is used for hash buckets: * Hhead [i] = EMPTY ; */ Nv [i] = 1 ; W [i] = 1 ; Elen [i] = 0 ; Degree [i] = Len [i] ; } #ifndef NDEBUG AMD_DEBUG1 (("\n======Nel "ID" initial\n", nel)) ; AMD_dump (n, Pe, Iw, Len, iwlen, pfree, Nv, Next, Last, Head, Elen, Degree, W, -1) ; #endif /* initialize wflg */ wbig = Int_MAX - n ; wflg = clear_flag (0, wbig, W, n) ; /* --------------------------------------------------------------------- */ /* initialize degree lists and eliminate dense and empty rows */ /* --------------------------------------------------------------------- */ ndense = 0 ; for (i = 0 ; i < n ; i++) { deg = Degree [i] ; ASSERT (deg >= 0 && deg < n) ; if (deg == 0) { /* ------------------------------------------------------------- * we have a variable that can be eliminated at once because * there is no off-diagonal non-zero in its row. Note that * Nv [i] = 1 for an empty variable i. It is treated just * the same as an eliminated element i. * ------------------------------------------------------------- */ Elen [i] = FLIP (1) ; nel++ ; Pe [i] = EMPTY ; W [i] = 0 ; } else if (deg > dense) { /* ------------------------------------------------------------- * Dense variables are not treated as elements, but as unordered, * non-principal variables that have no parent. They do not take * part in the postorder, since Nv [i] = 0. Note that the Fortran * version does not have this option. * ------------------------------------------------------------- */ AMD_DEBUG1 (("Dense node "ID" degree "ID"\n", i, deg)) ; ndense++ ; Nv [i] = 0 ; /* do not postorder this node */ Elen [i] = EMPTY ; nel++ ; Pe [i] = EMPTY ; } else { /* ------------------------------------------------------------- * place i in the degree list corresponding to its degree * ------------------------------------------------------------- */ inext = Head [deg] ; ASSERT (inext >= EMPTY && inext < n) ; if (inext != EMPTY) Last [inext] = i ; Next [i] = inext ; Head [deg] = i ; } } /* ========================================================================= */ /* WHILE (selecting pivots) DO */ /* ========================================================================= */ while (nel < n) { #ifndef NDEBUG AMD_DEBUG1 (("\n======Nel "ID"\n", nel)) ; if (AMD_debug >= 2) { AMD_dump (n, Pe, Iw, Len, iwlen, pfree, Nv, Next, Last, Head, Elen, Degree, W, nel) ; } #endif /* ========================================================================= */ /* GET PIVOT OF MINIMUM DEGREE */ /* ========================================================================= */ /* ----------------------------------------------------------------- */ /* find next supervariable for elimination */ /* ----------------------------------------------------------------- */ ASSERT (mindeg >= 0 && mindeg < n) ; for (deg = mindeg ; deg < n ; deg++) { me = Head [deg] ; if (me != EMPTY) break ; } mindeg = deg ; ASSERT (me >= 0 && me < n) ; AMD_DEBUG1 (("=================me: "ID"\n", me)) ; /* ----------------------------------------------------------------- */ /* remove chosen variable from link list */ /* ----------------------------------------------------------------- */ inext = Next [me] ; ASSERT (inext >= EMPTY && inext < n) ; if (inext != EMPTY) Last [inext] = EMPTY ; Head [deg] = inext ; /* ----------------------------------------------------------------- */ /* me represents the elimination of pivots nel to nel+Nv[me]-1. */ /* place me itself as the first in this set. */ /* ----------------------------------------------------------------- */ elenme = Elen [me] ; nvpiv = Nv [me] ; ASSERT (nvpiv > 0) ; nel += nvpiv ; /* ========================================================================= */ /* CONSTRUCT NEW ELEMENT */ /* ========================================================================= */ /* ----------------------------------------------------------------- * At this point, me is the pivotal supervariable. It will be * converted into the current element. Scan list of the pivotal * supervariable, me, setting tree pointers and constructing new list * of supervariables for the new element, me. p is a pointer to the * current position in the old list. * ----------------------------------------------------------------- */ /* flag the variable "me" as being in Lme by negating Nv [me] */ Nv [me] = -nvpiv ; degme = 0 ; ASSERT (Pe [me] >= 0 && Pe [me] < iwlen) ; if (elenme == 0) { /* ------------------------------------------------------------- */ /* construct the new element in place */ /* ------------------------------------------------------------- */ pme1 = Pe [me] ; pme2 = pme1 - 1 ; for (p = pme1 ; p <= pme1 + Len [me] - 1 ; p++) { i = Iw [p] ; ASSERT (i >= 0 && i < n && Nv [i] >= 0) ; nvi = Nv [i] ; if (nvi > 0) { /* ----------------------------------------------------- */ /* i is a principal variable not yet placed in Lme. */ /* store i in new list */ /* ----------------------------------------------------- */ /* flag i as being in Lme by negating Nv [i] */ degme += nvi ; Nv [i] = -nvi ; Iw [++pme2] = i ; /* ----------------------------------------------------- */ /* remove variable i from degree list. */ /* ----------------------------------------------------- */ ilast = Last [i] ; inext = Next [i] ; ASSERT (ilast >= EMPTY && ilast < n) ; ASSERT (inext >= EMPTY && inext < n) ; if (inext != EMPTY) Last [inext] = ilast ; if (ilast != EMPTY) { Next [ilast] = inext ; } else { /* i is at the head of the degree list */ ASSERT (Degree [i] >= 0 && Degree [i] < n) ; Head [Degree [i]] = inext ; } } } } else { /* ------------------------------------------------------------- */ /* construct the new element in empty space, Iw [pfree ...] */ /* ------------------------------------------------------------- */ p = Pe [me] ; pme1 = pfree ; slenme = Len [me] - elenme ; for (knt1 = 1 ; knt1 <= elenme + 1 ; knt1++) { if (knt1 > elenme) { /* search the supervariables in me. */ e = me ; pj = p ; ln = slenme ; AMD_DEBUG2 (("Search sv: "ID" "ID" "ID"\n", me,pj,ln)) ; } else { /* search the elements in me. */ e = Iw [p++] ; ASSERT (e >= 0 && e < n) ; pj = Pe [e] ; ln = Len [e] ; AMD_DEBUG2 (("Search element e "ID" in me "ID"\n", e,me)) ; ASSERT (Elen [e] < EMPTY && W [e] > 0 && pj >= 0) ; } ASSERT (ln >= 0 && (ln == 0 || (pj >= 0 && pj < iwlen))) ; /* --------------------------------------------------------- * search for different supervariables and add them to the * new list, compressing when necessary. this loop is * executed once for each element in the list and once for * all the supervariables in the list. * --------------------------------------------------------- */ for (knt2 = 1 ; knt2 <= ln ; knt2++) { i = Iw [pj++] ; ASSERT (i >= 0 && i < n && (i == me || Elen [i] >= EMPTY)); nvi = Nv [i] ; AMD_DEBUG2 ((": "ID" "ID" "ID" "ID"\n", i, Elen [i], Nv [i], wflg)) ; if (nvi > 0) { /* ------------------------------------------------- */ /* compress Iw, if necessary */ /* ------------------------------------------------- */ if (pfree >= iwlen) { AMD_DEBUG1 (("GARBAGE COLLECTION\n")) ; /* prepare for compressing Iw by adjusting pointers * and lengths so that the lists being searched in * the inner and outer loops contain only the * remaining entries. */ Pe [me] = p ; Len [me] -= knt1 ; /* check if nothing left of supervariable me */ if (Len [me] == 0) Pe [me] = EMPTY ; Pe [e] = pj ; Len [e] = ln - knt2 ; /* nothing left of element e */ if (Len [e] == 0) Pe [e] = EMPTY ; ncmpa++ ; /* one more garbage collection */ /* store first entry of each object in Pe */ /* FLIP the first entry in each object */ for (j = 0 ; j < n ; j++) { pn = Pe [j] ; if (pn >= 0) { ASSERT (pn >= 0 && pn < iwlen) ; Pe [j] = Iw [pn] ; Iw [pn] = FLIP (j) ; } } /* psrc/pdst point to source/destination */ psrc = 0 ; pdst = 0 ; pend = pme1 - 1 ; while (psrc <= pend) { /* search for next FLIP'd entry */ j = FLIP (Iw [psrc++]) ; if (j >= 0) { AMD_DEBUG2 (("Got object j: "ID"\n", j)) ; Iw [pdst] = Pe [j] ; Pe [j] = pdst++ ; lenj = Len [j] ; /* copy from source to destination */ for (knt3 = 0 ; knt3 <= lenj - 2 ; knt3++) { Iw [pdst++] = Iw [psrc++] ; } } } /* move the new partially-constructed element */ p1 = pdst ; for (psrc = pme1 ; psrc <= pfree-1 ; psrc++) { Iw [pdst++] = Iw [psrc] ; } pme1 = p1 ; pfree = pdst ; pj = Pe [e] ; p = Pe [me] ; } /* ------------------------------------------------- */ /* i is a principal variable not yet placed in Lme */ /* store i in new list */ /* ------------------------------------------------- */ /* flag i as being in Lme by negating Nv [i] */ degme += nvi ; Nv [i] = -nvi ; Iw [pfree++] = i ; AMD_DEBUG2 ((" s: "ID" nv "ID"\n", i, Nv [i])); /* ------------------------------------------------- */ /* remove variable i from degree link list */ /* ------------------------------------------------- */ ilast = Last [i] ; inext = Next [i] ; ASSERT (ilast >= EMPTY && ilast < n) ; ASSERT (inext >= EMPTY && inext < n) ; if (inext != EMPTY) Last [inext] = ilast ; if (ilast != EMPTY) { Next [ilast] = inext ; } else { /* i is at the head of the degree list */ ASSERT (Degree [i] >= 0 && Degree [i] < n) ; Head [Degree [i]] = inext ; } } } if (e != me) { /* set tree pointer and flag to indicate element e is * absorbed into new element me (the parent of e is me) */ AMD_DEBUG1 ((" Element "ID" => "ID"\n", e, me)) ; Pe [e] = FLIP (me) ; W [e] = 0 ; } } pme2 = pfree - 1 ; } /* ----------------------------------------------------------------- */ /* me has now been converted into an element in Iw [pme1..pme2] */ /* ----------------------------------------------------------------- */ /* degme holds the external degree of new element */ Degree [me] = degme ; Pe [me] = pme1 ; Len [me] = pme2 - pme1 + 1 ; ASSERT (Pe [me] >= 0 && Pe [me] < iwlen) ; Elen [me] = FLIP (nvpiv + degme) ; /* FLIP (Elen (me)) is now the degree of pivot (including * diagonal part). */ #ifndef NDEBUG AMD_DEBUG2 (("New element structure: length= "ID"\n", pme2-pme1+1)) ; for (pme = pme1 ; pme <= pme2 ; pme++) AMD_DEBUG3 ((" "ID"", Iw[pme])); AMD_DEBUG3 (("\n")) ; #endif /* ----------------------------------------------------------------- */ /* make sure that wflg is not too large. */ /* ----------------------------------------------------------------- */ /* With the current value of wflg, wflg+n must not cause integer * overflow */ wflg = clear_flag (wflg, wbig, W, n) ; /* ========================================================================= */ /* COMPUTE (W [e] - wflg) = |Le\Lme| FOR ALL ELEMENTS */ /* ========================================================================= */ /* ----------------------------------------------------------------- * Scan 1: compute the external degrees of previous elements with * respect to the current element. That is: * (W [e] - wflg) = |Le \ Lme| * for each element e that appears in any supervariable in Lme. The * notation Le refers to the pattern (list of supervariables) of a * previous element e, where e is not yet absorbed, stored in * Iw [Pe [e] + 1 ... Pe [e] + Len [e]]. The notation Lme * refers to the pattern of the current element (stored in * Iw [pme1..pme2]). If aggressive absorption is enabled, and * (W [e] - wflg) becomes zero, then the element e will be absorbed * in Scan 2. * ----------------------------------------------------------------- */ AMD_DEBUG2 (("me: ")) ; for (pme = pme1 ; pme <= pme2 ; pme++) { i = Iw [pme] ; ASSERT (i >= 0 && i < n) ; eln = Elen [i] ; AMD_DEBUG3 ((""ID" Elen "ID": \n", i, eln)) ; if (eln > 0) { /* note that Nv [i] has been negated to denote i in Lme: */ nvi = -Nv [i] ; ASSERT (nvi > 0 && Pe [i] >= 0 && Pe [i] < iwlen) ; wnvi = wflg - nvi ; for (p = Pe [i] ; p <= Pe [i] + eln - 1 ; p++) { e = Iw [p] ; ASSERT (e >= 0 && e < n) ; we = W [e] ; AMD_DEBUG4 ((" e "ID" we "ID" ", e, we)) ; if (we >= wflg) { /* unabsorbed element e has been seen in this loop */ AMD_DEBUG4 ((" unabsorbed, first time seen")) ; we -= nvi ; } else if (we != 0) { /* e is an unabsorbed element */ /* this is the first we have seen e in all of Scan 1 */ AMD_DEBUG4 ((" unabsorbed")) ; we = Degree [e] + wnvi ; } AMD_DEBUG4 (("\n")) ; W [e] = we ; } } } AMD_DEBUG2 (("\n")) ; /* ========================================================================= */ /* DEGREE UPDATE AND ELEMENT ABSORPTION */ /* ========================================================================= */ /* ----------------------------------------------------------------- * Scan 2: for each i in Lme, sum up the degree of Lme (which is * degme), plus the sum of the external degrees of each Le for the * elements e appearing within i, plus the supervariables in i. * Place i in hash list. * ----------------------------------------------------------------- */ for (pme = pme1 ; pme <= pme2 ; pme++) { i = Iw [pme] ; ASSERT (i >= 0 && i < n && Nv [i] < 0 && Elen [i] >= 0) ; AMD_DEBUG2 (("Updating: i "ID" "ID" "ID"\n", i, Elen[i], Len [i])); p1 = Pe [i] ; p2 = p1 + Elen [i] - 1 ; pn = p1 ; hash = 0 ; deg = 0 ; ASSERT (p1 >= 0 && p1 < iwlen && p2 >= -1 && p2 < iwlen) ; /* ------------------------------------------------------------- */ /* scan the element list associated with supervariable i */ /* ------------------------------------------------------------- */ /* UMFPACK/MA38-style approximate degree: */ if (aggressive) { for (p = p1 ; p <= p2 ; p++) { e = Iw [p] ; ASSERT (e >= 0 && e < n) ; we = W [e] ; if (we != 0) { /* e is an unabsorbed element */ /* dext = | Le \ Lme | */ dext = we - wflg ; if (dext > 0) { deg += dext ; Iw [pn++] = e ; hash += e ; AMD_DEBUG4 ((" e: "ID" hash = "ID"\n",e,hash)) ; } else { /* external degree of e is zero, absorb e into me*/ AMD_DEBUG1 ((" Element "ID" =>"ID" (aggressive)\n", e, me)) ; ASSERT (dext == 0) ; Pe [e] = FLIP (me) ; W [e] = 0 ; } } } } else { for (p = p1 ; p <= p2 ; p++) { e = Iw [p] ; ASSERT (e >= 0 && e < n) ; we = W [e] ; if (we != 0) { /* e is an unabsorbed element */ dext = we - wflg ; ASSERT (dext >= 0) ; deg += dext ; Iw [pn++] = e ; hash += e ; AMD_DEBUG4 ((" e: "ID" hash = "ID"\n",e,hash)) ; } } } /* count the number of elements in i (including me): */ Elen [i] = pn - p1 + 1 ; /* ------------------------------------------------------------- */ /* scan the supervariables in the list associated with i */ /* ------------------------------------------------------------- */ /* The bulk of the AMD run time is typically spent in this loop, * particularly if the matrix has many dense rows that are not * removed prior to ordering. */ p3 = pn ; p4 = p1 + Len [i] ; for (p = p2 + 1 ; p < p4 ; p++) { j = Iw [p] ; ASSERT (j >= 0 && j < n) ; nvj = Nv [j] ; if (nvj > 0) { /* j is unabsorbed, and not in Lme. */ /* add to degree and add to new list */ deg += nvj ; Iw [pn++] = j ; hash += j ; AMD_DEBUG4 ((" s: "ID" hash "ID" Nv[j]= "ID"\n", j, hash, nvj)) ; } } /* ------------------------------------------------------------- */ /* update the degree and check for mass elimination */ /* ------------------------------------------------------------- */ /* with aggressive absorption, deg==0 is identical to the * Elen [i] == 1 && p3 == pn test, below. */ ASSERT (IMPLIES (aggressive, (deg==0) == (Elen[i]==1 && p3==pn))) ; if (Elen [i] == 1 && p3 == pn) { /* --------------------------------------------------------- */ /* mass elimination */ /* --------------------------------------------------------- */ /* There is nothing left of this node except for an edge to * the current pivot element. Elen [i] is 1, and there are * no variables adjacent to node i. Absorb i into the * current pivot element, me. Note that if there are two or * more mass eliminations, fillin due to mass elimination is * possible within the nvpiv-by-nvpiv pivot block. It is this * step that causes AMD's analysis to be an upper bound. * * The reason is that the selected pivot has a lower * approximate degree than the true degree of the two mass * eliminated nodes. There is no edge between the two mass * eliminated nodes. They are merged with the current pivot * anyway. * * No fillin occurs in the Schur complement, in any case, * and this effect does not decrease the quality of the * ordering itself, just the quality of the nonzero and * flop count analysis. It also means that the post-ordering * is not an exact elimination tree post-ordering. */ AMD_DEBUG1 ((" MASS i "ID" => parent e "ID"\n", i, me)) ; Pe [i] = FLIP (me) ; nvi = -Nv [i] ; degme -= nvi ; nvpiv += nvi ; nel += nvi ; Nv [i] = 0 ; Elen [i] = EMPTY ; } else { /* --------------------------------------------------------- */ /* update the upper-bound degree of i */ /* --------------------------------------------------------- */ /* the following degree does not yet include the size * of the current element, which is added later: */ Degree [i] = MIN (Degree [i], deg) ; /* --------------------------------------------------------- */ /* add me to the list for i */ /* --------------------------------------------------------- */ /* move first supervariable to end of list */ Iw [pn] = Iw [p3] ; /* move first element to end of element part of list */ Iw [p3] = Iw [p1] ; /* add new element, me, to front of list. */ Iw [p1] = me ; /* store the new length of the list in Len [i] */ Len [i] = pn - p1 + 1 ; /* --------------------------------------------------------- */ /* place in hash bucket. Save hash key of i in Last [i]. */ /* --------------------------------------------------------- */ /* NOTE: this can fail if hash is negative, because the ANSI C * standard does not define a % b when a and/or b are negative. * That's why hash is defined as an unsigned Int, to avoid this * problem. */ hash = hash % n ; ASSERT (((Int) hash) >= 0 && ((Int) hash) < n) ; /* if the Hhead array is not used: */ j = Head [hash] ; if (j <= EMPTY) { /* degree list is empty, hash head is FLIP (j) */ Next [i] = FLIP (j) ; Head [hash] = FLIP (i) ; } else { /* degree list is not empty, use Last [Head [hash]] as * hash head. */ Next [i] = Last [j] ; Last [j] = i ; } /* if a separate Hhead array is used: * Next [i] = Hhead [hash] ; Hhead [hash] = i ; */ Last [i] = hash ; } } Degree [me] = degme ; /* ----------------------------------------------------------------- */ /* Clear the counter array, W [...], by incrementing wflg. */ /* ----------------------------------------------------------------- */ /* make sure that wflg+n does not cause integer overflow */ lemax = MAX (lemax, degme) ; wflg += lemax ; wflg = clear_flag (wflg, wbig, W, n) ; /* at this point, W [0..n-1] < wflg holds */ /* ========================================================================= */ /* SUPERVARIABLE DETECTION */ /* ========================================================================= */ AMD_DEBUG1 (("Detecting supervariables:\n")) ; for (pme = pme1 ; pme <= pme2 ; pme++) { i = Iw [pme] ; ASSERT (i >= 0 && i < n) ; AMD_DEBUG2 (("Consider i "ID" nv "ID"\n", i, Nv [i])) ; if (Nv [i] < 0) { /* i is a principal variable in Lme */ /* --------------------------------------------------------- * examine all hash buckets with 2 or more variables. We do * this by examing all unique hash keys for supervariables in * the pattern Lme of the current element, me * --------------------------------------------------------- */ /* let i = head of hash bucket, and empty the hash bucket */ ASSERT (Last [i] >= 0 && Last [i] < n) ; hash = Last [i] ; /* if Hhead array is not used: */ j = Head [hash] ; if (j == EMPTY) { /* hash bucket and degree list are both empty */ i = EMPTY ; } else if (j < EMPTY) { /* degree list is empty */ i = FLIP (j) ; Head [hash] = EMPTY ; } else { /* degree list is not empty, restore Last [j] of head j */ i = Last [j] ; Last [j] = EMPTY ; } /* if separate Hhead array is used: * i = Hhead [hash] ; Hhead [hash] = EMPTY ; */ ASSERT (i >= EMPTY && i < n) ; AMD_DEBUG2 (("----i "ID" hash "ID"\n", i, hash)) ; while (i != EMPTY && Next [i] != EMPTY) { /* ----------------------------------------------------- * this bucket has one or more variables following i. * scan all of them to see if i can absorb any entries * that follow i in hash bucket. Scatter i into w. * ----------------------------------------------------- */ ln = Len [i] ; eln = Elen [i] ; ASSERT (ln >= 0 && eln >= 0) ; ASSERT (Pe [i] >= 0 && Pe [i] < iwlen) ; /* do not flag the first element in the list (me) */ for (p = Pe [i] + 1 ; p <= Pe [i] + ln - 1 ; p++) { ASSERT (Iw [p] >= 0 && Iw [p] < n) ; W [Iw [p]] = wflg ; } /* ----------------------------------------------------- */ /* scan every other entry j following i in bucket */ /* ----------------------------------------------------- */ jlast = i ; j = Next [i] ; ASSERT (j >= EMPTY && j < n) ; while (j != EMPTY) { /* ------------------------------------------------- */ /* check if j and i have identical nonzero pattern */ /* ------------------------------------------------- */ AMD_DEBUG3 (("compare i "ID" and j "ID"\n", i,j)) ; /* check if i and j have the same Len and Elen */ ASSERT (Len [j] >= 0 && Elen [j] >= 0) ; ASSERT (Pe [j] >= 0 && Pe [j] < iwlen) ; ok = (Len [j] == ln) && (Elen [j] == eln) ; /* skip the first element in the list (me) */ for (p = Pe [j] + 1 ; ok && p <= Pe [j] + ln - 1 ; p++) { ASSERT (Iw [p] >= 0 && Iw [p] < n) ; if (W [Iw [p]] != wflg) ok = 0 ; } if (ok) { /* --------------------------------------------- */ /* found it! j can be absorbed into i */ /* --------------------------------------------- */ AMD_DEBUG1 (("found it! j "ID" => i "ID"\n", j,i)); Pe [j] = FLIP (i) ; /* both Nv [i] and Nv [j] are negated since they */ /* are in Lme, and the absolute values of each */ /* are the number of variables in i and j: */ Nv [i] += Nv [j] ; Nv [j] = 0 ; Elen [j] = EMPTY ; /* delete j from hash bucket */ ASSERT (j != Next [j]) ; j = Next [j] ; Next [jlast] = j ; } else { /* j cannot be absorbed into i */ jlast = j ; ASSERT (j != Next [j]) ; j = Next [j] ; } ASSERT (j >= EMPTY && j < n) ; } /* ----------------------------------------------------- * no more variables can be absorbed into i * go to next i in bucket and clear flag array * ----------------------------------------------------- */ wflg++ ; i = Next [i] ; ASSERT (i >= EMPTY && i < n) ; } } } AMD_DEBUG2 (("detect done\n")) ; /* ========================================================================= */ /* RESTORE DEGREE LISTS AND REMOVE NONPRINCIPAL SUPERVARIABLES FROM ELEMENT */ /* ========================================================================= */ p = pme1 ; nleft = n - nel ; for (pme = pme1 ; pme <= pme2 ; pme++) { i = Iw [pme] ; ASSERT (i >= 0 && i < n) ; nvi = -Nv [i] ; AMD_DEBUG3 (("Restore i "ID" "ID"\n", i, nvi)) ; if (nvi > 0) { /* i is a principal variable in Lme */ /* restore Nv [i] to signify that i is principal */ Nv [i] = nvi ; /* --------------------------------------------------------- */ /* compute the external degree (add size of current element) */ /* --------------------------------------------------------- */ deg = Degree [i] + degme - nvi ; deg = MIN (deg, nleft - nvi) ; ASSERT (IMPLIES (aggressive, deg > 0) && deg >= 0 && deg < n) ; /* --------------------------------------------------------- */ /* place the supervariable at the head of the degree list */ /* --------------------------------------------------------- */ inext = Head [deg] ; ASSERT (inext >= EMPTY && inext < n) ; if (inext != EMPTY) Last [inext] = i ; Next [i] = inext ; Last [i] = EMPTY ; Head [deg] = i ; /* --------------------------------------------------------- */ /* save the new degree, and find the minimum degree */ /* --------------------------------------------------------- */ mindeg = MIN (mindeg, deg) ; Degree [i] = deg ; /* --------------------------------------------------------- */ /* place the supervariable in the element pattern */ /* --------------------------------------------------------- */ Iw [p++] = i ; } } AMD_DEBUG2 (("restore done\n")) ; /* ========================================================================= */ /* FINALIZE THE NEW ELEMENT */ /* ========================================================================= */ AMD_DEBUG2 (("ME = "ID" DONE\n", me)) ; Nv [me] = nvpiv ; /* save the length of the list for the new element me */ Len [me] = p - pme1 ; if (Len [me] == 0) { /* there is nothing left of the current pivot element */ /* it is a root of the assembly tree */ Pe [me] = EMPTY ; W [me] = 0 ; } if (elenme != 0) { /* element was not constructed in place: deallocate part of */ /* it since newly nonprincipal variables may have been removed */ pfree = p ; } /* The new element has nvpiv pivots and the size of the contribution * block for a multifrontal method is degme-by-degme, not including * the "dense" rows/columns. If the "dense" rows/columns are included, * the frontal matrix is no larger than * (degme+ndense)-by-(degme+ndense). */ if (Info != (double *) NULL) { f = nvpiv ; r = degme + ndense ; dmax = MAX (dmax, f + r) ; /* number of nonzeros in L (excluding the diagonal) */ lnzme = f*r + (f-1)*f/2 ; lnz += lnzme ; /* number of divide operations for LDL' and for LU */ ndiv += lnzme ; /* number of multiply-subtract pairs for LU */ s = f*r*r + r*(f-1)*f + (f-1)*f*(2*f-1)/6 ; nms_lu += s ; /* number of multiply-subtract pairs for LDL' */ nms_ldl += (s + lnzme)/2 ; } #ifndef NDEBUG AMD_DEBUG2 (("finalize done nel "ID" n "ID"\n ::::\n", nel, n)) ; for (pme = Pe [me] ; pme <= Pe [me] + Len [me] - 1 ; pme++) { AMD_DEBUG3 ((" "ID"", Iw [pme])) ; } AMD_DEBUG3 (("\n")) ; #endif } /* ========================================================================= */ /* DONE SELECTING PIVOTS */ /* ========================================================================= */ if (Info != (double *) NULL) { /* count the work to factorize the ndense-by-ndense submatrix */ f = ndense ; dmax = MAX (dmax, (double) ndense) ; /* number of nonzeros in L (excluding the diagonal) */ lnzme = (f-1)*f/2 ; lnz += lnzme ; /* number of divide operations for LDL' and for LU */ ndiv += lnzme ; /* number of multiply-subtract pairs for LU */ s = (f-1)*f*(2*f-1)/6 ; nms_lu += s ; /* number of multiply-subtract pairs for LDL' */ nms_ldl += (s + lnzme)/2 ; /* number of nz's in L (excl. diagonal) */ Info [AMD_LNZ] = lnz ; /* number of divide ops for LU and LDL' */ Info [AMD_NDIV] = ndiv ; /* number of multiply-subtract pairs for LDL' */ Info [AMD_NMULTSUBS_LDL] = nms_ldl ; /* number of multiply-subtract pairs for LU */ Info [AMD_NMULTSUBS_LU] = nms_lu ; /* number of "dense" rows/columns */ Info [AMD_NDENSE] = ndense ; /* largest front is dmax-by-dmax */ Info [AMD_DMAX] = dmax ; /* number of garbage collections in AMD */ Info [AMD_NCMPA] = ncmpa ; /* successful ordering */ Info [AMD_STATUS] = AMD_OK ; } /* ========================================================================= */ /* POST-ORDERING */ /* ========================================================================= */ /* ------------------------------------------------------------------------- * Variables at this point: * * Pe: holds the elimination tree. The parent of j is FLIP (Pe [j]), * or EMPTY if j is a root. The tree holds both elements and * non-principal (unordered) variables absorbed into them. * Dense variables are non-principal and unordered. * * Elen: holds the size of each element, including the diagonal part. * FLIP (Elen [e]) > 0 if e is an element. For unordered * variables i, Elen [i] is EMPTY. * * Nv: Nv [e] > 0 is the number of pivots represented by the element e. * For unordered variables i, Nv [i] is zero. * * Contents no longer needed: * W, Iw, Len, Degree, Head, Next, Last. * * The matrix itself has been destroyed. * * n: the size of the matrix. * No other scalars needed (pfree, iwlen, etc.) * ------------------------------------------------------------------------- */ /* restore Pe */ for (i = 0 ; i < n ; i++) { Pe [i] = FLIP (Pe [i]) ; } /* restore Elen, for output information, and for postordering */ for (i = 0 ; i < n ; i++) { Elen [i] = FLIP (Elen [i]) ; } /* Now the parent of j is Pe [j], or EMPTY if j is a root. Elen [e] > 0 * is the size of element e. Elen [i] is EMPTY for unordered variable i. */ #ifndef NDEBUG AMD_DEBUG2 (("\nTree:\n")) ; for (i = 0 ; i < n ; i++) { AMD_DEBUG2 ((" "ID" parent: "ID" ", i, Pe [i])) ; ASSERT (Pe [i] >= EMPTY && Pe [i] < n) ; if (Nv [i] > 0) { /* this is an element */ e = i ; AMD_DEBUG2 ((" element, size is "ID"\n", Elen [i])) ; ASSERT (Elen [e] > 0) ; } AMD_DEBUG2 (("\n")) ; } AMD_DEBUG2 (("\nelements:\n")) ; for (e = 0 ; e < n ; e++) { if (Nv [e] > 0) { AMD_DEBUG3 (("Element e= "ID" size "ID" nv "ID" \n", e, Elen [e], Nv [e])) ; } } AMD_DEBUG2 (("\nvariables:\n")) ; for (i = 0 ; i < n ; i++) { Int cnt ; if (Nv [i] == 0) { AMD_DEBUG3 (("i unordered: "ID"\n", i)) ; j = Pe [i] ; cnt = 0 ; AMD_DEBUG3 ((" j: "ID"\n", j)) ; if (j == EMPTY) { AMD_DEBUG3 ((" i is a dense variable\n")) ; } else { ASSERT (j >= 0 && j < n) ; while (Nv [j] == 0) { AMD_DEBUG3 ((" j : "ID"\n", j)) ; j = Pe [j] ; AMD_DEBUG3 ((" j:: "ID"\n", j)) ; cnt++ ; if (cnt > n) break ; } e = j ; AMD_DEBUG3 ((" got to e: "ID"\n", e)) ; } } } #endif /* ========================================================================= */ /* compress the paths of the variables */ /* ========================================================================= */ for (i = 0 ; i < n ; i++) { if (Nv [i] == 0) { /* ------------------------------------------------------------- * i is an un-ordered row. Traverse the tree from i until * reaching an element, e. The element, e, was the principal * supervariable of i and all nodes in the path from i to when e * was selected as pivot. * ------------------------------------------------------------- */ AMD_DEBUG1 (("Path compression, i unordered: "ID"\n", i)) ; j = Pe [i] ; ASSERT (j >= EMPTY && j < n) ; AMD_DEBUG3 ((" j: "ID"\n", j)) ; if (j == EMPTY) { /* Skip a dense variable. It has no parent. */ AMD_DEBUG3 ((" i is a dense variable\n")) ; continue ; } /* while (j is a variable) */ while (Nv [j] == 0) { AMD_DEBUG3 ((" j : "ID"\n", j)) ; j = Pe [j] ; AMD_DEBUG3 ((" j:: "ID"\n", j)) ; ASSERT (j >= 0 && j < n) ; } /* got to an element e */ e = j ; AMD_DEBUG3 (("got to e: "ID"\n", e)) ; /* ------------------------------------------------------------- * traverse the path again from i to e, and compress the path * (all nodes point to e). Path compression allows this code to * compute in O(n) time. * ------------------------------------------------------------- */ j = i ; /* while (j is a variable) */ while (Nv [j] == 0) { jnext = Pe [j] ; AMD_DEBUG3 (("j "ID" jnext "ID"\n", j, jnext)) ; Pe [j] = e ; j = jnext ; ASSERT (j >= 0 && j < n) ; } } } /* ========================================================================= */ /* postorder the assembly tree */ /* ========================================================================= */ AMD_postorder (n, Pe, Nv, Elen, W, /* output order */ Head, Next, Last) ; /* workspace */ /* ========================================================================= */ /* compute output permutation and inverse permutation */ /* ========================================================================= */ /* W [e] = k means that element e is the kth element in the new * order. e is in the range 0 to n-1, and k is in the range 0 to * the number of elements. Use Head for inverse order. */ for (k = 0 ; k < n ; k++) { Head [k] = EMPTY ; Next [k] = EMPTY ; } for (e = 0 ; e < n ; e++) { k = W [e] ; ASSERT ((k == EMPTY) == (Nv [e] == 0)) ; if (k != EMPTY) { ASSERT (k >= 0 && k < n) ; Head [k] = e ; } } /* construct output inverse permutation in Next, * and permutation in Last */ nel = 0 ; for (k = 0 ; k < n ; k++) { e = Head [k] ; if (e == EMPTY) break ; ASSERT (e >= 0 && e < n && Nv [e] > 0) ; Next [e] = nel ; nel += Nv [e] ; } ASSERT (nel == n - ndense) ; /* order non-principal variables (dense, & those merged into supervar's) */ for (i = 0 ; i < n ; i++) { if (Nv [i] == 0) { e = Pe [i] ; ASSERT (e >= EMPTY && e < n) ; if (e != EMPTY) { /* This is an unordered variable that was merged * into element e via supernode detection or mass * elimination of i when e became the pivot element. * Place i in order just before e. */ ASSERT (Next [i] == EMPTY && Nv [e] > 0) ; Next [i] = Next [e] ; Next [e]++ ; } else { /* This is a dense unordered variable, with no parent. * Place it last in the output order. */ Next [i] = nel++ ; } } } ASSERT (nel == n) ; AMD_DEBUG2 (("\n\nPerm:\n")) ; for (i = 0 ; i < n ; i++) { k = Next [i] ; ASSERT (k >= 0 && k < n) ; Last [k] = i ; AMD_DEBUG2 ((" perm ["ID"] = "ID"\n", k, i)) ; } } ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/amd_valid.c0000644000175000017500000000651511632352620021273 0ustar csheltoncshelton/* ========================================================================= */ /* === AMD_valid =========================================================== */ /* ========================================================================= */ /* ------------------------------------------------------------------------- */ /* AMD, Copyright (c) Timothy A. Davis, */ /* Patrick R. Amestoy, and Iain S. Duff. See ../README.txt for License. */ /* email: davis at cise.ufl.edu CISE Department, Univ. of Florida. */ /* web: http://www.cise.ufl.edu/research/sparse/amd */ /* ------------------------------------------------------------------------- */ /* Check if a column-form matrix is valid or not. The matrix A is * n_row-by-n_col. The row indices of entries in column j are in * Ai [Ap [j] ... Ap [j+1]-1]. Required conditions are: * * n_row >= 0 * n_col >= 0 * nz = Ap [n_col] >= 0 number of entries in the matrix * Ap [0] == 0 * Ap [j] <= Ap [j+1] for all j in the range 0 to n_col. * Ai [0 ... nz-1] must be in the range 0 to n_row-1. * * If any of the above conditions hold, AMD_INVALID is returned. If the * following condition holds, AMD_OK_BUT_JUMBLED is returned (a warning, * not an error): * * row indices in Ai [Ap [j] ... Ap [j+1]-1] are not sorted in ascending * order, and/or duplicate entries exist. * * Otherwise, AMD_OK is returned. * * In v1.2 and earlier, this function returned TRUE if the matrix was valid * (now returns AMD_OK), or FALSE otherwise (now returns AMD_INVALID or * AMD_OK_BUT_JUMBLED). */ #include "amd_internal.h" GLOBAL Int AMD_valid ( /* inputs, not modified on output: */ Int n_row, /* A is n_row-by-n_col */ Int n_col, const Int Ap [ ], /* column pointers of A, of size n_col+1 */ const Int Ai [ ] /* row indices of A, of size nz = Ap [n_col] */ ) { Int nz, j, p1, p2, ilast, i, p, result = AMD_OK ; if (n_row < 0 || n_col < 0 || Ap == NULL || Ai == NULL) { return (AMD_INVALID) ; } nz = Ap [n_col] ; if (Ap [0] != 0 || nz < 0) { /* column pointers must start at Ap [0] = 0, and Ap [n] must be >= 0 */ AMD_DEBUG0 (("column 0 pointer bad or nz < 0\n")) ; return (AMD_INVALID) ; } for (j = 0 ; j < n_col ; j++) { p1 = Ap [j] ; p2 = Ap [j+1] ; AMD_DEBUG2 (("\nColumn: "ID" p1: "ID" p2: "ID"\n", j, p1, p2)) ; if (p1 > p2) { /* column pointers must be ascending */ AMD_DEBUG0 (("column "ID" pointer bad\n", j)) ; return (AMD_INVALID) ; } ilast = EMPTY ; for (p = p1 ; p < p2 ; p++) { i = Ai [p] ; AMD_DEBUG3 (("row: "ID"\n", i)) ; if (i < 0 || i >= n_row) { /* row index out of range */ AMD_DEBUG0 (("index out of range, col "ID" row "ID"\n", j, i)); return (AMD_INVALID) ; } if (i <= ilast) { /* row index unsorted, or duplicate entry present */ AMD_DEBUG1 (("index unsorted/dupl col "ID" row "ID"\n", j, i)); result = AMD_OK_BUT_JUMBLED ; } ilast = i ; } } return (result) ; } ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/amd_control.c0000644000175000017500000000370111632352620021646 0ustar csheltoncshelton/* ========================================================================= */ /* === AMD_control ========================================================= */ /* ========================================================================= */ /* ------------------------------------------------------------------------- */ /* AMD, Copyright (c) Timothy A. Davis, */ /* Patrick R. Amestoy, and Iain S. Duff. See ../README.txt for License. */ /* email: davis at cise.ufl.edu CISE Department, Univ. of Florida. */ /* web: http://www.cise.ufl.edu/research/sparse/amd */ /* ------------------------------------------------------------------------- */ /* User-callable. Prints the control parameters for AMD. See amd.h * for details. If the Control array is not present, the defaults are * printed instead. */ #include "amd_internal.h" GLOBAL void AMD_control ( double Control [ ] ) { double alpha ; Int aggressive ; if (Control != (double *) NULL) { alpha = Control [AMD_DENSE] ; aggressive = Control [AMD_AGGRESSIVE] != 0 ; } else { alpha = AMD_DEFAULT_DENSE ; aggressive = AMD_DEFAULT_AGGRESSIVE ; } PRINTF (("\nAMD version %d.%d.%d, %s: approximate minimum degree ordering\n" " dense row parameter: %g\n", AMD_MAIN_VERSION, AMD_SUB_VERSION, AMD_SUBSUB_VERSION, AMD_DATE, alpha)) ; if (alpha < 0) { PRINTF ((" no rows treated as dense\n")) ; } else { PRINTF (( " (rows with more than max (%g * sqrt (n), 16) entries are\n" " considered \"dense\", and placed last in output permutation)\n", alpha)) ; } if (aggressive) { PRINTF ((" aggressive absorption: yes\n")) ; } else { PRINTF ((" aggressive absorption: no\n")) ; } PRINTF ((" size of AMD integer: %d\n\n", sizeof (Int))) ; } ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/amd_order.c0000644000175000017500000001451611632352620021307 0ustar csheltoncshelton/* ========================================================================= */ /* === AMD_order =========================================================== */ /* ========================================================================= */ /* ------------------------------------------------------------------------- */ /* AMD, Copyright (c) Timothy A. Davis, */ /* Patrick R. Amestoy, and Iain S. Duff. See ../README.txt for License. */ /* email: davis at cise.ufl.edu CISE Department, Univ. of Florida. */ /* web: http://www.cise.ufl.edu/research/sparse/amd */ /* ------------------------------------------------------------------------- */ /* User-callable AMD minimum degree ordering routine. See amd.h for * documentation. */ #include "amd_internal.h" /* ========================================================================= */ /* === AMD_order =========================================================== */ /* ========================================================================= */ GLOBAL Int AMD_order ( Int n, const Int Ap [ ], const Int Ai [ ], Int P [ ], double Control [ ], double Info [ ] ) { Int *Len, *S, nz, i, *Pinv, info, status, *Rp, *Ri, *Cp, *Ci, ok ; size_t nzaat, slen ; double mem = 0 ; #ifndef NDEBUG AMD_debug_init ("amd") ; #endif /* clear the Info array, if it exists */ info = Info != (double *) NULL ; if (info) { for (i = 0 ; i < AMD_INFO ; i++) { Info [i] = EMPTY ; } Info [AMD_N] = n ; Info [AMD_STATUS] = AMD_OK ; } /* make sure inputs exist and n is >= 0 */ if (Ai == (Int *) NULL || Ap == (Int *) NULL || P == (Int *) NULL || n < 0) { if (info) Info [AMD_STATUS] = AMD_INVALID ; return (AMD_INVALID) ; /* arguments are invalid */ } if (n == 0) { return (AMD_OK) ; /* n is 0 so there's nothing to do */ } nz = Ap [n] ; if (info) { Info [AMD_NZ] = nz ; } if (nz < 0) { if (info) Info [AMD_STATUS] = AMD_INVALID ; return (AMD_INVALID) ; } /* check if n or nz will cause size_t overflow */ if (((size_t) n) >= SIZE_T_MAX / sizeof (Int) || ((size_t) nz) >= SIZE_T_MAX / sizeof (Int)) { if (info) Info [AMD_STATUS] = AMD_OUT_OF_MEMORY ; return (AMD_OUT_OF_MEMORY) ; /* problem too large */ } /* check the input matrix: AMD_OK, AMD_INVALID, or AMD_OK_BUT_JUMBLED */ status = AMD_valid (n, n, Ap, Ai) ; if (status == AMD_INVALID) { if (info) Info [AMD_STATUS] = AMD_INVALID ; return (AMD_INVALID) ; /* matrix is invalid */ } /* allocate two size-n integer workspaces */ Len = amd_malloc (n * sizeof (Int)) ; Pinv = amd_malloc (n * sizeof (Int)) ; mem += n ; mem += n ; if (!Len || !Pinv) { /* :: out of memory :: */ amd_free (Len) ; amd_free (Pinv) ; if (info) Info [AMD_STATUS] = AMD_OUT_OF_MEMORY ; return (AMD_OUT_OF_MEMORY) ; } if (status == AMD_OK_BUT_JUMBLED) { /* sort the input matrix and remove duplicate entries */ AMD_DEBUG1 (("Matrix is jumbled\n")) ; Rp = amd_malloc ((n+1) * sizeof (Int)) ; Ri = amd_malloc (MAX (nz,1) * sizeof (Int)) ; mem += (n+1) ; mem += MAX (nz,1) ; if (!Rp || !Ri) { /* :: out of memory :: */ amd_free (Rp) ; amd_free (Ri) ; amd_free (Len) ; amd_free (Pinv) ; if (info) Info [AMD_STATUS] = AMD_OUT_OF_MEMORY ; return (AMD_OUT_OF_MEMORY) ; } /* use Len and Pinv as workspace to create R = A' */ AMD_preprocess (n, Ap, Ai, Rp, Ri, Len, Pinv) ; Cp = Rp ; Ci = Ri ; } else { /* order the input matrix as-is. No need to compute R = A' first */ Rp = NULL ; Ri = NULL ; Cp = (Int *) Ap ; Ci = (Int *) Ai ; } /* --------------------------------------------------------------------- */ /* determine the symmetry and count off-diagonal nonzeros in A+A' */ /* --------------------------------------------------------------------- */ nzaat = AMD_aat (n, Cp, Ci, Len, P, Info) ; AMD_DEBUG1 (("nzaat: %g\n", (double) nzaat)) ; ASSERT ((MAX (nz-n, 0) <= nzaat) && (nzaat <= 2 * (size_t) nz)) ; /* --------------------------------------------------------------------- */ /* allocate workspace for matrix, elbow room, and 6 size-n vectors */ /* --------------------------------------------------------------------- */ S = NULL ; slen = nzaat ; /* space for matrix */ ok = ((slen + nzaat/5) >= slen) ; /* check for size_t overflow */ slen += nzaat/5 ; /* add elbow room */ for (i = 0 ; ok && i < 7 ; i++) { ok = ((slen + n) > slen) ; /* check for size_t overflow */ slen += n ; /* size-n elbow room, 6 size-n work */ } mem += slen ; ok = ok && (slen < SIZE_T_MAX / sizeof (Int)) ; /* check for overflow */ ok = ok && (slen < Int_MAX) ; /* S[i] for Int i must be OK */ if (ok) { S = amd_malloc (slen * sizeof (Int)) ; } AMD_DEBUG1 (("slen %g\n", (double) slen)) ; if (!S) { /* :: out of memory :: (or problem too large) */ amd_free (Rp) ; amd_free (Ri) ; amd_free (Len) ; amd_free (Pinv) ; if (info) Info [AMD_STATUS] = AMD_OUT_OF_MEMORY ; return (AMD_OUT_OF_MEMORY) ; } if (info) { /* memory usage, in bytes. */ Info [AMD_MEMORY] = mem * sizeof (Int) ; } /* --------------------------------------------------------------------- */ /* order the matrix */ /* --------------------------------------------------------------------- */ AMD_1 (n, Cp, Ci, P, Pinv, Len, slen, S, Control, Info) ; /* --------------------------------------------------------------------- */ /* free the workspace */ /* --------------------------------------------------------------------- */ amd_free (Rp) ; amd_free (Ri) ; amd_free (Len) ; amd_free (Pinv) ; amd_free (S) ; if (info) Info [AMD_STATUS] = status ; return (status) ; /* successful ordering */ } ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/COPYING0000644000175000017500000006362511632352620020247 0ustar csheltoncshelton GNU LESSER GENERAL PUBLIC LICENSE Version 2.1, February 1999 Copyright (C) 1991, 1999 Free Software Foundation, Inc. 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. 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Here is a sample; alter the names: Yoyodyne, Inc., hereby disclaims all copyright interest in the library `Frob' (a library for tweaking knobs) written by James Random Hacker. , 1 April 1990 Ty Coon, President of Vice That's all there is to it! ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/amd_post_tree.c0000644000175000017500000001070511632352620022174 0ustar csheltoncshelton/* ========================================================================= */ /* === AMD_post_tree ======================================================= */ /* ========================================================================= */ /* ------------------------------------------------------------------------- */ /* AMD, Copyright (c) Timothy A. Davis, */ /* Patrick R. Amestoy, and Iain S. Duff. See ../README.txt for License. */ /* email: davis at cise.ufl.edu CISE Department, Univ. of Florida. */ /* web: http://www.cise.ufl.edu/research/sparse/amd */ /* ------------------------------------------------------------------------- */ /* Post-ordering of a supernodal elimination tree. */ #include "amd_internal.h" GLOBAL Int AMD_post_tree ( Int root, /* root of the tree */ Int k, /* start numbering at k */ Int Child [ ], /* input argument of size nn, undefined on * output. Child [i] is the head of a link * list of all nodes that are children of node * i in the tree. */ const Int Sibling [ ], /* input argument of size nn, not modified. * If f is a node in the link list of the * children of node i, then Sibling [f] is the * next child of node i. */ Int Order [ ], /* output order, of size nn. Order [i] = k * if node i is the kth node of the reordered * tree. */ Int Stack [ ] /* workspace of size nn */ #ifndef NDEBUG , Int nn /* nodes are in the range 0..nn-1. */ #endif ) { Int f, head, h, i ; #if 0 /* --------------------------------------------------------------------- */ /* recursive version (Stack [ ] is not used): */ /* --------------------------------------------------------------------- */ /* this is simple, but can caouse stack overflow if nn is large */ i = root ; for (f = Child [i] ; f != EMPTY ; f = Sibling [f]) { k = AMD_post_tree (f, k, Child, Sibling, Order, Stack, nn) ; } Order [i] = k++ ; return (k) ; #endif /* --------------------------------------------------------------------- */ /* non-recursive version, using an explicit stack */ /* --------------------------------------------------------------------- */ /* push root on the stack */ head = 0 ; Stack [0] = root ; while (head >= 0) { /* get head of stack */ ASSERT (head < nn) ; i = Stack [head] ; AMD_DEBUG1 (("head of stack "ID" \n", i)) ; ASSERT (i >= 0 && i < nn) ; if (Child [i] != EMPTY) { /* the children of i are not yet ordered */ /* push each child onto the stack in reverse order */ /* so that small ones at the head of the list get popped first */ /* and the biggest one at the end of the list gets popped last */ for (f = Child [i] ; f != EMPTY ; f = Sibling [f]) { head++ ; ASSERT (head < nn) ; ASSERT (f >= 0 && f < nn) ; } h = head ; ASSERT (head < nn) ; for (f = Child [i] ; f != EMPTY ; f = Sibling [f]) { ASSERT (h > 0) ; Stack [h--] = f ; AMD_DEBUG1 (("push "ID" on stack\n", f)) ; ASSERT (f >= 0 && f < nn) ; } ASSERT (Stack [h] == i) ; /* delete child list so that i gets ordered next time we see it */ Child [i] = EMPTY ; } else { /* the children of i (if there were any) are already ordered */ /* remove i from the stack and order it. Front i is kth front */ head-- ; AMD_DEBUG1 (("pop "ID" order "ID"\n", i, k)) ; Order [i] = k++ ; ASSERT (k <= nn) ; } #ifndef NDEBUG AMD_DEBUG1 (("\nStack:")) ; for (h = head ; h >= 0 ; h--) { Int j = Stack [h] ; AMD_DEBUG1 ((" "ID, j)) ; ASSERT (j >= 0 && j < nn) ; } AMD_DEBUG1 (("\n\n")) ; ASSERT (head < nn) ; #endif } return (k) ; } ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/amd_aat.c0000644000175000017500000001340111632352620020731 0ustar csheltoncshelton/* ========================================================================= */ /* === AMD_aat ============================================================= */ /* ========================================================================= */ /* ------------------------------------------------------------------------- */ /* AMD, Copyright (c) Timothy A. Davis, */ /* Patrick R. Amestoy, and Iain S. Duff. See ../README.txt for License. */ /* email: davis at cise.ufl.edu CISE Department, Univ. of Florida. */ /* web: http://www.cise.ufl.edu/research/sparse/amd */ /* ------------------------------------------------------------------------- */ /* AMD_aat: compute the symmetry of the pattern of A, and count the number of * nonzeros each column of A+A' (excluding the diagonal). Assumes the input * matrix has no errors, with sorted columns and no duplicates * (AMD_valid (n, n, Ap, Ai) must be AMD_OK, but this condition is not * checked). */ #include "amd_internal.h" GLOBAL size_t AMD_aat /* returns nz in A+A' */ ( Int n, const Int Ap [ ], const Int Ai [ ], Int Len [ ], /* Len [j]: length of column j of A+A', excl diagonal*/ Int Tp [ ], /* workspace of size n */ double Info [ ] ) { Int p1, p2, p, i, j, pj, pj2, k, nzdiag, nzboth, nz ; double sym ; size_t nzaat ; #ifndef NDEBUG AMD_debug_init ("AMD AAT") ; for (k = 0 ; k < n ; k++) Tp [k] = EMPTY ; ASSERT (AMD_valid (n, n, Ap, Ai) == AMD_OK) ; #endif if (Info != (double *) NULL) { /* clear the Info array, if it exists */ for (i = 0 ; i < AMD_INFO ; i++) { Info [i] = EMPTY ; } Info [AMD_STATUS] = AMD_OK ; } for (k = 0 ; k < n ; k++) { Len [k] = 0 ; } nzdiag = 0 ; nzboth = 0 ; nz = Ap [n] ; for (k = 0 ; k < n ; k++) { p1 = Ap [k] ; p2 = Ap [k+1] ; AMD_DEBUG2 (("\nAAT Column: "ID" p1: "ID" p2: "ID"\n", k, p1, p2)) ; /* construct A+A' */ for (p = p1 ; p < p2 ; ) { /* scan the upper triangular part of A */ j = Ai [p] ; if (j < k) { /* entry A (j,k) is in the strictly upper triangular part, * add both A (j,k) and A (k,j) to the matrix A+A' */ Len [j]++ ; Len [k]++ ; AMD_DEBUG3 ((" upper ("ID","ID") ("ID","ID")\n", j,k, k,j)); p++ ; } else if (j == k) { /* skip the diagonal */ p++ ; nzdiag++ ; break ; } else /* j > k */ { /* first entry below the diagonal */ break ; } /* scan lower triangular part of A, in column j until reaching * row k. Start where last scan left off. */ ASSERT (Tp [j] != EMPTY) ; ASSERT (Ap [j] <= Tp [j] && Tp [j] <= Ap [j+1]) ; pj2 = Ap [j+1] ; for (pj = Tp [j] ; pj < pj2 ; ) { i = Ai [pj] ; if (i < k) { /* A (i,j) is only in the lower part, not in upper. * add both A (i,j) and A (j,i) to the matrix A+A' */ Len [i]++ ; Len [j]++ ; AMD_DEBUG3 ((" lower ("ID","ID") ("ID","ID")\n", i,j, j,i)) ; pj++ ; } else if (i == k) { /* entry A (k,j) in lower part and A (j,k) in upper */ pj++ ; nzboth++ ; break ; } else /* i > k */ { /* consider this entry later, when k advances to i */ break ; } } Tp [j] = pj ; } /* Tp [k] points to the entry just below the diagonal in column k */ Tp [k] = p ; } /* clean up, for remaining mismatched entries */ for (j = 0 ; j < n ; j++) { for (pj = Tp [j] ; pj < Ap [j+1] ; pj++) { i = Ai [pj] ; /* A (i,j) is only in the lower part, not in upper. * add both A (i,j) and A (j,i) to the matrix A+A' */ Len [i]++ ; Len [j]++ ; AMD_DEBUG3 ((" lower cleanup ("ID","ID") ("ID","ID")\n", i,j, j,i)) ; } } /* --------------------------------------------------------------------- */ /* compute the symmetry of the nonzero pattern of A */ /* --------------------------------------------------------------------- */ /* Given a matrix A, the symmetry of A is: * B = tril (spones (A), -1) + triu (spones (A), 1) ; * sym = nnz (B & B') / nnz (B) ; * or 1 if nnz (B) is zero. */ if (nz == nzdiag) { sym = 1 ; } else { sym = (2 * (double) nzboth) / ((double) (nz - nzdiag)) ; } nzaat = 0 ; for (k = 0 ; k < n ; k++) { nzaat += Len [k] ; } AMD_DEBUG1 (("AMD nz in A+A', excluding diagonal (nzaat) = %g\n", (double) nzaat)) ; AMD_DEBUG1 ((" nzboth: "ID" nz: "ID" nzdiag: "ID" symmetry: %g\n", nzboth, nz, nzdiag, sym)) ; if (Info != (double *) NULL) { Info [AMD_STATUS] = AMD_OK ; Info [AMD_N] = n ; Info [AMD_NZ] = nz ; Info [AMD_SYMMETRY] = sym ; /* symmetry of pattern of A */ Info [AMD_NZDIAG] = nzdiag ; /* nonzeros on diagonal of A */ Info [AMD_NZ_A_PLUS_AT] = nzaat ; /* nonzeros in A+A' */ } return (nzaat) ; } ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/amd_internal.h0000644000175000017500000000571711632352620022020 0ustar csheltoncshelton/* amd_internal.h */ /* Written by Andrew Makhorin . */ #ifndef AMD_INTERNAL_H #define AMD_INTERNAL_H /* AMD will be exceedingly slow when running in debug mode. */ #if 1 #define NDEBUG #endif #include "amd.h" #define _GLPSTD_STDIO #include "glpenv.h" #define Int int #define ID "%d" #define Int_MAX INT_MAX #define SIZE_T_MAX ((size_t)(-1)) #define EMPTY (-1) #define FLIP(i) (-(i)-2) #define UNFLIP(i) ((i < EMPTY) ? FLIP (i) : (i)) #define MAX(a,b) (((a) > (b)) ? (a) : (b)) #define MIN(a,b) (((a) < (b)) ? (a) : (b)) #define IMPLIES(p, q) (!(p) || (q)) #define GLOBAL #define AMD_order amd_order #define AMD_defaults amd_defaults #define AMD_control amd_control #define AMD_info amd_info #define AMD_1 amd_1 #define AMD_2 amd_2 #define AMD_valid amd_valid #define AMD_aat amd_aat #define AMD_postorder amd_postorder #define AMD_post_tree amd_post_tree #define AMD_dump amd_dump #define AMD_debug amd_debug #define AMD_debug_init amd_debug_init #define AMD_preprocess amd_preprocess #define amd_malloc xmalloc #if 0 /* 24/V-2009 */ #define amd_free xfree #else #define amd_free(ptr) { if ((ptr) != NULL) xfree(ptr); } #endif #define amd_printf xprintf #define PRINTF(params) { amd_printf params; } #ifndef NDEBUG #define ASSERT(expr) xassert(expr) #define AMD_DEBUG0(params) { PRINTF(params); } #define AMD_DEBUG1(params) { if (AMD_debug >= 1) PRINTF(params); } #define AMD_DEBUG2(params) { if (AMD_debug >= 2) PRINTF(params); } #define AMD_DEBUG3(params) { if (AMD_debug >= 3) PRINTF(params); } #define AMD_DEBUG4(params) { if (AMD_debug >= 4) PRINTF(params); } #else #define ASSERT(expression) #define AMD_DEBUG0(params) #define AMD_DEBUG1(params) #define AMD_DEBUG2(params) #define AMD_DEBUG3(params) #define AMD_DEBUG4(params) #endif #define amd_aat _glp_amd_aat size_t AMD_aat(Int n, const Int Ap[], const Int Ai[], Int Len[], Int Tp[], double Info[]); #define amd_1 _glp_amd_1 void AMD_1(Int n, const Int Ap[], const Int Ai[], Int P[], Int Pinv[], Int Len[], Int slen, Int S[], double Control[], double Info[]); #define amd_postorder _glp_amd_postorder void AMD_postorder(Int nn, Int Parent[], Int Npiv[], Int Fsize[], Int Order[], Int Child[], Int Sibling[], Int Stack[]); #define amd_post_tree _glp_amd_post_tree #ifndef NDEBUG Int AMD_post_tree(Int root, Int k, Int Child[], const Int Sibling[], Int Order[], Int Stack[], Int nn); #else Int AMD_post_tree(Int root, Int k, Int Child[], const Int Sibling[], Int Order[], Int Stack[]); #endif #define amd_preprocess _glp_amd_preprocess void AMD_preprocess(Int n, const Int Ap[], const Int Ai[], Int Rp[], Int Ri[], Int W[], Int Flag[]); #define amd_debug _glp_amd_debug extern Int AMD_debug; #define amd_debug_init _glp_amd_debug_init void AMD_debug_init(char *s); #define amd_dump _glp_amd_dump void AMD_dump(Int n, Int Pe[], Int Iw[], Int Len[], Int iwlen, Int pfree, Int Nv[], Int Next[], Int Last[], Int Head[], Int Elen[], Int Degree[], Int W[], Int nel); #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/README0000644000175000017500000000461711632352620020070 0ustar csheltoncsheltonNOTE: Files in this subdirectory are NOT part of the GLPK package, but are used with GLPK. The original code was modified according to GLPK requirements by Andrew Makhorin . ************************************************************************ AMD Version 2.2, Copyright (C) 2007 by Timothy A. Davis, Patrick R. Amestoy, and Iain S. Duff. All Rights Reserved. Description: AMD is a set of routines for pre-ordering sparse matrices prior to Cholesky or LU factorization, using the approximate minimum degree ordering algorithm. Written in ANSI/ISO C with a MATLAB interface, and in Fortran 77. Authors: Timothy A. Davis (davis at cise.ufl.edu), University of Florida. Patrick R. Amestoy, ENSEEIHT, Toulouse, France. Iain S. Duff, Rutherford Appleton Laboratory, UK. AMD License: Your use or distribution of AMD or any modified version of AMD implies that you agree to this License. This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA. Permission is hereby granted to use or copy this program under the terms of the GNU LGPL, provided that the Copyright, this License, and the Availability of the original version is retained on all copies. User documentation of any code that uses this code or any modified version of this code must cite the Copyright, this License, the Availability note, and "Used by permission." Permission to modify the code and to distribute modified code is granted, provided the Copyright, this License, and the Availability note are retained, and a notice that the code was modified is included. AMD is available under alternate licences; contact T. Davis for details. Availability: http://www.cise.ufl.edu/research/sparse/amd ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/amd_1.c0000644000175000017500000001504311632352620020330 0ustar csheltoncshelton/* ========================================================================= */ /* === AMD_1 =============================================================== */ /* ========================================================================= */ /* ------------------------------------------------------------------------- */ /* AMD, Copyright (c) Timothy A. Davis, */ /* Patrick R. Amestoy, and Iain S. Duff. See ../README.txt for License. */ /* email: davis at cise.ufl.edu CISE Department, Univ. of Florida. */ /* web: http://www.cise.ufl.edu/research/sparse/amd */ /* ------------------------------------------------------------------------- */ /* AMD_1: Construct A+A' for a sparse matrix A and perform the AMD ordering. * * The n-by-n sparse matrix A can be unsymmetric. It is stored in MATLAB-style * compressed-column form, with sorted row indices in each column, and no * duplicate entries. Diagonal entries may be present, but they are ignored. * Row indices of column j of A are stored in Ai [Ap [j] ... Ap [j+1]-1]. * Ap [0] must be zero, and nz = Ap [n] is the number of entries in A. The * size of the matrix, n, must be greater than or equal to zero. * * This routine must be preceded by a call to AMD_aat, which computes the * number of entries in each row/column in A+A', excluding the diagonal. * Len [j], on input, is the number of entries in row/column j of A+A'. This * routine constructs the matrix A+A' and then calls AMD_2. No error checking * is performed (this was done in AMD_valid). */ #include "amd_internal.h" GLOBAL void AMD_1 ( Int n, /* n > 0 */ const Int Ap [ ], /* input of size n+1, not modified */ const Int Ai [ ], /* input of size nz = Ap [n], not modified */ Int P [ ], /* size n output permutation */ Int Pinv [ ], /* size n output inverse permutation */ Int Len [ ], /* size n input, undefined on output */ Int slen, /* slen >= sum (Len [0..n-1]) + 7n, * ideally slen = 1.2 * sum (Len) + 8n */ Int S [ ], /* size slen workspace */ double Control [ ], /* input array of size AMD_CONTROL */ double Info [ ] /* output array of size AMD_INFO */ ) { Int i, j, k, p, pfree, iwlen, pj, p1, p2, pj2, *Iw, *Pe, *Nv, *Head, *Elen, *Degree, *s, *W, *Sp, *Tp ; /* --------------------------------------------------------------------- */ /* construct the matrix for AMD_2 */ /* --------------------------------------------------------------------- */ ASSERT (n > 0) ; iwlen = slen - 6*n ; s = S ; Pe = s ; s += n ; Nv = s ; s += n ; Head = s ; s += n ; Elen = s ; s += n ; Degree = s ; s += n ; W = s ; s += n ; Iw = s ; s += iwlen ; ASSERT (AMD_valid (n, n, Ap, Ai) == AMD_OK) ; /* construct the pointers for A+A' */ Sp = Nv ; /* use Nv and W as workspace for Sp and Tp [ */ Tp = W ; pfree = 0 ; for (j = 0 ; j < n ; j++) { Pe [j] = pfree ; Sp [j] = pfree ; pfree += Len [j] ; } /* Note that this restriction on iwlen is slightly more restrictive than * what is strictly required in AMD_2. AMD_2 can operate with no elbow * room at all, but it will be very slow. For better performance, at * least size-n elbow room is enforced. */ ASSERT (iwlen >= pfree + n) ; #ifndef NDEBUG for (p = 0 ; p < iwlen ; p++) Iw [p] = EMPTY ; #endif for (k = 0 ; k < n ; k++) { AMD_DEBUG1 (("Construct row/column k= "ID" of A+A'\n", k)) ; p1 = Ap [k] ; p2 = Ap [k+1] ; /* construct A+A' */ for (p = p1 ; p < p2 ; ) { /* scan the upper triangular part of A */ j = Ai [p] ; ASSERT (j >= 0 && j < n) ; if (j < k) { /* entry A (j,k) in the strictly upper triangular part */ ASSERT (Sp [j] < (j == n-1 ? pfree : Pe [j+1])) ; ASSERT (Sp [k] < (k == n-1 ? pfree : Pe [k+1])) ; Iw [Sp [j]++] = k ; Iw [Sp [k]++] = j ; p++ ; } else if (j == k) { /* skip the diagonal */ p++ ; break ; } else /* j > k */ { /* first entry below the diagonal */ break ; } /* scan lower triangular part of A, in column j until reaching * row k. Start where last scan left off. */ ASSERT (Ap [j] <= Tp [j] && Tp [j] <= Ap [j+1]) ; pj2 = Ap [j+1] ; for (pj = Tp [j] ; pj < pj2 ; ) { i = Ai [pj] ; ASSERT (i >= 0 && i < n) ; if (i < k) { /* A (i,j) is only in the lower part, not in upper */ ASSERT (Sp [i] < (i == n-1 ? pfree : Pe [i+1])) ; ASSERT (Sp [j] < (j == n-1 ? pfree : Pe [j+1])) ; Iw [Sp [i]++] = j ; Iw [Sp [j]++] = i ; pj++ ; } else if (i == k) { /* entry A (k,j) in lower part and A (j,k) in upper */ pj++ ; break ; } else /* i > k */ { /* consider this entry later, when k advances to i */ break ; } } Tp [j] = pj ; } Tp [k] = p ; } /* clean up, for remaining mismatched entries */ for (j = 0 ; j < n ; j++) { for (pj = Tp [j] ; pj < Ap [j+1] ; pj++) { i = Ai [pj] ; ASSERT (i >= 0 && i < n) ; /* A (i,j) is only in the lower part, not in upper */ ASSERT (Sp [i] < (i == n-1 ? pfree : Pe [i+1])) ; ASSERT (Sp [j] < (j == n-1 ? pfree : Pe [j+1])) ; Iw [Sp [i]++] = j ; Iw [Sp [j]++] = i ; } } #ifndef NDEBUG for (j = 0 ; j < n-1 ; j++) ASSERT (Sp [j] == Pe [j+1]) ; ASSERT (Sp [n-1] == pfree) ; #endif /* Tp and Sp no longer needed ] */ /* --------------------------------------------------------------------- */ /* order the matrix */ /* --------------------------------------------------------------------- */ AMD_2 (n, Pe, Iw, Len, iwlen, pfree, Nv, Pinv, P, Head, Elen, Degree, W, Control, Info) ; } ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/amd.h0000644000175000017500000000327611632352620020122 0ustar csheltoncshelton/* amd.h */ /* Written by Andrew Makhorin . */ #ifndef GLPAMD_H #define GLPAMD_H #define AMD_DATE "May 31, 2007" #define AMD_VERSION_CODE(main, sub) ((main) * 1000 + (sub)) #define AMD_MAIN_VERSION 2 #define AMD_SUB_VERSION 2 #define AMD_SUBSUB_VERSION 0 #define AMD_VERSION AMD_VERSION_CODE(AMD_MAIN_VERSION, AMD_SUB_VERSION) #define AMD_CONTROL 5 #define AMD_INFO 20 #define AMD_DENSE 0 #define AMD_AGGRESSIVE 1 #define AMD_DEFAULT_DENSE 10.0 #define AMD_DEFAULT_AGGRESSIVE 1 #define AMD_STATUS 0 #define AMD_N 1 #define AMD_NZ 2 #define AMD_SYMMETRY 3 #define AMD_NZDIAG 4 #define AMD_NZ_A_PLUS_AT 5 #define AMD_NDENSE 6 #define AMD_MEMORY 7 #define AMD_NCMPA 8 #define AMD_LNZ 9 #define AMD_NDIV 10 #define AMD_NMULTSUBS_LDL 11 #define AMD_NMULTSUBS_LU 12 #define AMD_DMAX 13 #define AMD_OK 0 #define AMD_OUT_OF_MEMORY (-1) #define AMD_INVALID (-2) #define AMD_OK_BUT_JUMBLED 1 #define amd_order _glp_amd_order int amd_order(int n, const int Ap[], const int Ai[], int P[], double Control[], double Info[]); #define amd_2 _glp_amd_2 void amd_2(int n, int Pe[], int Iw[], int Len[], int iwlen, int pfree, int Nv[], int Next[], int Last[], int Head[], int Elen[], int Degree[], int W[], double Control[], double Info[]); #define amd_valid _glp_amd_valid int amd_valid(int n_row, int n_col, const int Ap[], const int Ai[]); #define amd_defaults _glp_amd_defaults void amd_defaults(double Control[]); #define amd_control _glp_amd_control void amd_control(double Control[]); #define amd_info _glp_amd_info void amd_info(double Info[]); #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/amd/amd_postorder.c0000644000175000017500000001543111632352620022212 0ustar csheltoncshelton/* ========================================================================= */ /* === AMD_postorder ======================================================= */ /* ========================================================================= */ /* ------------------------------------------------------------------------- */ /* AMD, Copyright (c) Timothy A. Davis, */ /* Patrick R. Amestoy, and Iain S. Duff. See ../README.txt for License. */ /* email: davis at cise.ufl.edu CISE Department, Univ. of Florida. */ /* web: http://www.cise.ufl.edu/research/sparse/amd */ /* ------------------------------------------------------------------------- */ /* Perform a postordering (via depth-first search) of an assembly tree. */ #include "amd_internal.h" GLOBAL void AMD_postorder ( /* inputs, not modified on output: */ Int nn, /* nodes are in the range 0..nn-1 */ Int Parent [ ], /* Parent [j] is the parent of j, or EMPTY if root */ Int Nv [ ], /* Nv [j] > 0 number of pivots represented by node j, * or zero if j is not a node. */ Int Fsize [ ], /* Fsize [j]: size of node j */ /* output, not defined on input: */ Int Order [ ], /* output post-order */ /* workspaces of size nn: */ Int Child [ ], Int Sibling [ ], Int Stack [ ] ) { Int i, j, k, parent, frsize, f, fprev, maxfrsize, bigfprev, bigf, fnext ; for (j = 0 ; j < nn ; j++) { Child [j] = EMPTY ; Sibling [j] = EMPTY ; } /* --------------------------------------------------------------------- */ /* place the children in link lists - bigger elements tend to be last */ /* --------------------------------------------------------------------- */ for (j = nn-1 ; j >= 0 ; j--) { if (Nv [j] > 0) { /* this is an element */ parent = Parent [j] ; if (parent != EMPTY) { /* place the element in link list of the children its parent */ /* bigger elements will tend to be at the end of the list */ Sibling [j] = Child [parent] ; Child [parent] = j ; } } } #ifndef NDEBUG { Int nels, ff, nchild ; AMD_DEBUG1 (("\n\n================================ AMD_postorder:\n")); nels = 0 ; for (j = 0 ; j < nn ; j++) { if (Nv [j] > 0) { AMD_DEBUG1 (( ""ID" : nels "ID" npiv "ID" size "ID " parent "ID" maxfr "ID"\n", j, nels, Nv [j], Fsize [j], Parent [j], Fsize [j])) ; /* this is an element */ /* dump the link list of children */ nchild = 0 ; AMD_DEBUG1 ((" Children: ")) ; for (ff = Child [j] ; ff != EMPTY ; ff = Sibling [ff]) { AMD_DEBUG1 ((ID" ", ff)) ; ASSERT (Parent [ff] == j) ; nchild++ ; ASSERT (nchild < nn) ; } AMD_DEBUG1 (("\n")) ; parent = Parent [j] ; if (parent != EMPTY) { ASSERT (Nv [parent] > 0) ; } nels++ ; } } } AMD_DEBUG1 (("\n\nGo through the children of each node, and put\n" "the biggest child last in each list:\n")) ; #endif /* --------------------------------------------------------------------- */ /* place the largest child last in the list of children for each node */ /* --------------------------------------------------------------------- */ for (i = 0 ; i < nn ; i++) { if (Nv [i] > 0 && Child [i] != EMPTY) { #ifndef NDEBUG Int nchild ; AMD_DEBUG1 (("Before partial sort, element "ID"\n", i)) ; nchild = 0 ; for (f = Child [i] ; f != EMPTY ; f = Sibling [f]) { ASSERT (f >= 0 && f < nn) ; AMD_DEBUG1 ((" f: "ID" size: "ID"\n", f, Fsize [f])) ; nchild++ ; ASSERT (nchild <= nn) ; } #endif /* find the biggest element in the child list */ fprev = EMPTY ; maxfrsize = EMPTY ; bigfprev = EMPTY ; bigf = EMPTY ; for (f = Child [i] ; f != EMPTY ; f = Sibling [f]) { ASSERT (f >= 0 && f < nn) ; frsize = Fsize [f] ; if (frsize >= maxfrsize) { /* this is the biggest seen so far */ maxfrsize = frsize ; bigfprev = fprev ; bigf = f ; } fprev = f ; } ASSERT (bigf != EMPTY) ; fnext = Sibling [bigf] ; AMD_DEBUG1 (("bigf "ID" maxfrsize "ID" bigfprev "ID" fnext "ID " fprev " ID"\n", bigf, maxfrsize, bigfprev, fnext, fprev)) ; if (fnext != EMPTY) { /* if fnext is EMPTY then bigf is already at the end of list */ if (bigfprev == EMPTY) { /* delete bigf from the element of the list */ Child [i] = fnext ; } else { /* delete bigf from the middle of the list */ Sibling [bigfprev] = fnext ; } /* put bigf at the end of the list */ Sibling [bigf] = EMPTY ; ASSERT (Child [i] != EMPTY) ; ASSERT (fprev != bigf) ; ASSERT (fprev != EMPTY) ; Sibling [fprev] = bigf ; } #ifndef NDEBUG AMD_DEBUG1 (("After partial sort, element "ID"\n", i)) ; for (f = Child [i] ; f != EMPTY ; f = Sibling [f]) { ASSERT (f >= 0 && f < nn) ; AMD_DEBUG1 ((" "ID" "ID"\n", f, Fsize [f])) ; ASSERT (Nv [f] > 0) ; nchild-- ; } ASSERT (nchild == 0) ; #endif } } /* --------------------------------------------------------------------- */ /* postorder the assembly tree */ /* --------------------------------------------------------------------- */ for (i = 0 ; i < nn ; i++) { Order [i] = EMPTY ; } k = 0 ; for (i = 0 ; i < nn ; i++) { if (Parent [i] == EMPTY && Nv [i] > 0) { AMD_DEBUG1 (("Root of assembly tree "ID"\n", i)) ; k = AMD_post_tree (i, k, Child, Sibling, Order, Stack #ifndef NDEBUG , nn #endif ) ; } } } ctbnrle.1.1.1/glpk/glpk-4.47/src/glpapi02.c0000644000175000017500000003227711632352620020234 0ustar csheltoncshelton/* glpapi02.c (problem retrieving routines) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" /*********************************************************************** * NAME * * glp_get_prob_name - retrieve problem name * * SYNOPSIS * * const char *glp_get_prob_name(glp_prob *lp); * * RETURNS * * The routine glp_get_prob_name returns a pointer to an internal * buffer, which contains symbolic name of the problem. However, if the * problem has no assigned name, the routine returns NULL. */ const char *glp_get_prob_name(glp_prob *lp) { char *name; name = lp->name; return name; } /*********************************************************************** * NAME * * glp_get_obj_name - retrieve objective function name * * SYNOPSIS * * const char *glp_get_obj_name(glp_prob *lp); * * RETURNS * * The routine glp_get_obj_name returns a pointer to an internal * buffer, which contains a symbolic name of the objective function. * However, if the objective function has no assigned name, the routine * returns NULL. */ const char *glp_get_obj_name(glp_prob *lp) { char *name; name = lp->obj; return name; } /*********************************************************************** * NAME * * glp_get_obj_dir - retrieve optimization direction flag * * SYNOPSIS * * int glp_get_obj_dir(glp_prob *lp); * * RETURNS * * The routine glp_get_obj_dir returns the optimization direction flag * (i.e. "sense" of the objective function): * * GLP_MIN - minimization; * GLP_MAX - maximization. */ int glp_get_obj_dir(glp_prob *lp) { int dir = lp->dir; return dir; } /*********************************************************************** * NAME * * glp_get_num_rows - retrieve number of rows * * SYNOPSIS * * int glp_get_num_rows(glp_prob *lp); * * RETURNS * * The routine glp_get_num_rows returns the current number of rows in * the specified problem object. */ int glp_get_num_rows(glp_prob *lp) { int m = lp->m; return m; } /*********************************************************************** * NAME * * glp_get_num_cols - retrieve number of columns * * SYNOPSIS * * int glp_get_num_cols(glp_prob *lp); * * RETURNS * * The routine glp_get_num_cols returns the current number of columns * in the specified problem object. */ int glp_get_num_cols(glp_prob *lp) { int n = lp->n; return n; } /*********************************************************************** * NAME * * glp_get_row_name - retrieve row name * * SYNOPSIS * * const char *glp_get_row_name(glp_prob *lp, int i); * * RETURNS * * The routine glp_get_row_name returns a pointer to an internal * buffer, which contains symbolic name of i-th row. However, if i-th * row has no assigned name, the routine returns NULL. */ const char *glp_get_row_name(glp_prob *lp, int i) { char *name; if (!(1 <= i && i <= lp->m)) xerror("glp_get_row_name: i = %d; row number out of range\n", i); name = lp->row[i]->name; return name; } /*********************************************************************** * NAME * * glp_get_col_name - retrieve column name * * SYNOPSIS * * const char *glp_get_col_name(glp_prob *lp, int j); * * RETURNS * * The routine glp_get_col_name returns a pointer to an internal * buffer, which contains symbolic name of j-th column. However, if j-th * column has no assigned name, the routine returns NULL. */ const char *glp_get_col_name(glp_prob *lp, int j) { char *name; if (!(1 <= j && j <= lp->n)) xerror("glp_get_col_name: j = %d; column number out of range\n" , j); name = lp->col[j]->name; return name; } /*********************************************************************** * NAME * * glp_get_row_type - retrieve row type * * SYNOPSIS * * int glp_get_row_type(glp_prob *lp, int i); * * RETURNS * * The routine glp_get_row_type returns the type of i-th row, i.e. the * type of corresponding auxiliary variable, as follows: * * GLP_FR - free (unbounded) variable; * GLP_LO - variable with lower bound; * GLP_UP - variable with upper bound; * GLP_DB - double-bounded variable; * GLP_FX - fixed variable. */ int glp_get_row_type(glp_prob *lp, int i) { if (!(1 <= i && i <= lp->m)) xerror("glp_get_row_type: i = %d; row number out of range\n", i); return lp->row[i]->type; } /*********************************************************************** * NAME * * glp_get_row_lb - retrieve row lower bound * * SYNOPSIS * * double glp_get_row_lb(glp_prob *lp, int i); * * RETURNS * * The routine glp_get_row_lb returns the lower bound of i-th row, i.e. * the lower bound of corresponding auxiliary variable. However, if the * row has no lower bound, the routine returns -DBL_MAX. */ double glp_get_row_lb(glp_prob *lp, int i) { double lb; if (!(1 <= i && i <= lp->m)) xerror("glp_get_row_lb: i = %d; row number out of range\n", i); switch (lp->row[i]->type) { case GLP_FR: case GLP_UP: lb = -DBL_MAX; break; case GLP_LO: case GLP_DB: case GLP_FX: lb = lp->row[i]->lb; break; default: xassert(lp != lp); } return lb; } /*********************************************************************** * NAME * * glp_get_row_ub - retrieve row upper bound * * SYNOPSIS * * double glp_get_row_ub(glp_prob *lp, int i); * * RETURNS * * The routine glp_get_row_ub returns the upper bound of i-th row, i.e. * the upper bound of corresponding auxiliary variable. However, if the * row has no upper bound, the routine returns +DBL_MAX. */ double glp_get_row_ub(glp_prob *lp, int i) { double ub; if (!(1 <= i && i <= lp->m)) xerror("glp_get_row_ub: i = %d; row number out of range\n", i); switch (lp->row[i]->type) { case GLP_FR: case GLP_LO: ub = +DBL_MAX; break; case GLP_UP: case GLP_DB: case GLP_FX: ub = lp->row[i]->ub; break; default: xassert(lp != lp); } return ub; } /*********************************************************************** * NAME * * glp_get_col_type - retrieve column type * * SYNOPSIS * * int glp_get_col_type(glp_prob *lp, int j); * * RETURNS * * The routine glp_get_col_type returns the type of j-th column, i.e. * the type of corresponding structural variable, as follows: * * GLP_FR - free (unbounded) variable; * GLP_LO - variable with lower bound; * GLP_UP - variable with upper bound; * GLP_DB - double-bounded variable; * GLP_FX - fixed variable. */ int glp_get_col_type(glp_prob *lp, int j) { if (!(1 <= j && j <= lp->n)) xerror("glp_get_col_type: j = %d; column number out of range\n" , j); return lp->col[j]->type; } /*********************************************************************** * NAME * * glp_get_col_lb - retrieve column lower bound * * SYNOPSIS * * double glp_get_col_lb(glp_prob *lp, int j); * * RETURNS * * The routine glp_get_col_lb returns the lower bound of j-th column, * i.e. the lower bound of corresponding structural variable. However, * if the column has no lower bound, the routine returns -DBL_MAX. */ double glp_get_col_lb(glp_prob *lp, int j) { double lb; if (!(1 <= j && j <= lp->n)) xerror("glp_get_col_lb: j = %d; column number out of range\n", j); switch (lp->col[j]->type) { case GLP_FR: case GLP_UP: lb = -DBL_MAX; break; case GLP_LO: case GLP_DB: case GLP_FX: lb = lp->col[j]->lb; break; default: xassert(lp != lp); } return lb; } /*********************************************************************** * NAME * * glp_get_col_ub - retrieve column upper bound * * SYNOPSIS * * double glp_get_col_ub(glp_prob *lp, int j); * * RETURNS * * The routine glp_get_col_ub returns the upper bound of j-th column, * i.e. the upper bound of corresponding structural variable. However, * if the column has no upper bound, the routine returns +DBL_MAX. */ double glp_get_col_ub(glp_prob *lp, int j) { double ub; if (!(1 <= j && j <= lp->n)) xerror("glp_get_col_ub: j = %d; column number out of range\n", j); switch (lp->col[j]->type) { case GLP_FR: case GLP_LO: ub = +DBL_MAX; break; case GLP_UP: case GLP_DB: case GLP_FX: ub = lp->col[j]->ub; break; default: xassert(lp != lp); } return ub; } /*********************************************************************** * NAME * * glp_get_obj_coef - retrieve obj. coefficient or constant term * * SYNOPSIS * * double glp_get_obj_coef(glp_prob *lp, int j); * * RETURNS * * The routine glp_get_obj_coef returns the objective coefficient at * j-th structural variable (column) of the specified problem object. * * If the parameter j is zero, the routine returns the constant term * ("shift") of the objective function. */ double glp_get_obj_coef(glp_prob *lp, int j) { if (!(0 <= j && j <= lp->n)) xerror("glp_get_obj_coef: j = %d; column number out of range\n" , j); return j == 0 ? lp->c0 : lp->col[j]->coef; } /*********************************************************************** * NAME * * glp_get_num_nz - retrieve number of constraint coefficients * * SYNOPSIS * * int glp_get_num_nz(glp_prob *lp); * * RETURNS * * The routine glp_get_num_nz returns the number of (non-zero) elements * in the constraint matrix of the specified problem object. */ int glp_get_num_nz(glp_prob *lp) { int nnz = lp->nnz; return nnz; } /*********************************************************************** * NAME * * glp_get_mat_row - retrieve row of the constraint matrix * * SYNOPSIS * * int glp_get_mat_row(glp_prob *lp, int i, int ind[], double val[]); * * DESCRIPTION * * The routine glp_get_mat_row scans (non-zero) elements of i-th row * of the constraint matrix of the specified problem object and stores * their column indices and numeric values to locations ind[1], ..., * ind[len] and val[1], ..., val[len], respectively, where 0 <= len <= n * is the number of elements in i-th row, n is the number of columns. * * The parameter ind and/or val can be specified as NULL, in which case * corresponding information is not stored. * * RETURNS * * The routine glp_get_mat_row returns the length len, i.e. the number * of (non-zero) elements in i-th row. */ int glp_get_mat_row(glp_prob *lp, int i, int ind[], double val[]) { GLPAIJ *aij; int len; if (!(1 <= i && i <= lp->m)) xerror("glp_get_mat_row: i = %d; row number out of range\n", i); len = 0; for (aij = lp->row[i]->ptr; aij != NULL; aij = aij->r_next) { len++; if (ind != NULL) ind[len] = aij->col->j; if (val != NULL) val[len] = aij->val; } xassert(len <= lp->n); return len; } /*********************************************************************** * NAME * * glp_get_mat_col - retrieve column of the constraint matrix * * SYNOPSIS * * int glp_get_mat_col(glp_prob *lp, int j, int ind[], double val[]); * * DESCRIPTION * * The routine glp_get_mat_col scans (non-zero) elements of j-th column * of the constraint matrix of the specified problem object and stores * their row indices and numeric values to locations ind[1], ..., * ind[len] and val[1], ..., val[len], respectively, where 0 <= len <= m * is the number of elements in j-th column, m is the number of rows. * * The parameter ind or/and val can be specified as NULL, in which case * corresponding information is not stored. * * RETURNS * * The routine glp_get_mat_col returns the length len, i.e. the number * of (non-zero) elements in j-th column. */ int glp_get_mat_col(glp_prob *lp, int j, int ind[], double val[]) { GLPAIJ *aij; int len; if (!(1 <= j && j <= lp->n)) xerror("glp_get_mat_col: j = %d; column number out of range\n", j); len = 0; for (aij = lp->col[j]->ptr; aij != NULL; aij = aij->c_next) { len++; if (ind != NULL) ind[len] = aij->row->i; if (val != NULL) val[len] = aij->val; } xassert(len <= lp->m); return len; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpapi19.c0000644000175000017500000001071511632352620020235 0ustar csheltoncshelton/* glpapi19.c (driver to MiniSat solver) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpapi.h" #include "minisat/minisat.h" int glp_minisat1(glp_prob *P) { /* solve CNF-SAT problem with MiniSat solver */ solver *s; GLPAIJ *aij; int i, j, len, ret, *ind; double sum; /* check problem object */ if (P == NULL || P->magic != GLP_PROB_MAGIC) xerror("glp_minisat1: P = %p; invalid problem object\n", P); if (P->tree != NULL) xerror("glp_minisat1: operation not allowed\n"); /* integer solution is currently undefined */ P->mip_stat = GLP_UNDEF; P->mip_obj = 0.0; /* check that problem object encodes CNF-SAT instance */ if (glp_check_cnfsat(P) != 0) { xprintf("glp_minisat1: problem object does not encode CNF-SAT " "instance\n"); ret = GLP_EDATA; goto done; } /* solve CNF-SAT problem */ xprintf("Solving CNF-SAT problem...\n"); xprintf("Instance has %d variable%s, %d clause%s, and %d literal%" "s\n", P->n, P->n == 1 ? "" : "s", P->m, P->m == 1 ? "" : "s", P->nnz, P->nnz == 1 ? "" : "s"); /* if CNF-SAT has no clauses, it is satisfiable */ if (P->m == 0) { P->mip_stat = GLP_OPT; for (j = 1; j <= P->n; j++) P->col[j]->mipx = 0.0; goto fini; } /* if CNF-SAT has an empty clause, it is unsatisfiable */ for (i = 1; i <= P->m; i++) { if (P->row[i]->ptr == NULL) { P->mip_stat = GLP_NOFEAS; goto fini; } } /* prepare input data for the solver */ s = solver_new(); solver_setnvars(s, P->n); ind = xcalloc(1+P->n, sizeof(int)); for (i = 1; i <= P->m; i++) { len = 0; for (aij = P->row[i]->ptr; aij != NULL; aij = aij->r_next) { ind[++len] = toLit(aij->col->j-1); if (aij->val < 0.0) ind[len] = lit_neg(ind[len]); } xassert(len > 0); xassert(solver_addclause(s, &ind[1], &ind[1+len])); } xfree(ind); /* call the solver */ s->verbosity = 1; if (solver_solve(s, 0, 0)) { /* instance is reported as satisfiable */ P->mip_stat = GLP_OPT; /* copy solution to the problem object */ xassert(s->model.size == P->n); for (j = 1; j <= P->n; j++) { P->col[j]->mipx = s->model.ptr[j-1] == l_True ? 1.0 : 0.0; } /* compute row values */ for (i = 1; i <= P->m; i++) { sum = 0; for (aij = P->row[i]->ptr; aij != NULL; aij = aij->r_next) sum += aij->val * aij->col->mipx; P->row[i]->mipx = sum; } /* check integer feasibility */ for (i = 1; i <= P->m; i++) { if (P->row[i]->mipx < P->row[i]->lb) { /* solution is wrong */ P->mip_stat = GLP_UNDEF; break; } } } else { /* instance is reported as unsatisfiable */ P->mip_stat = GLP_NOFEAS; } solver_delete(s); fini: /* report the instance status */ if (P->mip_stat == GLP_OPT) { xprintf("SATISFIABLE\n"); ret = 0; } else if (P->mip_stat == GLP_NOFEAS) { xprintf("UNSATISFIABLE\n"); ret = 0; } else { xprintf("glp_minisat1: solver failed\n"); ret = GLP_EFAIL; } done: return ret; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpscf.h0000644000175000017500000001075211632352620020073 0ustar csheltoncshelton/* glpscf.h (Schur complement factorization) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #ifndef GLPSCF_H #define GLPSCF_H /*********************************************************************** * The structure SCF defines the following factorization of a square * nxn matrix C (which is the Schur complement): * * F * C = U * P, * * where F is a square transforming matrix, U is an upper triangular * matrix, P is a permutation matrix. * * It is assumed that matrix C is small and dense, so matrices F and U * are stored in the dense format by rows as follows: * * 1 n n_max 1 n n_max * 1 * * * * * * x x x x 1 * * * * * * x x x x * * * * * * * x x x x . * * * * * x x x x * * * * * * * x x x x . . * * * * x x x x * * * * * * * x x x x . . . * * * x x x x * * * * * * * x x x x . . . . * * x x x x * n * * * * * * x x x x n . . . . . * x x x x * x x x x x x x x x x . . . . . . x x x x * x x x x x x x x x x . . . . . . . x x x * x x x x x x x x x x . . . . . . . . x x * n_max x x x x x x x x x x n_max . . . . . . . . . x * * matrix F matrix U * * where '*' are matrix elements, 'x' are reserved locations. * * Permutation matrix P is stored in row-like format. * * Matrix C normally is not stored. * * REFERENCES * * 1. M.A.Saunders, "LUSOL: A basis package for constrained optimiza- * tion," SCCM, Stanford University, 2006. * * 2. M.A.Saunders, "Notes 5: Basis Updates," CME 318, Stanford Univer- * sity, Spring 2006. * * 3. M.A.Saunders, "Notes 6: LUSOL---a Basis Factorization Package," * ibid. */ typedef struct SCF SCF; struct SCF { /* Schur complement factorization */ int n_max; /* maximal order of matrices C, F, U, P; n_max >= 1 */ int n; /* current order of matrices C, F, U, P; n >= 0 */ double *f; /* double f[1+n_max*n_max]; */ /* matrix F stored by rows */ double *u; /* double u[1+n_max*(n_max+1)/2]; */ /* upper triangle of matrix U stored by rows */ int *p; /* int p[1+n_max]; */ /* matrix P; p[i] = j means that P[i,j] = 1 */ int t_opt; /* type of transformation used to restore triangular structure of matrix U: */ #define SCF_TBG 1 /* Bartels-Golub elimination */ #define SCF_TGR 2 /* Givens plane rotation */ int rank; /* estimated rank of matrices C and U */ double *c; /* double c[1+n_max*n_max]; */ /* matrix C stored in the same format as matrix F and used only for debugging; normally this array is not allocated */ double *w; /* double w[1+n_max]; */ /* working array */ }; /* return codes: */ #define SCF_ESING 1 /* singular matrix */ #define SCF_ELIMIT 2 /* update limit reached */ #define scf_create_it _glp_scf_create_it SCF *scf_create_it(int n_max); /* create Schur complement factorization */ #define scf_update_exp _glp_scf_update_exp int scf_update_exp(SCF *scf, const double x[], const double y[], double z); /* update factorization on expanding C */ #define scf_solve_it _glp_scf_solve_it void scf_solve_it(SCF *scf, int tr, double x[]); /* solve either system C * x = b or C' * x = b */ #define scf_reset_it _glp_scf_reset_it void scf_reset_it(SCF *scf); /* reset factorization for empty matrix C */ #define scf_delete_it _glp_scf_delete_it void scf_delete_it(SCF *scf); /* delete Schur complement factorization */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpspx01.c0000644000175000017500000030117311632352620020266 0ustar csheltoncshelton/* glpspx01.c (primal simplex method) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpspx.h" struct csa { /* common storage area */ /*--------------------------------------------------------------*/ /* LP data */ int m; /* number of rows (auxiliary variables), m > 0 */ int n; /* number of columns (structural variables), n > 0 */ char *type; /* char type[1+m+n]; */ /* type[0] is not used; type[k], 1 <= k <= m+n, is the type of variable x[k]: GLP_FR - free variable GLP_LO - variable with lower bound GLP_UP - variable with upper bound GLP_DB - double-bounded variable GLP_FX - fixed variable */ double *lb; /* double lb[1+m+n]; */ /* lb[0] is not used; lb[k], 1 <= k <= m+n, is an lower bound of variable x[k]; if x[k] has no lower bound, lb[k] is zero */ double *ub; /* double ub[1+m+n]; */ /* ub[0] is not used; ub[k], 1 <= k <= m+n, is an upper bound of variable x[k]; if x[k] has no upper bound, ub[k] is zero; if x[k] is of fixed type, ub[k] is the same as lb[k] */ double *coef; /* double coef[1+m+n]; */ /* coef[0] is not used; coef[k], 1 <= k <= m+n, is an objective coefficient at variable x[k] (note that on phase I auxiliary variables also may have non-zero objective coefficients) */ /*--------------------------------------------------------------*/ /* original objective function */ double *obj; /* double obj[1+n]; */ /* obj[0] is a constant term of the original objective function; obj[j], 1 <= j <= n, is an original objective coefficient at structural variable x[m+j] */ double zeta; /* factor used to scale original objective coefficients; its sign defines original optimization direction: zeta > 0 means minimization, zeta < 0 means maximization */ /*--------------------------------------------------------------*/ /* constraint matrix A; it has m rows and n columns and is stored by columns */ int *A_ptr; /* int A_ptr[1+n+1]; */ /* A_ptr[0] is not used; A_ptr[j], 1 <= j <= n, is starting position of j-th column in arrays A_ind and A_val; note that A_ptr[1] is always 1; A_ptr[n+1] indicates the position after the last element in arrays A_ind and A_val */ int *A_ind; /* int A_ind[A_ptr[n+1]]; */ /* row indices */ double *A_val; /* double A_val[A_ptr[n+1]]; */ /* non-zero element values */ /*--------------------------------------------------------------*/ /* basis header */ int *head; /* int head[1+m+n]; */ /* head[0] is not used; head[i], 1 <= i <= m, is the ordinal number of basic variable xB[i]; head[i] = k means that xB[i] = x[k] and i-th column of matrix B is k-th column of matrix (I|-A); head[m+j], 1 <= j <= n, is the ordinal number of non-basic variable xN[j]; head[m+j] = k means that xN[j] = x[k] and j-th column of matrix N is k-th column of matrix (I|-A) */ char *stat; /* char stat[1+n]; */ /* stat[0] is not used; stat[j], 1 <= j <= n, is the status of non-basic variable xN[j], which defines its active bound: GLP_NL - lower bound is active GLP_NU - upper bound is active GLP_NF - free variable GLP_NS - fixed variable */ /*--------------------------------------------------------------*/ /* matrix B is the basis matrix; it is composed from columns of the augmented constraint matrix (I|-A) corresponding to basic variables and stored in a factorized (invertable) form */ int valid; /* factorization is valid only if this flag is set */ BFD *bfd; /* BFD bfd[1:m,1:m]; */ /* factorized (invertable) form of the basis matrix */ /*--------------------------------------------------------------*/ /* matrix N is a matrix composed from columns of the augmented constraint matrix (I|-A) corresponding to non-basic variables except fixed ones; it is stored by rows and changes every time the basis changes */ int *N_ptr; /* int N_ptr[1+m+1]; */ /* N_ptr[0] is not used; N_ptr[i], 1 <= i <= m, is starting position of i-th row in arrays N_ind and N_val; note that N_ptr[1] is always 1; N_ptr[m+1] indicates the position after the last element in arrays N_ind and N_val */ int *N_len; /* int N_len[1+m]; */ /* N_len[0] is not used; N_len[i], 1 <= i <= m, is length of i-th row (0 to n) */ int *N_ind; /* int N_ind[N_ptr[m+1]]; */ /* column indices */ double *N_val; /* double N_val[N_ptr[m+1]]; */ /* non-zero element values */ /*--------------------------------------------------------------*/ /* working parameters */ int phase; /* search phase: 0 - not determined yet 1 - search for primal feasible solution 2 - search for optimal solution */ glp_long tm_beg; /* time value at the beginning of the search */ int it_beg; /* simplex iteration count at the beginning of the search */ int it_cnt; /* simplex iteration count; it increases by one every time the basis changes (including the case when a non-basic variable jumps to its opposite bound) */ int it_dpy; /* simplex iteration count at the most recent display output */ /*--------------------------------------------------------------*/ /* basic solution components */ double *bbar; /* double bbar[1+m]; */ /* bbar[0] is not used; bbar[i], 1 <= i <= m, is primal value of basic variable xB[i] (if xB[i] is free, its primal value is not updated) */ double *cbar; /* double cbar[1+n]; */ /* cbar[0] is not used; cbar[j], 1 <= j <= n, is reduced cost of non-basic variable xN[j] (if xN[j] is fixed, its reduced cost is not updated) */ /*--------------------------------------------------------------*/ /* the following pricing technique options may be used: GLP_PT_STD - standard ("textbook") pricing; GLP_PT_PSE - projected steepest edge; GLP_PT_DVX - Devex pricing (not implemented yet); in case of GLP_PT_STD the reference space is not used, and all steepest edge coefficients are set to 1 */ int refct; /* this count is set to an initial value when the reference space is defined and decreases by one every time the basis changes; once this count reaches zero, the reference space is redefined again */ char *refsp; /* char refsp[1+m+n]; */ /* refsp[0] is not used; refsp[k], 1 <= k <= m+n, is the flag which means that variable x[k] belongs to the current reference space */ double *gamma; /* double gamma[1+n]; */ /* gamma[0] is not used; gamma[j], 1 <= j <= n, is the steepest edge coefficient for non-basic variable xN[j]; if xN[j] is fixed, gamma[j] is not used and just set to 1 */ /*--------------------------------------------------------------*/ /* non-basic variable xN[q] chosen to enter the basis */ int q; /* index of the non-basic variable xN[q] chosen, 1 <= q <= n; if the set of eligible non-basic variables is empty and thus no variable has been chosen, q is set to 0 */ /*--------------------------------------------------------------*/ /* pivot column of the simplex table corresponding to non-basic variable xN[q] chosen is the following vector: T * e[q] = - inv(B) * N * e[q] = - inv(B) * N[q], where B is the current basis matrix, N[q] is a column of the matrix (I|-A) corresponding to xN[q] */ int tcol_nnz; /* number of non-zero components, 0 <= nnz <= m */ int *tcol_ind; /* int tcol_ind[1+m]; */ /* tcol_ind[0] is not used; tcol_ind[t], 1 <= t <= nnz, is an index of non-zero component, i.e. tcol_ind[t] = i means that tcol_vec[i] != 0 */ double *tcol_vec; /* double tcol_vec[1+m]; */ /* tcol_vec[0] is not used; tcol_vec[i], 1 <= i <= m, is a numeric value of i-th component of the column */ double tcol_max; /* infinity (maximum) norm of the column (max |tcol_vec[i]|) */ int tcol_num; /* number of significant non-zero components, which means that: |tcol_vec[i]| >= eps for i in tcol_ind[1,...,num], |tcol_vec[i]| < eps for i in tcol_ind[num+1,...,nnz], where eps is a pivot tolerance */ /*--------------------------------------------------------------*/ /* basic variable xB[p] chosen to leave the basis */ int p; /* index of the basic variable xB[p] chosen, 1 <= p <= m; p = 0 means that no basic variable reaches its bound; p < 0 means that non-basic variable xN[q] reaches its opposite bound before any basic variable */ int p_stat; /* new status (GLP_NL, GLP_NU, or GLP_NS) to be assigned to xB[p] once it has left the basis */ double teta; /* change of non-basic variable xN[q] (see above), on which xB[p] (or, if p < 0, xN[q] itself) reaches its bound */ /*--------------------------------------------------------------*/ /* pivot row of the simplex table corresponding to basic variable xB[p] chosen is the following vector: T' * e[p] = - N' * inv(B') * e[p] = - N' * rho, where B' is a matrix transposed to the current basis matrix, N' is a matrix, whose rows are columns of the matrix (I|-A) corresponding to non-basic non-fixed variables */ int trow_nnz; /* number of non-zero components, 0 <= nnz <= n */ int *trow_ind; /* int trow_ind[1+n]; */ /* trow_ind[0] is not used; trow_ind[t], 1 <= t <= nnz, is an index of non-zero component, i.e. trow_ind[t] = j means that trow_vec[j] != 0 */ double *trow_vec; /* int trow_vec[1+n]; */ /* trow_vec[0] is not used; trow_vec[j], 1 <= j <= n, is a numeric value of j-th component of the row */ /*--------------------------------------------------------------*/ /* working arrays */ double *work1; /* double work1[1+m]; */ double *work2; /* double work2[1+m]; */ double *work3; /* double work3[1+m]; */ double *work4; /* double work4[1+m]; */ }; static const double kappa = 0.10; /*********************************************************************** * alloc_csa - allocate common storage area * * This routine allocates all arrays in the common storage area (CSA) * and returns a pointer to the CSA. */ static struct csa *alloc_csa(glp_prob *lp) { struct csa *csa; int m = lp->m; int n = lp->n; int nnz = lp->nnz; csa = xmalloc(sizeof(struct csa)); xassert(m > 0 && n > 0); csa->m = m; csa->n = n; csa->type = xcalloc(1+m+n, sizeof(char)); csa->lb = xcalloc(1+m+n, sizeof(double)); csa->ub = xcalloc(1+m+n, sizeof(double)); csa->coef = xcalloc(1+m+n, sizeof(double)); csa->obj = xcalloc(1+n, sizeof(double)); csa->A_ptr = xcalloc(1+n+1, sizeof(int)); csa->A_ind = xcalloc(1+nnz, sizeof(int)); csa->A_val = xcalloc(1+nnz, sizeof(double)); csa->head = xcalloc(1+m+n, sizeof(int)); csa->stat = xcalloc(1+n, sizeof(char)); csa->N_ptr = xcalloc(1+m+1, sizeof(int)); csa->N_len = xcalloc(1+m, sizeof(int)); csa->N_ind = NULL; /* will be allocated later */ csa->N_val = NULL; /* will be allocated later */ csa->bbar = xcalloc(1+m, sizeof(double)); csa->cbar = xcalloc(1+n, sizeof(double)); csa->refsp = xcalloc(1+m+n, sizeof(char)); csa->gamma = xcalloc(1+n, sizeof(double)); csa->tcol_ind = xcalloc(1+m, sizeof(int)); csa->tcol_vec = xcalloc(1+m, sizeof(double)); csa->trow_ind = xcalloc(1+n, sizeof(int)); csa->trow_vec = xcalloc(1+n, sizeof(double)); csa->work1 = xcalloc(1+m, sizeof(double)); csa->work2 = xcalloc(1+m, sizeof(double)); csa->work3 = xcalloc(1+m, sizeof(double)); csa->work4 = xcalloc(1+m, sizeof(double)); return csa; } /*********************************************************************** * init_csa - initialize common storage area * * This routine initializes all data structures in the common storage * area (CSA). */ static void alloc_N(struct csa *csa); static void build_N(struct csa *csa); static void init_csa(struct csa *csa, glp_prob *lp) { int m = csa->m; int n = csa->n; char *type = csa->type; double *lb = csa->lb; double *ub = csa->ub; double *coef = csa->coef; double *obj = csa->obj; int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; double *A_val = csa->A_val; int *head = csa->head; char *stat = csa->stat; char *refsp = csa->refsp; double *gamma = csa->gamma; int i, j, k, loc; double cmax; /* auxiliary variables */ for (i = 1; i <= m; i++) { GLPROW *row = lp->row[i]; type[i] = (char)row->type; lb[i] = row->lb * row->rii; ub[i] = row->ub * row->rii; coef[i] = 0.0; } /* structural variables */ for (j = 1; j <= n; j++) { GLPCOL *col = lp->col[j]; type[m+j] = (char)col->type; lb[m+j] = col->lb / col->sjj; ub[m+j] = col->ub / col->sjj; coef[m+j] = col->coef * col->sjj; } /* original objective function */ obj[0] = lp->c0; memcpy(&obj[1], &coef[m+1], n * sizeof(double)); /* factor used to scale original objective coefficients */ cmax = 0.0; for (j = 1; j <= n; j++) if (cmax < fabs(obj[j])) cmax = fabs(obj[j]); if (cmax == 0.0) cmax = 1.0; switch (lp->dir) { case GLP_MIN: csa->zeta = + 1.0 / cmax; break; case GLP_MAX: csa->zeta = - 1.0 / cmax; break; default: xassert(lp != lp); } #if 1 if (fabs(csa->zeta) < 1.0) csa->zeta *= 1000.0; #endif /* matrix A (by columns) */ loc = 1; for (j = 1; j <= n; j++) { GLPAIJ *aij; A_ptr[j] = loc; for (aij = lp->col[j]->ptr; aij != NULL; aij = aij->c_next) { A_ind[loc] = aij->row->i; A_val[loc] = aij->row->rii * aij->val * aij->col->sjj; loc++; } } A_ptr[n+1] = loc; xassert(loc == lp->nnz+1); /* basis header */ xassert(lp->valid); memcpy(&head[1], &lp->head[1], m * sizeof(int)); k = 0; for (i = 1; i <= m; i++) { GLPROW *row = lp->row[i]; if (row->stat != GLP_BS) { k++; xassert(k <= n); head[m+k] = i; stat[k] = (char)row->stat; } } for (j = 1; j <= n; j++) { GLPCOL *col = lp->col[j]; if (col->stat != GLP_BS) { k++; xassert(k <= n); head[m+k] = m + j; stat[k] = (char)col->stat; } } xassert(k == n); /* factorization of matrix B */ csa->valid = 1, lp->valid = 0; csa->bfd = lp->bfd, lp->bfd = NULL; /* matrix N (by rows) */ alloc_N(csa); build_N(csa); /* working parameters */ csa->phase = 0; csa->tm_beg = xtime(); csa->it_beg = csa->it_cnt = lp->it_cnt; csa->it_dpy = -1; /* reference space and steepest edge coefficients */ csa->refct = 0; memset(&refsp[1], 0, (m+n) * sizeof(char)); for (j = 1; j <= n; j++) gamma[j] = 1.0; return; } /*********************************************************************** * invert_B - compute factorization of the basis matrix * * This routine computes factorization of the current basis matrix B. * * If the operation is successful, the routine returns zero, otherwise * non-zero. */ static int inv_col(void *info, int i, int ind[], double val[]) { /* this auxiliary routine returns row indices and numeric values of non-zero elements of i-th column of the basis matrix */ struct csa *csa = info; int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; double *A_val = csa->A_val; int *head = csa->head; int k, len, ptr, t; #ifdef GLP_DEBUG xassert(1 <= i && i <= m); #endif k = head[i]; /* B[i] is k-th column of (I|-A) */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif if (k <= m) { /* B[i] is k-th column of submatrix I */ len = 1; ind[1] = k; val[1] = 1.0; } else { /* B[i] is (k-m)-th column of submatrix (-A) */ ptr = A_ptr[k-m]; len = A_ptr[k-m+1] - ptr; memcpy(&ind[1], &A_ind[ptr], len * sizeof(int)); memcpy(&val[1], &A_val[ptr], len * sizeof(double)); for (t = 1; t <= len; t++) val[t] = - val[t]; } return len; } static int invert_B(struct csa *csa) { int ret; ret = bfd_factorize(csa->bfd, csa->m, NULL, inv_col, csa); csa->valid = (ret == 0); return ret; } /*********************************************************************** * update_B - update factorization of the basis matrix * * This routine replaces i-th column of the basis matrix B by k-th * column of the augmented constraint matrix (I|-A) and then updates * the factorization of B. * * If the factorization has been successfully updated, the routine * returns zero, otherwise non-zero. */ static int update_B(struct csa *csa, int i, int k) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif int ret; #ifdef GLP_DEBUG xassert(1 <= i && i <= m); xassert(1 <= k && k <= m+n); #endif if (k <= m) { /* new i-th column of B is k-th column of I */ int ind[1+1]; double val[1+1]; ind[1] = k; val[1] = 1.0; xassert(csa->valid); ret = bfd_update_it(csa->bfd, i, 0, 1, ind, val); } else { /* new i-th column of B is (k-m)-th column of (-A) */ int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; double *A_val = csa->A_val; double *val = csa->work1; int beg, end, ptr, len; beg = A_ptr[k-m]; end = A_ptr[k-m+1]; len = 0; for (ptr = beg; ptr < end; ptr++) val[++len] = - A_val[ptr]; xassert(csa->valid); ret = bfd_update_it(csa->bfd, i, 0, len, &A_ind[beg-1], val); } csa->valid = (ret == 0); return ret; } /*********************************************************************** * error_ftran - compute residual vector r = h - B * x * * This routine computes the residual vector r = h - B * x, where B is * the current basis matrix, h is the vector of right-hand sides, x is * the solution vector. */ static void error_ftran(struct csa *csa, double h[], double x[], double r[]) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; double *A_val = csa->A_val; int *head = csa->head; int i, k, beg, end, ptr; double temp; /* compute the residual vector: r = h - B * x = h - B[1] * x[1] - ... - B[m] * x[m], where B[1], ..., B[m] are columns of matrix B */ memcpy(&r[1], &h[1], m * sizeof(double)); for (i = 1; i <= m; i++) { temp = x[i]; if (temp == 0.0) continue; k = head[i]; /* B[i] is k-th column of (I|-A) */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif if (k <= m) { /* B[i] is k-th column of submatrix I */ r[k] -= temp; } else { /* B[i] is (k-m)-th column of submatrix (-A) */ beg = A_ptr[k-m]; end = A_ptr[k-m+1]; for (ptr = beg; ptr < end; ptr++) r[A_ind[ptr]] += A_val[ptr] * temp; } } return; } /*********************************************************************** * refine_ftran - refine solution of B * x = h * * This routine performs one iteration to refine the solution of * the system B * x = h, where B is the current basis matrix, h is the * vector of right-hand sides, x is the solution vector. */ static void refine_ftran(struct csa *csa, double h[], double x[]) { int m = csa->m; double *r = csa->work1; double *d = csa->work1; int i; /* compute the residual vector r = h - B * x */ error_ftran(csa, h, x, r); /* compute the correction vector d = inv(B) * r */ xassert(csa->valid); bfd_ftran(csa->bfd, d); /* refine the solution vector (new x) = (old x) + d */ for (i = 1; i <= m; i++) x[i] += d[i]; return; } /*********************************************************************** * error_btran - compute residual vector r = h - B'* x * * This routine computes the residual vector r = h - B'* x, where B' * is a matrix transposed to the current basis matrix, h is the vector * of right-hand sides, x is the solution vector. */ static void error_btran(struct csa *csa, double h[], double x[], double r[]) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; double *A_val = csa->A_val; int *head = csa->head; int i, k, beg, end, ptr; double temp; /* compute the residual vector r = b - B'* x */ for (i = 1; i <= m; i++) { /* r[i] := b[i] - (i-th column of B)'* x */ k = head[i]; /* B[i] is k-th column of (I|-A) */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif temp = h[i]; if (k <= m) { /* B[i] is k-th column of submatrix I */ temp -= x[k]; } else { /* B[i] is (k-m)-th column of submatrix (-A) */ beg = A_ptr[k-m]; end = A_ptr[k-m+1]; for (ptr = beg; ptr < end; ptr++) temp += A_val[ptr] * x[A_ind[ptr]]; } r[i] = temp; } return; } /*********************************************************************** * refine_btran - refine solution of B'* x = h * * This routine performs one iteration to refine the solution of the * system B'* x = h, where B' is a matrix transposed to the current * basis matrix, h is the vector of right-hand sides, x is the solution * vector. */ static void refine_btran(struct csa *csa, double h[], double x[]) { int m = csa->m; double *r = csa->work1; double *d = csa->work1; int i; /* compute the residual vector r = h - B'* x */ error_btran(csa, h, x, r); /* compute the correction vector d = inv(B') * r */ xassert(csa->valid); bfd_btran(csa->bfd, d); /* refine the solution vector (new x) = (old x) + d */ for (i = 1; i <= m; i++) x[i] += d[i]; return; } /*********************************************************************** * alloc_N - allocate matrix N * * This routine determines maximal row lengths of matrix N, sets its * row pointers, and then allocates arrays N_ind and N_val. * * Note that some fixed structural variables may temporarily become * double-bounded, so corresponding columns of matrix A should not be * ignored on calculating maximal row lengths of matrix N. */ static void alloc_N(struct csa *csa) { int m = csa->m; int n = csa->n; int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; int *N_ptr = csa->N_ptr; int *N_len = csa->N_len; int i, j, beg, end, ptr; /* determine number of non-zeros in each row of the augmented constraint matrix (I|-A) */ for (i = 1; i <= m; i++) N_len[i] = 1; for (j = 1; j <= n; j++) { beg = A_ptr[j]; end = A_ptr[j+1]; for (ptr = beg; ptr < end; ptr++) N_len[A_ind[ptr]]++; } /* determine maximal row lengths of matrix N and set its row pointers */ N_ptr[1] = 1; for (i = 1; i <= m; i++) { /* row of matrix N cannot have more than n non-zeros */ if (N_len[i] > n) N_len[i] = n; N_ptr[i+1] = N_ptr[i] + N_len[i]; } /* now maximal number of non-zeros in matrix N is known */ csa->N_ind = xcalloc(N_ptr[m+1], sizeof(int)); csa->N_val = xcalloc(N_ptr[m+1], sizeof(double)); return; } /*********************************************************************** * add_N_col - add column of matrix (I|-A) to matrix N * * This routine adds j-th column to matrix N which is k-th column of * the augmented constraint matrix (I|-A). (It is assumed that old j-th * column was previously removed from matrix N.) */ static void add_N_col(struct csa *csa, int j, int k) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif int *N_ptr = csa->N_ptr; int *N_len = csa->N_len; int *N_ind = csa->N_ind; double *N_val = csa->N_val; int pos; #ifdef GLP_DEBUG xassert(1 <= j && j <= n); xassert(1 <= k && k <= m+n); #endif if (k <= m) { /* N[j] is k-th column of submatrix I */ pos = N_ptr[k] + (N_len[k]++); #ifdef GLP_DEBUG xassert(pos < N_ptr[k+1]); #endif N_ind[pos] = j; N_val[pos] = 1.0; } else { /* N[j] is (k-m)-th column of submatrix (-A) */ int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; double *A_val = csa->A_val; int i, beg, end, ptr; beg = A_ptr[k-m]; end = A_ptr[k-m+1]; for (ptr = beg; ptr < end; ptr++) { i = A_ind[ptr]; /* row number */ pos = N_ptr[i] + (N_len[i]++); #ifdef GLP_DEBUG xassert(pos < N_ptr[i+1]); #endif N_ind[pos] = j; N_val[pos] = - A_val[ptr]; } } return; } /*********************************************************************** * del_N_col - remove column of matrix (I|-A) from matrix N * * This routine removes j-th column from matrix N which is k-th column * of the augmented constraint matrix (I|-A). */ static void del_N_col(struct csa *csa, int j, int k) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif int *N_ptr = csa->N_ptr; int *N_len = csa->N_len; int *N_ind = csa->N_ind; double *N_val = csa->N_val; int pos, head, tail; #ifdef GLP_DEBUG xassert(1 <= j && j <= n); xassert(1 <= k && k <= m+n); #endif if (k <= m) { /* N[j] is k-th column of submatrix I */ /* find element in k-th row of N */ head = N_ptr[k]; for (pos = head; N_ind[pos] != j; pos++) /* nop */; /* and remove it from the row list */ tail = head + (--N_len[k]); #ifdef GLP_DEBUG xassert(pos <= tail); #endif N_ind[pos] = N_ind[tail]; N_val[pos] = N_val[tail]; } else { /* N[j] is (k-m)-th column of submatrix (-A) */ int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; int i, beg, end, ptr; beg = A_ptr[k-m]; end = A_ptr[k-m+1]; for (ptr = beg; ptr < end; ptr++) { i = A_ind[ptr]; /* row number */ /* find element in i-th row of N */ head = N_ptr[i]; for (pos = head; N_ind[pos] != j; pos++) /* nop */; /* and remove it from the row list */ tail = head + (--N_len[i]); #ifdef GLP_DEBUG xassert(pos <= tail); #endif N_ind[pos] = N_ind[tail]; N_val[pos] = N_val[tail]; } } return; } /*********************************************************************** * build_N - build matrix N for current basis * * This routine builds matrix N for the current basis from columns * of the augmented constraint matrix (I|-A) corresponding to non-basic * non-fixed variables. */ static void build_N(struct csa *csa) { int m = csa->m; int n = csa->n; int *head = csa->head; char *stat = csa->stat; int *N_len = csa->N_len; int j, k; /* N := empty matrix */ memset(&N_len[1], 0, m * sizeof(int)); /* go through non-basic columns of matrix (I|-A) */ for (j = 1; j <= n; j++) { if (stat[j] != GLP_NS) { /* xN[j] is non-fixed; add j-th column to matrix N which is k-th column of matrix (I|-A) */ k = head[m+j]; /* x[k] = xN[j] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif add_N_col(csa, j, k); } } return; } /*********************************************************************** * get_xN - determine current value of non-basic variable xN[j] * * This routine returns the current value of non-basic variable xN[j], * which is a value of its active bound. */ static double get_xN(struct csa *csa, int j) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif double *lb = csa->lb; double *ub = csa->ub; int *head = csa->head; char *stat = csa->stat; int k; double xN; #ifdef GLP_DEBUG xassert(1 <= j && j <= n); #endif k = head[m+j]; /* x[k] = xN[j] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif switch (stat[j]) { case GLP_NL: /* x[k] is on its lower bound */ xN = lb[k]; break; case GLP_NU: /* x[k] is on its upper bound */ xN = ub[k]; break; case GLP_NF: /* x[k] is free non-basic variable */ xN = 0.0; break; case GLP_NS: /* x[k] is fixed non-basic variable */ xN = lb[k]; break; default: xassert(stat != stat); } return xN; } /*********************************************************************** * eval_beta - compute primal values of basic variables * * This routine computes current primal values of all basic variables: * * beta = - inv(B) * N * xN, * * where B is the current basis matrix, N is a matrix built of columns * of matrix (I|-A) corresponding to non-basic variables, and xN is the * vector of current values of non-basic variables. */ static void eval_beta(struct csa *csa, double beta[]) { int m = csa->m; int n = csa->n; int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; double *A_val = csa->A_val; int *head = csa->head; double *h = csa->work2; int i, j, k, beg, end, ptr; double xN; /* compute the right-hand side vector: h := - N * xN = - N[1] * xN[1] - ... - N[n] * xN[n], where N[1], ..., N[n] are columns of matrix N */ for (i = 1; i <= m; i++) h[i] = 0.0; for (j = 1; j <= n; j++) { k = head[m+j]; /* x[k] = xN[j] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif /* determine current value of xN[j] */ xN = get_xN(csa, j); if (xN == 0.0) continue; if (k <= m) { /* N[j] is k-th column of submatrix I */ h[k] -= xN; } else { /* N[j] is (k-m)-th column of submatrix (-A) */ beg = A_ptr[k-m]; end = A_ptr[k-m+1]; for (ptr = beg; ptr < end; ptr++) h[A_ind[ptr]] += xN * A_val[ptr]; } } /* solve system B * beta = h */ memcpy(&beta[1], &h[1], m * sizeof(double)); xassert(csa->valid); bfd_ftran(csa->bfd, beta); /* and refine the solution */ refine_ftran(csa, h, beta); return; } /*********************************************************************** * eval_pi - compute vector of simplex multipliers * * This routine computes the vector of current simplex multipliers: * * pi = inv(B') * cB, * * where B' is a matrix transposed to the current basis matrix, cB is * a subvector of objective coefficients at basic variables. */ static void eval_pi(struct csa *csa, double pi[]) { int m = csa->m; double *c = csa->coef; int *head = csa->head; double *cB = csa->work2; int i; /* construct the right-hand side vector cB */ for (i = 1; i <= m; i++) cB[i] = c[head[i]]; /* solve system B'* pi = cB */ memcpy(&pi[1], &cB[1], m * sizeof(double)); xassert(csa->valid); bfd_btran(csa->bfd, pi); /* and refine the solution */ refine_btran(csa, cB, pi); return; } /*********************************************************************** * eval_cost - compute reduced cost of non-basic variable xN[j] * * This routine computes the current reduced cost of non-basic variable * xN[j]: * * d[j] = cN[j] - N'[j] * pi, * * where cN[j] is the objective coefficient at variable xN[j], N[j] is * a column of the augmented constraint matrix (I|-A) corresponding to * xN[j], pi is the vector of simplex multipliers. */ static double eval_cost(struct csa *csa, double pi[], int j) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif double *coef = csa->coef; int *head = csa->head; int k; double dj; #ifdef GLP_DEBUG xassert(1 <= j && j <= n); #endif k = head[m+j]; /* x[k] = xN[j] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif dj = coef[k]; if (k <= m) { /* N[j] is k-th column of submatrix I */ dj -= pi[k]; } else { /* N[j] is (k-m)-th column of submatrix (-A) */ int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; double *A_val = csa->A_val; int beg, end, ptr; beg = A_ptr[k-m]; end = A_ptr[k-m+1]; for (ptr = beg; ptr < end; ptr++) dj += A_val[ptr] * pi[A_ind[ptr]]; } return dj; } /*********************************************************************** * eval_bbar - compute and store primal values of basic variables * * This routine computes primal values of all basic variables and then * stores them in the solution array. */ static void eval_bbar(struct csa *csa) { eval_beta(csa, csa->bbar); return; } /*********************************************************************** * eval_cbar - compute and store reduced costs of non-basic variables * * This routine computes reduced costs of all non-basic variables and * then stores them in the solution array. */ static void eval_cbar(struct csa *csa) { #ifdef GLP_DEBUG int m = csa->m; #endif int n = csa->n; #ifdef GLP_DEBUG int *head = csa->head; #endif double *cbar = csa->cbar; double *pi = csa->work3; int j; #ifdef GLP_DEBUG int k; #endif /* compute simplex multipliers */ eval_pi(csa, pi); /* compute and store reduced costs */ for (j = 1; j <= n; j++) { #ifdef GLP_DEBUG k = head[m+j]; /* x[k] = xN[j] */ xassert(1 <= k && k <= m+n); #endif cbar[j] = eval_cost(csa, pi, j); } return; } /*********************************************************************** * reset_refsp - reset the reference space * * This routine resets (redefines) the reference space used in the * projected steepest edge pricing algorithm. */ static void reset_refsp(struct csa *csa) { int m = csa->m; int n = csa->n; int *head = csa->head; char *refsp = csa->refsp; double *gamma = csa->gamma; int j, k; xassert(csa->refct == 0); csa->refct = 1000; memset(&refsp[1], 0, (m+n) * sizeof(char)); for (j = 1; j <= n; j++) { k = head[m+j]; /* x[k] = xN[j] */ refsp[k] = 1; gamma[j] = 1.0; } return; } /*********************************************************************** * eval_gamma - compute steepest edge coefficient * * This routine computes the steepest edge coefficient for non-basic * variable xN[j] using its direct definition: * * gamma[j] = delta[j] + sum alfa[i,j]^2, * i in R * * where delta[j] = 1, if xN[j] is in the current reference space, * and 0 otherwise; R is a set of basic variables xB[i], which are in * the current reference space; alfa[i,j] are elements of the current * simplex table. * * NOTE: The routine is intended only for debugginig purposes. */ static double eval_gamma(struct csa *csa, int j) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif int *head = csa->head; char *refsp = csa->refsp; double *alfa = csa->work3; double *h = csa->work3; int i, k; double gamma; #ifdef GLP_DEBUG xassert(1 <= j && j <= n); #endif k = head[m+j]; /* x[k] = xN[j] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif /* construct the right-hand side vector h = - N[j] */ for (i = 1; i <= m; i++) h[i] = 0.0; if (k <= m) { /* N[j] is k-th column of submatrix I */ h[k] = -1.0; } else { /* N[j] is (k-m)-th column of submatrix (-A) */ int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; double *A_val = csa->A_val; int beg, end, ptr; beg = A_ptr[k-m]; end = A_ptr[k-m+1]; for (ptr = beg; ptr < end; ptr++) h[A_ind[ptr]] = A_val[ptr]; } /* solve system B * alfa = h */ xassert(csa->valid); bfd_ftran(csa->bfd, alfa); /* compute gamma */ gamma = (refsp[k] ? 1.0 : 0.0); for (i = 1; i <= m; i++) { k = head[i]; #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif if (refsp[k]) gamma += alfa[i] * alfa[i]; } return gamma; } /*********************************************************************** * chuzc - choose non-basic variable (column of the simplex table) * * This routine chooses non-basic variable xN[q], which has largest * weighted reduced cost: * * |d[q]| / sqrt(gamma[q]) = max |d[j]| / sqrt(gamma[j]), * j in J * * where J is a subset of eligible non-basic variables xN[j], d[j] is * reduced cost of xN[j], gamma[j] is the steepest edge coefficient. * * The working objective function is always minimized, so the sign of * d[q] determines direction, in which xN[q] has to change: * * if d[q] < 0, xN[q] has to increase; * * if d[q] > 0, xN[q] has to decrease. * * If |d[j]| <= tol_dj, where tol_dj is a specified tolerance, xN[j] * is not included in J and therefore ignored. (It is assumed that the * working objective row is appropriately scaled, i.e. max|c[k]| = 1.) * * If J is empty and no variable has been chosen, q is set to 0. */ static void chuzc(struct csa *csa, double tol_dj) { int n = csa->n; char *stat = csa->stat; double *cbar = csa->cbar; double *gamma = csa->gamma; int j, q; double dj, best, temp; /* nothing is chosen so far */ q = 0, best = 0.0; /* look through the list of non-basic variables */ for (j = 1; j <= n; j++) { dj = cbar[j]; switch (stat[j]) { case GLP_NL: /* xN[j] can increase */ if (dj >= - tol_dj) continue; break; case GLP_NU: /* xN[j] can decrease */ if (dj <= + tol_dj) continue; break; case GLP_NF: /* xN[j] can change in any direction */ if (- tol_dj <= dj && dj <= + tol_dj) continue; break; case GLP_NS: /* xN[j] cannot change at all */ continue; default: xassert(stat != stat); } /* xN[j] is eligible non-basic variable; choose one which has largest weighted reduced cost */ #ifdef GLP_DEBUG xassert(gamma[j] > 0.0); #endif temp = (dj * dj) / gamma[j]; if (best < temp) q = j, best = temp; } /* store the index of non-basic variable xN[q] chosen */ csa->q = q; return; } /*********************************************************************** * eval_tcol - compute pivot column of the simplex table * * This routine computes the pivot column of the simplex table, which * corresponds to non-basic variable xN[q] chosen. * * The pivot column is the following vector: * * tcol = T * e[q] = - inv(B) * N * e[q] = - inv(B) * N[q], * * where B is the current basis matrix, N[q] is a column of the matrix * (I|-A) corresponding to variable xN[q]. */ static void eval_tcol(struct csa *csa) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif int *head = csa->head; int q = csa->q; int *tcol_ind = csa->tcol_ind; double *tcol_vec = csa->tcol_vec; double *h = csa->tcol_vec; int i, k, nnz; #ifdef GLP_DEBUG xassert(1 <= q && q <= n); #endif k = head[m+q]; /* x[k] = xN[q] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif /* construct the right-hand side vector h = - N[q] */ for (i = 1; i <= m; i++) h[i] = 0.0; if (k <= m) { /* N[q] is k-th column of submatrix I */ h[k] = -1.0; } else { /* N[q] is (k-m)-th column of submatrix (-A) */ int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; double *A_val = csa->A_val; int beg, end, ptr; beg = A_ptr[k-m]; end = A_ptr[k-m+1]; for (ptr = beg; ptr < end; ptr++) h[A_ind[ptr]] = A_val[ptr]; } /* solve system B * tcol = h */ xassert(csa->valid); bfd_ftran(csa->bfd, tcol_vec); /* construct sparse pattern of the pivot column */ nnz = 0; for (i = 1; i <= m; i++) { if (tcol_vec[i] != 0.0) tcol_ind[++nnz] = i; } csa->tcol_nnz = nnz; return; } /*********************************************************************** * refine_tcol - refine pivot column of the simplex table * * This routine refines the pivot column of the simplex table assuming * that it was previously computed by the routine eval_tcol. */ static void refine_tcol(struct csa *csa) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif int *head = csa->head; int q = csa->q; int *tcol_ind = csa->tcol_ind; double *tcol_vec = csa->tcol_vec; double *h = csa->work3; int i, k, nnz; #ifdef GLP_DEBUG xassert(1 <= q && q <= n); #endif k = head[m+q]; /* x[k] = xN[q] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif /* construct the right-hand side vector h = - N[q] */ for (i = 1; i <= m; i++) h[i] = 0.0; if (k <= m) { /* N[q] is k-th column of submatrix I */ h[k] = -1.0; } else { /* N[q] is (k-m)-th column of submatrix (-A) */ int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; double *A_val = csa->A_val; int beg, end, ptr; beg = A_ptr[k-m]; end = A_ptr[k-m+1]; for (ptr = beg; ptr < end; ptr++) h[A_ind[ptr]] = A_val[ptr]; } /* refine solution of B * tcol = h */ refine_ftran(csa, h, tcol_vec); /* construct sparse pattern of the pivot column */ nnz = 0; for (i = 1; i <= m; i++) { if (tcol_vec[i] != 0.0) tcol_ind[++nnz] = i; } csa->tcol_nnz = nnz; return; } /*********************************************************************** * sort_tcol - sort pivot column of the simplex table * * This routine reorders the list of non-zero elements of the pivot * column to put significant elements, whose magnitude is not less than * a specified tolerance, in front of the list, and stores the number * of significant elements in tcol_num. */ static void sort_tcol(struct csa *csa, double tol_piv) { #ifdef GLP_DEBUG int m = csa->m; #endif int nnz = csa->tcol_nnz; int *tcol_ind = csa->tcol_ind; double *tcol_vec = csa->tcol_vec; int i, num, pos; double big, eps, temp; /* compute infinity (maximum) norm of the column */ big = 0.0; for (pos = 1; pos <= nnz; pos++) { #ifdef GLP_DEBUG i = tcol_ind[pos]; xassert(1 <= i && i <= m); #endif temp = fabs(tcol_vec[tcol_ind[pos]]); if (big < temp) big = temp; } csa->tcol_max = big; /* determine absolute pivot tolerance */ eps = tol_piv * (1.0 + 0.01 * big); /* move significant column components to front of the list */ for (num = 0; num < nnz; ) { i = tcol_ind[nnz]; if (fabs(tcol_vec[i]) < eps) nnz--; else { num++; tcol_ind[nnz] = tcol_ind[num]; tcol_ind[num] = i; } } csa->tcol_num = num; return; } /*********************************************************************** * chuzr - choose basic variable (row of the simplex table) * * This routine chooses basic variable xB[p], which reaches its bound * first on changing non-basic variable xN[q] in valid direction. * * The parameter rtol is a relative tolerance used to relax bounds of * basic variables. If rtol = 0, the routine implements the standard * ratio test. Otherwise, if rtol > 0, the routine implements Harris' * two-pass ratio test. In the latter case rtol should be about three * times less than a tolerance used to check primal feasibility. */ static void chuzr(struct csa *csa, double rtol) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif char *type = csa->type; double *lb = csa->lb; double *ub = csa->ub; double *coef = csa->coef; int *head = csa->head; int phase = csa->phase; double *bbar = csa->bbar; double *cbar = csa->cbar; int q = csa->q; int *tcol_ind = csa->tcol_ind; double *tcol_vec = csa->tcol_vec; int tcol_num = csa->tcol_num; int i, i_stat, k, p, p_stat, pos; double alfa, big, delta, s, t, teta, tmax; #ifdef GLP_DEBUG xassert(1 <= q && q <= n); #endif /* s := - sign(d[q]), where d[q] is reduced cost of xN[q] */ #ifdef GLP_DEBUG xassert(cbar[q] != 0.0); #endif s = (cbar[q] > 0.0 ? -1.0 : +1.0); /*** FIRST PASS ***/ k = head[m+q]; /* x[k] = xN[q] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif if (type[k] == GLP_DB) { /* xN[q] has both lower and upper bounds */ p = -1, p_stat = 0, teta = ub[k] - lb[k], big = 1.0; } else { /* xN[q] has no opposite bound */ p = 0, p_stat = 0, teta = DBL_MAX, big = 0.0; } /* walk through significant elements of the pivot column */ for (pos = 1; pos <= tcol_num; pos++) { i = tcol_ind[pos]; #ifdef GLP_DEBUG xassert(1 <= i && i <= m); #endif k = head[i]; /* x[k] = xB[i] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif alfa = s * tcol_vec[i]; #ifdef GLP_DEBUG xassert(alfa != 0.0); #endif /* xB[i] = ... + alfa * xN[q] + ..., and due to s we need to consider the only case when xN[q] is increasing */ if (alfa > 0.0) { /* xB[i] is increasing */ if (phase == 1 && coef[k] < 0.0) { /* xB[i] violates its lower bound, which plays the role of an upper bound on phase I */ delta = rtol * (1.0 + kappa * fabs(lb[k])); t = ((lb[k] + delta) - bbar[i]) / alfa; i_stat = GLP_NL; } else if (phase == 1 && coef[k] > 0.0) { /* xB[i] violates its upper bound, which plays the role of an lower bound on phase I */ continue; } else if (type[k] == GLP_UP || type[k] == GLP_DB || type[k] == GLP_FX) { /* xB[i] is within its bounds and has an upper bound */ delta = rtol * (1.0 + kappa * fabs(ub[k])); t = ((ub[k] + delta) - bbar[i]) / alfa; i_stat = GLP_NU; } else { /* xB[i] is within its bounds and has no upper bound */ continue; } } else { /* xB[i] is decreasing */ if (phase == 1 && coef[k] > 0.0) { /* xB[i] violates its upper bound, which plays the role of an lower bound on phase I */ delta = rtol * (1.0 + kappa * fabs(ub[k])); t = ((ub[k] - delta) - bbar[i]) / alfa; i_stat = GLP_NU; } else if (phase == 1 && coef[k] < 0.0) { /* xB[i] violates its lower bound, which plays the role of an upper bound on phase I */ continue; } else if (type[k] == GLP_LO || type[k] == GLP_DB || type[k] == GLP_FX) { /* xB[i] is within its bounds and has an lower bound */ delta = rtol * (1.0 + kappa * fabs(lb[k])); t = ((lb[k] - delta) - bbar[i]) / alfa; i_stat = GLP_NL; } else { /* xB[i] is within its bounds and has no lower bound */ continue; } } /* t is a change of xN[q], on which xB[i] reaches its bound (possibly relaxed); since the basic solution is assumed to be primal feasible (or pseudo feasible on phase I), t has to be non-negative by definition; however, it may happen that xB[i] slightly (i.e. within a tolerance) violates its bound, that leads to negative t; in the latter case, if xB[i] is chosen, negative t means that xN[q] changes in wrong direction; if pivot alfa[i,q] is close to zero, even small bound violation of xB[i] may lead to a large change of xN[q] in wrong direction; let, for example, xB[i] >= 0 and in the current basis its value be -5e-9; let also xN[q] be on its zero bound and should increase; from the ratio test rule it follows that the pivot alfa[i,q] < 0; however, if alfa[i,q] is, say, -1e-9, the change of xN[q] in wrong direction is 5e-9 / (-1e-9) = -5, and using it for updating values of other basic variables will give absolutely wrong results; therefore, if t is negative, we should replace it by exact zero assuming that xB[i] is exactly on its bound, and the violation appears due to round-off errors */ if (t < 0.0) t = 0.0; /* apply minimal ratio test */ if (teta > t || teta == t && big < fabs(alfa)) p = i, p_stat = i_stat, teta = t, big = fabs(alfa); } /* the second pass is skipped in the following cases: */ /* if the standard ratio test is used */ if (rtol == 0.0) goto done; /* if xN[q] reaches its opposite bound or if no basic variable has been chosen on the first pass */ if (p <= 0) goto done; /* if xB[p] is a blocking variable, i.e. if it prevents xN[q] from any change */ if (teta == 0.0) goto done; /*** SECOND PASS ***/ /* here tmax is a maximal change of xN[q], on which the solution remains primal feasible (or pseudo feasible on phase I) within a tolerance */ #if 0 tmax = (1.0 + 10.0 * DBL_EPSILON) * teta; #else tmax = teta; #endif /* nothing is chosen so far */ p = 0, p_stat = 0, teta = DBL_MAX, big = 0.0; /* walk through significant elements of the pivot column */ for (pos = 1; pos <= tcol_num; pos++) { i = tcol_ind[pos]; #ifdef GLP_DEBUG xassert(1 <= i && i <= m); #endif k = head[i]; /* x[k] = xB[i] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif alfa = s * tcol_vec[i]; #ifdef GLP_DEBUG xassert(alfa != 0.0); #endif /* xB[i] = ... + alfa * xN[q] + ..., and due to s we need to consider the only case when xN[q] is increasing */ if (alfa > 0.0) { /* xB[i] is increasing */ if (phase == 1 && coef[k] < 0.0) { /* xB[i] violates its lower bound, which plays the role of an upper bound on phase I */ t = (lb[k] - bbar[i]) / alfa; i_stat = GLP_NL; } else if (phase == 1 && coef[k] > 0.0) { /* xB[i] violates its upper bound, which plays the role of an lower bound on phase I */ continue; } else if (type[k] == GLP_UP || type[k] == GLP_DB || type[k] == GLP_FX) { /* xB[i] is within its bounds and has an upper bound */ t = (ub[k] - bbar[i]) / alfa; i_stat = GLP_NU; } else { /* xB[i] is within its bounds and has no upper bound */ continue; } } else { /* xB[i] is decreasing */ if (phase == 1 && coef[k] > 0.0) { /* xB[i] violates its upper bound, which plays the role of an lower bound on phase I */ t = (ub[k] - bbar[i]) / alfa; i_stat = GLP_NU; } else if (phase == 1 && coef[k] < 0.0) { /* xB[i] violates its lower bound, which plays the role of an upper bound on phase I */ continue; } else if (type[k] == GLP_LO || type[k] == GLP_DB || type[k] == GLP_FX) { /* xB[i] is within its bounds and has an lower bound */ t = (lb[k] - bbar[i]) / alfa; i_stat = GLP_NL; } else { /* xB[i] is within its bounds and has no lower bound */ continue; } } /* (see comments for the first pass) */ if (t < 0.0) t = 0.0; /* t is a change of xN[q], on which xB[i] reaches its bound; if t <= tmax, all basic variables can violate their bounds only within relaxation tolerance delta; we can use this freedom and choose basic variable having largest influence coefficient to avoid possible numeric instability */ if (t <= tmax && big < fabs(alfa)) p = i, p_stat = i_stat, teta = t, big = fabs(alfa); } /* something must be chosen on the second pass */ xassert(p != 0); done: /* store the index and status of basic variable xB[p] chosen */ csa->p = p; if (p > 0 && type[head[p]] == GLP_FX) csa->p_stat = GLP_NS; else csa->p_stat = p_stat; /* store corresponding change of non-basic variable xN[q] */ #ifdef GLP_DEBUG xassert(teta >= 0.0); #endif csa->teta = s * teta; return; } /*********************************************************************** * eval_rho - compute pivot row of the inverse * * This routine computes the pivot (p-th) row of the inverse inv(B), * which corresponds to basic variable xB[p] chosen: * * rho = inv(B') * e[p], * * where B' is a matrix transposed to the current basis matrix, e[p] * is unity vector. */ static void eval_rho(struct csa *csa, double rho[]) { int m = csa->m; int p = csa->p; double *e = rho; int i; #ifdef GLP_DEBUG xassert(1 <= p && p <= m); #endif /* construct the right-hand side vector e[p] */ for (i = 1; i <= m; i++) e[i] = 0.0; e[p] = 1.0; /* solve system B'* rho = e[p] */ xassert(csa->valid); bfd_btran(csa->bfd, rho); return; } /*********************************************************************** * refine_rho - refine pivot row of the inverse * * This routine refines the pivot row of the inverse inv(B) assuming * that it was previously computed by the routine eval_rho. */ static void refine_rho(struct csa *csa, double rho[]) { int m = csa->m; int p = csa->p; double *e = csa->work3; int i; #ifdef GLP_DEBUG xassert(1 <= p && p <= m); #endif /* construct the right-hand side vector e[p] */ for (i = 1; i <= m; i++) e[i] = 0.0; e[p] = 1.0; /* refine solution of B'* rho = e[p] */ refine_btran(csa, e, rho); return; } /*********************************************************************** * eval_trow - compute pivot row of the simplex table * * This routine computes the pivot row of the simplex table, which * corresponds to basic variable xB[p] chosen. * * The pivot row is the following vector: * * trow = T'* e[p] = - N'* inv(B') * e[p] = - N' * rho, * * where rho is the pivot row of the inverse inv(B) previously computed * by the routine eval_rho. * * Note that elements of the pivot row corresponding to fixed non-basic * variables are not computed. */ static void eval_trow(struct csa *csa, double rho[]) { int m = csa->m; int n = csa->n; #ifdef GLP_DEBUG char *stat = csa->stat; #endif int *N_ptr = csa->N_ptr; int *N_len = csa->N_len; int *N_ind = csa->N_ind; double *N_val = csa->N_val; int *trow_ind = csa->trow_ind; double *trow_vec = csa->trow_vec; int i, j, beg, end, ptr, nnz; double temp; /* clear the pivot row */ for (j = 1; j <= n; j++) trow_vec[j] = 0.0; /* compute the pivot row as a linear combination of rows of the matrix N: trow = - rho[1] * N'[1] - ... - rho[m] * N'[m] */ for (i = 1; i <= m; i++) { temp = rho[i]; if (temp == 0.0) continue; /* trow := trow - rho[i] * N'[i] */ beg = N_ptr[i]; end = beg + N_len[i]; for (ptr = beg; ptr < end; ptr++) { #ifdef GLP_DEBUG j = N_ind[ptr]; xassert(1 <= j && j <= n); xassert(stat[j] != GLP_NS); #endif trow_vec[N_ind[ptr]] -= temp * N_val[ptr]; } } /* construct sparse pattern of the pivot row */ nnz = 0; for (j = 1; j <= n; j++) { if (trow_vec[j] != 0.0) trow_ind[++nnz] = j; } csa->trow_nnz = nnz; return; } /*********************************************************************** * update_bbar - update values of basic variables * * This routine updates values of all basic variables for the adjacent * basis. */ static void update_bbar(struct csa *csa) { #ifdef GLP_DEBUG int m = csa->m; int n = csa->n; #endif double *bbar = csa->bbar; int q = csa->q; int tcol_nnz = csa->tcol_nnz; int *tcol_ind = csa->tcol_ind; double *tcol_vec = csa->tcol_vec; int p = csa->p; double teta = csa->teta; int i, pos; #ifdef GLP_DEBUG xassert(1 <= q && q <= n); xassert(p < 0 || 1 <= p && p <= m); #endif /* if xN[q] leaves the basis, compute its value in the adjacent basis, where it will replace xB[p] */ if (p > 0) bbar[p] = get_xN(csa, q) + teta; /* update values of other basic variables (except xB[p], because it will be replaced by xN[q]) */ if (teta == 0.0) goto done; for (pos = 1; pos <= tcol_nnz; pos++) { i = tcol_ind[pos]; /* skip xB[p] */ if (i == p) continue; /* (change of xB[i]) = alfa[i,q] * (change of xN[q]) */ bbar[i] += tcol_vec[i] * teta; } done: return; } /*********************************************************************** * reeval_cost - recompute reduced cost of non-basic variable xN[q] * * This routine recomputes reduced cost of non-basic variable xN[q] for * the current basis more accurately using its direct definition: * * d[q] = cN[q] - N'[q] * pi = * * = cN[q] - N'[q] * (inv(B') * cB) = * * = cN[q] - (cB' * inv(B) * N[q]) = * * = cN[q] + cB' * (pivot column). * * It is assumed that the pivot column of the simplex table is already * computed. */ static double reeval_cost(struct csa *csa) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif double *coef = csa->coef; int *head = csa->head; int q = csa->q; int tcol_nnz = csa->tcol_nnz; int *tcol_ind = csa->tcol_ind; double *tcol_vec = csa->tcol_vec; int i, pos; double dq; #ifdef GLP_DEBUG xassert(1 <= q && q <= n); #endif dq = coef[head[m+q]]; for (pos = 1; pos <= tcol_nnz; pos++) { i = tcol_ind[pos]; #ifdef GLP_DEBUG xassert(1 <= i && i <= m); #endif dq += coef[head[i]] * tcol_vec[i]; } return dq; } /*********************************************************************** * update_cbar - update reduced costs of non-basic variables * * This routine updates reduced costs of all (except fixed) non-basic * variables for the adjacent basis. */ static void update_cbar(struct csa *csa) { #ifdef GLP_DEBUG int n = csa->n; #endif double *cbar = csa->cbar; int q = csa->q; int trow_nnz = csa->trow_nnz; int *trow_ind = csa->trow_ind; double *trow_vec = csa->trow_vec; int j, pos; double new_dq; #ifdef GLP_DEBUG xassert(1 <= q && q <= n); #endif /* compute reduced cost of xB[p] in the adjacent basis, where it will replace xN[q] */ #ifdef GLP_DEBUG xassert(trow_vec[q] != 0.0); #endif new_dq = (cbar[q] /= trow_vec[q]); /* update reduced costs of other non-basic variables (except xN[q], because it will be replaced by xB[p]) */ for (pos = 1; pos <= trow_nnz; pos++) { j = trow_ind[pos]; /* skip xN[q] */ if (j == q) continue; cbar[j] -= trow_vec[j] * new_dq; } return; } /*********************************************************************** * update_gamma - update steepest edge coefficients * * This routine updates steepest-edge coefficients for the adjacent * basis. */ static void update_gamma(struct csa *csa) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif char *type = csa->type; int *A_ptr = csa->A_ptr; int *A_ind = csa->A_ind; double *A_val = csa->A_val; int *head = csa->head; char *refsp = csa->refsp; double *gamma = csa->gamma; int q = csa->q; int tcol_nnz = csa->tcol_nnz; int *tcol_ind = csa->tcol_ind; double *tcol_vec = csa->tcol_vec; int p = csa->p; int trow_nnz = csa->trow_nnz; int *trow_ind = csa->trow_ind; double *trow_vec = csa->trow_vec; double *u = csa->work3; int i, j, k, pos, beg, end, ptr; double gamma_q, delta_q, pivot, s, t, t1, t2; #ifdef GLP_DEBUG xassert(1 <= p && p <= m); xassert(1 <= q && q <= n); #endif /* the basis changes, so decrease the count */ xassert(csa->refct > 0); csa->refct--; /* recompute gamma[q] for the current basis more accurately and compute auxiliary vector u */ gamma_q = delta_q = (refsp[head[m+q]] ? 1.0 : 0.0); for (i = 1; i <= m; i++) u[i] = 0.0; for (pos = 1; pos <= tcol_nnz; pos++) { i = tcol_ind[pos]; if (refsp[head[i]]) { u[i] = t = tcol_vec[i]; gamma_q += t * t; } else u[i] = 0.0; } xassert(csa->valid); bfd_btran(csa->bfd, u); /* update gamma[k] for other non-basic variables (except fixed variables and xN[q], because it will be replaced by xB[p]) */ pivot = trow_vec[q]; #ifdef GLP_DEBUG xassert(pivot != 0.0); #endif for (pos = 1; pos <= trow_nnz; pos++) { j = trow_ind[pos]; /* skip xN[q] */ if (j == q) continue; /* compute t */ t = trow_vec[j] / pivot; /* compute inner product s = N'[j] * u */ k = head[m+j]; /* x[k] = xN[j] */ if (k <= m) s = u[k]; else { s = 0.0; beg = A_ptr[k-m]; end = A_ptr[k-m+1]; for (ptr = beg; ptr < end; ptr++) s -= A_val[ptr] * u[A_ind[ptr]]; } /* compute gamma[k] for the adjacent basis */ t1 = gamma[j] + t * t * gamma_q + 2.0 * t * s; t2 = (refsp[k] ? 1.0 : 0.0) + delta_q * t * t; gamma[j] = (t1 >= t2 ? t1 : t2); if (gamma[j] < DBL_EPSILON) gamma[j] = DBL_EPSILON; } /* compute gamma[q] for the adjacent basis */ if (type[head[p]] == GLP_FX) gamma[q] = 1.0; else { gamma[q] = gamma_q / (pivot * pivot); if (gamma[q] < DBL_EPSILON) gamma[q] = DBL_EPSILON; } return; } /*********************************************************************** * err_in_bbar - compute maximal relative error in primal solution * * This routine returns maximal relative error: * * max |beta[i] - bbar[i]| / (1 + |beta[i]|), * * where beta and bbar are, respectively, directly computed and the * current (updated) values of basic variables. * * NOTE: The routine is intended only for debugginig purposes. */ static double err_in_bbar(struct csa *csa) { int m = csa->m; double *bbar = csa->bbar; int i; double e, emax, *beta; beta = xcalloc(1+m, sizeof(double)); eval_beta(csa, beta); emax = 0.0; for (i = 1; i <= m; i++) { e = fabs(beta[i] - bbar[i]) / (1.0 + fabs(beta[i])); if (emax < e) emax = e; } xfree(beta); return emax; } /*********************************************************************** * err_in_cbar - compute maximal relative error in dual solution * * This routine returns maximal relative error: * * max |cost[j] - cbar[j]| / (1 + |cost[j]|), * * where cost and cbar are, respectively, directly computed and the * current (updated) reduced costs of non-basic non-fixed variables. * * NOTE: The routine is intended only for debugginig purposes. */ static double err_in_cbar(struct csa *csa) { int m = csa->m; int n = csa->n; char *stat = csa->stat; double *cbar = csa->cbar; int j; double e, emax, cost, *pi; pi = xcalloc(1+m, sizeof(double)); eval_pi(csa, pi); emax = 0.0; for (j = 1; j <= n; j++) { if (stat[j] == GLP_NS) continue; cost = eval_cost(csa, pi, j); e = fabs(cost - cbar[j]) / (1.0 + fabs(cost)); if (emax < e) emax = e; } xfree(pi); return emax; } /*********************************************************************** * err_in_gamma - compute maximal relative error in steepest edge cff. * * This routine returns maximal relative error: * * max |gamma'[j] - gamma[j]| / (1 + |gamma'[j]), * * where gamma'[j] and gamma[j] are, respectively, directly computed * and the current (updated) steepest edge coefficients for non-basic * non-fixed variable x[j]. * * NOTE: The routine is intended only for debugginig purposes. */ static double err_in_gamma(struct csa *csa) { int n = csa->n; char *stat = csa->stat; double *gamma = csa->gamma; int j; double e, emax, temp; emax = 0.0; for (j = 1; j <= n; j++) { if (stat[j] == GLP_NS) { xassert(gamma[j] == 1.0); continue; } temp = eval_gamma(csa, j); e = fabs(temp - gamma[j]) / (1.0 + fabs(temp)); if (emax < e) emax = e; } return emax; } /*********************************************************************** * change_basis - change basis header * * This routine changes the basis header to make it corresponding to * the adjacent basis. */ static void change_basis(struct csa *csa) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; char *type = csa->type; #endif int *head = csa->head; char *stat = csa->stat; int q = csa->q; int p = csa->p; int p_stat = csa->p_stat; int k; #ifdef GLP_DEBUG xassert(1 <= q && q <= n); #endif if (p < 0) { /* xN[q] goes to its opposite bound */ #ifdef GLP_DEBUG k = head[m+q]; /* x[k] = xN[q] */ xassert(1 <= k && k <= m+n); xassert(type[k] == GLP_DB); #endif switch (stat[q]) { case GLP_NL: /* xN[q] increases */ stat[q] = GLP_NU; break; case GLP_NU: /* xN[q] decreases */ stat[q] = GLP_NL; break; default: xassert(stat != stat); } } else { /* xB[p] leaves the basis, xN[q] enters the basis */ #ifdef GLP_DEBUG xassert(1 <= p && p <= m); k = head[p]; /* x[k] = xB[p] */ switch (p_stat) { case GLP_NL: /* xB[p] goes to its lower bound */ xassert(type[k] == GLP_LO || type[k] == GLP_DB); break; case GLP_NU: /* xB[p] goes to its upper bound */ xassert(type[k] == GLP_UP || type[k] == GLP_DB); break; case GLP_NS: /* xB[p] goes to its fixed value */ xassert(type[k] == GLP_NS); break; default: xassert(p_stat != p_stat); } #endif /* xB[p] <-> xN[q] */ k = head[p], head[p] = head[m+q], head[m+q] = k; stat[q] = (char)p_stat; } return; } /*********************************************************************** * set_aux_obj - construct auxiliary objective function * * The auxiliary objective function is a separable piecewise linear * convex function, which is the sum of primal infeasibilities: * * z = t[1] + ... + t[m+n] -> minimize, * * where: * * / lb[k] - x[k], if x[k] < lb[k] * | * t[k] = < 0, if lb[k] <= x[k] <= ub[k] * | * \ x[k] - ub[k], if x[k] > ub[k] * * This routine computes objective coefficients for the current basis * and returns the number of non-zero terms t[k]. */ static int set_aux_obj(struct csa *csa, double tol_bnd) { int m = csa->m; int n = csa->n; char *type = csa->type; double *lb = csa->lb; double *ub = csa->ub; double *coef = csa->coef; int *head = csa->head; double *bbar = csa->bbar; int i, k, cnt = 0; double eps; /* use a bit more restrictive tolerance */ tol_bnd *= 0.90; /* clear all objective coefficients */ for (k = 1; k <= m+n; k++) coef[k] = 0.0; /* walk through the list of basic variables */ for (i = 1; i <= m; i++) { k = head[i]; /* x[k] = xB[i] */ if (type[k] == GLP_LO || type[k] == GLP_DB || type[k] == GLP_FX) { /* x[k] has lower bound */ eps = tol_bnd * (1.0 + kappa * fabs(lb[k])); if (bbar[i] < lb[k] - eps) { /* and violates it */ coef[k] = -1.0; cnt++; } } if (type[k] == GLP_UP || type[k] == GLP_DB || type[k] == GLP_FX) { /* x[k] has upper bound */ eps = tol_bnd * (1.0 + kappa * fabs(ub[k])); if (bbar[i] > ub[k] + eps) { /* and violates it */ coef[k] = +1.0; cnt++; } } } return cnt; } /*********************************************************************** * set_orig_obj - restore original objective function * * This routine assigns scaled original objective coefficients to the * working objective function. */ static void set_orig_obj(struct csa *csa) { int m = csa->m; int n = csa->n; double *coef = csa->coef; double *obj = csa->obj; double zeta = csa->zeta; int i, j; for (i = 1; i <= m; i++) coef[i] = 0.0; for (j = 1; j <= n; j++) coef[m+j] = zeta * obj[j]; return; } /*********************************************************************** * check_stab - check numerical stability of basic solution * * If the current basic solution is primal feasible (or pseudo feasible * on phase I) within a tolerance, this routine returns zero, otherwise * it returns non-zero. */ static int check_stab(struct csa *csa, double tol_bnd) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif char *type = csa->type; double *lb = csa->lb; double *ub = csa->ub; double *coef = csa->coef; int *head = csa->head; int phase = csa->phase; double *bbar = csa->bbar; int i, k; double eps; /* walk through the list of basic variables */ for (i = 1; i <= m; i++) { k = head[i]; /* x[k] = xB[i] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif if (phase == 1 && coef[k] < 0.0) { /* x[k] must not be greater than its lower bound */ #ifdef GLP_DEBUG xassert(type[k] == GLP_LO || type[k] == GLP_DB || type[k] == GLP_FX); #endif eps = tol_bnd * (1.0 + kappa * fabs(lb[k])); if (bbar[i] > lb[k] + eps) return 1; } else if (phase == 1 && coef[k] > 0.0) { /* x[k] must not be less than its upper bound */ #ifdef GLP_DEBUG xassert(type[k] == GLP_UP || type[k] == GLP_DB || type[k] == GLP_FX); #endif eps = tol_bnd * (1.0 + kappa * fabs(ub[k])); if (bbar[i] < ub[k] - eps) return 1; } else { /* either phase = 1 and coef[k] = 0, or phase = 2 */ if (type[k] == GLP_LO || type[k] == GLP_DB || type[k] == GLP_FX) { /* x[k] must not be less than its lower bound */ eps = tol_bnd * (1.0 + kappa * fabs(lb[k])); if (bbar[i] < lb[k] - eps) return 1; } if (type[k] == GLP_UP || type[k] == GLP_DB || type[k] == GLP_FX) { /* x[k] must not be greater then its upper bound */ eps = tol_bnd * (1.0 + kappa * fabs(ub[k])); if (bbar[i] > ub[k] + eps) return 1; } } } /* basic solution is primal feasible within a tolerance */ return 0; } /*********************************************************************** * check_feas - check primal feasibility of basic solution * * If the current basic solution is primal feasible within a tolerance, * this routine returns zero, otherwise it returns non-zero. */ static int check_feas(struct csa *csa, double tol_bnd) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; char *type = csa->type; #endif double *lb = csa->lb; double *ub = csa->ub; double *coef = csa->coef; int *head = csa->head; double *bbar = csa->bbar; int i, k; double eps; xassert(csa->phase == 1); /* walk through the list of basic variables */ for (i = 1; i <= m; i++) { k = head[i]; /* x[k] = xB[i] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif if (coef[k] < 0.0) { /* check if x[k] still violates its lower bound */ #ifdef GLP_DEBUG xassert(type[k] == GLP_LO || type[k] == GLP_DB || type[k] == GLP_FX); #endif eps = tol_bnd * (1.0 + kappa * fabs(lb[k])); if (bbar[i] < lb[k] - eps) return 1; } else if (coef[k] > 0.0) { /* check if x[k] still violates its upper bound */ #ifdef GLP_DEBUG xassert(type[k] == GLP_UP || type[k] == GLP_DB || type[k] == GLP_FX); #endif eps = tol_bnd * (1.0 + kappa * fabs(ub[k])); if (bbar[i] > ub[k] + eps) return 1; } } /* basic solution is primal feasible within a tolerance */ return 0; } /*********************************************************************** * eval_obj - compute original objective function * * This routine computes the current value of the original objective * function. */ static double eval_obj(struct csa *csa) { int m = csa->m; int n = csa->n; double *obj = csa->obj; int *head = csa->head; double *bbar = csa->bbar; int i, j, k; double sum; sum = obj[0]; /* walk through the list of basic variables */ for (i = 1; i <= m; i++) { k = head[i]; /* x[k] = xB[i] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif if (k > m) sum += obj[k-m] * bbar[i]; } /* walk through the list of non-basic variables */ for (j = 1; j <= n; j++) { k = head[m+j]; /* x[k] = xN[j] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif if (k > m) sum += obj[k-m] * get_xN(csa, j); } return sum; } /*********************************************************************** * display - display the search progress * * This routine displays some information about the search progress * that includes: * * the search phase; * * the number of simplex iterations performed by the solver; * * the original objective value; * * the sum of (scaled) primal infeasibilities; * * the number of basic fixed variables. */ static void display(struct csa *csa, const glp_smcp *parm, int spec) { int m = csa->m; #ifdef GLP_DEBUG int n = csa->n; #endif char *type = csa->type; double *lb = csa->lb; double *ub = csa->ub; int phase = csa->phase; int *head = csa->head; double *bbar = csa->bbar; int i, k, cnt; double sum; if (parm->msg_lev < GLP_MSG_ON) goto skip; if (parm->out_dly > 0 && 1000.0 * xdifftime(xtime(), csa->tm_beg) < parm->out_dly) goto skip; if (csa->it_cnt == csa->it_dpy) goto skip; if (!spec && csa->it_cnt % parm->out_frq != 0) goto skip; /* compute the sum of primal infeasibilities and determine the number of basic fixed variables */ sum = 0.0, cnt = 0; for (i = 1; i <= m; i++) { k = head[i]; /* x[k] = xB[i] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif if (type[k] == GLP_LO || type[k] == GLP_DB || type[k] == GLP_FX) { /* x[k] has lower bound */ if (bbar[i] < lb[k]) sum += (lb[k] - bbar[i]); } if (type[k] == GLP_UP || type[k] == GLP_DB || type[k] == GLP_FX) { /* x[k] has upper bound */ if (bbar[i] > ub[k]) sum += (bbar[i] - ub[k]); } if (type[k] == GLP_FX) cnt++; } xprintf("%c%6d: obj = %17.9e infeas = %10.3e (%d)\n", phase == 1 ? ' ' : '*', csa->it_cnt, eval_obj(csa), sum, cnt); csa->it_dpy = csa->it_cnt; skip: return; } /*********************************************************************** * store_sol - store basic solution back to the problem object * * This routine stores basic solution components back to the problem * object. */ static void store_sol(struct csa *csa, glp_prob *lp, int p_stat, int d_stat, int ray) { int m = csa->m; int n = csa->n; double zeta = csa->zeta; int *head = csa->head; char *stat = csa->stat; double *bbar = csa->bbar; double *cbar = csa->cbar; int i, j, k; #ifdef GLP_DEBUG xassert(lp->m == m); xassert(lp->n == n); #endif /* basis factorization */ #ifdef GLP_DEBUG xassert(!lp->valid && lp->bfd == NULL); xassert(csa->valid && csa->bfd != NULL); #endif lp->valid = 1, csa->valid = 0; lp->bfd = csa->bfd, csa->bfd = NULL; memcpy(&lp->head[1], &head[1], m * sizeof(int)); /* basic solution status */ lp->pbs_stat = p_stat; lp->dbs_stat = d_stat; /* objective function value */ lp->obj_val = eval_obj(csa); /* simplex iteration count */ lp->it_cnt = csa->it_cnt; /* unbounded ray */ lp->some = ray; /* basic variables */ for (i = 1; i <= m; i++) { k = head[i]; /* x[k] = xB[i] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif if (k <= m) { GLPROW *row = lp->row[k]; row->stat = GLP_BS; row->bind = i; row->prim = bbar[i] / row->rii; row->dual = 0.0; } else { GLPCOL *col = lp->col[k-m]; col->stat = GLP_BS; col->bind = i; col->prim = bbar[i] * col->sjj; col->dual = 0.0; } } /* non-basic variables */ for (j = 1; j <= n; j++) { k = head[m+j]; /* x[k] = xN[j] */ #ifdef GLP_DEBUG xassert(1 <= k && k <= m+n); #endif if (k <= m) { GLPROW *row = lp->row[k]; row->stat = stat[j]; row->bind = 0; #if 0 row->prim = get_xN(csa, j) / row->rii; #else switch (stat[j]) { case GLP_NL: row->prim = row->lb; break; case GLP_NU: row->prim = row->ub; break; case GLP_NF: row->prim = 0.0; break; case GLP_NS: row->prim = row->lb; break; default: xassert(stat != stat); } #endif row->dual = (cbar[j] * row->rii) / zeta; } else { GLPCOL *col = lp->col[k-m]; col->stat = stat[j]; col->bind = 0; #if 0 col->prim = get_xN(csa, j) * col->sjj; #else switch (stat[j]) { case GLP_NL: col->prim = col->lb; break; case GLP_NU: col->prim = col->ub; break; case GLP_NF: col->prim = 0.0; break; case GLP_NS: col->prim = col->lb; break; default: xassert(stat != stat); } #endif col->dual = (cbar[j] / col->sjj) / zeta; } } return; } /*********************************************************************** * free_csa - deallocate common storage area * * This routine frees all the memory allocated to arrays in the common * storage area (CSA). */ static void free_csa(struct csa *csa) { xfree(csa->type); xfree(csa->lb); xfree(csa->ub); xfree(csa->coef); xfree(csa->obj); xfree(csa->A_ptr); xfree(csa->A_ind); xfree(csa->A_val); xfree(csa->head); xfree(csa->stat); xfree(csa->N_ptr); xfree(csa->N_len); xfree(csa->N_ind); xfree(csa->N_val); xfree(csa->bbar); xfree(csa->cbar); xfree(csa->refsp); xfree(csa->gamma); xfree(csa->tcol_ind); xfree(csa->tcol_vec); xfree(csa->trow_ind); xfree(csa->trow_vec); xfree(csa->work1); xfree(csa->work2); xfree(csa->work3); xfree(csa->work4); xfree(csa); return; } /*********************************************************************** * spx_primal - core LP solver based on the primal simplex method * * SYNOPSIS * * #include "glpspx.h" * int spx_primal(glp_prob *lp, const glp_smcp *parm); * * DESCRIPTION * * The routine spx_primal is a core LP solver based on the two-phase * primal simplex method. * * RETURNS * * 0 LP instance has been successfully solved. * * GLP_EITLIM * Iteration limit has been exhausted. * * GLP_ETMLIM * Time limit has been exhausted. * * GLP_EFAIL * The solver failed to solve LP instance. */ int spx_primal(glp_prob *lp, const glp_smcp *parm) { struct csa *csa; int binv_st = 2; /* status of basis matrix factorization: 0 - invalid; 1 - just computed; 2 - updated */ int bbar_st = 0; /* status of primal values of basic variables: 0 - invalid; 1 - just computed; 2 - updated */ int cbar_st = 0; /* status of reduced costs of non-basic variables: 0 - invalid; 1 - just computed; 2 - updated */ int rigorous = 0; /* rigorous mode flag; this flag is used to enable iterative refinement on computing pivot rows and columns of the simplex table */ int check = 0; int p_stat, d_stat, ret; /* allocate and initialize the common storage area */ csa = alloc_csa(lp); init_csa(csa, lp); if (parm->msg_lev >= GLP_MSG_DBG) xprintf("Objective scale factor = %g\n", csa->zeta); loop: /* main loop starts here */ /* compute factorization of the basis matrix */ if (binv_st == 0) { ret = invert_B(csa); if (ret != 0) { if (parm->msg_lev >= GLP_MSG_ERR) { xprintf("Error: unable to factorize the basis matrix (%d" ")\n", ret); xprintf("Sorry, basis recovery procedure not implemented" " yet\n"); } xassert(!lp->valid && lp->bfd == NULL); lp->bfd = csa->bfd, csa->bfd = NULL; lp->pbs_stat = lp->dbs_stat = GLP_UNDEF; lp->obj_val = 0.0; lp->it_cnt = csa->it_cnt; lp->some = 0; ret = GLP_EFAIL; goto done; } csa->valid = 1; binv_st = 1; /* just computed */ /* invalidate basic solution components */ bbar_st = cbar_st = 0; } /* compute primal values of basic variables */ if (bbar_st == 0) { eval_bbar(csa); bbar_st = 1; /* just computed */ /* determine the search phase, if not determined yet */ if (csa->phase == 0) { if (set_aux_obj(csa, parm->tol_bnd) > 0) { /* current basic solution is primal infeasible */ /* start to minimize the sum of infeasibilities */ csa->phase = 1; } else { /* current basic solution is primal feasible */ /* start to minimize the original objective function */ set_orig_obj(csa); csa->phase = 2; } xassert(check_stab(csa, parm->tol_bnd) == 0); /* working objective coefficients have been changed, so invalidate reduced costs */ cbar_st = 0; display(csa, parm, 1); } /* make sure that the current basic solution remains primal feasible (or pseudo feasible on phase I) */ if (check_stab(csa, parm->tol_bnd)) { /* there are excessive bound violations due to round-off errors */ if (parm->msg_lev >= GLP_MSG_ERR) xprintf("Warning: numerical instability (primal simplex," " phase %s)\n", csa->phase == 1 ? "I" : "II"); /* restart the search */ csa->phase = 0; binv_st = 0; rigorous = 5; goto loop; } } xassert(csa->phase == 1 || csa->phase == 2); /* on phase I we do not need to wait until the current basic solution becomes dual feasible; it is sufficient to make sure that no basic variable violates its bounds */ if (csa->phase == 1 && !check_feas(csa, parm->tol_bnd)) { /* the current basis is primal feasible; switch to phase II */ csa->phase = 2; set_orig_obj(csa); cbar_st = 0; display(csa, parm, 1); } /* compute reduced costs of non-basic variables */ if (cbar_st == 0) { eval_cbar(csa); cbar_st = 1; /* just computed */ } /* redefine the reference space, if required */ switch (parm->pricing) { case GLP_PT_STD: break; case GLP_PT_PSE: if (csa->refct == 0) reset_refsp(csa); break; default: xassert(parm != parm); } /* at this point the basis factorization and all basic solution components are valid */ xassert(binv_st && bbar_st && cbar_st); /* check accuracy of current basic solution components (only for debugging) */ if (check) { double e_bbar = err_in_bbar(csa); double e_cbar = err_in_cbar(csa); double e_gamma = (parm->pricing == GLP_PT_PSE ? err_in_gamma(csa) : 0.0); xprintf("e_bbar = %10.3e; e_cbar = %10.3e; e_gamma = %10.3e\n", e_bbar, e_cbar, e_gamma); xassert(e_bbar <= 1e-5 && e_cbar <= 1e-5 && e_gamma <= 1e-3); } /* check if the iteration limit has been exhausted */ if (parm->it_lim < INT_MAX && csa->it_cnt - csa->it_beg >= parm->it_lim) { if (bbar_st != 1 || csa->phase == 2 && cbar_st != 1) { if (bbar_st != 1) bbar_st = 0; if (csa->phase == 2 && cbar_st != 1) cbar_st = 0; goto loop; } display(csa, parm, 1); if (parm->msg_lev >= GLP_MSG_ALL) xprintf("ITERATION LIMIT EXCEEDED; SEARCH TERMINATED\n"); switch (csa->phase) { case 1: p_stat = GLP_INFEAS; set_orig_obj(csa); eval_cbar(csa); break; case 2: p_stat = GLP_FEAS; break; default: xassert(csa != csa); } chuzc(csa, parm->tol_dj); d_stat = (csa->q == 0 ? GLP_FEAS : GLP_INFEAS); store_sol(csa, lp, p_stat, d_stat, 0); ret = GLP_EITLIM; goto done; } /* check if the time limit has been exhausted */ if (parm->tm_lim < INT_MAX && 1000.0 * xdifftime(xtime(), csa->tm_beg) >= parm->tm_lim) { if (bbar_st != 1 || csa->phase == 2 && cbar_st != 1) { if (bbar_st != 1) bbar_st = 0; if (csa->phase == 2 && cbar_st != 1) cbar_st = 0; goto loop; } display(csa, parm, 1); if (parm->msg_lev >= GLP_MSG_ALL) xprintf("TIME LIMIT EXCEEDED; SEARCH TERMINATED\n"); switch (csa->phase) { case 1: p_stat = GLP_INFEAS; set_orig_obj(csa); eval_cbar(csa); break; case 2: p_stat = GLP_FEAS; break; default: xassert(csa != csa); } chuzc(csa, parm->tol_dj); d_stat = (csa->q == 0 ? GLP_FEAS : GLP_INFEAS); store_sol(csa, lp, p_stat, d_stat, 0); ret = GLP_ETMLIM; goto done; } /* display the search progress */ display(csa, parm, 0); /* choose non-basic variable xN[q] */ chuzc(csa, parm->tol_dj); if (csa->q == 0) { if (bbar_st != 1 || cbar_st != 1) { if (bbar_st != 1) bbar_st = 0; if (cbar_st != 1) cbar_st = 0; goto loop; } display(csa, parm, 1); switch (csa->phase) { case 1: if (parm->msg_lev >= GLP_MSG_ALL) xprintf("PROBLEM HAS NO FEASIBLE SOLUTION\n"); p_stat = GLP_NOFEAS; set_orig_obj(csa); eval_cbar(csa); chuzc(csa, parm->tol_dj); d_stat = (csa->q == 0 ? GLP_FEAS : GLP_INFEAS); break; case 2: if (parm->msg_lev >= GLP_MSG_ALL) xprintf("OPTIMAL SOLUTION FOUND\n"); p_stat = d_stat = GLP_FEAS; break; default: xassert(csa != csa); } store_sol(csa, lp, p_stat, d_stat, 0); ret = 0; goto done; } /* compute pivot column of the simplex table */ eval_tcol(csa); if (rigorous) refine_tcol(csa); sort_tcol(csa, parm->tol_piv); /* check accuracy of the reduced cost of xN[q] */ { double d1 = csa->cbar[csa->q]; /* less accurate */ double d2 = reeval_cost(csa); /* more accurate */ xassert(d1 != 0.0); if (fabs(d1 - d2) > 1e-5 * (1.0 + fabs(d2)) || !(d1 < 0.0 && d2 < 0.0 || d1 > 0.0 && d2 > 0.0)) { if (parm->msg_lev >= GLP_MSG_DBG) xprintf("d1 = %.12g; d2 = %.12g\n", d1, d2); if (cbar_st != 1 || !rigorous) { if (cbar_st != 1) cbar_st = 0; rigorous = 5; goto loop; } } /* replace cbar[q] by more accurate value keeping its sign */ if (d1 > 0.0) csa->cbar[csa->q] = (d2 > 0.0 ? d2 : +DBL_EPSILON); else csa->cbar[csa->q] = (d2 < 0.0 ? d2 : -DBL_EPSILON); } /* choose basic variable xB[p] */ switch (parm->r_test) { case GLP_RT_STD: chuzr(csa, 0.0); break; case GLP_RT_HAR: chuzr(csa, 0.30 * parm->tol_bnd); break; default: xassert(parm != parm); } if (csa->p == 0) { if (bbar_st != 1 || cbar_st != 1 || !rigorous) { if (bbar_st != 1) bbar_st = 0; if (cbar_st != 1) cbar_st = 0; rigorous = 1; goto loop; } display(csa, parm, 1); switch (csa->phase) { case 1: if (parm->msg_lev >= GLP_MSG_ERR) xprintf("Error: unable to choose basic variable on ph" "ase I\n"); xassert(!lp->valid && lp->bfd == NULL); lp->bfd = csa->bfd, csa->bfd = NULL; lp->pbs_stat = lp->dbs_stat = GLP_UNDEF; lp->obj_val = 0.0; lp->it_cnt = csa->it_cnt; lp->some = 0; ret = GLP_EFAIL; break; case 2: if (parm->msg_lev >= GLP_MSG_ALL) xprintf("PROBLEM HAS UNBOUNDED SOLUTION\n"); store_sol(csa, lp, GLP_FEAS, GLP_NOFEAS, csa->head[csa->m+csa->q]); ret = 0; break; default: xassert(csa != csa); } goto done; } /* check if the pivot element is acceptable */ if (csa->p > 0) { double piv = csa->tcol_vec[csa->p]; double eps = 1e-5 * (1.0 + 0.01 * csa->tcol_max); if (fabs(piv) < eps) { if (parm->msg_lev >= GLP_MSG_DBG) xprintf("piv = %.12g; eps = %g\n", piv, eps); if (!rigorous) { rigorous = 5; goto loop; } } } /* now xN[q] and xB[p] have been chosen anyhow */ /* compute pivot row of the simplex table */ if (csa->p > 0) { double *rho = csa->work4; eval_rho(csa, rho); if (rigorous) refine_rho(csa, rho); eval_trow(csa, rho); } /* accuracy check based on the pivot element */ if (csa->p > 0) { double piv1 = csa->tcol_vec[csa->p]; /* more accurate */ double piv2 = csa->trow_vec[csa->q]; /* less accurate */ xassert(piv1 != 0.0); if (fabs(piv1 - piv2) > 1e-8 * (1.0 + fabs(piv1)) || !(piv1 > 0.0 && piv2 > 0.0 || piv1 < 0.0 && piv2 < 0.0)) { if (parm->msg_lev >= GLP_MSG_DBG) xprintf("piv1 = %.12g; piv2 = %.12g\n", piv1, piv2); if (binv_st != 1 || !rigorous) { if (binv_st != 1) binv_st = 0; rigorous = 5; goto loop; } /* use more accurate version in the pivot row */ if (csa->trow_vec[csa->q] == 0.0) { csa->trow_nnz++; xassert(csa->trow_nnz <= csa->n); csa->trow_ind[csa->trow_nnz] = csa->q; } csa->trow_vec[csa->q] = piv1; } } /* update primal values of basic variables */ update_bbar(csa); bbar_st = 2; /* updated */ /* update reduced costs of non-basic variables */ if (csa->p > 0) { update_cbar(csa); cbar_st = 2; /* updated */ /* on phase I objective coefficient of xB[p] in the adjacent basis becomes zero */ if (csa->phase == 1) { int k = csa->head[csa->p]; /* x[k] = xB[p] -> xN[q] */ csa->cbar[csa->q] -= csa->coef[k]; csa->coef[k] = 0.0; } } /* update steepest edge coefficients */ if (csa->p > 0) { switch (parm->pricing) { case GLP_PT_STD: break; case GLP_PT_PSE: if (csa->refct > 0) update_gamma(csa); break; default: xassert(parm != parm); } } /* update factorization of the basis matrix */ if (csa->p > 0) { ret = update_B(csa, csa->p, csa->head[csa->m+csa->q]); if (ret == 0) binv_st = 2; /* updated */ else { csa->valid = 0; binv_st = 0; /* invalid */ } } /* update matrix N */ if (csa->p > 0) { del_N_col(csa, csa->q, csa->head[csa->m+csa->q]); if (csa->type[csa->head[csa->p]] != GLP_FX) add_N_col(csa, csa->q, csa->head[csa->p]); } /* change the basis header */ change_basis(csa); /* iteration complete */ csa->it_cnt++; if (rigorous > 0) rigorous--; goto loop; done: /* deallocate the common storage area */ free_csa(csa); /* return to the calling program */ return ret; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/src/glpavl.c0000644000175000017500000002621611632352620020077 0ustar csheltoncshelton/* glpavl.c (binary search tree) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, * Moscow Aviation Institute, Moscow, Russia. All rights reserved. * E-mail: . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "glpavl.h" AVL *avl_create_tree(int (*fcmp)(void *info, const void *key1, const void *key2), void *info) { /* create AVL tree */ AVL *tree; tree = xmalloc(sizeof(AVL)); tree->pool = dmp_create_pool(); tree->root = NULL; tree->fcmp = fcmp; tree->info = info; tree->size = 0; tree->height = 0; return tree; } int avl_strcmp(void *info, const void *key1, const void *key2) { /* compare character string keys */ xassert(info == info); return strcmp(key1, key2); } static AVLNODE *rotate_subtree(AVL *tree, AVLNODE *node); AVLNODE *avl_insert_node(AVL *tree, const void *key) { /* insert new node into AVL tree */ AVLNODE *p, *q, *r; short int flag; /* find an appropriate point for insertion */ p = NULL; q = tree->root; while (q != NULL) { p = q; if (tree->fcmp(tree->info, key, p->key) <= 0) { flag = 0; q = p->left; p->rank++; } else { flag = 1; q = p->right; } } /* create new node and insert it into the tree */ r = dmp_get_atom(tree->pool, sizeof(AVLNODE)); r->key = key; r->type = 0; r->link = NULL; r->rank = 1; r->up = p; r->flag = (short int)(p == NULL ? 0 : flag); r->bal = 0; r->left = NULL; r->right = NULL; tree->size++; if (p == NULL) tree->root = r; else if (flag == 0) p->left = r; else p->right = r; /* go upstairs to the root and correct all subtrees affected by insertion */ while (p != NULL) { if (flag == 0) { /* the height of the left subtree of [p] is increased */ if (p->bal > 0) { p->bal = 0; break; } if (p->bal < 0) { rotate_subtree(tree, p); break; } p->bal = -1; flag = p->flag; p = p->up; } else { /* the height of the right subtree of [p] is increased */ if (p->bal < 0) { p->bal = 0; break; } if (p->bal > 0) { rotate_subtree(tree, p); break; } p->bal = +1; flag = p->flag; p = p->up; } } /* if the root has been reached, the height of the entire tree is increased */ if (p == NULL) tree->height++; return r; } void avl_set_node_type(AVLNODE *node, int type) { /* assign the type field of specified node */ node->type = type; return; } void avl_set_node_link(AVLNODE *node, void *link) { /* assign the link field of specified node */ node->link = link; return; } AVLNODE *avl_find_node(AVL *tree, const void *key) { /* find node in AVL tree */ AVLNODE *p; int c; p = tree->root; while (p != NULL) { c = tree->fcmp(tree->info, key, p->key); if (c == 0) break; p = (c < 0 ? p->left : p->right); } return p; } int avl_get_node_type(AVLNODE *node) { /* retrieve the type field of specified node */ return node->type; } void *avl_get_node_link(AVLNODE *node) { /* retrieve the link field of specified node */ return node->link; } static AVLNODE *find_next_node(AVL *tree, AVLNODE *node) { /* find next node in AVL tree */ AVLNODE *p, *q; if (tree->root == NULL) return NULL; p = node; q = (p == NULL ? tree->root : p->right); if (q == NULL) { /* go upstairs from the left subtree */ for (;;) { q = p->up; if (q == NULL) break; if (p->flag == 0) break; p = q; } } else { /* go downstairs into the right subtree */ for (;;) { p = q->left; if (p == NULL) break; q = p; } } return q; } void avl_delete_node(AVL *tree, AVLNODE *node) { /* delete specified node from AVL tree */ AVLNODE *f, *p, *q, *r, *s, *x, *y; short int flag; p = node; /* if both subtrees of the specified node are non-empty, the node should be interchanged with the next one, at least one subtree of which is always empty */ if (p->left == NULL || p->right == NULL) goto skip; f = p->up; q = p->left; r = find_next_node(tree, p); s = r->right; if (p->right == r) { if (f == NULL) tree->root = r; else if (p->flag == 0) f->left = r; else f->right = r; r->rank = p->rank; r->up = f; r->flag = p->flag; r->bal = p->bal; r->left = q; r->right = p; q->up = r; p->rank = 1; p->up = r; p->flag = 1; p->bal = (short int)(s == NULL ? 0 : +1); p->left = NULL; p->right = s; if (s != NULL) s->up = p; } else { x = p->right; y = r->up; if (f == NULL) tree->root = r; else if (p->flag == 0) f->left = r; else f->right = r; r->rank = p->rank; r->up = f; r->flag = p->flag; r->bal = p->bal; r->left = q; r->right = x; q->up = r; x->up = r; y->left = p; p->rank = 1; p->up = y; p->flag = 0; p->bal = (short int)(s == NULL ? 0 : +1); p->left = NULL; p->right = s; if (s != NULL) s->up = p; } skip: /* now the specified node [p] has at least one empty subtree; go upstairs to the root and adjust the rank field of all nodes affected by deletion */ q = p; f = q->up; while (f != NULL) { if (q->flag == 0) f->rank--; q = f; f = q->up; } /* delete the specified node from the tree */ f = p->up; flag = p->flag; q = p->left != NULL ? p->left : p->right; if (f == NULL) tree->root = q; else if (flag == 0) f->left = q; else f->right = q; if (q != NULL) q->up = f, q->flag = flag; tree->size--; /* go upstairs to the root and correct all subtrees affected by deletion */ while (f != NULL) { if (flag == 0) { /* the height of the left subtree of [f] is decreased */ if (f->bal == 0) { f->bal = +1; break; } if (f->bal < 0) f->bal = 0; else { f = rotate_subtree(tree, f); if (f->bal < 0) break; } flag = f->flag; f = f->up; } else { /* the height of the right subtree of [f] is decreased */ if (f->bal == 0) { f->bal = -1; break; } if (f->bal > 0) f->bal = 0; else { f = rotate_subtree(tree, f); if (f->bal > 0) break; } flag = f->flag; f = f->up; } } /* if the root has been reached, the height of the entire tree is decreased */ if (f == NULL) tree->height--; /* returns the deleted node to the memory pool */ dmp_free_atom(tree->pool, p, sizeof(AVLNODE)); return; } static AVLNODE *rotate_subtree(AVL *tree, AVLNODE *node) { /* restore balance of AVL subtree */ AVLNODE *f, *p, *q, *r, *x, *y; xassert(node != NULL); p = node; if (p->bal < 0) { /* perform negative (left) rotation */ f = p->up; q = p->left; r = q->right; if (q->bal <= 0) { /* perform single negative rotation */ if (f == NULL) tree->root = q; else if (p->flag == 0) f->left = q; else f->right = q; p->rank -= q->rank; q->up = f; q->flag = p->flag; q->bal++; q->right = p; p->up = q; p->flag = 1; p->bal = (short int)(-q->bal); p->left = r; if (r != NULL) r->up = p, r->flag = 0; node = q; } else { /* perform double negative rotation */ x = r->left; y = r->right; if (f == NULL) tree->root = r; else if (p->flag == 0) f->left = r; else f->right = r; p->rank -= (q->rank + r->rank); r->rank += q->rank; p->bal = (short int)(r->bal >= 0 ? 0 : +1); q->bal = (short int)(r->bal <= 0 ? 0 : -1); r->up = f; r->flag = p->flag; r->bal = 0; r->left = q; r->right = p; p->up = r; p->flag = 1; p->left = y; q->up = r; q->flag = 0; q->right = x; if (x != NULL) x->up = q, x->flag = 1; if (y != NULL) y->up = p, y->flag = 0; node = r; } } else { /* perform positive (right) rotation */ f = p->up; q = p->right; r = q->left; if (q->bal >= 0) { /* perform single positive rotation */ if (f == NULL) tree->root = q; else if (p->flag == 0) f->left = q; else f->right = q; q->rank += p->rank; q->up = f; q->flag = p->flag; q->bal--; q->left = p; p->up = q; p->flag = 0; p->bal = (short int)(-q->bal); p->right = r; if (r != NULL) r->up = p, r->flag = 1; node = q; } else { /* perform double positive rotation */ x = r->left; y = r->right; if (f == NULL) tree->root = r; else if (p->flag == 0) f->left = r; else f->right = r; q->rank -= r->rank; r->rank += p->rank; p->bal = (short int)(r->bal <= 0 ? 0 : -1); q->bal = (short int)(r->bal >= 0 ? 0 : +1); r->up = f; r->flag = p->flag; r->bal = 0; r->left = p; r->right = q; p->up = r; p->flag = 0; p->right = x; q->up = r; q->flag = 1; q->left = y; if (x != NULL) x->up = p, x->flag = 1; if (y != NULL) y->up = q, y->flag = 0; node = r; } } return node; } void avl_delete_tree(AVL *tree) { /* delete AVL tree */ dmp_delete_pool(tree->pool); xfree(tree); return; } /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/install-sh0000755000175000017500000003253711632352620017666 0ustar csheltoncshelton#!/bin/sh # install - install a program, script, or datafile scriptversion=2009-04-28.21; # UTC # This originates from X11R5 (mit/util/scripts/install.sh), which was # later released in X11R6 (xc/config/util/install.sh) with the # following copyright and license. # # Copyright (C) 1994 X Consortium # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to # deal in the Software without restriction, including without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # X CONSORTIUM BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN # AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNEC- # TION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # # Except as contained in this notice, the name of the X Consortium shall not # be used in advertising or otherwise to promote the sale, use or other deal- # ings in this Software without prior written authorization from the X Consor- # tium. # # # FSF changes to this file are in the public domain. # # Calling this script install-sh is preferred over install.sh, to prevent # `make' implicit rules from creating a file called install from it # when there is no Makefile. # # This script is compatible with the BSD install script, but was written # from scratch. nl=' ' IFS=" "" $nl" # set DOITPROG to echo to test this script # Don't use :- since 4.3BSD and earlier shells don't like it. doit=${DOITPROG-} if test -z "$doit"; then doit_exec=exec else doit_exec=$doit fi # Put in absolute file names if you don't have them in your path; # or use environment vars. chgrpprog=${CHGRPPROG-chgrp} chmodprog=${CHMODPROG-chmod} chownprog=${CHOWNPROG-chown} cmpprog=${CMPPROG-cmp} cpprog=${CPPROG-cp} mkdirprog=${MKDIRPROG-mkdir} mvprog=${MVPROG-mv} rmprog=${RMPROG-rm} stripprog=${STRIPPROG-strip} posix_glob='?' initialize_posix_glob=' test "$posix_glob" != "?" || { if (set -f) 2>/dev/null; then posix_glob= else posix_glob=: fi } ' posix_mkdir= # Desired mode of installed file. mode=0755 chgrpcmd= chmodcmd=$chmodprog chowncmd= mvcmd=$mvprog rmcmd="$rmprog -f" stripcmd= src= dst= dir_arg= dst_arg= copy_on_change=false no_target_directory= usage="\ Usage: $0 [OPTION]... [-T] SRCFILE DSTFILE or: $0 [OPTION]... SRCFILES... DIRECTORY or: $0 [OPTION]... -t DIRECTORY SRCFILES... or: $0 [OPTION]... -d DIRECTORIES... In the 1st form, copy SRCFILE to DSTFILE. In the 2nd and 3rd, copy all SRCFILES to DIRECTORY. In the 4th, create DIRECTORIES. Options: --help display this help and exit. --version display version info and exit. -c (ignored) -C install only if different (preserve the last data modification time) -d create directories instead of installing files. -g GROUP $chgrpprog installed files to GROUP. -m MODE $chmodprog installed files to MODE. -o USER $chownprog installed files to USER. -s $stripprog installed files. -t DIRECTORY install into DIRECTORY. -T report an error if DSTFILE is a directory. 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In this case, the final cleanup might fail but the new # file should still install successfully. { test ! -f "$dst" || $doit $rmcmd -f "$dst" 2>/dev/null || { $doit $mvcmd -f "$dst" "$rmtmp" 2>/dev/null && { $doit $rmcmd -f "$rmtmp" 2>/dev/null; :; } } || { echo "$0: cannot unlink or rename $dst" >&2 (exit 1); exit 1 } } && # Now rename the file to the real destination. $doit $mvcmd "$dsttmp" "$dst" } fi || exit 1 trap '' 0 fi done # Local variables: # eval: (add-hook 'write-file-hooks 'time-stamp) # time-stamp-start: "scriptversion=" # time-stamp-format: "%:y-%02m-%02d.%02H" # time-stamp-time-zone: "UTC" # time-stamp-end: "; # UTC" # End: ctbnrle.1.1.1/glpk/glpk-4.47/w64/0000755000175000017500000000000011632352620016270 5ustar csheltoncsheltonctbnrle.1.1.1/glpk/glpk-4.47/w64/makefile_VC_DLL0000644000175000017500000000735111632352620021061 0ustar csheltoncshelton## Build GLPK DLL with Microsoft Visual Studio Express ## CFLAGS = \ /I. \ /I..\src \ /I..\src\amd \ /I..\src\colamd \ /I..\src\minisat \ /I..\src\zlib \ /DHAVE_CONFIG_H=1 \ /D_CRT_SECURE_NO_WARNINGS=1 \ /nologo \ /W3 \ /O2 OBJSET = \ ..\src\glpapi01.obj \ ..\src\glpapi02.obj \ ..\src\glpapi03.obj \ ..\src\glpapi04.obj \ ..\src\glpapi05.obj \ ..\src\glpapi06.obj \ ..\src\glpapi07.obj \ ..\src\glpapi08.obj \ ..\src\glpapi09.obj \ ..\src\glpapi10.obj \ ..\src\glpapi11.obj \ ..\src\glpapi12.obj \ ..\src\glpapi13.obj \ ..\src\glpapi14.obj \ ..\src\glpapi15.obj \ ..\src\glpapi16.obj \ ..\src\glpapi17.obj \ ..\src\glpapi18.obj \ ..\src\glpapi19.obj \ ..\src\glpapi20.obj \ ..\src\glpapi21.obj \ ..\src\glpavl.obj \ ..\src\glpbfd.obj \ ..\src\glpbfx.obj \ ..\src\glpcpx.obj \ ..\src\glpdmp.obj \ ..\src\glpdmx.obj \ ..\src\glpenv01.obj \ ..\src\glpenv02.obj \ ..\src\glpenv03.obj \ ..\src\glpenv04.obj \ ..\src\glpenv05.obj \ ..\src\glpenv06.obj \ ..\src\glpenv07.obj \ ..\src\glpenv08.obj \ ..\src\glpfhv.obj \ ..\src\glpgmp.obj \ ..\src\glphbm.obj \ ..\src\glpini01.obj \ ..\src\glpini02.obj \ ..\src\glpios01.obj \ ..\src\glpios02.obj \ ..\src\glpios03.obj \ ..\src\glpios04.obj \ ..\src\glpios05.obj \ ..\src\glpios06.obj \ ..\src\glpios07.obj \ ..\src\glpios08.obj \ ..\src\glpios09.obj \ ..\src\glpios10.obj \ ..\src\glpios11.obj \ ..\src\glpios12.obj \ ..\src\glpipm.obj \ ..\src\glplib01.obj \ ..\src\glplib02.obj \ ..\src\glplib03.obj \ ..\src\glplpf.obj \ ..\src\glplpx01.obj \ ..\src\glplpx02.obj \ ..\src\glplpx03.obj \ ..\src\glpluf.obj \ ..\src\glplux.obj \ ..\src\glpmat.obj \ ..\src\glpmpl01.obj \ ..\src\glpmpl02.obj \ ..\src\glpmpl03.obj \ ..\src\glpmpl04.obj \ ..\src\glpmpl05.obj \ ..\src\glpmpl06.obj \ ..\src\glpmps.obj \ ..\src\glpnet01.obj \ ..\src\glpnet02.obj \ ..\src\glpnet03.obj \ ..\src\glpnet04.obj \ ..\src\glpnet05.obj \ ..\src\glpnet06.obj \ ..\src\glpnet07.obj \ ..\src\glpnet08.obj \ ..\src\glpnet09.obj \ ..\src\glpnpp01.obj \ ..\src\glpnpp02.obj \ ..\src\glpnpp03.obj \ ..\src\glpnpp04.obj \ ..\src\glpnpp05.obj \ ..\src\glpnpp06.obj \ ..\src\glpqmd.obj \ ..\src\glprgr.obj \ ..\src\glprng01.obj \ ..\src\glprng02.obj \ ..\src\glpscf.obj \ ..\src\glpscl.obj \ ..\src\glpsdf.obj \ ..\src\glpspm.obj \ ..\src\glpspx01.obj \ ..\src\glpspx02.obj \ ..\src\glpsql.obj \ ..\src\glpssx01.obj \ ..\src\glpssx02.obj \ ..\src\glptsp.obj \ ..\src\amd\amd_1.obj \ ..\src\amd\amd_2.obj \ ..\src\amd\amd_aat.obj \ ..\src\amd\amd_control.obj \ ..\src\amd\amd_defaults.obj \ ..\src\amd\amd_dump.obj \ ..\src\amd\amd_info.obj \ ..\src\amd\amd_order.obj \ ..\src\amd\amd_post_tree.obj \ ..\src\amd\amd_postorder.obj \ ..\src\amd\amd_preprocess.obj \ ..\src\amd\amd_valid.obj \ ..\src\colamd\colamd.obj \ ..\src\minisat\minisat.obj \ ..\src\zlib\adler32.obj \ ..\src\zlib\compress.obj \ ..\src\zlib\crc32.obj \ ..\src\zlib\deflate.obj \ ..\src\zlib\gzclose.obj \ ..\src\zlib\gzlib.obj \ ..\src\zlib\gzread.obj \ ..\src\zlib\gzwrite.obj \ ..\src\zlib\infback.obj \ ..\src\zlib\inffast.obj \ ..\src\zlib\inflate.obj \ ..\src\zlib\inftrees.obj \ ..\src\zlib\trees.obj \ ..\src\zlib\uncompr.obj \ ..\src\zlib\zio.obj \ ..\src\zlib\zutil.obj .c.obj: cl.exe $(CFLAGS) /Fo$*.obj /c $*.c all: glpk_4_47.dll glpsol.exe glpk_4_47.dll: $(OBJSET) cl.exe $(CFLAGS) /LD /Feglpk_4_47.dll \ ..\src\*.obj \ ..\src\amd\*.obj \ ..\src\colamd\*.obj \ ..\src\minisat\*.obj \ ..\src\zlib\*.obj \ glpk_4_47.def glpsol.exe: ..\examples\glpsol.obj glpk_4_47.dll cl.exe $(CFLAGS) /Feglpsol.exe \ ..\examples\glpsol.obj glpk_4_47.lib check: glpsol.exe .\glpsol.exe --version .\glpsol.exe --mps ..\examples\plan.mps ## eof ## ctbnrle.1.1.1/glpk/glpk-4.47/w64/Build_GLPK_with_VC9.bat0000644000175000017500000000051711632352620022353 0ustar csheltoncsheltonrem Build GLPK with Microsoft Visual Studio Express 2008 rem NOTE: Make sure that HOME variable specifies correct path set HOME="C:\Program Files (x86)\Microsoft Visual Studio 9.0\VC" call %HOME%\bin\vcvars64.bat copy config_VC config.h %HOME%\bin\nmake.exe /f Makefile_VC %HOME%\bin\nmake.exe /f Makefile_VC check pause ctbnrle.1.1.1/glpk/glpk-4.47/w64/readme.txt0000644000175000017500000000166711632352620020300 0ustar csheltoncsheltonThis directory contains batch files and other stuff which you can use to build GLPK for 64-bit Windows with the native C/C++ compilers. Before running the batch file do the following: 1. Make sure that you have installed the compiler you are going to use to build GLPK. 2. Look into corresponding batch file (just right-click it and choose 'Edit' in the popup menu; DO NOT choose 'Open'). Make sure that HOME variable specifies correct path to the compiler directory; if not, make necessary changes. To run the batch file just double-click it and wait a bit while the Make utility does its job. The message 'OPTIMAL SOLUTION FOUND' in the MS-DOS window means that all is OK. If you do not see it, something is wrong. Once GLPK has been successfully built, there must appear two files in this directory: glpk.lib, which is the GLPK object library, and glpsol.exe, which is the stand-alone GLPK LP/MIP solver. ctbnrle.1.1.1/glpk/glpk-4.47/w64/Build_GLPK_with_VC10.bat0000644000175000017500000000052111632352620022416 0ustar csheltoncsheltonrem Build GLPK with Microsoft Visual Studio Express 2010 rem NOTE: Make sure that HOME variable specifies correct path set HOME="C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC" call %HOME%\vcvarsall.bat x64 copy config_VC config.h %HOME%\bin\nmake.exe /f Makefile_VC %HOME%\bin\nmake.exe /f Makefile_VC check pause ctbnrle.1.1.1/glpk/glpk-4.47/w64/Build_GLPK_with_VC10_DLL.bat0000644000175000017500000000053511632352620023116 0ustar csheltoncsheltonrem Build GLPK DLL with Microsoft Visual Studio Express 2010 rem NOTE: Make sure that HOME variable specifies correct path set HOME="C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC" call %HOME%\vcvarsall.bat x64 copy config_VC config.h %HOME%\bin\nmake.exe /f Makefile_VC_DLL %HOME%\bin\nmake.exe /f Makefile_VC_DLL check pause ctbnrle.1.1.1/glpk/glpk-4.47/w64/makefile_VC0000644000175000017500000000724011632352620020363 0ustar csheltoncshelton## Build GLPK with Microsoft Visual Studio Express ## CFLAGS = \ /I. \ /I..\src \ /I..\src\amd \ /I..\src\colamd \ /I..\src\minisat \ /I..\src\zlib \ /DHAVE_CONFIG_H=1 \ /D_CRT_SECURE_NO_WARNINGS=1 \ /nologo \ /W3 \ /O2 OBJSET = \ ..\src\glpapi01.obj \ ..\src\glpapi02.obj \ ..\src\glpapi03.obj \ ..\src\glpapi04.obj \ ..\src\glpapi05.obj \ ..\src\glpapi06.obj \ ..\src\glpapi07.obj \ ..\src\glpapi08.obj \ ..\src\glpapi09.obj \ ..\src\glpapi10.obj \ ..\src\glpapi11.obj \ ..\src\glpapi12.obj \ ..\src\glpapi13.obj \ ..\src\glpapi14.obj \ ..\src\glpapi15.obj \ ..\src\glpapi16.obj \ ..\src\glpapi17.obj \ ..\src\glpapi18.obj \ ..\src\glpapi19.obj \ ..\src\glpapi20.obj \ ..\src\glpapi21.obj \ ..\src\glpavl.obj \ ..\src\glpbfd.obj \ ..\src\glpbfx.obj \ ..\src\glpcpx.obj \ ..\src\glpdmp.obj \ ..\src\glpdmx.obj \ ..\src\glpenv01.obj \ ..\src\glpenv02.obj \ ..\src\glpenv03.obj \ ..\src\glpenv04.obj \ ..\src\glpenv05.obj \ ..\src\glpenv06.obj \ ..\src\glpenv07.obj \ ..\src\glpenv08.obj \ ..\src\glpfhv.obj \ ..\src\glpgmp.obj \ ..\src\glphbm.obj \ ..\src\glpini01.obj \ ..\src\glpini02.obj \ ..\src\glpios01.obj \ ..\src\glpios02.obj \ ..\src\glpios03.obj \ ..\src\glpios04.obj \ ..\src\glpios05.obj \ ..\src\glpios06.obj \ ..\src\glpios07.obj \ ..\src\glpios08.obj \ ..\src\glpios09.obj \ ..\src\glpios10.obj \ ..\src\glpios11.obj \ ..\src\glpios12.obj \ ..\src\glpipm.obj \ ..\src\glplib01.obj \ ..\src\glplib02.obj \ ..\src\glplib03.obj \ ..\src\glplpf.obj \ ..\src\glplpx01.obj \ ..\src\glplpx02.obj \ ..\src\glplpx03.obj \ ..\src\glpluf.obj \ ..\src\glplux.obj \ ..\src\glpmat.obj \ ..\src\glpmpl01.obj \ ..\src\glpmpl02.obj \ ..\src\glpmpl03.obj \ ..\src\glpmpl04.obj \ ..\src\glpmpl05.obj \ ..\src\glpmpl06.obj \ ..\src\glpmps.obj \ ..\src\glpnet01.obj \ ..\src\glpnet02.obj \ ..\src\glpnet03.obj \ ..\src\glpnet04.obj \ ..\src\glpnet05.obj \ ..\src\glpnet06.obj \ ..\src\glpnet07.obj \ ..\src\glpnet08.obj \ ..\src\glpnet09.obj \ ..\src\glpnpp01.obj \ ..\src\glpnpp02.obj \ ..\src\glpnpp03.obj \ ..\src\glpnpp04.obj \ ..\src\glpnpp05.obj \ ..\src\glpnpp06.obj \ ..\src\glpqmd.obj \ ..\src\glprgr.obj \ ..\src\glprng01.obj \ ..\src\glprng02.obj \ ..\src\glpscf.obj \ ..\src\glpscl.obj \ ..\src\glpsdf.obj \ ..\src\glpspm.obj \ ..\src\glpspx01.obj \ ..\src\glpspx02.obj \ ..\src\glpsql.obj \ ..\src\glpssx01.obj \ ..\src\glpssx02.obj \ ..\src\glptsp.obj \ ..\src\amd\amd_1.obj \ ..\src\amd\amd_2.obj \ ..\src\amd\amd_aat.obj \ ..\src\amd\amd_control.obj \ ..\src\amd\amd_defaults.obj \ ..\src\amd\amd_dump.obj \ ..\src\amd\amd_info.obj \ ..\src\amd\amd_order.obj \ ..\src\amd\amd_post_tree.obj \ ..\src\amd\amd_postorder.obj \ ..\src\amd\amd_preprocess.obj \ ..\src\amd\amd_valid.obj \ ..\src\colamd\colamd.obj \ ..\src\minisat\minisat.obj \ ..\src\zlib\adler32.obj \ ..\src\zlib\compress.obj \ ..\src\zlib\crc32.obj \ ..\src\zlib\deflate.obj \ ..\src\zlib\gzclose.obj \ ..\src\zlib\gzlib.obj \ ..\src\zlib\gzread.obj \ ..\src\zlib\gzwrite.obj \ ..\src\zlib\infback.obj \ ..\src\zlib\inffast.obj \ ..\src\zlib\inflate.obj \ ..\src\zlib\inftrees.obj \ ..\src\zlib\trees.obj \ ..\src\zlib\uncompr.obj \ ..\src\zlib\zio.obj \ ..\src\zlib\zutil.obj .c.obj: cl.exe $(CFLAGS) /Fo$*.obj /c $*.c all: glpk.lib glpsol.exe glpk.lib: $(OBJSET) lib.exe /out:glpk.lib \ ..\src\*.obj \ ..\src\amd\*.obj \ ..\src\colamd\*.obj \ ..\src\minisat\*.obj \ ..\src\zlib\*.obj glpsol.exe: ..\examples\glpsol.obj glpk.lib cl.exe $(CFLAGS) /Feglpsol.exe \ ..\examples\glpsol.obj glpk.lib check: glpsol.exe .\glpsol.exe --version .\glpsol.exe --mps ..\examples\plan.mps ## eof ## ctbnrle.1.1.1/glpk/glpk-4.47/w64/config_VC0000644000175000017500000000046411632352620020054 0ustar csheltoncshelton/* GLPK configuration file (Microsoft Visual Studio Express) */ #define __WOE__ 1 #define ODBC_DLNAME "odbc32.dll" /* ODBC shared library name if this feature is enabled */ #if 0 #define MYSQL_DLNAME "libmysql.dll" /* MySQL shared library name if this feature is enabled */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/w64/glpk_4_47.def0000644000175000017500000005750711632352620020460 0ustar csheltoncsheltonLIBRARY glpk_4_47 VERSION 4.47 DESCRIPTION "GNU Linear Programming Kit" EXPORTS ;; from adler32 ;; _glp_zlib_adler32 _glp_zlib_adler32_combine _glp_zlib_adler32_combine64 ;; from amd_1 ;; _glp_amd_1 ;; from amd_2 ;; _glp_amd_2 ;; from amd_aat ;; _glp_amd_aat ;; from amd_control ;; _glp_amd_control ;; from amd_defaults ;; _glp_amd_defaults ;; from amd_dump ;; ;; from amd_info ;; _glp_amd_info ;; from amd_order ;; _glp_amd_order ;; from amd_post_tree ;; _glp_amd_post_tree ;; from amd_postorder ;; _glp_amd_postorder ;; from amd_preprocess ;; _glp_amd_preprocess ;; from amd_valid ;; _glp_amd_valid ;; from colamd ;; _glp_colamd _glp_colamd_recommended _glp_colamd_report _glp_colamd_set_defaults _glp_symamd _glp_symamd_report ;; from compress ;; _glp_zlib_compress _glp_zlib_compress2 _glp_zlib_compressBound ;; from crc32 ;; _glp_zlib_crc32 _glp_zlib_crc32_combine _glp_zlib_crc32_combine64 _glp_zlib_get_crc_table ;; from deflate ;; _glp_zlib_deflate _glp_zlib_deflateBound _glp_zlib_deflateCopy _glp_zlib_deflateEnd _glp_zlib_deflateInit2_ _glp_zlib_deflateInit_ _glp_zlib_deflateParams _glp_zlib_deflatePrime _glp_zlib_deflateReset _glp_zlib_deflateSetDictionary _glp_zlib_deflateSetHeader _glp_zlib_deflateTune _glp_zlib_deflate_copyright ;; from glpapi01 ;; glp_add_cols glp_add_rows glp_check_dup glp_copy_prob glp_create_prob glp_del_cols glp_del_rows glp_delete_prob glp_erase_prob glp_load_matrix glp_set_col_bnds glp_set_col_name glp_set_mat_col glp_set_mat_row glp_set_obj_coef glp_set_obj_dir glp_set_obj_name glp_set_prob_name glp_set_row_bnds glp_set_row_name glp_sort_matrix ;; from glpapi02 ;; glp_get_col_lb glp_get_col_name glp_get_col_type glp_get_col_ub glp_get_mat_col glp_get_mat_row glp_get_num_cols glp_get_num_nz glp_get_num_rows glp_get_obj_coef glp_get_obj_dir glp_get_obj_name glp_get_prob_name glp_get_row_lb glp_get_row_name glp_get_row_type glp_get_row_ub ;; from glpapi03 ;; glp_create_index glp_delete_index glp_find_col glp_find_row ;; from glpapi04 ;; glp_get_rii glp_get_sjj glp_set_rii glp_set_sjj glp_unscale_prob ;; from glpapi05 ;; glp_set_col_stat glp_set_row_stat glp_std_basis ;; from glpapi06 ;; glp_get_col_dual glp_get_col_prim glp_get_col_stat glp_get_dual_stat glp_get_obj_val glp_get_prim_stat glp_get_row_dual glp_get_row_prim glp_get_row_stat glp_get_status glp_get_unbnd_ray glp_init_smcp glp_simplex ;; from glpapi07 ;; glp_exact ;; from glpapi08 ;; glp_init_iptcp glp_interior glp_ipt_col_dual glp_ipt_col_prim glp_ipt_obj_val glp_ipt_row_dual glp_ipt_row_prim glp_ipt_status ;; from glpapi09 ;; _glp_intopt1 glp_get_col_kind glp_get_num_bin glp_get_num_int glp_init_iocp glp_intopt glp_mip_col_val glp_mip_obj_val glp_mip_row_val glp_mip_status glp_set_col_kind ;; from glpapi10 ;; _glp_check_kkt ;; from glpapi11 ;; glp_print_ipt glp_print_mip glp_print_ranges glp_print_sol glp_read_ipt glp_read_mip glp_read_sol glp_write_ipt glp_write_mip glp_write_sol ;; from glpapi12 ;; _glp_analyze_row glp_analyze_bound glp_analyze_coef glp_bf_exists glp_bf_updated glp_btran glp_dual_rtest glp_eval_tab_col glp_eval_tab_row glp_factorize glp_ftran glp_get_bfcp glp_get_bhead glp_get_col_bind glp_get_row_bind glp_prim_rtest glp_set_bfcp glp_transform_col glp_transform_row glp_warm_up ;; from glpapi13 ;; glp_ios_add_row glp_ios_best_node glp_ios_branch_upon glp_ios_can_branch glp_ios_clear_pool glp_ios_curr_node glp_ios_del_row glp_ios_get_prob glp_ios_heur_sol glp_ios_mip_gap glp_ios_next_node glp_ios_node_bound glp_ios_node_data glp_ios_node_level glp_ios_pool_size glp_ios_prev_node glp_ios_reason glp_ios_row_attr glp_ios_select_node glp_ios_terminate glp_ios_tree_size glp_ios_up_node ;; from glpapi14 ;; _glp_mpl_init_rand glp_mpl_alloc_wksp glp_mpl_build_prob glp_mpl_free_wksp glp_mpl_generate glp_mpl_postsolve glp_mpl_read_data glp_mpl_read_model ;; from glpapi15 ;; glp_add_arc glp_add_vertices glp_create_graph glp_create_v_index glp_del_arc glp_del_vertices glp_delete_graph glp_delete_v_index glp_erase_graph glp_find_vertex glp_read_graph glp_set_graph_name glp_set_vertex_name glp_write_graph ;; from glpapi16 ;; glp_strong_comp glp_top_sort glp_weak_comp ;; from glpapi17 ;; glp_asnprob_hall glp_asnprob_lp glp_asnprob_okalg glp_check_asnprob glp_cpp glp_maxflow_ffalg glp_maxflow_lp glp_mincost_lp glp_mincost_okalg ;; from glpapi18 ;; glp_wclique_exact ;; from glpapi19 ;; glp_minisat1 ;; from glpapi20 ;; glp_intfeas1 ;; from glpapi21 ;; glp_main ;; from glpavl ;; _glp_avl_create_tree _glp_avl_delete_node _glp_avl_delete_tree _glp_avl_find_node _glp_avl_get_node_link _glp_avl_get_node_type _glp_avl_insert_node _glp_avl_set_node_link _glp_avl_set_node_type _glp_avl_strcmp ;; from glpbfd ;; _glp_bfd_btran _glp_bfd_create_it _glp_bfd_delete_it _glp_bfd_factorize _glp_bfd_ftran _glp_bfd_get_count _glp_bfd_set_parm _glp_bfd_update_it ;; from glpbfx ;; _glp_bfx_btran _glp_bfx_create_binv _glp_bfx_delete_binv _glp_bfx_factorize _glp_bfx_ftran _glp_bfx_update ;; from glpcpx ;; glp_init_cpxcp glp_read_lp glp_write_lp ;; from glpdmp ;; _glp_dmp_create_pool _glp_dmp_delete_pool _glp_dmp_free_atom _glp_dmp_get_atom _glp_dmp_in_use ;; from glpdmx ;; glp_check_cnfsat glp_read_asnprob glp_read_ccdata glp_read_cnfsat glp_read_maxflow glp_read_mincost glp_read_prob glp_write_asnprob glp_write_ccdata glp_write_cnfsat glp_write_maxflow glp_write_mincost glp_write_prob ;; from glpenv01 ;; _glp_get_env_ptr glp_free_env glp_init_env glp_version ;; from glpenv02 ;; _glp_tls_get_ptr _glp_tls_set_ptr ;; from glpenv03 ;; glp_close_tee glp_open_tee glp_printf glp_term_hook glp_term_out glp_vprintf ;; from glpenv04 ;; glp_assert_ glp_error_ glp_error_hook ;; from glpenv05 ;; glp_calloc glp_free glp_malloc glp_mem_limit glp_mem_usage ;; from glpenv06 ;; glp_difftime glp_time ;; from glpenv07 ;; _glp_lib_err_msg _glp_lib_xerrmsg _glp_lib_xfclose _glp_lib_xfeof _glp_lib_xferror _glp_lib_xfflush _glp_lib_xfgetc _glp_lib_xfopen _glp_lib_xfprintf _glp_lib_xfputc ;; from glpenv08 ;; _glp_xdlclose _glp_xdlopen _glp_xdlsym ;; from glpfhv ;; _glp_fhv_btran _glp_fhv_create_it _glp_fhv_delete_it _glp_fhv_factorize _glp_fhv_ftran _glp_fhv_h_solve _glp_fhv_update_it ;; from glpgmp ;; _glp_gmp_free_atom _glp_gmp_free_mem _glp_gmp_get_atom _glp_gmp_get_work _glp_gmp_pool_count _glp_mpq_abs _glp_mpq_add _glp_mpq_canonicalize _glp_mpq_clear _glp_mpq_cmp _glp_mpq_div _glp_mpq_get_d _glp_mpq_init _glp_mpq_mul _glp_mpq_neg _glp_mpq_out_str _glp_mpq_set _glp_mpq_set_d _glp_mpq_set_si _glp_mpq_sgn _glp_mpq_sub _glp_mpz_abs _glp_mpz_add _glp_mpz_clear _glp_mpz_cmp _glp_mpz_div _glp_mpz_gcd _glp_mpz_get_d _glp_mpz_get_d_2exp _glp_mpz_init _glp_mpz_mul _glp_mpz_neg _glp_mpz_out_str _glp_mpz_set _glp_mpz_set_si _glp_mpz_sgn _glp_mpz_sub _glp_mpz_swap ;; from glphbm ;; _glp_hbm_free_mat _glp_hbm_read_mat ;; from glpini01 ;; glp_adv_basis ;; from glpini02 ;; glp_cpx_basis ;; from glpios01 ;; _glp_ios_add_row _glp_ios_best_node _glp_ios_clear_pool _glp_ios_clone_node _glp_ios_create_pool _glp_ios_create_tree _glp_ios_del_row _glp_ios_delete_node _glp_ios_delete_pool _glp_ios_delete_tree _glp_ios_eval_degrad _glp_ios_find_row _glp_ios_freeze_node _glp_ios_is_hopeful _glp_ios_relative_gap _glp_ios_revive_node _glp_ios_round_bound _glp_ios_solve_node ;; from glpios02 ;; _glp_ios_preprocess_node ;; from glpios03 ;; _glp_ios_driver ;; from glpios04 ;; _glp_ios_check_vec _glp_ios_clean_vec _glp_ios_clear_vec _glp_ios_copy_vec _glp_ios_create_vec _glp_ios_delete_vec _glp_ios_get_vj _glp_ios_linear_comb _glp_ios_set_vj ;; from glpios05 ;; _glp_ios_gmi_gen ;; from glpios06 ;; _glp_ios_mir_gen _glp_ios_mir_init _glp_ios_mir_term ;; from glpios07 ;; _glp_ios_cov_gen ;; from glpios08 ;; _glp_ios_clq_gen _glp_ios_clq_init _glp_ios_clq_term ;; from glpios09 ;; _glp_ios_choose_var _glp_ios_pcost_branch _glp_ios_pcost_free _glp_ios_pcost_init _glp_ios_pcost_update ;; from glpios10 ;; _glp_ios_feas_pump ;; from glpios11 ;; _glp_ios_process_cuts ;; from glpios12 ;; _glp_ios_choose_node ;; from glpipm ;; _glp_ipm_solve ;; from glplib01 ;; _glp_lib_bigdiv _glp_lib_bigmul ;; from glplib02 ;; _glp_lib_xladd _glp_lib_xlcmp _glp_lib_xldiv _glp_lib_xlmul _glp_lib_xlneg _glp_lib_xlset _glp_lib_xlsub _glp_lib_xltoa _glp_lib_xltod ;; from glplib03 ;; _glp_lib_fp2rat _glp_lib_gcd _glp_lib_gcdn _glp_lib_jdate _glp_lib_jday _glp_lib_lcm _glp_lib_lcmn _glp_lib_round2n _glp_lib_str2int _glp_lib_str2num _glp_lib_strrev _glp_lib_strspx _glp_lib_strtrim ;; from glplpf ;; _glp_lpf_btran _glp_lpf_create_it _glp_lpf_delete_it _glp_lpf_factorize _glp_lpf_ftran _glp_lpf_update_it ;; from glplpx01 ;; _glp_lpx_add_cols _glp_lpx_add_rows _glp_lpx_adv_basis _glp_lpx_check_int _glp_lpx_check_kkt _glp_lpx_cpx_basis _glp_lpx_create_index _glp_lpx_create_prob _glp_lpx_del_cols _glp_lpx_del_rows _glp_lpx_delete_index _glp_lpx_delete_prob _glp_lpx_dual_ratio_test _glp_lpx_eval_tab_col _glp_lpx_eval_tab_row _glp_lpx_exact _glp_lpx_find_col _glp_lpx_find_row _glp_lpx_get_class _glp_lpx_get_col_bnds _glp_lpx_get_col_dual _glp_lpx_get_col_info _glp_lpx_get_col_kind _glp_lpx_get_col_lb _glp_lpx_get_col_name _glp_lpx_get_col_prim _glp_lpx_get_col_stat _glp_lpx_get_col_type _glp_lpx_get_col_ub _glp_lpx_get_dual_stat _glp_lpx_get_int_parm _glp_lpx_get_mat_col _glp_lpx_get_mat_row _glp_lpx_get_num_bin _glp_lpx_get_num_cols _glp_lpx_get_num_int _glp_lpx_get_num_nz _glp_lpx_get_num_rows _glp_lpx_get_obj_coef _glp_lpx_get_obj_dir _glp_lpx_get_obj_name _glp_lpx_get_obj_val _glp_lpx_get_prim_stat _glp_lpx_get_prob_name _glp_lpx_get_ray_info _glp_lpx_get_real_parm _glp_lpx_get_row_bnds _glp_lpx_get_row_dual _glp_lpx_get_row_info _glp_lpx_get_row_lb _glp_lpx_get_row_name _glp_lpx_get_row_prim _glp_lpx_get_row_stat _glp_lpx_get_row_type _glp_lpx_get_row_ub _glp_lpx_get_status _glp_lpx_integer _glp_lpx_interior _glp_lpx_intopt _glp_lpx_ipt_col_dual _glp_lpx_ipt_col_prim _glp_lpx_ipt_obj_val _glp_lpx_ipt_row_dual _glp_lpx_ipt_row_prim _glp_lpx_ipt_status _glp_lpx_is_b_avail _glp_lpx_load_matrix _glp_lpx_main _glp_lpx_mip_col_val _glp_lpx_mip_obj_val _glp_lpx_mip_row_val _glp_lpx_mip_status _glp_lpx_prim_ratio_test _glp_lpx_print_ips _glp_lpx_print_mip _glp_lpx_print_prob _glp_lpx_print_sens_bnds _glp_lpx_print_sol _glp_lpx_read_bas _glp_lpx_read_cpxlp _glp_lpx_read_freemps _glp_lpx_read_model _glp_lpx_read_mps _glp_lpx_reset_parms _glp_lpx_scale_prob _glp_lpx_set_class _glp_lpx_set_col_bnds _glp_lpx_set_col_kind _glp_lpx_set_col_name _glp_lpx_set_col_stat _glp_lpx_set_int_parm _glp_lpx_set_mat_col _glp_lpx_set_mat_row _glp_lpx_set_obj_coef _glp_lpx_set_obj_dir _glp_lpx_set_obj_name _glp_lpx_set_prob_name _glp_lpx_set_real_parm _glp_lpx_set_row_bnds _glp_lpx_set_row_name _glp_lpx_set_row_stat _glp_lpx_simplex _glp_lpx_std_basis _glp_lpx_transform_col _glp_lpx_transform_row _glp_lpx_unscale_prob _glp_lpx_warm_up _glp_lpx_write_bas _glp_lpx_write_cpxlp _glp_lpx_write_freemps _glp_lpx_write_mps ;; from glplpx02 ;; _glp_put_mip_soln _glp_put_solution ;; from glplpx03 ;; _glp_lpx_write_pb ;; from glpluf ;; _glp_luf_a_solve _glp_luf_create_it _glp_luf_defrag_sva _glp_luf_delete_it _glp_luf_enlarge_col _glp_luf_enlarge_row _glp_luf_f_solve _glp_luf_factorize _glp_luf_v_solve ;; from glplux ;; _glp_lux_create _glp_lux_decomp _glp_lux_delete _glp_lux_f_solve _glp_lux_solve _glp_lux_v_solve ;; from glpmat ;; _glp_mat_adat_numeric _glp_mat_adat_symbolic _glp_mat_amd_order1 _glp_mat_check_fvs _glp_mat_check_pattern _glp_mat_chol_numeric _glp_mat_chol_symbolic _glp_mat_min_degree _glp_mat_symamd_ord _glp_mat_transpose _glp_mat_u_solve _glp_mat_ut_solve ;; from glpmpl01 ;; _glp_mpl_append_block _glp_mpl_append_char _glp_mpl_append_slot _glp_mpl_arg_list_len _glp_mpl_branched_expression _glp_mpl_check_statement _glp_mpl_close_scope _glp_mpl_constraint_statement _glp_mpl_create_arg_list _glp_mpl_create_block _glp_mpl_create_domain _glp_mpl_display_statement _glp_mpl_domain_arity _glp_mpl_elemset_argument _glp_mpl_end_statement _glp_mpl_enter_context _glp_mpl_error_dimension _glp_mpl_error_following _glp_mpl_error_preceding _glp_mpl_expand_arg_list _glp_mpl_expression_0 _glp_mpl_expression_1 _glp_mpl_expression_10 _glp_mpl_expression_11 _glp_mpl_expression_12 _glp_mpl_expression_13 _glp_mpl_expression_2 _glp_mpl_expression_3 _glp_mpl_expression_4 _glp_mpl_expression_5 _glp_mpl_expression_6 _glp_mpl_expression_7 _glp_mpl_expression_8 _glp_mpl_expression_9 _glp_mpl_expression_list _glp_mpl_for_statement _glp_mpl_function_reference _glp_mpl_get_char _glp_mpl_get_token _glp_mpl_indexing_expression _glp_mpl_is_keyword _glp_mpl_is_reserved _glp_mpl_iterated_expression _glp_mpl_literal_set _glp_mpl_make_binary _glp_mpl_make_code _glp_mpl_make_ternary _glp_mpl_make_unary _glp_mpl_model_section _glp_mpl_numeric_argument _glp_mpl_numeric_literal _glp_mpl_object_reference _glp_mpl_objective_statement _glp_mpl_parameter_statement _glp_mpl_primary_expression _glp_mpl_print_context _glp_mpl_printf_statement _glp_mpl_set_expression _glp_mpl_set_statement _glp_mpl_simple_statement _glp_mpl_solve_statement _glp_mpl_string_literal _glp_mpl_subscript_list _glp_mpl_symbolic_argument _glp_mpl_table_statement _glp_mpl_unget_token _glp_mpl_variable_statement ;; from glpmpl02 ;; _glp_mpl_create_slice _glp_mpl_data_section _glp_mpl_delete_slice _glp_mpl_expand_slice _glp_mpl_fake_slice _glp_mpl_is_literal _glp_mpl_is_number _glp_mpl_is_symbol _glp_mpl_matrix_format _glp_mpl_parameter_data _glp_mpl_plain_format _glp_mpl_read_number _glp_mpl_read_slice _glp_mpl_read_symbol _glp_mpl_read_value _glp_mpl_select_parameter _glp_mpl_select_set _glp_mpl_set_data _glp_mpl_set_default _glp_mpl_simple_format _glp_mpl_slice_arity _glp_mpl_slice_dimen _glp_mpl_tabbing_format _glp_mpl_tabular_format ;; from glpmpl03 ;; _glp_mpl_add_member _glp_mpl_add_tuple _glp_mpl_arelset_member _glp_mpl_arelset_size _glp_mpl_assign_dummy_index _glp_mpl_build_subtuple _glp_mpl_check_elem_set _glp_mpl_check_then_add _glp_mpl_check_value_num _glp_mpl_check_value_sym _glp_mpl_clean_check _glp_mpl_clean_code _glp_mpl_clean_constraint _glp_mpl_clean_display _glp_mpl_clean_domain _glp_mpl_clean_for _glp_mpl_clean_parameter _glp_mpl_clean_printf _glp_mpl_clean_set _glp_mpl_clean_statement _glp_mpl_clean_table _glp_mpl_clean_variable _glp_mpl_compare_strings _glp_mpl_compare_symbols _glp_mpl_compare_tuples _glp_mpl_concat_symbols _glp_mpl_constant_term _glp_mpl_copy_elemset _glp_mpl_copy_formula _glp_mpl_copy_string _glp_mpl_copy_symbol _glp_mpl_copy_tuple _glp_mpl_create_arelset _glp_mpl_create_array _glp_mpl_create_elemset _glp_mpl_create_string _glp_mpl_create_symbol_num _glp_mpl_create_symbol_str _glp_mpl_create_tuple _glp_mpl_delete_array _glp_mpl_delete_elemset _glp_mpl_delete_formula _glp_mpl_delete_string _glp_mpl_delete_symbol _glp_mpl_delete_tuple _glp_mpl_delete_value _glp_mpl_enter_domain_block _glp_mpl_eval_elemset _glp_mpl_eval_formula _glp_mpl_eval_logical _glp_mpl_eval_member_con _glp_mpl_eval_member_num _glp_mpl_eval_member_set _glp_mpl_eval_member_sym _glp_mpl_eval_member_var _glp_mpl_eval_numeric _glp_mpl_eval_symbolic _glp_mpl_eval_tuple _glp_mpl_eval_whole_con _glp_mpl_eval_whole_par _glp_mpl_eval_whole_set _glp_mpl_eval_whole_var _glp_mpl_eval_within_domain _glp_mpl_execute_check _glp_mpl_execute_display _glp_mpl_execute_for _glp_mpl_execute_printf _glp_mpl_execute_statement _glp_mpl_execute_table _glp_mpl_expand_tuple _glp_mpl_fetch_string _glp_mpl_find_member _glp_mpl_find_tuple _glp_mpl_format_symbol _glp_mpl_format_tuple _glp_mpl_fp_add _glp_mpl_fp_atan _glp_mpl_fp_atan2 _glp_mpl_fp_cos _glp_mpl_fp_div _glp_mpl_fp_exp _glp_mpl_fp_idiv _glp_mpl_fp_irand224 _glp_mpl_fp_less _glp_mpl_fp_log _glp_mpl_fp_log10 _glp_mpl_fp_mod _glp_mpl_fp_mul _glp_mpl_fp_normal _glp_mpl_fp_normal01 _glp_mpl_fp_power _glp_mpl_fp_round _glp_mpl_fp_sin _glp_mpl_fp_sqrt _glp_mpl_fp_sub _glp_mpl_fp_trunc _glp_mpl_fp_uniform01 _glp_mpl_free_dca _glp_mpl_get_domain_tuple _glp_mpl_is_member _glp_mpl_linear_comb _glp_mpl_loop_within_domain _glp_mpl_out_of_domain _glp_mpl_reduce_terms _glp_mpl_remove_constant _glp_mpl_set_cross _glp_mpl_set_diff _glp_mpl_set_inter _glp_mpl_set_symdiff _glp_mpl_set_union _glp_mpl_single_variable _glp_mpl_tab_get_arg _glp_mpl_tab_get_name _glp_mpl_tab_get_num _glp_mpl_tab_get_str _glp_mpl_tab_get_type _glp_mpl_tab_num_args _glp_mpl_tab_num_flds _glp_mpl_tab_set_num _glp_mpl_tab_set_str _glp_mpl_take_member_con _glp_mpl_take_member_num _glp_mpl_take_member_set _glp_mpl_take_member_sym _glp_mpl_take_member_var _glp_mpl_tuple_dimen _glp_mpl_uniform _glp_mpl_update_dummy_indices ;; from glpmpl04 ;; _glp_mpl_alloc_content _glp_mpl_build_problem _glp_mpl_clean_model _glp_mpl_close_input _glp_mpl_error _glp_mpl_flush_output _glp_mpl_generate _glp_mpl_generate_model _glp_mpl_get_col_bnds _glp_mpl_get_col_kind _glp_mpl_get_col_name _glp_mpl_get_mat_row _glp_mpl_get_num_cols _glp_mpl_get_num_rows _glp_mpl_get_prob_name _glp_mpl_get_row_bnds _glp_mpl_get_row_c0 _glp_mpl_get_row_kind _glp_mpl_get_row_name _glp_mpl_has_solve_stmt _glp_mpl_initialize _glp_mpl_open_input _glp_mpl_open_output _glp_mpl_postsolve _glp_mpl_postsolve_model _glp_mpl_put_col_soln _glp_mpl_put_row_soln _glp_mpl_read_char _glp_mpl_read_data _glp_mpl_read_model _glp_mpl_terminate _glp_mpl_warning _glp_mpl_write_char _glp_mpl_write_text ;; from glpmpl05 ;; _glp_mpl_fn_gmtime _glp_mpl_fn_str2time _glp_mpl_fn_time2str ;; from glpmpl06 ;; _glp_mpl_tab_drv_close _glp_mpl_tab_drv_open _glp_mpl_tab_drv_read _glp_mpl_tab_drv_write ;; from glpmps ;; glp_init_mpscp glp_read_mps glp_write_mps ;; from glpnet01 ;; _glp_mc21a ;; from glpnet02 ;; _glp_mc13d ;; from glpnet03 ;; glp_netgen ;; from glpnet04 ;; glp_gridgen ;; from glpnet05 ;; glp_rmfgen ;; from glpnet06 ;; _glp_okalg ;; from glpnet07 ;; _glp_ffalg ;; from glpnet08 ;; _glp_wclique ;; from glpnet09 ;; _glp_kellerman ;; from glpnpp01 ;; _glp_npp_activate_col _glp_npp_activate_row _glp_npp_add_aij _glp_npp_add_col _glp_npp_add_row _glp_npp_build_prob _glp_npp_col_nnz _glp_npp_create_wksp _glp_npp_deactivate_col _glp_npp_deactivate_row _glp_npp_del_aij _glp_npp_del_col _glp_npp_del_row _glp_npp_delete_wksp _glp_npp_erase_row _glp_npp_insert_col _glp_npp_insert_row _glp_npp_load_prob _glp_npp_postprocess _glp_npp_push_tse _glp_npp_remove_col _glp_npp_remove_row _glp_npp_row_nnz _glp_npp_unload_sol ;; from glpnpp02 ;; _glp_npp_dbnd_col _glp_npp_fixed_col _glp_npp_free_col _glp_npp_free_row _glp_npp_geq_row _glp_npp_lbnd_col _glp_npp_leq_row _glp_npp_make_equality _glp_npp_make_fixed _glp_npp_ubnd_col ;; from glpnpp03 ;; _glp_npp_analyze_row _glp_npp_empty_col _glp_npp_empty_row _glp_npp_eq_doublet _glp_npp_eq_singlet _glp_npp_forcing_row _glp_npp_implied_bounds _glp_npp_implied_free _glp_npp_implied_lower _glp_npp_implied_slack _glp_npp_implied_upper _glp_npp_implied_value _glp_npp_inactive_bound _glp_npp_ineq_singlet ;; from glpnpp04 ;; _glp_npp_binarize_prob _glp_npp_hidden_covering _glp_npp_hidden_packing _glp_npp_implied_packing _glp_npp_is_covering _glp_npp_is_packing _glp_npp_is_partitioning _glp_npp_reduce_ineq_coef ;; from glpnpp05 ;; _glp_npp_clean_prob _glp_npp_improve_bounds _glp_npp_integer _glp_npp_process_col _glp_npp_process_prob _glp_npp_process_row _glp_npp_simplex ;; from glpnpp06 ;; _glp_npp_sat_encode_clause _glp_npp_sat_encode_geq _glp_npp_sat_encode_leq _glp_npp_sat_encode_pack _glp_npp_sat_encode_prob _glp_npp_sat_encode_row _glp_npp_sat_encode_sum2 _glp_npp_sat_encode_sum3 _glp_npp_sat_encode_sum_ax _glp_npp_sat_fixed_col _glp_npp_sat_free_row _glp_npp_sat_is_bin_comb _glp_npp_sat_is_cover_ineq _glp_npp_sat_is_pack_ineq _glp_npp_sat_is_partn_eq _glp_npp_sat_normalize_clause _glp_npp_sat_num_neg_coef _glp_npp_sat_num_pos_coef _glp_npp_sat_reverse_row _glp_npp_sat_split_pack ;; from glpqmd ;; _glp_qmd_genqmd _glp_qmd_qmdmrg _glp_qmd_qmdqt _glp_qmd_qmdrch _glp_qmd_qmdupd ;; from glprgr ;; _glp_rgr_write_bmp16 ;; from glprng01 ;; _glp_rng_create_rand _glp_rng_delete_rand _glp_rng_init_rand _glp_rng_next_rand _glp_rng_unif_rand ;; from glprng02 ;; _glp_rng_unif_01 _glp_rng_uniform ;; from glpscf ;; _glp_scf_create_it _glp_scf_delete_it _glp_scf_reset_it _glp_scf_solve_it _glp_scf_update_exp ;; from glpscl ;; glp_scale_prob ;; from glpsdf ;; glp_sdf_close_file glp_sdf_error glp_sdf_line glp_sdf_open_file glp_sdf_read_int glp_sdf_read_item glp_sdf_read_num glp_sdf_read_text glp_sdf_set_jump glp_sdf_warning ;; from glpspm ;; _glp_spm_add_mat _glp_spm_add_num _glp_spm_add_sym _glp_spm_check_per _glp_spm_count_nnz _glp_spm_create_mat _glp_spm_create_per _glp_spm_delete_mat _glp_spm_delete_per _glp_spm_drop_zeros _glp_spm_mul_mat _glp_spm_mul_num _glp_spm_mul_sym _glp_spm_new_elem _glp_spm_read_hbm _glp_spm_read_mat _glp_spm_show_mat _glp_spm_test_mat_d _glp_spm_test_mat_e _glp_spm_transpose _glp_spm_write_mat ;; from glpspx01 ;; _glp_spx_primal ;; from glpspx02 ;; _glp_spx_dual ;; from glpsql ;; _glp_db_iodbc_close _glp_db_iodbc_open _glp_db_iodbc_read _glp_db_iodbc_write _glp_db_mysql_close _glp_db_mysql_open _glp_db_mysql_read _glp_db_mysql_write ;; from glpssx01 ;; _glp_ssx_change_basis _glp_ssx_chuzc _glp_ssx_chuzr _glp_ssx_create _glp_ssx_delete _glp_ssx_eval_bbar _glp_ssx_eval_cbar _glp_ssx_eval_col _glp_ssx_eval_dj _glp_ssx_eval_pi _glp_ssx_eval_rho _glp_ssx_eval_row _glp_ssx_factorize _glp_ssx_get_xNj _glp_ssx_update_bbar _glp_ssx_update_cbar _glp_ssx_update_pi ;; from glpssx02 ;; _glp_ssx_driver _glp_ssx_phase_I _glp_ssx_phase_II ;; from glptsp ;; _glp_tsp_distance _glp_tsp_free_data _glp_tsp_read_data ;; from gzclose ;; _glp_zlib_gzclose ;; from gzlib ;; _glp_zlib_gz_error _glp_zlib_gzbuffer _glp_zlib_gzclearerr _glp_zlib_gzdopen _glp_zlib_gzeof _glp_zlib_gzerror _glp_zlib_gzoffset _glp_zlib_gzoffset64 _glp_zlib_gzopen _glp_zlib_gzopen64 _glp_zlib_gzrewind _glp_zlib_gzseek _glp_zlib_gzseek64 _glp_zlib_gztell _glp_zlib_gztell64 ;; from gzread ;; _glp_zlib_gzclose_r _glp_zlib_gzdirect _glp_zlib_gzgetc _glp_zlib_gzgets _glp_zlib_gzread _glp_zlib_gzungetc ;; from gzwrite ;; _glp_zlib_gzclose_w _glp_zlib_gzflush _glp_zlib_gzprintf _glp_zlib_gzputc _glp_zlib_gzputs _glp_zlib_gzsetparams _glp_zlib_gzwrite ;; from infback ;; _glp_zlib_inflateBack _glp_zlib_inflateBackEnd _glp_zlib_inflateBackInit_ ;; from inffast ;; _glp_zlib_inflate_fast ;; from inflate ;; _glp_zlib_inflate _glp_zlib_inflateCopy _glp_zlib_inflateEnd _glp_zlib_inflateGetHeader _glp_zlib_inflateInit2_ _glp_zlib_inflateInit_ _glp_zlib_inflateMark _glp_zlib_inflatePrime _glp_zlib_inflateReset _glp_zlib_inflateReset2 _glp_zlib_inflateSetDictionary _glp_zlib_inflateSync _glp_zlib_inflateSyncPoint _glp_zlib_inflateUndermine ;; from inftrees ;; _glp_zlib_inflate_copyright _glp_zlib_inflate_table ;; from minisat ;; _glp_minisat_addclause _glp_minisat_delete _glp_minisat_nclauses _glp_minisat_nconflicts _glp_minisat_new _glp_minisat_nvars _glp_minisat_propagate _glp_minisat_reducedb _glp_minisat_setnvars _glp_minisat_simplify _glp_minisat_solve ;; from trees ;; _glp_zlib_dist_code _glp_zlib_length_code _glp_zlib_tr_align _glp_zlib_tr_flush_block _glp_zlib_tr_init _glp_zlib_tr_stored_block _glp_zlib_tr_tally ;; from uncompr ;; _glp_zlib_uncompress ;; from zio ;; _glp_zlib_close _glp_zlib_lseek _glp_zlib_open _glp_zlib_read _glp_zlib_write ;; from zutil ;; _glp_zlib_zError _glp_zlib_z_errmsg _glp_zlib_zcalloc _glp_zlib_zcfree _glp_zlib_zlibCompileFlags _glp_zlib_zlibVersion ctbnrle.1.1.1/glpk/glpk-4.47/w64/Build_GLPK_with_VC9_DLL.bat0000644000175000017500000000053311632352620023044 0ustar csheltoncsheltonrem Build GLPK DLL with Microsoft Visual Studio Express 2008 rem NOTE: Make sure that HOME variable specifies correct path set HOME="C:\Program Files (x86)\Microsoft Visual Studio 9.0\VC" call %HOME%\bin\vcvars64.bat copy config_VC config.h %HOME%\bin\nmake.exe /f Makefile_VC_DLL %HOME%\bin\nmake.exe /f Makefile_VC_DLL check pause ctbnrle.1.1.1/glpk/glpk-4.47/NEWS0000644000175000017500000021710111632352620016351 0ustar csheltoncsheltonGLPK 4.47 (release date: Sep 09, 2011) The new API routine glp_intfeas1 was added to the package. This routine is a tentative implementation of the integer (0-1) feasibility solver based on the CNF-SAT solver (which currently is MiniSat). It may be used in the same way as glp_intopt to find either any integer feasible solution or a solution, for which the objective function is not worse than the specified value. Detailed description of this routine can be found in the document "CNF Satisfiability Problem", which is included in the distribution (see doc/cnfsat.pdf). The following two options were added to glpsol: --minisat translate 0-1 feasibility problem to CNF-SAT problem and solve it with glp_intfeas1/MiniSat (if the problem instance is already in CNF-SAT format, no translation is performed) --objbnd bound add inequality obj <= bound (minimization) or obj >= bound (maximization) to 0-1 feasibility problem (this option assumes --minisat) The paint-by-numbers puzzle model (pbn.mod) included in the distribution is a nice example of the 0-1 feasibility problem, which can be efficiently solved with glp_intfeas1/MiniSat. This model along with a brief instruction (pbn.pdf) and benchmark examples from encoded in GNU MathProg (*.dat) can be found in subdirectory examples/pbn/. The glpsol lp/mip solver was modified to bypass postprocessing of MathProg models if the solution reported is neither optimal nor feasible. A minor bug in examples/Makefile.am was fixed to correctly build glpk in a separate directory. Thanks to Marco Atzeri for bug report and patch. GLPK 4.46 (release date: Aug 09, 2011) The following new API routines were added: glp_read_cnfsat read CNF-SAT problem data in DIMACS format glp_check_cnfsat check for CNF-SAT problem instance glp_write_cnfsat write CNF-SAT problem data in DIMACS format glp_minisat1 solve CNF-SAT problem instance with MiniSat The routine glp_minisat1 is a driver to MiniSat, a CNF-SAT solver developed by Niklas Een and Niklas Sorensson, Chalmers University of Technology, Sweden. This routine is similar to the routine glp_intopt, however, it is intended to solve a 0-1 programming problem instance, which is the MIP translation of a CNF-SAT problem instance. Detailed description of these new API routines can be found in the document "CNF Satisfiability Problem", which is included in the distribution (see files doc/cnfsat.tex and doc/cnfsat.pdf). The following new glpsol command-line options were added: --cnf filename read CNF-SAT problem instance in DIMACS format from filename and translate it to MIP --wcnf filename write CNF-SAT problem instance in DIMACS format to filename --minisat solve CNF-SAT problem instance with MiniSat solver The zlib compression library (version 1.2.5) was ANSIfied, modified according to GLPK requirements and included in the distribution as an external software module. Thus, now this feature is platform independent. Some bugs were fixed in the SQL table driver. Thanks to Xypron . GLPK 4.45 (release date: Dec 05, 2010) This is a bug-fix release. Several bugs/typos were fixed. Thanks to Xypron , Robbie Morrison , and Ali Baharev for reports. Some glpk documents was re-formatted and merged into a single document. Now the glpk documentation consists of the following three main documents (all included in the distribution): GLPK: Reference Manual GLPK: Graph and Network Routines Modeling Language GNU MathProg: Language Reference GLPK 4.44 (release date: Jun 03, 2010) The following suffixes for variables and constraints were implemented in the MathProg language: .lb (lower bound), .ub (upper bound), .status (status in the solution), .val (primal value), and .dual (dual value). Thanks to Xypron for draft implementation and testing. Now the MathProg language allows comment records (marked by '#' in the very first position) in CSV data files read with the table statements. Note that the comment records may appear only in the beginning of a CSV data file. The API routine glp_cpp to solve the Critical Path Problem was added and documented. GLPK 4.43 (release date: Feb 20, 2010) This is a maintainer release. `configure.ac' was changed to allow building the package under Mac OS and Darwin with ODBC support. Thanks to Xypron for suggestions and Noli Sicad for testing. The SQL table driver was improved to process NULL data. Thanks to Xypron . Some bugs were fixed in the LP/MIP preprocessor. GLPK 4.42 (release date: Jan 13, 2010) The following new API routines were added: glp_check_dup check for duplicate elements in sparse matrix glp_sort_matrix sort elements of the constraint matrix glp_read_prob read problem data in GLPK format glp_write_prob write problem data in GLPK format glp_analyze_bound analyze active bound of non-basic variable glp_analyze_coef analyze objective coefficient at basic variable glp_print_ranges print sensitivity analysis report (this routine replaces lpx_print_sens_bnds and makes it deprecated) For description of these new routines and the GLPK LP/MIP format see a new edition of the reference manual included in the distribution. (Chapter "Graph and network API routines" was carried out from the main reference manual and included in the distribution as a separate document.) The following new command-line options were added to the stand- alone solver glpsol: --glp filename read problem data in GLPK format --wglp filename write problem data in GLPK format --ranges filename print sensitivity analysis report (this option replaces --bounds) Now all GLPK routines performing file I/O support special filenames "/dev/stdin", "/dev/stdout", and "/dev/stderr", which can be specified in the same way as regular filenames. This feature is plaform-independent. GLPK 4.41 (release date: Dec 21, 2009) The following new API routies were added: glp_transform_row transform explicitly specified row glp_transform_col transform explicitly specified column glp_prim_rtest perform primal ratio test glp_dual_rtest perform dual ratio test For description of these new routines see a new edition of the reference manual included in the distribution. The following API routines are deprecated: lpx_transform_row, lpx_transform_col, lpx_prim_ratio_test, lpx_dual_ratio_test. Some improvements were made in the MIP solver (glp_intopt). The SQL table driver used to read/write data in MathProg models was changed to allow multiple arguments separated by semicolon in SQL statements. Thanks to Xypron . Two new options were added to the glpsol stand-alone solver: --seed value (to initialize the pseudo-random number generator used in MathProg models with specified value), and --ini filename (to use a basis previously saved with -w option as an initial basis on solving similar LP's). Two new MathProg example models were included. Thanks to Nigel Galloway and Noli Sicad for contribution. Scripts to build GLPK with Microsoft Visual Studio 2010 for both 32-bit and 64-bit Windows were included. Thanks to Xypron for contribution and testing. GLPK 4.40 (release date: Nov 03, 2009) The following new API routines were added: glp_del_vertices remove vertices from graph glp_del_arc remove arc from graph glp_wclique_exact find maximum weight clique with the exact algorithm developed by Prof. P. Ostergard glp_read_ccdata read graph in DIMACS clique/coloring format glp_write_ccdata write graph in DIMACS clique/coloring format For description of these new routines see a new edition of the reference manual included in the distribution. The hybrid pseudocost branching heuristic was included in the MIP solver. It is available on API level (iocp.br_tech should be set to GLP_BR_PCH) and in the stand-alone solver glpsol (via the command-line option --pcost). This heuristic may be useful on solving hard MIP instances. The branching heuristic by Driebeck and Tomlin (used in the MIP solver by default) was changed to switch to branching on most fractional variable if an lower bound of degradation of the objective is close to zero for all branching candidates. A bug was fixed in the LP preprocessor (routine npp_empty_col). Thanks to Stefan Vigerske for the bug report. A bug was fixed and some improvements were made in the FPUMP heuristic module. Thanks to Xypron . A bug was fixed in the API routine glp_warm_up (dual feasibility test was incorrect in maximization case). Thanks to Uday Venkatadri for the bug report. GLPK 4.39 (release date: Jul 26, 2009) The following new API routines were added: glp_warm_up "warm up" LP basis glp_set_vertex_name assign (change) vertex name glp_create_v_index create vertex name index glp_find_vertex find vertex by its name glp_delete_v_index delete vertex name index glp_read_asnprob read assignment problem data in DIMACS format glp_write_asnprob write assignment problem data in DIMACS format glp_check_asnprob check correctness of assignment problem data glp_asnprob_lp convert assignment problem to LP glp_asnprob_okalg solve assignment problem with the out-of-kilter algorithm glp_asnprob_hall find bipartite matching of maxumum cardinality with Hall's algorithm Also were added some API routines to read plain data files. The API routines glp_read_lp and glp_write_lp to read/write files in CPLEX LP format were re-implemented. Now glp_write_lp correctly writes double-bounded (ranged) rows by introducing slack variables rather than by duplicating the rows. For description of these new routines see a new edition of the reference manual included in the distribution. The 'xfree(NULL)' bug was fixed in the AMD routines. Thanks to Niels Klitgord for bug report. The message "Crashing..." was changed to "Constructing initial basis..." due to suggestion by Thomas Kahle . Some typos were corrected in glpsol output messages. Thanks to Xypron for patch. GLPK 4.38 (release date: May 02, 2009) API routines glp_read_mps and glp_write_mps were improved. Some improvements were made in the dual simplex routines. Two external software modules AMD and COLAMD were included in the distribution (for more details please see src/amd/README and src/colamd/README). Now they are used in the interior-point solver to reorder the matrix prior to Cholesky factorization. API routine glp_ipt_status may return two new statuses due to changes in the routine glp_interior. For details please see the reference manual included in the distribution. A minor bug was fixed in the graph/network routines. Thanks to Nelson H. F. Beebe for bug report. GLPK 4.37 (release date: Mar 29, 2009) The 0-1 Feasibility Pump heuristic was included in the GLPK integer optimizer glp_intopt. On API level the heuristic can be enabled by setting the parameter fp_heur in glp_iocp to GLP_ON. This feature is also available in the solver glpsol through command-line option '--fpump'. For more details please see the reference manual included in the distribution. The following new API routines were added: glp_print_sol write basic solution in printable format glp_print_ipt write interior-point solution in printable format glp_print_mip write MIP solution in printable format glp_read_graph read (di)graph from plain text file glp_write_graph write (di)graph to plain text file glp_weak_comp find all weakly connected components glp_strong_comp find all strongly connected components The following API routines are deprecated: lpx_print_sol, lpx_print_ips, lpx_print_mip, lpx_print_prob (the latter is equivalent to glp_write_lp). A bug was fixed in the interior-point solver (glp_interior) to correctly compute dual solution components when the problem is scaled. The files configure.ac and Makefile.am were changed: (a) to allow using autoreconf/autoheader; (b) to allow building the package in a directory other than its source directory. Thanks to Marco Atzeri for bug report. An example model in the GNU MathProg language was added. Thanks to Larry D'Agostino for contribution. GLPK 4.36 (release date: Feb 06, 2009) The following new API routines were added to the package: glp_mincost_okalg find minimum-cost flow with out-of-kilter algorithm glp_maxflow_ffalg find maximal flow with Ford-Fulkerson algorithm For detailed description of these new routines and related data structures see chapter "Graph and Network API Routines" in a new edition of the reference manual included in the distribution. The following two new command-line options were added to the solver glpsol: --mincost read min-cost flow data in DIMACS format --maxflow read maximum flow data in DIMACS format Duplicate symbols in the header glpk.h were removed to allow using swig. Thanks to Kelly Westbrooks and Nigel Galloway for suggestion. A minor defect was fixed in the routine glp_write_lp. Thanks to Sebastien Briais for bug report. A minor bug was fixed in the SQL module. Thanks to Xypron for patch. Some new example models in the GNU MathProg modeling language were added. Thanks to Sebastian Nowozin and Nigel Galloway for contribution. GLPK 4.35 (release date: Jan 09, 2009) The following new API routines were added to the package: glp_create_graph create graph glp_set_graph_name assign (change) graph name glp_add_vertices add new vertices to graph glp_add_arc add new arc to graph glp_erase_graph erase graph content glp_delete_graph delete graph glp_read_mincost read minimum cost flow problem data in DIMACS format glp_write_mincost write minimum cost flow problem data in DIMACS format glp_mincost_lp convert minimum cost flow problem to LP glp_netgen Klingman's network problem generator glp_gridgen grid-like network problem generator glp_read_maxflow read maximum flow problem data in DIMACS format glp_write_maxflow write maximum flow problem data in DIMACS format glp_maxflow_lp convert maximum flow problem to LP glp_rmfgen Goldfarb's maximum flow problem generator For detailed description of these new routines and related data structures see chapter "Graph and Network API Routines" in a new edition of the reference manual included in the distribution. A minor change were made in the internal routine xputc. Thanks to Luiz Bettoni for suggestion. A minor bug was fixed in the internal routine mpl_fn_time2str. Thanks to Stefan Vigerske for bug report. GLPK 4.34 (release date: Dec 04, 2008) The GNU MathProg modeling language was supplemented with three new built-in functions: gmtime obtaining current calendar time str2time converting character string to calendar time time2str converting calendar time to character string (Thanks to Xypron .) For detailed description of these functions see Appendix A in the document "Modeling Language GNU MathProg", a new edition of which was included in the distribution. A bug was fixed in the MIP solver. Thanks to Nigel Galloway for bug report. A new makefile was added to build the GLPK DLL with Microsoft Visual Studio Express 2008 for 64-bit Windows. Thanks to Xypron for contribution and testing. GLPK 4.33 (release date: Oct 30, 2008) The following new API routines were added to the package: glp_copy_prob copy problem object content glp_exact solve LP in exact arithmetic (makes lpx_exact deprecated) glp_get_unbnd_ray determine variable causing unboundedness (makes lpx_get_ray_info deprecated) glp_interior solve LP with interior-point method (makes lpx_interior deprecated) The following new API routines for processing models written in the GNU Mathprog language were added to the package: glp_mpl_alloc_wksp allocate the translator workspace glp_mpl_read_model read and translate model section glp_mpl_read_data read and translate data section glp_mpl_generate generate the model glp_mpl_build_prob build LP/MIP instance from the model glp_mpl_postsolve postsolve the model glp_mpl_free_wksp deallocate the translator workspace (These routines make lpx_read_model deprecated.) For description of all these new API routines see the reference manual included in the distribution. A crude implementation of CPLEX-like interface to GLPK API was added to the package. Currently it allows using GLPK as a core LP solver for Concorde, a well known computer code for solving the symmetric TSP. For details see examples/cplex/README. Some bugs were fixed in the SQL table driver. Thanks to Xypron . GLPK 4.32 (release date: Oct 03, 2008) The following new features were included in the MIP solver (the API routine glp_intopt): * MIP presolver * mixed cover cut generator * clique cut generator * Euclidean reduction of the objective value Due to changes the routine glp_intopt may additionally return GLP_ENOPFS, GLP_ENODFS, and GLP_EMIPGAP. The API routines lpx_integer are lpx_intopt are deprecated, since they are completely superseded by glp_intopt. The following new branch-and-cut API routines were added: glp_ios_row_attr determine additional row attributes glp_ios_pool_size determine current size of the cut pool glp_ios_add_row add constraint to the cut pool glp_ios_del_row delete constraint from the cut pool glp_ios_clear_pool delete all constraints from the cut pool For description of these new routines see the reference manual included in the distribution. The stand-alone solver glpsol was changed to allow multiple data files. A new edition of the supplement "Using Data Tables in the GNU MathProg Modeling Language" was included. As usual, some bugs were fixed (in the MathProg translator). Thanks to Xypron . GLPK 4.31 (release date: Sep 02, 2008) The core LP solver based on the dual simplex method was re-implemented and now it provides both phases I and II. The following new API routines were added: glp_scale_prob automatic scaling of problem data glp_std_basis construct standard initial LP basis glp_adv_basis construct advanced initial LP basis glp_cpx_basis construct Bixby's initial LP basis For description of these new routines see the reference manual included in the distribution. The following API routines are deprecated: lpx_scale_prob, lpx_std_basis, lpx_adv_basis, lpx_cpx_basis. Necessary changes were made in memory allocation routines to resolve portability issues for 64-bit platforms. New version of the routine lpx_write_pb to write problem data in OPB (pseudo boolean format) was added to the package. Thanks to Oscar Gustafsson for the contribution. Two new makefiles were added to build the package for 32- and 64-bit Windows with Microsoft Visual Studio Express 2008. Thanks to Heinrich Schuchardt (aka Xypron) for the contribution and testing. Two new makefiles were added to build the package with Digital Mars C/C++ 8.50 and Open Watcom C/C++ 1.6 (for 32-bit Windows). GLPK 4.30 (release date: Aug 13, 2008) The core LP solver based on the primal simplex method was re-implemented to allow its further improvements. Currently the new version provides the same features as the old one, however, it is a bit faster and more numerically stable. Some changes were made in the MathProg translator to allow <, <=, >=, and > on comparing symbolic values. Thanks to Heinrich Schuchardt for patches. Internal routine set_d_eps in the exact LP solver was changed to prevent approximation errors in case of integral data. Thanks to Markus Pilz for bug report. GLPK 4.29 (release date: Jul 06, 2008) The configure script was changed to disable all optional features by default. For details please see file INSTALL. The following new API routines were added: glp_erase_prob erase problem object content glp_read_mps read problem data in MPS format glp_write_mps write problem data in MPS format glp_read_lp read problem data in CPLEX LP format glp_write_lp write problem data in CPLEX LP format For description of these new routines see the reference manual included in the distribution. The following API routines are deprecated: lpx_read_mps, lpx_read_freemps, lpx_write_mps, lpx_write_freemps, lpx_read_cpxlp, and lpx_write_cpxlp. Two bugs were fixed. Thanks to Anne-Laurence Putz and Xypron for bug report. GLPK 4.28 (release date: Mar 25, 2008) The iODBC and MySQL table drivers, which allows transmitting data between MathProg model objects and relational databases, were re-implemented to replace a static linking by a dynamic linking to corresponding shared libraries. Many thanks to Heinrich Schuchardt for the contribution, Rafael Laboissiere for useful advices concerning the shared library support under GNU/Linux, and Vijay Patil for testing this feature under Windows XP. A new optional feature was added to the package. This feature is based on the zlib data compression library and allows GLPK API routines and the stand-alone solver to read and write compressed data files performing compression/decompression "on the fly" (compressed data files are recognized by suffix `.gz' in the file name). It may be useful in case of large MPS files to save the disk space (up to ten times). The `configure' script was re-implemented. Now it supports the following specific options: --with-gmp Enable using the GNU MP bignum library --without-gmp Disable using the GNU MP bignum library --with-zlib Enable using the zlib data compression library --without-zlib Disable using the zlib data compression library --enable-dl Enable shared library support (auto check) --enable-dl=ltdl Enable shared library support (GNU) --enable-dl=dlfcn Enable shared library support (POSIX) --disable-dl Disable shared library support --enable-odbc Enable using ODBC table driver --disable-odbc Disable using ODBC table driver --enable-mysql Enable using MySQL table driver --disable-mysql Disable using MySQL table driver For more details please see file INSTALL. GLPK 4.27 (release date: Mar 02, 2008) Three new table drivers were added to the MathProg translator: xBASE built-in table driver, which allows reading and writing data in .dbf format (only C and N fields are supported); MySQL table driver, which provides connection to a MySQL database; iODBC table driver, which provides connection to a database through ODBC. The MySQL and iODBC table drivers were contributed to GLPK by Heinrich Schuchardt . The table driver is a program module which allows transmitting data between MathProg model objects and external data tables. For detailed description of the table statement and table drivers see the document "Using Data Tables in the GNU MathProg Modeling Language" (file doc/tables.txt) included in the distribution. Some examples which demonstrate using MySQL and iODBC table drivers can be found in subdirectory examples/sql. GLPK 4.26 (release date: Feb 17, 2008) The table statement was implemented in the GNU MathProg modeling language. This new feature allows reading data from external tables into model objects such as sets and parameters as well as writing results of computations to external tables. A table is a (unordered) set of records, where each record consists of the same number of fields, and each field is provided with a unique symbolic name called the field name. Currently the GLPK package has the only built-in table driver, which supports tables in the CSV (comma-separated values) file format. This format is very simple and supported by almost all spreadsheets and database management systems. Detailed description of the table statement and CSV format can be found in file doc/tables.txt, included in the distribution. GLPK 4.25 (release date: Dec 19, 2007) A tentative implementation of Gomory's mixed integer cuts was included in the branch-and-cut solver. To enable generating Gomory's cuts the control parameter gmi_cuts passed to the routine glp_intopt should be set to GLP_ON. This feature is also available in the solver glpsol through command-line option '--gomory'. For more details please see the reference manual included in the distribution. GLPK 4.24 (release date: Nov 21, 2007) A tentative implementation of MIR (mixed integer rounding) cuts was included in the MIP solver. To enable generating MIR cuts the control parameter mir_cuts passed to the routine glp_intopt should be set to GLP_ON. This feature is also available in the stand-alone solver glpsol via command-line option '--mir'. For more details please see the reference manual included in the distribution. The implementation is mainly based on the following two papers: 1. H. Marchand and L. A. Wolsey. Aggregation and mixed integer rounding to solve MIPs. CORE discussion paper 9839, CORE, Universite catholique de Louvain, June 1998. 2. G. Andreello, A. Caprara, and M. Fischetti. Embedding cuts in a Branch&Cut framework. Preliminary draft, October 2003. MIR cuts can be generated on any level of the search tree that makes the GLPK MIP solver to be a real branch-and-cut solver. A bug was fixed in the routine lpx_write_cpxlp. If a variable x has upper bound and no lower bound, it should appear in the bounds section as "-inf <= x <= u", not as "x <= u". Thanks to Enric Rodriguez for the bug report. GLPK 4.23 (release date: Oct 28, 2007) The following new API routines were added: glp_read_sol read basic solution from text file glp_write_sol write basic solution to text file glp_read_ipt read interior-point solution from text file glp_write_ipt write interior-point solution to text file glp_read_mip read MIP solution from text file glp_write_mip write MIP solution to text file For description of these routines and corresponding file formats see Chapter "API Routines", Section "Utility routines" in the reference manual included in the distribution. Advanced API routine glp_free_env was added. It may be used by the application program to free all resources allocated by GLPK routines. The following three new command-line options were added to the solver glpsol: --mipgap tol set relative MIP gap tolerance -r filename read solution from filename -w filename write solution to filename GLPK 4.22 (release date: Sep 19, 2007) This is a maintainer release. A bug was fixed in the MIP preprocessor (ios_preprocess_node). Thanks to Roberto Bagnara (Department of Mathematics, University of Parma, Italy) for the bug report. A bug was fixed in the MIP preprocessor (col_implied_bounds), due to which constraint coefficients with small magnitude could lead to wrong implied bounds of structural variables. A similar bug was fixed in the routine reduce_bounds. A bug was fixed in the routines glp_set_mat_row and glp_set_mat_col. (The bug appeared due to incorrect removing zero elements from the row/column lists.) A bug was fixed in the API routines lpx_read_mps and lpx_read_freemps, due to which bounds of type LI specified in BOUNDS section were incorrectly processed. A call to standard function vsprintf was replaced by a call to vsnprintf for security reasons. Many thanks to Peter T. Breuer and Rafael Laboissiere . GLPK 4.21 (release date: Aug 28, 2007) Additional reasons for calling the callback routine used in the MIP solver (glp_intopt) were introduced. Currently the following reasons are supported: * request for subproblem selection * request for preprocessing * request for row generation * request for heuristic solution * request for cut generation * request for branching * better integer solution found A basic preprocessing component used to improve subproblem formulations by tightening bounds of variables was included in the MIP solver. Depending on the control parameter pp_tech passed to the routine glp_intopt the preprocessing can be performed either on the root level or on all levels (default) or can be disabled. Backtracking heuristic used by default in the MIP solver was changed to the "best local bound". For more details see Chapter "Advanced API routines", Section "Branch-and-bound interface routines" in a new edition of the reference manual included in the distribution. GLPK 4.20 (release date: Jul 26, 2007) API routine lpx_integer was replaced by API routine glp_intopt, which provides equivalent functionality and additionally allows the application to control the solution process by means of the user-written callback routine, which is called by the solver at various points of the branch-and-bound algorithm. Besides, the new MIP solver allows generating "lazy" constraints and cutting planes on all levels of the branch-and-bound tree, not only on the root level. The routine lpx_integer is also still available for the backward compatibility. The following new advanced API routines, which may be called from the B&B callback routine, were included in the package: glp_ios_reason determine reason for calling callback routine glp_ios_get_prob access the problem object glp_ios_tree_size determine size of the branch-and-bound tree glp_ios_curr_node determine current active subproblem glp_ios_next_node determine next active subproblem glp_ios_prev_node determine previous active subproblem glp_ios_up_node determine parent subproblem glp_ios_node_level determine subproblem level glp_ios_node_bound determine subproblem local bound glp_ios_mip_gap compute relative MIP gap glp_ios_heur_sol provide solution found by heuristic glp_ios_terminate terminate the solution process For description of these routines see Chapter "Advanced API routines", Section "Branch-and-bound interface routines" in a new edition of the reference manual, which was included in the distribution. Old version of the integer optimization suite (IOS) as well as TSP solver tspsol based on it are no longer supported and were removed from the package. A minor error in the MIP presolver was fixed; thanks to Graham Rockwell for the bug report. GLPK 4.19 (release date: Jul 05, 2007) The principal change is upgrading to GPLv3. A serious bug in the routine glp_del_cols was fixed; thanks to Cedric[FR] for the bug report. The bug appeared because on deleting non-basic columns the basis header remained valid, however, contained invalid (old) column ordinal numbers. A new advanced API routine glp_mem_limit was added. The case GLP_EBOUND was added to the routine lpx_simplex. Thanks to Cameron Kellough for the bug report. An API routine lpx_write_pb to write the problem instance in OPB (pseudo boolean) format format was added. Thanks to Oscar Gustafsson for the contribution. Two new options --wpb and --wnpb were added to glpsol to write the problem instance in OPB format. GLPK 4.18 (release date: Jun 25, 2007) The following new API routines were added: glp_set_rii set (change) row scale factor glp_set_sjj set (change) column scale factor glp_get_rii retrieve row scale factor glp_get_sjj retrieve column scale factor glp_simplex solve LP problem with the simplex method (this routine replaces lpx_simplex, which is also available for backward compatibility) glp_init_smcp initialize simplex method control params glp_bf_exists check if the basis factorization exists glp_factorize compute the basis factorization glp_bf_updated check if the basis factorization has been updated glp_get_bfcp retrieve basis factorization control params glp_set_bfcp change basis factorization control params glp_get_bhead retrieve the basis header information glp_get_row_bind retrieve row index in the basis header glp_get_col_bind retrieve column index in the basis header glp_ftran perform forward transformation glp_btran perform backward transformation For description of all these routines see a new edition of the reference manual included in the distribution. Type names ulong_t and uldiv_t were changed to glp_ulong and glp_uldiv to avoid conflicts with standard type names on some platforms. Thanks to Boris Wirtz for the bug report. Some new examples in the MathProg language were added. Thanks to Sebastian Nowozin . GLPK 4.17 (release date: May 26, 2007) API routines glp_set_mat_row, glp_set_mat_col, and glp_load_mat were modified to allow zero constraint coefficients (which are not stored in the constraint matrix). Note that constraint coefficients with duplicate row/column indices are not allowed. Another form of LP basis factorization was implemented in the package. It is based on LU-factorization of an initial basis and Schur complement to reflect changes in the basis. Currently the implementation is incomplete and provides only updating the factorization on replacing a column of the basis matrix. On API level the user can set the control parameter LPX_K_BFTYPE to choose between the folloiwng forms of LP basis factorization to be used in the simplex method routines: 1) LU + Forrest-Tomlin update; 2) LU + Schur complement + Bartels-Golub update; 3) LU + Schur complement + Givens rotation update. The GLPK implementation is similar to LUSOL/LUMOD developed by Michael A. Saunders. The user can choose the form of LP basis factorzation used by the simplex method routines by specifying the folloiwng options of glpsol: --luf, --cbg, --cgr. GLPK 4.16 (release date: May 05, 2007) A number of basic GLPK API routines, which now are in the stable stable, were renamed to be prefixed with 'glp_'. Note that all these routines are available via their old names prefixed with 'lpx_' that keeps the downward compatibility with older versions of the package. Three new GLPK API routines were added to the package: glp_version, glp_term_hook, and glp_mem_usage; for more details see a new edition of the GLPK reference manual included in the distribution. The routine glp_version reports the actual version of the GLPK library and also can be used (along with the header glpk.h) in Autotools specification files to check if the GLPK library has been installed. The header glpk.h was changed to conform to C++ environment. GLPK 4.15 (release date: Feb 18, 2007) Autotools specification files (configure.ac, Makefile.am) were changed to use GNU Libtool. This allows building the static as well as shared GLPK library. GLPK 4.14 (release date: Feb 05, 2007) Now GLPK conforms to ILP32, LLP64, and LP64 programming models (the latter seems to be the ultimate choice regarding 64-bit architectures). Note that GLPK itself is a 32-bit application, and the conformity only means that the package works correctly on all these arenae. Nevertheless, on 64-bit platforms it is possible to use more than 4GB of memory, if necessary. GLPK 4.13 (release date: Nov 13, 2006) A tentative implementation of the "exact" simplex method based on bignum (rational) arithmetic was included in the package. On API level this new feature is available through the routine lpx_exact, which is similar to the routine lpx_simplex. In the solver glpsol this feature is available through two new command-line options: --exact and --xcheck. If the '--exact' option is specified, glpsol solves LP instance using the exact simplex method; in case of MIP it is used to obtain optimal solution of LP relaxation. If the --xcheck option is specified, LP instance (or LP relaxation) is solved using the standard (floating-point) simplex method, however, then glpsol calls the exact simplex routine to make sure that the final LP basis is exactly optimal, and if it is not, to perform some additional simplex iterations in exact arithmetic. GLPK 4.12 (release date: Nov 08, 2006) A tentative implementation of some simplex method routines based on exact (bignum) arithmetic was included in the package. Currently these routines provide computing LU-factorization of the basis matrix and computing components of basic solution. These routines were used to implement a routine, which checks primal and dual feasibility of basic solution exactly, i.e. in rational numbers, without round-off errors. In glpsol this feature is available through the command-line option --xcheck. GLPK has its own low-level routines implementing operations on integer and rational numbers that makes it independent on other software packages. However, to attain a much better performance it is highly recommended to install (before configuring GLPK) the GNU Multiple Precision Arithmetic Library (GMP). Using GMP makes computations 100-200 times faster. GLPK 4.11 (release date: Jul 25, 2006) Three new built-in functions in the modeling language were implemented: card (cardinality of set), length (length of character string), and substr (substring of character string). Another improvement concerns the printf statement which now allows redirecting its output to a specified file. These new features are illustrated in example models crypto.mod and graph.mod included in the distribution. For more details see the document "Modeling Language GNU MathProg". Four batch files (along with corresponding makefiles) were included in the distribution to simplify building GLPK under MS Windows; see them in subdirectory 'w32'. GLPK 4.10 (release date: May 11, 2006) Cutting planes of two new classes were implemented: mixed cover cuts and clique cuts. On API level this feature can be enabled by setting control parameter LPX_K_USECUTS passed to the routine lpx_intopt. In glpsol this feature is available through the command-line options --cover and --clique. For more details see the reference manual. Now the routines lpx_read_mps and lpx_read_freemps support LI bound type. It is similar to LO, however, indicates the column as of integer kind. GLPK 4.9 (release date: Jan 17, 2006) An advanced MIP solver was implemented. It includes: - basic presolving technique (removing free, singleton and redundant rows, improving bounds of columns, removing fixed columns, reducing constraint coefficents); - generating cutting planes to improve LP relaxation (currently only Gomory's mixed integer cuts are implemented); - using the branch-and-bound method to solve resultant MIP; - recovering solution of the original MIP. The solver is available on API level via the routine lpx_intopt (see the reference manual). It is similar to the routine lpx_integer, however, does not require initial solution of LP relaxation. The solver is also available in the command-line utility glpsol via two options: --intopt (only presolving) and --cuts (assumes --intopt plus generating cuts). Note that efficiency of the MIP solver strongly depends on the internal structure of the problem to be solved. For some hard instances it is very efficient, but for other instances it may be significantly worse than the standard branch-and-bound. For some comparative benchmarks see doc/bench1.txt. Well, what else... Three built-in functions were added to MathProg: sin, cos, and atan (the latter allows one or two arguments). Some bugs were fixed. Several new examples in MathProg were included: color.mod (graph coloring problem), tsp.mod (traveling salesman problem), and pbn.mod (paint-by-numbers puzzle). GLPK 4.8 (release date: Jan 12, 2005) Core simplex method and interior-point method routines were re-implemented and now they use a new, "storage-by-rows" sparse matrix format (unlike previous versions where linked lists were used to represent sparse matrices). For details see ChangeLog. Also a minor bug was fixed in API routine lpx_read_cpxlp. GLPK 4.7 (release date: Aug 23, 2004) Now GLPK supports free MPS format. Two new API routines lpx_read_freemps (to read problem data in free MPS format) and lpx_write_freemps (to write problem data in free MPS format) were added. This feature is also available in the solver glpsol via new command-line options --freemps and --wfreemps. For more details see the GLPK reference manual. API routines lpx_read_cpxlp and lpx_write_cpxlp for reading and writing problem data in CPLEX LP format were re-implemented to allow long symbolic names (up to 255 characters). The following three modules were temporarily removed from the GLPK distribution due to licensing problems: DELI (an interface module to Delphi), GLPKMEX (an interface module to Matlab), and JNI (an interface module to Java). GLPK 4.6 (release date: Aug 04, 2004) Three new statements were implemented in the GNU MathProg language: solve, printf, and for. Their detailed description can be found in the GLPK documentation included in the distribution. (See also a sample model, examples/queens.mod, which illustrates using these new statements.) Two new API routines were added to the package: lpx_read_prob and lpx_write_prob. They allow reading/writing problem data in GNU LP low-level text format. Three new command-line options were implemented in the LP/MIP solver glpsol: --glp (to read problem data in GNU LP format), --wglp (to write problem data in GNU LP format), and --name (to change problem name). Now glpsol also supports processing models where the new statements (see above) are used. A new version of GLPKMEX, a Matlab MEX interface to GLPK, was included. For more details see contrib/glpkmex/ChangeLog. GLPK 4.5 (release date: Jul 19, 2004) The branch-and-bound solver was completely re-implemented. Some modifications were made in memory allocation routines that allows using the package on 64-bit platforms. For more details see ChangeLog. GLPK 4.4 (release date: Jan 17, 2004) All API routines were re-implemented using new data structures. The new implementation provides the same specifications and functionality of API routines as the old one, however, it has some important advantages, in particular: * linked lists are used everywhere that allows creating and modifying the problem object as efficiently as possible * all data stored in the problem object are non-scaled (even if the internal scaling is used) that prevents distortion of the original problem data * solution components obtained by the solver remain available even if the problem object has been modified * no solver-specific data are used in the new data structures that allows attaching any external lp/mip solver using GLPK API as an uniform interface Note that some API routines became obsolete being replaced by new, more convenient routines. These obsolete routines are kept for backward compatibility, however, they will be removed in the future. For more details please see ChangeLog and the GLPK Reference Manual. New edition of the GLPK Reference Manual was included in the distribution. GLPKMEX, a Matlab MEX interface to GLPK package, contributed by Nicolo Giorgetti was included in the distribution. GLPK FAQ contributed by Harley Mackenzie was included in the distribution. GLPK 4.3 (release date: Dec 12, 2003) The bug, due to which the standard math library is not linked on building the package on some platforms, was fixed. The following new built-in functions were added to the MathProg language: round, trunc, Irand224, Uniform01, Uniform, Normal01, Normal. For details see the language description. The MathProg syntax was changed to allow writing 'subj to' that means 'subject to'. The new api routine lpx_get_ray_info was added. It is intended to determine which (non-basic) variable causes unboundness. For details see the reference manual. The module glpmps.c was changed to avoid compilation errors on building the package on Mac OS X. Several typos was fixed and some new material was added to the GLPK documentation. GLPK 4.2 (release date: Nov 14, 2003) A preliminary implementation of the Integer Optimization Suite (IOS) was included in the package. The Branch-and-Cut Framework being completely superseded by IOS was removed from the package. New API routine lpx_print_sens_bnds intended for bounds sensitivity analysis was contributed to GLPK by Brady Hunsaker . This function is also available in the solver glpsol (via command-line option --bounds). An improved version of GLPK JNI (Java Native Interface) was contributed by Chris Rosebrugh . GLPK DELI (Delphi Interface) was contributed by Ivo van Baren . Several makefiles were added to allow compiling GLPK on some non-GNU 32-bit platforms: * Windows single-threaded static library, Visual C++ 6.0 * Windows multi-threaded dynamic library, Visual C++ 6.0 * Windows single-threaded static library, Borland C++ 5.2 * DOS single-threaded static library, Digital Mars C++ 7.50 And, of course, some bugs were fixed. For more details see ChangeLog. GLPK 4.1 (release date: Aug 23, 2003) Some improvements were made in the lp/mip solver routines and several bugs were fixed in the model translator. For more details see ChangeLog. GLPK 4.0 (release date: May 06, 2003) Now GLPK supports the GNU MathProg modeling language, which is a subset of the AMPL modeling language. The document "GLPK: Modeling Language GNU MathProg" included in the distribution is a complete description of GNU MathProg. (See the files lang.latex, lang.dvi, and lang.ps in the subdirectory 'doc'. See also some examples in the subdirectory 'sample'.) New version of the solver glpsol, which supports models written in GNU MathProg, was implemented. (Brief instructions how to use glpsol can be found in the GNU MathProg documentation.) The GLPK/L modeling language is no more supported. The reason is that GNU MathProg being much more powerful completely supersedes all features of GLPK/L. GLPK 3.3 (release date: Mar 25, 2003) LP PRESOLVER ------------ Now the routine lpx_simplex (which is a driver to the simplex method for solving LP) is provided with the built-in LP presolver, which is a program that transforms the original LP problem to an equivalent LP problem, which may be easier for solving with the simplex method than the original one. Once the transformed LP has been solver, the presolver transforms its basic solution back to a corresponding basic solution of the original problem. For details about this feature please see the GLPK reference manual. Currently the LP presolver implements the following features: * removing empty rows; * removing empty columns; * removing free rows; * removing fixed columns; * removing row singletons, which have the form of equations; * removing row singletons, which have the form of inequalities; * removing column singletons, which are implied slack variables; * fixing and removing column singletons, which are implied free variables; * removing forcing rows that involves fixing and removing the corresponding columns; * checking for primal and dual infeasibilities. The LP presolver is also used by default in the stand-alone program glpsol. In order *not* to use it, the option --nopresol should be specified in the command-line. CHANGES IN GLPK/L ----------------- The syntax and semantics of the GLPK/L modeling language was changed to allow declaration of "interval" sets. This means that now the user can declare a set, for example, as: set task = [8:11]; that is exactly equivalent to the following declaration: set task = (task_8, task_9, task_10, task_11); For details see the language description. JAVA INTERFACE -------------- Now GLPK includes the package GLPK JNI (Java Native Interface) that implements Java binding for GLPK. It allows Java programs to utilize GLPK in solving LP and MIP problems. For details see a brief user's guide in the subdirectory contrib/java-binding. This package was developed and programmed by Yuri Victorovich , who contributed it to GLPK. GLPK 3.2.4 (release date: Feb 18, 2003) This is a bug-fix release. For details see ChangeLog. GLPK 3.2.3 (release date: Nov 11, 2002) A new implementation of the api routine lpx_integer which now is based on the b&b driver (which is based on the implicit enumeration suite) was included in the package. This new implementation has exactly the same functionality as the old version, so all changes are transparent to the api user. Four new api routines were included in the package: lpx_check_kkt checks Karush-Kuhn-Tucker optmality conditions; lpx_read_bas reads predifined basis in MPS format; lpx_write_bas writes current basis in MPS format; lpx_write_lpt writes problem data in CPLEX LP format. Also other minor improvements were made (for details see the file 'ChangeLog'). GLPK 3.2.2 (release date: Oct 14, 2002) The api routine lpx_read_lpt was included in the package. It is similar to the routine lpx_read_mps and intended to read LP/MIP data prepared in CPLEX LP format. Description of this format is given in the GLPK reference manual, a new edition of which was also included in the distribution (see the files 'refman.latex', 'refman.dvi', 'refman.ps' in the subdirectory 'doc'). In order to use data files in CPLEX LP format with the solver glpsol the option '--lpt' should be specified in the command line. Several bugs were fixed and some minor improvements were made (for details see the file 'ChangeLog'). GLPK 3.2.1 (release date: Aug 12, 2002) Now GLPK includes a preliminary implementation of the branch-and-cut framework, which is a set of data structures and routines intended for developing branch-and-cut methods for solving mixed-integer and combinatorial optimization problems. Detailed decsription of the branch-and-cut framework is given in the document "GLPK: A Preliminary Implementation of the Branch-And-Cut Framework" included in the distribution (see the file 'brcut.txt' in the subdirectory 'doc'). In order to illustrate how the GLPK branch-and-cut framework can be used for solving a particular optimization problem there is an example included in the package. This is a stand-alone program, TSPSOL, which is intended for solving to optimality the symmetric Traveling Salesman Problem (TSP), a classical problem of the combinatorial optimization (see the file 'tspsol.c' in the subdirectory 'sample'). GLPK 3.2 (release date: Jul 15, 2002) New edition of the document "GLPK: Reference Manual" was included (see the files 'refman.latex', 'refman.dvi', and 'refman.ps' in the subdirectory 'doc'). New edition of the document "GLPK: Modeling Language GLPK/L" was included (see the files 'lang.latex', 'lang.dvi', and 'lang.ps' in the subdirectory 'doc'). The following new API routines were added to the package: lpx_transform_row (transform explicitly specified row); lpx_transform_col (transform explicitly specified column); lpx_prim_ratio_test (perform primal ratio test); lpx_dual_ratio_test (perform dual ratio test); lpx_interior (solve LP problem using interior point method); lpx_get_ips_stat (query status of interior point solution); lpx_get_ips_row (obtain row interior point solution); lpx_get_ips_col (obtain column interior point solution); lpx_get_ips_obj (obtain interior point value of obj.func.); lpx_read_lpm (read LP/MIP model written in GLPK/L); lpx_write_mps (write problem data using MPS format); lpx_print_ips (print interior point solution). Detailed description of all these new API routines are given in the new edition of the reference manual. New version of the stand-alone solver glpsol (which is based on the new API) was implemented. So long as the new API (introduced in glpk 3.0) now provides all the functions, which were provided by the old API, the old API routines were removed from the package at all. GLPK 3.1 (release date: May 27, 2002) A preliminary implementation of new API routines was completed and included in the package. These new API routines provide much more flexible interaction between the application program, LP/MIP problem instances, and solver routines. Based on completely changed data structures they are, however, similar to the API routines and provide the same functionality. Please note that three routines, namely, solving LPs using interior point method, reading model written in the GLPK/L modeling language, and writing problem data in the MPS format, are not implemented in the new API, however, these routines are planned to be implemented in the next version of the package. A description of the new API routines is given in the document "GLPK Reference Manual", a draft edition of which is included in the package (see the files 'refman.latex', 'refman.dvi', and 'refman.ps' in the subdirectory 'doc'). Although the old API routines are kept in the package, they are no longer supported and will be removed in the future. GLPK 3.0.8 (release date: May 13, 2002) A preliminary implementation of new API routines was included in the package. These new API routines are intended to provide much more flexible interaction between the application program, LP/MIP problem and solver routines. See the document "New GLPK API Routines" (the file 'newapi.txt' in the subdirectory 'doc') also included in the package. The api routines glp_simplex2, glp_call_ipm1, glp_call_bbm1 were renamed, respectively, to glp_simplex, glp_interior, glp_integer in order to reflect changes in implementation. The api routines glp_call_rsm1, glp_simplex1, glp_pivot_in, glp_pivout_out were removed from the package since they are completely supreseded by the new API routines (however, these routines still can be found in the subdirectory 'oldsrc'). Please consult a new edition of the document "GLPK User's Guide" about all these changes in the existing api routines. The document "GLPK Library Reference" was removed from the package (into the subdirectory 'oldsrc') since it describes the obsolete library routines, most of which are no longer used. GLPK 3.0.7 (release date: Apr 22, 2002) A new, more efficient implementation of the primal/dual simplex method was included in the package. Due to some improvements the simplex-based solver allows solving many LP problems faster and provides more reliable results. Note that the new implementation is currently incomplete and available only via the api routine glp_simplex2. All the changes are transparent on API level. GLPK 3.0.6 (release date: Mar 28, 2002) New version of LU-factorization and basis maintenance routines (based on Forrest-Tomlin updating technique) was implemented. Since these new routines functionally supersede some routines (which implement other forms of the basis matrix) and make them obsolete, the latter were removed from the package (they still can be found in the subdirectory 'oldsrc'). All the changes are transparent on API level. GLPK 3.0.5 (release date: Jan 29, 2002) New edition of the document "GLPK User's Guide" was included in the distribution. Now it describes all additional API routines, which were recently added to the package. Structure of the package was re-organized in order to make its maintenance easier (all small files in the subdurectory 'source' were merged in bigger units). These changes are transparent for the user. GLPK 3.0.4 (release date: Dec 10, 2001) A new, more efficient implementation of the two-phase primal simplex method was included in the package. Due to some new features (an advanced initial basis, projected steepest edge, recursive updating values and reduced costs) the new LP solver is faster and numerically more stable than the old one. The new LP solver is available as API routine glp_simplex2 and has the same purpose as API routine glp_call_rsm1. For detailed specification see the file 'newapi.txt' in the directory 'doc'. Now the new LP solver is also used by default to solve an initial LP problem in the branch-and-bound routine glp_call_bbm1 instead the routine rsm1_driver. Note that the branch-and-bound procedure itself is still based on rsm1_driver. The new LP solver is also used as default solver in GLPSOL for solving LP and MIP problems. In order to choose the old solver the option '--old-sim' can be specified in the command line. GLPK 3.0.3 (release date: Oct 03, 2001) Some minor changes were made in the simplex method routines in order to improve numerical stability of the method. GLPK 3.0.2 (release date: Sep 24, 2001) A new implementation of the basis maintaining routines was included in the package. These routines, which are based on so called FHV-factorization (a variety of LU-factorization) of the basis matrix and Gustavson's data structures, allows performing the main operations faster at the expense of some worsening numerical accuracy. AFI (Advanced Form of the Inverse), which is the form of the basis matrix based on FHV-factorization, is available via the parameter form = 3 (on API level) or via the option --afi (in GLPSOL solver). GLPK 3.0.1 (release date: Aug 01, 2001) Old GLPK API routines have been removed from the package. New GLPK API routines were added: - scaling routines; - a routine for writing problem data in MPS format; - a comprehensive driver to the simplex method; - basis maintaining routines. A description of the new API routines is given in the document "Additional GLPK API Routines". This document is included into the distribution in plain text format (see the file 'newapi.txt' in the subdirectory 'doc'). Now the distribution includes a non-trivial example of using GLPK as a base LP solver for Concorde, a well known program that solves Traveling Salesman Problem (TSP). For further details see comments in the file 'sample/lpglpk30.c'. GLPK 3.0 (release date: Jul 19, 2001) Now GLPK is provided with new API, which being more flexible can be used in more complex algorithmic schemes. New edition of the document "GLPK User's Guide" is included in the distribution. Now it completely corresponds to the new GLPK API routines. Old API routines are not removed yet from the package, however they became obsolete and therefore should not be used. Since now the header glpk.h corresponds to new API, in order to compile existing programs that use old GLPK API routines the statement #define GLP_OLD_API should be inserted before the statement #include "glpk.h" GLPK 2.4.1 (release date: Jun 14, 2001) The document "Modeling language GLPK/L" is included into the distribution in texinfo format. New edition of the document "GLPK User's Guide" is included in the distribution. Now it describes all additional API routines which were recently added to the package. GLPK 2.4 (release date: May 10, 2001) Now GLPK includes an implementation of a preliminary version of the GLPK/L modeling language. This language is intended for writing mathematcal programming models. The name GLPK/L is derived from GNU Linear Programming Kit Language. A brief description of the GLPK/L language is given in the document "GLPK/L Modeling Language: A Brief Description". This document is included into the distribution in plain text format (see the file 'language.txt' in the subdirectory 'doc'). The language processor (which is a program that analyzes model description written in GLPK/L and translates it to internal data structures) is available as the GLPK API routine. The stand-alone solver GLPSOL now is able: a) to process model descriptions written in the GLPK/L language; b) to solve pure LP problems using the interior point method (therefore the program GLPIPM was removed from the package). GLPK 2.3 (release date: Apr 09, 2001) New edition of the document "GLPK User's Guide" is included in the distribution. Now it describes all additional API routines which were recently added to the package. The MIP solver was fully re-programmed in order to improve its robustness and performance. In particular, a basis recovering procedure was implemented (this procedure allows switching to the primal simplex method in case when the dual simplex method fails). GLPK 2.2 (release date: Mar 15, 2001) Now GLPK includes a tentative implementation of the branch-and-bound procedure based on the dual simplex method for mixed integer linear programming (MIP). Complete description of this new feature of the package is given in the preliminary document "Mixed Integer Linear Programming Using GLPK Version 2.2 (Supplement to GLPK User's Guide)". This document is included into the distribution in plain text format (see the file 'mip.txt' in the subdirectory 'doc'). The MIP solver (glp_integer) can be used as GLPK API routine in the same way as the pure LP solver (glp_simplex). The stand-alone program 'glpsol' is now able to solve LP as well as MIP problems. Note that the current version of GLPK MIP solver is based on easiest heuristics for branching and backtrackng. Therefore the solver is fit mainly for MIP problems which are not very hard and have few integer variables. GLPK 2.1 (release date: Feb 19, 2001) The document "GLPK Implementation of the Revised Simplex Method" is included into the distribution. This document describes most of routines related to the revised simplex method. GLPK 2.0 (release date: Jan 25, 2001) Now GLPK includes a tentative implementation of the primal-dual interior point method for large-scale linear programming. The interior point solver can be used as GLPK API routine in the same manner as the simplex method solver (glp_simplex): ret = glp_interior(); Note that currently the interior point solver implemented in GLPK doesn't include many important features, in particular: * it can't process dense columns; therefore if the problem has dense columns, the solving will be extremely inefficient; * it has no special features against numerical unstability; some problems may cause premature termination of the solving when the matrix A*D*A' becomes ill-conditioned; * it computes only values of primal (auxiliary and structural) variables and doesn't compute values of dual variables (i.e. reduced costs) which are just set to zero; * it doesn't identify optimal basis corresponding to the found interior point solution; all variables in the found solution are just marked as basic variables. GLPK also includes a stand-alone program 'glpipm' which is a demo based on the interior point method. 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See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with GNU Libtool; see the file COPYING. If not, a copy # can be downloaded from http://www.gnu.org/licenses/gpl.html, or # obtained by writing to the Free Software Foundation, Inc., # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. ]) # serial 56 LT_INIT # LT_PREREQ(VERSION) # ------------------ # Complain and exit if this libtool version is less that VERSION. m4_defun([LT_PREREQ], [m4_if(m4_version_compare(m4_defn([LT_PACKAGE_VERSION]), [$1]), -1, [m4_default([$3], [m4_fatal([Libtool version $1 or higher is required], 63)])], [$2])]) # _LT_CHECK_BUILDDIR # ------------------ # Complain if the absolute build directory name contains unusual characters m4_defun([_LT_CHECK_BUILDDIR], [case `pwd` in *\ * | *\ *) AC_MSG_WARN([Libtool does not cope well with whitespace in `pwd`]) ;; esac ]) # LT_INIT([OPTIONS]) # ------------------ AC_DEFUN([LT_INIT], [AC_PREREQ([2.58])dnl We use AC_INCLUDES_DEFAULT AC_REQUIRE([AC_CONFIG_AUX_DIR_DEFAULT])dnl AC_BEFORE([$0], [LT_LANG])dnl AC_BEFORE([$0], [LT_OUTPUT])dnl AC_BEFORE([$0], [LTDL_INIT])dnl m4_require([_LT_CHECK_BUILDDIR])dnl dnl Autoconf doesn't catch unexpanded LT_ macros by default: m4_pattern_forbid([^_?LT_[A-Z_]+$])dnl m4_pattern_allow([^(_LT_EOF|LT_DLGLOBAL|LT_DLLAZY_OR_NOW|LT_MULTI_MODULE)$])dnl dnl aclocal doesn't pull ltoptions.m4, ltsugar.m4, or ltversion.m4 dnl unless we require an AC_DEFUNed macro: AC_REQUIRE([LTOPTIONS_VERSION])dnl AC_REQUIRE([LTSUGAR_VERSION])dnl AC_REQUIRE([LTVERSION_VERSION])dnl AC_REQUIRE([LTOBSOLETE_VERSION])dnl m4_require([_LT_PROG_LTMAIN])dnl _LT_SHELL_INIT([SHELL=${CONFIG_SHELL-/bin/sh}]) dnl Parse OPTIONS _LT_SET_OPTIONS([$0], [$1]) # This can be used to rebuild libtool when needed LIBTOOL_DEPS="$ltmain" # Always use our own libtool. LIBTOOL='$(SHELL) $(top_builddir)/libtool' AC_SUBST(LIBTOOL)dnl _LT_SETUP # Only expand once: m4_define([LT_INIT]) ])# LT_INIT # Old names: AU_ALIAS([AC_PROG_LIBTOOL], [LT_INIT]) AU_ALIAS([AM_PROG_LIBTOOL], [LT_INIT]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AC_PROG_LIBTOOL], []) dnl AC_DEFUN([AM_PROG_LIBTOOL], []) # _LT_CC_BASENAME(CC) # ------------------- # Calculate cc_basename. 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In configure, this macro expands # each variable declared with _LT_DECL (and _LT_TAGDECL) into: # # ='`$ECHO "$" | $SED "$delay_single_quote_subst"`' m4_defun([_LT_CONFIG_STATUS_DECLARATIONS], [m4_foreach([_lt_var], m4_quote(lt_decl_all_varnames), [m4_n([_LT_CONFIG_STATUS_DECLARE(_lt_var)])])]) # _LT_LIBTOOL_TAGS # ---------------- # Output comment and list of tags supported by the script m4_defun([_LT_LIBTOOL_TAGS], [_LT_FORMAT_COMMENT([The names of the tagged configurations supported by this script])dnl available_tags="_LT_TAGS"dnl ]) # _LT_LIBTOOL_DECLARE(VARNAME, [TAG]) # ----------------------------------- # Extract the dictionary values for VARNAME (optionally with TAG) and # expand to a commented shell variable setting: # # # Some comment about what VAR is for. # visible_name=$lt_internal_name m4_define([_LT_LIBTOOL_DECLARE], [_LT_FORMAT_COMMENT(m4_quote(lt_dict_fetch([lt_decl_dict], [$1], [description])))[]dnl m4_pushdef([_libtool_name], m4_quote(lt_dict_fetch([lt_decl_dict], [$1], [libtool_name])))[]dnl m4_case(m4_quote(lt_dict_fetch([lt_decl_dict], [$1], [value])), [0], [_libtool_name=[$]$1], [1], [_libtool_name=$lt_[]$1], [2], [_libtool_name=$lt_[]$1], [_libtool_name=lt_dict_fetch([lt_decl_dict], [$1], [value])])[]dnl m4_ifval([$2], [_$2])[]m4_popdef([_libtool_name])[]dnl ]) # _LT_LIBTOOL_CONFIG_VARS # ----------------------- # Produce commented declarations of non-tagged libtool config variables # suitable for insertion in the LIBTOOL CONFIG section of the `libtool' # script. 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#endif void fnord () { int i=42; } int main () { void *self = dlopen (0, LT_DLGLOBAL|LT_DLLAZY_OR_NOW); int status = $lt_dlunknown; if (self) { if (dlsym (self,"fnord")) status = $lt_dlno_uscore; else { if (dlsym( self,"_fnord")) status = $lt_dlneed_uscore; else puts (dlerror ()); } /* dlclose (self); */ } else puts (dlerror ()); return status; }] _LT_EOF if AC_TRY_EVAL(ac_link) && test -s conftest${ac_exeext} 2>/dev/null; then (./conftest; exit; ) >&AS_MESSAGE_LOG_FD 2>/dev/null lt_status=$? case x$lt_status in x$lt_dlno_uscore) $1 ;; x$lt_dlneed_uscore) $2 ;; x$lt_dlunknown|x*) $3 ;; esac else : # compilation failed $3 fi fi rm -fr conftest* ])# _LT_TRY_DLOPEN_SELF # LT_SYS_DLOPEN_SELF # ------------------ AC_DEFUN([LT_SYS_DLOPEN_SELF], [m4_require([_LT_HEADER_DLFCN])dnl if test "x$enable_dlopen" != xyes; then enable_dlopen=unknown enable_dlopen_self=unknown enable_dlopen_self_static=unknown else lt_cv_dlopen=no lt_cv_dlopen_libs= case $host_os in beos*) lt_cv_dlopen="load_add_on" lt_cv_dlopen_libs= lt_cv_dlopen_self=yes ;; mingw* | pw32* | cegcc*) lt_cv_dlopen="LoadLibrary" lt_cv_dlopen_libs= ;; cygwin*) lt_cv_dlopen="dlopen" lt_cv_dlopen_libs= ;; darwin*) # if libdl is installed we need to link against it AC_CHECK_LIB([dl], [dlopen], [lt_cv_dlopen="dlopen" lt_cv_dlopen_libs="-ldl"],[ lt_cv_dlopen="dyld" lt_cv_dlopen_libs= lt_cv_dlopen_self=yes ]) ;; *) AC_CHECK_FUNC([shl_load], [lt_cv_dlopen="shl_load"], [AC_CHECK_LIB([dld], [shl_load], [lt_cv_dlopen="shl_load" lt_cv_dlopen_libs="-ldld"], [AC_CHECK_FUNC([dlopen], [lt_cv_dlopen="dlopen"], [AC_CHECK_LIB([dl], [dlopen], [lt_cv_dlopen="dlopen" lt_cv_dlopen_libs="-ldl"], [AC_CHECK_LIB([svld], [dlopen], [lt_cv_dlopen="dlopen" lt_cv_dlopen_libs="-lsvld"], [AC_CHECK_LIB([dld], [dld_link], [lt_cv_dlopen="dld_link" lt_cv_dlopen_libs="-ldld"]) ]) ]) ]) ]) ]) ;; esac if test "x$lt_cv_dlopen" != xno; then enable_dlopen=yes else enable_dlopen=no fi case $lt_cv_dlopen in dlopen) save_CPPFLAGS="$CPPFLAGS" test "x$ac_cv_header_dlfcn_h" = xyes && CPPFLAGS="$CPPFLAGS -DHAVE_DLFCN_H" save_LDFLAGS="$LDFLAGS" wl=$lt_prog_compiler_wl eval LDFLAGS=\"\$LDFLAGS $export_dynamic_flag_spec\" save_LIBS="$LIBS" LIBS="$lt_cv_dlopen_libs $LIBS" AC_CACHE_CHECK([whether a program can dlopen itself], lt_cv_dlopen_self, [dnl _LT_TRY_DLOPEN_SELF( lt_cv_dlopen_self=yes, lt_cv_dlopen_self=yes, lt_cv_dlopen_self=no, lt_cv_dlopen_self=cross) ]) if test "x$lt_cv_dlopen_self" = xyes; then wl=$lt_prog_compiler_wl eval LDFLAGS=\"\$LDFLAGS $lt_prog_compiler_static\" AC_CACHE_CHECK([whether a statically linked program can dlopen itself], lt_cv_dlopen_self_static, [dnl _LT_TRY_DLOPEN_SELF( lt_cv_dlopen_self_static=yes, lt_cv_dlopen_self_static=yes, lt_cv_dlopen_self_static=no, lt_cv_dlopen_self_static=cross) ]) fi CPPFLAGS="$save_CPPFLAGS" LDFLAGS="$save_LDFLAGS" LIBS="$save_LIBS" ;; esac case $lt_cv_dlopen_self in yes|no) enable_dlopen_self=$lt_cv_dlopen_self ;; *) enable_dlopen_self=unknown ;; esac case $lt_cv_dlopen_self_static in yes|no) enable_dlopen_self_static=$lt_cv_dlopen_self_static ;; *) enable_dlopen_self_static=unknown ;; esac fi _LT_DECL([dlopen_support], [enable_dlopen], [0], [Whether dlopen is supported]) _LT_DECL([dlopen_self], [enable_dlopen_self], [0], [Whether dlopen of programs is supported]) _LT_DECL([dlopen_self_static], [enable_dlopen_self_static], [0], [Whether dlopen of statically linked programs is supported]) ])# LT_SYS_DLOPEN_SELF # Old name: AU_ALIAS([AC_LIBTOOL_DLOPEN_SELF], [LT_SYS_DLOPEN_SELF]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AC_LIBTOOL_DLOPEN_SELF], []) # _LT_COMPILER_C_O([TAGNAME]) # --------------------------- # Check to see if options -c and -o are simultaneously supported by compiler. # This macro does not hard code the compiler like AC_PROG_CC_C_O. m4_defun([_LT_COMPILER_C_O], [m4_require([_LT_DECL_SED])dnl m4_require([_LT_FILEUTILS_DEFAULTS])dnl m4_require([_LT_TAG_COMPILER])dnl AC_CACHE_CHECK([if $compiler supports -c -o file.$ac_objext], [_LT_TAGVAR(lt_cv_prog_compiler_c_o, $1)], [_LT_TAGVAR(lt_cv_prog_compiler_c_o, $1)=no $RM -r conftest 2>/dev/null mkdir conftest cd conftest mkdir out echo "$lt_simple_compile_test_code" > conftest.$ac_ext lt_compiler_flag="-o out/conftest2.$ac_objext" # Insert the option either (1) after the last *FLAGS variable, or # (2) before a word containing "conftest.", or (3) at the end. # Note that $ac_compile itself does not contain backslashes and begins # with a dollar sign (not a hyphen), so the echo should work correctly. lt_compile=`echo "$ac_compile" | $SED \ -e 's:.*FLAGS}\{0,1\} :&$lt_compiler_flag :; t' \ -e 's: [[^ ]]*conftest\.: $lt_compiler_flag&:; t' \ -e 's:$: $lt_compiler_flag:'` (eval echo "\"\$as_me:$LINENO: $lt_compile\"" >&AS_MESSAGE_LOG_FD) (eval "$lt_compile" 2>out/conftest.err) ac_status=$? cat out/conftest.err >&AS_MESSAGE_LOG_FD echo "$as_me:$LINENO: \$? = $ac_status" >&AS_MESSAGE_LOG_FD if (exit $ac_status) && test -s out/conftest2.$ac_objext then # The compiler can only warn and ignore the option if not recognized # So say no if there are warnings $ECHO "$_lt_compiler_boilerplate" | $SED '/^$/d' > out/conftest.exp $SED '/^$/d; /^ *+/d' out/conftest.err >out/conftest.er2 if test ! -s out/conftest.er2 || diff out/conftest.exp out/conftest.er2 >/dev/null; then _LT_TAGVAR(lt_cv_prog_compiler_c_o, $1)=yes fi fi chmod u+w . 2>&AS_MESSAGE_LOG_FD $RM conftest* # SGI C++ compiler will create directory out/ii_files/ for # template instantiation test -d out/ii_files && $RM out/ii_files/* && rmdir out/ii_files $RM out/* && rmdir out cd .. $RM -r conftest $RM conftest* ]) _LT_TAGDECL([compiler_c_o], [lt_cv_prog_compiler_c_o], [1], [Does compiler simultaneously support -c and -o options?]) ])# _LT_COMPILER_C_O # _LT_COMPILER_FILE_LOCKS([TAGNAME]) # ---------------------------------- # Check to see if we can do hard links to lock some files if needed m4_defun([_LT_COMPILER_FILE_LOCKS], [m4_require([_LT_ENABLE_LOCK])dnl m4_require([_LT_FILEUTILS_DEFAULTS])dnl _LT_COMPILER_C_O([$1]) hard_links="nottested" if test "$_LT_TAGVAR(lt_cv_prog_compiler_c_o, $1)" = no && test "$need_locks" != no; then # do not overwrite the value of need_locks provided by the user AC_MSG_CHECKING([if we can lock with hard links]) hard_links=yes $RM conftest* ln conftest.a conftest.b 2>/dev/null && hard_links=no touch conftest.a ln conftest.a conftest.b 2>&5 || hard_links=no ln conftest.a conftest.b 2>/dev/null && hard_links=no AC_MSG_RESULT([$hard_links]) if test "$hard_links" = no; then AC_MSG_WARN([`$CC' does not support `-c -o', so `make -j' may be unsafe]) need_locks=warn fi else need_locks=no fi _LT_DECL([], [need_locks], [1], [Must we lock files when doing compilation?]) ])# _LT_COMPILER_FILE_LOCKS # _LT_CHECK_OBJDIR # ---------------- m4_defun([_LT_CHECK_OBJDIR], [AC_CACHE_CHECK([for objdir], [lt_cv_objdir], [rm -f .libs 2>/dev/null mkdir .libs 2>/dev/null if test -d .libs; then lt_cv_objdir=.libs else # MS-DOS does not allow filenames that begin with a dot. lt_cv_objdir=_libs fi rmdir .libs 2>/dev/null]) objdir=$lt_cv_objdir _LT_DECL([], [objdir], [0], [The name of the directory that contains temporary libtool files])dnl m4_pattern_allow([LT_OBJDIR])dnl AC_DEFINE_UNQUOTED(LT_OBJDIR, "$lt_cv_objdir/", [Define to the sub-directory in which libtool stores uninstalled libraries.]) ])# _LT_CHECK_OBJDIR # _LT_LINKER_HARDCODE_LIBPATH([TAGNAME]) # -------------------------------------- # Check hardcoding attributes. m4_defun([_LT_LINKER_HARDCODE_LIBPATH], [AC_MSG_CHECKING([how to hardcode library paths into programs]) _LT_TAGVAR(hardcode_action, $1)= if test -n "$_LT_TAGVAR(hardcode_libdir_flag_spec, $1)" || test -n "$_LT_TAGVAR(runpath_var, $1)" || test "X$_LT_TAGVAR(hardcode_automatic, $1)" = "Xyes" ; then # We can hardcode non-existent directories. if test "$_LT_TAGVAR(hardcode_direct, $1)" != no && # If the only mechanism to avoid hardcoding is shlibpath_var, we # have to relink, otherwise we might link with an installed library # when we should be linking with a yet-to-be-installed one ## test "$_LT_TAGVAR(hardcode_shlibpath_var, $1)" != no && test "$_LT_TAGVAR(hardcode_minus_L, $1)" != no; then # Linking always hardcodes the temporary library directory. _LT_TAGVAR(hardcode_action, $1)=relink else # We can link without hardcoding, and we can hardcode nonexisting dirs. _LT_TAGVAR(hardcode_action, $1)=immediate fi else # We cannot hardcode anything, or else we can only hardcode existing # directories. _LT_TAGVAR(hardcode_action, $1)=unsupported fi AC_MSG_RESULT([$_LT_TAGVAR(hardcode_action, $1)]) if test "$_LT_TAGVAR(hardcode_action, $1)" = relink || test "$_LT_TAGVAR(inherit_rpath, $1)" = yes; then # Fast installation is not supported enable_fast_install=no elif test "$shlibpath_overrides_runpath" = yes || test "$enable_shared" = no; then # Fast installation is not necessary enable_fast_install=needless fi _LT_TAGDECL([], [hardcode_action], [0], [How to hardcode a shared library path into an executable]) ])# _LT_LINKER_HARDCODE_LIBPATH # _LT_CMD_STRIPLIB # ---------------- m4_defun([_LT_CMD_STRIPLIB], [m4_require([_LT_DECL_EGREP]) striplib= old_striplib= AC_MSG_CHECKING([whether stripping libraries is possible]) if test -n "$STRIP" && $STRIP -V 2>&1 | $GREP "GNU strip" >/dev/null; then test -z "$old_striplib" && old_striplib="$STRIP --strip-debug" test -z "$striplib" && striplib="$STRIP --strip-unneeded" AC_MSG_RESULT([yes]) else # FIXME - insert some real tests, host_os isn't really good enough case $host_os in darwin*) if test -n "$STRIP" ; then striplib="$STRIP -x" old_striplib="$STRIP -S" AC_MSG_RESULT([yes]) else AC_MSG_RESULT([no]) fi ;; *) AC_MSG_RESULT([no]) ;; esac fi _LT_DECL([], [old_striplib], [1], [Commands to strip libraries]) _LT_DECL([], [striplib], [1]) ])# _LT_CMD_STRIPLIB # _LT_SYS_DYNAMIC_LINKER([TAG]) # ----------------------------- # PORTME Fill in your ld.so characteristics m4_defun([_LT_SYS_DYNAMIC_LINKER], [AC_REQUIRE([AC_CANONICAL_HOST])dnl m4_require([_LT_DECL_EGREP])dnl m4_require([_LT_FILEUTILS_DEFAULTS])dnl m4_require([_LT_DECL_OBJDUMP])dnl m4_require([_LT_DECL_SED])dnl m4_require([_LT_CHECK_SHELL_FEATURES])dnl AC_MSG_CHECKING([dynamic linker characteristics]) m4_if([$1], [], [ if test "$GCC" = yes; then case $host_os in darwin*) lt_awk_arg="/^libraries:/,/LR/" ;; *) lt_awk_arg="/^libraries:/" ;; esac case $host_os in mingw* | cegcc*) lt_sed_strip_eq="s,=\([[A-Za-z]]:\),\1,g" ;; *) lt_sed_strip_eq="s,=/,/,g" ;; esac lt_search_path_spec=`$CC -print-search-dirs | awk $lt_awk_arg | $SED -e "s/^libraries://" -e $lt_sed_strip_eq` case $lt_search_path_spec in *\;*) # if the path contains ";" then we assume it to be the separator # otherwise default to the standard path separator (i.e. ":") - it is # assumed that no part of a normal pathname contains ";" but that should # okay in the real world where ";" in dirpaths is itself problematic. lt_search_path_spec=`$ECHO "$lt_search_path_spec" | $SED 's/;/ /g'` ;; *) lt_search_path_spec=`$ECHO "$lt_search_path_spec" | $SED "s/$PATH_SEPARATOR/ /g"` ;; esac # Ok, now we have the path, separated by spaces, we can step through it # and add multilib dir if necessary. lt_tmp_lt_search_path_spec= lt_multi_os_dir=`$CC $CPPFLAGS $CFLAGS $LDFLAGS -print-multi-os-directory 2>/dev/null` for lt_sys_path in $lt_search_path_spec; do if test -d "$lt_sys_path/$lt_multi_os_dir"; then lt_tmp_lt_search_path_spec="$lt_tmp_lt_search_path_spec $lt_sys_path/$lt_multi_os_dir" else test -d "$lt_sys_path" && \ lt_tmp_lt_search_path_spec="$lt_tmp_lt_search_path_spec $lt_sys_path" fi done lt_search_path_spec=`$ECHO "$lt_tmp_lt_search_path_spec" | awk ' BEGIN {RS=" "; FS="/|\n";} { lt_foo=""; lt_count=0; for (lt_i = NF; lt_i > 0; lt_i--) { if ($lt_i != "" && $lt_i != ".") { if ($lt_i == "..") { lt_count++; } else { if (lt_count == 0) { lt_foo="/" $lt_i lt_foo; } else { lt_count--; } } } } if (lt_foo != "") { lt_freq[[lt_foo]]++; } if (lt_freq[[lt_foo]] == 1) { print lt_foo; } }'` # AWK program above erroneously prepends '/' to C:/dos/paths # for these hosts. case $host_os in mingw* | cegcc*) lt_search_path_spec=`$ECHO "$lt_search_path_spec" |\ $SED 's,/\([[A-Za-z]]:\),\1,g'` ;; esac sys_lib_search_path_spec=`$ECHO "$lt_search_path_spec" | $lt_NL2SP` else sys_lib_search_path_spec="/lib /usr/lib /usr/local/lib" fi]) library_names_spec= libname_spec='lib$name' soname_spec= shrext_cmds=".so" postinstall_cmds= postuninstall_cmds= finish_cmds= finish_eval= shlibpath_var= shlibpath_overrides_runpath=unknown version_type=none dynamic_linker="$host_os ld.so" sys_lib_dlsearch_path_spec="/lib /usr/lib" need_lib_prefix=unknown hardcode_into_libs=no # when you set need_version to no, make sure it does not cause -set_version # flags to be left without arguments need_version=unknown case $host_os in aix3*) version_type=linux library_names_spec='${libname}${release}${shared_ext}$versuffix $libname.a' shlibpath_var=LIBPATH # AIX 3 has no versioning support, so we append a major version to the name. soname_spec='${libname}${release}${shared_ext}$major' ;; aix[[4-9]]*) version_type=linux need_lib_prefix=no need_version=no hardcode_into_libs=yes if test "$host_cpu" = ia64; then # AIX 5 supports IA64 library_names_spec='${libname}${release}${shared_ext}$major ${libname}${release}${shared_ext}$versuffix $libname${shared_ext}' shlibpath_var=LD_LIBRARY_PATH else # With GCC up to 2.95.x, collect2 would create an import file # for dependence libraries. 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esac # HP-UX runs *really* slowly unless shared libraries are mode 555, ... postinstall_cmds='chmod 555 $lib' # or fails outright, so override atomically: install_override_mode=555 ;; interix[[3-9]]*) version_type=linux need_lib_prefix=no need_version=no library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major ${libname}${shared_ext}' soname_spec='${libname}${release}${shared_ext}$major' dynamic_linker='Interix 3.x ld.so.1 (PE, like ELF)' shlibpath_var=LD_LIBRARY_PATH shlibpath_overrides_runpath=no hardcode_into_libs=yes ;; irix5* | irix6* | nonstopux*) case $host_os in nonstopux*) version_type=nonstopux ;; *) if test "$lt_cv_prog_gnu_ld" = yes; then version_type=linux else version_type=irix fi ;; esac need_lib_prefix=no need_version=no soname_spec='${libname}${release}${shared_ext}$major' library_names_spec='${libname}${release}${shared_ext}$versuffix ${libname}${release}${shared_ext}$major ${libname}${release}${shared_ext} $libname${shared_ext}' case $host_os in irix5* | nonstopux*) libsuff= shlibsuff= ;; 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Nevertheless, you *** may want to report the problem to your system manager and/or to *** bug-libtool@gnu.org _LT_EOF fi ;; esac fi break fi done IFS="$lt_save_ifs" MAGIC_CMD="$lt_save_MAGIC_CMD" ;; esac]) MAGIC_CMD="$lt_cv_path_MAGIC_CMD" if test -n "$MAGIC_CMD"; then AC_MSG_RESULT($MAGIC_CMD) else AC_MSG_RESULT(no) fi _LT_DECL([], [MAGIC_CMD], [0], [Used to examine libraries when file_magic_cmd begins with "file"])dnl ])# _LT_PATH_TOOL_PREFIX # Old name: AU_ALIAS([AC_PATH_TOOL_PREFIX], [_LT_PATH_TOOL_PREFIX]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AC_PATH_TOOL_PREFIX], []) # _LT_PATH_MAGIC # -------------- # find a file program which can recognize a shared library m4_defun([_LT_PATH_MAGIC], [_LT_PATH_TOOL_PREFIX(${ac_tool_prefix}file, /usr/bin$PATH_SEPARATOR$PATH) if test -z "$lt_cv_path_MAGIC_CMD"; then if test -n "$ac_tool_prefix"; then _LT_PATH_TOOL_PREFIX(file, /usr/bin$PATH_SEPARATOR$PATH) else MAGIC_CMD=: fi fi ])# _LT_PATH_MAGIC # LT_PATH_LD # ---------- # find the pathname to the GNU or non-GNU linker AC_DEFUN([LT_PATH_LD], [AC_REQUIRE([AC_PROG_CC])dnl AC_REQUIRE([AC_CANONICAL_HOST])dnl AC_REQUIRE([AC_CANONICAL_BUILD])dnl m4_require([_LT_DECL_SED])dnl m4_require([_LT_DECL_EGREP])dnl m4_require([_LT_PROG_ECHO_BACKSLASH])dnl AC_ARG_WITH([gnu-ld], [AS_HELP_STRING([--with-gnu-ld], [assume the C compiler uses GNU ld @<:@default=no@:>@])], [test "$withval" = no || with_gnu_ld=yes], [with_gnu_ld=no])dnl ac_prog=ld if test "$GCC" = yes; then # Check if gcc -print-prog-name=ld gives a path. 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[[\\/]]* | ?:[[\\/]]*) re_direlt='/[[^/]][[^/]]*/\.\./' # Canonicalize the pathname of ld ac_prog=`$ECHO "$ac_prog"| $SED 's%\\\\%/%g'` while $ECHO "$ac_prog" | $GREP "$re_direlt" > /dev/null 2>&1; do ac_prog=`$ECHO $ac_prog| $SED "s%$re_direlt%/%"` done test -z "$LD" && LD="$ac_prog" ;; "") # If it fails, then pretend we aren't using GCC. ac_prog=ld ;; *) # If it is relative, then search for the first ld in PATH. with_gnu_ld=unknown ;; esac elif test "$with_gnu_ld" = yes; then AC_MSG_CHECKING([for GNU ld]) else AC_MSG_CHECKING([for non-GNU ld]) fi AC_CACHE_VAL(lt_cv_path_LD, [if test -z "$LD"; then lt_save_ifs="$IFS"; IFS=$PATH_SEPARATOR for ac_dir in $PATH; do IFS="$lt_save_ifs" test -z "$ac_dir" && ac_dir=. if test -f "$ac_dir/$ac_prog" || test -f "$ac_dir/$ac_prog$ac_exeext"; then lt_cv_path_LD="$ac_dir/$ac_prog" # Check to see if the program is GNU ld. 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See mingw*. lt_cv_deplibs_check_method='file_magic file format pe-arm-.*little(.*architecture: arm)?' lt_cv_file_magic_cmd='$OBJDUMP -f' ;; darwin* | rhapsody*) lt_cv_deplibs_check_method=pass_all ;; freebsd* | dragonfly*) if echo __ELF__ | $CC -E - | $GREP __ELF__ > /dev/null; then case $host_cpu in i*86 ) # Not sure whether the presence of OpenBSD here was a mistake. # Let's accept both of them until this is cleared up. lt_cv_deplibs_check_method='file_magic (FreeBSD|OpenBSD|DragonFly)/i[[3-9]]86 (compact )?demand paged shared library' lt_cv_file_magic_cmd=/usr/bin/file lt_cv_file_magic_test_file=`echo /usr/lib/libc.so.*` ;; esac else lt_cv_deplibs_check_method=pass_all fi ;; gnu*) lt_cv_deplibs_check_method=pass_all ;; haiku*) lt_cv_deplibs_check_method=pass_all ;; hpux10.20* | hpux11*) lt_cv_file_magic_cmd=/usr/bin/file case $host_cpu in ia64*) lt_cv_deplibs_check_method='file_magic (s[[0-9]][[0-9]][[0-9]]|ELF-[[0-9]][[0-9]]) shared object file - IA64' lt_cv_file_magic_test_file=/usr/lib/hpux32/libc.so ;; hppa*64*) [lt_cv_deplibs_check_method='file_magic (s[0-9][0-9][0-9]|ELF[ -][0-9][0-9])(-bit)?( [LM]SB)? shared object( file)?[, -]* PA-RISC [0-9]\.[0-9]'] lt_cv_file_magic_test_file=/usr/lib/pa20_64/libc.sl ;; *) lt_cv_deplibs_check_method='file_magic (s[[0-9]][[0-9]][[0-9]]|PA-RISC[[0-9]]\.[[0-9]]) shared library' lt_cv_file_magic_test_file=/usr/lib/libc.sl ;; esac ;; interix[[3-9]]*) # PIC code is broken on Interix 3.x, that's why |\.a not |_pic\.a here lt_cv_deplibs_check_method='match_pattern /lib[[^/]]+(\.so|\.a)$' ;; irix5* | irix6* | nonstopux*) case $LD in *-32|*"-32 ") libmagic=32-bit;; *-n32|*"-n32 ") libmagic=N32;; *-64|*"-64 ") libmagic=64-bit;; *) libmagic=never-match;; esac lt_cv_deplibs_check_method=pass_all ;; # This must be Linux ELF. linux* | k*bsd*-gnu | kopensolaris*-gnu) lt_cv_deplibs_check_method=pass_all ;; netbsd*) if echo __ELF__ | $CC -E - | $GREP __ELF__ > /dev/null; then lt_cv_deplibs_check_method='match_pattern /lib[[^/]]+(\.so\.[[0-9]]+\.[[0-9]]+|_pic\.a)$' else lt_cv_deplibs_check_method='match_pattern /lib[[^/]]+(\.so|_pic\.a)$' fi ;; newos6*) lt_cv_deplibs_check_method='file_magic ELF [[0-9]][[0-9]]*-bit [[ML]]SB (executable|dynamic lib)' lt_cv_file_magic_cmd=/usr/bin/file lt_cv_file_magic_test_file=/usr/lib/libnls.so ;; *nto* | *qnx*) lt_cv_deplibs_check_method=pass_all ;; openbsd*) if test -z "`echo __ELF__ | $CC -E - | $GREP __ELF__`" || test "$host_os-$host_cpu" = "openbsd2.8-powerpc"; then lt_cv_deplibs_check_method='match_pattern /lib[[^/]]+(\.so\.[[0-9]]+\.[[0-9]]+|\.so|_pic\.a)$' else lt_cv_deplibs_check_method='match_pattern /lib[[^/]]+(\.so\.[[0-9]]+\.[[0-9]]+|_pic\.a)$' fi ;; osf3* | osf4* | osf5*) lt_cv_deplibs_check_method=pass_all ;; rdos*) lt_cv_deplibs_check_method=pass_all ;; solaris*) lt_cv_deplibs_check_method=pass_all ;; sysv5* | sco3.2v5* | sco5v6* | unixware* | OpenUNIX* | sysv4*uw2*) lt_cv_deplibs_check_method=pass_all ;; sysv4 | sysv4.3*) case $host_vendor in motorola) lt_cv_deplibs_check_method='file_magic ELF [[0-9]][[0-9]]*-bit [[ML]]SB (shared object|dynamic lib) M[[0-9]][[0-9]]* Version [[0-9]]' lt_cv_file_magic_test_file=`echo /usr/lib/libc.so*` ;; ncr) lt_cv_deplibs_check_method=pass_all ;; sequent) lt_cv_file_magic_cmd='/bin/file' lt_cv_deplibs_check_method='file_magic ELF [[0-9]][[0-9]]*-bit [[LM]]SB (shared object|dynamic lib )' ;; sni) lt_cv_file_magic_cmd='/bin/file' lt_cv_deplibs_check_method="file_magic ELF [[0-9]][[0-9]]*-bit [[LM]]SB dynamic lib" lt_cv_file_magic_test_file=/lib/libc.so ;; siemens) lt_cv_deplibs_check_method=pass_all ;; pc) lt_cv_deplibs_check_method=pass_all ;; esac ;; tpf*) lt_cv_deplibs_check_method=pass_all ;; esac ]) file_magic_cmd=$lt_cv_file_magic_cmd deplibs_check_method=$lt_cv_deplibs_check_method test -z "$deplibs_check_method" && deplibs_check_method=unknown _LT_DECL([], [deplibs_check_method], [1], [Method to check whether dependent libraries are shared objects]) _LT_DECL([], [file_magic_cmd], [1], [Command to use when deplibs_check_method == "file_magic"]) ])# _LT_CHECK_MAGIC_METHOD # LT_PATH_NM # ---------- # find the pathname to a BSD- or MS-compatible name lister AC_DEFUN([LT_PATH_NM], [AC_REQUIRE([AC_PROG_CC])dnl AC_CACHE_CHECK([for BSD- or MS-compatible name lister (nm)], lt_cv_path_NM, [if test -n "$NM"; then # Let the user override the test. lt_cv_path_NM="$NM" else lt_nm_to_check="${ac_tool_prefix}nm" if test -n "$ac_tool_prefix" && test "$build" = "$host"; then lt_nm_to_check="$lt_nm_to_check nm" fi for lt_tmp_nm in $lt_nm_to_check; do lt_save_ifs="$IFS"; IFS=$PATH_SEPARATOR for ac_dir in $PATH /usr/ccs/bin/elf /usr/ccs/bin /usr/ucb /bin; do IFS="$lt_save_ifs" test -z "$ac_dir" && ac_dir=. tmp_nm="$ac_dir/$lt_tmp_nm" if test -f "$tmp_nm" || test -f "$tmp_nm$ac_exeext" ; then # Check to see if the nm accepts a BSD-compat flag. # Adding the `sed 1q' prevents false positives on HP-UX, which says: # nm: unknown option "B" ignored # Tru64's nm complains that /dev/null is an invalid object file case `"$tmp_nm" -B /dev/null 2>&1 | sed '1q'` in */dev/null* | *'Invalid file or object type'*) lt_cv_path_NM="$tmp_nm -B" break ;; *) case `"$tmp_nm" -p /dev/null 2>&1 | sed '1q'` in */dev/null*) lt_cv_path_NM="$tmp_nm -p" break ;; *) lt_cv_path_NM=${lt_cv_path_NM="$tmp_nm"} # keep the first match, but continue # so that we can try to find one that supports BSD flags ;; esac ;; esac fi done IFS="$lt_save_ifs" done : ${lt_cv_path_NM=no} fi]) if test "$lt_cv_path_NM" != "no"; then NM="$lt_cv_path_NM" else # Didn't find any BSD compatible name lister, look for dumpbin. if test -n "$DUMPBIN"; then : # Let the user override the test. else AC_CHECK_TOOLS(DUMPBIN, [dumpbin "link -dump"], :) case `$DUMPBIN -symbols /dev/null 2>&1 | sed '1q'` in *COFF*) DUMPBIN="$DUMPBIN -symbols" ;; *) DUMPBIN=: ;; esac fi AC_SUBST([DUMPBIN]) if test "$DUMPBIN" != ":"; then NM="$DUMPBIN" fi fi test -z "$NM" && NM=nm AC_SUBST([NM]) _LT_DECL([], [NM], [1], [A BSD- or MS-compatible name lister])dnl AC_CACHE_CHECK([the name lister ($NM) interface], [lt_cv_nm_interface], [lt_cv_nm_interface="BSD nm" echo "int some_variable = 0;" > conftest.$ac_ext (eval echo "\"\$as_me:$LINENO: $ac_compile\"" >&AS_MESSAGE_LOG_FD) (eval "$ac_compile" 2>conftest.err) cat conftest.err >&AS_MESSAGE_LOG_FD (eval echo "\"\$as_me:$LINENO: $NM \\\"conftest.$ac_objext\\\"\"" >&AS_MESSAGE_LOG_FD) (eval "$NM \"conftest.$ac_objext\"" 2>conftest.err > conftest.out) cat conftest.err >&AS_MESSAGE_LOG_FD (eval echo "\"\$as_me:$LINENO: output\"" >&AS_MESSAGE_LOG_FD) cat conftest.out >&AS_MESSAGE_LOG_FD if $GREP 'External.*some_variable' conftest.out > /dev/null; then lt_cv_nm_interface="MS dumpbin" fi rm -f conftest*]) ])# LT_PATH_NM # Old names: AU_ALIAS([AM_PROG_NM], [LT_PATH_NM]) AU_ALIAS([AC_PROG_NM], [LT_PATH_NM]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AM_PROG_NM], []) dnl AC_DEFUN([AC_PROG_NM], []) # _LT_CHECK_SHAREDLIB_FROM_LINKLIB # ---------------------- # how to determine the name of the shared library # associated with a specific link library. # -- PORTME fill in with the dynamic library characteristics m4_defun([_LT_CHECK_SHAREDLIB_FROM_LINKLIB], [m4_require([_LT_DECL_EGREP]) m4_require([_LT_DECL_OBJDUMP]) m4_require([_LT_DECL_DLLTOOL]) AC_CACHE_CHECK([how to associate runtime and link libraries], lt_cv_sharedlib_from_linklib_cmd, [lt_cv_sharedlib_from_linklib_cmd='unknown' case $host_os in cygwin* | mingw* | pw32* | cegcc*) # two different shell functions defined in ltmain.sh # decide which to use based on capabilities of $DLLTOOL case `$DLLTOOL --help 2>&1` in *--identify-strict*) lt_cv_sharedlib_from_linklib_cmd=func_cygming_dll_for_implib ;; *) lt_cv_sharedlib_from_linklib_cmd=func_cygming_dll_for_implib_fallback ;; esac ;; *) # fallback: assume linklib IS sharedlib lt_cv_sharedlib_from_linklib_cmd="$ECHO" ;; esac ]) sharedlib_from_linklib_cmd=$lt_cv_sharedlib_from_linklib_cmd test -z "$sharedlib_from_linklib_cmd" && sharedlib_from_linklib_cmd=$ECHO _LT_DECL([], [sharedlib_from_linklib_cmd], [1], [Command to associate shared and link libraries]) ])# _LT_CHECK_SHAREDLIB_FROM_LINKLIB # LT_LIB_M # -------- # check for math library AC_DEFUN([LT_LIB_M], [AC_REQUIRE([AC_CANONICAL_HOST])dnl LIBM= case $host in *-*-beos* | *-*-cegcc* | *-*-cygwin* | *-*-haiku* | *-*-pw32* | *-*-darwin*) # These system don't have libm, or don't need it ;; *-ncr-sysv4.3*) AC_CHECK_LIB(mw, _mwvalidcheckl, LIBM="-lmw") AC_CHECK_LIB(m, cos, LIBM="$LIBM -lm") ;; *) AC_CHECK_LIB(m, cos, LIBM="-lm") ;; esac AC_SUBST([LIBM]) ])# LT_LIB_M # Old name: AU_ALIAS([AC_CHECK_LIBM], [LT_LIB_M]) dnl aclocal-1.4 backwards compatibility: dnl AC_DEFUN([AC_CHECK_LIBM], []) # _LT_COMPILER_NO_RTTI([TAGNAME]) # ------------------------------- m4_defun([_LT_COMPILER_NO_RTTI], [m4_require([_LT_TAG_COMPILER])dnl _LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1)= if test "$GCC" = yes; then case $cc_basename in nvcc*) _LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1)=' -Xcompiler -fno-builtin' ;; *) _LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1)=' -fno-builtin' ;; esac _LT_COMPILER_OPTION([if $compiler supports -fno-rtti -fno-exceptions], lt_cv_prog_compiler_rtti_exceptions, [-fno-rtti -fno-exceptions], [], [_LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1)="$_LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1) -fno-rtti -fno-exceptions"]) fi _LT_TAGDECL([no_builtin_flag], [lt_prog_compiler_no_builtin_flag], [1], [Compiler flag to turn off builtin functions]) ])# _LT_COMPILER_NO_RTTI # _LT_CMD_GLOBAL_SYMBOLS # ---------------------- m4_defun([_LT_CMD_GLOBAL_SYMBOLS], [AC_REQUIRE([AC_CANONICAL_HOST])dnl AC_REQUIRE([AC_PROG_CC])dnl AC_REQUIRE([AC_PROG_AWK])dnl AC_REQUIRE([LT_PATH_NM])dnl AC_REQUIRE([LT_PATH_LD])dnl m4_require([_LT_DECL_SED])dnl m4_require([_LT_DECL_EGREP])dnl m4_require([_LT_TAG_COMPILER])dnl # Check for command to grab the raw symbol name followed by C symbol from nm. AC_MSG_CHECKING([command to parse $NM output from $compiler object]) AC_CACHE_VAL([lt_cv_sys_global_symbol_pipe], [ # These are sane defaults that work on at least a few old systems. # [They come from Ultrix. What could be older than Ultrix?!! ;)] # Character class describing NM global symbol codes. symcode='[[BCDEGRST]]' # Regexp to match symbols that can be accessed directly from C. sympat='\([[_A-Za-z]][[_A-Za-z0-9]]*\)' # Define system-specific variables. case $host_os in aix*) symcode='[[BCDT]]' ;; cygwin* | mingw* | pw32* | cegcc*) symcode='[[ABCDGISTW]]' ;; hpux*) if test "$host_cpu" = ia64; then symcode='[[ABCDEGRST]]' fi ;; irix* | nonstopux*) symcode='[[BCDEGRST]]' ;; osf*) symcode='[[BCDEGQRST]]' ;; solaris*) symcode='[[BDRT]]' ;; sco3.2v5*) symcode='[[DT]]' ;; sysv4.2uw2*) symcode='[[DT]]' ;; sysv5* | sco5v6* | unixware* | OpenUNIX*) symcode='[[ABDT]]' ;; sysv4) symcode='[[DFNSTU]]' ;; esac # If we're using GNU nm, then use its standard symbol codes. case `$NM -V 2>&1` in *GNU* | *'with BFD'*) symcode='[[ABCDGIRSTW]]' ;; esac # Transform an extracted symbol line into a proper C declaration. # Some systems (esp. on ia64) link data and code symbols differently, # so use this general approach. lt_cv_sys_global_symbol_to_cdecl="sed -n -e 's/^T .* \(.*\)$/extern int \1();/p' -e 's/^$symcode* .* \(.*\)$/extern char \1;/p'" # Transform an extracted symbol line into symbol name and symbol address lt_cv_sys_global_symbol_to_c_name_address="sed -n -e 's/^: \([[^ ]]*\)[[ ]]*$/ {\\\"\1\\\", (void *) 0},/p' -e 's/^$symcode* \([[^ ]]*\) \([[^ ]]*\)$/ {\"\2\", (void *) \&\2},/p'" lt_cv_sys_global_symbol_to_c_name_address_lib_prefix="sed -n -e 's/^: \([[^ ]]*\)[[ ]]*$/ {\\\"\1\\\", (void *) 0},/p' -e 's/^$symcode* \([[^ ]]*\) \(lib[[^ ]]*\)$/ {\"\2\", (void *) \&\2},/p' -e 's/^$symcode* \([[^ ]]*\) \([[^ ]]*\)$/ {\"lib\2\", (void *) \&\2},/p'" # Handle CRLF in mingw tool chain opt_cr= case $build_os in mingw*) opt_cr=`$ECHO 'x\{0,1\}' | tr x '\015'` # option cr in regexp ;; esac # Try without a prefix underscore, then with it. for ac_symprfx in "" "_"; do # Transform symcode, sympat, and symprfx into a raw symbol and a C symbol. symxfrm="\\1 $ac_symprfx\\2 \\2" # Write the raw and C identifiers. if test "$lt_cv_nm_interface" = "MS dumpbin"; then # Fake it for dumpbin and say T for any non-static function # and D for any global variable. # Also find C++ and __fastcall symbols from MSVC++, # which start with @ or ?. lt_cv_sys_global_symbol_pipe="$AWK ['"\ " {last_section=section; section=\$ 3};"\ " /Section length .*#relocs.*(pick any)/{hide[last_section]=1};"\ " \$ 0!~/External *\|/{next};"\ " / 0+ UNDEF /{next}; / UNDEF \([^|]\)*()/{next};"\ " {if(hide[section]) next};"\ " {f=0}; \$ 0~/\(\).*\|/{f=1}; {printf f ? \"T \" : \"D \"};"\ " {split(\$ 0, a, /\||\r/); split(a[2], s)};"\ " s[1]~/^[@?]/{print s[1], s[1]; next};"\ " s[1]~prfx {split(s[1],t,\"@\"); print t[1], substr(t[1],length(prfx))}"\ " ' prfx=^$ac_symprfx]" else lt_cv_sys_global_symbol_pipe="sed -n -e 's/^.*[[ ]]\($symcode$symcode*\)[[ ]][[ ]]*$ac_symprfx$sympat$opt_cr$/$symxfrm/p'" fi # Check to see that the pipe works correctly. pipe_works=no rm -f conftest* cat > conftest.$ac_ext <<_LT_EOF #ifdef __cplusplus extern "C" { #endif char nm_test_var; void nm_test_func(void); void nm_test_func(void){} #ifdef __cplusplus } #endif int main(){nm_test_var='a';nm_test_func();return(0);} _LT_EOF if AC_TRY_EVAL(ac_compile); then # Now try to grab the symbols. nlist=conftest.nm if AC_TRY_EVAL(NM conftest.$ac_objext \| "$lt_cv_sys_global_symbol_pipe" \> $nlist) && test -s "$nlist"; then # Try sorting and uniquifying the output. if sort "$nlist" | uniq > "$nlist"T; then mv -f "$nlist"T "$nlist" else rm -f "$nlist"T fi # Make sure that we snagged all the symbols we need. if $GREP ' nm_test_var$' "$nlist" >/dev/null; then if $GREP ' nm_test_func$' "$nlist" >/dev/null; then cat <<_LT_EOF > conftest.$ac_ext #ifdef __cplusplus extern "C" { #endif _LT_EOF # Now generate the symbol file. eval "$lt_cv_sys_global_symbol_to_cdecl"' < "$nlist" | $GREP -v main >> conftest.$ac_ext' cat <<_LT_EOF >> conftest.$ac_ext /* The mapping between symbol names and symbols. */ const struct { const char *name; void *address; } lt__PROGRAM__LTX_preloaded_symbols[[]] = { { "@PROGRAM@", (void *) 0 }, _LT_EOF $SED "s/^$symcode$symcode* \(.*\) \(.*\)$/ {\"\2\", (void *) \&\2},/" < "$nlist" | $GREP -v main >> conftest.$ac_ext cat <<\_LT_EOF >> conftest.$ac_ext {0, (void *) 0} }; /* This works around a problem in FreeBSD linker */ #ifdef FREEBSD_WORKAROUND static const void *lt_preloaded_setup() { return lt__PROGRAM__LTX_preloaded_symbols; } #endif #ifdef __cplusplus } #endif _LT_EOF # Now try linking the two files. mv conftest.$ac_objext conftstm.$ac_objext lt_save_LIBS="$LIBS" lt_save_CFLAGS="$CFLAGS" LIBS="conftstm.$ac_objext" CFLAGS="$CFLAGS$_LT_TAGVAR(lt_prog_compiler_no_builtin_flag, $1)" if AC_TRY_EVAL(ac_link) && test -s conftest${ac_exeext}; then pipe_works=yes fi LIBS="$lt_save_LIBS" CFLAGS="$lt_save_CFLAGS" else echo "cannot find nm_test_func in $nlist" >&AS_MESSAGE_LOG_FD fi else echo "cannot find nm_test_var in $nlist" >&AS_MESSAGE_LOG_FD fi else echo "cannot run $lt_cv_sys_global_symbol_pipe" >&AS_MESSAGE_LOG_FD fi else echo "$progname: failed program was:" >&AS_MESSAGE_LOG_FD cat conftest.$ac_ext >&5 fi rm -rf conftest* conftst* # Do not use the global_symbol_pipe unless it works. if test "$pipe_works" = yes; then break else lt_cv_sys_global_symbol_pipe= fi done ]) if test -z "$lt_cv_sys_global_symbol_pipe"; then lt_cv_sys_global_symbol_to_cdecl= fi if test -z "$lt_cv_sys_global_symbol_pipe$lt_cv_sys_global_symbol_to_cdecl"; then AC_MSG_RESULT(failed) else AC_MSG_RESULT(ok) fi _LT_DECL([global_symbol_pipe], [lt_cv_sys_global_symbol_pipe], [1], [Take the output of nm and produce a listing of raw symbols and C names]) _LT_DECL([global_symbol_to_cdecl], [lt_cv_sys_global_symbol_to_cdecl], [1], [Transform the output of nm in a proper C declaration]) _LT_DECL([global_symbol_to_c_name_address], [lt_cv_sys_global_symbol_to_c_name_address], [1], [Transform the output of nm in a C name address pair]) _LT_DECL([global_symbol_to_c_name_address_lib_prefix], [lt_cv_sys_global_symbol_to_c_name_address_lib_prefix], [1], [Transform the output of nm in a C name address pair when lib prefix is needed]) ]) # _LT_CMD_GLOBAL_SYMBOLS # _LT_COMPILER_PIC([TAGNAME]) # --------------------------- m4_defun([_LT_COMPILER_PIC], [m4_require([_LT_TAG_COMPILER])dnl _LT_TAGVAR(lt_prog_compiler_wl, $1)= _LT_TAGVAR(lt_prog_compiler_pic, $1)= _LT_TAGVAR(lt_prog_compiler_static, $1)= AC_MSG_CHECKING([for $compiler option to produce PIC]) m4_if([$1], [CXX], [ # C++ specific cases for pic, static, wl, etc. if test "$GXX" = yes; then _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' case $host_os in aix*) # All AIX code is PIC. if test "$host_cpu" = ia64; then # AIX 5 now supports IA64 processor _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' fi ;; amigaos*) case $host_cpu in powerpc) # see comment about AmigaOS4 .so support _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; m68k) # FIXME: we need at least 68020 code to build shared libraries, but # adding the `-m68020' flag to GCC prevents building anything better, # like `-m68040'. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-m68020 -resident32 -malways-restore-a4' ;; esac ;; beos* | irix5* | irix6* | nonstopux* | osf3* | osf4* | osf5*) # PIC is the default for these OSes. ;; mingw* | cygwin* | os2* | pw32* | cegcc*) # This hack is so that the source file can tell whether it is being # built for inclusion in a dll (and should export symbols for example). # Although the cygwin gcc ignores -fPIC, still need this for old-style # (--disable-auto-import) libraries m4_if([$1], [GCJ], [], [_LT_TAGVAR(lt_prog_compiler_pic, $1)='-DDLL_EXPORT']) ;; darwin* | rhapsody*) # PIC is the default on this platform # Common symbols not allowed in MH_DYLIB files _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fno-common' ;; *djgpp*) # DJGPP does not support shared libraries at all _LT_TAGVAR(lt_prog_compiler_pic, $1)= ;; haiku*) # PIC is the default for Haiku. # The "-static" flag exists, but is broken. _LT_TAGVAR(lt_prog_compiler_static, $1)= ;; interix[[3-9]]*) # Interix 3.x gcc -fpic/-fPIC options generate broken code. # Instead, we relocate shared libraries at runtime. ;; sysv4*MP*) if test -d /usr/nec; then _LT_TAGVAR(lt_prog_compiler_pic, $1)=-Kconform_pic fi ;; hpux*) # PIC is the default for 64-bit PA HP-UX, but not for 32-bit # PA HP-UX. On IA64 HP-UX, PIC is the default but the pic flag # sets the default TLS model and affects inlining. case $host_cpu in hppa*64*) ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; esac ;; *qnx* | *nto*) # QNX uses GNU C++, but need to define -shared option too, otherwise # it will coredump. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC -shared' ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; esac else case $host_os in aix[[4-9]]*) # All AIX code is PIC. if test "$host_cpu" = ia64; then # AIX 5 now supports IA64 processor _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' else _LT_TAGVAR(lt_prog_compiler_static, $1)='-bnso -bI:/lib/syscalls.exp' fi ;; chorus*) case $cc_basename in cxch68*) # Green Hills C++ Compiler # _LT_TAGVAR(lt_prog_compiler_static, $1)="--no_auto_instantiation -u __main -u __premain -u _abort -r $COOL_DIR/lib/libOrb.a $MVME_DIR/lib/CC/libC.a $MVME_DIR/lib/classix/libcx.s.a" ;; esac ;; dgux*) case $cc_basename in ec++*) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' ;; ghcx*) # Green Hills C++ Compiler _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' ;; *) ;; esac ;; freebsd* | dragonfly*) # FreeBSD uses GNU C++ ;; hpux9* | hpux10* | hpux11*) case $cc_basename in CC*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_static, $1)='${wl}-a ${wl}archive' if test "$host_cpu" != ia64; then _LT_TAGVAR(lt_prog_compiler_pic, $1)='+Z' fi ;; aCC*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_static, $1)='${wl}-a ${wl}archive' case $host_cpu in hppa*64*|ia64*) # +Z the default ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='+Z' ;; esac ;; *) ;; esac ;; interix*) # This is c89, which is MS Visual C++ (no shared libs) # Anyone wants to do a port? ;; irix5* | irix6* | nonstopux*) case $cc_basename in CC*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' # CC pic flag -KPIC is the default. ;; *) ;; esac ;; linux* | k*bsd*-gnu | kopensolaris*-gnu) case $cc_basename in KCC*) # KAI C++ Compiler _LT_TAGVAR(lt_prog_compiler_wl, $1)='--backend -Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; ecpc* ) # old Intel C++ for x86_64 which still supported -KPIC. _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' ;; icpc* ) # Intel C++, used to be incompatible with GCC. # ICC 10 doesn't accept -KPIC any more. _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' ;; pgCC* | pgcpp*) # Portland Group C++ compiler _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fpic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; cxx*) # Compaq C++ # Make sure the PIC flag is empty. It appears that all Alpha # Linux and Compaq Tru64 Unix objects are PIC. _LT_TAGVAR(lt_prog_compiler_pic, $1)= _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; xlc* | xlC* | bgxl[[cC]]* | mpixl[[cC]]*) # IBM XL 8.0, 9.0 on PPC and BlueGene _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-qpic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-qstaticlink' ;; *) case `$CC -V 2>&1 | sed 5q` in *Sun\ C*) # Sun C++ 5.9 _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Qoption ld ' ;; esac ;; esac ;; lynxos*) ;; m88k*) ;; mvs*) case $cc_basename in cxx*) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-W c,exportall' ;; *) ;; esac ;; netbsd*) ;; *qnx* | *nto*) # QNX uses GNU C++, but need to define -shared option too, otherwise # it will coredump. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC -shared' ;; osf3* | osf4* | osf5*) case $cc_basename in KCC*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='--backend -Wl,' ;; RCC*) # Rational C++ 2.4.1 _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' ;; cxx*) # Digital/Compaq C++ _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' # Make sure the PIC flag is empty. It appears that all Alpha # Linux and Compaq Tru64 Unix objects are PIC. _LT_TAGVAR(lt_prog_compiler_pic, $1)= _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; *) ;; esac ;; psos*) ;; solaris*) case $cc_basename in CC*) # Sun C++ 4.2, 5.x and Centerline C++ _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Qoption ld ' ;; gcx*) # Green Hills C++ Compiler _LT_TAGVAR(lt_prog_compiler_pic, $1)='-PIC' ;; *) ;; esac ;; sunos4*) case $cc_basename in CC*) # Sun C++ 4.x _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; lcc*) # Lucid _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' ;; *) ;; esac ;; sysv5* | unixware* | sco3.2v5* | sco5v6* | OpenUNIX*) case $cc_basename in CC*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; esac ;; tandem*) case $cc_basename in NCC*) # NonStop-UX NCC 3.20 _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' ;; *) ;; esac ;; vxworks*) ;; *) _LT_TAGVAR(lt_prog_compiler_can_build_shared, $1)=no ;; esac fi ], [ if test "$GCC" = yes; then _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' case $host_os in aix*) # All AIX code is PIC. if test "$host_cpu" = ia64; then # AIX 5 now supports IA64 processor _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' fi ;; amigaos*) case $host_cpu in powerpc) # see comment about AmigaOS4 .so support _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; m68k) # FIXME: we need at least 68020 code to build shared libraries, but # adding the `-m68020' flag to GCC prevents building anything better, # like `-m68040'. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-m68020 -resident32 -malways-restore-a4' ;; esac ;; beos* | irix5* | irix6* | nonstopux* | osf3* | osf4* | osf5*) # PIC is the default for these OSes. ;; mingw* | cygwin* | pw32* | os2* | cegcc*) # This hack is so that the source file can tell whether it is being # built for inclusion in a dll (and should export symbols for example). # Although the cygwin gcc ignores -fPIC, still need this for old-style # (--disable-auto-import) libraries m4_if([$1], [GCJ], [], [_LT_TAGVAR(lt_prog_compiler_pic, $1)='-DDLL_EXPORT']) ;; darwin* | rhapsody*) # PIC is the default on this platform # Common symbols not allowed in MH_DYLIB files _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fno-common' ;; haiku*) # PIC is the default for Haiku. # The "-static" flag exists, but is broken. _LT_TAGVAR(lt_prog_compiler_static, $1)= ;; hpux*) # PIC is the default for 64-bit PA HP-UX, but not for 32-bit # PA HP-UX. On IA64 HP-UX, PIC is the default but the pic flag # sets the default TLS model and affects inlining. case $host_cpu in hppa*64*) # +Z the default ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; esac ;; interix[[3-9]]*) # Interix 3.x gcc -fpic/-fPIC options generate broken code. # Instead, we relocate shared libraries at runtime. ;; msdosdjgpp*) # Just because we use GCC doesn't mean we suddenly get shared libraries # on systems that don't support them. _LT_TAGVAR(lt_prog_compiler_can_build_shared, $1)=no enable_shared=no ;; *nto* | *qnx*) # QNX uses GNU C++, but need to define -shared option too, otherwise # it will coredump. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC -shared' ;; sysv4*MP*) if test -d /usr/nec; then _LT_TAGVAR(lt_prog_compiler_pic, $1)=-Kconform_pic fi ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' ;; esac case $cc_basename in nvcc*) # Cuda Compiler Driver 2.2 _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Xlinker ' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-Xcompiler -fPIC' ;; esac else # PORTME Check for flag to pass linker flags through the system compiler. case $host_os in aix*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' if test "$host_cpu" = ia64; then # AIX 5 now supports IA64 processor _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' else _LT_TAGVAR(lt_prog_compiler_static, $1)='-bnso -bI:/lib/syscalls.exp' fi ;; mingw* | cygwin* | pw32* | os2* | cegcc*) # This hack is so that the source file can tell whether it is being # built for inclusion in a dll (and should export symbols for example). m4_if([$1], [GCJ], [], [_LT_TAGVAR(lt_prog_compiler_pic, $1)='-DDLL_EXPORT']) ;; hpux9* | hpux10* | hpux11*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' # PIC is the default for IA64 HP-UX and 64-bit HP-UX, but # not for PA HP-UX. case $host_cpu in hppa*64*|ia64*) # +Z the default ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)='+Z' ;; esac # Is there a better lt_prog_compiler_static that works with the bundled CC? _LT_TAGVAR(lt_prog_compiler_static, $1)='${wl}-a ${wl}archive' ;; irix5* | irix6* | nonstopux*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' # PIC (with -KPIC) is the default. _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; linux* | k*bsd*-gnu | kopensolaris*-gnu) case $cc_basename in # old Intel for x86_64 which still supported -KPIC. ecc*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' ;; # icc used to be incompatible with GCC. # ICC 10 doesn't accept -KPIC any more. icc* | ifort*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-static' ;; # Lahey Fortran 8.1. lf95*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='--shared' _LT_TAGVAR(lt_prog_compiler_static, $1)='--static' ;; pgcc* | pgf77* | pgf90* | pgf95* | pgfortran*) # Portland Group compilers (*not* the Pentium gcc compiler, # which looks to be a dead project) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fpic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; ccc*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' # All Alpha code is PIC. _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; xl* | bgxl* | bgf* | mpixl*) # IBM XL C 8.0/Fortran 10.1, 11.1 on PPC and BlueGene _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-qpic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-qstaticlink' ;; *) case `$CC -V 2>&1 | sed 5q` in *Sun\ F* | *Sun*Fortran*) # Sun Fortran 8.3 passes all unrecognized flags to the linker _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' _LT_TAGVAR(lt_prog_compiler_wl, $1)='' ;; *Sun\ C*) # Sun C 5.9 _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' ;; esac ;; esac ;; newsos6) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; *nto* | *qnx*) # QNX uses GNU C++, but need to define -shared option too, otherwise # it will coredump. _LT_TAGVAR(lt_prog_compiler_pic, $1)='-fPIC -shared' ;; osf3* | osf4* | osf5*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' # All OSF/1 code is PIC. _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; rdos*) _LT_TAGVAR(lt_prog_compiler_static, $1)='-non_shared' ;; solaris*) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' case $cc_basename in f77* | f90* | f95*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Qoption ld ';; *) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,';; esac ;; sunos4*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Qoption ld ' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-PIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; sysv4 | sysv4.2uw2* | sysv4.3*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; sysv4*MP*) if test -d /usr/nec ;then _LT_TAGVAR(lt_prog_compiler_pic, $1)='-Kconform_pic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' fi ;; sysv5* | unixware* | sco3.2v5* | sco5v6* | OpenUNIX*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_pic, $1)='-KPIC' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; unicos*) _LT_TAGVAR(lt_prog_compiler_wl, $1)='-Wl,' _LT_TAGVAR(lt_prog_compiler_can_build_shared, $1)=no ;; uts4*) _LT_TAGVAR(lt_prog_compiler_pic, $1)='-pic' _LT_TAGVAR(lt_prog_compiler_static, $1)='-Bstatic' ;; *) _LT_TAGVAR(lt_prog_compiler_can_build_shared, $1)=no ;; esac fi ]) case $host_os in # For platforms which do not support PIC, -DPIC is meaningless: *djgpp*) _LT_TAGVAR(lt_prog_compiler_pic, $1)= ;; *) _LT_TAGVAR(lt_prog_compiler_pic, $1)="$_LT_TAGVAR(lt_prog_compiler_pic, $1)@&t@m4_if([$1],[],[ -DPIC],[m4_if([$1],[CXX],[ -DPIC],[])])" ;; esac AC_MSG_RESULT([$_LT_TAGVAR(lt_prog_compiler_pic, $1)]) _LT_TAGDECL([wl], [lt_prog_compiler_wl], [1], [How to pass a linker flag through the compiler]) # # Check to make sure the PIC flag actually works. # if test -n "$_LT_TAGVAR(lt_prog_compiler_pic, $1)"; then _LT_COMPILER_OPTION([if $compiler PIC flag $_LT_TAGVAR(lt_prog_compiler_pic, $1) works], [_LT_TAGVAR(lt_cv_prog_compiler_pic_works, $1)], [$_LT_TAGVAR(lt_prog_compiler_pic, $1)@&t@m4_if([$1],[],[ -DPIC],[m4_if([$1],[CXX],[ -DPIC],[])])], [], [case $_LT_TAGVAR(lt_prog_compiler_pic, $1) in "" | " "*) ;; 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solaris*) _LT_TAGVAR(no_undefined_flag, $1)=' -z defs' if test "$GCC" = yes; then wlarc='${wl}' _LT_TAGVAR(archive_cmds, $1)='$CC -shared ${wl}-z ${wl}text ${wl}-h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags' _LT_TAGVAR(archive_expsym_cmds, $1)='echo "{ global:" > $lib.exp~cat $export_symbols | $SED -e "s/\(.*\)/\1;/" >> $lib.exp~echo "local: *; };" >> $lib.exp~ $CC -shared ${wl}-z ${wl}text ${wl}-M ${wl}$lib.exp ${wl}-h ${wl}$soname -o $lib $libobjs $deplibs $compiler_flags~$RM $lib.exp' else case `$CC -V 2>&1` in *"Compilers 5.0"*) wlarc='' _LT_TAGVAR(archive_cmds, $1)='$LD -G${allow_undefined_flag} -h $soname -o $lib $libobjs $deplibs $linker_flags' _LT_TAGVAR(archive_expsym_cmds, $1)='echo "{ global:" > $lib.exp~cat $export_symbols | $SED -e "s/\(.*\)/\1;/" >> $lib.exp~echo "local: *; };" >> $lib.exp~ $LD -G${allow_undefined_flag} -M $lib.exp -h $soname -o $lib $libobjs $deplibs $linker_flags~$RM $lib.exp' ;; *) wlarc='${wl}' _LT_TAGVAR(archive_cmds, $1)='$CC -G${allow_undefined_flag} -h $soname -o $lib $libobjs $deplibs $compiler_flags' _LT_TAGVAR(archive_expsym_cmds, $1)='echo "{ global:" > $lib.exp~cat $export_symbols | $SED -e "s/\(.*\)/\1;/" >> $lib.exp~echo "local: *; };" >> $lib.exp~ $CC -G${allow_undefined_flag} -M $lib.exp -h $soname -o $lib $libobjs $deplibs $compiler_flags~$RM $lib.exp' ;; esac fi _LT_TAGVAR(hardcode_libdir_flag_spec, $1)='-R$libdir' _LT_TAGVAR(hardcode_shlibpath_var, $1)=no case $host_os in solaris2.[[0-5]] | solaris2.[[0-5]].*) ;; *) # The compiler driver will combine and reorder linker options, # but understands `-z linker_flag'. GCC discards it without `$wl', # but is careful enough not to reorder. # Supported since Solaris 2.6 (maybe 2.5.1?) if test "$GCC" = yes; then _LT_TAGVAR(whole_archive_flag_spec, $1)='${wl}-z ${wl}allextract$convenience ${wl}-z ${wl}defaultextract' else _LT_TAGVAR(whole_archive_flag_spec, $1)='-z allextract$convenience -z defaultextract' fi ;; esac _LT_TAGVAR(link_all_deplibs, $1)=yes ;; sunos4*) if test "x$host_vendor" = xsequent; then # Use $CC to link under sequent, because it throws in some extra .o # files that make .init and .fini sections work. _LT_TAGVAR(archive_cmds, $1)='$CC -G ${wl}-h $soname -o $lib $libobjs $deplibs $compiler_flags' else _LT_TAGVAR(archive_cmds, $1)='$LD -assert pure-text -Bstatic -o $lib $libobjs $deplibs $linker_flags' fi _LT_TAGVAR(hardcode_libdir_flag_spec, $1)='-L$libdir' _LT_TAGVAR(hardcode_direct, $1)=yes _LT_TAGVAR(hardcode_minus_L, $1)=yes _LT_TAGVAR(hardcode_shlibpath_var, $1)=no ;; sysv4) case $host_vendor in sni) _LT_TAGVAR(archive_cmds, $1)='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' _LT_TAGVAR(hardcode_direct, $1)=yes # is this really true??? ;; siemens) ## LD is ld it makes a PLAMLIB ## CC just makes a GrossModule. _LT_TAGVAR(archive_cmds, $1)='$LD -G -o $lib $libobjs $deplibs $linker_flags' _LT_TAGVAR(reload_cmds, $1)='$CC -r -o $output$reload_objs' _LT_TAGVAR(hardcode_direct, $1)=no ;; motorola) _LT_TAGVAR(archive_cmds, $1)='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' _LT_TAGVAR(hardcode_direct, $1)=no #Motorola manual says yes, but my tests say they lie ;; esac runpath_var='LD_RUN_PATH' _LT_TAGVAR(hardcode_shlibpath_var, $1)=no ;; sysv4.3*) _LT_TAGVAR(archive_cmds, $1)='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' _LT_TAGVAR(hardcode_shlibpath_var, $1)=no _LT_TAGVAR(export_dynamic_flag_spec, $1)='-Bexport' ;; sysv4*MP*) if test -d /usr/nec; then _LT_TAGVAR(archive_cmds, $1)='$LD -G -h $soname -o $lib $libobjs $deplibs $linker_flags' _LT_TAGVAR(hardcode_shlibpath_var, $1)=no runpath_var=LD_RUN_PATH hardcode_runpath_var=yes _LT_TAGVAR(ld_shlibs, $1)=yes fi ;; sysv4*uw2* | sysv5OpenUNIX* | sysv5UnixWare7.[[01]].[[10]]* | unixware7* | sco3.2v5.0.[[024]]*) _LT_TAGVAR(no_undefined_flag, $1)='${wl}-z,text' _LT_TAGVAR(archive_cmds_need_lc, $1)=no _LT_TAGVAR(hardcode_shlibpath_var, $1)=no runpath_var='LD_RUN_PATH' if test "$GCC" = yes; then _LT_TAGVAR(archive_cmds, $1)='$CC -shared ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' _LT_TAGVAR(archive_expsym_cmds, $1)='$CC -shared ${wl}-Bexport:$export_symbols ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' else _LT_TAGVAR(archive_cmds, $1)='$CC -G ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' _LT_TAGVAR(archive_expsym_cmds, $1)='$CC -G ${wl}-Bexport:$export_symbols ${wl}-h,$soname -o $lib $libobjs $deplibs $compiler_flags' fi ;; sysv5* | sco3.2v5* | sco5v6*) # Note: We can NOT use -z defs as we might desire, because we do not # link with -lc, and that would cause any symbols used from libc to # always be unresolved, which means just about no library would # ever link correctly. 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(KAI) C++ Compiler # KCC will only create a shared library if the output file # ends with ".so" (or ".sl" for HP-UX), so rename the library # to its proper name (with version) after linking. _LT_TAGVAR(archive_cmds, $1)='tempext=`echo $shared_ext | $SED -e '\''s/\([[^()0-9A-Za-z{}]]\)/\\\\\1/g'\''`; templib=`echo $lib | $SED -e "s/\${tempext}\..*/.so/"`; $CC $predep_objects $libobjs $deplibs $postdep_objects $compiler_flags --soname $soname -o \$templib; mv \$templib $lib' _LT_TAGVAR(archive_expsym_cmds, $1)='tempext=`echo $shared_ext | $SED -e '\''s/\([[^()0-9A-Za-z{}]]\)/\\\\\1/g'\''`; templib=`echo $lib | $SED -e "s/\${tempext}\..*/.so/"`; $CC $predep_objects $libobjs $deplibs $postdep_objects $compiler_flags --soname $soname -o \$templib ${wl}-retain-symbols-file,$export_symbols; mv \$templib $lib' # Commands to make compiler produce verbose output that lists # what "hidden" libraries, object files and flags are used when # linking a shared library. # # There doesn't appear to be a way to prevent this compiler from # explicitly linking system object files so we need to strip them # from the output so that they don't get included in the library # dependencies. output_verbose_link_cmd='templist=`$CC $CFLAGS -v conftest.$objext -o libconftest$shared_ext 2>&1 | $GREP "ld"`; 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*) # Version 8.0 or newer tmp_idyn= case $host_cpu in ia64*) tmp_idyn=' -i_dynamic';; esac _LT_TAGVAR(archive_cmds, $1)='$CC -shared'"$tmp_idyn"' $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname -o $lib' _LT_TAGVAR(archive_expsym_cmds, $1)='$CC -shared'"$tmp_idyn"' $libobjs $deplibs $compiler_flags ${wl}-soname $wl$soname ${wl}-retain-symbols-file $wl$export_symbols -o $lib' ;; esac _LT_TAGVAR(archive_cmds_need_lc, $1)=no _LT_TAGVAR(hardcode_libdir_flag_spec, $1)='${wl}-rpath,$libdir' _LT_TAGVAR(export_dynamic_flag_spec, $1)='${wl}--export-dynamic' _LT_TAGVAR(whole_archive_flag_spec, $1)='${wl}--whole-archive$convenience ${wl}--no-whole-archive' ;; pgCC* | pgcpp*) # Portland Group C++ compiler case `$CC -V` in *pgCC\ [[1-5]].* | *pgcpp\ [[1-5]].*) _LT_TAGVAR(prelink_cmds, $1)='tpldir=Template.dir~ rm -rf $tpldir~ $CC --prelink_objects --instantiation_dir $tpldir $objs $libobjs $compile_deplibs~ compile_command="$compile_command `find $tpldir -name \*.o | $NL2SP`"' _LT_TAGVAR(old_archive_cmds, $1)='tpldir=Template.dir~ rm -rf $tpldir~ $CC --prelink_objects --instantiation_dir $tpldir $oldobjs$old_deplibs~ $AR $AR_FLAGS $oldlib$oldobjs$old_deplibs `find $tpldir -name \*.o | $NL2SP`~ $RANLIB $oldlib' _LT_TAGVAR(archive_cmds, $1)='tpldir=Template.dir~ rm -rf $tpldir~ $CC --prelink_objects --instantiation_dir $tpldir $predep_objects $libobjs $deplibs $convenience $postdep_objects~ $CC -shared $pic_flag $predep_objects $libobjs $deplibs `find $tpldir -name \*.o | $NL2SP` $postdep_objects $compiler_flags ${wl}-soname ${wl}$soname -o $lib' _LT_TAGVAR(archive_expsym_cmds, $1)='tpldir=Template.dir~ rm -rf $tpldir~ $CC --prelink_objects --instantiation_dir $tpldir $predep_objects $libobjs $deplibs $convenience $postdep_objects~ $CC -shared $pic_flag $predep_objects $libobjs $deplibs `find $tpldir -name \*.o | $NL2SP` $postdep_objects $compiler_flags ${wl}-soname ${wl}$soname ${wl}-retain-symbols-file ${wl}$export_symbols -o $lib' ;; *) # Version 6 and above use weak symbols _LT_TAGVAR(archive_cmds, $1)='$CC -shared $pic_flag $predep_objects $libobjs $deplibs $postdep_objects $compiler_flags ${wl}-soname ${wl}$soname -o $lib' _LT_TAGVAR(archive_expsym_cmds, $1)='$CC -shared $pic_flag $predep_objects $libobjs $deplibs $postdep_objects $compiler_flags ${wl}-soname ${wl}$soname ${wl}-retain-symbols-file ${wl}$export_symbols -o $lib' ;; esac _LT_TAGVAR(hardcode_libdir_flag_spec, $1)='${wl}--rpath ${wl}$libdir' _LT_TAGVAR(export_dynamic_flag_spec, $1)='${wl}--export-dynamic' _LT_TAGVAR(whole_archive_flag_spec, $1)='${wl}--whole-archive`for conv in $convenience\"\"; do test -n \"$conv\" && new_convenience=\"$new_convenience,$conv\"; done; func_echo_all \"$new_convenience\"` ${wl}--no-whole-archive' ;; cxx*) # Compaq C++ _LT_TAGVAR(archive_cmds, $1)='$CC -shared $predep_objects $libobjs $deplibs $postdep_objects $compiler_flags ${wl}-soname $wl$soname -o $lib' _LT_TAGVAR(archive_expsym_cmds, $1)='$CC -shared $predep_objects $libobjs $deplibs $postdep_objects $compiler_flags ${wl}-soname $wl$soname -o $lib ${wl}-retain-symbols-file $wl$export_symbols' runpath_var=LD_RUN_PATH _LT_TAGVAR(hardcode_libdir_flag_spec, $1)='-rpath $libdir' _LT_TAGVAR(hardcode_libdir_separator, $1)=: # Commands to make compiler produce verbose output that lists # what "hidden" libraries, object files and flags are used when # linking a shared library. # # There doesn't appear to be a way to prevent this compiler from # explicitly linking system object files so we need to strip them # from the output so that they don't get included in the library # dependencies. output_verbose_link_cmd='templist=`$CC -shared $CFLAGS -v conftest.$objext 2>&1 | $GREP "ld"`; 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for conv in $convenience\"\"; do test -z \"$conv\" || new_convenience=\"$new_convenience,$conv\"; done; func_echo_all \"$new_convenience\"` ${wl}--no-whole-archive' _LT_TAGVAR(compiler_needs_object, $1)=yes # Not sure whether something based on # $CC $CFLAGS -v conftest.$objext -o libconftest$shared_ext 2>&1 # would be better. output_verbose_link_cmd='func_echo_all' # Archives containing C++ object files must be created using # "CC -xar", where "CC" is the Sun C++ compiler. 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Vaughan, 2004 # # This file is free software; the Free Software Foundation gives # unlimited permission to copy and/or distribute it, with or without # modifications, as long as this notice is preserved. # serial 6 ltsugar.m4 # This is to help aclocal find these macros, as it can't see m4_define. AC_DEFUN([LTSUGAR_VERSION], [m4_if([0.1])]) # lt_join(SEP, ARG1, [ARG2...]) # ----------------------------- # Produce ARG1SEPARG2...SEPARGn, omitting [] arguments and their # associated separator. # Needed until we can rely on m4_join from Autoconf 2.62, since all earlier # versions in m4sugar had bugs. m4_define([lt_join], [m4_if([$#], [1], [], [$#], [2], [[$2]], [m4_if([$2], [], [], [[$2]_])$0([$1], m4_shift(m4_shift($@)))])]) m4_define([_lt_join], [m4_if([$#$2], [2], [], [m4_if([$2], [], [], [[$1$2]])$0([$1], m4_shift(m4_shift($@)))])]) # lt_car(LIST) # lt_cdr(LIST) # ------------ # Manipulate m4 lists. # These macros are necessary as long as will still need to support # Autoconf-2.59 which quotes differently. m4_define([lt_car], [[$1]]) m4_define([lt_cdr], [m4_if([$#], 0, [m4_fatal([$0: cannot be called without arguments])], [$#], 1, [], [m4_dquote(m4_shift($@))])]) m4_define([lt_unquote], $1) # lt_append(MACRO-NAME, STRING, [SEPARATOR]) # ------------------------------------------ # Redefine MACRO-NAME to hold its former content plus `SEPARATOR'`STRING'. # Note that neither SEPARATOR nor STRING are expanded; they are appended # to MACRO-NAME as is (leaving the expansion for when MACRO-NAME is invoked). # No SEPARATOR is output if MACRO-NAME was previously undefined (different # than defined and empty). # # This macro is needed until we can rely on Autoconf 2.62, since earlier # versions of m4sugar mistakenly expanded SEPARATOR but not STRING. m4_define([lt_append], [m4_define([$1], m4_ifdef([$1], [m4_defn([$1])[$3]])[$2])]) # lt_combine(SEP, PREFIX-LIST, INFIX, SUFFIX1, [SUFFIX2...]) # ---------------------------------------------------------- # Produce a SEP delimited list of all paired combinations of elements of # PREFIX-LIST with SUFFIX1 through SUFFIXn. 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It'll see the old AC_DEFUN # in /usr/share/aclocal/libtool.m4 and remember it, then when it sees us # using a macro with the same name in our local m4/libtool.m4 it'll # pull the old libtool.m4 in (it doesn't see our shiny new m4_define # and doesn't know about Autoconf macros at all.) # # So we provide this file, which has a silly filename so it's always # included after everything else. This provides aclocal with the # AC_DEFUNs it wants, but when m4 processes it, it doesn't do anything # because those macros already exist, or will be overwritten later. # We use AC_DEFUN over AU_DEFUN for compatibility with aclocal-1.6. # # Anytime we withdraw an AC_DEFUN or AU_DEFUN, remember to add it here. # Yes, that means every name once taken will need to remain here until # we give up compatibility with versions before 1.7, at which point # we need to keep only those names which we still refer to. # This is to help aclocal find these macros, as it can't see m4_define. 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There is NO # warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # GNU Libtool is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # As a special exception to the GNU General Public License, # if you distribute this file as part of a program or library that # is built using GNU Libtool, you may include this file under the # same distribution terms that you use for the rest of that program. # # GNU Libtool is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with GNU Libtool; see the file COPYING. If not, a copy # can be downloaded from http://www.gnu.org/licenses/gpl.html, # or obtained by writing to the Free Software Foundation, Inc., # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # Usage: $progname [OPTION]... [MODE-ARG]... # # Provide generalized library-building support services. # # --config show all configuration variables # --debug enable verbose shell tracing # -n, --dry-run display commands without modifying any files # --features display basic configuration information and exit # --mode=MODE use operation mode MODE # --preserve-dup-deps don't remove duplicate dependency libraries # --quiet, --silent don't print informational messages # --no-quiet, --no-silent # print informational messages (default) # --tag=TAG use configuration variables from tag TAG # -v, --verbose print more informational messages than default # --no-verbose don't print the extra informational messages # --version print version information # -h, --help, --help-all print short, long, or detailed help message # # MODE must be one of the following: # # clean remove files from the build directory # compile compile a source file into a libtool object # execute automatically set library path, then run a program # finish complete the installation of libtool libraries # install install libraries or executables # link create a library or an executable # uninstall remove libraries from an installed directory # # MODE-ARGS vary depending on the MODE. 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Use a colon delimited # list incase some portion of path contains whitespace. my_dir_list="$my_directory_path:$my_dir_list" # If the last portion added has no slash in it, the list is done case $my_directory_path in */*) ;; *) break ;; esac # ...otherwise throw away the child directory and loop my_directory_path=`$ECHO "$my_directory_path" | $SED -e "$dirname"` done my_dir_list=`$ECHO "$my_dir_list" | $SED 's,:*$,,'` save_mkdir_p_IFS="$IFS"; IFS=':' for my_dir in $my_dir_list; do IFS="$save_mkdir_p_IFS" # mkdir can fail with a `File exist' error if two processes # try to create one of the directories concurrently. Don't # stop in that case! $MKDIR "$my_dir" 2>/dev/null || : done IFS="$save_mkdir_p_IFS" # Bail out if we (or some other process) failed to create a directory. test -d "$my_directory_path" || \ func_fatal_error "Failed to create \`$1'" fi } # func_mktempdir [string] # Make a temporary directory that won't clash with other running # libtool processes, and avoids race conditions if possible. If # given, STRING is the basename for that directory. func_mktempdir () { my_template="${TMPDIR-/tmp}/${1-$progname}" if test "$opt_dry_run" = ":"; then # Return a directory name, but don't create it in dry-run mode my_tmpdir="${my_template}-$$" else # If mktemp works, use that first and foremost my_tmpdir=`mktemp -d "${my_template}-XXXXXXXX" 2>/dev/null` if test ! -d "$my_tmpdir"; then # Failing that, at least try and use $RANDOM to avoid a race my_tmpdir="${my_template}-${RANDOM-0}$$" save_mktempdir_umask=`umask` umask 0077 $MKDIR "$my_tmpdir" umask $save_mktempdir_umask fi # If we're not in dry-run mode, bomb out on failure test -d "$my_tmpdir" || \ func_fatal_error "cannot create temporary directory \`$my_tmpdir'" fi $ECHO "$my_tmpdir" } # func_quote_for_eval arg # Aesthetically quote ARG to be evaled later. # This function returns two values: FUNC_QUOTE_FOR_EVAL_RESULT # is double-quoted, suitable for a subsequent eval, whereas # FUNC_QUOTE_FOR_EVAL_UNQUOTED_RESULT has merely all characters # which are still active within double quotes backslashified. func_quote_for_eval () { case $1 in *[\\\`\"\$]*) func_quote_for_eval_unquoted_result=`$ECHO "$1" | $SED "$sed_quote_subst"` ;; *) func_quote_for_eval_unquoted_result="$1" ;; esac case $func_quote_for_eval_unquoted_result in # Double-quote args containing shell metacharacters to delay # word splitting, command substitution and and variable # expansion for a subsequent eval. # Many Bourne shells cannot handle close brackets correctly # in scan sets, so we specify it separately. *[\[\~\#\^\&\*\(\)\{\}\|\;\<\>\?\'\ \ ]*|*]*|"") func_quote_for_eval_result="\"$func_quote_for_eval_unquoted_result\"" ;; *) func_quote_for_eval_result="$func_quote_for_eval_unquoted_result" esac } # func_quote_for_expand arg # Aesthetically quote ARG to be evaled later; same as above, # but do not quote variable references. func_quote_for_expand () { case $1 in *[\\\`\"]*) my_arg=`$ECHO "$1" | $SED \ -e "$double_quote_subst" -e "$sed_double_backslash"` ;; *) my_arg="$1" ;; esac case $my_arg in # Double-quote args containing shell metacharacters to delay # word splitting and command substitution for a subsequent eval. # Many Bourne shells cannot handle close brackets correctly # in scan sets, so we specify it separately. *[\[\~\#\^\&\*\(\)\{\}\|\;\<\>\?\'\ \ ]*|*]*|"") my_arg="\"$my_arg\"" ;; esac func_quote_for_expand_result="$my_arg" } # func_show_eval cmd [fail_exp] # Unless opt_silent is true, then output CMD. Then, if opt_dryrun is # not true, evaluate CMD. If the evaluation of CMD fails, and FAIL_EXP # is given, then evaluate it. func_show_eval () { my_cmd="$1" my_fail_exp="${2-:}" ${opt_silent-false} || { func_quote_for_expand "$my_cmd" eval "func_echo $func_quote_for_expand_result" } if ${opt_dry_run-false}; then :; else eval "$my_cmd" my_status=$? if test "$my_status" -eq 0; then :; else eval "(exit $my_status); $my_fail_exp" fi fi } # func_show_eval_locale cmd [fail_exp] # Unless opt_silent is true, then output CMD. Then, if opt_dryrun is # not true, evaluate CMD. If the evaluation of CMD fails, and FAIL_EXP # is given, then evaluate it. Use the saved locale for evaluation. func_show_eval_locale () { my_cmd="$1" my_fail_exp="${2-:}" ${opt_silent-false} || { func_quote_for_expand "$my_cmd" eval "func_echo $func_quote_for_expand_result" } if ${opt_dry_run-false}; then :; else eval "$lt_user_locale $my_cmd" my_status=$? eval "$lt_safe_locale" if test "$my_status" -eq 0; then :; else eval "(exit $my_status); $my_fail_exp" fi fi } # func_tr_sh # Turn $1 into a string suitable for a shell variable name. # Result is stored in $func_tr_sh_result. All characters # not in the set a-zA-Z0-9_ are replaced with '_'. Further, # if $1 begins with a digit, a '_' is prepended as well. func_tr_sh () { case "$1" in [0-9]* | *[!a-zA-Z0-9_]*) func_tr_sh_result=`$ECHO "$1" | $SED 's/^\([0-9]\)/_\1/; s/[^a-zA-Z0-9_]/_/g'` ;; * ) func_tr_sh_result=$1 ;; esac } # func_version # Echo version message to standard output and exit. func_version () { $SED -n '/(C)/!b go :more /\./!{ N s/\n# // b more } :go /^# '$PROGRAM' (GNU /,/# warranty; / { s/^# // s/^# *$// s/\((C)\)[ 0-9,-]*\( [1-9][0-9]*\)/\1\2/ p }' < "$progpath" exit $? } # func_usage # Echo short help message to standard output and exit. func_usage () { $SED -n '/^# Usage:/,/^# *-h/ { s/^# // s/^# *$// s/\$progname/'$progname'/ p }' < "$progpath" echo $ECHO "run \`$progname --help | more' for full usage" exit $? } # func_help [NOEXIT] # Echo long help message to standard output and exit, # unless 'noexit' is passed as argument. func_help () { $SED -n '/^# Usage:/,/# Report bugs to/ { s/^# // s/^# *$// s*\$progname*'$progname'* s*\$host*'"$host"'* s*\$SHELL*'"$SHELL"'* s*\$LTCC*'"$LTCC"'* s*\$LTCFLAGS*'"$LTCFLAGS"'* s*\$LD*'"$LD"'* s/\$with_gnu_ld/'"$with_gnu_ld"'/ s/\$automake_version/'"`(automake --version) 2>/dev/null |$SED 1q`"'/ s/\$autoconf_version/'"`(autoconf --version) 2>/dev/null |$SED 1q`"'/ p }' < "$progpath" ret=$? if test -z "$1"; then exit $ret fi } # func_missing_arg argname # Echo program name prefixed message to standard error and set global # exit_cmd. func_missing_arg () { func_error "missing argument for $1" exit_cmd=exit } exit_cmd=: magic="%%%MAGIC variable%%%" magic_exe="%%%MAGIC EXE variable%%%" # Global variables. # $mode is unset nonopt= execute_dlfiles= preserve_args= lo2o="s/\\.lo\$/.${objext}/" o2lo="s/\\.${objext}\$/.lo/" extracted_archives= extracted_serial=0 opt_dry_run=false opt_duplicate_deps=false opt_silent=false opt_debug=: # If this variable is set in any of the actions, the command in it # will be execed at the end. This prevents here-documents from being # left over by shells. exec_cmd= # func_fatal_configuration arg... # Echo program name prefixed message to standard error, followed by # a configuration failure hint, and exit. func_fatal_configuration () { func_error ${1+"$@"} func_error "See the $PACKAGE documentation for more information." func_fatal_error "Fatal configuration error." } # func_config # Display the configuration for all the tags in this script. func_config () { re_begincf='^# ### BEGIN LIBTOOL' re_endcf='^# ### END LIBTOOL' # Default configuration. $SED "1,/$re_begincf CONFIG/d;/$re_endcf CONFIG/,\$d" < "$progpath" # Now print the configurations for the tags. for tagname in $taglist; do $SED -n "/$re_begincf TAG CONFIG: $tagname\$/,/$re_endcf TAG CONFIG: $tagname\$/p" < "$progpath" done exit $? } # func_features # Display the features supported by this script. func_features () { echo "host: $host" if test "$build_libtool_libs" = yes; then echo "enable shared libraries" else echo "disable shared libraries" fi if test "$build_old_libs" = yes; then echo "enable static libraries" else echo "disable static libraries" fi exit $? } # func_enable_tag tagname # Verify that TAGNAME is valid, and either flag an error and exit, or # enable the TAGNAME tag. We also add TAGNAME to the global $taglist # variable here. func_enable_tag () { # Global variable: tagname="$1" re_begincf="^# ### BEGIN LIBTOOL TAG CONFIG: $tagname\$" re_endcf="^# ### END LIBTOOL TAG CONFIG: $tagname\$" sed_extractcf="/$re_begincf/,/$re_endcf/p" # Validate tagname. case $tagname in *[!-_A-Za-z0-9,/]*) func_fatal_error "invalid tag name: $tagname" ;; esac # Don't test for the "default" C tag, as we know it's # there but not specially marked. case $tagname in CC) ;; *) if $GREP "$re_begincf" "$progpath" >/dev/null 2>&1; then taglist="$taglist $tagname" # Evaluate the configuration. Be careful to quote the path # and the sed script, to avoid splitting on whitespace, but # also don't use non-portable quotes within backquotes within # quotes we have to do it in 2 steps: extractedcf=`$SED -n -e "$sed_extractcf" < "$progpath"` eval "$extractedcf" else func_error "ignoring unknown tag $tagname" fi ;; esac } # Parse options once, thoroughly. This comes as soon as possible in # the script to make things like `libtool --version' happen quickly. { # Shorthand for --mode=foo, only valid as the first argument case $1 in clean|clea|cle|cl) shift; set dummy --mode clean ${1+"$@"}; shift ;; compile|compil|compi|comp|com|co|c) shift; set dummy --mode compile ${1+"$@"}; shift ;; execute|execut|execu|exec|exe|ex|e) shift; set dummy --mode execute ${1+"$@"}; shift ;; finish|finis|fini|fin|fi|f) shift; set dummy --mode finish ${1+"$@"}; shift ;; install|instal|insta|inst|ins|in|i) shift; set dummy --mode install ${1+"$@"}; shift ;; link|lin|li|l) shift; set dummy --mode link ${1+"$@"}; shift ;; uninstall|uninstal|uninsta|uninst|unins|unin|uni|un|u) shift; set dummy --mode uninstall ${1+"$@"}; shift ;; esac # Parse non-mode specific arguments: while test "$#" -gt 0; do opt="$1" shift case $opt in --config) func_config ;; --debug) preserve_args="$preserve_args $opt" func_echo "enabling shell trace mode" opt_debug='set -x' $opt_debug ;; -dlopen) test "$#" -eq 0 && func_missing_arg "$opt" && break execute_dlfiles="$execute_dlfiles $1" shift ;; --dry-run | -n) opt_dry_run=: ;; --features) func_features ;; --finish) mode="finish" ;; --mode) test "$#" -eq 0 && func_missing_arg "$opt" && break case $1 in # Valid mode arguments: clean) ;; compile) ;; execute) ;; finish) ;; install) ;; link) ;; relink) ;; uninstall) ;; # Catch anything else as an error *) func_error "invalid argument for $opt" exit_cmd=exit break ;; esac mode="$1" shift ;; --preserve-dup-deps) opt_duplicate_deps=: ;; --quiet|--silent) preserve_args="$preserve_args $opt" opt_silent=: opt_verbose=false ;; --no-quiet|--no-silent) preserve_args="$preserve_args $opt" opt_silent=false ;; --verbose| -v) preserve_args="$preserve_args $opt" opt_silent=false opt_verbose=: ;; --no-verbose) preserve_args="$preserve_args $opt" opt_verbose=false ;; --tag) test "$#" -eq 0 && func_missing_arg "$opt" && break preserve_args="$preserve_args $opt $1" func_enable_tag "$1" # tagname is set here shift ;; # Separate optargs to long options: -dlopen=*|--mode=*|--tag=*) func_opt_split "$opt" set dummy "$func_opt_split_opt" "$func_opt_split_arg" ${1+"$@"} shift ;; -\?|-h) func_usage ;; --help) opt_help=: ;; --help-all) opt_help=': help-all' ;; --version) func_version ;; -*) func_fatal_help "unrecognized option \`$opt'" ;; *) nonopt="$opt" break ;; esac done case $host in *cygwin* | *mingw* | *pw32* | *cegcc*) # don't eliminate duplications in $postdeps and $predeps opt_duplicate_compiler_generated_deps=: ;; *) opt_duplicate_compiler_generated_deps=$opt_duplicate_deps ;; esac # Having warned about all mis-specified options, bail out if # anything was wrong. $exit_cmd $EXIT_FAILURE } # func_check_version_match # Ensure that we are using m4 macros, and libtool script from the same # release of libtool. func_check_version_match () { if test "$package_revision" != "$macro_revision"; then if test "$VERSION" != "$macro_version"; then if test -z "$macro_version"; then cat >&2 <<_LT_EOF $progname: Version mismatch error. This is $PACKAGE $VERSION, but the $progname: definition of this LT_INIT comes from an older release. $progname: You should recreate aclocal.m4 with macros from $PACKAGE $VERSION $progname: and run autoconf again. _LT_EOF else cat >&2 <<_LT_EOF $progname: Version mismatch error. This is $PACKAGE $VERSION, but the $progname: definition of this LT_INIT comes from $PACKAGE $macro_version. $progname: You should recreate aclocal.m4 with macros from $PACKAGE $VERSION $progname: and run autoconf again. _LT_EOF fi else cat >&2 <<_LT_EOF $progname: Version mismatch error. This is $PACKAGE $VERSION, revision $package_revision, $progname: but the definition of this LT_INIT comes from revision $macro_revision. $progname: You should recreate aclocal.m4 with macros from revision $package_revision $progname: of $PACKAGE $VERSION and run autoconf again. _LT_EOF fi exit $EXIT_MISMATCH fi } ## ----------- ## ## Main. ## ## ----------- ## $opt_help || { # Sanity checks first: func_check_version_match if test "$build_libtool_libs" != yes && test "$build_old_libs" != yes; then func_fatal_configuration "not configured to build any kind of library" fi test -z "$mode" && func_fatal_error "error: you must specify a MODE." # Darwin sucks eval std_shrext=\"$shrext_cmds\" # Only execute mode is allowed to have -dlopen flags. if test -n "$execute_dlfiles" && test "$mode" != execute; then func_error "unrecognized option \`-dlopen'" $ECHO "$help" 1>&2 exit $EXIT_FAILURE fi # Change the help message to a mode-specific one. generic_help="$help" help="Try \`$progname --help --mode=$mode' for more information." } # func_lalib_p file # True iff FILE is a libtool `.la' library or `.lo' object file. # This function is only a basic sanity check; it will hardly flush out # determined imposters. func_lalib_p () { test -f "$1" && $SED -e 4q "$1" 2>/dev/null \ | $GREP "^# Generated by .*$PACKAGE" > /dev/null 2>&1 } # func_lalib_unsafe_p file # True iff FILE is a libtool `.la' library or `.lo' object file. # This function implements the same check as func_lalib_p without # resorting to external programs. To this end, it redirects stdin and # closes it afterwards, without saving the original file descriptor. # As a safety measure, use it only where a negative result would be # fatal anyway. Works if `file' does not exist. func_lalib_unsafe_p () { lalib_p=no if test -f "$1" && test -r "$1" && exec 5<&0 <"$1"; then for lalib_p_l in 1 2 3 4 do read lalib_p_line case "$lalib_p_line" in \#\ Generated\ by\ *$PACKAGE* ) lalib_p=yes; break;; esac done exec 0<&5 5<&- fi test "$lalib_p" = yes } # func_ltwrapper_script_p file # True iff FILE is a libtool wrapper script # This function is only a basic sanity check; it will hardly flush out # determined imposters. func_ltwrapper_script_p () { func_lalib_p "$1" } # func_ltwrapper_executable_p file # True iff FILE is a libtool wrapper executable # This function is only a basic sanity check; it will hardly flush out # determined imposters. func_ltwrapper_executable_p () { func_ltwrapper_exec_suffix= case $1 in *.exe) ;; *) func_ltwrapper_exec_suffix=.exe ;; esac $GREP "$magic_exe" "$1$func_ltwrapper_exec_suffix" >/dev/null 2>&1 } # func_ltwrapper_scriptname file # Assumes file is an ltwrapper_executable # uses $file to determine the appropriate filename for a # temporary ltwrapper_script. func_ltwrapper_scriptname () { func_ltwrapper_scriptname_result="" if func_ltwrapper_executable_p "$1"; then func_dirname_and_basename "$1" "" "." func_stripname '' '.exe' "$func_basename_result" func_ltwrapper_scriptname_result="$func_dirname_result/$objdir/${func_stripname_result}_ltshwrapper" fi } # func_ltwrapper_p file # True iff FILE is a libtool wrapper script or wrapper executable # This function is only a basic sanity check; it will hardly flush out # determined imposters. func_ltwrapper_p () { func_ltwrapper_script_p "$1" || func_ltwrapper_executable_p "$1" } # func_execute_cmds commands fail_cmd # Execute tilde-delimited COMMANDS. # If FAIL_CMD is given, eval that upon failure. # FAIL_CMD may read-access the current command in variable CMD! func_execute_cmds () { $opt_debug save_ifs=$IFS; IFS='~' for cmd in $1; do IFS=$save_ifs eval cmd=\"$cmd\" func_show_eval "$cmd" "${2-:}" done IFS=$save_ifs } # func_source file # Source FILE, adding directory component if necessary. # Note that it is not necessary on cygwin/mingw to append a dot to # FILE even if both FILE and FILE.exe exist: automatic-append-.exe # behavior happens only for exec(3), not for open(2)! Also, sourcing # `FILE.' does not work on cygwin managed mounts. func_source () { $opt_debug case $1 in */* | *\\*) . "$1" ;; *) . "./$1" ;; esac } # func_infer_tag arg # Infer tagged configuration to use if any are available and # if one wasn't chosen via the "--tag" command line option. # Only attempt this if the compiler in the base compile # command doesn't match the default compiler. # arg is usually of the form 'gcc ...' func_infer_tag () { $opt_debug if test -n "$available_tags" && test -z "$tagname"; then CC_quoted= for arg in $CC; do func_quote_for_eval "$arg" CC_quoted="$CC_quoted $func_quote_for_eval_result" done CC_expanded=`func_echo_all $CC` CC_quoted_expanded=`func_echo_all $CC_quoted` case $@ in # Blanks in the command may have been stripped by the calling shell, # but not from the CC environment variable when configure was run. " $CC "* | "$CC "* | " $CC_expanded "* | "$CC_expanded "* | \ " $CC_quoted"* | "$CC_quoted "* | " $CC_quoted_expanded "* | "$CC_quoted_expanded "*) ;; # Blanks at the start of $base_compile will cause this to fail # if we don't check for them as well. *) for z in $available_tags; do if $GREP "^# ### BEGIN LIBTOOL TAG CONFIG: $z$" < "$progpath" > /dev/null; then # Evaluate the configuration. eval "`${SED} -n -e '/^# ### BEGIN LIBTOOL TAG CONFIG: '$z'$/,/^# ### END LIBTOOL TAG CONFIG: '$z'$/p' < $progpath`" CC_quoted= for arg in $CC; do # Double-quote args containing other shell metacharacters. func_quote_for_eval "$arg" CC_quoted="$CC_quoted $func_quote_for_eval_result" done CC_expanded=`func_echo_all $CC` CC_quoted_expanded=`func_echo_all $CC_quoted` case "$@ " in " $CC "* | "$CC "* | " $CC_expanded "* | "$CC_expanded "* | \ " $CC_quoted"* | "$CC_quoted "* | " $CC_quoted_expanded "* | "$CC_quoted_expanded "*) # The compiler in the base compile command matches # the one in the tagged configuration. # Assume this is the tagged configuration we want. tagname=$z break ;; esac fi done # If $tagname still isn't set, then no tagged configuration # was found and let the user know that the "--tag" command # line option must be used. if test -z "$tagname"; then func_echo "unable to infer tagged configuration" func_fatal_error "specify a tag with \`--tag'" # else # func_verbose "using $tagname tagged configuration" fi ;; esac fi } # func_write_libtool_object output_name pic_name nonpic_name # Create a libtool object file (analogous to a ".la" file), # but don't create it if we're doing a dry run. func_write_libtool_object () { write_libobj=${1} if test "$build_libtool_libs" = yes; then write_lobj=\'${2}\' else write_lobj=none fi if test "$build_old_libs" = yes; then write_oldobj=\'${3}\' else write_oldobj=none fi $opt_dry_run || { cat >${write_libobj}T <?"'"'"' &()|`$[]' \ && func_warning "libobj name \`$libobj' may not contain shell special characters." func_dirname_and_basename "$obj" "/" "" objname="$func_basename_result" xdir="$func_dirname_result" lobj=${xdir}$objdir/$objname test -z "$base_compile" && \ func_fatal_help "you must specify a compilation command" # Delete any leftover library objects. if test "$build_old_libs" = yes; then removelist="$obj $lobj $libobj ${libobj}T" else removelist="$lobj $libobj ${libobj}T" fi # On Cygwin there's no "real" PIC flag so we must build both object types case $host_os in cygwin* | mingw* | pw32* | os2* | cegcc*) pic_mode=default ;; esac if test "$pic_mode" = no && test "$deplibs_check_method" != pass_all; then # non-PIC code in shared libraries is not supported pic_mode=default fi # Calculate the filename of the output object if compiler does # not support -o with -c if test "$compiler_c_o" = no; then output_obj=`$ECHO "$srcfile" | $SED 's%^.*/%%; s%\.[^.]*$%%'`.${objext} lockfile="$output_obj.lock" else output_obj= need_locks=no lockfile= fi # Lock this critical section if it is needed # We use this script file to make the link, it avoids creating a new file if test "$need_locks" = yes; then until $opt_dry_run || ln "$progpath" "$lockfile" 2>/dev/null; do func_echo "Waiting for $lockfile to be removed" sleep 2 done elif test "$need_locks" = warn; then if test -f "$lockfile"; then $ECHO "\ *** ERROR, $lockfile exists and contains: `cat $lockfile 2>/dev/null` This indicates that another process is trying to use the same temporary object file, and libtool could not work around it because your compiler does not support \`-c' and \`-o' together. If you repeat this compilation, it may succeed, by chance, but you had better avoid parallel builds (make -j) in this platform, or get a better compiler." $opt_dry_run || $RM $removelist exit $EXIT_FAILURE fi removelist="$removelist $output_obj" $ECHO "$srcfile" > "$lockfile" fi $opt_dry_run || $RM $removelist removelist="$removelist $lockfile" trap '$opt_dry_run || $RM $removelist; exit $EXIT_FAILURE' 1 2 15 if test -n "$fix_srcfile_path"; then eval srcfile=\"$fix_srcfile_path\" fi func_quote_for_eval "$srcfile" qsrcfile=$func_quote_for_eval_result # Only build a PIC object if we are building libtool libraries. if test "$build_libtool_libs" = yes; then # Without this assignment, base_compile gets emptied. fbsd_hideous_sh_bug=$base_compile if test "$pic_mode" != no; then command="$base_compile $qsrcfile $pic_flag" else # Don't build PIC code command="$base_compile $qsrcfile" fi func_mkdir_p "$xdir$objdir" if test -z "$output_obj"; then # Place PIC objects in $objdir command="$command -o $lobj" fi func_show_eval_locale "$command" \ 'test -n "$output_obj" && $RM $removelist; exit $EXIT_FAILURE' if test "$need_locks" = warn && test "X`cat $lockfile 2>/dev/null`" != "X$srcfile"; then $ECHO "\ *** ERROR, $lockfile contains: `cat $lockfile 2>/dev/null` but it should contain: $srcfile This indicates that another process is trying to use the same temporary object file, and libtool could not work around it because your compiler does not support \`-c' and \`-o' together. If you repeat this compilation, it may succeed, by chance, but you had better avoid parallel builds (make -j) in this platform, or get a better compiler." $opt_dry_run || $RM $removelist exit $EXIT_FAILURE fi # Just move the object if needed, then go on to compile the next one if test -n "$output_obj" && test "X$output_obj" != "X$lobj"; then func_show_eval '$MV "$output_obj" "$lobj"' \ 'error=$?; $opt_dry_run || $RM $removelist; exit $error' fi # Allow error messages only from the first compilation. if test "$suppress_opt" = yes; then suppress_output=' >/dev/null 2>&1' fi fi # Only build a position-dependent object if we build old libraries. if test "$build_old_libs" = yes; then if test "$pic_mode" != yes; then # Don't build PIC code command="$base_compile $qsrcfile$pie_flag" else command="$base_compile $qsrcfile $pic_flag" fi if test "$compiler_c_o" = yes; then command="$command -o $obj" fi # Suppress compiler output if we already did a PIC compilation. command="$command$suppress_output" func_show_eval_locale "$command" \ '$opt_dry_run || $RM $removelist; exit $EXIT_FAILURE' if test "$need_locks" = warn && test "X`cat $lockfile 2>/dev/null`" != "X$srcfile"; then $ECHO "\ *** ERROR, $lockfile contains: `cat $lockfile 2>/dev/null` but it should contain: $srcfile This indicates that another process is trying to use the same temporary object file, and libtool could not work around it because your compiler does not support \`-c' and \`-o' together. If you repeat this compilation, it may succeed, by chance, but you had better avoid parallel builds (make -j) in this platform, or get a better compiler." $opt_dry_run || $RM $removelist exit $EXIT_FAILURE fi # Just move the object if needed if test -n "$output_obj" && test "X$output_obj" != "X$obj"; then func_show_eval '$MV "$output_obj" "$obj"' \ 'error=$?; $opt_dry_run || $RM $removelist; exit $error' fi fi $opt_dry_run || { func_write_libtool_object "$libobj" "$objdir/$objname" "$objname" # Unlock the critical section if it was locked if test "$need_locks" != no; then removelist=$lockfile $RM "$lockfile" fi } exit $EXIT_SUCCESS } $opt_help || { test "$mode" = compile && func_mode_compile ${1+"$@"} } func_mode_help () { # We need to display help for each of the modes. case $mode in "") # Generic help is extracted from the usage comments # at the start of this file. func_help ;; clean) $ECHO \ "Usage: $progname [OPTION]... --mode=clean RM [RM-OPTION]... FILE... Remove files from the build directory. RM is the name of the program to use to delete files associated with each FILE (typically \`/bin/rm'). RM-OPTIONS are options (such as \`-f') to be passed to RM. If FILE is a libtool library, object or program, all the files associated with it are deleted. Otherwise, only FILE itself is deleted using RM." ;; compile) $ECHO \ "Usage: $progname [OPTION]... --mode=compile COMPILE-COMMAND... SOURCEFILE Compile a source file into a libtool library object. This mode accepts the following additional options: -o OUTPUT-FILE set the output file name to OUTPUT-FILE -no-suppress do not suppress compiler output for multiple passes -prefer-pic try to building PIC objects only -prefer-non-pic try to building non-PIC objects only -shared do not build a \`.o' file suitable for static linking -static only build a \`.o' file suitable for static linking -Wc,FLAG pass FLAG directly to the compiler COMPILE-COMMAND is a command to be used in creating a \`standard' object file from the given SOURCEFILE. The output file name is determined by removing the directory component from SOURCEFILE, then substituting the C source code suffix \`.c' with the library object suffix, \`.lo'." ;; execute) $ECHO \ "Usage: $progname [OPTION]... --mode=execute COMMAND [ARGS]... Automatically set library path, then run a program. This mode accepts the following additional options: -dlopen FILE add the directory containing FILE to the library path This mode sets the library path environment variable according to \`-dlopen' flags. If any of the ARGS are libtool executable wrappers, then they are translated into their corresponding uninstalled binary, and any of their required library directories are added to the library path. Then, COMMAND is executed, with ARGS as arguments." ;; finish) $ECHO \ "Usage: $progname [OPTION]... --mode=finish [LIBDIR]... Complete the installation of libtool libraries. Each LIBDIR is a directory that contains libtool libraries. The commands that this mode executes may require superuser privileges. Use the \`--dry-run' option if you just want to see what would be executed." ;; install) $ECHO \ "Usage: $progname [OPTION]... --mode=install INSTALL-COMMAND... Install executables or libraries. INSTALL-COMMAND is the installation command. The first component should be either the \`install' or \`cp' program. The following components of INSTALL-COMMAND are treated specially: -inst-prefix-dir PREFIX-DIR Use PREFIX-DIR as a staging area for installation The rest of the components are interpreted as arguments to that command (only BSD-compatible install options are recognized)." ;; link) $ECHO \ "Usage: $progname [OPTION]... --mode=link LINK-COMMAND... Link object files or libraries together to form another library, or to create an executable program. LINK-COMMAND is a command using the C compiler that you would use to create a program from several object files. The following components of LINK-COMMAND are treated specially: -all-static do not do any dynamic linking at all -avoid-version do not add a version suffix if possible -bindir BINDIR specify path to binaries directory (for systems where libraries must be found in the PATH setting at runtime) -dlopen FILE \`-dlpreopen' FILE if it cannot be dlopened at runtime -dlpreopen FILE link in FILE and add its symbols to lt_preloaded_symbols -export-dynamic allow symbols from OUTPUT-FILE to be resolved with dlsym(3) -export-symbols SYMFILE try to export only the symbols listed in SYMFILE -export-symbols-regex REGEX try to export only the symbols matching REGEX -LLIBDIR search LIBDIR for required installed libraries -lNAME OUTPUT-FILE requires the installed library libNAME -module build a library that can dlopened -no-fast-install disable the fast-install mode -no-install link a not-installable executable -no-undefined declare that a library does not refer to external symbols -o OUTPUT-FILE create OUTPUT-FILE from the specified objects -objectlist FILE Use a list of object files found in FILE to specify objects -precious-files-regex REGEX don't remove output files matching REGEX -release RELEASE specify package release information -rpath LIBDIR the created library will eventually be installed in LIBDIR -R[ ]LIBDIR add LIBDIR to the runtime path of programs and libraries -shared only do dynamic linking of libtool libraries -shrext SUFFIX override the standard shared library file extension -static do not do any dynamic linking of uninstalled libtool libraries -static-libtool-libs do not do any dynamic linking of libtool libraries -version-info CURRENT[:REVISION[:AGE]] specify library version info [each variable defaults to 0] -weak LIBNAME declare that the target provides the LIBNAME interface -Wc,FLAG -Xcompiler FLAG pass linker-specific FLAG directly to the compiler -Wl,FLAG -Xlinker FLAG pass linker-specific FLAG directly to the linker -XCClinker FLAG pass link-specific FLAG to the compiler driver (CC) All other options (arguments beginning with \`-') are ignored. Every other argument is treated as a filename. Files ending in \`.la' are treated as uninstalled libtool libraries, other files are standard or library object files. If the OUTPUT-FILE ends in \`.la', then a libtool library is created, only library objects (\`.lo' files) may be specified, and \`-rpath' is required, except when creating a convenience library. If OUTPUT-FILE ends in \`.a' or \`.lib', then a standard library is created using \`ar' and \`ranlib', or on Windows using \`lib'. If OUTPUT-FILE ends in \`.lo' or \`.${objext}', then a reloadable object file is created, otherwise an executable program is created." ;; uninstall) $ECHO \ "Usage: $progname [OPTION]... --mode=uninstall RM [RM-OPTION]... FILE... Remove libraries from an installation directory. RM is the name of the program to use to delete files associated with each FILE (typically \`/bin/rm'). RM-OPTIONS are options (such as \`-f') to be passed to RM. If FILE is a libtool library, all the files associated with it are deleted. Otherwise, only FILE itself is deleted using RM." ;; *) func_fatal_help "invalid operation mode \`$mode'" ;; esac echo $ECHO "Try \`$progname --help' for more information about other modes." } # Now that we've collected a possible --mode arg, show help if necessary if $opt_help; then if test "$opt_help" = :; then func_mode_help else { func_help noexit for mode in compile link execute install finish uninstall clean; do func_mode_help done } | sed -n '1p; 2,$s/^Usage:/ or: /p' { func_help noexit for mode in compile link execute install finish uninstall clean; do echo func_mode_help done } | sed '1d /^When reporting/,/^Report/{ H d } $x /information about other modes/d /more detailed .*MODE/d s/^Usage:.*--mode=\([^ ]*\) .*/Description of \1 mode:/' fi exit $? fi # func_mode_execute arg... func_mode_execute () { $opt_debug # The first argument is the command name. cmd="$nonopt" test -z "$cmd" && \ func_fatal_help "you must specify a COMMAND" # Handle -dlopen flags immediately. for file in $execute_dlfiles; do test -f "$file" \ || func_fatal_help "\`$file' is not a file" dir= case $file in *.la) # Check to see that this really is a libtool archive. func_lalib_unsafe_p "$file" \ || func_fatal_help "\`$lib' is not a valid libtool archive" # Read the libtool library. dlname= library_names= func_source "$file" # Skip this library if it cannot be dlopened. if test -z "$dlname"; then # Warn if it was a shared library. test -n "$library_names" && \ func_warning "\`$file' was not linked with \`-export-dynamic'" continue fi func_dirname "$file" "" "." dir="$func_dirname_result" if test -f "$dir/$objdir/$dlname"; then dir="$dir/$objdir" else if test ! -f "$dir/$dlname"; then func_fatal_error "cannot find \`$dlname' in \`$dir' or \`$dir/$objdir'" fi fi ;; *.lo) # Just add the directory containing the .lo file. func_dirname "$file" "" "." dir="$func_dirname_result" ;; *) func_warning "\`-dlopen' is ignored for non-libtool libraries and objects" continue ;; esac # Get the absolute pathname. absdir=`cd "$dir" && pwd` test -n "$absdir" && dir="$absdir" # Now add the directory to shlibpath_var. if eval "test -z \"\$$shlibpath_var\""; then eval "$shlibpath_var=\"\$dir\"" else eval "$shlibpath_var=\"\$dir:\$$shlibpath_var\"" fi done # This variable tells wrapper scripts just to set shlibpath_var # rather than running their programs. libtool_execute_magic="$magic" # Check if any of the arguments is a wrapper script. args= for file do case $file in -* | *.la | *.lo ) ;; *) # Do a test to see if this is really a libtool program. if func_ltwrapper_script_p "$file"; then func_source "$file" # Transform arg to wrapped name. file="$progdir/$program" elif func_ltwrapper_executable_p "$file"; then func_ltwrapper_scriptname "$file" func_source "$func_ltwrapper_scriptname_result" # Transform arg to wrapped name. file="$progdir/$program" fi ;; esac # Quote arguments (to preserve shell metacharacters). func_quote_for_eval "$file" args="$args $func_quote_for_eval_result" done if test "X$opt_dry_run" = Xfalse; then if test -n "$shlibpath_var"; then # Export the shlibpath_var. eval "export $shlibpath_var" fi # Restore saved environment variables for lt_var in LANG LANGUAGE LC_ALL LC_CTYPE LC_COLLATE LC_MESSAGES do eval "if test \"\${save_$lt_var+set}\" = set; then $lt_var=\$save_$lt_var; export $lt_var else $lt_unset $lt_var fi" done # Now prepare to actually exec the command. exec_cmd="\$cmd$args" else # Display what would be done. if test -n "$shlibpath_var"; then eval "\$ECHO \"\$shlibpath_var=\$$shlibpath_var\"" echo "export $shlibpath_var" fi $ECHO "$cmd$args" exit $EXIT_SUCCESS fi } test "$mode" = execute && func_mode_execute ${1+"$@"} # func_mode_finish arg... func_mode_finish () { $opt_debug libdirs="$nonopt" admincmds= if test -n "$finish_cmds$finish_eval" && test -n "$libdirs"; then for dir do libdirs="$libdirs $dir" done for libdir in $libdirs; do if test -n "$finish_cmds"; then # Do each command in the finish commands. func_execute_cmds "$finish_cmds" 'admincmds="$admincmds '"$cmd"'"' fi if test -n "$finish_eval"; then # Do the single finish_eval. eval cmds=\"$finish_eval\" $opt_dry_run || eval "$cmds" || admincmds="$admincmds $cmds" fi done fi # Exit here if they wanted silent mode. $opt_silent && exit $EXIT_SUCCESS echo "----------------------------------------------------------------------" echo "Libraries have been installed in:" for libdir in $libdirs; do $ECHO " $libdir" done echo echo "If you ever happen to want to link against installed libraries" echo "in a given directory, LIBDIR, you must either use libtool, and" echo "specify the full pathname of the library, or use the \`-LLIBDIR'" echo "flag during linking and do at least one of the following:" if test -n "$shlibpath_var"; then echo " - add LIBDIR to the \`$shlibpath_var' environment variable" echo " during execution" fi if test -n "$runpath_var"; then echo " - add LIBDIR to the \`$runpath_var' environment variable" echo " during linking" fi if test -n "$hardcode_libdir_flag_spec"; then libdir=LIBDIR eval flag=\"$hardcode_libdir_flag_spec\" $ECHO " - use the \`$flag' linker flag" fi if test -n "$admincmds"; then $ECHO " - have your system administrator run these commands:$admincmds" fi if test -f /etc/ld.so.conf; then echo " - have your system administrator add LIBDIR to \`/etc/ld.so.conf'" fi echo echo "See any operating system documentation about shared libraries for" case $host in solaris2.[6789]|solaris2.1[0-9]) echo "more information, such as the ld(1), crle(1) and ld.so(8) manual" echo "pages." ;; *) echo "more information, such as the ld(1) and ld.so(8) manual pages." ;; esac echo "----------------------------------------------------------------------" exit $EXIT_SUCCESS } test "$mode" = finish && func_mode_finish ${1+"$@"} # func_mode_install arg... func_mode_install () { $opt_debug # There may be an optional sh(1) argument at the beginning of # install_prog (especially on Windows NT). if test "$nonopt" = "$SHELL" || test "$nonopt" = /bin/sh || # Allow the use of GNU shtool's install command. case $nonopt in *shtool*) :;; *) false;; esac; then # Aesthetically quote it. func_quote_for_eval "$nonopt" install_prog="$func_quote_for_eval_result " arg=$1 shift else install_prog= arg=$nonopt fi # The real first argument should be the name of the installation program. # Aesthetically quote it. func_quote_for_eval "$arg" install_prog="$install_prog$func_quote_for_eval_result" install_shared_prog=$install_prog case " $install_prog " in *[\\\ /]cp\ *) install_cp=: ;; *) install_cp=false ;; esac # We need to accept at least all the BSD install flags. dest= files= opts= prev= install_type= isdir=no stripme= no_mode=: for arg do arg2= if test -n "$dest"; then files="$files $dest" dest=$arg continue fi case $arg in -d) isdir=yes ;; -f) if $install_cp; then :; else prev=$arg fi ;; -g | -m | -o) prev=$arg ;; -s) stripme=" -s" continue ;; -*) ;; *) # If the previous option needed an argument, then skip it. if test -n "$prev"; then if test "x$prev" = x-m && test -n "$install_override_mode"; then arg2=$install_override_mode no_mode=false fi prev= else dest=$arg continue fi ;; esac # Aesthetically quote the argument. func_quote_for_eval "$arg" install_prog="$install_prog $func_quote_for_eval_result" if test -n "$arg2"; then func_quote_for_eval "$arg2" fi install_shared_prog="$install_shared_prog $func_quote_for_eval_result" done test -z "$install_prog" && \ func_fatal_help "you must specify an install program" test -n "$prev" && \ func_fatal_help "the \`$prev' option requires an argument" if test -n "$install_override_mode" && $no_mode; then if $install_cp; then :; else func_quote_for_eval "$install_override_mode" install_shared_prog="$install_shared_prog -m $func_quote_for_eval_result" fi fi if test -z "$files"; then if test -z "$dest"; then func_fatal_help "no file or destination specified" else func_fatal_help "you must specify a destination" fi fi # Strip any trailing slash from the destination. func_stripname '' '/' "$dest" dest=$func_stripname_result # Check to see that the destination is a directory. test -d "$dest" && isdir=yes if test "$isdir" = yes; then destdir="$dest" destname= else func_dirname_and_basename "$dest" "" "." destdir="$func_dirname_result" destname="$func_basename_result" # Not a directory, so check to see that there is only one file specified. set dummy $files; shift test "$#" -gt 1 && \ func_fatal_help "\`$dest' is not a directory" fi case $destdir in [\\/]* | [A-Za-z]:[\\/]*) ;; *) for file in $files; do case $file in *.lo) ;; *) func_fatal_help "\`$destdir' must be an absolute directory name" ;; esac done ;; esac # This variable tells wrapper scripts just to set variables rather # than running their programs. libtool_install_magic="$magic" staticlibs= future_libdirs= current_libdirs= for file in $files; do # Do each installation. case $file in *.$libext) # Do the static libraries later. staticlibs="$staticlibs $file" ;; *.la) # Check to see that this really is a libtool archive. func_lalib_unsafe_p "$file" \ || func_fatal_help "\`$file' is not a valid libtool archive" library_names= old_library= relink_command= func_source "$file" # Add the libdir to current_libdirs if it is the destination. if test "X$destdir" = "X$libdir"; then case "$current_libdirs " in *" $libdir "*) ;; *) current_libdirs="$current_libdirs $libdir" ;; esac else # Note the libdir as a future libdir. case "$future_libdirs " in *" $libdir "*) ;; *) future_libdirs="$future_libdirs $libdir" ;; esac fi func_dirname "$file" "/" "" dir="$func_dirname_result" dir="$dir$objdir" if test -n "$relink_command"; then # Determine the prefix the user has applied to our future dir. inst_prefix_dir=`$ECHO "$destdir" | $SED -e "s%$libdir\$%%"` # Don't allow the user to place us outside of our expected # location b/c this prevents finding dependent libraries that # are installed to the same prefix. # At present, this check doesn't affect windows .dll's that # are installed into $libdir/../bin (currently, that works fine) # but it's something to keep an eye on. test "$inst_prefix_dir" = "$destdir" && \ func_fatal_error "error: cannot install \`$file' to a directory not ending in $libdir" if test -n "$inst_prefix_dir"; then # Stick the inst_prefix_dir data into the link command. relink_command=`$ECHO "$relink_command" | $SED "s%@inst_prefix_dir@%-inst-prefix-dir $inst_prefix_dir%"` else relink_command=`$ECHO "$relink_command" | $SED "s%@inst_prefix_dir@%%"` fi func_warning "relinking \`$file'" func_show_eval "$relink_command" \ 'func_fatal_error "error: relink \`$file'\'' with the above command before installing it"' fi # See the names of the shared library. set dummy $library_names; shift if test -n "$1"; then realname="$1" shift srcname="$realname" test -n "$relink_command" && srcname="$realname"T # Install the shared library and build the symlinks. func_show_eval "$install_shared_prog $dir/$srcname $destdir/$realname" \ 'exit $?' tstripme="$stripme" case $host_os in cygwin* | mingw* | pw32* | cegcc*) case $realname in *.dll.a) tstripme="" ;; esac ;; esac if test -n "$tstripme" && test -n "$striplib"; then func_show_eval "$striplib $destdir/$realname" 'exit $?' fi if test "$#" -gt 0; then # Delete the old symlinks, and create new ones. # Try `ln -sf' first, because the `ln' binary might depend on # the symlink we replace! Solaris /bin/ln does not understand -f, # so we also need to try rm && ln -s. for linkname do test "$linkname" != "$realname" \ && func_show_eval "(cd $destdir && { $LN_S -f $realname $linkname || { $RM $linkname && $LN_S $realname $linkname; }; })" done fi # Do each command in the postinstall commands. lib="$destdir/$realname" func_execute_cmds "$postinstall_cmds" 'exit $?' fi # Install the pseudo-library for information purposes. func_basename "$file" name="$func_basename_result" instname="$dir/$name"i func_show_eval "$install_prog $instname $destdir/$name" 'exit $?' # Maybe install the static library, too. test -n "$old_library" && staticlibs="$staticlibs $dir/$old_library" ;; *.lo) # Install (i.e. copy) a libtool object. # Figure out destination file name, if it wasn't already specified. if test -n "$destname"; then destfile="$destdir/$destname" else func_basename "$file" destfile="$func_basename_result" destfile="$destdir/$destfile" fi # Deduce the name of the destination old-style object file. case $destfile in *.lo) func_lo2o "$destfile" staticdest=$func_lo2o_result ;; *.$objext) staticdest="$destfile" destfile= ;; *) func_fatal_help "cannot copy a libtool object to \`$destfile'" ;; esac # Install the libtool object if requested. test -n "$destfile" && \ func_show_eval "$install_prog $file $destfile" 'exit $?' # Install the old object if enabled. if test "$build_old_libs" = yes; then # Deduce the name of the old-style object file. func_lo2o "$file" staticobj=$func_lo2o_result func_show_eval "$install_prog \$staticobj \$staticdest" 'exit $?' fi exit $EXIT_SUCCESS ;; *) # Figure out destination file name, if it wasn't already specified. if test -n "$destname"; then destfile="$destdir/$destname" else func_basename "$file" destfile="$func_basename_result" destfile="$destdir/$destfile" fi # If the file is missing, and there is a .exe on the end, strip it # because it is most likely a libtool script we actually want to # install stripped_ext="" case $file in *.exe) if test ! -f "$file"; then func_stripname '' '.exe' "$file" file=$func_stripname_result stripped_ext=".exe" fi ;; esac # Do a test to see if this is really a libtool program. case $host in *cygwin* | *mingw*) if func_ltwrapper_executable_p "$file"; then func_ltwrapper_scriptname "$file" wrapper=$func_ltwrapper_scriptname_result else func_stripname '' '.exe' "$file" wrapper=$func_stripname_result fi ;; *) wrapper=$file ;; esac if func_ltwrapper_script_p "$wrapper"; then notinst_deplibs= relink_command= func_source "$wrapper" # Check the variables that should have been set. test -z "$generated_by_libtool_version" && \ func_fatal_error "invalid libtool wrapper script \`$wrapper'" finalize=yes for lib in $notinst_deplibs; do # Check to see that each library is installed. libdir= if test -f "$lib"; then func_source "$lib" fi libfile="$libdir/"`$ECHO "$lib" | $SED 's%^.*/%%g'` ### testsuite: skip nested quoting test if test -n "$libdir" && test ! -f "$libfile"; then func_warning "\`$lib' has not been installed in \`$libdir'" finalize=no fi done relink_command= func_source "$wrapper" outputname= if test "$fast_install" = no && test -n "$relink_command"; then $opt_dry_run || { if test "$finalize" = yes; then tmpdir=`func_mktempdir` func_basename "$file$stripped_ext" file="$func_basename_result" outputname="$tmpdir/$file" # Replace the output file specification. relink_command=`$ECHO "$relink_command" | $SED 's%@OUTPUT@%'"$outputname"'%g'` $opt_silent || { func_quote_for_expand "$relink_command" eval "func_echo $func_quote_for_expand_result" } if eval "$relink_command"; then : else func_error "error: relink \`$file' with the above command before installing it" $opt_dry_run || ${RM}r "$tmpdir" continue fi file="$outputname" else func_warning "cannot relink \`$file'" fi } else # Install the binary that we compiled earlier. file=`$ECHO "$file$stripped_ext" | $SED "s%\([^/]*\)$%$objdir/\1%"` fi fi # remove .exe since cygwin /usr/bin/install will append another # one anyway case $install_prog,$host in */usr/bin/install*,*cygwin*) case $file:$destfile in *.exe:*.exe) # this is ok ;; *.exe:*) destfile=$destfile.exe ;; *:*.exe) func_stripname '' '.exe' "$destfile" destfile=$func_stripname_result ;; esac ;; esac func_show_eval "$install_prog\$stripme \$file \$destfile" 'exit $?' $opt_dry_run || if test -n "$outputname"; then ${RM}r "$tmpdir" fi ;; esac done for file in $staticlibs; do func_basename "$file" name="$func_basename_result" # Set up the ranlib parameters. oldlib="$destdir/$name" func_show_eval "$install_prog \$file \$oldlib" 'exit $?' if test -n "$stripme" && test -n "$old_striplib"; then func_show_eval "$old_striplib $oldlib" 'exit $?' fi # Do each command in the postinstall commands. func_execute_cmds "$old_postinstall_cmds" 'exit $?' done test -n "$future_libdirs" && \ func_warning "remember to run \`$progname --finish$future_libdirs'" if test -n "$current_libdirs"; then # Maybe just do a dry run. $opt_dry_run && current_libdirs=" -n$current_libdirs" exec_cmd='$SHELL $progpath $preserve_args --finish$current_libdirs' else exit $EXIT_SUCCESS fi } test "$mode" = install && func_mode_install ${1+"$@"} # func_generate_dlsyms outputname originator pic_p # Extract symbols from dlprefiles and create ${outputname}S.o with # a dlpreopen symbol table. func_generate_dlsyms () { $opt_debug my_outputname="$1" my_originator="$2" my_pic_p="${3-no}" my_prefix=`$ECHO "$my_originator" | sed 's%[^a-zA-Z0-9]%_%g'` my_dlsyms= if test -n "$dlfiles$dlprefiles" || test "$dlself" != no; then if test -n "$NM" && test -n "$global_symbol_pipe"; then my_dlsyms="${my_outputname}S.c" else func_error "not configured to extract global symbols from dlpreopened files" fi fi if test -n "$my_dlsyms"; then case $my_dlsyms in "") ;; *.c) # Discover the nlist of each of the dlfiles. nlist="$output_objdir/${my_outputname}.nm" func_show_eval "$RM $nlist ${nlist}S ${nlist}T" # Parse the name list into a source file. func_verbose "creating $output_objdir/$my_dlsyms" $opt_dry_run || $ECHO > "$output_objdir/$my_dlsyms" "\ /* $my_dlsyms - symbol resolution table for \`$my_outputname' dlsym emulation. */ /* Generated by $PROGRAM (GNU $PACKAGE$TIMESTAMP) $VERSION */ #ifdef __cplusplus extern \"C\" { #endif #if defined(__GNUC__) && (((__GNUC__ == 4) && (__GNUC_MINOR__ >= 4)) || (__GNUC__ > 4)) #pragma GCC diagnostic ignored \"-Wstrict-prototypes\" #endif /* External symbol declarations for the compiler. */\ " if test "$dlself" = yes; then func_verbose "generating symbol list for \`$output'" $opt_dry_run || echo ': @PROGRAM@ ' > "$nlist" # Add our own program objects to the symbol list. progfiles=`$ECHO "$objs$old_deplibs" | $SP2NL | $SED "$lo2o" | $NL2SP` for progfile in $progfiles; do func_verbose "extracting global C symbols from \`$progfile'" $opt_dry_run || eval "$NM $progfile | $global_symbol_pipe >> '$nlist'" done if test -n "$exclude_expsyms"; then $opt_dry_run || { eval '$EGREP -v " ($exclude_expsyms)$" "$nlist" > "$nlist"T' eval '$MV "$nlist"T "$nlist"' } fi if test -n "$export_symbols_regex"; then $opt_dry_run || { eval '$EGREP -e "$export_symbols_regex" "$nlist" > "$nlist"T' eval '$MV "$nlist"T "$nlist"' } fi # Prepare the list of exported symbols if test -z "$export_symbols"; then export_symbols="$output_objdir/$outputname.exp" $opt_dry_run || { $RM $export_symbols eval "${SED} -n -e '/^: @PROGRAM@ $/d' -e 's/^.* \(.*\)$/\1/p' "'< "$nlist" > "$export_symbols"' case $host in *cygwin* | *mingw* | *cegcc* ) eval "echo EXPORTS "'> "$output_objdir/$outputname.def"' eval 'cat "$export_symbols" >> "$output_objdir/$outputname.def"' ;; esac } else $opt_dry_run || { eval "${SED} -e 's/\([].[*^$]\)/\\\\\1/g' -e 's/^/ /' -e 's/$/$/'"' < "$export_symbols" > "$output_objdir/$outputname.exp"' eval '$GREP -f "$output_objdir/$outputname.exp" < "$nlist" > "$nlist"T' eval '$MV "$nlist"T "$nlist"' case $host in *cygwin* | *mingw* | *cegcc* ) eval "echo EXPORTS "'> "$output_objdir/$outputname.def"' eval 'cat "$nlist" >> "$output_objdir/$outputname.def"' ;; esac } fi fi for dlprefile in $dlprefiles; do func_verbose "extracting global C symbols from \`$dlprefile'" func_basename "$dlprefile" name="$func_basename_result" case $host in *cygwin* | *mingw* | *cegcc* ) # if an import library, we need to obtain dlname if func_win32_import_lib_p "$dlprefile"; then func_tr_sh "$dlprefile" eval "curr_lafile=\$libfile_$func_tr_sh_result" dlprefile_dlbasename="" if test -n "$curr_lafile" && func_lalib_p "$curr_lafile"; then # Use subshell, to avoid clobbering current variable values dlprefile_dlname=`source "$curr_lafile" && echo "$dlname"` if test -n "$dlprefile_dlname" ; then func_basename "$dlprefile_dlname" dlprefile_dlbasename="$func_basename_result" else # no lafile. user explicitly requested -dlpreopen . eval '$sharedlib_from_linklib "$dlprefile"' dlprefile_dlbasename=$sharedlib_from_linklib_result fi fi $opt_dry_run || { if test -n "$dlprefile_dlbasename" ; then eval '$ECHO ": $dlprefile_dlbasename" >> "$nlist"' else func_warning "Could not compute DLL name from $name" eval '$ECHO ": $name " >> "$nlist"' fi eval "$NM $dlprefile 2>/dev/null | $global_symbol_pipe | $SED -e '/I __imp/d' -e 's/I __nm_/D /;s/_nm__//' >> '$nlist'" } else # not an import lib $opt_dry_run || { eval '$ECHO ": $name " >> "$nlist"' eval "$NM $dlprefile 2>/dev/null | $global_symbol_pipe >> '$nlist'" } fi ;; *) $opt_dry_run || { eval '$ECHO ": $name " >> "$nlist"' eval "$NM $dlprefile 2>/dev/null | $global_symbol_pipe >> '$nlist'" } ;; esac done $opt_dry_run || { # Make sure we have at least an empty file. test -f "$nlist" || : > "$nlist" if test -n "$exclude_expsyms"; then $EGREP -v " ($exclude_expsyms)$" "$nlist" > "$nlist"T $MV "$nlist"T "$nlist" fi # Try sorting and uniquifying the output. if $GREP -v "^: " < "$nlist" | if sort -k 3 /dev/null 2>&1; then sort -k 3 else sort +2 fi | uniq > "$nlist"S; then : else $GREP -v "^: " < "$nlist" > "$nlist"S fi if test -f "$nlist"S; then eval "$global_symbol_to_cdecl"' < "$nlist"S >> "$output_objdir/$my_dlsyms"' else echo '/* NONE */' >> "$output_objdir/$my_dlsyms" fi echo >> "$output_objdir/$my_dlsyms" "\ /* The mapping between symbol names and symbols. */ typedef struct { const char *name; void *address; } lt_dlsymlist; " case $host in *cygwin* | *mingw* | *cegcc* ) echo >> "$output_objdir/$my_dlsyms" "\ /* DATA imports from DLLs on WIN32 con't be const, because runtime relocations are performed -- see ld's documentation on pseudo-relocs. */" lt_dlsym_const= ;; *osf5*) echo >> "$output_objdir/$my_dlsyms" "\ /* This system does not cope well with relocations in const data */" lt_dlsym_const= ;; *) lt_dlsym_const=const ;; esac echo >> "$output_objdir/$my_dlsyms" "\ extern $lt_dlsym_const lt_dlsymlist lt_${my_prefix}_LTX_preloaded_symbols[]; $lt_dlsym_const lt_dlsymlist lt_${my_prefix}_LTX_preloaded_symbols[] = {\ { \"$my_originator\", (void *) 0 }," case $need_lib_prefix in no) eval "$global_symbol_to_c_name_address" < "$nlist" >> "$output_objdir/$my_dlsyms" ;; *) eval "$global_symbol_to_c_name_address_lib_prefix" < "$nlist" >> "$output_objdir/$my_dlsyms" ;; esac echo >> "$output_objdir/$my_dlsyms" "\ {0, (void *) 0} }; /* This works around a problem in FreeBSD linker */ #ifdef FREEBSD_WORKAROUND static const void *lt_preloaded_setup() { return lt_${my_prefix}_LTX_preloaded_symbols; } #endif #ifdef __cplusplus } #endif\ " } # !$opt_dry_run pic_flag_for_symtable= case "$compile_command " in *" -static "*) ;; *) case $host in # compiling the symbol table file with pic_flag works around # a FreeBSD bug that causes programs to crash when -lm is # linked before any other PIC object. But we must not use # pic_flag when linking with -static. The problem exists in # FreeBSD 2.2.6 and is fixed in FreeBSD 3.1. *-*-freebsd2*|*-*-freebsd3.0*|*-*-freebsdelf3.0*) pic_flag_for_symtable=" $pic_flag -DFREEBSD_WORKAROUND" ;; *-*-hpux*) pic_flag_for_symtable=" $pic_flag" ;; *) if test "X$my_pic_p" != Xno; then pic_flag_for_symtable=" $pic_flag" fi ;; esac ;; esac symtab_cflags= for arg in $LTCFLAGS; do case $arg in -pie | -fpie | -fPIE) ;; *) symtab_cflags="$symtab_cflags $arg" ;; esac done # Now compile the dynamic symbol file. func_show_eval '(cd $output_objdir && $LTCC$symtab_cflags -c$no_builtin_flag$pic_flag_for_symtable "$my_dlsyms")' 'exit $?' # Clean up the generated files. func_show_eval '$RM "$output_objdir/$my_dlsyms" "$nlist" "${nlist}S" "${nlist}T"' # Transform the symbol file into the correct name. symfileobj="$output_objdir/${my_outputname}S.$objext" case $host in *cygwin* | *mingw* | *cegcc* ) if test -f "$output_objdir/$my_outputname.def"; then compile_command=`$ECHO "$compile_command" | $SED "s%@SYMFILE@%$output_objdir/$my_outputname.def $symfileobj%"` finalize_command=`$ECHO "$finalize_command" | $SED "s%@SYMFILE@%$output_objdir/$my_outputname.def $symfileobj%"` else compile_command=`$ECHO "$compile_command" | $SED "s%@SYMFILE@%$symfileobj%"` finalize_command=`$ECHO "$finalize_command" | $SED "s%@SYMFILE@%$symfileobj%"` fi ;; *) compile_command=`$ECHO "$compile_command" | $SED "s%@SYMFILE@%$symfileobj%"` finalize_command=`$ECHO "$finalize_command" | $SED "s%@SYMFILE@%$symfileobj%"` ;; esac ;; *) func_fatal_error "unknown suffix for \`$my_dlsyms'" ;; esac else # We keep going just in case the user didn't refer to # lt_preloaded_symbols. The linker will fail if global_symbol_pipe # really was required. # Nullify the symbol file. compile_command=`$ECHO "$compile_command" | $SED "s% @SYMFILE@%%"` finalize_command=`$ECHO "$finalize_command" | $SED "s% @SYMFILE@%%"` fi } # func_win32_libid arg # return the library type of file 'arg' # # Need a lot of goo to handle *both* DLLs and import libs # Has to be a shell function in order to 'eat' the argument # that is supplied when $file_magic_command is called. # Despite the name, also deal with 64 bit binaries. func_win32_libid () { $opt_debug win32_libid_type="unknown" win32_fileres=`file -L $1 2>/dev/null` case $win32_fileres in *ar\ archive\ import\ library*) # definitely import win32_libid_type="x86 archive import" ;; *ar\ archive*) # could be an import, or static if eval $OBJDUMP -f $1 | $SED -e '10q' 2>/dev/null | $EGREP 'file format (pei?-i386(.*architecture: i386)?|pe-arm-wince|pe-x86-64)' >/dev/null; then win32_nmres=`eval $NM -f posix -A $1 | $SED -n -e ' 1,100{ / I /{ s,.*,import, p q } }'` case $win32_nmres in import*) win32_libid_type="x86 archive import";; *) win32_libid_type="x86 archive static";; esac fi ;; *DLL*) win32_libid_type="x86 DLL" ;; *executable*) # but shell scripts are "executable" too... case $win32_fileres in *MS\ Windows\ PE\ Intel*) win32_libid_type="x86 DLL" ;; esac ;; esac $ECHO "$win32_libid_type" } # func_cygming_dll_for_implib ARG # # Platform-specific function to extract the # name of the DLL associated with the specified # import library ARG. # Invoked by eval'ing the libtool variable # $sharedlib_from_linklib_cmd # Result is available in the variable # $sharedlib_from_linklib_result func_cygming_dll_for_implib () { $opt_debug sharedlib_from_linklib_result=`$DLLTOOL --identify-strict --identify "$1"` } # func_cygming_dll_for_implib_core SECTION_NAME LIBNAMEs # # The is the core of a fallback implementation of a # platform-specific function to extract the name of the # DLL associated with the specified import library LIBNAME. # # SECTION_NAME is either .idata$6 or .idata$7, depending # on the platform and compiler that created the implib. # # Echos the name of the DLL associated with the # specified import library. func_cygming_dll_for_implib_fallback_core () { $opt_debug $OBJDUMP -s --section "$1" "$2" 2>/dev/null | sed '/^Contents of section '"$1"':/{ # Place marker at beginning of archive member dllname section s/.*/====MARK====/ p d } # These lines can sometimes be longer than 43 characters, but # are always uninteresting /:[ \t]*file format pe[i]\{,1\}-i386$/d /^In archive [^:]*:/d # Ensure marker is printed /^====MARK====/p # Remove all lines with less than 43 characters /^.\{43\}/!d # From remoaining lines, remove first 43 characters s/^.\{43\}//' | sed -n ' # Join marker and all lines until next marker into a single line /^====MARK====/ b para H $ b para b :para x s/\n//g # Remove the marker s/^====MARK====// # Remove trailing dots and whitespace s/[\. \t]*$// # Print /./p ' | # we now have a list, one entry per line, of the stringified # contents of the appropriate section of all members of the # archive which possess that section. Heuristic: eliminate # all those which have a first or second character that is # a '.' (that is, objdump's representation of an unprintable # character.) This should work for all archives with less than # 0x302f exports -- but will fail for DLLs whose name actually # begins with a literal '.' or a single character followed by # a '.'. # # Of those that remain, print the first one. sed -e '/^\./d' -e '/^.\./d' | sed -n -e '1p' } # func_cygming_gnu_implib_p ARG # This predicate returns with zero status (TRUE) if # ARG is a GNU/binutils-style import library. Returns # with nonzero status (FALSE) otherwise. func_cygming_gnu_implib_p () { $opt_debug func_cygming_gnu_implib_tmp=`eval "\$NM \$1 | \$global_symbol_pipe | \$EGREP ' (_head_[A-Za-z0-9_]+_dll|[A-Za-z0-9_]+_dll_iname)\\\$'"` test -n "$func_cygming_gnu_implib_tmp" } # func_cygming_ms_implib_p ARG # This predicate returns with zero status (TRUE) if # ARG is an MS-style import library. Returns # with nonzero status (FALSE) otherwise. func_cygming_ms_implib_p () { $opt_debug func_cygming_ms_implib_tmp=`eval "\$NM \$1 | \$global_symbol_pipe | grep '_NULL_IMPORT_DESCRIPTOR'"` test -n "$func_cygming_ms_implib_tmp" } # func_cygming_dll_for_implib_fallback ARG # Platform-specific function to extract the # name of the DLL associated with the specified # import library ARG. # # This fallback implementation is for use when $DLLTOOL # does not support the --identify-strict option. # Invoked by eval'ing the libtool variable # $sharedlib_from_linklib_cmd # Result is available in the variable # $sharedlib_from_linklib_result func_cygming_dll_for_implib_fallback () { $opt_debug if func_cygming_gnu_implib_p "$1" ; then # binutils import library sharedlib_from_linklib_result=`func_cygming_dll_for_implib_fallback_core '.idata$7' "$1"` elif func_cygming_ms_implib_p "$1" ; then # ms-generated import library sharedlib_from_linklib_result=`func_cygming_dll_for_implib_fallback_core '.idata$6' "$1"` else # unknown sharedlib_from_linklib_result="" fi } # func_extract_an_archive dir oldlib func_extract_an_archive () { $opt_debug f_ex_an_ar_dir="$1"; shift f_ex_an_ar_oldlib="$1" if test "$lock_old_archive_extraction" = yes; then lockfile=$f_ex_an_ar_oldlib.lock until $opt_dry_run || ln "$progpath" "$lockfile" 2>/dev/null; do func_echo "Waiting for $lockfile to be removed" sleep 2 done fi func_show_eval "(cd \$f_ex_an_ar_dir && $AR x \"\$f_ex_an_ar_oldlib\")" \ 'stat=$?; rm -f "$lockfile"; exit $stat' if test "$lock_old_archive_extraction" = yes; then $opt_dry_run || rm -f "$lockfile" fi if ($AR t "$f_ex_an_ar_oldlib" | sort | sort -uc >/dev/null 2>&1); then : else func_fatal_error "object name conflicts in archive: $f_ex_an_ar_dir/$f_ex_an_ar_oldlib" fi } # func_extract_archives gentop oldlib ... func_extract_archives () { $opt_debug my_gentop="$1"; shift my_oldlibs=${1+"$@"} my_oldobjs="" my_xlib="" my_xabs="" my_xdir="" for my_xlib in $my_oldlibs; do # Extract the objects. case $my_xlib in [\\/]* | [A-Za-z]:[\\/]*) my_xabs="$my_xlib" ;; *) my_xabs=`pwd`"/$my_xlib" ;; esac func_basename "$my_xlib" my_xlib="$func_basename_result" my_xlib_u=$my_xlib while :; do case " $extracted_archives " in *" $my_xlib_u "*) func_arith $extracted_serial + 1 extracted_serial=$func_arith_result my_xlib_u=lt$extracted_serial-$my_xlib ;; *) break ;; esac done extracted_archives="$extracted_archives $my_xlib_u" my_xdir="$my_gentop/$my_xlib_u" func_mkdir_p "$my_xdir" case $host in *-darwin*) func_verbose "Extracting $my_xabs" # Do not bother doing anything if just a dry run $opt_dry_run || { darwin_orig_dir=`pwd` cd $my_xdir || exit $? darwin_archive=$my_xabs darwin_curdir=`pwd` darwin_base_archive=`basename "$darwin_archive"` darwin_arches=`$LIPO -info "$darwin_archive" 2>/dev/null | $GREP Architectures 2>/dev/null || true` if test -n "$darwin_arches"; then darwin_arches=`$ECHO "$darwin_arches" | $SED -e 's/.*are://'` darwin_arch= func_verbose "$darwin_base_archive has multiple architectures $darwin_arches" for darwin_arch in $darwin_arches ; do func_mkdir_p "unfat-$$/${darwin_base_archive}-${darwin_arch}" $LIPO -thin $darwin_arch -output "unfat-$$/${darwin_base_archive}-${darwin_arch}/${darwin_base_archive}" "${darwin_archive}" cd "unfat-$$/${darwin_base_archive}-${darwin_arch}" func_extract_an_archive "`pwd`" "${darwin_base_archive}" cd "$darwin_curdir" $RM "unfat-$$/${darwin_base_archive}-${darwin_arch}/${darwin_base_archive}" done # $darwin_arches ## Okay now we've a bunch of thin objects, gotta fatten them up :) darwin_filelist=`find unfat-$$ -type f -name \*.o -print -o -name \*.lo -print | $SED -e "$basename" | sort -u` darwin_file= darwin_files= for darwin_file in $darwin_filelist; do darwin_files=`find unfat-$$ -name $darwin_file -print | $NL2SP` $LIPO -create -output "$darwin_file" $darwin_files done # $darwin_filelist $RM -rf unfat-$$ cd "$darwin_orig_dir" else cd $darwin_orig_dir func_extract_an_archive "$my_xdir" "$my_xabs" fi # $darwin_arches } # !$opt_dry_run ;; *) func_extract_an_archive "$my_xdir" "$my_xabs" ;; esac my_oldobjs="$my_oldobjs "`find $my_xdir -name \*.$objext -print -o -name \*.lo -print | $NL2SP` done func_extract_archives_result="$my_oldobjs" } # func_emit_wrapper [arg=no] # # Emit a libtool wrapper script on stdout. # Don't directly open a file because we may want to # incorporate the script contents within a cygwin/mingw # wrapper executable. Must ONLY be called from within # func_mode_link because it depends on a number of variables # set therein. # # ARG is the value that the WRAPPER_SCRIPT_BELONGS_IN_OBJDIR # variable will take. If 'yes', then the emitted script # will assume that the directory in which it is stored is # the $objdir directory. This is a cygwin/mingw-specific # behavior. func_emit_wrapper () { func_emit_wrapper_arg1=${1-no} $ECHO "\ #! $SHELL # $output - temporary wrapper script for $objdir/$outputname # Generated by $PROGRAM (GNU $PACKAGE$TIMESTAMP) $VERSION # # The $output program cannot be directly executed until all the libtool # libraries that it depends on are installed. # # This wrapper script should never be moved out of the build directory. # If it is, it will not operate correctly. # Sed substitution that helps us do robust quoting. It backslashifies # metacharacters that are still active within double-quoted strings. sed_quote_subst='$sed_quote_subst' # Be Bourne compatible if test -n \"\${ZSH_VERSION+set}\" && (emulate sh) >/dev/null 2>&1; then emulate sh NULLCMD=: # Zsh 3.x and 4.x performs word splitting on \${1+\"\$@\"}, which # is contrary to our usage. Disable this feature. alias -g '\${1+\"\$@\"}'='\"\$@\"' setopt NO_GLOB_SUBST else case \`(set -o) 2>/dev/null\` in *posix*) set -o posix;; esac fi BIN_SH=xpg4; export BIN_SH # for Tru64 DUALCASE=1; export DUALCASE # for MKS sh # The HP-UX ksh and POSIX shell print the target directory to stdout # if CDPATH is set. (unset CDPATH) >/dev/null 2>&1 && unset CDPATH relink_command=\"$relink_command\" # This environment variable determines our operation mode. if test \"\$libtool_install_magic\" = \"$magic\"; then # install mode needs the following variables: generated_by_libtool_version='$macro_version' notinst_deplibs='$notinst_deplibs' else # When we are sourced in execute mode, \$file and \$ECHO are already set. if test \"\$libtool_execute_magic\" != \"$magic\"; then file=\"\$0\"" qECHO=`$ECHO "$ECHO" | $SED "$sed_quote_subst"` $ECHO "\ # A function that is used when there is no print builtin or printf. func_fallback_echo () { eval 'cat <<_LTECHO_EOF \$1 _LTECHO_EOF' } ECHO=\"$qECHO\" fi # Very basic option parsing. These options are (a) specific to # the libtool wrapper, (b) are identical between the wrapper # /script/ and the wrapper /executable/ which is used only on # windows platforms, and (c) all exist in the "--lt-" namespace # (application programs are unlikely to have options which match # this pattern). # # There are only two supported options: --lt-debug and # --lt-dump-script. There is, deliberately, no --lt-help. # # The first argument to this parsing function should be the # script's $0 value, followed by "$@". lt_option_debug= func_parse_lt_options () { lt_script_arg0=\$0 shift for lt_opt do case \"\$lt_opt\" in --lt-debug) lt_option_debug=1 ;; --lt-dump-script) lt_dump_D=\`\$ECHO \"X\$lt_script_arg0\" | $SED -e 's/^X//' -e 's%/[^/]*$%%'\` test \"X\$lt_dump_D\" = \"X\$lt_script_arg0\" && lt_dump_D=. lt_dump_F=\`\$ECHO \"X\$lt_script_arg0\" | $SED -e 's/^X//' -e 's%^.*/%%'\` cat \"\$lt_dump_D/\$lt_dump_F\" exit 0 ;; --lt-*) \$ECHO \"Unrecognized --lt- option: '\$lt_opt'\" 1>&2 exit 1 ;; esac done } # Sets opts_contain_lt_result to \"yes\" if the # supplied arguments contain any elements in the # --lt-* namespace. Empty otherwise. opts_contain_lt_result= func_opts_contain_lt () { opts_contain_lt_result= for lt_option do case \"\$lt_option\" in --lt-*) opts_contain_lt_result=yes break ;; *) ;; esac done } # Used when --lt-debug. Prints its arguments to stdout # (redirection is the responsibility of the caller) func_lt_dump_args () { lt_dump_args_N=1; for lt_arg do \$ECHO \"(main) newargz[\$lt_dump_args_N] : \$lt_arg\" lt_dump_args_N=\`expr \$lt_dump_args_N + 1\` done } # Core function for launching the target application func_exec_program_core () { " case $host in # Backslashes separate directories on plain windows *-*-mingw | *-*-os2* | *-cegcc*) $ECHO "\ if test -n \"\$lt_option_debug\"; then \$ECHO \"(main) lt_argv_zero : \$progdir\\\\\$program\" 1>&2 func_lt_dump_args \${1+\"\$@\"} 1>&2 fi exec \"\$progdir\\\\\$program\" \${1+\"\$@\"} " ;; *) $ECHO "\ if test -n \"\$lt_option_debug\"; then \$ECHO \"(main) lt_argv_zero : \$progdir/\$program\" 1>&2 func_lt_dump_args \${1+\"\$@\"} 1>&2 fi exec \"\$progdir/\$program\" \${1+\"\$@\"} " ;; esac $ECHO "\ \$ECHO \"\$0: cannot exec \$program \$*\" 1>&2 exit 1 } # A function to encapsulate launching the target application # Strips options in the --lt-* namespace from \$@ and # launches target application with the remaining arguments. func_exec_program () { func_opts_contain_lt \${1+\"\$@\"} if test -n \"\$opts_contain_lt_result\"; then # the following is adapted from _AC_INIT_PREPARE, except # (1) we don't care about duplicates, and # (2) we strip out --lt-*, not --no-create/--no-recursion/--silent lt_wrapper_args= for lt_wr_arg do case \$lt_wr_arg in --lt-*) continue ;; *\\'*) lt_wr_arg=\`\$ECHO \"X\$lt_wr_arg\" | $SED -e \"s/^X//\" -e \"s/'/'\\\\\\\\\\\\\\\\''/g\"\` ;; esac lt_wrapper_args=\"\$lt_wrapper_args '\$lt_wr_arg'\" done eval func_exec_program_core \$lt_wrapper_args else func_exec_program_core \${1+\"\$@\"} fi } # Parse options func_parse_lt_options \"\$0\" \${1+\"\$@\"} # Find the directory that this script lives in. thisdir=\`\$ECHO \"\$file\" | $SED 's%/[^/]*$%%'\` test \"x\$thisdir\" = \"x\$file\" && thisdir=. # Follow symbolic links until we get to the real thisdir. file=\`ls -ld \"\$file\" | $SED -n 's/.*-> //p'\` while test -n \"\$file\"; do destdir=\`\$ECHO \"\$file\" | $SED 's%/[^/]*\$%%'\` # If there was a directory component, then change thisdir. if test \"x\$destdir\" != \"x\$file\"; then case \"\$destdir\" in [\\\\/]* | [A-Za-z]:[\\\\/]*) thisdir=\"\$destdir\" ;; *) thisdir=\"\$thisdir/\$destdir\" ;; esac fi file=\`\$ECHO \"\$file\" | $SED 's%^.*/%%'\` file=\`ls -ld \"\$thisdir/\$file\" | $SED -n 's/.*-> //p'\` done # Usually 'no', except on cygwin/mingw when embedded into # the cwrapper. WRAPPER_SCRIPT_BELONGS_IN_OBJDIR=$func_emit_wrapper_arg1 if test \"\$WRAPPER_SCRIPT_BELONGS_IN_OBJDIR\" = \"yes\"; then # special case for '.' if test \"\$thisdir\" = \".\"; then thisdir=\`pwd\` fi # remove .libs from thisdir case \"\$thisdir\" in *[\\\\/]$objdir ) thisdir=\`\$ECHO \"\$thisdir\" | $SED 's%[\\\\/][^\\\\/]*$%%'\` ;; $objdir ) thisdir=. ;; esac fi # Try to get the absolute directory name. absdir=\`cd \"\$thisdir\" && pwd\` test -n \"\$absdir\" && thisdir=\"\$absdir\" " if test "$fast_install" = yes; then $ECHO "\ program=lt-'$outputname'$exeext progdir=\"\$thisdir/$objdir\" if test ! -f \"\$progdir/\$program\" || { file=\`ls -1dt \"\$progdir/\$program\" \"\$progdir/../\$program\" 2>/dev/null | ${SED} 1q\`; \\ test \"X\$file\" != \"X\$progdir/\$program\"; }; then file=\"\$\$-\$program\" if test ! -d \"\$progdir\"; then $MKDIR \"\$progdir\" else $RM \"\$progdir/\$file\" fi" $ECHO "\ # relink executable if necessary if test -n \"\$relink_command\"; then if relink_command_output=\`eval \$relink_command 2>&1\`; then : else $ECHO \"\$relink_command_output\" >&2 $RM \"\$progdir/\$file\" exit 1 fi fi $MV \"\$progdir/\$file\" \"\$progdir/\$program\" 2>/dev/null || { $RM \"\$progdir/\$program\"; $MV \"\$progdir/\$file\" \"\$progdir/\$program\"; } $RM \"\$progdir/\$file\" fi" else $ECHO "\ program='$outputname' progdir=\"\$thisdir/$objdir\" " fi $ECHO "\ if test -f \"\$progdir/\$program\"; then" # Export our shlibpath_var if we have one. if test "$shlibpath_overrides_runpath" = yes && test -n "$shlibpath_var" && test -n "$temp_rpath"; then $ECHO "\ # Add our own library path to $shlibpath_var $shlibpath_var=\"$temp_rpath\$$shlibpath_var\" # Some systems cannot cope with colon-terminated $shlibpath_var # The second colon is a workaround for a bug in BeOS R4 sed $shlibpath_var=\`\$ECHO \"\$$shlibpath_var\" | $SED 's/::*\$//'\` export $shlibpath_var " fi # fixup the dll searchpath if we need to. if test -n "$dllsearchpath"; then $ECHO "\ # Add the dll search path components to the executable PATH PATH=$dllsearchpath:\$PATH " fi $ECHO "\ if test \"\$libtool_execute_magic\" != \"$magic\"; then # Run the actual program with our arguments. func_exec_program \${1+\"\$@\"} fi else # The program doesn't exist. \$ECHO \"\$0: error: \\\`\$progdir/\$program' does not exist\" 1>&2 \$ECHO \"This script is just a wrapper for \$program.\" 1>&2 \$ECHO \"See the $PACKAGE documentation for more information.\" 1>&2 exit 1 fi fi\ " } #################################### # PATH CONVERSION HELPER FUNCTIONS # #################################### # func_wine_to_win32_path ARG # Helper function used by path conversion functions # when $build is *nix, and $host is mingw, cygwin, # or some other win32 environment. Relies on a # correctly configured wine environment available, # with the winepath program in $build's $PATH. # # ARG is the $build path to be converted to win32 format. # result is available in $func_wine_to_win32_path_result # result is empty on error (or when arg is empty) func_wine_to_win32_path () { $opt_debug lt_sed_naive_backslashify='s|\\\\*|\\|g;s|/|\\|g;s|\\|\\\\|g' func_wine_to_win32_path_result="$1" if test -n "$1"; then # Unfortunately, winepath does not exit with a non-zero # error code, so we are forced to check the contents of # stdout. On the other hand, if the command is not # found, the shell will set an exit code of 127 and print # *an error message* to stdout. So we must check for both # error code of zero AND non-empty stdout, which explains # the odd construction: func_to_host_path_tmp1=`winepath -w "$1" 2>/dev/null` if test "$?" -eq 0 && test -n "${func_wine_to_win32_path_tmp}"; then func_to_host_path_result=`$ECHO "$func_wine_to_win32_path_tmp" | $SED -e "$lt_sed_naive_backslashify"` else func_wine_to_win32_path_result= fi fi } # end: func_wine_to_win32_path # func_wine_to_win32_pathlist ARG # Helper function used by path conversion functions # when $build is *nix, and $host is mingw, cygwin, # or some other win32 environment. Relies on a # correctly configured wine environment available, # with the winepath program in $build's $PATH. # Assumes ARG has no leading or trailing path separator # characters. # # ARG is pathlist to be converted from $build format to win32. # Result is available in $func_wine_to_win32_pathlist_result # Unconvertible paths in pathlist are skipped; if no paths # are convertible, result may be empty. func_wine_to_win32_pathlist () { $opt_debug # unfortunately, winepath doesn't convert pathlists func_wine_to_win32_pathlist_result="" if test -n "$1"; then func_wine_to_win32_pathlist_oldIFS=$IFS IFS=: for func_wine_to_win32_pathlist_f in $1; do IFS=$func_wine_to_win32_pathlist_oldIFS if test -n "$func_wine_to_win32_pathlist_f" ; then func_wine_to_win32_path "$func_wine_to_win32_pathlist_f" if test -n "$func_wine_to_win32_path_result" ; then if test -z "$func_wine_to_win32_pathlist_result"; then func_wine_to_win32_pathlist_result="$func_wine_to_win32_path_result" else func_append func_wine_to_win32_pathlist_result ";$func_wine_to_win32_path_result" fi fi fi done IFS=$func_wine_to_win32_pathlist_oldIFS fi } # end: func_wine_to_win32_pathlist # func_cygpath ARGS... # a wrapper around calling the cygpath program via # LT_CYGPATH, when $host is *nix and cygwin is # hosted via a wine environment (or, rarely, when # host is mingw -- that is, msys). # # Result is available in func_cygpath_result, which # may be empty on error. Can accomodate both paths # and pathlists (with appropriate options). # # ARGS are the typical arguments and options for # the cygpath program. Usually, the last argument # is the path or pathlist to be converted. # # The full *nix (or msys) path to the cygpath program must be # specified in the LT_CYGPATH environment variable. This # is because (a) the cygpath program shouldn't be in $PATH, # because it usually lives in cygwin's bin/ directory -- # along with *cygwin* versions of sed, id, cp. If the *nix (or # msys) host environment had those programs in its $PATH, many # bad things could happen. (b) especially in cygwin-1.7, multiple # installations (with separate "mount tables" in # /etc/fstab) can coexist on the same Win32 # instance. The cygpath.exe for cygwin installation #N in # /bin automatically deduces the appropriate # ../etc/fstab file. Therefore, it matters which cygpath.exe # is used. LT_CYGPATH may be replaced or supplemented by an # LT_INIT-activated configure option in the future. func_cygpath () { $opt_debug if test -n "$LT_CYGPATH" && test -f "$LT_CYGPATH"; then func_cygpath_result=`$LT_CYGPATH "$@" 2>/dev/null` if test "$?" -ne 0; then # on failure, ensure result is empty func_cygpath_result= fi else func_cygpath_result= func_error "LT_CYGPATH is empty or specifies non-existant file: \`$LT_CYGPATH'" fi } #end: func_cygpath # func_msys_to_win32 ARG # Converts ARG from msys (unix-ish) format to # win32 format. Can accomodate both paths and pathlists. # Result is available in func_msys_to_win32_result. func_msys_to_win32 () { $opt_debug lt_sed_naive_backslashify='s|\\\\*|\\|g;s|/|\\|g;s|\\|\\\\|g' # awkward: cmd appends spaces to result func_msys_to_win32_result=`( cmd //c echo "$1" ) 2>/dev/null | $SED -e 's/[ ]*$//' -e "$lt_sed_naive_backslashify"` } #end: func_msys_to_win32 # func_path_convert_check ARG1 ARG2 # Verify that ARG1 (a path in $build format) was # converted to $host format in ARG2. Otherwise, emit # an error message, but continue (resetting # func_to_host_path_result to ARG1). func_path_convert_check () { $opt_debug if test -z "$2" && test -n "$1" ; then func_error "Could not determine host path corresponding to" func_error " \`$1'" func_error "Continuing, but uninstalled executables may not work." # Fallback: func_to_host_path_result="$1" fi } # end func_path_convert_check # func_pathlist_convert_check FROM_PATHSEP TO_PATHSEP FROM_PATHLIST TO_PATHLIST # Verify that FROM_PATHLIST (a path in $build format) was converted # $host format in TO_PATHLIST. Otherwise, emit an error message, but # continue, resetting func_to_host_path_result to a simplistic # fallback value (see below). func_pathlist_convert_check () { $opt_debug if test -z "$4" && test -n "$3"; then func_error "Could not determine the host path(s) corresponding to" func_error " \`$3'" func_error "Continuing, but uninstalled executables may not work." # Fallback. If even this fallback fails, the fix is not to # complicate the expression below, but for the user to provide, # in that situation, whatever elements are missing from the # environment so that the actual pathlist conversion functions # work properly (for instance, a working wine installation # with winepath so that path translation in the cross-to-mingw # case works). if test "x$1" != "x$2"; then lt_replace_pathsep_chars="s|$1|$2|g" func_to_host_pathlist_result=`echo "$3" |\ $SED -e "$lt_replace_pathsep_chars"` else func_to_host_pathlist_result="$3" fi fi } # end func_pathlist_convert_check # func_pathlist_front_back_pathsep FRONTPAT BACKPAT REPL ORIG # Modifies func_to_host_pathlist_result by prepending REPL # if ORIG matches FRONTPAT and appending REPL if ORIG matches # BACKPAT. func_pathlist_front_back_pathsep () { $opt_debug case "$4" in $1 ) func_to_host_pathlist_result="$3$func_to_host_pathlist_result" ;; esac case "$4" in $2 ) func_append func_to_host_pathlist_result "$3" ;; esac } # end func_pathlist_front_back_pathsep ############################################# # $build to $host PATH CONVERSION FUNCTIONS # ############################################# # invoked via `eval $to_host_path_cmd ARG' # # At present, the following path conversions are supported: # $build $host # mingw (msys) mingw [e.g. native] # cygwin mingw # *nix + wine mingw # mingw (msys) cygwin [*] [**] # *nix + wine cygwin [**] # where wine is equipped with the `winepath' executable. # [*] available, but not officially supported. See comments with # func_msys_to_cygwin_path_convert. # [**] requires environment variable $LT_CYGPATH. See comments # with func_cygpath. # In each case, ARG is the path to be converted from $build # to $host format. the result will be available in # $func_to_host_path_result. # func_to_host_path ARG # converts the path ARG from $build format to $host # format. func_to_host_path () { $opt_debug eval '$to_host_path_cmd "$1"' } # end func_to_host_path # func_noop_path_convert ARG # A no-op path conversion function for use when $build == $host. # or when there is no required (or known) conversion function # between $build and $host. func_noop_path_convert () { $opt_debug func_to_host_path_result="$1" } # end func_noop_path_convert # func_msys_to_mingw_path_convert ARG # A path conversion function for use with "native" mingw # builds -- that is, when $host is *mingw*, and $build # is *mingw* (which is to say, msys). In this case, the # msys shell automatically converts paths for any non-msys # applications it launches, but that facility isn't available # from inside the cwrapper. # # ARG is the path to be converted; the result is available # in func_to_host_path_result. func_msys_to_mingw_path_convert () { $opt_debug func_to_host_path_result="$1" if test -n "$1"; then func_msys_to_win32 "$1" func_to_host_path_result="$func_msys_to_win32_result" fi func_path_convert_check "$1" "$func_to_host_path_result" } # end func_msys_to_mingw_path_convert # func_cygwin_to_mingw_path_convert ARG # A path conversion function for use when $host is *mingw* # but $build is *cygwin*. In this case, the cygpath program # provided by the $build environment is sufficient for all # conversions. # # ARG is the path to be converted; the result is available # in func_to_host_path_result. func_cygwin_to_mingw_path_convert () { $opt_debug func_to_host_path_result="$1" if test -n "$1"; then # because $build is cygwin, we call "the" cygpath # in $PATH; no need to use LT_CYGPATH in this case. func_to_host_path_result=`cygpath -m "$1"` fi func_path_convert_check "$1" "$func_to_host_path_result" } # end func_cygwin_to_mingw_path_convert # func_nix_to_mingw_path_convert ARG # A path conversion function for use when $host is *mingw* # but $build is some *nix variant. In this case, we assume # that a wine environment with a working winepath executable # is available in $build's $PATH. # # ARG is the path to be converted; the result is available # in func_to_host_path_result. func_nix_to_mingw_path_convert () { $opt_debug func_to_host_path_result="$1" if test -n "$1"; then func_wine_to_win32_path "$1" func_to_host_path_result="$func_wine_to_win32_path_result" fi func_path_convert_check "$1" "$func_to_host_path_result" } # end func_nix_to_mingw_path_convert # func_msys_to_cygwin_path_convert ARG # A path conversion function for use when $host is *cygwin* # but $build is *mingw* (that is, msys). This implies running # a cross build from msys to cygwin -- but msys has notorious # problems executing cygwin apps, because of conflicts between # cygwin1.dll and msys-1.0.dll. However, we'll try it. First, # convert from msys to win32, then use func_cygpath to convert # from win32 to cygwin. Requires LT_CYGPATH. # # ARG is the path to be converted; the result is available # in func_to_host_path_result. func_msys_to_cygwin_path_convert () { $opt_debug func_to_host_path_result="$1" if test -n "$1"; then func_msys_to_win32 "$1" func_cygpath -u "$func_msys_to_win32_result" func_to_host_path_result="$func_cygpath_result" fi func_path_convert_check "$1" "$func_to_host_path_result" } # end func_msys_to_cygwin_path_convert # func_nix_to_cygwin_path_convert ARG # A path conversion function for use when $host is *cygwin* # but $build is some *nix variant. In this case, we assume # that a wine environment with a working winepath executable # is available in $build's $PATH, and that cygwin is installed # within that wine environment. Requires LT_CYGPATH (see # func_cygpath). # # ARG is the path to be converted; the result is available # in func_to_host_path_result. func_nix_to_cygwin_path_convert () { $opt_debug func_to_host_path_result="$1" if test -n "$1"; then # convert from *nix to win32, then use cygpath to # convert from win32 to cygwin. func_wine_to_win32_path "$1" func_cygpath -u "$func_wine_to_win32_path_result" func_to_host_path_result="$func_cygpath_result" fi func_path_convert_check "$1" "$func_to_host_path_result" } # end func_nix_to_cygwin_path_convert ################################################# # $build to $host PATHLIST CONVERSION FUNCTIONS # ################################################# # invoked via `eval $to_host_pathlist_cmd ARG' # # At present, the following pathlist conversions are supported: # $build $host # mingw (msys) mingw [e.g. native] # cygwin mingw # *nix + wine mingw # mingw (msys) cygwin [*] [**] # *nix + wine cygwin [**] # where wine is equipped with the `winepath' executable. # [*] available, but not officially supported. See comments with # func_msys_to_cygwin_pathlist_convert. # [**] requires environment variable $LT_CYGPATH. See comments # with func_cygpath. # In each case, ARG is the pathlist to be converted from # $build to $host format. the result will be available in # $func_to_host_pathlist_result. # # Path separators are also converted from $build format to # $host format. If ARG begins or ends with a path separator # character, it is preserved (but converted to $host format) # on output. # func_init_to_host_pathlist_cmd # Ensures that function "pointer" variable # $to_host_pathlist_cmd is set to the appropriate # value, based on the value of $to_host_path_cmd. # # ASSUMPTIONS: all such conversion functions are # named using the following convention: # path conversion function : xxxxxx_path_convert () # pathlist conversion function: xxxxxx_pathlist_convert () # where, for any given $build/$host combination the 'xxxxxx' # value is the same. to_host_pathlist_cmd= func_init_to_host_pathlist_cmd () { $opt_debug if test -z "$to_host_pathlist_cmd"; then func_stripname '' '_path_convert' "$to_host_path_cmd" to_host_pathlist_cmd="${func_stripname_result}_pathlist_convert" fi } # func_to_host_pathlist ARG # converts the pathlist ARG from $build format to $host # format. func_to_host_pathlist () { $opt_debug func_init_to_host_pathlist_cmd eval '$to_host_pathlist_cmd "$1"' } # end func_to_host_pathlist # func_noop_pathlist_convert ARG # A no-op pathlist conversion function for use when $build == $host, # or when there is no required (or known) conversion function # between $build and $host. func_noop_pathlist_convert () { $opt_debug func_to_host_pathlist_result="$1" } # end func_noop_pathlist_convert # func_msys_to_mingw_pathlist_convert ARG # A pathlist conversion function for use with "native" mingw # builds -- that is, when $host is *mingw*, and $build # is *mingw* (which is to say, msys). In this case, the # msys shell automatically converts pathlists for any non-msys # applications it launches, but that facility isn't available # from inside the cwrapper. # # ARG is the pathlist to be converted; the result is available # in func_to_host_pathlist_result. func_msys_to_mingw_pathlist_convert () { $opt_debug func_to_host_pathlist_result="$1" if test -n "$1"; then # Remove leading and trailing path separator characters from # ARG. msys behavior is inconsistent here, cygpath turns them # into '.;' and ';.', and winepath ignores them completely. func_stripname : : "$1" func_to_host_pathlist_tmp1=$func_stripname_result func_msys_to_win32 "$func_to_host_pathlist_tmp1" func_to_host_pathlist_result="$func_msys_to_win32_result" func_pathlist_convert_check ":" ";" \ "$func_to_host_pathlist_tmp1" "$func_to_host_pathlist_result" func_pathlist_front_back_pathsep ":*" "*:" ";" "$1" fi } # end func_msys_to_mingw_pathlist_convert # func_cygwin_to_mingw_pathlist_convert ARG # A pathlist conversion function for use when $host is *mingw* # but $build is *cygwin*. In this case, the cygpath program # provided by the $build environment is sufficient for all # conversions. # # ARG is the pathlist to be converted; the result is available # in func_to_host_pathlist_result. func_cygwin_to_mingw_pathlist_convert () { $opt_debug func_to_host_pathlist_result="$1" if test -n "$1"; then # Remove leading and trailing path separator characters from # ARG. msys behavior is inconsistent here, cygpath turns them # into '.;' and ';.', and winepath ignores them completely. func_stripname : : "$1" func_to_host_pathlist_tmp1=$func_stripname_result func_to_host_pathlist_result=`cygpath -m -p "$func_to_host_pathlist_tmp1"` func_pathlist_convert_check ":" ";" \ "$func_to_host_pathlist_tmp1" "$func_to_host_pathlist_result" func_pathlist_front_back_pathsep ":*" "*:" ";" "$1" fi } # end func_cygwin_to_mingw_pathlist_convert # func_nix_to_mingw_pathlist_convert ARG # A pathlist conversion function for use when $host is *mingw* # but $build is some *nix variant. In this case, we assume # that a wine environment with a working winepath executable # is available in $build's $PATH. # # ARG is the pathlist to be converted; the result is available # in func_to_host_pathlist_result. func_nix_to_mingw_pathlist_convert () { $opt_debug func_to_host_pathlist_result="$1" if test -n "$1"; then # Remove leading and trailing path separator characters from # ARG. msys behavior is inconsistent here, cygpath turns them # into '.;' and ';.', and winepath ignores them completely. func_stripname : : "$1" func_to_host_pathlist_tmp1=$func_stripname_result func_wine_to_win32_pathlist "$func_to_host_pathlist_tmp1" func_to_host_pathlist_result="$func_wine_to_win32_pathlist_result" func_pathlist_convert_check ":" ";" \ "$func_to_host_pathlist_tmp1" "$func_to_host_pathlist_result" func_pathlist_front_back_pathsep ":*" "*:" ";" "$1" fi } # end func_nix_to_mingw_pathlist_convert # func_msys_to_cygwin_pathlist_convert ARG # A pathlist conversion function for use when $host is *cygwin* # but $build is *mingw* (that is, msys). This implies running # a cross build from msys to cygwin -- but msys has notorious # problems executing cygwin apps, because of conflicts between # cygwin1.dll and msys-1.0.dll. However, we'll try it. First, # convert from msys to win32, then use func_cygpath to convert # from win32 to cygwin. Requires LT_CYGPATH. # # ARG is the pathlist to be converted; the result is available # in func_to_host_pathlist_result. func_msys_to_cygwin_pathlist_convert () { $opt_debug func_to_host_pathlist_result="$1" if test -n "$1"; then # Remove leading and trailing path separator characters from # ARG. msys behavior is inconsistent here, cygpath turns them # into '.;' and ';.', and winepath ignores them completely. func_stripname : : "$1" func_to_host_pathlist_tmp1=$func_stripname_result func_msys_to_win32 "$func_to_host_pathlist_tmp1" func_cygpath -u -p "$func_msys_to_win32_result" func_to_host_pathlist_result="$func_cygpath_result" func_pathlist_convert_check ":" ":" \ "$func_to_host_pathlist_tmp1" "$func_to_host_pathlist_result" func_pathlist_front_back_pathsep ":*" "*:" ":" "$1" fi } # end func_msys_to_cygwin_pathlist_convert # func_nix_to_cygwin_pathlist_convert ARG # A pathlist conversion function for use when $host is *cygwin* # but $build is some *nix variant. In this case, we assume # that a wine environment with a working winepath executable # is available in $build's $PATH, and that cygwin is installed # within that wine environment. Requires LT_CYGPATH (see # func_cygpath). # # ARG is the pathlist to be converted; the result is available # in func_to_host_pathlist_result. func_nix_to_cygwin_pathlist_convert () { $opt_debug func_to_host_pathlist_result="$1" if test -n "$1"; then # Remove leading and trailing path separator characters from # ARG. msys behavior is inconsistent here, cygpath turns them # into '.;' and ';.', and winepath ignores them completely. func_stripname : : "$1" func_to_host_pathlist_tmp1=$func_stripname_result func_wine_to_win32_pathlist "$func_to_host_pathlist_tmp1" func_cygpath -u -p "$func_wine_to_win32_pathlist_result" func_to_host_pathlist_result="$func_cygpath_result" func_pathlist_convert_check ":" ":" \ "$func_to_host_pathlist_tmp1" "$func_to_host_pathlist_result" func_pathlist_front_back_pathsep ":*" "*:" ":" "$1" fi } # end func_nix_to_cygwin_pathlist_convert # func_emit_cwrapperexe_src # emit the source code for a wrapper executable on stdout # Must ONLY be called from within func_mode_link because # it depends on a number of variable set therein. func_emit_cwrapperexe_src () { cat < #include #ifdef _MSC_VER # include # include # include #else # include # include # ifdef __CYGWIN__ # include # endif #endif #include #include #include #include #include #include #include #include /* declarations of non-ANSI functions */ #if defined(__MINGW32__) # ifdef __STRICT_ANSI__ int _putenv (const char *); # endif #elif defined(__CYGWIN__) # ifdef __STRICT_ANSI__ char *realpath (const char *, char *); int putenv (char *); int setenv (const char *, const char *, int); # endif /* #elif defined (other platforms) ... */ #endif /* portability defines, excluding path handling macros */ #if defined(_MSC_VER) # define setmode _setmode # define stat _stat # define chmod _chmod # define getcwd _getcwd # define putenv _putenv # define S_IXUSR _S_IEXEC # ifndef _INTPTR_T_DEFINED # define _INTPTR_T_DEFINED # define intptr_t int # endif #elif defined(__MINGW32__) # define setmode _setmode # define stat _stat # define chmod _chmod # define getcwd _getcwd # define putenv _putenv #elif defined(__CYGWIN__) # define HAVE_SETENV # define FOPEN_WB "wb" /* #elif defined (other platforms) ... */ #endif #if defined(PATH_MAX) # define LT_PATHMAX PATH_MAX #elif defined(MAXPATHLEN) # define LT_PATHMAX MAXPATHLEN #else # define LT_PATHMAX 1024 #endif #ifndef S_IXOTH # define S_IXOTH 0 #endif #ifndef S_IXGRP # define S_IXGRP 0 #endif /* path handling portability macros */ #ifndef DIR_SEPARATOR # define DIR_SEPARATOR '/' # define PATH_SEPARATOR ':' #endif #if defined (_WIN32) || defined (__MSDOS__) || defined (__DJGPP__) || \ defined (__OS2__) # define HAVE_DOS_BASED_FILE_SYSTEM # define FOPEN_WB "wb" # ifndef DIR_SEPARATOR_2 # define DIR_SEPARATOR_2 '\\' # endif # ifndef PATH_SEPARATOR_2 # define PATH_SEPARATOR_2 ';' # endif #endif #ifndef DIR_SEPARATOR_2 # define IS_DIR_SEPARATOR(ch) ((ch) == DIR_SEPARATOR) #else /* DIR_SEPARATOR_2 */ # define IS_DIR_SEPARATOR(ch) \ (((ch) == DIR_SEPARATOR) || ((ch) == DIR_SEPARATOR_2)) #endif /* DIR_SEPARATOR_2 */ #ifndef PATH_SEPARATOR_2 # define IS_PATH_SEPARATOR(ch) ((ch) == PATH_SEPARATOR) #else /* PATH_SEPARATOR_2 */ # define IS_PATH_SEPARATOR(ch) ((ch) == PATH_SEPARATOR_2) #endif /* PATH_SEPARATOR_2 */ #ifndef FOPEN_WB # define FOPEN_WB "w" #endif #ifndef _O_BINARY # define _O_BINARY 0 #endif #define XMALLOC(type, num) ((type *) xmalloc ((num) * sizeof(type))) #define XFREE(stale) do { \ if (stale) { free ((void *) stale); stale = 0; } \ } while (0) #if defined(LT_DEBUGWRAPPER) static int lt_debug = 1; #else static int lt_debug = 0; #endif const char *program_name = NULL; void *xmalloc (size_t num); char *xstrdup (const char *string); const char *base_name (const char *name); char *find_executable (const char *wrapper); char *chase_symlinks (const char *pathspec); int make_executable (const char *path); int check_executable (const char *path); char *strendzap (char *str, const char *pat); void lt_debugprintf (const char *fmt, ...); void lt_fatal (const char *message, ...); void lt_setenv (const char *name, const char *value); char *lt_extend_str (const char *orig_value, const char *add, int to_end); void lt_update_exe_path (const char *name, const char *value); void lt_update_lib_path (const char *name, const char *value); char **prepare_spawn (char **argv); void lt_dump_script (FILE *f); EOF cat <")); for (i = 0; i < newargc; i++) { lt_debugprintf ("(main) newargz[%d] : %s\n", i, (newargz[i] ? newargz[i] : "")); } EOF case $host_os in mingw*) cat <<"EOF" /* execv doesn't actually work on mingw as expected on unix */ newargz = prepare_spawn (newargz); rval = _spawnv (_P_WAIT, lt_argv_zero, (const char * const *) newargz); if (rval == -1) { /* failed to start process */ lt_debugprintf ("(main) failed to launch target \"%s\": errno = %d\n", lt_argv_zero, errno); return 127; } return rval; EOF ;; *) cat <<"EOF" execv (lt_argv_zero, newargz); return rval; /* =127, but avoids unused variable warning */ EOF ;; esac cat <<"EOF" } void * xmalloc (size_t num) { void *p = (void *) malloc (num); if (!p) lt_fatal ("Memory exhausted"); return p; } char * xstrdup (const char *string) { return string ? strcpy ((char *) xmalloc (strlen (string) + 1), string) : NULL; } const char * base_name (const char *name) { const char *base; #if defined (HAVE_DOS_BASED_FILE_SYSTEM) /* Skip over the disk name in MSDOS pathnames. */ if (isalpha ((unsigned char) name[0]) && name[1] == ':') name += 2; #endif for (base = name; *name; name++) if (IS_DIR_SEPARATOR (*name)) base = name + 1; return base; } int check_executable (const char *path) { struct stat st; lt_debugprintf ("(check_executable) : %s\n", path ? (*path ? path : "EMPTY!") : "NULL!"); if ((!path) || (!*path)) return 0; if ((stat (path, &st) >= 0) && (st.st_mode & (S_IXUSR | S_IXGRP | S_IXOTH))) return 1; else return 0; } int make_executable (const char *path) { int rval = 0; struct stat st; lt_debugprintf ("(make_executable) : %s\n", path ? (*path ? path : "EMPTY!") : "NULL!"); if ((!path) || (!*path)) return 0; if (stat (path, &st) >= 0) { rval = chmod (path, st.st_mode | S_IXOTH | S_IXGRP | S_IXUSR); } return rval; } /* Searches for the full path of the wrapper. Returns newly allocated full path name if found, NULL otherwise Does not chase symlinks, even on platforms that support them. */ char * find_executable (const char *wrapper) { int has_slash = 0; const char *p; const char *p_next; /* static buffer for getcwd */ char tmp[LT_PATHMAX + 1]; int tmp_len; char *concat_name; lt_debugprintf ("(find_executable) : %s\n", wrapper ? (*wrapper ? wrapper : "EMPTY!") : "NULL!"); if ((wrapper == NULL) || (*wrapper == '\0')) return NULL; /* Absolute path? */ #if defined (HAVE_DOS_BASED_FILE_SYSTEM) if (isalpha ((unsigned char) wrapper[0]) && wrapper[1] == ':') { concat_name = xstrdup (wrapper); if (check_executable (concat_name)) return concat_name; XFREE (concat_name); } else { #endif if (IS_DIR_SEPARATOR (wrapper[0])) { concat_name = xstrdup (wrapper); if (check_executable (concat_name)) return concat_name; XFREE (concat_name); } #if defined (HAVE_DOS_BASED_FILE_SYSTEM) } #endif for (p = wrapper; *p; p++) if (*p == '/') { has_slash = 1; break; } if (!has_slash) { /* no slashes; search PATH */ const char *path = getenv ("PATH"); if (path != NULL) { for (p = path; *p; p = p_next) { const char *q; size_t p_len; for (q = p; *q; q++) if (IS_PATH_SEPARATOR (*q)) break; p_len = q - p; p_next = (*q == '\0' ? q : q + 1); if (p_len == 0) { /* empty path: current directory */ if (getcwd (tmp, LT_PATHMAX) == NULL) lt_fatal ("getcwd failed"); tmp_len = strlen (tmp); concat_name = XMALLOC (char, tmp_len + 1 + strlen (wrapper) + 1); memcpy (concat_name, tmp, tmp_len); concat_name[tmp_len] = '/'; strcpy (concat_name + tmp_len + 1, wrapper); } else { concat_name = XMALLOC (char, p_len + 1 + strlen (wrapper) + 1); memcpy (concat_name, p, p_len); concat_name[p_len] = '/'; strcpy (concat_name + p_len + 1, wrapper); } if (check_executable (concat_name)) return concat_name; XFREE (concat_name); } } /* not found in PATH; assume curdir */ } /* Relative path | not found in path: prepend cwd */ if (getcwd (tmp, LT_PATHMAX) == NULL) lt_fatal ("getcwd failed"); tmp_len = strlen (tmp); concat_name = XMALLOC (char, tmp_len + 1 + strlen (wrapper) + 1); memcpy (concat_name, tmp, tmp_len); concat_name[tmp_len] = '/'; strcpy (concat_name + tmp_len + 1, wrapper); if (check_executable (concat_name)) return concat_name; XFREE (concat_name); return NULL; } char * chase_symlinks (const char *pathspec) { #ifndef S_ISLNK return xstrdup (pathspec); #else char buf[LT_PATHMAX]; struct stat s; char *tmp_pathspec = xstrdup (pathspec); char *p; int has_symlinks = 0; while (strlen (tmp_pathspec) && !has_symlinks) { lt_debugprintf ("checking path component for symlinks: %s\n", tmp_pathspec); if (lstat (tmp_pathspec, &s) == 0) { if (S_ISLNK (s.st_mode) != 0) { has_symlinks = 1; break; } /* search backwards for last DIR_SEPARATOR */ p = tmp_pathspec + strlen (tmp_pathspec) - 1; while ((p > tmp_pathspec) && (!IS_DIR_SEPARATOR (*p))) p--; if ((p == tmp_pathspec) && (!IS_DIR_SEPARATOR (*p))) { /* no more DIR_SEPARATORS left */ break; } *p = '\0'; } else { char *errstr = strerror (errno); lt_fatal ("Error accessing file %s (%s)", tmp_pathspec, errstr); } } XFREE (tmp_pathspec); if (!has_symlinks) { return xstrdup (pathspec); } tmp_pathspec = realpath (pathspec, buf); if (tmp_pathspec == 0) { lt_fatal ("Could not follow symlinks for %s", pathspec); } return xstrdup (tmp_pathspec); #endif } char * strendzap (char *str, const char *pat) { size_t len, patlen; assert (str != NULL); assert (pat != NULL); len = strlen (str); patlen = strlen (pat); if (patlen <= len) { str += len - patlen; if (strcmp (str, pat) == 0) *str = '\0'; } return str; } void lt_debugprintf (const char *fmt, ...) { va_list args; if (lt_debug) { va_start (args, fmt); (void) vfprintf (stderr, fmt, args); va_end (args); } } static void lt_error_core (int exit_status, const char *mode, const char *message, va_list ap) { fprintf (stderr, "%s: %s: ", program_name, mode); vfprintf (stderr, message, ap); fprintf (stderr, ".\n"); if (exit_status >= 0) exit (exit_status); } void lt_fatal (const char *message, ...) { va_list ap; va_start (ap, message); lt_error_core (EXIT_FAILURE, "FATAL", message, ap); va_end (ap); } void lt_setenv (const char *name, const char *value) { lt_debugprintf ("(lt_setenv) setting '%s' to '%s'\n", (name ? name : ""), (value ? value : "")); { #ifdef HAVE_SETENV /* always make a copy, for consistency with !HAVE_SETENV */ char *str = xstrdup (value); setenv (name, str, 1); #else int len = strlen (name) + 1 + strlen (value) + 1; char *str = XMALLOC (char, len); sprintf (str, "%s=%s", name, value); if (putenv (str) != EXIT_SUCCESS) { XFREE (str); } #endif } } char * lt_extend_str (const char *orig_value, const char *add, int to_end) { char *new_value; if (orig_value && *orig_value) { int orig_value_len = strlen (orig_value); int add_len = strlen (add); new_value = XMALLOC (char, add_len + orig_value_len + 1); if (to_end) { strcpy (new_value, orig_value); strcpy (new_value + orig_value_len, add); } else { strcpy (new_value, add); strcpy (new_value + add_len, orig_value); } } else { new_value = xstrdup (add); } return new_value; } void lt_update_exe_path (const char *name, const char *value) { lt_debugprintf ("(lt_update_exe_path) modifying '%s' by prepending '%s'\n", (name ? name : ""), (value ? value : "")); if (name && *name && value && *value) { char *new_value = lt_extend_str (getenv (name), value, 0); /* some systems can't cope with a ':'-terminated path #' */ int len = strlen (new_value); while (((len = strlen (new_value)) > 0) && IS_PATH_SEPARATOR (new_value[len-1])) { new_value[len-1] = '\0'; } lt_setenv (name, new_value); XFREE (new_value); } } void lt_update_lib_path (const char *name, const char *value) { lt_debugprintf ("(lt_update_lib_path) modifying '%s' by prepending '%s'\n", (name ? name : ""), (value ? value : "")); if (name && *name && value && *value) { char *new_value = lt_extend_str (getenv (name), value, 0); lt_setenv (name, new_value); XFREE (new_value); } } EOF case $host_os in mingw*) cat <<"EOF" /* Prepares an argument vector before calling spawn(). Note that spawn() does not by itself call the command interpreter (getenv ("COMSPEC") != NULL ? getenv ("COMSPEC") : ({ OSVERSIONINFO v; v.dwOSVersionInfoSize = sizeof(OSVERSIONINFO); GetVersionEx(&v); v.dwPlatformId == VER_PLATFORM_WIN32_NT; }) ? "cmd.exe" : "command.com"). Instead it simply concatenates the arguments, separated by ' ', and calls CreateProcess(). We must quote the arguments since Win32 CreateProcess() interprets characters like ' ', '\t', '\\', '"' (but not '<' and '>') in a special way: - Space and tab are interpreted as delimiters. They are not treated as delimiters if they are surrounded by double quotes: "...". - Unescaped double quotes are removed from the input. Their only effect is that within double quotes, space and tab are treated like normal characters. - Backslashes not followed by double quotes are not special. - But 2*n+1 backslashes followed by a double quote become n backslashes followed by a double quote (n >= 0): \" -> " \\\" -> \" \\\\\" -> \\" */ #define SHELL_SPECIAL_CHARS "\"\\ \001\002\003\004\005\006\007\010\011\012\013\014\015\016\017\020\021\022\023\024\025\026\027\030\031\032\033\034\035\036\037" #define SHELL_SPACE_CHARS " \001\002\003\004\005\006\007\010\011\012\013\014\015\016\017\020\021\022\023\024\025\026\027\030\031\032\033\034\035\036\037" char ** prepare_spawn (char **argv) { size_t argc; char **new_argv; size_t i; /* Count number of arguments. */ for (argc = 0; argv[argc] != NULL; argc++) ; /* Allocate new argument vector. */ new_argv = XMALLOC (char *, argc + 1); /* Put quoted arguments into the new argument vector. */ for (i = 0; i < argc; i++) { const char *string = argv[i]; if (string[0] == '\0') new_argv[i] = xstrdup ("\"\""); else if (strpbrk (string, SHELL_SPECIAL_CHARS) != NULL) { int quote_around = (strpbrk (string, SHELL_SPACE_CHARS) != NULL); size_t length; unsigned int backslashes; const char *s; char *quoted_string; char *p; length = 0; backslashes = 0; if (quote_around) length++; for (s = string; *s != '\0'; s++) { char c = *s; if (c == '"') length += backslashes + 1; length++; if (c == '\\') backslashes++; else backslashes = 0; } if (quote_around) length += backslashes + 1; quoted_string = XMALLOC (char, length + 1); p = quoted_string; backslashes = 0; if (quote_around) *p++ = '"'; for (s = string; *s != '\0'; s++) { char c = *s; if (c == '"') { unsigned int j; for (j = backslashes + 1; j > 0; j--) *p++ = '\\'; } *p++ = c; if (c == '\\') backslashes++; else backslashes = 0; } if (quote_around) { unsigned int j; for (j = backslashes; j > 0; j--) *p++ = '\\'; *p++ = '"'; } *p = '\0'; new_argv[i] = quoted_string; } else new_argv[i] = (char *) string; } new_argv[argc] = NULL; return new_argv; } EOF ;; esac cat <<"EOF" void lt_dump_script (FILE* f) { EOF func_emit_wrapper yes | $SED -e 's/\([\\"]\)/\\\1/g' \ -e 's/^/ fputs ("/' -e 's/$/\\n", f);/' cat <<"EOF" } EOF } # end: func_emit_cwrapperexe_src # func_emit_exe_manifest # emit a Win32 UAC manifest for executable on stdout # Must ONLY be called from within func_mode_link because # it depends on a number of variable set therein. func_emit_exe_manifest () { cat < EOF } # func_win32_import_lib_p ARG # True if ARG is an import lib, as indicated by $file_magic_cmd func_win32_import_lib_p () { $opt_debug case `eval $file_magic_cmd \"\$1\" 2>/dev/null | $SED -e 10q` in *import*) : ;; *) false ;; esac } # func_mode_link arg... func_mode_link () { $opt_debug case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-os2* | *-cegcc*) # It is impossible to link a dll without this setting, and # we shouldn't force the makefile maintainer to figure out # which system we are compiling for in order to pass an extra # flag for every libtool invocation. # allow_undefined=no # FIXME: Unfortunately, there are problems with the above when trying # to make a dll which has undefined symbols, in which case not # even a static library is built. For now, we need to specify # -no-undefined on the libtool link line when we can be certain # that all symbols are satisfied, otherwise we get a static library. allow_undefined=yes ;; *) allow_undefined=yes ;; esac libtool_args=$nonopt base_compile="$nonopt $@" compile_command=$nonopt finalize_command=$nonopt compile_rpath= finalize_rpath= compile_shlibpath= finalize_shlibpath= convenience= old_convenience= deplibs= old_deplibs= compiler_flags= linker_flags= dllsearchpath= lib_search_path=`pwd` inst_prefix_dir= new_inherited_linker_flags= avoid_version=no bindir= dlfiles= dlprefiles= dlself=no export_dynamic=no export_symbols= export_symbols_regex= generated= libobjs= ltlibs= module=no no_install=no objs= non_pic_objects= precious_files_regex= prefer_static_libs=no preload=no prev= prevarg= release= rpath= xrpath= perm_rpath= temp_rpath= thread_safe=no vinfo= vinfo_number=no weak_libs= single_module="${wl}-single_module" func_infer_tag $base_compile # We need to know -static, to get the right output filenames. for arg do case $arg in -shared) test "$build_libtool_libs" != yes && \ func_fatal_configuration "can not build a shared library" build_old_libs=no break ;; -all-static | -static | -static-libtool-libs) case $arg in -all-static) if test "$build_libtool_libs" = yes && test -z "$link_static_flag"; then func_warning "complete static linking is impossible in this configuration" fi if test -n "$link_static_flag"; then dlopen_self=$dlopen_self_static fi prefer_static_libs=yes ;; -static) if test -z "$pic_flag" && test -n "$link_static_flag"; then dlopen_self=$dlopen_self_static fi prefer_static_libs=built ;; -static-libtool-libs) if test -z "$pic_flag" && test -n "$link_static_flag"; then dlopen_self=$dlopen_self_static fi prefer_static_libs=yes ;; esac build_libtool_libs=no build_old_libs=yes break ;; esac done # See if our shared archives depend on static archives. test -n "$old_archive_from_new_cmds" && build_old_libs=yes # Go through the arguments, transforming them on the way. while test "$#" -gt 0; do arg="$1" shift func_quote_for_eval "$arg" qarg=$func_quote_for_eval_unquoted_result func_append libtool_args " $func_quote_for_eval_result" # If the previous option needs an argument, assign it. if test -n "$prev"; then case $prev in output) func_append compile_command " @OUTPUT@" func_append finalize_command " @OUTPUT@" ;; esac case $prev in bindir) bindir="$arg" prev= continue ;; dlfiles|dlprefiles) if test "$preload" = no; then # Add the symbol object into the linking commands. func_append compile_command " @SYMFILE@" func_append finalize_command " @SYMFILE@" preload=yes fi case $arg in *.la | *.lo) ;; # We handle these cases below. force) if test "$dlself" = no; then dlself=needless export_dynamic=yes fi prev= continue ;; self) if test "$prev" = dlprefiles; then dlself=yes elif test "$prev" = dlfiles && test "$dlopen_self" != yes; then dlself=yes else dlself=needless export_dynamic=yes fi prev= continue ;; *) if test "$prev" = dlfiles; then dlfiles="$dlfiles $arg" else dlprefiles="$dlprefiles $arg" fi prev= continue ;; esac ;; expsyms) export_symbols="$arg" test -f "$arg" \ || func_fatal_error "symbol file \`$arg' does not exist" prev= continue ;; expsyms_regex) export_symbols_regex="$arg" prev= continue ;; framework) case $host in *-*-darwin*) case "$deplibs " in *" $qarg.ltframework "*) ;; *) deplibs="$deplibs $qarg.ltframework" # this is fixed later ;; esac ;; esac prev= continue ;; inst_prefix) inst_prefix_dir="$arg" prev= continue ;; objectlist) if test -f "$arg"; then save_arg=$arg moreargs= for fil in `cat "$save_arg"` do # moreargs="$moreargs $fil" arg=$fil # A libtool-controlled object. # Check to see that this really is a libtool object. if func_lalib_unsafe_p "$arg"; then pic_object= non_pic_object= # Read the .lo file func_source "$arg" if test -z "$pic_object" || test -z "$non_pic_object" || test "$pic_object" = none && test "$non_pic_object" = none; then func_fatal_error "cannot find name of object for \`$arg'" fi # Extract subdirectory from the argument. func_dirname "$arg" "/" "" xdir="$func_dirname_result" if test "$pic_object" != none; then # Prepend the subdirectory the object is found in. pic_object="$xdir$pic_object" if test "$prev" = dlfiles; then if test "$build_libtool_libs" = yes && test "$dlopen_support" = yes; then dlfiles="$dlfiles $pic_object" prev= continue else # If libtool objects are unsupported, then we need to preload. prev=dlprefiles fi fi # CHECK ME: I think I busted this. -Ossama if test "$prev" = dlprefiles; then # Preload the old-style object. dlprefiles="$dlprefiles $pic_object" prev= fi # A PIC object. func_append libobjs " $pic_object" arg="$pic_object" fi # Non-PIC object. if test "$non_pic_object" != none; then # Prepend the subdirectory the object is found in. non_pic_object="$xdir$non_pic_object" # A standard non-PIC object func_append non_pic_objects " $non_pic_object" if test -z "$pic_object" || test "$pic_object" = none ; then arg="$non_pic_object" fi else # If the PIC object exists, use it instead. # $xdir was prepended to $pic_object above. non_pic_object="$pic_object" func_append non_pic_objects " $non_pic_object" fi else # Only an error if not doing a dry-run. if $opt_dry_run; then # Extract subdirectory from the argument. func_dirname "$arg" "/" "" xdir="$func_dirname_result" func_lo2o "$arg" pic_object=$xdir$objdir/$func_lo2o_result non_pic_object=$xdir$func_lo2o_result func_append libobjs " $pic_object" func_append non_pic_objects " $non_pic_object" else func_fatal_error "\`$arg' is not a valid libtool object" fi fi done else func_fatal_error "link input file \`$arg' does not exist" fi arg=$save_arg prev= continue ;; precious_regex) precious_files_regex="$arg" prev= continue ;; release) release="-$arg" prev= continue ;; rpath | xrpath) # We need an absolute path. case $arg in [\\/]* | [A-Za-z]:[\\/]*) ;; *) func_fatal_error "only absolute run-paths are allowed" ;; esac if test "$prev" = rpath; then case "$rpath " in *" $arg "*) ;; *) rpath="$rpath $arg" ;; esac else case "$xrpath " in *" $arg "*) ;; *) xrpath="$xrpath $arg" ;; esac fi prev= continue ;; shrext) shrext_cmds="$arg" prev= continue ;; weak) weak_libs="$weak_libs $arg" prev= continue ;; xcclinker) linker_flags="$linker_flags $qarg" compiler_flags="$compiler_flags $qarg" prev= func_append compile_command " $qarg" func_append finalize_command " $qarg" continue ;; xcompiler) compiler_flags="$compiler_flags $qarg" prev= func_append compile_command " $qarg" func_append finalize_command " $qarg" continue ;; xlinker) linker_flags="$linker_flags $qarg" compiler_flags="$compiler_flags $wl$qarg" prev= func_append compile_command " $wl$qarg" func_append finalize_command " $wl$qarg" continue ;; *) eval "$prev=\"\$arg\"" prev= continue ;; esac fi # test -n "$prev" prevarg="$arg" case $arg in -all-static) if test -n "$link_static_flag"; then # See comment for -static flag below, for more details. func_append compile_command " $link_static_flag" func_append finalize_command " $link_static_flag" fi continue ;; -allow-undefined) # FIXME: remove this flag sometime in the future. func_fatal_error "\`-allow-undefined' must not be used because it is the default" ;; -avoid-version) avoid_version=yes continue ;; -bindir) prev=bindir continue ;; -dlopen) prev=dlfiles continue ;; -dlpreopen) prev=dlprefiles continue ;; -export-dynamic) export_dynamic=yes continue ;; -export-symbols | -export-symbols-regex) if test -n "$export_symbols" || test -n "$export_symbols_regex"; then func_fatal_error "more than one -exported-symbols argument is not allowed" fi if test "X$arg" = "X-export-symbols"; then prev=expsyms else prev=expsyms_regex fi continue ;; -framework) prev=framework continue ;; -inst-prefix-dir) prev=inst_prefix continue ;; # The native IRIX linker understands -LANG:*, -LIST:* and -LNO:* # so, if we see these flags be careful not to treat them like -L -L[A-Z][A-Z]*:*) case $with_gcc/$host in no/*-*-irix* | /*-*-irix*) func_append compile_command " $arg" func_append finalize_command " $arg" ;; esac continue ;; -L*) func_stripname '-L' '' "$arg" dir=$func_stripname_result if test -z "$dir"; then if test "$#" -gt 0; then func_fatal_error "require no space between \`-L' and \`$1'" else func_fatal_error "need path for \`-L' option" fi fi # We need an absolute path. case $dir in [\\/]* | [A-Za-z]:[\\/]*) ;; *) absdir=`cd "$dir" && pwd` test -z "$absdir" && \ func_fatal_error "cannot determine absolute directory name of \`$dir'" dir="$absdir" ;; esac case "$deplibs " in *" -L$dir "*) ;; *) deplibs="$deplibs -L$dir" lib_search_path="$lib_search_path $dir" ;; esac case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-os2* | *-cegcc*) testbindir=`$ECHO "$dir" | $SED 's*/lib$*/bin*'` case :$dllsearchpath: in *":$dir:"*) ;; ::) dllsearchpath=$dir;; *) dllsearchpath="$dllsearchpath:$dir";; esac case :$dllsearchpath: in *":$testbindir:"*) ;; ::) dllsearchpath=$testbindir;; *) dllsearchpath="$dllsearchpath:$testbindir";; esac ;; esac continue ;; -l*) if test "X$arg" = "X-lc" || test "X$arg" = "X-lm"; then case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-beos* | *-cegcc* | *-*-haiku*) # These systems don't actually have a C or math library (as such) continue ;; *-*-os2*) # These systems don't actually have a C library (as such) test "X$arg" = "X-lc" && continue ;; *-*-openbsd* | *-*-freebsd* | *-*-dragonfly*) # Do not include libc due to us having libc/libc_r. test "X$arg" = "X-lc" && continue ;; *-*-rhapsody* | *-*-darwin1.[012]) # Rhapsody C and math libraries are in the System framework deplibs="$deplibs System.ltframework" continue ;; *-*-sco3.2v5* | *-*-sco5v6*) # Causes problems with __ctype test "X$arg" = "X-lc" && continue ;; *-*-sysv4.2uw2* | *-*-sysv5* | *-*-unixware* | *-*-OpenUNIX*) # Compiler inserts libc in the correct place for threads to work test "X$arg" = "X-lc" && continue ;; esac elif test "X$arg" = "X-lc_r"; then case $host in *-*-openbsd* | *-*-freebsd* | *-*-dragonfly*) # Do not include libc_r directly, use -pthread flag. continue ;; esac fi deplibs="$deplibs $arg" continue ;; -module) module=yes continue ;; # Tru64 UNIX uses -model [arg] to determine the layout of C++ # classes, name mangling, and exception handling. # Darwin uses the -arch flag to determine output architecture. -model|-arch|-isysroot) compiler_flags="$compiler_flags $arg" func_append compile_command " $arg" func_append finalize_command " $arg" prev=xcompiler continue ;; -mt|-mthreads|-kthread|-Kthread|-pthread|-pthreads|--thread-safe|-threads) compiler_flags="$compiler_flags $arg" func_append compile_command " $arg" func_append finalize_command " $arg" case "$new_inherited_linker_flags " in *" $arg "*) ;; * ) new_inherited_linker_flags="$new_inherited_linker_flags $arg" ;; esac continue ;; -multi_module) single_module="${wl}-multi_module" continue ;; -no-fast-install) fast_install=no continue ;; -no-install) case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-os2* | *-*-darwin* | *-cegcc*) # The PATH hackery in wrapper scripts is required on Windows # and Darwin in order for the loader to find any dlls it needs. func_warning "\`-no-install' is ignored for $host" func_warning "assuming \`-no-fast-install' instead" fast_install=no ;; *) no_install=yes ;; esac continue ;; -no-undefined) allow_undefined=no continue ;; -objectlist) prev=objectlist continue ;; -o) prev=output ;; -precious-files-regex) prev=precious_regex continue ;; -release) prev=release continue ;; -rpath) prev=rpath continue ;; -R) prev=xrpath continue ;; -R*) func_stripname '-R' '' "$arg" dir=$func_stripname_result # We need an absolute path. case $dir in [\\/]* | [A-Za-z]:[\\/]*) ;; *) func_fatal_error "only absolute run-paths are allowed" ;; esac case "$xrpath " in *" $dir "*) ;; *) xrpath="$xrpath $dir" ;; esac continue ;; -shared) # The effects of -shared are defined in a previous loop. continue ;; -shrext) prev=shrext continue ;; -static | -static-libtool-libs) # The effects of -static are defined in a previous loop. # We used to do the same as -all-static on platforms that # didn't have a PIC flag, but the assumption that the effects # would be equivalent was wrong. It would break on at least # Digital Unix and AIX. continue ;; -thread-safe) thread_safe=yes continue ;; -version-info) prev=vinfo continue ;; -version-number) prev=vinfo vinfo_number=yes continue ;; -weak) prev=weak continue ;; -Wc,*) func_stripname '-Wc,' '' "$arg" args=$func_stripname_result arg= save_ifs="$IFS"; IFS=',' for flag in $args; do IFS="$save_ifs" func_quote_for_eval "$flag" arg="$arg $func_quote_for_eval_result" compiler_flags="$compiler_flags $func_quote_for_eval_result" done IFS="$save_ifs" func_stripname ' ' '' "$arg" arg=$func_stripname_result ;; -Wl,*) func_stripname '-Wl,' '' "$arg" args=$func_stripname_result arg= save_ifs="$IFS"; IFS=',' for flag in $args; do IFS="$save_ifs" func_quote_for_eval "$flag" arg="$arg $wl$func_quote_for_eval_result" compiler_flags="$compiler_flags $wl$func_quote_for_eval_result" linker_flags="$linker_flags $func_quote_for_eval_result" done IFS="$save_ifs" func_stripname ' ' '' "$arg" arg=$func_stripname_result ;; -Xcompiler) prev=xcompiler continue ;; -Xlinker) prev=xlinker continue ;; -XCClinker) prev=xcclinker continue ;; # -msg_* for osf cc -msg_*) func_quote_for_eval "$arg" arg="$func_quote_for_eval_result" ;; # -64, -mips[0-9] enable 64-bit mode on the SGI compiler # -r[0-9][0-9]* specifies the processor on the SGI compiler # -xarch=*, -xtarget=* enable 64-bit mode on the Sun compiler # +DA*, +DD* enable 64-bit mode on the HP compiler # -q* pass through compiler args for the IBM compiler # -m*, -t[45]*, -txscale* pass through architecture-specific # compiler args for GCC # -F/path gives path to uninstalled frameworks, gcc on darwin # -p, -pg, --coverage, -fprofile-* pass through profiling flag for GCC # @file GCC response files # -tp=* Portland pgcc target processor selection # -{shared,static}-libgcc, -static-{libgfortran|libstdc++}: force GCC # to link against specified runtime library. -64|-mips[0-9]|-r[0-9][0-9]*|-xarch=*|-xtarget=*|+DA*|+DD*|-q*|-m*| \ -t[45]*|-txscale*|-p|-pg|--coverage|-fprofile-*|-F*|@*|-tp=*| \ -shared-libgcc|-static-libgcc|-static-libgfortran|-static-libstdc++) func_quote_for_eval "$arg" arg="$func_quote_for_eval_result" func_append compile_command " $arg" func_append finalize_command " $arg" compiler_flags="$compiler_flags $arg" continue ;; # Some other compiler flag. -* | +*) func_quote_for_eval "$arg" arg="$func_quote_for_eval_result" ;; *.$objext) # A standard object. objs="$objs $arg" ;; *.lo) # A libtool-controlled object. # Check to see that this really is a libtool object. if func_lalib_unsafe_p "$arg"; then pic_object= non_pic_object= # Read the .lo file func_source "$arg" if test -z "$pic_object" || test -z "$non_pic_object" || test "$pic_object" = none && test "$non_pic_object" = none; then func_fatal_error "cannot find name of object for \`$arg'" fi # Extract subdirectory from the argument. func_dirname "$arg" "/" "" xdir="$func_dirname_result" if test "$pic_object" != none; then # Prepend the subdirectory the object is found in. pic_object="$xdir$pic_object" if test "$prev" = dlfiles; then if test "$build_libtool_libs" = yes && test "$dlopen_support" = yes; then dlfiles="$dlfiles $pic_object" prev= continue else # If libtool objects are unsupported, then we need to preload. prev=dlprefiles fi fi # CHECK ME: I think I busted this. -Ossama if test "$prev" = dlprefiles; then # Preload the old-style object. dlprefiles="$dlprefiles $pic_object" prev= fi # A PIC object. func_append libobjs " $pic_object" arg="$pic_object" fi # Non-PIC object. if test "$non_pic_object" != none; then # Prepend the subdirectory the object is found in. non_pic_object="$xdir$non_pic_object" # A standard non-PIC object func_append non_pic_objects " $non_pic_object" if test -z "$pic_object" || test "$pic_object" = none ; then arg="$non_pic_object" fi else # If the PIC object exists, use it instead. # $xdir was prepended to $pic_object above. non_pic_object="$pic_object" func_append non_pic_objects " $non_pic_object" fi else # Only an error if not doing a dry-run. if $opt_dry_run; then # Extract subdirectory from the argument. func_dirname "$arg" "/" "" xdir="$func_dirname_result" func_lo2o "$arg" pic_object=$xdir$objdir/$func_lo2o_result non_pic_object=$xdir$func_lo2o_result func_append libobjs " $pic_object" func_append non_pic_objects " $non_pic_object" else func_fatal_error "\`$arg' is not a valid libtool object" fi fi ;; *.$libext) # An archive. deplibs="$deplibs $arg" old_deplibs="$old_deplibs $arg" continue ;; *.la) # A libtool-controlled library. if test "$prev" = dlfiles; then # This library was specified with -dlopen. dlfiles="$dlfiles $arg" prev= elif test "$prev" = dlprefiles; then # The library was specified with -dlpreopen. dlprefiles="$dlprefiles $arg" prev= else deplibs="$deplibs $arg" fi continue ;; # Some other compiler argument. *) # Unknown arguments in both finalize_command and compile_command need # to be aesthetically quoted because they are evaled later. func_quote_for_eval "$arg" arg="$func_quote_for_eval_result" ;; esac # arg # Now actually substitute the argument into the commands. if test -n "$arg"; then func_append compile_command " $arg" func_append finalize_command " $arg" fi done # argument parsing loop test -n "$prev" && \ func_fatal_help "the \`$prevarg' option requires an argument" if test "$export_dynamic" = yes && test -n "$export_dynamic_flag_spec"; then eval arg=\"$export_dynamic_flag_spec\" func_append compile_command " $arg" func_append finalize_command " $arg" fi oldlibs= # calculate the name of the file, without its directory func_basename "$output" outputname="$func_basename_result" libobjs_save="$libobjs" if test -n "$shlibpath_var"; then # get the directories listed in $shlibpath_var eval shlib_search_path=\`\$ECHO \"\${$shlibpath_var}\" \| \$SED \'s/:/ /g\'\` else shlib_search_path= fi eval sys_lib_search_path=\"$sys_lib_search_path_spec\" eval sys_lib_dlsearch_path=\"$sys_lib_dlsearch_path_spec\" func_dirname "$output" "/" "" output_objdir="$func_dirname_result$objdir" # Create the object directory. func_mkdir_p "$output_objdir" # Determine the type of output case $output in "") func_fatal_help "you must specify an output file" ;; *.$libext) linkmode=oldlib ;; *.lo | *.$objext) linkmode=obj ;; *.la) linkmode=lib ;; *) linkmode=prog ;; # Anything else should be a program. esac specialdeplibs= libs= # Find all interdependent deplibs by searching for libraries # that are linked more than once (e.g. -la -lb -la) for deplib in $deplibs; do if $opt_duplicate_deps ; then case "$libs " in *" $deplib "*) specialdeplibs="$specialdeplibs $deplib" ;; esac fi libs="$libs $deplib" done if test "$linkmode" = lib; then libs="$predeps $libs $compiler_lib_search_path $postdeps" # Compute libraries that are listed more than once in $predeps # $postdeps and mark them as special (i.e., whose duplicates are # not to be eliminated). pre_post_deps= if $opt_duplicate_compiler_generated_deps; then for pre_post_dep in $predeps $postdeps; do case "$pre_post_deps " in *" $pre_post_dep "*) specialdeplibs="$specialdeplibs $pre_post_deps" ;; esac pre_post_deps="$pre_post_deps $pre_post_dep" done fi pre_post_deps= fi deplibs= newdependency_libs= newlib_search_path= need_relink=no # whether we're linking any uninstalled libtool libraries notinst_deplibs= # not-installed libtool libraries notinst_path= # paths that contain not-installed libtool libraries case $linkmode in lib) passes="conv dlpreopen link" for file in $dlfiles $dlprefiles; do case $file in *.la) ;; *) func_fatal_help "libraries can \`-dlopen' only libtool libraries: $file" ;; esac done ;; prog) compile_deplibs= finalize_deplibs= alldeplibs=no newdlfiles= newdlprefiles= passes="conv scan dlopen dlpreopen link" ;; *) passes="conv" ;; esac for pass in $passes; do # The preopen pass in lib mode reverses $deplibs; put it back here # so that -L comes before libs that need it for instance... if test "$linkmode,$pass" = "lib,link"; then ## FIXME: Find the place where the list is rebuilt in the wrong ## order, and fix it there properly tmp_deplibs= for deplib in $deplibs; do tmp_deplibs="$deplib $tmp_deplibs" done deplibs="$tmp_deplibs" fi if test "$linkmode,$pass" = "lib,link" || test "$linkmode,$pass" = "prog,scan"; then libs="$deplibs" deplibs= fi if test "$linkmode" = prog; then case $pass in dlopen) libs="$dlfiles" ;; dlpreopen) libs="$dlprefiles" ;; link) libs="$deplibs %DEPLIBS% $dependency_libs" ;; esac fi if test "$linkmode,$pass" = "lib,dlpreopen"; then # Collect and forward deplibs of preopened libtool libs for lib in $dlprefiles; do # Ignore non-libtool-libs dependency_libs= case $lib in *.la) func_source "$lib" ;; esac # Collect preopened libtool deplibs, except any this library # has declared as weak libs for deplib in $dependency_libs; do func_basename "$deplib" deplib_base=$func_basename_result case " $weak_libs " in *" $deplib_base "*) ;; *) deplibs="$deplibs $deplib" ;; esac done done libs="$dlprefiles" fi if test "$pass" = dlopen; then # Collect dlpreopened libraries save_deplibs="$deplibs" deplibs= fi for deplib in $libs; do lib= found=no case $deplib in -mt|-mthreads|-kthread|-Kthread|-pthread|-pthreads|--thread-safe|-threads) if test "$linkmode,$pass" = "prog,link"; then compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" else compiler_flags="$compiler_flags $deplib" if test "$linkmode" = lib ; then case "$new_inherited_linker_flags " in *" $deplib "*) ;; * ) new_inherited_linker_flags="$new_inherited_linker_flags $deplib" ;; esac fi fi continue ;; -l*) if test "$linkmode" != lib && test "$linkmode" != prog; then func_warning "\`-l' is ignored for archives/objects" continue fi func_stripname '-l' '' "$deplib" name=$func_stripname_result if test "$linkmode" = lib; then searchdirs="$newlib_search_path $lib_search_path $compiler_lib_search_dirs $sys_lib_search_path $shlib_search_path" else searchdirs="$newlib_search_path $lib_search_path $sys_lib_search_path $shlib_search_path" fi for searchdir in $searchdirs; do for search_ext in .la $std_shrext .so .a; do # Search the libtool library lib="$searchdir/lib${name}${search_ext}" if test -f "$lib"; then if test "$search_ext" = ".la"; then found=yes else found=no fi break 2 fi done done if test "$found" != yes; then # deplib doesn't seem to be a libtool library if test "$linkmode,$pass" = "prog,link"; then compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" else deplibs="$deplib $deplibs" test "$linkmode" = lib && newdependency_libs="$deplib $newdependency_libs" fi continue else # deplib is a libtool library # If $allow_libtool_libs_with_static_runtimes && $deplib is a stdlib, # We need to do some special things here, and not later. if test "X$allow_libtool_libs_with_static_runtimes" = "Xyes" ; then case " $predeps $postdeps " in *" $deplib "*) if func_lalib_p "$lib"; then library_names= old_library= func_source "$lib" for l in $old_library $library_names; do ll="$l" done if test "X$ll" = "X$old_library" ; then # only static version available found=no func_dirname "$lib" "" "." ladir="$func_dirname_result" lib=$ladir/$old_library if test "$linkmode,$pass" = "prog,link"; then compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" else deplibs="$deplib $deplibs" test "$linkmode" = lib && newdependency_libs="$deplib $newdependency_libs" fi continue fi fi ;; *) ;; esac fi fi ;; # -l *.ltframework) if test "$linkmode,$pass" = "prog,link"; then compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" else deplibs="$deplib $deplibs" if test "$linkmode" = lib ; then case "$new_inherited_linker_flags " in *" $deplib "*) ;; * ) new_inherited_linker_flags="$new_inherited_linker_flags $deplib" ;; esac fi fi continue ;; -L*) case $linkmode in lib) deplibs="$deplib $deplibs" test "$pass" = conv && continue newdependency_libs="$deplib $newdependency_libs" func_stripname '-L' '' "$deplib" newlib_search_path="$newlib_search_path $func_stripname_result" ;; prog) if test "$pass" = conv; then deplibs="$deplib $deplibs" continue fi if test "$pass" = scan; then deplibs="$deplib $deplibs" else compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" fi func_stripname '-L' '' "$deplib" newlib_search_path="$newlib_search_path $func_stripname_result" ;; *) func_warning "\`-L' is ignored for archives/objects" ;; esac # linkmode continue ;; # -L -R*) if test "$pass" = link; then func_stripname '-R' '' "$deplib" dir=$func_stripname_result # Make sure the xrpath contains only unique directories. case "$xrpath " in *" $dir "*) ;; *) xrpath="$xrpath $dir" ;; esac fi deplibs="$deplib $deplibs" continue ;; *.la) lib="$deplib" ;; *.$libext) if test "$pass" = conv; then deplibs="$deplib $deplibs" continue fi case $linkmode in lib) # Linking convenience modules into shared libraries is allowed, # but linking other static libraries is non-portable. case " $dlpreconveniencelibs " in *" $deplib "*) ;; *) valid_a_lib=no case $deplibs_check_method in match_pattern*) set dummy $deplibs_check_method; shift match_pattern_regex=`expr "$deplibs_check_method" : "$1 \(.*\)"` if eval "\$ECHO \"$deplib\"" 2>/dev/null | $SED 10q \ | $EGREP "$match_pattern_regex" > /dev/null; then valid_a_lib=yes fi ;; pass_all) valid_a_lib=yes ;; esac if test "$valid_a_lib" != yes; then echo $ECHO "*** Warning: Trying to link with static lib archive $deplib." echo "*** I have the capability to make that library automatically link in when" echo "*** you link to this library. But I can only do this if you have a" echo "*** shared version of the library, which you do not appear to have" echo "*** because the file extensions .$libext of this argument makes me believe" echo "*** that it is just a static archive that I should not use here." else echo $ECHO "*** Warning: Linking the shared library $output against the" $ECHO "*** static library $deplib is not portable!" deplibs="$deplib $deplibs" fi ;; esac continue ;; prog) if test "$pass" != link; then deplibs="$deplib $deplibs" else compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" fi continue ;; esac # linkmode ;; # *.$libext *.lo | *.$objext) if test "$pass" = conv; then deplibs="$deplib $deplibs" elif test "$linkmode" = prog; then if test "$pass" = dlpreopen || test "$dlopen_support" != yes || test "$build_libtool_libs" = no; then # If there is no dlopen support or we're linking statically, # we need to preload. newdlprefiles="$newdlprefiles $deplib" compile_deplibs="$deplib $compile_deplibs" finalize_deplibs="$deplib $finalize_deplibs" else newdlfiles="$newdlfiles $deplib" fi fi continue ;; %DEPLIBS%) alldeplibs=yes continue ;; esac # case $deplib if test "$found" = yes || test -f "$lib"; then : else func_fatal_error "cannot find the library \`$lib' or unhandled argument \`$deplib'" fi # Check to see that this really is a libtool archive. func_lalib_unsafe_p "$lib" \ || func_fatal_error "\`$lib' is not a valid libtool archive" func_dirname "$lib" "" "." ladir="$func_dirname_result" dlname= dlopen= dlpreopen= libdir= library_names= old_library= inherited_linker_flags= # If the library was installed with an old release of libtool, # it will not redefine variables installed, or shouldnotlink installed=yes shouldnotlink=no avoidtemprpath= # Read the .la file func_source "$lib" # Convert "-framework foo" to "foo.ltframework" if test -n "$inherited_linker_flags"; then tmp_inherited_linker_flags=`$ECHO "$inherited_linker_flags" | $SED 's/-framework \([^ $]*\)/\1.ltframework/g'` for tmp_inherited_linker_flag in $tmp_inherited_linker_flags; do case " $new_inherited_linker_flags " in *" $tmp_inherited_linker_flag "*) ;; *) new_inherited_linker_flags="$new_inherited_linker_flags $tmp_inherited_linker_flag";; esac done fi dependency_libs=`$ECHO " $dependency_libs" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` if test "$linkmode,$pass" = "lib,link" || test "$linkmode,$pass" = "prog,scan" || { test "$linkmode" != prog && test "$linkmode" != lib; }; then test -n "$dlopen" && dlfiles="$dlfiles $dlopen" test -n "$dlpreopen" && dlprefiles="$dlprefiles $dlpreopen" fi if test "$pass" = conv; then # Only check for convenience libraries deplibs="$lib $deplibs" if test -z "$libdir"; then if test -z "$old_library"; then func_fatal_error "cannot find name of link library for \`$lib'" fi # It is a libtool convenience library, so add in its objects. convenience="$convenience $ladir/$objdir/$old_library" old_convenience="$old_convenience $ladir/$objdir/$old_library" elif test "$linkmode" != prog && test "$linkmode" != lib; then func_fatal_error "\`$lib' is not a convenience library" fi tmp_libs= for deplib in $dependency_libs; do deplibs="$deplib $deplibs" if $opt_duplicate_deps ; then case "$tmp_libs " in *" $deplib "*) specialdeplibs="$specialdeplibs $deplib" ;; esac fi tmp_libs="$tmp_libs $deplib" done continue fi # $pass = conv # Get the name of the library we link against. linklib= for l in $old_library $library_names; do linklib="$l" done if test -z "$linklib"; then func_fatal_error "cannot find name of link library for \`$lib'" fi # This library was specified with -dlopen. if test "$pass" = dlopen; then if test -z "$libdir"; then func_fatal_error "cannot -dlopen a convenience library: \`$lib'" fi if test -z "$dlname" || test "$dlopen_support" != yes || test "$build_libtool_libs" = no; then # If there is no dlname, no dlopen support or we're linking # statically, we need to preload. We also need to preload any # dependent libraries so libltdl's deplib preloader doesn't # bomb out in the load deplibs phase. dlprefiles="$dlprefiles $lib $dependency_libs" else newdlfiles="$newdlfiles $lib" fi continue fi # $pass = dlopen # We need an absolute path. case $ladir in [\\/]* | [A-Za-z]:[\\/]*) abs_ladir="$ladir" ;; *) abs_ladir=`cd "$ladir" && pwd` if test -z "$abs_ladir"; then func_warning "cannot determine absolute directory name of \`$ladir'" func_warning "passing it literally to the linker, although it might fail" abs_ladir="$ladir" fi ;; esac func_basename "$lib" laname="$func_basename_result" # Find the relevant object directory and library name. if test "X$installed" = Xyes; then if test ! -f "$libdir/$linklib" && test -f "$abs_ladir/$linklib"; then func_warning "library \`$lib' was moved." dir="$ladir" absdir="$abs_ladir" libdir="$abs_ladir" else dir="$libdir" absdir="$libdir" fi test "X$hardcode_automatic" = Xyes && avoidtemprpath=yes else if test ! -f "$ladir/$objdir/$linklib" && test -f "$abs_ladir/$linklib"; then dir="$ladir" absdir="$abs_ladir" # Remove this search path later notinst_path="$notinst_path $abs_ladir" else dir="$ladir/$objdir" absdir="$abs_ladir/$objdir" # Remove this search path later notinst_path="$notinst_path $abs_ladir" fi fi # $installed = yes func_stripname 'lib' '.la' "$laname" name=$func_stripname_result # This library was specified with -dlpreopen. if test "$pass" = dlpreopen; then if test -z "$libdir" && test "$linkmode" = prog; then func_fatal_error "only libraries may -dlpreopen a convenience library: \`$lib'" fi case "$host" in # special handling for platforms with PE-DLLs. *cygwin* | *mingw* | *cegcc* ) # Linker will automatically link against shared library if both # static and shared are present. Therefore, ensure we extract # symbols from the import library if a shared library is present # (otherwise, the dlopen module name will be incorrect). We do # this by putting the import library name into $newdlprefiles. # We recover the dlopen module name by 'saving' the la file # name in a special purpose variable, and (later) extracting the # dlname from the la file. if test -n "$dlname"; then func_tr_sh "$dir/$linklib" eval "libfile_$func_tr_sh_result=\$abs_ladir/\$laname" newdlprefiles="$newdlprefiles $dir/$linklib" else newdlprefiles="$newdlprefiles $dir/$old_library" # Keep a list of preopened convenience libraries to check # that they are being used correctly in the link pass. test -z "$libdir" && \ dlpreconveniencelibs="$dlpreconveniencelibs $dir/$old_library" fi ;; * ) # Prefer using a static library (so that no silly _DYNAMIC symbols # are required to link). if test -n "$old_library"; then newdlprefiles="$newdlprefiles $dir/$old_library" # Keep a list of preopened convenience libraries to check # that they are being used correctly in the link pass. test -z "$libdir" && \ dlpreconveniencelibs="$dlpreconveniencelibs $dir/$old_library" # Otherwise, use the dlname, so that lt_dlopen finds it. elif test -n "$dlname"; then newdlprefiles="$newdlprefiles $dir/$dlname" else newdlprefiles="$newdlprefiles $dir/$linklib" fi ;; esac fi # $pass = dlpreopen if test -z "$libdir"; then # Link the convenience library if test "$linkmode" = lib; then deplibs="$dir/$old_library $deplibs" elif test "$linkmode,$pass" = "prog,link"; then compile_deplibs="$dir/$old_library $compile_deplibs" finalize_deplibs="$dir/$old_library $finalize_deplibs" else deplibs="$lib $deplibs" # used for prog,scan pass fi continue fi if test "$linkmode" = prog && test "$pass" != link; then newlib_search_path="$newlib_search_path $ladir" deplibs="$lib $deplibs" linkalldeplibs=no if test "$link_all_deplibs" != no || test -z "$library_names" || test "$build_libtool_libs" = no; then linkalldeplibs=yes fi tmp_libs= for deplib in $dependency_libs; do case $deplib in -L*) func_stripname '-L' '' "$deplib" newlib_search_path="$newlib_search_path $func_stripname_result" ;; esac # Need to link against all dependency_libs? if test "$linkalldeplibs" = yes; then deplibs="$deplib $deplibs" else # Need to hardcode shared library paths # or/and link against static libraries newdependency_libs="$deplib $newdependency_libs" fi if $opt_duplicate_deps ; then case "$tmp_libs " in *" $deplib "*) specialdeplibs="$specialdeplibs $deplib" ;; esac fi tmp_libs="$tmp_libs $deplib" done # for deplib continue fi # $linkmode = prog... if test "$linkmode,$pass" = "prog,link"; then if test -n "$library_names" && { { test "$prefer_static_libs" = no || test "$prefer_static_libs,$installed" = "built,yes"; } || test -z "$old_library"; }; then # We need to hardcode the library path if test -n "$shlibpath_var" && test -z "$avoidtemprpath" ; then # Make sure the rpath contains only unique directories. case "$temp_rpath:" in *"$absdir:"*) ;; *) temp_rpath="$temp_rpath$absdir:" ;; esac fi # Hardcode the library path. # Skip directories that are in the system default run-time # search path. case " $sys_lib_dlsearch_path " in *" $absdir "*) ;; *) case "$compile_rpath " in *" $absdir "*) ;; *) compile_rpath="$compile_rpath $absdir" esac ;; esac case " $sys_lib_dlsearch_path " in *" $libdir "*) ;; *) case "$finalize_rpath " in *" $libdir "*) ;; *) finalize_rpath="$finalize_rpath $libdir" esac ;; esac fi # $linkmode,$pass = prog,link... if test "$alldeplibs" = yes && { test "$deplibs_check_method" = pass_all || { test "$build_libtool_libs" = yes && test -n "$library_names"; }; }; then # We only need to search for static libraries continue fi fi link_static=no # Whether the deplib will be linked statically use_static_libs=$prefer_static_libs if test "$use_static_libs" = built && test "$installed" = yes; then use_static_libs=no fi if test -n "$library_names" && { test "$use_static_libs" = no || test -z "$old_library"; }; then case $host in *cygwin* | *mingw* | *cegcc*) # No point in relinking DLLs because paths are not encoded notinst_deplibs="$notinst_deplibs $lib" need_relink=no ;; *) if test "$installed" = no; then notinst_deplibs="$notinst_deplibs $lib" need_relink=yes fi ;; esac # This is a shared library # Warn about portability, can't link against -module's on some # systems (darwin). Don't bleat about dlopened modules though! dlopenmodule="" for dlpremoduletest in $dlprefiles; do if test "X$dlpremoduletest" = "X$lib"; then dlopenmodule="$dlpremoduletest" break fi done if test -z "$dlopenmodule" && test "$shouldnotlink" = yes && test "$pass" = link; then echo if test "$linkmode" = prog; then $ECHO "*** Warning: Linking the executable $output against the loadable module" else $ECHO "*** Warning: Linking the shared library $output against the loadable module" fi $ECHO "*** $linklib is not portable!" fi if test "$linkmode" = lib && test "$hardcode_into_libs" = yes; then # Hardcode the library path. # Skip directories that are in the system default run-time # search path. case " $sys_lib_dlsearch_path " in *" $absdir "*) ;; *) case "$compile_rpath " in *" $absdir "*) ;; *) compile_rpath="$compile_rpath $absdir" esac ;; esac case " $sys_lib_dlsearch_path " in *" $libdir "*) ;; *) case "$finalize_rpath " in *" $libdir "*) ;; *) finalize_rpath="$finalize_rpath $libdir" esac ;; esac fi if test -n "$old_archive_from_expsyms_cmds"; then # figure out the soname set dummy $library_names shift realname="$1" shift libname=`eval "\\$ECHO \"$libname_spec\""` # use dlname if we got it. it's perfectly good, no? if test -n "$dlname"; then soname="$dlname" elif test -n "$soname_spec"; then # bleh windows case $host in *cygwin* | mingw* | *cegcc*) func_arith $current - $age major=$func_arith_result versuffix="-$major" ;; esac eval soname=\"$soname_spec\" else soname="$realname" fi # Make a new name for the extract_expsyms_cmds to use soroot="$soname" func_basename "$soroot" soname="$func_basename_result" func_stripname 'lib' '.dll' "$soname" newlib=libimp-$func_stripname_result.a # If the library has no export list, then create one now if test -f "$output_objdir/$soname-def"; then : else func_verbose "extracting exported symbol list from \`$soname'" func_execute_cmds "$extract_expsyms_cmds" 'exit $?' fi # Create $newlib if test -f "$output_objdir/$newlib"; then :; else func_verbose "generating import library for \`$soname'" func_execute_cmds "$old_archive_from_expsyms_cmds" 'exit $?' fi # make sure the library variables are pointing to the new library dir=$output_objdir linklib=$newlib fi # test -n "$old_archive_from_expsyms_cmds" if test "$linkmode" = prog || test "$mode" != relink; then add_shlibpath= add_dir= add= lib_linked=yes case $hardcode_action in immediate | unsupported) if test "$hardcode_direct" = no; then add="$dir/$linklib" case $host in *-*-sco3.2v5.0.[024]*) add_dir="-L$dir" ;; *-*-sysv4*uw2*) add_dir="-L$dir" ;; *-*-sysv5OpenUNIX* | *-*-sysv5UnixWare7.[01].[10]* | \ *-*-unixware7*) add_dir="-L$dir" ;; *-*-darwin* ) # if the lib is a (non-dlopened) module then we can not # link against it, someone is ignoring the earlier warnings if /usr/bin/file -L $add 2> /dev/null | $GREP ": [^:]* bundle" >/dev/null ; then if test "X$dlopenmodule" != "X$lib"; then $ECHO "*** Warning: lib $linklib is a module, not a shared library" if test -z "$old_library" ; then echo echo "*** And there doesn't seem to be a static archive available" echo "*** The link will probably fail, sorry" else add="$dir/$old_library" fi elif test -n "$old_library"; then add="$dir/$old_library" fi fi esac elif test "$hardcode_minus_L" = no; then case $host in *-*-sunos*) add_shlibpath="$dir" ;; esac add_dir="-L$dir" add="-l$name" elif test "$hardcode_shlibpath_var" = no; then add_shlibpath="$dir" add="-l$name" else lib_linked=no fi ;; relink) if test "$hardcode_direct" = yes && test "$hardcode_direct_absolute" = no; then add="$dir/$linklib" elif test "$hardcode_minus_L" = yes; then add_dir="-L$dir" # Try looking first in the location we're being installed to. if test -n "$inst_prefix_dir"; then case $libdir in [\\/]*) add_dir="$add_dir -L$inst_prefix_dir$libdir" ;; esac fi add="-l$name" elif test "$hardcode_shlibpath_var" = yes; then add_shlibpath="$dir" add="-l$name" else lib_linked=no fi ;; *) lib_linked=no ;; esac if test "$lib_linked" != yes; then func_fatal_configuration "unsupported hardcode properties" fi if test -n "$add_shlibpath"; then case :$compile_shlibpath: in *":$add_shlibpath:"*) ;; *) compile_shlibpath="$compile_shlibpath$add_shlibpath:" ;; esac fi if test "$linkmode" = prog; then test -n "$add_dir" && compile_deplibs="$add_dir $compile_deplibs" test -n "$add" && compile_deplibs="$add $compile_deplibs" else test -n "$add_dir" && deplibs="$add_dir $deplibs" test -n "$add" && deplibs="$add $deplibs" if test "$hardcode_direct" != yes && test "$hardcode_minus_L" != yes && test "$hardcode_shlibpath_var" = yes; then case :$finalize_shlibpath: in *":$libdir:"*) ;; *) finalize_shlibpath="$finalize_shlibpath$libdir:" ;; esac fi fi fi if test "$linkmode" = prog || test "$mode" = relink; then add_shlibpath= add_dir= add= # Finalize command for both is simple: just hardcode it. if test "$hardcode_direct" = yes && test "$hardcode_direct_absolute" = no; then add="$libdir/$linklib" elif test "$hardcode_minus_L" = yes; then add_dir="-L$libdir" add="-l$name" elif test "$hardcode_shlibpath_var" = yes; then case :$finalize_shlibpath: in *":$libdir:"*) ;; *) finalize_shlibpath="$finalize_shlibpath$libdir:" ;; esac add="-l$name" elif test "$hardcode_automatic" = yes; then if test -n "$inst_prefix_dir" && test -f "$inst_prefix_dir$libdir/$linklib" ; then add="$inst_prefix_dir$libdir/$linklib" else add="$libdir/$linklib" fi else # We cannot seem to hardcode it, guess we'll fake it. add_dir="-L$libdir" # Try looking first in the location we're being installed to. if test -n "$inst_prefix_dir"; then case $libdir in [\\/]*) add_dir="$add_dir -L$inst_prefix_dir$libdir" ;; esac fi add="-l$name" fi if test "$linkmode" = prog; then test -n "$add_dir" && finalize_deplibs="$add_dir $finalize_deplibs" test -n "$add" && finalize_deplibs="$add $finalize_deplibs" else test -n "$add_dir" && deplibs="$add_dir $deplibs" test -n "$add" && deplibs="$add $deplibs" fi fi elif test "$linkmode" = prog; then # Here we assume that one of hardcode_direct or hardcode_minus_L # is not unsupported. This is valid on all known static and # shared platforms. if test "$hardcode_direct" != unsupported; then test -n "$old_library" && linklib="$old_library" compile_deplibs="$dir/$linklib $compile_deplibs" finalize_deplibs="$dir/$linklib $finalize_deplibs" else compile_deplibs="-l$name -L$dir $compile_deplibs" finalize_deplibs="-l$name -L$dir $finalize_deplibs" fi elif test "$build_libtool_libs" = yes; then # Not a shared library if test "$deplibs_check_method" != pass_all; then # We're trying link a shared library against a static one # but the system doesn't support it. # Just print a warning and add the library to dependency_libs so # that the program can be linked against the static library. echo $ECHO "*** Warning: This system can not link to static lib archive $lib." echo "*** I have the capability to make that library automatically link in when" echo "*** you link to this library. But I can only do this if you have a" echo "*** shared version of the library, which you do not appear to have." if test "$module" = yes; then echo "*** But as you try to build a module library, libtool will still create " echo "*** a static module, that should work as long as the dlopening application" echo "*** is linked with the -dlopen flag to resolve symbols at runtime." if test -z "$global_symbol_pipe"; then echo echo "*** However, this would only work if libtool was able to extract symbol" echo "*** lists from a program, using \`nm' or equivalent, but libtool could" echo "*** not find such a program. So, this module is probably useless." echo "*** \`nm' from GNU binutils and a full rebuild may help." fi if test "$build_old_libs" = no; then build_libtool_libs=module build_old_libs=yes else build_libtool_libs=no fi fi else deplibs="$dir/$old_library $deplibs" link_static=yes fi fi # link shared/static library? if test "$linkmode" = lib; then if test -n "$dependency_libs" && { test "$hardcode_into_libs" != yes || test "$build_old_libs" = yes || test "$link_static" = yes; }; then # Extract -R from dependency_libs temp_deplibs= for libdir in $dependency_libs; do case $libdir in -R*) func_stripname '-R' '' "$libdir" temp_xrpath=$func_stripname_result case " $xrpath " in *" $temp_xrpath "*) ;; *) xrpath="$xrpath $temp_xrpath";; esac;; *) temp_deplibs="$temp_deplibs $libdir";; esac done dependency_libs="$temp_deplibs" fi newlib_search_path="$newlib_search_path $absdir" # Link against this library test "$link_static" = no && newdependency_libs="$abs_ladir/$laname $newdependency_libs" # ... and its dependency_libs tmp_libs= for deplib in $dependency_libs; do newdependency_libs="$deplib $newdependency_libs" if $opt_duplicate_deps ; then case "$tmp_libs " in *" $deplib "*) specialdeplibs="$specialdeplibs $deplib" ;; esac fi tmp_libs="$tmp_libs $deplib" done if test "$link_all_deplibs" != no; then # Add the search paths of all dependency libraries for deplib in $dependency_libs; do path= case $deplib in -L*) path="$deplib" ;; *.la) func_dirname "$deplib" "" "." dir="$func_dirname_result" # We need an absolute path. case $dir in [\\/]* | [A-Za-z]:[\\/]*) absdir="$dir" ;; *) absdir=`cd "$dir" && pwd` if test -z "$absdir"; then func_warning "cannot determine absolute directory name of \`$dir'" absdir="$dir" fi ;; esac if $GREP "^installed=no" $deplib > /dev/null; then case $host in *-*-darwin*) depdepl= eval deplibrary_names=`${SED} -n -e 's/^library_names=\(.*\)$/\1/p' $deplib` if test -n "$deplibrary_names" ; then for tmp in $deplibrary_names ; do depdepl=$tmp done if test -f "$absdir/$objdir/$depdepl" ; then depdepl="$absdir/$objdir/$depdepl" darwin_install_name=`${OTOOL} -L $depdepl | awk '{if (NR == 2) {print $1;exit}}'` if test -z "$darwin_install_name"; then darwin_install_name=`${OTOOL64} -L $depdepl | awk '{if (NR == 2) {print $1;exit}}'` fi compiler_flags="$compiler_flags ${wl}-dylib_file ${wl}${darwin_install_name}:${depdepl}" linker_flags="$linker_flags -dylib_file ${darwin_install_name}:${depdepl}" path= fi fi ;; *) path="-L$absdir/$objdir" ;; esac else eval libdir=`${SED} -n -e 's/^libdir=\(.*\)$/\1/p' $deplib` test -z "$libdir" && \ func_fatal_error "\`$deplib' is not a valid libtool archive" test "$absdir" != "$libdir" && \ func_warning "\`$deplib' seems to be moved" path="-L$absdir" fi ;; esac case " $deplibs " in *" $path "*) ;; *) deplibs="$path $deplibs" ;; esac done fi # link_all_deplibs != no fi # linkmode = lib done # for deplib in $libs if test "$pass" = link; then if test "$linkmode" = "prog"; then compile_deplibs="$new_inherited_linker_flags $compile_deplibs" finalize_deplibs="$new_inherited_linker_flags $finalize_deplibs" else compiler_flags="$compiler_flags "`$ECHO " $new_inherited_linker_flags" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` fi fi dependency_libs="$newdependency_libs" if test "$pass" = dlpreopen; then # Link the dlpreopened libraries before other libraries for deplib in $save_deplibs; do deplibs="$deplib $deplibs" done fi if test "$pass" != dlopen; then if test "$pass" != conv; then # Make sure lib_search_path contains only unique directories. lib_search_path= for dir in $newlib_search_path; do case "$lib_search_path " in *" $dir "*) ;; *) lib_search_path="$lib_search_path $dir" ;; esac done newlib_search_path= fi if test "$linkmode,$pass" != "prog,link"; then vars="deplibs" else vars="compile_deplibs finalize_deplibs" fi for var in $vars dependency_libs; do # Add libraries to $var in reverse order eval tmp_libs=\"\$$var\" new_libs= for deplib in $tmp_libs; do # FIXME: Pedantically, this is the right thing to do, so # that some nasty dependency loop isn't accidentally # broken: #new_libs="$deplib $new_libs" # Pragmatically, this seems to cause very few problems in # practice: case $deplib in -L*) new_libs="$deplib $new_libs" ;; -R*) ;; *) # And here is the reason: when a library appears more # than once as an explicit dependence of a library, or # is implicitly linked in more than once by the # compiler, it is considered special, and multiple # occurrences thereof are not removed. Compare this # with having the same library being listed as a # dependency of multiple other libraries: in this case, # we know (pedantically, we assume) the library does not # need to be listed more than once, so we keep only the # last copy. This is not always right, but it is rare # enough that we require users that really mean to play # such unportable linking tricks to link the library # using -Wl,-lname, so that libtool does not consider it # for duplicate removal. case " $specialdeplibs " in *" $deplib "*) new_libs="$deplib $new_libs" ;; *) case " $new_libs " in *" $deplib "*) ;; *) new_libs="$deplib $new_libs" ;; esac ;; esac ;; esac done tmp_libs= for deplib in $new_libs; do case $deplib in -L*) case " $tmp_libs " in *" $deplib "*) ;; *) tmp_libs="$tmp_libs $deplib" ;; esac ;; *) tmp_libs="$tmp_libs $deplib" ;; esac done eval $var=\"$tmp_libs\" done # for var fi # Last step: remove runtime libs from dependency_libs # (they stay in deplibs) tmp_libs= for i in $dependency_libs ; do case " $predeps $postdeps $compiler_lib_search_path " in *" $i "*) i="" ;; esac if test -n "$i" ; then tmp_libs="$tmp_libs $i" fi done dependency_libs=$tmp_libs done # for pass if test "$linkmode" = prog; then dlfiles="$newdlfiles" fi if test "$linkmode" = prog || test "$linkmode" = lib; then dlprefiles="$newdlprefiles" fi case $linkmode in oldlib) if test -n "$dlfiles$dlprefiles" || test "$dlself" != no; then func_warning "\`-dlopen' is ignored for archives" fi case " $deplibs" in *\ -l* | *\ -L*) func_warning "\`-l' and \`-L' are ignored for archives" ;; esac test -n "$rpath" && \ func_warning "\`-rpath' is ignored for archives" test -n "$xrpath" && \ func_warning "\`-R' is ignored for archives" test -n "$vinfo" && \ func_warning "\`-version-info/-version-number' is ignored for archives" test -n "$release" && \ func_warning "\`-release' is ignored for archives" test -n "$export_symbols$export_symbols_regex" && \ func_warning "\`-export-symbols' is ignored for archives" # Now set the variables for building old libraries. build_libtool_libs=no oldlibs="$output" objs="$objs$old_deplibs" ;; lib) # Make sure we only generate libraries of the form `libNAME.la'. case $outputname in lib*) func_stripname 'lib' '.la' "$outputname" name=$func_stripname_result eval shared_ext=\"$shrext_cmds\" eval libname=\"$libname_spec\" ;; *) test "$module" = no && \ func_fatal_help "libtool library \`$output' must begin with \`lib'" if test "$need_lib_prefix" != no; then # Add the "lib" prefix for modules if required func_stripname '' '.la' "$outputname" name=$func_stripname_result eval shared_ext=\"$shrext_cmds\" eval libname=\"$libname_spec\" else func_stripname '' '.la' "$outputname" libname=$func_stripname_result fi ;; esac if test -n "$objs"; then if test "$deplibs_check_method" != pass_all; then func_fatal_error "cannot build libtool library \`$output' from non-libtool objects on this host:$objs" else echo $ECHO "*** Warning: Linking the shared library $output against the non-libtool" $ECHO "*** objects $objs is not portable!" libobjs="$libobjs $objs" fi fi test "$dlself" != no && \ func_warning "\`-dlopen self' is ignored for libtool libraries" set dummy $rpath shift test "$#" -gt 1 && \ func_warning "ignoring multiple \`-rpath's for a libtool library" install_libdir="$1" oldlibs= if test -z "$rpath"; then if test "$build_libtool_libs" = yes; then # Building a libtool convenience library. # Some compilers have problems with a `.al' extension so # convenience libraries should have the same extension an # archive normally would. oldlibs="$output_objdir/$libname.$libext $oldlibs" build_libtool_libs=convenience build_old_libs=yes fi test -n "$vinfo" && \ func_warning "\`-version-info/-version-number' is ignored for convenience libraries" test -n "$release" && \ func_warning "\`-release' is ignored for convenience libraries" else # Parse the version information argument. save_ifs="$IFS"; IFS=':' set dummy $vinfo 0 0 0 shift IFS="$save_ifs" test -n "$7" && \ func_fatal_help "too many parameters to \`-version-info'" # convert absolute version numbers to libtool ages # this retains compatibility with .la files and attempts # to make the code below a bit more comprehensible case $vinfo_number in yes) number_major="$1" number_minor="$2" number_revision="$3" # # There are really only two kinds -- those that # use the current revision as the major version # and those that subtract age and use age as # a minor version. But, then there is irix # which has an extra 1 added just for fun # case $version_type in darwin|linux|osf|windows|none) func_arith $number_major + $number_minor current=$func_arith_result age="$number_minor" revision="$number_revision" ;; freebsd-aout|freebsd-elf|qnx|sunos) current="$number_major" revision="$number_minor" age="0" ;; irix|nonstopux) func_arith $number_major + $number_minor current=$func_arith_result age="$number_minor" revision="$number_minor" lt_irix_increment=no ;; esac ;; no) current="$1" revision="$2" age="$3" ;; esac # Check that each of the things are valid numbers. case $current in 0|[1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9]|[1-9][0-9][0-9][0-9][0-9]) ;; *) func_error "CURRENT \`$current' must be a nonnegative integer" func_fatal_error "\`$vinfo' is not valid version information" ;; esac case $revision in 0|[1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9]|[1-9][0-9][0-9][0-9][0-9]) ;; *) func_error "REVISION \`$revision' must be a nonnegative integer" func_fatal_error "\`$vinfo' is not valid version information" ;; esac case $age in 0|[1-9]|[1-9][0-9]|[1-9][0-9][0-9]|[1-9][0-9][0-9][0-9]|[1-9][0-9][0-9][0-9][0-9]) ;; *) func_error "AGE \`$age' must be a nonnegative integer" func_fatal_error "\`$vinfo' is not valid version information" ;; esac if test "$age" -gt "$current"; then func_error "AGE \`$age' is greater than the current interface number \`$current'" func_fatal_error "\`$vinfo' is not valid version information" fi # Calculate the version variables. major= versuffix= verstring= case $version_type in none) ;; darwin) # Like Linux, but with the current version available in # verstring for coding it into the library header func_arith $current - $age major=.$func_arith_result versuffix="$major.$age.$revision" # Darwin ld doesn't like 0 for these options... func_arith $current + 1 minor_current=$func_arith_result xlcverstring="${wl}-compatibility_version ${wl}$minor_current ${wl}-current_version ${wl}$minor_current.$revision" verstring="-compatibility_version $minor_current -current_version $minor_current.$revision" ;; freebsd-aout) major=".$current" versuffix=".$current.$revision"; ;; freebsd-elf) major=".$current" versuffix=".$current" ;; irix | nonstopux) if test "X$lt_irix_increment" = "Xno"; then func_arith $current - $age else func_arith $current - $age + 1 fi major=$func_arith_result case $version_type in nonstopux) verstring_prefix=nonstopux ;; *) verstring_prefix=sgi ;; esac verstring="$verstring_prefix$major.$revision" # Add in all the interfaces that we are compatible with. loop=$revision while test "$loop" -ne 0; do func_arith $revision - $loop iface=$func_arith_result func_arith $loop - 1 loop=$func_arith_result verstring="$verstring_prefix$major.$iface:$verstring" done # Before this point, $major must not contain `.'. major=.$major versuffix="$major.$revision" ;; linux) func_arith $current - $age major=.$func_arith_result versuffix="$major.$age.$revision" ;; osf) func_arith $current - $age major=.$func_arith_result versuffix=".$current.$age.$revision" verstring="$current.$age.$revision" # Add in all the interfaces that we are compatible with. loop=$age while test "$loop" -ne 0; do func_arith $current - $loop iface=$func_arith_result func_arith $loop - 1 loop=$func_arith_result verstring="$verstring:${iface}.0" done # Make executables depend on our current version. verstring="$verstring:${current}.0" ;; qnx) major=".$current" versuffix=".$current" ;; sunos) major=".$current" versuffix=".$current.$revision" ;; windows) # Use '-' rather than '.', since we only want one # extension on DOS 8.3 filesystems. func_arith $current - $age major=$func_arith_result versuffix="-$major" ;; *) func_fatal_configuration "unknown library version type \`$version_type'" ;; esac # Clear the version info if we defaulted, and they specified a release. if test -z "$vinfo" && test -n "$release"; then major= case $version_type in darwin) # we can't check for "0.0" in archive_cmds due to quoting # problems, so we reset it completely verstring= ;; *) verstring="0.0" ;; esac if test "$need_version" = no; then versuffix= else versuffix=".0.0" fi fi # Remove version info from name if versioning should be avoided if test "$avoid_version" = yes && test "$need_version" = no; then major= versuffix= verstring="" fi # Check to see if the archive will have undefined symbols. if test "$allow_undefined" = yes; then if test "$allow_undefined_flag" = unsupported; then func_warning "undefined symbols not allowed in $host shared libraries" build_libtool_libs=no build_old_libs=yes fi else # Don't allow undefined symbols. allow_undefined_flag="$no_undefined_flag" fi fi func_generate_dlsyms "$libname" "$libname" "yes" libobjs="$libobjs $symfileobj" test "X$libobjs" = "X " && libobjs= if test "$mode" != relink; then # Remove our outputs, but don't remove object files since they # may have been created when compiling PIC objects. removelist= tempremovelist=`$ECHO "$output_objdir/*"` for p in $tempremovelist; do case $p in *.$objext | *.gcno) ;; $output_objdir/$outputname | $output_objdir/$libname.* | $output_objdir/${libname}${release}.*) if test "X$precious_files_regex" != "X"; then if $ECHO "$p" | $EGREP -e "$precious_files_regex" >/dev/null 2>&1 then continue fi fi removelist="$removelist $p" ;; *) ;; esac done test -n "$removelist" && \ func_show_eval "${RM}r \$removelist" fi # Now set the variables for building old libraries. if test "$build_old_libs" = yes && test "$build_libtool_libs" != convenience ; then oldlibs="$oldlibs $output_objdir/$libname.$libext" # Transform .lo files to .o files. oldobjs="$objs "`$ECHO "$libobjs" | $SP2NL | $SED "/\.${libext}$/d; $lo2o" | $NL2SP` fi # Eliminate all temporary directories. #for path in $notinst_path; do # lib_search_path=`$ECHO "$lib_search_path " | $SED "s% $path % %g"` # deplibs=`$ECHO "$deplibs " | $SED "s% -L$path % %g"` # dependency_libs=`$ECHO "$dependency_libs " | $SED "s% -L$path % %g"` #done if test -n "$xrpath"; then # If the user specified any rpath flags, then add them. temp_xrpath= for libdir in $xrpath; do temp_xrpath="$temp_xrpath -R$libdir" case "$finalize_rpath " in *" $libdir "*) ;; *) finalize_rpath="$finalize_rpath $libdir" ;; esac done if test "$hardcode_into_libs" != yes || test "$build_old_libs" = yes; then dependency_libs="$temp_xrpath $dependency_libs" fi fi # Make sure dlfiles contains only unique files that won't be dlpreopened old_dlfiles="$dlfiles" dlfiles= for lib in $old_dlfiles; do case " $dlprefiles $dlfiles " in *" $lib "*) ;; *) dlfiles="$dlfiles $lib" ;; esac done # Make sure dlprefiles contains only unique files old_dlprefiles="$dlprefiles" dlprefiles= for lib in $old_dlprefiles; do case "$dlprefiles " in *" $lib "*) ;; *) dlprefiles="$dlprefiles $lib" ;; esac done if test "$build_libtool_libs" = yes; then if test -n "$rpath"; then case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-os2* | *-*-beos* | *-cegcc* | *-*-haiku*) # these systems don't actually have a c library (as such)! ;; *-*-rhapsody* | *-*-darwin1.[012]) # Rhapsody C library is in the System framework deplibs="$deplibs System.ltframework" ;; *-*-netbsd*) # Don't link with libc until the a.out ld.so is fixed. ;; *-*-openbsd* | *-*-freebsd* | *-*-dragonfly*) # Do not include libc due to us having libc/libc_r. ;; *-*-sco3.2v5* | *-*-sco5v6*) # Causes problems with __ctype ;; *-*-sysv4.2uw2* | *-*-sysv5* | *-*-unixware* | *-*-OpenUNIX*) # Compiler inserts libc in the correct place for threads to work ;; *) # Add libc to deplibs on all other systems if necessary. if test "$build_libtool_need_lc" = "yes"; then deplibs="$deplibs -lc" fi ;; esac fi # Transform deplibs into only deplibs that can be linked in shared. name_save=$name libname_save=$libname release_save=$release versuffix_save=$versuffix major_save=$major # I'm not sure if I'm treating the release correctly. I think # release should show up in the -l (ie -lgmp5) so we don't want to # add it in twice. Is that correct? release="" versuffix="" major="" newdeplibs= droppeddeps=no case $deplibs_check_method in pass_all) # Don't check for shared/static. Everything works. # This might be a little naive. We might want to check # whether the library exists or not. But this is on # osf3 & osf4 and I'm not really sure... Just # implementing what was already the behavior. newdeplibs=$deplibs ;; test_compile) # This code stresses the "libraries are programs" paradigm to its # limits. Maybe even breaks it. We compile a program, linking it # against the deplibs as a proxy for the library. Then we can check # whether they linked in statically or dynamically with ldd. $opt_dry_run || $RM conftest.c cat > conftest.c </dev/null` for potent_lib in $potential_libs; do # Follow soft links. if ls -lLd "$potent_lib" 2>/dev/null | $GREP " -> " >/dev/null; then continue fi # The statement above tries to avoid entering an # endless loop below, in case of cyclic links. # We might still enter an endless loop, since a link # loop can be closed while we follow links, # but so what? potlib="$potent_lib" while test -h "$potlib" 2>/dev/null; do potliblink=`ls -ld $potlib | ${SED} 's/.* -> //'` case $potliblink in [\\/]* | [A-Za-z]:[\\/]*) potlib="$potliblink";; *) potlib=`$ECHO "$potlib" | $SED 's,[^/]*$,,'`"$potliblink";; esac done if eval $file_magic_cmd \"\$potlib\" 2>/dev/null | $SED -e 10q | $EGREP "$file_magic_regex" > /dev/null; then newdeplibs="$newdeplibs $a_deplib" a_deplib="" break 2 fi done done fi if test -n "$a_deplib" ; then droppeddeps=yes echo $ECHO "*** Warning: linker path does not have real file for library $a_deplib." echo "*** I have the capability to make that library automatically link in when" echo "*** you link to this library. But I can only do this if you have a" echo "*** shared version of the library, which you do not appear to have" echo "*** because I did check the linker path looking for a file starting" if test -z "$potlib" ; then $ECHO "*** with $libname but no candidates were found. (...for file magic test)" else $ECHO "*** with $libname and none of the candidates passed a file format test" $ECHO "*** using a file magic. 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Last file checked: $potlib" fi fi ;; *) # Add a -L argument. newdeplibs="$newdeplibs $a_deplib" ;; esac done # Gone through all deplibs. ;; none | unknown | *) newdeplibs="" tmp_deplibs=`$ECHO " $deplibs" | $SED 's/ -lc$//; s/ -[LR][^ ]*//g'` if test "X$allow_libtool_libs_with_static_runtimes" = "Xyes" ; then for i in $predeps $postdeps ; do # can't use Xsed below, because $i might contain '/' tmp_deplibs=`$ECHO " $tmp_deplibs" | $SED "s,$i,,"` done fi case $tmp_deplibs in *[!\ \ ]*) echo if test "X$deplibs_check_method" = "Xnone"; then echo "*** Warning: inter-library dependencies are not supported in this platform." else echo "*** Warning: inter-library dependencies are not known to be supported." fi echo "*** All declared inter-library dependencies are being dropped." droppeddeps=yes ;; esac ;; esac versuffix=$versuffix_save major=$major_save release=$release_save libname=$libname_save name=$name_save case $host in *-*-rhapsody* | *-*-darwin1.[012]) # On Rhapsody replace the C library with the System framework newdeplibs=`$ECHO " $newdeplibs" | $SED 's/ -lc / System.ltframework /'` ;; esac if test "$droppeddeps" = yes; then if test "$module" = yes; then echo echo "*** Warning: libtool could not satisfy all declared inter-library" $ECHO "*** dependencies of module $libname. Therefore, libtool will create" echo "*** a static module, that should work as long as the dlopening" echo "*** application is linked with the -dlopen flag." if test -z "$global_symbol_pipe"; then echo echo "*** However, this would only work if libtool was able to extract symbol" echo "*** lists from a program, using \`nm' or equivalent, but libtool could" echo "*** not find such a program. So, this module is probably useless." echo "*** \`nm' from GNU binutils and a full rebuild may help." fi if test "$build_old_libs" = no; then oldlibs="$output_objdir/$libname.$libext" build_libtool_libs=module build_old_libs=yes else build_libtool_libs=no fi else echo "*** The inter-library dependencies that have been dropped here will be" echo "*** automatically added whenever a program is linked with this library" echo "*** or is declared to -dlopen it." if test "$allow_undefined" = no; then echo echo "*** Since this library must not contain undefined symbols," echo "*** because either the platform does not support them or" echo "*** it was explicitly requested with -no-undefined," echo "*** libtool will only create a static version of it." if test "$build_old_libs" = no; then oldlibs="$output_objdir/$libname.$libext" build_libtool_libs=module build_old_libs=yes else build_libtool_libs=no fi fi fi fi # Done checking deplibs! deplibs=$newdeplibs fi # Time to change all our "foo.ltframework" stuff back to "-framework foo" case $host in *-*-darwin*) newdeplibs=`$ECHO " $newdeplibs" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` new_inherited_linker_flags=`$ECHO " $new_inherited_linker_flags" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` deplibs=`$ECHO " $deplibs" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` ;; esac # move library search paths that coincide with paths to not yet # installed libraries to the beginning of the library search list new_libs= for path in $notinst_path; do case " $new_libs " in *" -L$path/$objdir "*) ;; *) case " $deplibs " in *" -L$path/$objdir "*) new_libs="$new_libs -L$path/$objdir" ;; esac ;; esac done for deplib in $deplibs; do case $deplib in -L*) case " $new_libs " in *" $deplib "*) ;; *) new_libs="$new_libs $deplib" ;; esac ;; *) new_libs="$new_libs $deplib" ;; esac done deplibs="$new_libs" # All the library-specific variables (install_libdir is set above). library_names= old_library= dlname= # Test again, we may have decided not to build it any more if test "$build_libtool_libs" = yes; then if test "$hardcode_into_libs" = yes; then # Hardcode the library paths hardcode_libdirs= dep_rpath= rpath="$finalize_rpath" test "$mode" != relink && rpath="$compile_rpath$rpath" for libdir in $rpath; do if test -n "$hardcode_libdir_flag_spec"; then if test -n "$hardcode_libdir_separator"; then if test -z "$hardcode_libdirs"; then hardcode_libdirs="$libdir" else # Just accumulate the unique libdirs. case $hardcode_libdir_separator$hardcode_libdirs$hardcode_libdir_separator in *"$hardcode_libdir_separator$libdir$hardcode_libdir_separator"*) ;; *) hardcode_libdirs="$hardcode_libdirs$hardcode_libdir_separator$libdir" ;; esac fi else eval flag=\"$hardcode_libdir_flag_spec\" dep_rpath="$dep_rpath $flag" fi elif test -n "$runpath_var"; then case "$perm_rpath " in *" $libdir "*) ;; *) perm_rpath="$perm_rpath $libdir" ;; esac fi done # Substitute the hardcoded libdirs into the rpath. if test -n "$hardcode_libdir_separator" && test -n "$hardcode_libdirs"; then libdir="$hardcode_libdirs" if test -n "$hardcode_libdir_flag_spec_ld"; then eval dep_rpath=\"$hardcode_libdir_flag_spec_ld\" else eval dep_rpath=\"$hardcode_libdir_flag_spec\" fi fi if test -n "$runpath_var" && test -n "$perm_rpath"; then # We should set the runpath_var. rpath= for dir in $perm_rpath; do rpath="$rpath$dir:" done eval "$runpath_var='$rpath\$$runpath_var'; export $runpath_var" fi test -n "$dep_rpath" && deplibs="$dep_rpath $deplibs" fi shlibpath="$finalize_shlibpath" test "$mode" != relink && shlibpath="$compile_shlibpath$shlibpath" if test -n "$shlibpath"; then eval "$shlibpath_var='$shlibpath\$$shlibpath_var'; export $shlibpath_var" fi # Get the real and link names of the library. eval shared_ext=\"$shrext_cmds\" eval library_names=\"$library_names_spec\" set dummy $library_names shift realname="$1" shift if test -n "$soname_spec"; then eval soname=\"$soname_spec\" else soname="$realname" fi if test -z "$dlname"; then dlname=$soname fi lib="$output_objdir/$realname" linknames= for link do linknames="$linknames $link" done # Use standard objects if they are pic test -z "$pic_flag" && libobjs=`$ECHO "$libobjs" | $SP2NL | $SED "$lo2o" | $NL2SP` test "X$libobjs" = "X " && libobjs= delfiles= if test -n "$export_symbols" && test -n "$include_expsyms"; then $opt_dry_run || cp "$export_symbols" "$output_objdir/$libname.uexp" export_symbols="$output_objdir/$libname.uexp" delfiles="$delfiles $export_symbols" fi orig_export_symbols= case $host_os in cygwin* | mingw* | cegcc*) if test -n "$export_symbols" && test -z "$export_symbols_regex"; then # exporting using user supplied symfile if test "x`$SED 1q $export_symbols`" != xEXPORTS; then # and it's NOT already a .def file. Must figure out # which of the given symbols are data symbols and tag # them as such. So, trigger use of export_symbols_cmds. # export_symbols gets reassigned inside the "prepare # the list of exported symbols" if statement, so the # include_expsyms logic still works. orig_export_symbols="$export_symbols" export_symbols= always_export_symbols=yes fi fi ;; esac # Prepare the list of exported symbols if test -z "$export_symbols"; then if test "$always_export_symbols" = yes || test -n "$export_symbols_regex"; then func_verbose "generating symbol list for \`$libname.la'" export_symbols="$output_objdir/$libname.exp" $opt_dry_run || $RM $export_symbols cmds=$export_symbols_cmds save_ifs="$IFS"; IFS='~' for cmd in $cmds; do IFS="$save_ifs" eval cmd=\"$cmd\" func_len " $cmd" len=$func_len_result if test "$len" -lt "$max_cmd_len" || test "$max_cmd_len" -le -1; then func_show_eval "$cmd" 'exit $?' skipped_export=false else # The command line is too long to execute in one step. func_verbose "using reloadable object file for export list..." skipped_export=: # Break out early, otherwise skipped_export may be # set to false by a later but shorter cmd. break fi done IFS="$save_ifs" if test -n "$export_symbols_regex" && test "X$skipped_export" != "X:"; then func_show_eval '$EGREP -e "$export_symbols_regex" "$export_symbols" > "${export_symbols}T"' func_show_eval '$MV "${export_symbols}T" "$export_symbols"' fi fi fi if test -n "$export_symbols" && test -n "$include_expsyms"; then tmp_export_symbols="$export_symbols" test -n "$orig_export_symbols" && tmp_export_symbols="$orig_export_symbols" $opt_dry_run || eval '$ECHO "$include_expsyms" | $SP2NL >> "$tmp_export_symbols"' fi if test "X$skipped_export" != "X:" && test -n "$orig_export_symbols"; then # The given exports_symbols file has to be filtered, so filter it. func_verbose "filter symbol list for \`$libname.la' to tag DATA exports" # FIXME: $output_objdir/$libname.filter potentially contains lots of # 's' commands which not all seds can handle. GNU sed should be fine # though. Also, the filter scales superlinearly with the number of # global variables. join(1) would be nice here, but unfortunately # isn't a blessed tool. $opt_dry_run || $SED -e '/[ ,]DATA/!d;s,\(.*\)\([ \,].*\),s|^\1$|\1\2|,' < $export_symbols > $output_objdir/$libname.filter delfiles="$delfiles $export_symbols $output_objdir/$libname.filter" export_symbols=$output_objdir/$libname.def $opt_dry_run || $SED -f $output_objdir/$libname.filter < $orig_export_symbols > $export_symbols fi tmp_deplibs= for test_deplib in $deplibs; do case " $convenience " in *" $test_deplib "*) ;; *) tmp_deplibs="$tmp_deplibs $test_deplib" ;; esac done deplibs="$tmp_deplibs" if test -n "$convenience"; then if test -n "$whole_archive_flag_spec" && test "$compiler_needs_object" = yes && test -z "$libobjs"; then # extract the archives, so we have objects to list. # TODO: could optimize this to just extract one archive. whole_archive_flag_spec= fi if test -n "$whole_archive_flag_spec"; then save_libobjs=$libobjs eval libobjs=\"\$libobjs $whole_archive_flag_spec\" test "X$libobjs" = "X " && libobjs= else gentop="$output_objdir/${outputname}x" generated="$generated $gentop" func_extract_archives $gentop $convenience libobjs="$libobjs $func_extract_archives_result" test "X$libobjs" = "X " && libobjs= fi fi if test "$thread_safe" = yes && test -n "$thread_safe_flag_spec"; then eval flag=\"$thread_safe_flag_spec\" linker_flags="$linker_flags $flag" fi # Make a backup of the uninstalled library when relinking if test "$mode" = relink; then $opt_dry_run || eval '(cd $output_objdir && $RM ${realname}U && $MV $realname ${realname}U)' || exit $? fi # Do each of the archive commands. if test "$module" = yes && test -n "$module_cmds" ; then if test -n "$export_symbols" && test -n "$module_expsym_cmds"; then eval test_cmds=\"$module_expsym_cmds\" cmds=$module_expsym_cmds else eval test_cmds=\"$module_cmds\" cmds=$module_cmds fi else if test -n "$export_symbols" && test -n "$archive_expsym_cmds"; then eval test_cmds=\"$archive_expsym_cmds\" cmds=$archive_expsym_cmds else eval test_cmds=\"$archive_cmds\" cmds=$archive_cmds fi fi if test "X$skipped_export" != "X:" && func_len " $test_cmds" && len=$func_len_result && test "$len" -lt "$max_cmd_len" || test "$max_cmd_len" -le -1; then : else # The command line is too long to link in one step, link piecewise # or, if using GNU ld and skipped_export is not :, use a linker # script. # Save the value of $output and $libobjs because we want to # use them later. If we have whole_archive_flag_spec, we # want to use save_libobjs as it was before # whole_archive_flag_spec was expanded, because we can't # assume the linker understands whole_archive_flag_spec. # This may have to be revisited, in case too many # convenience libraries get linked in and end up exceeding # the spec. if test -z "$convenience" || test -z "$whole_archive_flag_spec"; then save_libobjs=$libobjs fi save_output=$output func_basename "$output" output_la=$func_basename_result # Clear the reloadable object creation command queue and # initialize k to one. test_cmds= concat_cmds= objlist= last_robj= k=1 if test -n "$save_libobjs" && test "X$skipped_export" != "X:" && test "$with_gnu_ld" = yes; then output=${output_objdir}/${output_la}.lnkscript func_verbose "creating GNU ld script: $output" echo 'INPUT (' > $output for obj in $save_libobjs do $ECHO "$obj" >> $output done echo ')' >> $output delfiles="$delfiles $output" elif test -n "$save_libobjs" && test "X$skipped_export" != "X:" && test "X$file_list_spec" != X; then output=${output_objdir}/${output_la}.lnk func_verbose "creating linker input file list: $output" : > $output set x $save_libobjs shift firstobj= if test "$compiler_needs_object" = yes; then firstobj="$1 " shift fi for obj do $ECHO "$obj" >> $output done delfiles="$delfiles $output" output=$firstobj\"$file_list_spec$output\" else if test -n "$save_libobjs"; then func_verbose "creating reloadable object files..." output=$output_objdir/$output_la-${k}.$objext eval test_cmds=\"$reload_cmds\" func_len " $test_cmds" len0=$func_len_result len=$len0 # Loop over the list of objects to be linked. for obj in $save_libobjs do func_len " $obj" func_arith $len + $func_len_result len=$func_arith_result if test "X$objlist" = X || test "$len" -lt "$max_cmd_len"; then func_append objlist " $obj" else # The command $test_cmds is almost too long, add a # command to the queue. if test "$k" -eq 1 ; then # The first file doesn't have a previous command to add. reload_objs=$objlist eval concat_cmds=\"$reload_cmds\" else # All subsequent reloadable object files will link in # the last one created. reload_objs="$objlist $last_robj" eval concat_cmds=\"\$concat_cmds~$reload_cmds~\$RM $last_robj\" fi last_robj=$output_objdir/$output_la-${k}.$objext func_arith $k + 1 k=$func_arith_result output=$output_objdir/$output_la-${k}.$objext objlist=" $obj" func_len " $last_robj" func_arith $len0 + $func_len_result len=$func_arith_result fi done # Handle the remaining objects by creating one last # reloadable object file. All subsequent reloadable object # files will link in the last one created. test -z "$concat_cmds" || concat_cmds=$concat_cmds~ reload_objs="$objlist $last_robj" eval concat_cmds=\"\${concat_cmds}$reload_cmds\" if test -n "$last_robj"; then eval concat_cmds=\"\${concat_cmds}~\$RM $last_robj\" fi delfiles="$delfiles $output" else output= fi if ${skipped_export-false}; then func_verbose "generating symbol list for \`$libname.la'" export_symbols="$output_objdir/$libname.exp" $opt_dry_run || $RM $export_symbols libobjs=$output # Append the command to create the export file. test -z "$concat_cmds" || concat_cmds=$concat_cmds~ eval concat_cmds=\"\$concat_cmds$export_symbols_cmds\" if test -n "$last_robj"; then eval concat_cmds=\"\$concat_cmds~\$RM $last_robj\" fi fi test -n "$save_libobjs" && func_verbose "creating a temporary reloadable object file: $output" # Loop through the commands generated above and execute them. save_ifs="$IFS"; IFS='~' for cmd in $concat_cmds; do IFS="$save_ifs" $opt_silent || { func_quote_for_expand "$cmd" eval "func_echo $func_quote_for_expand_result" } $opt_dry_run || eval "$cmd" || { lt_exit=$? # Restore the uninstalled library and exit if test "$mode" = relink; then ( cd "$output_objdir" && \ $RM "${realname}T" && \ $MV "${realname}U" "$realname" ) fi exit $lt_exit } done IFS="$save_ifs" if test -n "$export_symbols_regex" && ${skipped_export-false}; then func_show_eval '$EGREP -e "$export_symbols_regex" "$export_symbols" > "${export_symbols}T"' func_show_eval '$MV "${export_symbols}T" "$export_symbols"' fi fi if ${skipped_export-false}; then if test -n "$export_symbols" && test -n "$include_expsyms"; then tmp_export_symbols="$export_symbols" test -n "$orig_export_symbols" && tmp_export_symbols="$orig_export_symbols" $opt_dry_run || eval '$ECHO "$include_expsyms" | $SP2NL >> "$tmp_export_symbols"' fi if test -n "$orig_export_symbols"; then # The given exports_symbols file has to be filtered, so filter it. func_verbose "filter symbol list for \`$libname.la' to tag DATA exports" # FIXME: $output_objdir/$libname.filter potentially contains lots of # 's' commands which not all seds can handle. GNU sed should be fine # though. Also, the filter scales superlinearly with the number of # global variables. join(1) would be nice here, but unfortunately # isn't a blessed tool. $opt_dry_run || $SED -e '/[ ,]DATA/!d;s,\(.*\)\([ \,].*\),s|^\1$|\1\2|,' < $export_symbols > $output_objdir/$libname.filter delfiles="$delfiles $export_symbols $output_objdir/$libname.filter" export_symbols=$output_objdir/$libname.def $opt_dry_run || $SED -f $output_objdir/$libname.filter < $orig_export_symbols > $export_symbols fi fi libobjs=$output # Restore the value of output. output=$save_output if test -n "$convenience" && test -n "$whole_archive_flag_spec"; then eval libobjs=\"\$libobjs $whole_archive_flag_spec\" test "X$libobjs" = "X " && libobjs= fi # Expand the library linking commands again to reset the # value of $libobjs for piecewise linking. # Do each of the archive commands. if test "$module" = yes && test -n "$module_cmds" ; then if test -n "$export_symbols" && test -n "$module_expsym_cmds"; then cmds=$module_expsym_cmds else cmds=$module_cmds fi else if test -n "$export_symbols" && test -n "$archive_expsym_cmds"; then cmds=$archive_expsym_cmds else cmds=$archive_cmds fi fi fi if test -n "$delfiles"; then # Append the command to remove temporary files to $cmds. eval cmds=\"\$cmds~\$RM $delfiles\" fi # Add any objects from preloaded convenience libraries if test -n "$dlprefiles"; then gentop="$output_objdir/${outputname}x" generated="$generated $gentop" func_extract_archives $gentop $dlprefiles libobjs="$libobjs $func_extract_archives_result" test "X$libobjs" = "X " && libobjs= fi save_ifs="$IFS"; IFS='~' for cmd in $cmds; do IFS="$save_ifs" eval cmd=\"$cmd\" $opt_silent || { func_quote_for_expand "$cmd" eval "func_echo $func_quote_for_expand_result" } $opt_dry_run || eval "$cmd" || { lt_exit=$? # Restore the uninstalled library and exit if test "$mode" = relink; then ( cd "$output_objdir" && \ $RM "${realname}T" && \ $MV "${realname}U" "$realname" ) fi exit $lt_exit } done IFS="$save_ifs" # Restore the uninstalled library and exit if test "$mode" = relink; then $opt_dry_run || eval '(cd $output_objdir && $RM ${realname}T && $MV $realname ${realname}T && $MV ${realname}U $realname)' || exit $? if test -n "$convenience"; then if test -z "$whole_archive_flag_spec"; then func_show_eval '${RM}r "$gentop"' fi fi exit $EXIT_SUCCESS fi # Create links to the real library. for linkname in $linknames; do if test "$realname" != "$linkname"; then func_show_eval '(cd "$output_objdir" && $RM "$linkname" && $LN_S "$realname" "$linkname")' 'exit $?' fi done # If -module or -export-dynamic was specified, set the dlname. if test "$module" = yes || test "$export_dynamic" = yes; then # On all known operating systems, these are identical. dlname="$soname" fi fi ;; obj) if test -n "$dlfiles$dlprefiles" || test "$dlself" != no; then func_warning "\`-dlopen' is ignored for objects" fi case " $deplibs" in *\ -l* | *\ -L*) func_warning "\`-l' and \`-L' are ignored for objects" ;; esac test -n "$rpath" && \ func_warning "\`-rpath' is ignored for objects" test -n "$xrpath" && \ func_warning "\`-R' is ignored for objects" test -n "$vinfo" && \ func_warning "\`-version-info' is ignored for objects" test -n "$release" && \ func_warning "\`-release' is ignored for objects" case $output in *.lo) test -n "$objs$old_deplibs" && \ func_fatal_error "cannot build library object \`$output' from non-libtool objects" libobj=$output func_lo2o "$libobj" obj=$func_lo2o_result ;; *) libobj= obj="$output" ;; esac # Delete the old objects. $opt_dry_run || $RM $obj $libobj # Objects from convenience libraries. This assumes # single-version convenience libraries. Whenever we create # different ones for PIC/non-PIC, this we'll have to duplicate # the extraction. reload_conv_objs= gentop= # reload_cmds runs $LD directly, so let us get rid of # -Wl from whole_archive_flag_spec and hope we can get by with # turning comma into space.. wl= if test -n "$convenience"; then if test -n "$whole_archive_flag_spec"; then eval tmp_whole_archive_flags=\"$whole_archive_flag_spec\" reload_conv_objs=$reload_objs\ `$ECHO "$tmp_whole_archive_flags" | $SED 's|,| |g'` else gentop="$output_objdir/${obj}x" generated="$generated $gentop" func_extract_archives $gentop $convenience reload_conv_objs="$reload_objs $func_extract_archives_result" fi fi # Create the old-style object. reload_objs="$objs$old_deplibs "`$ECHO "$libobjs" | $SP2NL | $SED "/\.${libext}$/d; /\.lib$/d; $lo2o" | $NL2SP`" $reload_conv_objs" ### testsuite: skip nested quoting test output="$obj" func_execute_cmds "$reload_cmds" 'exit $?' # Exit if we aren't doing a library object file. if test -z "$libobj"; then if test -n "$gentop"; then func_show_eval '${RM}r "$gentop"' fi exit $EXIT_SUCCESS fi if test "$build_libtool_libs" != yes; then if test -n "$gentop"; then func_show_eval '${RM}r "$gentop"' fi # Create an invalid libtool object if no PIC, so that we don't # accidentally link it into a program. # $show "echo timestamp > $libobj" # $opt_dry_run || eval "echo timestamp > $libobj" || exit $? exit $EXIT_SUCCESS fi if test -n "$pic_flag" || test "$pic_mode" != default; then # Only do commands if we really have different PIC objects. reload_objs="$libobjs $reload_conv_objs" output="$libobj" func_execute_cmds "$reload_cmds" 'exit $?' fi if test -n "$gentop"; then func_show_eval '${RM}r "$gentop"' fi exit $EXIT_SUCCESS ;; prog) case $host in *cygwin*) func_stripname '' '.exe' "$output" output=$func_stripname_result.exe;; esac test -n "$vinfo" && \ func_warning "\`-version-info' is ignored for programs" test -n "$release" && \ func_warning "\`-release' is ignored for programs" test "$preload" = yes \ && test "$dlopen_support" = unknown \ && test "$dlopen_self" = unknown \ && test "$dlopen_self_static" = unknown && \ func_warning "\`LT_INIT([dlopen])' not used. Assuming no dlopen support." case $host in *-*-rhapsody* | *-*-darwin1.[012]) # On Rhapsody replace the C library is the System framework compile_deplibs=`$ECHO " $compile_deplibs" | $SED 's/ -lc / System.ltframework /'` finalize_deplibs=`$ECHO " $finalize_deplibs" | $SED 's/ -lc / System.ltframework /'` ;; esac case $host in *-*-darwin*) # Don't allow lazy linking, it breaks C++ global constructors # But is supposedly fixed on 10.4 or later (yay!). if test "$tagname" = CXX ; then case ${MACOSX_DEPLOYMENT_TARGET-10.0} in 10.[0123]) compile_command="$compile_command ${wl}-bind_at_load" finalize_command="$finalize_command ${wl}-bind_at_load" ;; esac fi # Time to change all our "foo.ltframework" stuff back to "-framework foo" compile_deplibs=`$ECHO " $compile_deplibs" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` finalize_deplibs=`$ECHO " $finalize_deplibs" | $SED 's% \([^ $]*\).ltframework% -framework \1%g'` ;; esac # move library search paths that coincide with paths to not yet # installed libraries to the beginning of the library search list new_libs= for path in $notinst_path; do case " $new_libs " in *" -L$path/$objdir "*) ;; *) case " $compile_deplibs " in *" -L$path/$objdir "*) new_libs="$new_libs -L$path/$objdir" ;; esac ;; esac done for deplib in $compile_deplibs; do case $deplib in -L*) case " $new_libs " in *" $deplib "*) ;; *) new_libs="$new_libs $deplib" ;; esac ;; *) new_libs="$new_libs $deplib" ;; esac done compile_deplibs="$new_libs" compile_command="$compile_command $compile_deplibs" finalize_command="$finalize_command $finalize_deplibs" if test -n "$rpath$xrpath"; then # If the user specified any rpath flags, then add them. for libdir in $rpath $xrpath; do # This is the magic to use -rpath. case "$finalize_rpath " in *" $libdir "*) ;; *) finalize_rpath="$finalize_rpath $libdir" ;; esac done fi # Now hardcode the library paths rpath= hardcode_libdirs= for libdir in $compile_rpath $finalize_rpath; do if test -n "$hardcode_libdir_flag_spec"; then if test -n "$hardcode_libdir_separator"; then if test -z "$hardcode_libdirs"; then hardcode_libdirs="$libdir" else # Just accumulate the unique libdirs. case $hardcode_libdir_separator$hardcode_libdirs$hardcode_libdir_separator in *"$hardcode_libdir_separator$libdir$hardcode_libdir_separator"*) ;; *) hardcode_libdirs="$hardcode_libdirs$hardcode_libdir_separator$libdir" ;; esac fi else eval flag=\"$hardcode_libdir_flag_spec\" rpath="$rpath $flag" fi elif test -n "$runpath_var"; then case "$perm_rpath " in *" $libdir "*) ;; *) perm_rpath="$perm_rpath $libdir" ;; esac fi case $host in *-*-cygwin* | *-*-mingw* | *-*-pw32* | *-*-os2* | *-cegcc*) testbindir=`${ECHO} "$libdir" | ${SED} -e 's*/lib$*/bin*'` case :$dllsearchpath: in *":$libdir:"*) ;; ::) dllsearchpath=$libdir;; *) dllsearchpath="$dllsearchpath:$libdir";; esac case :$dllsearchpath: in *":$testbindir:"*) ;; ::) dllsearchpath=$testbindir;; *) dllsearchpath="$dllsearchpath:$testbindir";; esac ;; esac done # Substitute the hardcoded libdirs into the rpath. if test -n "$hardcode_libdir_separator" && test -n "$hardcode_libdirs"; then libdir="$hardcode_libdirs" eval rpath=\" $hardcode_libdir_flag_spec\" fi compile_rpath="$rpath" rpath= hardcode_libdirs= for libdir in $finalize_rpath; do if test -n "$hardcode_libdir_flag_spec"; then if test -n "$hardcode_libdir_separator"; then if test -z "$hardcode_libdirs"; then hardcode_libdirs="$libdir" else # Just accumulate the unique libdirs. case $hardcode_libdir_separator$hardcode_libdirs$hardcode_libdir_separator in *"$hardcode_libdir_separator$libdir$hardcode_libdir_separator"*) ;; *) hardcode_libdirs="$hardcode_libdirs$hardcode_libdir_separator$libdir" ;; esac fi else eval flag=\"$hardcode_libdir_flag_spec\" rpath="$rpath $flag" fi elif test -n "$runpath_var"; then case "$finalize_perm_rpath " in *" $libdir "*) ;; *) finalize_perm_rpath="$finalize_perm_rpath $libdir" ;; esac fi done # Substitute the hardcoded libdirs into the rpath. if test -n "$hardcode_libdir_separator" && test -n "$hardcode_libdirs"; then libdir="$hardcode_libdirs" eval rpath=\" $hardcode_libdir_flag_spec\" fi finalize_rpath="$rpath" if test -n "$libobjs" && test "$build_old_libs" = yes; then # Transform all the library objects into standard objects. compile_command=`$ECHO "$compile_command" | $SP2NL | $SED "$lo2o" | $NL2SP` finalize_command=`$ECHO "$finalize_command" | $SP2NL | $SED "$lo2o" | $NL2SP` fi func_generate_dlsyms "$outputname" "@PROGRAM@" "no" # template prelinking step if test -n "$prelink_cmds"; then func_execute_cmds "$prelink_cmds" 'exit $?' fi wrappers_required=yes case $host in *cegcc* | *mingw32ce*) # Disable wrappers for cegcc and mingw32ce hosts, we are cross compiling anyway. wrappers_required=no ;; *cygwin* | *mingw* ) if test "$build_libtool_libs" != yes; then wrappers_required=no fi ;; *) if test "$need_relink" = no || test "$build_libtool_libs" != yes; then wrappers_required=no fi ;; esac if test "$wrappers_required" = no; then # Replace the output file specification. compile_command=`$ECHO "$compile_command" | $SED 's%@OUTPUT@%'"$output"'%g'` link_command="$compile_command$compile_rpath" # We have no uninstalled library dependencies, so finalize right now. exit_status=0 func_show_eval "$link_command" 'exit_status=$?' # Delete the generated files. if test -f "$output_objdir/${outputname}S.${objext}"; then func_show_eval '$RM "$output_objdir/${outputname}S.${objext}"' fi exit $exit_status fi if test -n "$compile_shlibpath$finalize_shlibpath"; then compile_command="$shlibpath_var=\"$compile_shlibpath$finalize_shlibpath\$$shlibpath_var\" $compile_command" fi if test -n "$finalize_shlibpath"; then finalize_command="$shlibpath_var=\"$finalize_shlibpath\$$shlibpath_var\" $finalize_command" fi compile_var= finalize_var= if test -n "$runpath_var"; then if test -n "$perm_rpath"; then # We should set the runpath_var. rpath= for dir in $perm_rpath; do rpath="$rpath$dir:" done compile_var="$runpath_var=\"$rpath\$$runpath_var\" " fi if test -n "$finalize_perm_rpath"; then # We should set the runpath_var. rpath= for dir in $finalize_perm_rpath; do rpath="$rpath$dir:" done finalize_var="$runpath_var=\"$rpath\$$runpath_var\" " fi fi if test "$no_install" = yes; then # We don't need to create a wrapper script. link_command="$compile_var$compile_command$compile_rpath" # Replace the output file specification. link_command=`$ECHO "$link_command" | $SED 's%@OUTPUT@%'"$output"'%g'` # Delete the old output file. $opt_dry_run || $RM $output # Link the executable and exit func_show_eval "$link_command" 'exit $?' exit $EXIT_SUCCESS fi if test "$hardcode_action" = relink; then # Fast installation is not supported link_command="$compile_var$compile_command$compile_rpath" relink_command="$finalize_var$finalize_command$finalize_rpath" func_warning "this platform does not like uninstalled shared libraries" func_warning "\`$output' will be relinked during installation" else if test "$fast_install" != no; then link_command="$finalize_var$compile_command$finalize_rpath" if test "$fast_install" = yes; then relink_command=`$ECHO "$compile_var$compile_command$compile_rpath" | $SED 's%@OUTPUT@%\$progdir/\$file%g'` else # fast_install is set to needless relink_command= fi else link_command="$compile_var$compile_command$compile_rpath" relink_command="$finalize_var$finalize_command$finalize_rpath" fi fi # Replace the output file specification. link_command=`$ECHO "$link_command" | $SED 's%@OUTPUT@%'"$output_objdir/$outputname"'%g'` # Delete the old output files. $opt_dry_run || $RM $output $output_objdir/$outputname $output_objdir/lt-$outputname func_show_eval "$link_command" 'exit $?' # Now create the wrapper script. func_verbose "creating $output" # Quote the relink command for shipping. if test -n "$relink_command"; then # Preserve any variables that may affect compiler behavior for var in $variables_saved_for_relink; do if eval test -z \"\${$var+set}\"; then relink_command="{ test -z \"\${$var+set}\" || $lt_unset $var || { $var=; export $var; }; }; $relink_command" elif eval var_value=\$$var; test -z "$var_value"; then relink_command="$var=; export $var; $relink_command" else func_quote_for_eval "$var_value" relink_command="$var=$func_quote_for_eval_result; export $var; $relink_command" fi done relink_command="(cd `pwd`; $relink_command)" relink_command=`$ECHO "$relink_command" | $SED "$sed_quote_subst"` fi # Only actually do things if not in dry run mode. $opt_dry_run || { # win32 will think the script is a binary if it has # a .exe suffix, so we strip it off here. case $output in *.exe) func_stripname '' '.exe' "$output" output=$func_stripname_result ;; esac # test for cygwin because mv fails w/o .exe extensions case $host in *cygwin*) exeext=.exe func_stripname '' '.exe' "$outputname" outputname=$func_stripname_result ;; *) exeext= ;; esac case $host in *cygwin* | *mingw* ) func_dirname_and_basename "$output" "" "." output_name=$func_basename_result output_path=$func_dirname_result cwrappersource="$output_path/$objdir/lt-$output_name.c" cwrapper="$output_path/$output_name.exe" $RM $cwrappersource $cwrapper trap "$RM $cwrappersource $cwrapper $cwrapper.manifest; exit $EXIT_FAILURE" 1 2 15 func_emit_cwrapperexe_src > $cwrappersource # The wrapper executable is built using the $host compiler, # because it contains $host paths and files. If cross- # compiling, it, like the target executable, must be # executed on the $host or under an emulation environment. $opt_dry_run || { $LTCC $LTCFLAGS -o $cwrapper $cwrappersource $STRIP $cwrapper } # Now, create the wrapper script for func_source use: func_ltwrapper_scriptname $cwrapper $RM $func_ltwrapper_scriptname_result trap "$RM $func_ltwrapper_scriptname_result; exit $EXIT_FAILURE" 1 2 15 $opt_dry_run || { # note: this script will not be executed, so do not chmod. if test "x$build" = "x$host" ; then # Create the UAC manifests first if necessary (but the # manifest files must have executable permission regardless). case $output_name in *instal*|*patch*|*setup*|*update*) func_emit_exe_manifest > $cwrapper.manifest func_emit_exe_manifest > $output_path/$objdir/$output_name.exe.manifest chmod +x $cwrapper.manifest chmod +x $output_path/$objdir/$output_name.exe.manifest ;; esac $cwrapper --lt-dump-script > $func_ltwrapper_scriptname_result else func_emit_wrapper no > $func_ltwrapper_scriptname_result fi } ;; * ) $RM $output trap "$RM $output; exit $EXIT_FAILURE" 1 2 15 func_emit_wrapper no > $output chmod +x $output ;; esac } exit $EXIT_SUCCESS ;; esac # See if we need to build an old-fashioned archive. for oldlib in $oldlibs; do if test "$build_libtool_libs" = convenience; then oldobjs="$libobjs_save $symfileobj" addlibs="$convenience" build_libtool_libs=no else if test "$build_libtool_libs" = module; then oldobjs="$libobjs_save" build_libtool_libs=no else oldobjs="$old_deplibs $non_pic_objects" if test "$preload" = yes && test -f "$symfileobj"; then oldobjs="$oldobjs $symfileobj" fi fi addlibs="$old_convenience" fi if test -n "$addlibs"; then gentop="$output_objdir/${outputname}x" generated="$generated $gentop" func_extract_archives $gentop $addlibs oldobjs="$oldobjs $func_extract_archives_result" fi # Do each command in the archive commands. if test -n "$old_archive_from_new_cmds" && test "$build_libtool_libs" = yes; then cmds=$old_archive_from_new_cmds else # Add any objects from preloaded convenience libraries if test -n "$dlprefiles"; then gentop="$output_objdir/${outputname}x" generated="$generated $gentop" func_extract_archives $gentop $dlprefiles oldobjs="$oldobjs $func_extract_archives_result" fi # POSIX demands no paths to be encoded in archives. We have # to avoid creating archives with duplicate basenames if we # might have to extract them afterwards, e.g., when creating a # static archive out of a convenience library, or when linking # the entirety of a libtool archive into another (currently # not supported by libtool). if (for obj in $oldobjs do func_basename "$obj" $ECHO "$func_basename_result" done | sort | sort -uc >/dev/null 2>&1); then : else echo "copying selected object files to avoid basename conflicts..." gentop="$output_objdir/${outputname}x" generated="$generated $gentop" func_mkdir_p "$gentop" save_oldobjs=$oldobjs oldobjs= counter=1 for obj in $save_oldobjs do func_basename "$obj" objbase="$func_basename_result" case " $oldobjs " in " ") oldobjs=$obj ;; *[\ /]"$objbase "*) while :; do # Make sure we don't pick an alternate name that also # overlaps. newobj=lt$counter-$objbase func_arith $counter + 1 counter=$func_arith_result case " $oldobjs " in *[\ /]"$newobj "*) ;; *) if test ! -f "$gentop/$newobj"; then break; fi ;; esac done func_show_eval "ln $obj $gentop/$newobj || cp $obj $gentop/$newobj" oldobjs="$oldobjs $gentop/$newobj" ;; *) oldobjs="$oldobjs $obj" ;; esac done fi eval cmds=\"$old_archive_cmds\" func_len " $cmds" len=$func_len_result if test "$len" -lt "$max_cmd_len" || test "$max_cmd_len" -le -1; then cmds=$old_archive_cmds else # the command line is too long to link in one step, link in parts func_verbose "using piecewise archive linking..." save_RANLIB=$RANLIB RANLIB=: objlist= concat_cmds= save_oldobjs=$oldobjs oldobjs= # Is there a better way of finding the last object in the list? for obj in $save_oldobjs do last_oldobj=$obj done eval test_cmds=\"$old_archive_cmds\" func_len " $test_cmds" len0=$func_len_result len=$len0 for obj in $save_oldobjs do func_len " $obj" func_arith $len + $func_len_result len=$func_arith_result func_append objlist " $obj" if test "$len" -lt "$max_cmd_len"; then : else # the above command should be used before it gets too long oldobjs=$objlist if test "$obj" = "$last_oldobj" ; then RANLIB=$save_RANLIB fi test -z "$concat_cmds" || concat_cmds=$concat_cmds~ eval concat_cmds=\"\${concat_cmds}$old_archive_cmds\" objlist= len=$len0 fi done RANLIB=$save_RANLIB oldobjs=$objlist if test "X$oldobjs" = "X" ; then eval cmds=\"\$concat_cmds\" else eval cmds=\"\$concat_cmds~\$old_archive_cmds\" fi fi fi func_execute_cmds "$cmds" 'exit $?' done test -n "$generated" && \ func_show_eval "${RM}r$generated" # Now create the libtool archive. case $output in *.la) old_library= test "$build_old_libs" = yes && old_library="$libname.$libext" func_verbose "creating $output" # Preserve any variables that may affect compiler behavior for var in $variables_saved_for_relink; do if eval test -z \"\${$var+set}\"; then relink_command="{ test -z \"\${$var+set}\" || $lt_unset $var || { $var=; export $var; }; }; $relink_command" elif eval var_value=\$$var; test -z "$var_value"; then relink_command="$var=; export $var; $relink_command" else func_quote_for_eval "$var_value" relink_command="$var=$func_quote_for_eval_result; export $var; $relink_command" fi done # Quote the link command for shipping. relink_command="(cd `pwd`; $SHELL $progpath $preserve_args --mode=relink $libtool_args @inst_prefix_dir@)" relink_command=`$ECHO "$relink_command" | $SED "$sed_quote_subst"` if test "$hardcode_automatic" = yes ; then relink_command= fi # Only create the output if not a dry run. $opt_dry_run || { for installed in no yes; do if test "$installed" = yes; then if test -z "$install_libdir"; then break fi output="$output_objdir/$outputname"i # Replace all uninstalled libtool libraries with the installed ones newdependency_libs= for deplib in $dependency_libs; do case $deplib in *.la) func_basename "$deplib" name="$func_basename_result" eval libdir=`${SED} -n -e 's/^libdir=\(.*\)$/\1/p' $deplib` test -z "$libdir" && \ func_fatal_error "\`$deplib' is not a valid libtool archive" newdependency_libs="$newdependency_libs $libdir/$name" ;; *) newdependency_libs="$newdependency_libs $deplib" ;; esac done dependency_libs="$newdependency_libs" newdlfiles= for lib in $dlfiles; do case $lib in *.la) func_basename "$lib" name="$func_basename_result" eval libdir=`${SED} -n -e 's/^libdir=\(.*\)$/\1/p' $lib` test -z "$libdir" && \ func_fatal_error "\`$lib' is not a valid libtool archive" newdlfiles="$newdlfiles $libdir/$name" ;; *) newdlfiles="$newdlfiles $lib" ;; esac done dlfiles="$newdlfiles" newdlprefiles= for lib in $dlprefiles; do case $lib in *.la) # Only pass preopened files to the pseudo-archive (for # eventual linking with the app. that links it) if we # didn't already link the preopened objects directly into # the library: func_basename "$lib" name="$func_basename_result" eval libdir=`${SED} -n -e 's/^libdir=\(.*\)$/\1/p' $lib` test -z "$libdir" && \ func_fatal_error "\`$lib' is not a valid libtool archive" newdlprefiles="$newdlprefiles $libdir/$name" ;; esac done dlprefiles="$newdlprefiles" else newdlfiles= for lib in $dlfiles; do case $lib in [\\/]* | [A-Za-z]:[\\/]*) abs="$lib" ;; *) abs=`pwd`"/$lib" ;; esac newdlfiles="$newdlfiles $abs" done dlfiles="$newdlfiles" newdlprefiles= for lib in $dlprefiles; do case $lib in [\\/]* | [A-Za-z]:[\\/]*) abs="$lib" ;; *) abs=`pwd`"/$lib" ;; esac newdlprefiles="$newdlprefiles $abs" done dlprefiles="$newdlprefiles" fi $RM $output # place dlname in correct position for cygwin # In fact, it would be nice if we could use this code for all target # systems that can't hard-code library paths into their executables # and that have no shared library path variable independent of PATH, # but it turns out we can't easily determine that from inspecting # libtool variables, so we have to hard-code the OSs to which it # applies here; at the moment, that means platforms that use the PE # object format with DLL files. See the long comment at the top of # tests/bindir.at for full details. tdlname=$dlname case $host,$output,$installed,$module,$dlname in *cygwin*,*lai,yes,no,*.dll | *mingw*,*lai,yes,no,*.dll | *cegcc*,*lai,yes,no,*.dll) # If a -bindir argument was supplied, place the dll there. if test "x$bindir" != x ; then func_relative_path "$install_libdir" "$bindir" tdlname=$func_relative_path_result$dlname else # Otherwise fall back on heuristic. tdlname=../bin/$dlname fi ;; esac $ECHO > $output "\ # $outputname - a libtool library file # Generated by $PROGRAM (GNU $PACKAGE$TIMESTAMP) $VERSION # # Please DO NOT delete this file! # It is necessary for linking the library. # The name that we can dlopen(3). dlname='$tdlname' # Names of this library. library_names='$library_names' # The name of the static archive. old_library='$old_library' # Linker flags that can not go in dependency_libs. inherited_linker_flags='$new_inherited_linker_flags' # Libraries that this one depends upon. dependency_libs='$dependency_libs' # Names of additional weak libraries provided by this library weak_library_names='$weak_libs' # Version information for $libname. current=$current age=$age revision=$revision # Is this an already installed library? installed=$installed # Should we warn about portability when linking against -modules? shouldnotlink=$module # Files to dlopen/dlpreopen dlopen='$dlfiles' dlpreopen='$dlprefiles' # Directory that this library needs to be installed in: libdir='$install_libdir'" if test "$installed" = no && test "$need_relink" = yes; then $ECHO >> $output "\ relink_command=\"$relink_command\"" fi done } # Do a symbolic link so that the libtool archive can be found in # LD_LIBRARY_PATH before the program is installed. func_show_eval '( cd "$output_objdir" && $RM "$outputname" && $LN_S "../$outputname" "$outputname" )' 'exit $?' ;; esac exit $EXIT_SUCCESS } { test "$mode" = link || test "$mode" = relink; } && func_mode_link ${1+"$@"} # func_mode_uninstall arg... func_mode_uninstall () { $opt_debug RM="$nonopt" files= rmforce= exit_status=0 # This variable tells wrapper scripts just to set variables rather # than running their programs. libtool_install_magic="$magic" for arg do case $arg in -f) RM="$RM $arg"; rmforce=yes ;; -*) RM="$RM $arg" ;; *) files="$files $arg" ;; esac done test -z "$RM" && \ func_fatal_help "you must specify an RM program" rmdirs= origobjdir="$objdir" for file in $files; do func_dirname "$file" "" "." dir="$func_dirname_result" if test "X$dir" = X.; then objdir="$origobjdir" else objdir="$dir/$origobjdir" fi func_basename "$file" name="$func_basename_result" test "$mode" = uninstall && objdir="$dir" # Remember objdir for removal later, being careful to avoid duplicates if test "$mode" = clean; then case " $rmdirs " in *" $objdir "*) ;; *) rmdirs="$rmdirs $objdir" ;; esac fi # Don't error if the file doesn't exist and rm -f was used. if { test -L "$file"; } >/dev/null 2>&1 || { test -h "$file"; } >/dev/null 2>&1 || test -f "$file"; then : elif test -d "$file"; then exit_status=1 continue elif test "$rmforce" = yes; then continue fi rmfiles="$file" case $name in *.la) # Possibly a libtool archive, so verify it. if func_lalib_p "$file"; then func_source $dir/$name # Delete the libtool libraries and symlinks. for n in $library_names; do rmfiles="$rmfiles $objdir/$n" done test -n "$old_library" && rmfiles="$rmfiles $objdir/$old_library" case "$mode" in clean) case " $library_names " in # " " in the beginning catches empty $dlname *" $dlname "*) ;; *) rmfiles="$rmfiles $objdir/$dlname" ;; esac test -n "$libdir" && rmfiles="$rmfiles $objdir/$name $objdir/${name}i" ;; uninstall) if test -n "$library_names"; then # Do each command in the postuninstall commands. func_execute_cmds "$postuninstall_cmds" 'test "$rmforce" = yes || exit_status=1' fi if test -n "$old_library"; then # Do each command in the old_postuninstall commands. func_execute_cmds "$old_postuninstall_cmds" 'test "$rmforce" = yes || exit_status=1' fi # FIXME: should reinstall the best remaining shared library. ;; esac fi ;; *.lo) # Possibly a libtool object, so verify it. if func_lalib_p "$file"; then # Read the .lo file func_source $dir/$name # Add PIC object to the list of files to remove. if test -n "$pic_object" && test "$pic_object" != none; then rmfiles="$rmfiles $dir/$pic_object" fi # Add non-PIC object to the list of files to remove. if test -n "$non_pic_object" && test "$non_pic_object" != none; then rmfiles="$rmfiles $dir/$non_pic_object" fi fi ;; *) if test "$mode" = clean ; then noexename=$name case $file in *.exe) func_stripname '' '.exe' "$file" file=$func_stripname_result func_stripname '' '.exe' "$name" noexename=$func_stripname_result # $file with .exe has already been added to rmfiles, # add $file without .exe rmfiles="$rmfiles $file" ;; esac # Do a test to see if this is a libtool program. if func_ltwrapper_p "$file"; then if func_ltwrapper_executable_p "$file"; then func_ltwrapper_scriptname "$file" relink_command= func_source $func_ltwrapper_scriptname_result rmfiles="$rmfiles $func_ltwrapper_scriptname_result" else relink_command= func_source $dir/$noexename fi # note $name still contains .exe if it was in $file originally # as does the version of $file that was added into $rmfiles rmfiles="$rmfiles $objdir/$name $objdir/${name}S.${objext}" rmfiles="$rmfiles ${name}.manifest $objdir/${name}.manifest" if test "$fast_install" = yes && test -n "$relink_command"; then rmfiles="$rmfiles $objdir/lt-$name $objdir/lt-${name}.manifest" fi if test "X$noexename" != "X$name" ; then rmfiles="$rmfiles $objdir/lt-${noexename}.c" fi fi fi ;; esac func_show_eval "$RM $rmfiles" 'exit_status=1' done objdir="$origobjdir" # Try to remove the ${objdir}s in the directories where we deleted files for dir in $rmdirs; do if test -d "$dir"; then func_show_eval "rmdir $dir >/dev/null 2>&1" fi done exit $exit_status } { test "$mode" = uninstall || test "$mode" = clean; } && func_mode_uninstall ${1+"$@"} test -z "$mode" && { help="$generic_help" func_fatal_help "you must specify a MODE" } test -z "$exec_cmd" && \ func_fatal_help "invalid operation mode \`$mode'" if test -n "$exec_cmd"; then eval exec "$exec_cmd" exit $EXIT_FAILURE fi exit $exit_status # The TAGs below are defined such that we never get into a situation # in which we disable both kinds of libraries. Given conflicting # choices, we go for a static library, that is the most portable, # since we can't tell whether shared libraries were disabled because # the user asked for that or because the platform doesn't support # them. This is particularly important on AIX, because we don't # support having both static and shared libraries enabled at the same # time on that platform, so we default to a shared-only configuration. # If a disable-shared tag is given, we'll fallback to a static-only # configuration. But we'll never go from static-only to shared-only. # ### BEGIN LIBTOOL TAG CONFIG: disable-shared build_libtool_libs=no build_old_libs=yes # ### END LIBTOOL TAG CONFIG: disable-shared # ### BEGIN LIBTOOL TAG CONFIG: disable-static build_old_libs=`case $build_libtool_libs in yes) echo no;; *) echo yes;; esac` # ### END LIBTOOL TAG CONFIG: disable-static # Local Variables: # mode:shell-script # sh-indentation:2 # End: # vi:sw=2 ctbnrle.1.1.1/glpk/glpk-4.47/COPYING0000644000175000017500000010451511632352620016711 0ustar csheltoncshelton GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The GNU General Public License is a free, copyleft license for software and other kinds of works. The licenses for most software and other practical works are designed to take away your freedom to share and change the works. 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You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007. Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law. 12. No Surrender of Others' Freedom. If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program. 13. Use with the GNU Affero General Public License. Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. 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IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 17. Interpretation of Sections 15 and 16. If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read . ctbnrle.1.1.1/glpk/glpk-4.47/configure.ac0000644000175000017500000000704411632352620020143 0ustar csheltoncsheltondnl Process this file with autoconf to produce a configure script AC_INIT([GLPK], [4.47], [bug-glpk@gnu.org]) AC_CONFIG_SRCDIR([src/glpk.h]) AC_CONFIG_MACRO_DIR([m4]) AM_INIT_AUTOMAKE AC_CONFIG_HEADERS([config.h]) AC_ARG_WITH(gmp, AC_HELP_STRING([--with-gmp], [use GNU MP bignum library [[default=no]]]), [case $withval in yes | no) ;; *) AC_MSG_ERROR([invalid value `$withval' for --with-gmp]);; esac], [with_gmp=no]) AC_ARG_ENABLE(dl, AC_HELP_STRING([--enable-dl], [enable shared library support [[default=no]]]), [case $enableval in yes | ltdl | dlfcn | no) ;; *) AC_MSG_ERROR([invalid value `$enableval' for --enable-dl]);; esac], [enable_dl=no]) AC_ARG_ENABLE(odbc, AC_HELP_STRING([--enable-odbc], [enable MathProg ODBC support [[default=no]]]), [case $enableval in yes | unix | no) ;; *) AC_MSG_ERROR([invalid value `$enableval' for --enable-odbc]);; esac], [enable_odbc=no]) AC_ARG_ENABLE(mysql, AC_HELP_STRING([--enable-mysql], [enable MathProg MySQL support [[default=no]]]), [case $enableval in yes | no) ;; *) AC_MSG_ERROR([invalid value `$enableval' for --enable-mysql]);; esac], [enable_mysql=no]) dnl Disable unnecessary libtool tests define([AC_LIBTOOL_LANG_CXX_CONFIG], [:]) define([AC_LIBTOOL_LANG_F77_CONFIG], [:]) define([AC_LIBTOOL_LANG_GCJ_CONFIG], [:]) dnl Check for programs AC_PROG_CC AC_PROG_INSTALL AC_PROG_LIBTOOL dnl Check for math library AC_CHECK_LIB([m], [exp]) dnl Check for header AC_CHECK_HEADER([sys/time.h], AC_DEFINE([HAVE_SYS_TIME_H], [1], [N/A])) dnl Check for gettimeofday function AC_CHECK_FUNC([gettimeofday], AC_DEFINE([HAVE_GETTIMEOFDAY], [1], [N/A])) AC_MSG_CHECKING([whether to use GNU MP bignum library]) if test "$with_gmp" = "yes"; then AC_MSG_RESULT([yes]) AC_DEFINE([HAVE_GMP], [1], [N/A]) LIBS="-lgmp $LIBS" else AC_MSG_RESULT([no]) fi AC_MSG_CHECKING([whether to enable shared library support]) if test "$enable_dl" = "yes"; then AC_MSG_RESULT([ltdl]) AC_DEFINE([HAVE_LTDL], [1], [N/A]) LIBS="-lltdl $LIBS" elif test "$enable_dl" = "ltdl"; then AC_MSG_RESULT([ltdl]) AC_DEFINE([HAVE_LTDL], [1], [N/A]) LIBS="-lltdl $LIBS" elif test "$enable_dl" = "dlfcn"; then AC_MSG_RESULT([dlfcn]) AC_DEFINE([HAVE_DLFCN], [1], [N/A]) else AC_MSG_RESULT([no]) fi case $host_os in darwin* | macosx*) LIBIODBC="libiodbc.dylib" LIBODBC="libodbc.dylib" LIBMYSQL="libmysqlclient.dylib" ;; *) LIBIODBC="libiodbc.so" LIBODBC="libodbc.so" LIBMYSQL="libmysqlclient.so" ;; esac AC_MSG_CHECKING([whether to enable MathProg ODBC support]) if test "$enable_odbc" = "yes"; then if test "$enable_dl" = "no"; then AC_MSG_ERROR([--enable-odbc requires --enable-dl]) fi AC_MSG_RESULT([yes]) AC_DEFINE_UNQUOTED([ODBC_DLNAME], ["$LIBIODBC"], [N/A]) elif test "$enable_odbc" = "unix"; then if test "$enable_dl" = "no"; then AC_MSG_ERROR([--enable-odbc requires --enable-dl]) fi AC_MSG_RESULT([unix]) AC_DEFINE_UNQUOTED([ODBC_DLNAME], ["$LIBODBC"], [N/A]) else AC_MSG_RESULT([no]) fi AC_MSG_CHECKING([whether to enable MathProg MySQL support]) if test "$enable_mysql" = "yes"; then if test "$enable_dl" = "no"; then AC_MSG_ERROR([--enable-mysql requires --enable-dl]) fi AC_MSG_RESULT([yes]) CPPFLAGS="-I/usr/include/mysql $CPPFLAGS" AC_DEFINE_UNQUOTED([MYSQL_DLNAME], ["$LIBMYSQL"], [N/A]) else AC_MSG_RESULT([no]) fi AC_CONFIG_FILES( [src/Makefile examples/Makefile Makefile]) AC_OUTPUT dnl eof ctbnrle.1.1.1/glpk/glpk-4.47/w32/0000755000175000017500000000000011632352620016263 5ustar csheltoncsheltonctbnrle.1.1.1/glpk/glpk-4.47/w32/Build_GLPK_with_VC9.bat0000644000175000017500000000051111632352620022340 0ustar csheltoncsheltonrem Build GLPK with Microsoft Visual Studio Express 2008 rem NOTE: Make sure that HOME variable specifies correct path set HOME="C:\Program Files\Microsoft Visual Studio 9.0\VC" call %HOME%\bin\vcvars32.bat copy config_VC config.h %HOME%\bin\nmake.exe /f Makefile_VC %HOME%\bin\nmake.exe /f Makefile_VC check pause ctbnrle.1.1.1/glpk/glpk-4.47/w32/readme.txt0000644000175000017500000000166711632352620020273 0ustar csheltoncsheltonThis directory contains batch files and other stuff which you can use to build GLPK for 32-bit Windows with the native C/C++ compilers. Before running the batch file do the following: 1. Make sure that you have installed the compiler you are going to use to build GLPK. 2. Look into corresponding batch file (just right-click it and choose 'Edit' in the popup menu; DO NOT choose 'Open'). Make sure that HOME variable specifies correct path to the compiler directory; if not, make necessary changes. To run the batch file just double-click it and wait a bit while the Make utility does its job. The message 'OPTIMAL SOLUTION FOUND' in the MS-DOS window means that all is OK. If you do not see it, something is wrong. Once GLPK has been successfully built, there must appear two files in this directory: glpk.lib, which is the GLPK object library, and glpsol.exe, which is the stand-alone GLPK LP/MIP solver. ctbnrle.1.1.1/glpk/glpk-4.47/w32/Build_GLPK_with_VC10.bat0000644000175000017500000000051311632352620022412 0ustar csheltoncsheltonrem Build GLPK with Microsoft Visual Studio Express 2010 rem NOTE: Make sure that HOME variable specifies correct path set HOME="C:\Program Files\Microsoft Visual Studio 10.0\VC" call %HOME%\vcvarsall.bat x86 copy config_VC config.h %HOME%\bin\nmake.exe /f Makefile_VC %HOME%\bin\nmake.exe /f Makefile_VC check pause ctbnrle.1.1.1/glpk/glpk-4.47/w32/Build_GLPK_with_VC10_DLL.bat0000644000175000017500000000052711632352620023112 0ustar csheltoncsheltonrem Build GLPK DLL with Microsoft Visual Studio Express 2010 rem NOTE: Make sure that HOME variable specifies correct path set HOME="C:\Program Files\Microsoft Visual Studio 10.0\VC" call %HOME%\vcvarsall.bat x86 copy config_VC config.h %HOME%\bin\nmake.exe /f Makefile_VC_DLL %HOME%\bin\nmake.exe /f Makefile_VC_DLL check pause ctbnrle.1.1.1/glpk/glpk-4.47/w32/config_VC0000644000175000017500000000046411632352620020047 0ustar csheltoncshelton/* GLPK configuration file (Microsoft Visual Studio Express) */ #define __WOE__ 1 #define ODBC_DLNAME "odbc32.dll" /* ODBC shared library name if this feature is enabled */ #if 0 #define MYSQL_DLNAME "libmysql.dll" /* MySQL shared library name if this feature is enabled */ #endif /* eof */ ctbnrle.1.1.1/glpk/glpk-4.47/w32/Makefile_VC_DLL0000644000175000017500000000735111632352620021014 0ustar csheltoncshelton## Build GLPK DLL with Microsoft Visual Studio Express ## CFLAGS = \ /I. \ /I..\src \ /I..\src\amd \ /I..\src\colamd \ /I..\src\minisat \ /I..\src\zlib \ /DHAVE_CONFIG_H=1 \ /D_CRT_SECURE_NO_WARNINGS=1 \ /nologo \ /W3 \ /O2 OBJSET = \ ..\src\glpapi01.obj \ ..\src\glpapi02.obj \ ..\src\glpapi03.obj \ ..\src\glpapi04.obj \ ..\src\glpapi05.obj \ ..\src\glpapi06.obj \ ..\src\glpapi07.obj \ ..\src\glpapi08.obj \ ..\src\glpapi09.obj \ ..\src\glpapi10.obj \ ..\src\glpapi11.obj \ ..\src\glpapi12.obj \ ..\src\glpapi13.obj \ ..\src\glpapi14.obj \ ..\src\glpapi15.obj \ ..\src\glpapi16.obj \ ..\src\glpapi17.obj \ ..\src\glpapi18.obj \ ..\src\glpapi19.obj \ ..\src\glpapi20.obj \ ..\src\glpapi21.obj \ ..\src\glpavl.obj \ ..\src\glpbfd.obj \ ..\src\glpbfx.obj \ ..\src\glpcpx.obj \ ..\src\glpdmp.obj \ ..\src\glpdmx.obj \ ..\src\glpenv01.obj \ ..\src\glpenv02.obj \ ..\src\glpenv03.obj \ ..\src\glpenv04.obj \ ..\src\glpenv05.obj \ ..\src\glpenv06.obj \ ..\src\glpenv07.obj \ ..\src\glpenv08.obj \ ..\src\glpfhv.obj \ ..\src\glpgmp.obj \ ..\src\glphbm.obj \ ..\src\glpini01.obj \ ..\src\glpini02.obj \ ..\src\glpios01.obj \ ..\src\glpios02.obj \ ..\src\glpios03.obj \ ..\src\glpios04.obj \ ..\src\glpios05.obj \ ..\src\glpios06.obj \ ..\src\glpios07.obj \ ..\src\glpios08.obj \ ..\src\glpios09.obj \ ..\src\glpios10.obj \ ..\src\glpios11.obj \ ..\src\glpios12.obj \ ..\src\glpipm.obj \ ..\src\glplib01.obj \ ..\src\glplib02.obj \ ..\src\glplib03.obj \ ..\src\glplpf.obj \ ..\src\glplpx01.obj \ ..\src\glplpx02.obj \ ..\src\glplpx03.obj \ ..\src\glpluf.obj \ ..\src\glplux.obj \ ..\src\glpmat.obj \ ..\src\glpmpl01.obj \ ..\src\glpmpl02.obj \ ..\src\glpmpl03.obj \ ..\src\glpmpl04.obj \ ..\src\glpmpl05.obj \ ..\src\glpmpl06.obj \ ..\src\glpmps.obj \ ..\src\glpnet01.obj \ ..\src\glpnet02.obj \ ..\src\glpnet03.obj \ ..\src\glpnet04.obj \ ..\src\glpnet05.obj \ ..\src\glpnet06.obj \ ..\src\glpnet07.obj \ ..\src\glpnet08.obj \ ..\src\glpnet09.obj \ ..\src\glpnpp01.obj \ ..\src\glpnpp02.obj \ ..\src\glpnpp03.obj \ ..\src\glpnpp04.obj \ ..\src\glpnpp05.obj \ ..\src\glpnpp06.obj \ ..\src\glpqmd.obj \ ..\src\glprgr.obj \ ..\src\glprng01.obj \ ..\src\glprng02.obj \ ..\src\glpscf.obj \ ..\src\glpscl.obj \ ..\src\glpsdf.obj \ ..\src\glpspm.obj \ ..\src\glpspx01.obj \ ..\src\glpspx02.obj \ ..\src\glpsql.obj \ ..\src\glpssx01.obj \ ..\src\glpssx02.obj \ ..\src\glptsp.obj \ ..\src\amd\amd_1.obj \ ..\src\amd\amd_2.obj \ ..\src\amd\amd_aat.obj \ ..\src\amd\amd_control.obj \ ..\src\amd\amd_defaults.obj \ ..\src\amd\amd_dump.obj \ ..\src\amd\amd_info.obj \ ..\src\amd\amd_order.obj \ ..\src\amd\amd_post_tree.obj \ ..\src\amd\amd_postorder.obj \ ..\src\amd\amd_preprocess.obj \ ..\src\amd\amd_valid.obj \ ..\src\colamd\colamd.obj \ ..\src\minisat\minisat.obj \ ..\src\zlib\adler32.obj \ ..\src\zlib\compress.obj \ ..\src\zlib\crc32.obj \ ..\src\zlib\deflate.obj \ ..\src\zlib\gzclose.obj \ ..\src\zlib\gzlib.obj \ ..\src\zlib\gzread.obj \ ..\src\zlib\gzwrite.obj \ ..\src\zlib\infback.obj \ ..\src\zlib\inffast.obj \ ..\src\zlib\inflate.obj \ ..\src\zlib\inftrees.obj \ ..\src\zlib\trees.obj \ ..\src\zlib\uncompr.obj \ ..\src\zlib\zio.obj \ ..\src\zlib\zutil.obj .c.obj: cl.exe $(CFLAGS) /Fo$*.obj /c $*.c all: glpk_4_47.dll glpsol.exe glpk_4_47.dll: $(OBJSET) cl.exe $(CFLAGS) /LD /Feglpk_4_47.dll \ ..\src\*.obj \ ..\src\amd\*.obj \ ..\src\colamd\*.obj \ ..\src\minisat\*.obj \ ..\src\zlib\*.obj \ glpk_4_47.def glpsol.exe: ..\examples\glpsol.obj glpk_4_47.dll cl.exe $(CFLAGS) /Feglpsol.exe \ ..\examples\glpsol.obj glpk_4_47.lib check: glpsol.exe .\glpsol.exe --version .\glpsol.exe --mps ..\examples\plan.mps ## eof ## ctbnrle.1.1.1/glpk/glpk-4.47/w32/Makefile_VC0000644000175000017500000000724011632352620020316 0ustar csheltoncshelton## Build GLPK with Microsoft Visual Studio Express ## CFLAGS = \ /I. \ /I..\src \ /I..\src\amd \ /I..\src\colamd \ /I..\src\minisat \ /I..\src\zlib \ /DHAVE_CONFIG_H=1 \ /D_CRT_SECURE_NO_WARNINGS=1 \ /nologo \ /W3 \ /O2 OBJSET = \ ..\src\glpapi01.obj \ ..\src\glpapi02.obj \ ..\src\glpapi03.obj \ ..\src\glpapi04.obj \ ..\src\glpapi05.obj \ ..\src\glpapi06.obj \ ..\src\glpapi07.obj \ ..\src\glpapi08.obj \ ..\src\glpapi09.obj \ ..\src\glpapi10.obj \ ..\src\glpapi11.obj \ ..\src\glpapi12.obj \ ..\src\glpapi13.obj \ ..\src\glpapi14.obj \ ..\src\glpapi15.obj \ ..\src\glpapi16.obj \ ..\src\glpapi17.obj \ ..\src\glpapi18.obj \ ..\src\glpapi19.obj \ ..\src\glpapi20.obj \ ..\src\glpapi21.obj \ ..\src\glpavl.obj \ ..\src\glpbfd.obj \ ..\src\glpbfx.obj \ ..\src\glpcpx.obj \ ..\src\glpdmp.obj \ ..\src\glpdmx.obj \ ..\src\glpenv01.obj \ ..\src\glpenv02.obj \ ..\src\glpenv03.obj \ ..\src\glpenv04.obj \ ..\src\glpenv05.obj \ ..\src\glpenv06.obj \ ..\src\glpenv07.obj \ ..\src\glpenv08.obj \ ..\src\glpfhv.obj \ ..\src\glpgmp.obj \ ..\src\glphbm.obj \ ..\src\glpini01.obj \ ..\src\glpini02.obj \ ..\src\glpios01.obj \ ..\src\glpios02.obj \ ..\src\glpios03.obj \ ..\src\glpios04.obj \ ..\src\glpios05.obj \ ..\src\glpios06.obj \ ..\src\glpios07.obj \ ..\src\glpios08.obj \ ..\src\glpios09.obj \ ..\src\glpios10.obj \ ..\src\glpios11.obj \ ..\src\glpios12.obj \ ..\src\glpipm.obj \ ..\src\glplib01.obj \ ..\src\glplib02.obj \ ..\src\glplib03.obj \ ..\src\glplpf.obj \ ..\src\glplpx01.obj \ ..\src\glplpx02.obj \ ..\src\glplpx03.obj \ ..\src\glpluf.obj \ ..\src\glplux.obj \ ..\src\glpmat.obj \ ..\src\glpmpl01.obj \ ..\src\glpmpl02.obj \ ..\src\glpmpl03.obj \ ..\src\glpmpl04.obj \ ..\src\glpmpl05.obj \ ..\src\glpmpl06.obj \ ..\src\glpmps.obj \ ..\src\glpnet01.obj \ ..\src\glpnet02.obj \ ..\src\glpnet03.obj \ ..\src\glpnet04.obj \ ..\src\glpnet05.obj \ ..\src\glpnet06.obj \ ..\src\glpnet07.obj \ ..\src\glpnet08.obj \ ..\src\glpnet09.obj \ ..\src\glpnpp01.obj \ ..\src\glpnpp02.obj \ ..\src\glpnpp03.obj \ ..\src\glpnpp04.obj \ ..\src\glpnpp05.obj \ ..\src\glpnpp06.obj \ ..\src\glpqmd.obj \ ..\src\glprgr.obj \ ..\src\glprng01.obj \ ..\src\glprng02.obj \ ..\src\glpscf.obj \ ..\src\glpscl.obj \ ..\src\glpsdf.obj \ ..\src\glpspm.obj \ ..\src\glpspx01.obj \ ..\src\glpspx02.obj \ ..\src\glpsql.obj \ ..\src\glpssx01.obj \ ..\src\glpssx02.obj \ ..\src\glptsp.obj \ ..\src\amd\amd_1.obj \ ..\src\amd\amd_2.obj \ ..\src\amd\amd_aat.obj \ ..\src\amd\amd_control.obj \ ..\src\amd\amd_defaults.obj \ ..\src\amd\amd_dump.obj \ ..\src\amd\amd_info.obj \ ..\src\amd\amd_order.obj \ ..\src\amd\amd_post_tree.obj \ ..\src\amd\amd_postorder.obj \ ..\src\amd\amd_preprocess.obj \ ..\src\amd\amd_valid.obj \ ..\src\colamd\colamd.obj \ ..\src\minisat\minisat.obj \ ..\src\zlib\adler32.obj \ ..\src\zlib\compress.obj \ ..\src\zlib\crc32.obj \ ..\src\zlib\deflate.obj \ ..\src\zlib\gzclose.obj \ ..\src\zlib\gzlib.obj \ ..\src\zlib\gzread.obj \ ..\src\zlib\gzwrite.obj \ ..\src\zlib\infback.obj \ ..\src\zlib\inffast.obj \ ..\src\zlib\inflate.obj \ ..\src\zlib\inftrees.obj \ ..\src\zlib\trees.obj \ ..\src\zlib\uncompr.obj \ ..\src\zlib\zio.obj \ ..\src\zlib\zutil.obj .c.obj: cl.exe $(CFLAGS) /Fo$*.obj /c $*.c all: glpk.lib glpsol.exe glpk.lib: $(OBJSET) lib.exe /out:glpk.lib \ ..\src\*.obj \ ..\src\amd\*.obj \ ..\src\colamd\*.obj \ ..\src\minisat\*.obj \ ..\src\zlib\*.obj glpsol.exe: ..\examples\glpsol.obj glpk.lib cl.exe $(CFLAGS) /Feglpsol.exe \ ..\examples\glpsol.obj glpk.lib check: glpsol.exe .\glpsol.exe --version .\glpsol.exe --mps ..\examples\plan.mps ## eof ## ctbnrle.1.1.1/glpk/glpk-4.47/w32/glpk_4_47.def0000644000175000017500000005750711632352620020453 0ustar csheltoncsheltonLIBRARY glpk_4_47 VERSION 4.47 DESCRIPTION "GNU Linear Programming Kit" EXPORTS ;; from adler32 ;; _glp_zlib_adler32 _glp_zlib_adler32_combine _glp_zlib_adler32_combine64 ;; from amd_1 ;; _glp_amd_1 ;; from amd_2 ;; _glp_amd_2 ;; from amd_aat ;; _glp_amd_aat ;; from amd_control ;; _glp_amd_control ;; from amd_defaults ;; _glp_amd_defaults ;; from amd_dump ;; ;; from amd_info ;; _glp_amd_info ;; from amd_order ;; _glp_amd_order ;; from amd_post_tree ;; _glp_amd_post_tree ;; from amd_postorder ;; _glp_amd_postorder ;; from amd_preprocess ;; _glp_amd_preprocess ;; from amd_valid ;; _glp_amd_valid ;; from colamd ;; _glp_colamd _glp_colamd_recommended _glp_colamd_report _glp_colamd_set_defaults _glp_symamd _glp_symamd_report ;; from compress ;; _glp_zlib_compress _glp_zlib_compress2 _glp_zlib_compressBound ;; from crc32 ;; _glp_zlib_crc32 _glp_zlib_crc32_combine _glp_zlib_crc32_combine64 _glp_zlib_get_crc_table ;; from deflate ;; _glp_zlib_deflate _glp_zlib_deflateBound _glp_zlib_deflateCopy _glp_zlib_deflateEnd _glp_zlib_deflateInit2_ _glp_zlib_deflateInit_ _glp_zlib_deflateParams _glp_zlib_deflatePrime _glp_zlib_deflateReset _glp_zlib_deflateSetDictionary _glp_zlib_deflateSetHeader _glp_zlib_deflateTune _glp_zlib_deflate_copyright ;; from glpapi01 ;; glp_add_cols glp_add_rows glp_check_dup glp_copy_prob glp_create_prob glp_del_cols glp_del_rows glp_delete_prob glp_erase_prob glp_load_matrix glp_set_col_bnds glp_set_col_name glp_set_mat_col glp_set_mat_row glp_set_obj_coef glp_set_obj_dir glp_set_obj_name glp_set_prob_name glp_set_row_bnds glp_set_row_name glp_sort_matrix ;; from glpapi02 ;; glp_get_col_lb glp_get_col_name glp_get_col_type glp_get_col_ub glp_get_mat_col glp_get_mat_row glp_get_num_cols glp_get_num_nz glp_get_num_rows glp_get_obj_coef glp_get_obj_dir glp_get_obj_name glp_get_prob_name glp_get_row_lb glp_get_row_name glp_get_row_type glp_get_row_ub ;; from glpapi03 ;; glp_create_index glp_delete_index glp_find_col glp_find_row ;; from glpapi04 ;; glp_get_rii glp_get_sjj glp_set_rii glp_set_sjj glp_unscale_prob ;; from glpapi05 ;; glp_set_col_stat glp_set_row_stat glp_std_basis ;; from glpapi06 ;; glp_get_col_dual glp_get_col_prim glp_get_col_stat glp_get_dual_stat glp_get_obj_val glp_get_prim_stat glp_get_row_dual glp_get_row_prim glp_get_row_stat glp_get_status glp_get_unbnd_ray glp_init_smcp glp_simplex ;; from glpapi07 ;; glp_exact ;; from glpapi08 ;; glp_init_iptcp glp_interior glp_ipt_col_dual glp_ipt_col_prim glp_ipt_obj_val glp_ipt_row_dual glp_ipt_row_prim glp_ipt_status ;; from glpapi09 ;; _glp_intopt1 glp_get_col_kind glp_get_num_bin glp_get_num_int glp_init_iocp glp_intopt glp_mip_col_val glp_mip_obj_val glp_mip_row_val glp_mip_status glp_set_col_kind ;; from glpapi10 ;; _glp_check_kkt ;; from glpapi11 ;; glp_print_ipt glp_print_mip glp_print_ranges glp_print_sol glp_read_ipt glp_read_mip glp_read_sol glp_write_ipt glp_write_mip glp_write_sol ;; from glpapi12 ;; _glp_analyze_row glp_analyze_bound glp_analyze_coef glp_bf_exists glp_bf_updated glp_btran glp_dual_rtest glp_eval_tab_col glp_eval_tab_row glp_factorize glp_ftran glp_get_bfcp glp_get_bhead glp_get_col_bind glp_get_row_bind glp_prim_rtest glp_set_bfcp glp_transform_col glp_transform_row glp_warm_up ;; from glpapi13 ;; glp_ios_add_row glp_ios_best_node glp_ios_branch_upon glp_ios_can_branch glp_ios_clear_pool glp_ios_curr_node glp_ios_del_row glp_ios_get_prob glp_ios_heur_sol glp_ios_mip_gap glp_ios_next_node glp_ios_node_bound glp_ios_node_data glp_ios_node_level glp_ios_pool_size glp_ios_prev_node glp_ios_reason glp_ios_row_attr glp_ios_select_node glp_ios_terminate glp_ios_tree_size glp_ios_up_node ;; from glpapi14 ;; _glp_mpl_init_rand glp_mpl_alloc_wksp glp_mpl_build_prob glp_mpl_free_wksp glp_mpl_generate glp_mpl_postsolve glp_mpl_read_data glp_mpl_read_model ;; from glpapi15 ;; glp_add_arc glp_add_vertices glp_create_graph glp_create_v_index glp_del_arc glp_del_vertices glp_delete_graph glp_delete_v_index glp_erase_graph glp_find_vertex glp_read_graph glp_set_graph_name glp_set_vertex_name glp_write_graph ;; from glpapi16 ;; glp_strong_comp glp_top_sort glp_weak_comp ;; from glpapi17 ;; glp_asnprob_hall glp_asnprob_lp glp_asnprob_okalg glp_check_asnprob glp_cpp glp_maxflow_ffalg glp_maxflow_lp glp_mincost_lp glp_mincost_okalg ;; from glpapi18 ;; glp_wclique_exact ;; from glpapi19 ;; glp_minisat1 ;; from glpapi20 ;; glp_intfeas1 ;; from glpapi21 ;; glp_main ;; from glpavl ;; _glp_avl_create_tree _glp_avl_delete_node _glp_avl_delete_tree _glp_avl_find_node _glp_avl_get_node_link _glp_avl_get_node_type _glp_avl_insert_node _glp_avl_set_node_link _glp_avl_set_node_type _glp_avl_strcmp ;; from glpbfd ;; _glp_bfd_btran _glp_bfd_create_it _glp_bfd_delete_it _glp_bfd_factorize _glp_bfd_ftran _glp_bfd_get_count _glp_bfd_set_parm _glp_bfd_update_it ;; from glpbfx ;; _glp_bfx_btran _glp_bfx_create_binv _glp_bfx_delete_binv _glp_bfx_factorize _glp_bfx_ftran _glp_bfx_update ;; from glpcpx ;; glp_init_cpxcp glp_read_lp glp_write_lp ;; from glpdmp ;; _glp_dmp_create_pool _glp_dmp_delete_pool _glp_dmp_free_atom _glp_dmp_get_atom _glp_dmp_in_use ;; from glpdmx ;; glp_check_cnfsat glp_read_asnprob glp_read_ccdata glp_read_cnfsat glp_read_maxflow glp_read_mincost glp_read_prob glp_write_asnprob glp_write_ccdata glp_write_cnfsat glp_write_maxflow glp_write_mincost glp_write_prob ;; from glpenv01 ;; _glp_get_env_ptr glp_free_env glp_init_env glp_version ;; from glpenv02 ;; _glp_tls_get_ptr _glp_tls_set_ptr ;; from glpenv03 ;; glp_close_tee glp_open_tee glp_printf glp_term_hook glp_term_out glp_vprintf ;; from glpenv04 ;; glp_assert_ glp_error_ glp_error_hook ;; from glpenv05 ;; glp_calloc glp_free glp_malloc glp_mem_limit glp_mem_usage ;; from glpenv06 ;; glp_difftime glp_time ;; from glpenv07 ;; _glp_lib_err_msg _glp_lib_xerrmsg _glp_lib_xfclose _glp_lib_xfeof _glp_lib_xferror _glp_lib_xfflush _glp_lib_xfgetc _glp_lib_xfopen _glp_lib_xfprintf _glp_lib_xfputc ;; from glpenv08 ;; _glp_xdlclose _glp_xdlopen _glp_xdlsym ;; from glpfhv ;; _glp_fhv_btran _glp_fhv_create_it _glp_fhv_delete_it _glp_fhv_factorize _glp_fhv_ftran _glp_fhv_h_solve _glp_fhv_update_it ;; from glpgmp ;; _glp_gmp_free_atom _glp_gmp_free_mem _glp_gmp_get_atom _glp_gmp_get_work _glp_gmp_pool_count _glp_mpq_abs _glp_mpq_add _glp_mpq_canonicalize _glp_mpq_clear _glp_mpq_cmp _glp_mpq_div _glp_mpq_get_d _glp_mpq_init _glp_mpq_mul _glp_mpq_neg _glp_mpq_out_str _glp_mpq_set _glp_mpq_set_d _glp_mpq_set_si _glp_mpq_sgn _glp_mpq_sub _glp_mpz_abs _glp_mpz_add _glp_mpz_clear _glp_mpz_cmp _glp_mpz_div _glp_mpz_gcd _glp_mpz_get_d _glp_mpz_get_d_2exp _glp_mpz_init _glp_mpz_mul _glp_mpz_neg _glp_mpz_out_str _glp_mpz_set _glp_mpz_set_si _glp_mpz_sgn _glp_mpz_sub _glp_mpz_swap ;; from glphbm ;; _glp_hbm_free_mat _glp_hbm_read_mat ;; from glpini01 ;; glp_adv_basis ;; from glpini02 ;; glp_cpx_basis ;; from glpios01 ;; _glp_ios_add_row _glp_ios_best_node _glp_ios_clear_pool _glp_ios_clone_node _glp_ios_create_pool _glp_ios_create_tree _glp_ios_del_row _glp_ios_delete_node _glp_ios_delete_pool _glp_ios_delete_tree _glp_ios_eval_degrad _glp_ios_find_row _glp_ios_freeze_node _glp_ios_is_hopeful _glp_ios_relative_gap _glp_ios_revive_node _glp_ios_round_bound _glp_ios_solve_node ;; from glpios02 ;; _glp_ios_preprocess_node ;; from glpios03 ;; _glp_ios_driver ;; from glpios04 ;; _glp_ios_check_vec _glp_ios_clean_vec _glp_ios_clear_vec _glp_ios_copy_vec _glp_ios_create_vec _glp_ios_delete_vec _glp_ios_get_vj _glp_ios_linear_comb _glp_ios_set_vj ;; from glpios05 ;; _glp_ios_gmi_gen ;; from glpios06 ;; _glp_ios_mir_gen _glp_ios_mir_init _glp_ios_mir_term ;; from glpios07 ;; _glp_ios_cov_gen ;; from glpios08 ;; _glp_ios_clq_gen _glp_ios_clq_init _glp_ios_clq_term ;; from glpios09 ;; _glp_ios_choose_var _glp_ios_pcost_branch _glp_ios_pcost_free _glp_ios_pcost_init _glp_ios_pcost_update ;; from glpios10 ;; _glp_ios_feas_pump ;; from glpios11 ;; _glp_ios_process_cuts ;; from glpios12 ;; _glp_ios_choose_node ;; from glpipm ;; _glp_ipm_solve ;; from glplib01 ;; _glp_lib_bigdiv _glp_lib_bigmul ;; from glplib02 ;; _glp_lib_xladd _glp_lib_xlcmp _glp_lib_xldiv _glp_lib_xlmul _glp_lib_xlneg _glp_lib_xlset _glp_lib_xlsub _glp_lib_xltoa _glp_lib_xltod ;; from glplib03 ;; _glp_lib_fp2rat _glp_lib_gcd _glp_lib_gcdn _glp_lib_jdate _glp_lib_jday _glp_lib_lcm _glp_lib_lcmn _glp_lib_round2n _glp_lib_str2int _glp_lib_str2num _glp_lib_strrev _glp_lib_strspx _glp_lib_strtrim ;; from glplpf ;; _glp_lpf_btran _glp_lpf_create_it _glp_lpf_delete_it _glp_lpf_factorize _glp_lpf_ftran _glp_lpf_update_it ;; from glplpx01 ;; _glp_lpx_add_cols _glp_lpx_add_rows _glp_lpx_adv_basis _glp_lpx_check_int _glp_lpx_check_kkt _glp_lpx_cpx_basis _glp_lpx_create_index _glp_lpx_create_prob _glp_lpx_del_cols _glp_lpx_del_rows _glp_lpx_delete_index _glp_lpx_delete_prob _glp_lpx_dual_ratio_test _glp_lpx_eval_tab_col _glp_lpx_eval_tab_row _glp_lpx_exact _glp_lpx_find_col _glp_lpx_find_row _glp_lpx_get_class _glp_lpx_get_col_bnds _glp_lpx_get_col_dual _glp_lpx_get_col_info _glp_lpx_get_col_kind _glp_lpx_get_col_lb _glp_lpx_get_col_name _glp_lpx_get_col_prim _glp_lpx_get_col_stat _glp_lpx_get_col_type _glp_lpx_get_col_ub _glp_lpx_get_dual_stat _glp_lpx_get_int_parm _glp_lpx_get_mat_col _glp_lpx_get_mat_row _glp_lpx_get_num_bin _glp_lpx_get_num_cols _glp_lpx_get_num_int _glp_lpx_get_num_nz _glp_lpx_get_num_rows _glp_lpx_get_obj_coef _glp_lpx_get_obj_dir _glp_lpx_get_obj_name _glp_lpx_get_obj_val _glp_lpx_get_prim_stat _glp_lpx_get_prob_name _glp_lpx_get_ray_info _glp_lpx_get_real_parm _glp_lpx_get_row_bnds _glp_lpx_get_row_dual _glp_lpx_get_row_info _glp_lpx_get_row_lb _glp_lpx_get_row_name _glp_lpx_get_row_prim _glp_lpx_get_row_stat _glp_lpx_get_row_type _glp_lpx_get_row_ub _glp_lpx_get_status _glp_lpx_integer _glp_lpx_interior _glp_lpx_intopt _glp_lpx_ipt_col_dual _glp_lpx_ipt_col_prim _glp_lpx_ipt_obj_val _glp_lpx_ipt_row_dual _glp_lpx_ipt_row_prim _glp_lpx_ipt_status _glp_lpx_is_b_avail _glp_lpx_load_matrix _glp_lpx_main _glp_lpx_mip_col_val _glp_lpx_mip_obj_val _glp_lpx_mip_row_val _glp_lpx_mip_status _glp_lpx_prim_ratio_test _glp_lpx_print_ips _glp_lpx_print_mip _glp_lpx_print_prob _glp_lpx_print_sens_bnds _glp_lpx_print_sol _glp_lpx_read_bas _glp_lpx_read_cpxlp _glp_lpx_read_freemps _glp_lpx_read_model _glp_lpx_read_mps _glp_lpx_reset_parms _glp_lpx_scale_prob _glp_lpx_set_class _glp_lpx_set_col_bnds _glp_lpx_set_col_kind _glp_lpx_set_col_name _glp_lpx_set_col_stat _glp_lpx_set_int_parm _glp_lpx_set_mat_col _glp_lpx_set_mat_row _glp_lpx_set_obj_coef _glp_lpx_set_obj_dir _glp_lpx_set_obj_name _glp_lpx_set_prob_name _glp_lpx_set_real_parm _glp_lpx_set_row_bnds _glp_lpx_set_row_name _glp_lpx_set_row_stat _glp_lpx_simplex _glp_lpx_std_basis _glp_lpx_transform_col _glp_lpx_transform_row _glp_lpx_unscale_prob _glp_lpx_warm_up _glp_lpx_write_bas _glp_lpx_write_cpxlp _glp_lpx_write_freemps _glp_lpx_write_mps ;; from glplpx02 ;; _glp_put_mip_soln _glp_put_solution ;; from glplpx03 ;; _glp_lpx_write_pb ;; from glpluf ;; _glp_luf_a_solve _glp_luf_create_it _glp_luf_defrag_sva _glp_luf_delete_it _glp_luf_enlarge_col _glp_luf_enlarge_row _glp_luf_f_solve _glp_luf_factorize _glp_luf_v_solve ;; from glplux ;; _glp_lux_create _glp_lux_decomp _glp_lux_delete _glp_lux_f_solve _glp_lux_solve _glp_lux_v_solve ;; from glpmat ;; _glp_mat_adat_numeric _glp_mat_adat_symbolic _glp_mat_amd_order1 _glp_mat_check_fvs _glp_mat_check_pattern _glp_mat_chol_numeric _glp_mat_chol_symbolic _glp_mat_min_degree _glp_mat_symamd_ord _glp_mat_transpose _glp_mat_u_solve _glp_mat_ut_solve ;; from glpmpl01 ;; _glp_mpl_append_block _glp_mpl_append_char _glp_mpl_append_slot _glp_mpl_arg_list_len _glp_mpl_branched_expression _glp_mpl_check_statement _glp_mpl_close_scope _glp_mpl_constraint_statement _glp_mpl_create_arg_list _glp_mpl_create_block _glp_mpl_create_domain _glp_mpl_display_statement _glp_mpl_domain_arity _glp_mpl_elemset_argument _glp_mpl_end_statement _glp_mpl_enter_context _glp_mpl_error_dimension _glp_mpl_error_following _glp_mpl_error_preceding _glp_mpl_expand_arg_list _glp_mpl_expression_0 _glp_mpl_expression_1 _glp_mpl_expression_10 _glp_mpl_expression_11 _glp_mpl_expression_12 _glp_mpl_expression_13 _glp_mpl_expression_2 _glp_mpl_expression_3 _glp_mpl_expression_4 _glp_mpl_expression_5 _glp_mpl_expression_6 _glp_mpl_expression_7 _glp_mpl_expression_8 _glp_mpl_expression_9 _glp_mpl_expression_list _glp_mpl_for_statement _glp_mpl_function_reference _glp_mpl_get_char _glp_mpl_get_token _glp_mpl_indexing_expression _glp_mpl_is_keyword _glp_mpl_is_reserved _glp_mpl_iterated_expression _glp_mpl_literal_set _glp_mpl_make_binary _glp_mpl_make_code _glp_mpl_make_ternary _glp_mpl_make_unary _glp_mpl_model_section _glp_mpl_numeric_argument _glp_mpl_numeric_literal _glp_mpl_object_reference _glp_mpl_objective_statement _glp_mpl_parameter_statement _glp_mpl_primary_expression _glp_mpl_print_context _glp_mpl_printf_statement _glp_mpl_set_expression _glp_mpl_set_statement _glp_mpl_simple_statement _glp_mpl_solve_statement _glp_mpl_string_literal _glp_mpl_subscript_list _glp_mpl_symbolic_argument _glp_mpl_table_statement _glp_mpl_unget_token _glp_mpl_variable_statement ;; from glpmpl02 ;; _glp_mpl_create_slice _glp_mpl_data_section _glp_mpl_delete_slice _glp_mpl_expand_slice _glp_mpl_fake_slice _glp_mpl_is_literal _glp_mpl_is_number _glp_mpl_is_symbol _glp_mpl_matrix_format _glp_mpl_parameter_data _glp_mpl_plain_format _glp_mpl_read_number _glp_mpl_read_slice _glp_mpl_read_symbol _glp_mpl_read_value _glp_mpl_select_parameter _glp_mpl_select_set _glp_mpl_set_data _glp_mpl_set_default _glp_mpl_simple_format _glp_mpl_slice_arity _glp_mpl_slice_dimen _glp_mpl_tabbing_format _glp_mpl_tabular_format ;; from glpmpl03 ;; _glp_mpl_add_member _glp_mpl_add_tuple _glp_mpl_arelset_member _glp_mpl_arelset_size _glp_mpl_assign_dummy_index _glp_mpl_build_subtuple _glp_mpl_check_elem_set _glp_mpl_check_then_add _glp_mpl_check_value_num _glp_mpl_check_value_sym _glp_mpl_clean_check _glp_mpl_clean_code _glp_mpl_clean_constraint _glp_mpl_clean_display _glp_mpl_clean_domain _glp_mpl_clean_for _glp_mpl_clean_parameter _glp_mpl_clean_printf _glp_mpl_clean_set _glp_mpl_clean_statement _glp_mpl_clean_table _glp_mpl_clean_variable _glp_mpl_compare_strings _glp_mpl_compare_symbols _glp_mpl_compare_tuples _glp_mpl_concat_symbols _glp_mpl_constant_term _glp_mpl_copy_elemset _glp_mpl_copy_formula _glp_mpl_copy_string _glp_mpl_copy_symbol _glp_mpl_copy_tuple _glp_mpl_create_arelset _glp_mpl_create_array _glp_mpl_create_elemset _glp_mpl_create_string _glp_mpl_create_symbol_num _glp_mpl_create_symbol_str _glp_mpl_create_tuple _glp_mpl_delete_array _glp_mpl_delete_elemset _glp_mpl_delete_formula _glp_mpl_delete_string _glp_mpl_delete_symbol _glp_mpl_delete_tuple _glp_mpl_delete_value _glp_mpl_enter_domain_block _glp_mpl_eval_elemset _glp_mpl_eval_formula _glp_mpl_eval_logical _glp_mpl_eval_member_con _glp_mpl_eval_member_num _glp_mpl_eval_member_set _glp_mpl_eval_member_sym _glp_mpl_eval_member_var _glp_mpl_eval_numeric _glp_mpl_eval_symbolic _glp_mpl_eval_tuple _glp_mpl_eval_whole_con _glp_mpl_eval_whole_par _glp_mpl_eval_whole_set _glp_mpl_eval_whole_var _glp_mpl_eval_within_domain _glp_mpl_execute_check _glp_mpl_execute_display _glp_mpl_execute_for _glp_mpl_execute_printf _glp_mpl_execute_statement _glp_mpl_execute_table _glp_mpl_expand_tuple _glp_mpl_fetch_string _glp_mpl_find_member _glp_mpl_find_tuple _glp_mpl_format_symbol _glp_mpl_format_tuple _glp_mpl_fp_add _glp_mpl_fp_atan _glp_mpl_fp_atan2 _glp_mpl_fp_cos _glp_mpl_fp_div _glp_mpl_fp_exp _glp_mpl_fp_idiv _glp_mpl_fp_irand224 _glp_mpl_fp_less _glp_mpl_fp_log _glp_mpl_fp_log10 _glp_mpl_fp_mod _glp_mpl_fp_mul _glp_mpl_fp_normal _glp_mpl_fp_normal01 _glp_mpl_fp_power _glp_mpl_fp_round _glp_mpl_fp_sin _glp_mpl_fp_sqrt _glp_mpl_fp_sub _glp_mpl_fp_trunc _glp_mpl_fp_uniform01 _glp_mpl_free_dca _glp_mpl_get_domain_tuple _glp_mpl_is_member _glp_mpl_linear_comb _glp_mpl_loop_within_domain _glp_mpl_out_of_domain _glp_mpl_reduce_terms _glp_mpl_remove_constant _glp_mpl_set_cross _glp_mpl_set_diff _glp_mpl_set_inter _glp_mpl_set_symdiff _glp_mpl_set_union _glp_mpl_single_variable _glp_mpl_tab_get_arg _glp_mpl_tab_get_name _glp_mpl_tab_get_num _glp_mpl_tab_get_str _glp_mpl_tab_get_type _glp_mpl_tab_num_args _glp_mpl_tab_num_flds _glp_mpl_tab_set_num _glp_mpl_tab_set_str _glp_mpl_take_member_con _glp_mpl_take_member_num _glp_mpl_take_member_set _glp_mpl_take_member_sym _glp_mpl_take_member_var _glp_mpl_tuple_dimen _glp_mpl_uniform _glp_mpl_update_dummy_indices ;; from glpmpl04 ;; _glp_mpl_alloc_content _glp_mpl_build_problem _glp_mpl_clean_model _glp_mpl_close_input _glp_mpl_error _glp_mpl_flush_output _glp_mpl_generate _glp_mpl_generate_model _glp_mpl_get_col_bnds _glp_mpl_get_col_kind _glp_mpl_get_col_name _glp_mpl_get_mat_row _glp_mpl_get_num_cols _glp_mpl_get_num_rows _glp_mpl_get_prob_name _glp_mpl_get_row_bnds _glp_mpl_get_row_c0 _glp_mpl_get_row_kind _glp_mpl_get_row_name _glp_mpl_has_solve_stmt _glp_mpl_initialize _glp_mpl_open_input _glp_mpl_open_output _glp_mpl_postsolve _glp_mpl_postsolve_model _glp_mpl_put_col_soln _glp_mpl_put_row_soln _glp_mpl_read_char _glp_mpl_read_data _glp_mpl_read_model _glp_mpl_terminate _glp_mpl_warning _glp_mpl_write_char _glp_mpl_write_text ;; from glpmpl05 ;; _glp_mpl_fn_gmtime _glp_mpl_fn_str2time _glp_mpl_fn_time2str ;; from glpmpl06 ;; _glp_mpl_tab_drv_close _glp_mpl_tab_drv_open _glp_mpl_tab_drv_read _glp_mpl_tab_drv_write ;; from glpmps ;; glp_init_mpscp glp_read_mps glp_write_mps ;; from glpnet01 ;; _glp_mc21a ;; from glpnet02 ;; _glp_mc13d ;; from glpnet03 ;; glp_netgen ;; from glpnet04 ;; glp_gridgen ;; from glpnet05 ;; glp_rmfgen ;; from glpnet06 ;; _glp_okalg ;; from glpnet07 ;; _glp_ffalg ;; from glpnet08 ;; _glp_wclique ;; from glpnet09 ;; _glp_kellerman ;; from glpnpp01 ;; _glp_npp_activate_col _glp_npp_activate_row _glp_npp_add_aij _glp_npp_add_col _glp_npp_add_row _glp_npp_build_prob _glp_npp_col_nnz _glp_npp_create_wksp _glp_npp_deactivate_col _glp_npp_deactivate_row _glp_npp_del_aij _glp_npp_del_col _glp_npp_del_row _glp_npp_delete_wksp _glp_npp_erase_row _glp_npp_insert_col _glp_npp_insert_row _glp_npp_load_prob _glp_npp_postprocess _glp_npp_push_tse _glp_npp_remove_col _glp_npp_remove_row _glp_npp_row_nnz _glp_npp_unload_sol ;; from glpnpp02 ;; _glp_npp_dbnd_col _glp_npp_fixed_col _glp_npp_free_col _glp_npp_free_row _glp_npp_geq_row _glp_npp_lbnd_col _glp_npp_leq_row _glp_npp_make_equality _glp_npp_make_fixed _glp_npp_ubnd_col ;; from glpnpp03 ;; _glp_npp_analyze_row _glp_npp_empty_col _glp_npp_empty_row _glp_npp_eq_doublet _glp_npp_eq_singlet _glp_npp_forcing_row _glp_npp_implied_bounds _glp_npp_implied_free _glp_npp_implied_lower _glp_npp_implied_slack _glp_npp_implied_upper _glp_npp_implied_value _glp_npp_inactive_bound _glp_npp_ineq_singlet ;; from glpnpp04 ;; _glp_npp_binarize_prob _glp_npp_hidden_covering _glp_npp_hidden_packing _glp_npp_implied_packing _glp_npp_is_covering _glp_npp_is_packing _glp_npp_is_partitioning _glp_npp_reduce_ineq_coef ;; from glpnpp05 ;; _glp_npp_clean_prob _glp_npp_improve_bounds _glp_npp_integer _glp_npp_process_col _glp_npp_process_prob _glp_npp_process_row _glp_npp_simplex ;; from glpnpp06 ;; _glp_npp_sat_encode_clause _glp_npp_sat_encode_geq _glp_npp_sat_encode_leq _glp_npp_sat_encode_pack _glp_npp_sat_encode_prob _glp_npp_sat_encode_row _glp_npp_sat_encode_sum2 _glp_npp_sat_encode_sum3 _glp_npp_sat_encode_sum_ax _glp_npp_sat_fixed_col _glp_npp_sat_free_row _glp_npp_sat_is_bin_comb _glp_npp_sat_is_cover_ineq _glp_npp_sat_is_pack_ineq _glp_npp_sat_is_partn_eq _glp_npp_sat_normalize_clause _glp_npp_sat_num_neg_coef _glp_npp_sat_num_pos_coef _glp_npp_sat_reverse_row _glp_npp_sat_split_pack ;; from glpqmd ;; _glp_qmd_genqmd _glp_qmd_qmdmrg _glp_qmd_qmdqt _glp_qmd_qmdrch _glp_qmd_qmdupd ;; from glprgr ;; _glp_rgr_write_bmp16 ;; from glprng01 ;; _glp_rng_create_rand _glp_rng_delete_rand _glp_rng_init_rand _glp_rng_next_rand _glp_rng_unif_rand ;; from glprng02 ;; _glp_rng_unif_01 _glp_rng_uniform ;; from glpscf ;; _glp_scf_create_it _glp_scf_delete_it _glp_scf_reset_it _glp_scf_solve_it _glp_scf_update_exp ;; from glpscl ;; glp_scale_prob ;; from glpsdf ;; glp_sdf_close_file glp_sdf_error glp_sdf_line glp_sdf_open_file glp_sdf_read_int glp_sdf_read_item glp_sdf_read_num glp_sdf_read_text glp_sdf_set_jump glp_sdf_warning ;; from glpspm ;; _glp_spm_add_mat _glp_spm_add_num _glp_spm_add_sym _glp_spm_check_per _glp_spm_count_nnz _glp_spm_create_mat _glp_spm_create_per _glp_spm_delete_mat _glp_spm_delete_per _glp_spm_drop_zeros _glp_spm_mul_mat _glp_spm_mul_num _glp_spm_mul_sym _glp_spm_new_elem _glp_spm_read_hbm _glp_spm_read_mat _glp_spm_show_mat _glp_spm_test_mat_d _glp_spm_test_mat_e _glp_spm_transpose _glp_spm_write_mat ;; from glpspx01 ;; _glp_spx_primal ;; from glpspx02 ;; _glp_spx_dual ;; from glpsql ;; _glp_db_iodbc_close _glp_db_iodbc_open _glp_db_iodbc_read _glp_db_iodbc_write _glp_db_mysql_close _glp_db_mysql_open _glp_db_mysql_read _glp_db_mysql_write ;; from glpssx01 ;; _glp_ssx_change_basis _glp_ssx_chuzc _glp_ssx_chuzr _glp_ssx_create _glp_ssx_delete _glp_ssx_eval_bbar _glp_ssx_eval_cbar _glp_ssx_eval_col _glp_ssx_eval_dj _glp_ssx_eval_pi _glp_ssx_eval_rho _glp_ssx_eval_row _glp_ssx_factorize _glp_ssx_get_xNj _glp_ssx_update_bbar _glp_ssx_update_cbar _glp_ssx_update_pi ;; from glpssx02 ;; _glp_ssx_driver _glp_ssx_phase_I _glp_ssx_phase_II ;; from glptsp ;; _glp_tsp_distance _glp_tsp_free_data _glp_tsp_read_data ;; from gzclose ;; _glp_zlib_gzclose ;; from gzlib ;; _glp_zlib_gz_error _glp_zlib_gzbuffer _glp_zlib_gzclearerr _glp_zlib_gzdopen _glp_zlib_gzeof _glp_zlib_gzerror _glp_zlib_gzoffset _glp_zlib_gzoffset64 _glp_zlib_gzopen _glp_zlib_gzopen64 _glp_zlib_gzrewind _glp_zlib_gzseek _glp_zlib_gzseek64 _glp_zlib_gztell _glp_zlib_gztell64 ;; from gzread ;; _glp_zlib_gzclose_r _glp_zlib_gzdirect _glp_zlib_gzgetc _glp_zlib_gzgets _glp_zlib_gzread _glp_zlib_gzungetc ;; from gzwrite ;; _glp_zlib_gzclose_w _glp_zlib_gzflush _glp_zlib_gzprintf _glp_zlib_gzputc _glp_zlib_gzputs _glp_zlib_gzsetparams _glp_zlib_gzwrite ;; from infback ;; _glp_zlib_inflateBack _glp_zlib_inflateBackEnd _glp_zlib_inflateBackInit_ ;; from inffast ;; _glp_zlib_inflate_fast ;; from inflate ;; _glp_zlib_inflate _glp_zlib_inflateCopy _glp_zlib_inflateEnd _glp_zlib_inflateGetHeader _glp_zlib_inflateInit2_ _glp_zlib_inflateInit_ _glp_zlib_inflateMark _glp_zlib_inflatePrime _glp_zlib_inflateReset _glp_zlib_inflateReset2 _glp_zlib_inflateSetDictionary _glp_zlib_inflateSync _glp_zlib_inflateSyncPoint _glp_zlib_inflateUndermine ;; from inftrees ;; _glp_zlib_inflate_copyright _glp_zlib_inflate_table ;; from minisat ;; _glp_minisat_addclause _glp_minisat_delete _glp_minisat_nclauses _glp_minisat_nconflicts _glp_minisat_new _glp_minisat_nvars _glp_minisat_propagate _glp_minisat_reducedb _glp_minisat_setnvars _glp_minisat_simplify _glp_minisat_solve ;; from trees ;; _glp_zlib_dist_code _glp_zlib_length_code _glp_zlib_tr_align _glp_zlib_tr_flush_block _glp_zlib_tr_init _glp_zlib_tr_stored_block _glp_zlib_tr_tally ;; from uncompr ;; _glp_zlib_uncompress ;; from zio ;; _glp_zlib_close _glp_zlib_lseek _glp_zlib_open _glp_zlib_read _glp_zlib_write ;; from zutil ;; _glp_zlib_zError _glp_zlib_z_errmsg _glp_zlib_zcalloc _glp_zlib_zcfree _glp_zlib_zlibCompileFlags _glp_zlib_zlibVersion ctbnrle.1.1.1/glpk/glpk-4.47/w32/Build_GLPK_with_VC9_DLL.bat0000644000175000017500000000052511632352620023040 0ustar csheltoncsheltonrem Build GLPK DLL with Microsoft Visual Studio Express 2008 rem NOTE: Make sure that HOME variable specifies correct path set HOME="C:\Program Files\Microsoft Visual Studio 9.0\VC" call %HOME%\bin\vcvars32.bat copy config_VC config.h %HOME%\bin\nmake.exe /f Makefile_VC_DLL %HOME%\bin\nmake.exe /f Makefile_VC_DLL check pause ctbnrle.1.1.1/glpk/glpk-4.47/Makefile_MMIX0000644000175000017500000000752111632352620020147 0ustar csheltoncshelton# Build GLPK for MMIX with GCC cross-compiler #**********************************************************************# # You can use this Makefile to build GLPK with GCC cross-compiler for # # MMIX. No configuring is needed. # # # # MMIX is a 64-bit RISC computer for the third millennium designed by # # Prof. Donald Knuth. For details see: # # # # http://www-cs-faculty.stanford.edu/~knuth/mmix.html # # http://en.wikipedia.org/wiki/MMIX # # http://www.malgil.com/mmix # # # # The GNU MMIX compiler tool-suite (GCC backend, binutils, and newlib) # # was developed by Hans-Peter Nilsson. You may follow his instructions # # to build and install the MMIX tools; see: # # # # http://bitrange.com/mmix/install.html # #**********************************************************************# GCC = mmix-gcc AR = mmix-ar MMIX = mmix CFLAGS = -O2 OBJSET = \ src/glpapi01.o \ src/glpapi02.o \ src/glpapi03.o \ src/glpapi04.o \ src/glpapi05.o \ src/glpapi06.o \ src/glpapi07.o \ src/glpapi08.o \ src/glpapi09.o \ src/glpapi10.o \ src/glpapi11.o \ src/glpapi12.o \ src/glpapi13.o \ src/glpapi14.o \ src/glpapi15.o \ src/glpapi16.o \ src/glpapi17.o \ src/glpapi18.o \ src/glpapi19.o \ src/glpapi20.o \ src/glpapi21.o \ src/glpavl.o \ src/glpbfd.o \ src/glpbfx.o \ src/glpcpx.o \ src/glpdmp.o \ src/glpdmx.o \ src/glpenv01.o \ src/glpenv02.o \ src/glpenv03.o \ src/glpenv04.o \ src/glpenv05.o \ src/glpenv06.o \ src/glpenv07.o \ src/glpenv08.o \ src/glpfhv.o \ src/glpgmp.o \ src/glphbm.o \ src/glpini01.o \ src/glpini02.o \ src/glpios01.o \ src/glpios02.o \ src/glpios03.o \ src/glpios04.o \ src/glpios05.o \ src/glpios06.o \ src/glpios07.o \ src/glpios08.o \ src/glpios09.o \ src/glpios10.o \ src/glpios11.o \ src/glpios12.o \ src/glpipm.o \ src/glplib01.o \ src/glplib02.o \ src/glplib03.o \ src/glplpf.o \ src/glplpx01.o \ src/glplpx02.o \ src/glplpx03.o \ src/glpluf.o \ src/glplux.o \ src/glpmat.o \ src/glpmpl01.o \ src/glpmpl02.o \ src/glpmpl03.o \ src/glpmpl04.o \ src/glpmpl05.o \ src/glpmpl06.o \ src/glpmps.o \ src/glpnet01.o \ src/glpnet02.o \ src/glpnet03.o \ src/glpnet04.o \ src/glpnet05.o \ src/glpnet06.o \ src/glpnet07.o \ src/glpnet08.o \ src/glpnet09.o \ src/glpnpp01.o \ src/glpnpp02.o \ src/glpnpp03.o \ src/glpnpp04.o \ src/glpnpp05.o \ src/glpnpp06.o \ src/glpqmd.o \ src/glprgr.o \ src/glprng01.o \ src/glprng02.o \ src/glpscf.o \ src/glpscl.o \ src/glpsdf.o \ src/glpspm.o \ src/glpspx01.o \ src/glpspx02.o \ src/glpsql.o \ src/glpssx01.o \ src/glpssx02.o \ src/glptsp.o \ src/amd/amd_1.o \ src/amd/amd_2.o \ src/amd/amd_aat.o \ src/amd/amd_control.o \ src/amd/amd_defaults.o \ src/amd/amd_dump.o \ src/amd/amd_info.o \ src/amd/amd_order.o \ src/amd/amd_post_tree.o \ src/amd/amd_postorder.o \ src/amd/amd_preprocess.o \ src/amd/amd_valid.o \ src/colamd/colamd.o \ src/minisat/minisat.o \ src/zlib/adler32.o \ src/zlib/compress.o \ src/zlib/crc32.o \ src/zlib/deflate.o \ src/zlib/gzclose.o \ src/zlib/gzlib.o \ src/zlib/gzread.o \ src/zlib/gzwrite.o \ src/zlib/infback.o \ src/zlib/inffast.o \ src/zlib/inflate.o \ src/zlib/inftrees.o \ src/zlib/trees.o \ src/zlib/uncompr.o \ src/zlib/zio.o \ src/zlib/zutil.o .c.o: $(GCC) $(CFLAGS) -Isrc -o $@ -c $< all: libglpk.a glpsol.mmo libglpk.a: $(OBJSET) $(AR) cru libglpk.a $(OBJSET) glpsol.mmo: examples/glpsol.o libglpk.a $(GCC) $(CFLAGS) -o glpsol.mmo examples/glpsol.o libglpk.a -lm check: glpsol.mmo $(MMIX) glpsol.mmo --mps examples/plan.mps ctbnrle.1.1.1/glpk/glpk-4.47/config.guess0000755000175000017500000012761511632352620020204 0ustar csheltoncshelton#! /bin/sh # Attempt to guess a canonical system name. # Copyright (C) 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, # 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009 # Free Software Foundation, Inc. timestamp='2009-11-20' # This file is free software; you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street - Fifth Floor, Boston, MA # 02110-1301, USA. # # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that program. # Originally written by Per Bothner. Please send patches (context # diff format) to and include a ChangeLog # entry. # # This script attempts to guess a canonical system name similar to # config.sub. If it succeeds, it prints the system name on stdout, and # exits with 0. Otherwise, it exits with 1. # # You can get the latest version of this script from: # http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD me=`echo "$0" | sed -e 's,.*/,,'` usage="\ Usage: $0 [OPTION] Output the configuration name of the system \`$me' is run on. Operation modes: -h, --help print this help, then exit -t, --time-stamp print date of last modification, then exit -v, --version print version number, then exit Report bugs and patches to ." version="\ GNU config.guess ($timestamp) Originally written by Per Bothner. Copyright (C) 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008 Free Software Foundation, Inc. This is free software; see the source for copying conditions. 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I hope that # covers most systems running today. This code pipes the CPU # types through head -n 1, so we only detect the type of CPU 0. ALPHA_CPU_TYPE=`/usr/sbin/psrinfo -v | sed -n -e 's/^ The alpha \(.*\) processor.*$/\1/p' | head -n 1` case "$ALPHA_CPU_TYPE" in "EV4 (21064)") UNAME_MACHINE="alpha" ;; "EV4.5 (21064)") UNAME_MACHINE="alpha" ;; "LCA4 (21066/21068)") UNAME_MACHINE="alpha" ;; "EV5 (21164)") UNAME_MACHINE="alphaev5" ;; "EV5.6 (21164A)") UNAME_MACHINE="alphaev56" ;; "EV5.6 (21164PC)") UNAME_MACHINE="alphapca56" ;; "EV5.7 (21164PC)") UNAME_MACHINE="alphapca57" ;; "EV6 (21264)") UNAME_MACHINE="alphaev6" ;; "EV6.7 (21264A)") UNAME_MACHINE="alphaev67" ;; "EV6.8CB (21264C)") UNAME_MACHINE="alphaev68" ;; "EV6.8AL (21264B)") UNAME_MACHINE="alphaev68" ;; "EV6.8CX (21264D)") UNAME_MACHINE="alphaev68" ;; "EV6.9A (21264/EV69A)") UNAME_MACHINE="alphaev69" ;; "EV7 (21364)") UNAME_MACHINE="alphaev7" ;; "EV7.9 (21364A)") UNAME_MACHINE="alphaev79" ;; esac # A Pn.n version is a patched version. # A Vn.n version is a released version. # A Tn.n version is a released field test version. # A Xn.n version is an unreleased experimental baselevel. # 1.2 uses "1.2" for uname -r. echo ${UNAME_MACHINE}-dec-osf`echo ${UNAME_RELEASE} | sed -e 's/^[PVTX]//' | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz'` exit ;; Alpha\ *:Windows_NT*:*) # How do we know it's Interix rather than the generic POSIX subsystem? # Should we change UNAME_MACHINE based on the output of uname instead # of the specific Alpha model? echo alpha-pc-interix exit ;; 21064:Windows_NT:50:3) echo alpha-dec-winnt3.5 exit ;; Amiga*:UNIX_System_V:4.0:*) echo m68k-unknown-sysv4 exit ;; *:[Aa]miga[Oo][Ss]:*:*) echo ${UNAME_MACHINE}-unknown-amigaos exit ;; *:[Mm]orph[Oo][Ss]:*:*) echo ${UNAME_MACHINE}-unknown-morphos exit ;; *:OS/390:*:*) echo i370-ibm-openedition exit ;; *:z/VM:*:*) echo s390-ibm-zvmoe exit ;; *:OS400:*:*) echo powerpc-ibm-os400 exit ;; arm:RISC*:1.[012]*:*|arm:riscix:1.[012]*:*) echo arm-acorn-riscix${UNAME_RELEASE} exit ;; arm:riscos:*:*|arm:RISCOS:*:*) echo arm-unknown-riscos exit ;; SR2?01:HI-UX/MPP:*:* | SR8000:HI-UX/MPP:*:*) echo hppa1.1-hitachi-hiuxmpp exit ;; Pyramid*:OSx*:*:* | MIS*:OSx*:*:* | MIS*:SMP_DC-OSx*:*:*) # akee@wpdis03.wpafb.af.mil (Earle F. Ake) contributed MIS and NILE. if test "`(/bin/universe) 2>/dev/null`" = att ; then echo pyramid-pyramid-sysv3 else echo pyramid-pyramid-bsd fi exit ;; NILE*:*:*:dcosx) echo pyramid-pyramid-svr4 exit ;; DRS?6000:unix:4.0:6*) echo sparc-icl-nx6 exit ;; DRS?6000:UNIX_SV:4.2*:7* | DRS?6000:isis:4.2*:7*) case `/usr/bin/uname -p` in sparc) echo sparc-icl-nx7; exit ;; esac ;; s390x:SunOS:*:*) echo ${UNAME_MACHINE}-ibm-solaris2`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; sun4H:SunOS:5.*:*) echo sparc-hal-solaris2`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; sun4*:SunOS:5.*:* | tadpole*:SunOS:5.*:*) echo sparc-sun-solaris2`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; i86pc:AuroraUX:5.*:* | i86xen:AuroraUX:5.*:*) echo i386-pc-auroraux${UNAME_RELEASE} exit ;; i86pc:SunOS:5.*:* | i86xen:SunOS:5.*:*) eval $set_cc_for_build SUN_ARCH="i386" # If there is a compiler, see if it is configured for 64-bit objects. # Note that the Sun cc does not turn __LP64__ into 1 like gcc does. # This test works for both compilers. if [ "$CC_FOR_BUILD" != 'no_compiler_found' ]; then if (echo '#ifdef __amd64'; echo IS_64BIT_ARCH; echo '#endif') | \ (CCOPTS= $CC_FOR_BUILD -E - 2>/dev/null) | \ grep IS_64BIT_ARCH >/dev/null then SUN_ARCH="x86_64" fi fi echo ${SUN_ARCH}-pc-solaris2`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; sun4*:SunOS:6*:*) # According to config.sub, this is the proper way to canonicalize # SunOS6. Hard to guess exactly what SunOS6 will be like, but # it's likely to be more like Solaris than SunOS4. echo sparc-sun-solaris3`echo ${UNAME_RELEASE}|sed -e 's/[^.]*//'` exit ;; sun4*:SunOS:*:*) case "`/usr/bin/arch -k`" in Series*|S4*) UNAME_RELEASE=`uname -v` ;; esac # Japanese Language versions have a version number like `4.1.3-JL'. echo sparc-sun-sunos`echo ${UNAME_RELEASE}|sed -e 's/-/_/'` exit ;; sun3*:SunOS:*:*) echo m68k-sun-sunos${UNAME_RELEASE} exit ;; sun*:*:4.2BSD:*) UNAME_RELEASE=`(sed 1q /etc/motd | awk '{print substr($5,1,3)}') 2>/dev/null` test "x${UNAME_RELEASE}" = "x" && UNAME_RELEASE=3 case "`/bin/arch`" in sun3) echo m68k-sun-sunos${UNAME_RELEASE} ;; sun4) echo sparc-sun-sunos${UNAME_RELEASE} ;; esac exit ;; aushp:SunOS:*:*) echo sparc-auspex-sunos${UNAME_RELEASE} exit ;; # The situation for MiNT is a little confusing. The machine name # can be virtually everything (everything which is not # "atarist" or "atariste" at least should have a processor # > m68000). The system name ranges from "MiNT" over "FreeMiNT" # to the lowercase version "mint" (or "freemint"). Finally # the system name "TOS" denotes a system which is actually not # MiNT. But MiNT is downward compatible to TOS, so this should # be no problem. atarist[e]:*MiNT:*:* | atarist[e]:*mint:*:* | atarist[e]:*TOS:*:*) echo m68k-atari-mint${UNAME_RELEASE} exit ;; atari*:*MiNT:*:* | atari*:*mint:*:* | atarist[e]:*TOS:*:*) echo m68k-atari-mint${UNAME_RELEASE} exit ;; *falcon*:*MiNT:*:* | *falcon*:*mint:*:* | *falcon*:*TOS:*:*) echo m68k-atari-mint${UNAME_RELEASE} exit ;; milan*:*MiNT:*:* | milan*:*mint:*:* | *milan*:*TOS:*:*) echo m68k-milan-mint${UNAME_RELEASE} exit ;; hades*:*MiNT:*:* | hades*:*mint:*:* | *hades*:*TOS:*:*) echo m68k-hades-mint${UNAME_RELEASE} exit ;; *:*MiNT:*:* | *:*mint:*:* | *:*TOS:*:*) echo m68k-unknown-mint${UNAME_RELEASE} exit ;; m68k:machten:*:*) echo m68k-apple-machten${UNAME_RELEASE} exit ;; powerpc:machten:*:*) echo powerpc-apple-machten${UNAME_RELEASE} exit ;; RISC*:Mach:*:*) echo mips-dec-mach_bsd4.3 exit ;; RISC*:ULTRIX:*:*) echo mips-dec-ultrix${UNAME_RELEASE} exit ;; VAX*:ULTRIX*:*:*) echo vax-dec-ultrix${UNAME_RELEASE} exit ;; 2020:CLIX:*:* | 2430:CLIX:*:*) echo clipper-intergraph-clix${UNAME_RELEASE} exit ;; mips:*:*:UMIPS | mips:*:*:RISCos) eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #ifdef __cplusplus #include /* for printf() prototype */ int main (int argc, char *argv[]) { #else int main (argc, argv) int argc; char *argv[]; { #endif #if defined (host_mips) && defined (MIPSEB) #if defined (SYSTYPE_SYSV) printf ("mips-mips-riscos%ssysv\n", argv[1]); exit (0); #endif #if defined (SYSTYPE_SVR4) printf ("mips-mips-riscos%ssvr4\n", argv[1]); exit (0); #endif #if defined (SYSTYPE_BSD43) || defined(SYSTYPE_BSD) printf ("mips-mips-riscos%sbsd\n", argv[1]); exit (0); #endif #endif exit (-1); } EOF $CC_FOR_BUILD -o $dummy $dummy.c && dummyarg=`echo "${UNAME_RELEASE}" | sed -n 's/\([0-9]*\).*/\1/p'` && SYSTEM_NAME=`$dummy $dummyarg` && { echo "$SYSTEM_NAME"; exit; } echo mips-mips-riscos${UNAME_RELEASE} exit ;; Motorola:PowerMAX_OS:*:*) echo powerpc-motorola-powermax exit ;; Motorola:*:4.3:PL8-*) echo powerpc-harris-powermax exit ;; Night_Hawk:*:*:PowerMAX_OS | Synergy:PowerMAX_OS:*:*) echo powerpc-harris-powermax exit ;; Night_Hawk:Power_UNIX:*:*) echo powerpc-harris-powerunix exit ;; m88k:CX/UX:7*:*) echo m88k-harris-cxux7 exit ;; m88k:*:4*:R4*) echo m88k-motorola-sysv4 exit ;; m88k:*:3*:R3*) echo m88k-motorola-sysv3 exit ;; AViiON:dgux:*:*) # DG/UX returns AViiON for all architectures UNAME_PROCESSOR=`/usr/bin/uname -p` if [ $UNAME_PROCESSOR = mc88100 ] || [ $UNAME_PROCESSOR = mc88110 ] then if [ ${TARGET_BINARY_INTERFACE}x = m88kdguxelfx ] || \ [ ${TARGET_BINARY_INTERFACE}x = x ] then echo m88k-dg-dgux${UNAME_RELEASE} else echo m88k-dg-dguxbcs${UNAME_RELEASE} fi else echo i586-dg-dgux${UNAME_RELEASE} fi exit ;; M88*:DolphinOS:*:*) # DolphinOS (SVR3) echo m88k-dolphin-sysv3 exit ;; M88*:*:R3*:*) # Delta 88k system running SVR3 echo m88k-motorola-sysv3 exit ;; XD88*:*:*:*) # Tektronix XD88 system running UTekV (SVR3) echo m88k-tektronix-sysv3 exit ;; Tek43[0-9][0-9]:UTek:*:*) # Tektronix 4300 system running UTek (BSD) echo m68k-tektronix-bsd exit ;; *:IRIX*:*:*) echo mips-sgi-irix`echo ${UNAME_RELEASE}|sed -e 's/-/_/g'` exit ;; ????????:AIX?:[12].1:2) # AIX 2.2.1 or AIX 2.1.1 is RT/PC AIX. echo romp-ibm-aix # uname -m gives an 8 hex-code CPU id exit ;; # Note that: echo "'`uname -s`'" gives 'AIX ' i*86:AIX:*:*) echo i386-ibm-aix exit ;; ia64:AIX:*:*) if [ -x /usr/bin/oslevel ] ; then IBM_REV=`/usr/bin/oslevel` else IBM_REV=${UNAME_VERSION}.${UNAME_RELEASE} fi echo ${UNAME_MACHINE}-ibm-aix${IBM_REV} exit ;; *:AIX:2:3) if grep bos325 /usr/include/stdio.h >/dev/null 2>&1; then eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #include main() { if (!__power_pc()) exit(1); puts("powerpc-ibm-aix3.2.5"); exit(0); } EOF if $CC_FOR_BUILD -o $dummy $dummy.c && SYSTEM_NAME=`$dummy` then echo "$SYSTEM_NAME" else echo rs6000-ibm-aix3.2.5 fi elif grep bos324 /usr/include/stdio.h >/dev/null 2>&1; then echo rs6000-ibm-aix3.2.4 else echo rs6000-ibm-aix3.2 fi exit ;; *:AIX:*:[456]) IBM_CPU_ID=`/usr/sbin/lsdev -C -c processor -S available | sed 1q | awk '{ print $1 }'` if /usr/sbin/lsattr -El ${IBM_CPU_ID} | grep ' POWER' >/dev/null 2>&1; then IBM_ARCH=rs6000 else IBM_ARCH=powerpc fi if [ -x /usr/bin/oslevel ] ; then IBM_REV=`/usr/bin/oslevel` else IBM_REV=${UNAME_VERSION}.${UNAME_RELEASE} fi echo ${IBM_ARCH}-ibm-aix${IBM_REV} exit ;; *:AIX:*:*) echo rs6000-ibm-aix exit ;; ibmrt:4.4BSD:*|romp-ibm:BSD:*) echo romp-ibm-bsd4.4 exit ;; ibmrt:*BSD:*|romp-ibm:BSD:*) # covers RT/PC BSD and echo romp-ibm-bsd${UNAME_RELEASE} # 4.3 with uname added to exit ;; # report: romp-ibm BSD 4.3 *:BOSX:*:*) echo rs6000-bull-bosx exit ;; DPX/2?00:B.O.S.:*:*) echo m68k-bull-sysv3 exit ;; 9000/[34]??:4.3bsd:1.*:*) echo m68k-hp-bsd exit ;; hp300:4.4BSD:*:* | 9000/[34]??:4.3bsd:2.*:*) echo m68k-hp-bsd4.4 exit ;; 9000/[34678]??:HP-UX:*:*) HPUX_REV=`echo ${UNAME_RELEASE}|sed -e 's/[^.]*.[0B]*//'` case "${UNAME_MACHINE}" in 9000/31? ) HP_ARCH=m68000 ;; 9000/[34]?? ) HP_ARCH=m68k ;; 9000/[678][0-9][0-9]) if [ -x /usr/bin/getconf ]; then sc_cpu_version=`/usr/bin/getconf SC_CPU_VERSION 2>/dev/null` sc_kernel_bits=`/usr/bin/getconf SC_KERNEL_BITS 2>/dev/null` case "${sc_cpu_version}" in 523) HP_ARCH="hppa1.0" ;; # CPU_PA_RISC1_0 528) HP_ARCH="hppa1.1" ;; # CPU_PA_RISC1_1 532) # CPU_PA_RISC2_0 case "${sc_kernel_bits}" in 32) HP_ARCH="hppa2.0n" ;; 64) HP_ARCH="hppa2.0w" ;; '') HP_ARCH="hppa2.0" ;; # HP-UX 10.20 esac ;; esac fi if [ "${HP_ARCH}" = "" ]; then eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #define _HPUX_SOURCE #include #include int main () { #if defined(_SC_KERNEL_BITS) long bits = sysconf(_SC_KERNEL_BITS); #endif long cpu = sysconf (_SC_CPU_VERSION); switch (cpu) { case CPU_PA_RISC1_0: puts ("hppa1.0"); break; case CPU_PA_RISC1_1: puts ("hppa1.1"); break; case CPU_PA_RISC2_0: #if defined(_SC_KERNEL_BITS) switch (bits) { case 64: puts ("hppa2.0w"); break; case 32: puts ("hppa2.0n"); break; default: puts ("hppa2.0"); break; } break; #else /* !defined(_SC_KERNEL_BITS) */ puts ("hppa2.0"); break; #endif default: puts ("hppa1.0"); break; } exit (0); } EOF (CCOPTS= $CC_FOR_BUILD -o $dummy $dummy.c 2>/dev/null) && HP_ARCH=`$dummy` test -z "$HP_ARCH" && HP_ARCH=hppa fi ;; esac if [ ${HP_ARCH} = "hppa2.0w" ] then eval $set_cc_for_build # hppa2.0w-hp-hpux* has a 64-bit kernel and a compiler generating # 32-bit code. hppa64-hp-hpux* has the same kernel and a compiler # generating 64-bit code. GNU and HP use different nomenclature: # # $ CC_FOR_BUILD=cc ./config.guess # => hppa2.0w-hp-hpux11.23 # $ CC_FOR_BUILD="cc +DA2.0w" ./config.guess # => hppa64-hp-hpux11.23 if echo __LP64__ | (CCOPTS= $CC_FOR_BUILD -E - 2>/dev/null) | grep -q __LP64__ then HP_ARCH="hppa2.0w" else HP_ARCH="hppa64" fi fi echo ${HP_ARCH}-hp-hpux${HPUX_REV} exit ;; ia64:HP-UX:*:*) HPUX_REV=`echo ${UNAME_RELEASE}|sed -e 's/[^.]*.[0B]*//'` echo ia64-hp-hpux${HPUX_REV} exit ;; 3050*:HI-UX:*:*) eval $set_cc_for_build sed 's/^ //' << EOF >$dummy.c #include int main () { long cpu = sysconf (_SC_CPU_VERSION); /* The order matters, because CPU_IS_HP_MC68K erroneously returns true for CPU_PA_RISC1_0. CPU_IS_PA_RISC returns correct results, however. */ if (CPU_IS_PA_RISC (cpu)) { switch (cpu) { case CPU_PA_RISC1_0: puts ("hppa1.0-hitachi-hiuxwe2"); break; case CPU_PA_RISC1_1: puts ("hppa1.1-hitachi-hiuxwe2"); break; case CPU_PA_RISC2_0: puts ("hppa2.0-hitachi-hiuxwe2"); break; default: puts ("hppa-hitachi-hiuxwe2"); break; } } else if (CPU_IS_HP_MC68K (cpu)) puts ("m68k-hitachi-hiuxwe2"); else puts ("unknown-hitachi-hiuxwe2"); exit (0); } EOF $CC_FOR_BUILD -o $dummy $dummy.c && SYSTEM_NAME=`$dummy` && { echo "$SYSTEM_NAME"; exit; } echo unknown-hitachi-hiuxwe2 exit ;; 9000/7??:4.3bsd:*:* | 9000/8?[79]:4.3bsd:*:* ) echo hppa1.1-hp-bsd exit ;; 9000/8??:4.3bsd:*:*) echo hppa1.0-hp-bsd exit ;; *9??*:MPE/iX:*:* | *3000*:MPE/iX:*:*) echo hppa1.0-hp-mpeix exit ;; hp7??:OSF1:*:* | hp8?[79]:OSF1:*:* ) echo hppa1.1-hp-osf exit ;; hp8??:OSF1:*:*) echo hppa1.0-hp-osf exit ;; i*86:OSF1:*:*) if [ -x /usr/sbin/sysversion ] ; then echo ${UNAME_MACHINE}-unknown-osf1mk else echo ${UNAME_MACHINE}-unknown-osf1 fi exit ;; parisc*:Lites*:*:*) echo hppa1.1-hp-lites exit ;; C1*:ConvexOS:*:* | convex:ConvexOS:C1*:*) echo c1-convex-bsd exit ;; C2*:ConvexOS:*:* | convex:ConvexOS:C2*:*) if getsysinfo -f scalar_acc then echo c32-convex-bsd else echo c2-convex-bsd fi exit ;; C34*:ConvexOS:*:* | convex:ConvexOS:C34*:*) echo c34-convex-bsd exit ;; C38*:ConvexOS:*:* | convex:ConvexOS:C38*:*) echo c38-convex-bsd exit ;; C4*:ConvexOS:*:* | convex:ConvexOS:C4*:*) echo c4-convex-bsd exit ;; CRAY*Y-MP:*:*:*) echo ymp-cray-unicos${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; CRAY*[A-Z]90:*:*:*) echo ${UNAME_MACHINE}-cray-unicos${UNAME_RELEASE} \ | sed -e 's/CRAY.*\([A-Z]90\)/\1/' \ -e y/ABCDEFGHIJKLMNOPQRSTUVWXYZ/abcdefghijklmnopqrstuvwxyz/ \ -e 's/\.[^.]*$/.X/' exit ;; CRAY*TS:*:*:*) echo t90-cray-unicos${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; CRAY*T3E:*:*:*) echo alphaev5-cray-unicosmk${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; CRAY*SV1:*:*:*) echo sv1-cray-unicos${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; *:UNICOS/mp:*:*) echo craynv-cray-unicosmp${UNAME_RELEASE} | sed -e 's/\.[^.]*$/.X/' exit ;; F30[01]:UNIX_System_V:*:* | F700:UNIX_System_V:*:*) FUJITSU_PROC=`uname -m | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz'` FUJITSU_SYS=`uname -p | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz' | sed -e 's/\///'` FUJITSU_REL=`echo ${UNAME_RELEASE} | sed -e 's/ /_/'` echo "${FUJITSU_PROC}-fujitsu-${FUJITSU_SYS}${FUJITSU_REL}" exit ;; 5000:UNIX_System_V:4.*:*) FUJITSU_SYS=`uname -p | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz' | sed -e 's/\///'` FUJITSU_REL=`echo ${UNAME_RELEASE} | tr 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' 'abcdefghijklmnopqrstuvwxyz' | sed -e 's/ /_/'` echo "sparc-fujitsu-${FUJITSU_SYS}${FUJITSU_REL}" exit ;; i*86:BSD/386:*:* | i*86:BSD/OS:*:* | *:Ascend\ Embedded/OS:*:*) echo ${UNAME_MACHINE}-pc-bsdi${UNAME_RELEASE} exit ;; sparc*:BSD/OS:*:*) echo sparc-unknown-bsdi${UNAME_RELEASE} exit ;; *:BSD/OS:*:*) echo ${UNAME_MACHINE}-unknown-bsdi${UNAME_RELEASE} exit ;; *:FreeBSD:*:*) case ${UNAME_MACHINE} in pc98) echo i386-unknown-freebsd`echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'` ;; amd64) echo x86_64-unknown-freebsd`echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'` ;; *) echo ${UNAME_MACHINE}-unknown-freebsd`echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'` ;; esac exit ;; i*:CYGWIN*:*) echo ${UNAME_MACHINE}-pc-cygwin exit ;; *:MINGW*:*) echo ${UNAME_MACHINE}-pc-mingw32 exit ;; i*:windows32*:*) # uname -m includes "-pc" on this system. echo ${UNAME_MACHINE}-mingw32 exit ;; i*:PW*:*) echo ${UNAME_MACHINE}-pc-pw32 exit ;; *:Interix*:*) case ${UNAME_MACHINE} in x86) echo i586-pc-interix${UNAME_RELEASE} exit ;; authenticamd | genuineintel | EM64T) echo x86_64-unknown-interix${UNAME_RELEASE} exit ;; IA64) echo ia64-unknown-interix${UNAME_RELEASE} exit ;; esac ;; [345]86:Windows_95:* | [345]86:Windows_98:* | [345]86:Windows_NT:*) echo i${UNAME_MACHINE}-pc-mks exit ;; 8664:Windows_NT:*) echo x86_64-pc-mks exit ;; i*:Windows_NT*:* | Pentium*:Windows_NT*:*) # How do we know it's Interix rather than the generic POSIX subsystem? # It also conflicts with pre-2.0 versions of AT&T UWIN. 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then echo i860-stardent-sysv${UNAME_RELEASE} # Stardent Vistra i860-SVR4 else # Add other i860-SVR4 vendors below as they are discovered. echo i860-unknown-sysv${UNAME_RELEASE} # Unknown i860-SVR4 fi exit ;; mini*:CTIX:SYS*5:*) # "miniframe" echo m68010-convergent-sysv exit ;; mc68k:UNIX:SYSTEM5:3.51m) echo m68k-convergent-sysv exit ;; M680?0:D-NIX:5.3:*) echo m68k-diab-dnix exit ;; M68*:*:R3V[5678]*:*) test -r /sysV68 && { echo 'm68k-motorola-sysv'; exit; } ;; 3[345]??:*:4.0:3.0 | 3[34]??A:*:4.0:3.0 | 3[34]??,*:*:4.0:3.0 | 3[34]??/*:*:4.0:3.0 | 4400:*:4.0:3.0 | 4850:*:4.0:3.0 | SKA40:*:4.0:3.0 | SDS2:*:4.0:3.0 | SHG2:*:4.0:3.0 | S7501*:*:4.0:3.0) OS_REL='' test -r /etc/.relid \ && OS_REL=.`sed -n 's/[^ ]* [^ ]* \([0-9][0-9]\).*/\1/p' < /etc/.relid` /bin/uname -p 2>/dev/null | grep 86 >/dev/null \ && { echo i486-ncr-sysv4.3${OS_REL}; exit; } /bin/uname -p 2>/dev/null | /bin/grep entium >/dev/null \ && { echo i586-ncr-sysv4.3${OS_REL}; exit; } ;; 3[34]??:*:4.0:* | 3[34]??,*:*:4.0:*) /bin/uname -p 2>/dev/null | grep 86 >/dev/null \ && { echo i486-ncr-sysv4; 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*:SINIX-*:*:*) if uname -p 2>/dev/null >/dev/null ; then UNAME_MACHINE=`(uname -p) 2>/dev/null` echo ${UNAME_MACHINE}-sni-sysv4 else echo ns32k-sni-sysv fi exit ;; PENTIUM:*:4.0*:*) # Unisys `ClearPath HMP IX 4000' SVR4/MP effort # says echo i586-unisys-sysv4 exit ;; *:UNIX_System_V:4*:FTX*) # From Gerald Hewes . # How about differentiating between stratus architectures? -djm echo hppa1.1-stratus-sysv4 exit ;; *:*:*:FTX*) # From seanf@swdc.stratus.com. echo i860-stratus-sysv4 exit ;; i*86:VOS:*:*) # From Paul.Green@stratus.com. echo ${UNAME_MACHINE}-stratus-vos exit ;; *:VOS:*:*) # From Paul.Green@stratus.com. echo hppa1.1-stratus-vos exit ;; mc68*:A/UX:*:*) echo m68k-apple-aux${UNAME_RELEASE} exit ;; news*:NEWS-OS:6*:*) echo mips-sony-newsos6 exit ;; R[34]000:*System_V*:*:* | R4000:UNIX_SYSV:*:* | R*000:UNIX_SV:*:*) if [ -d /usr/nec ]; then echo mips-nec-sysv${UNAME_RELEASE} else echo mips-unknown-sysv${UNAME_RELEASE} fi exit ;; BeBox:BeOS:*:*) # BeOS running on hardware made by Be, PPC only. echo powerpc-be-beos exit ;; BeMac:BeOS:*:*) # BeOS running on Mac or Mac clone, PPC only. echo powerpc-apple-beos exit ;; BePC:BeOS:*:*) # BeOS running on Intel PC compatible. echo i586-pc-beos exit ;; BePC:Haiku:*:*) # Haiku running on Intel PC compatible. echo i586-pc-haiku exit ;; SX-4:SUPER-UX:*:*) echo sx4-nec-superux${UNAME_RELEASE} exit ;; SX-5:SUPER-UX:*:*) echo sx5-nec-superux${UNAME_RELEASE} exit ;; SX-6:SUPER-UX:*:*) echo sx6-nec-superux${UNAME_RELEASE} exit ;; SX-7:SUPER-UX:*:*) echo sx7-nec-superux${UNAME_RELEASE} exit ;; SX-8:SUPER-UX:*:*) echo sx8-nec-superux${UNAME_RELEASE} exit ;; SX-8R:SUPER-UX:*:*) echo sx8r-nec-superux${UNAME_RELEASE} exit ;; Power*:Rhapsody:*:*) echo powerpc-apple-rhapsody${UNAME_RELEASE} exit ;; *:Rhapsody:*:*) echo ${UNAME_MACHINE}-apple-rhapsody${UNAME_RELEASE} exit ;; *:Darwin:*:*) UNAME_PROCESSOR=`uname -p` || UNAME_PROCESSOR=unknown case $UNAME_PROCESSOR in i386) eval $set_cc_for_build if [ "$CC_FOR_BUILD" != 'no_compiler_found' ]; then if (echo '#ifdef __LP64__'; echo IS_64BIT_ARCH; echo '#endif') | \ (CCOPTS= $CC_FOR_BUILD -E - 2>/dev/null) | \ grep IS_64BIT_ARCH >/dev/null then UNAME_PROCESSOR="x86_64" fi fi ;; unknown) UNAME_PROCESSOR=powerpc ;; esac echo ${UNAME_PROCESSOR}-apple-darwin${UNAME_RELEASE} exit ;; *:procnto*:*:* | *:QNX:[0123456789]*:*) UNAME_PROCESSOR=`uname -p` if test "$UNAME_PROCESSOR" = "x86"; then UNAME_PROCESSOR=i386 UNAME_MACHINE=pc fi echo ${UNAME_PROCESSOR}-${UNAME_MACHINE}-nto-qnx${UNAME_RELEASE} exit ;; *:QNX:*:4*) echo i386-pc-qnx exit ;; NSE-?:NONSTOP_KERNEL:*:*) echo nse-tandem-nsk${UNAME_RELEASE} exit ;; NSR-?:NONSTOP_KERNEL:*:*) echo nsr-tandem-nsk${UNAME_RELEASE} exit ;; *:NonStop-UX:*:*) echo mips-compaq-nonstopux exit ;; BS2000:POSIX*:*:*) echo bs2000-siemens-sysv exit ;; DS/*:UNIX_System_V:*:*) echo ${UNAME_MACHINE}-${UNAME_SYSTEM}-${UNAME_RELEASE} exit ;; *:Plan9:*:*) # "uname -m" is not consistent, so use $cputype instead. 386 # is converted to i386 for consistency with other x86 # operating systems. if test "$cputype" = "386"; then UNAME_MACHINE=i386 else UNAME_MACHINE="$cputype" fi echo ${UNAME_MACHINE}-unknown-plan9 exit ;; *:TOPS-10:*:*) echo pdp10-unknown-tops10 exit ;; *:TENEX:*:*) echo pdp10-unknown-tenex exit ;; KS10:TOPS-20:*:* | KL10:TOPS-20:*:* | TYPE4:TOPS-20:*:*) echo pdp10-dec-tops20 exit ;; XKL-1:TOPS-20:*:* | TYPE5:TOPS-20:*:*) echo pdp10-xkl-tops20 exit ;; *:TOPS-20:*:*) echo pdp10-unknown-tops20 exit ;; *:ITS:*:*) echo pdp10-unknown-its exit ;; SEI:*:*:SEIUX) echo mips-sei-seiux${UNAME_RELEASE} exit ;; *:DragonFly:*:*) echo ${UNAME_MACHINE}-unknown-dragonfly`echo ${UNAME_RELEASE}|sed -e 's/[-(].*//'` exit ;; *:*VMS:*:*) UNAME_MACHINE=`(uname -p) 2>/dev/null` case "${UNAME_MACHINE}" in A*) echo alpha-dec-vms ; exit ;; I*) echo ia64-dec-vms ; exit ;; V*) echo vax-dec-vms ; exit ;; esac ;; *:XENIX:*:SysV) echo i386-pc-xenix exit ;; i*86:skyos:*:*) echo ${UNAME_MACHINE}-pc-skyos`echo ${UNAME_RELEASE}` | sed -e 's/ .*$//' exit ;; i*86:rdos:*:*) echo ${UNAME_MACHINE}-pc-rdos exit ;; i*86:AROS:*:*) echo ${UNAME_MACHINE}-pc-aros exit ;; esac #echo '(No uname command or uname output not recognized.)' 1>&2 #echo "${UNAME_MACHINE}:${UNAME_SYSTEM}:${UNAME_RELEASE}:${UNAME_VERSION}" 1>&2 eval $set_cc_for_build cat >$dummy.c < # include #endif main () { #if defined (sony) #if defined (MIPSEB) /* BFD wants "bsd" instead of "newsos". Perhaps BFD should be changed, I don't know.... */ printf ("mips-sony-bsd\n"); exit (0); #else #include printf ("m68k-sony-newsos%s\n", #ifdef NEWSOS4 "4" #else "" #endif ); exit (0); #endif #endif #if defined (__arm) && defined (__acorn) && defined (__unix) printf ("arm-acorn-riscix\n"); exit (0); #endif #if defined (hp300) && !defined (hpux) printf ("m68k-hp-bsd\n"); exit (0); #endif #if defined (NeXT) #if !defined (__ARCHITECTURE__) #define __ARCHITECTURE__ "m68k" #endif int version; version=`(hostinfo | sed -n 's/.*NeXT Mach \([0-9]*\).*/\1/p') 2>/dev/null`; if (version < 4) printf ("%s-next-nextstep%d\n", __ARCHITECTURE__, version); else printf ("%s-next-openstep%d\n", __ARCHITECTURE__, version); exit (0); #endif #if defined (MULTIMAX) || defined (n16) #if defined (UMAXV) printf ("ns32k-encore-sysv\n"); exit (0); #else #if defined (CMU) printf ("ns32k-encore-mach\n"); exit (0); #else printf ("ns32k-encore-bsd\n"); exit (0); #endif #endif #endif #if defined (__386BSD__) printf ("i386-pc-bsd\n"); exit (0); #endif #if defined (sequent) #if defined (i386) printf ("i386-sequent-dynix\n"); exit (0); #endif #if defined (ns32000) printf ("ns32k-sequent-dynix\n"); exit (0); #endif #endif #if defined (_SEQUENT_) struct utsname un; uname(&un); if (strncmp(un.version, "V2", 2) == 0) { printf ("i386-sequent-ptx2\n"); exit (0); } if (strncmp(un.version, "V1", 2) == 0) { /* XXX is V1 correct? */ printf ("i386-sequent-ptx1\n"); exit (0); } printf ("i386-sequent-ptx\n"); exit (0); #endif #if defined (vax) # if !defined (ultrix) # include # if defined (BSD) # if BSD == 43 printf ("vax-dec-bsd4.3\n"); exit (0); # else # if BSD == 199006 printf ("vax-dec-bsd4.3reno\n"); exit (0); # else printf ("vax-dec-bsd\n"); exit (0); # endif # endif # else printf ("vax-dec-bsd\n"); exit (0); # endif # else printf ("vax-dec-ultrix\n"); exit (0); # endif #endif #if defined (alliant) && defined (i860) printf ("i860-alliant-bsd\n"); exit (0); #endif exit (1); } EOF $CC_FOR_BUILD -o $dummy $dummy.c 2>/dev/null && SYSTEM_NAME=`$dummy` && { echo "$SYSTEM_NAME"; exit; } # Apollos put the system type in the environment. test -d /usr/apollo && { echo ${ISP}-apollo-${SYSTYPE}; exit; } # Convex versions that predate uname can use getsysinfo(1) if [ -x /usr/convex/getsysinfo ] then case `getsysinfo -f cpu_type` in c1*) echo c1-convex-bsd exit ;; c2*) if getsysinfo -f scalar_acc then echo c32-convex-bsd else echo c2-convex-bsd fi exit ;; c34*) echo c34-convex-bsd exit ;; c38*) echo c38-convex-bsd exit ;; c4*) echo c4-convex-bsd exit ;; esac fi cat >&2 < in order to provide the needed information to handle your system. config.guess timestamp = $timestamp uname -m = `(uname -m) 2>/dev/null || echo unknown` uname -r = `(uname -r) 2>/dev/null || echo unknown` uname -s = `(uname -s) 2>/dev/null || echo unknown` uname -v = `(uname -v) 2>/dev/null || echo unknown` /usr/bin/uname -p = `(/usr/bin/uname -p) 2>/dev/null` /bin/uname -X = `(/bin/uname -X) 2>/dev/null` hostinfo = `(hostinfo) 2>/dev/null` /bin/universe = `(/bin/universe) 2>/dev/null` /usr/bin/arch -k = `(/usr/bin/arch -k) 2>/dev/null` /bin/arch = `(/bin/arch) 2>/dev/null` /usr/bin/oslevel = `(/usr/bin/oslevel) 2>/dev/null` /usr/convex/getsysinfo = `(/usr/convex/getsysinfo) 2>/dev/null` UNAME_MACHINE = ${UNAME_MACHINE} UNAME_RELEASE = ${UNAME_RELEASE} UNAME_SYSTEM = ${UNAME_SYSTEM} UNAME_VERSION = ${UNAME_VERSION} EOF exit 1 # Local variables: # eval: (add-hook 'write-file-hooks 'time-stamp) # time-stamp-start: "timestamp='" # time-stamp-format: "%:y-%02m-%02d" # time-stamp-end: "'" # End: ctbnrle.1.1.1/glpk/glpk-4.47/README0000644000175000017500000000301311632352620016525 0ustar csheltoncshelton Olga K. gewidmet GLPK (GNU Linear Programming Kit) Version 4.47 Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics, Moscow Aviation Institute, Moscow, Russia. All rights reserved. E-mail: . GLPK is part of the GNU Project released under the aegis of GNU. GLPK is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. See the file COPYING for the GNU General Public License. See the file INSTALL for compilation and installation instructions. The GLPK package is a set of routines written in ANSI C and organized in the form of a callable library. This package is intended for solving large-scale linear programming (LP), mixed integer linear programming (MIP), and other related problems. The GLPK package includes the following main components: * implementation of the simplex method; * implementation of the exact simplex method based on bignum (rational) arithmetic; * implementation of the primal-dual interior-point method; * implementation of the branch-and-cut method; * application program interface (API); * GNU MathProg modeling language (a subset of AMPL); * GLPSOL, a stand-alone LP/MIP solver. See GLPK webpage . Please report bugs to . ctbnrle.1.1.1/glpk/glpk-4.47/config.sub0000755000175000017500000010316711632352620017643 0ustar csheltoncshelton#! /bin/sh # Configuration validation subroutine script. # Copyright (C) 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, # 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009 # Free Software Foundation, Inc. timestamp='2009-11-20' # This file is (in principle) common to ALL GNU software. # The presence of a machine in this file suggests that SOME GNU software # can handle that machine. It does not imply ALL GNU software can. # # This file is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street - Fifth Floor, Boston, MA # 02110-1301, USA. # # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that program. # Please send patches to . Submit a context # diff and a properly formatted GNU ChangeLog entry. # # Configuration subroutine to validate and canonicalize a configuration type. # Supply the specified configuration type as an argument. # If it is invalid, we print an error message on stderr and exit with code 1. # Otherwise, we print the canonical config type on stdout and succeed. # You can get the latest version of this script from: # http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD # This file is supposed to be the same for all GNU packages # and recognize all the CPU types, system types and aliases # that are meaningful with *any* GNU software. # Each package is responsible for reporting which valid configurations # it does not support. The user should be able to distinguish # a failure to support a valid configuration from a meaningless # configuration. # The goal of this file is to map all the various variations of a given # machine specification into a single specification in the form: # CPU_TYPE-MANUFACTURER-OPERATING_SYSTEM # or in some cases, the newer four-part form: # CPU_TYPE-MANUFACTURER-KERNEL-OPERATING_SYSTEM # It is wrong to echo any other type of specification. me=`echo "$0" | sed -e 's,.*/,,'` usage="\ Usage: $0 [OPTION] CPU-MFR-OPSYS $0 [OPTION] ALIAS Canonicalize a configuration name. Operation modes: -h, --help print this help, then exit -t, --time-stamp print date of last modification, then exit -v, --version print version number, then exit Report bugs and patches to ." version="\ GNU config.sub ($timestamp) Copyright (C) 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE." help=" Try \`$me --help' for more information." # Parse command line while test $# -gt 0 ; do case $1 in --time-stamp | --time* | -t ) echo "$timestamp" ; exit ;; --version | -v ) echo "$version" ; exit ;; --help | --h* | -h ) echo "$usage"; exit ;; -- ) # Stop option processing shift; break ;; - ) # Use stdin as input. break ;; -* ) echo "$me: invalid option $1$help" exit 1 ;; *local*) # First pass through any local machine types. echo $1 exit ;; * ) break ;; esac done case $# in 0) echo "$me: missing argument$help" >&2 exit 1;; 1) ;; *) echo "$me: too many arguments$help" >&2 exit 1;; esac # Separate what the user gave into CPU-COMPANY and OS or KERNEL-OS (if any). # Here we must recognize all the valid KERNEL-OS combinations. maybe_os=`echo $1 | sed 's/^\(.*\)-\([^-]*-[^-]*\)$/\2/'` case $maybe_os in nto-qnx* | linux-gnu* | linux-dietlibc | linux-newlib* | linux-uclibc* | \ uclinux-uclibc* | uclinux-gnu* | kfreebsd*-gnu* | knetbsd*-gnu* | netbsd*-gnu* | \ kopensolaris*-gnu* | \ storm-chaos* | os2-emx* | rtmk-nova*) os=-$maybe_os basic_machine=`echo $1 | sed 's/^\(.*\)-\([^-]*-[^-]*\)$/\1/'` ;; *) basic_machine=`echo $1 | sed 's/-[^-]*$//'` if [ $basic_machine != $1 ] then os=`echo $1 | sed 's/.*-/-/'` else os=; fi ;; esac ### Let's recognize common machines as not being operating systems so ### that things like config.sub decstation-3100 work. We also ### recognize some manufacturers as not being operating systems, so we ### can provide default operating systems below. case $os in -sun*os*) # Prevent following clause from handling this invalid input. ;; -dec* | -mips* | -sequent* | -encore* | -pc532* | -sgi* | -sony* | \ -att* | -7300* | -3300* | -delta* | -motorola* | -sun[234]* | \ -unicom* | -ibm* | -next | -hp | -isi* | -apollo | -altos* | \ -convergent* | -ncr* | -news | -32* | -3600* | -3100* | -hitachi* |\ -c[123]* | -convex* | -sun | -crds | -omron* | -dg | -ultra | -tti* | \ -harris | -dolphin | -highlevel | -gould | -cbm | -ns | -masscomp | \ -apple | -axis | -knuth | -cray | -microblaze) os= basic_machine=$1 ;; -bluegene*) os=-cnk ;; -sim | -cisco | -oki | -wec | -winbond) os= basic_machine=$1 ;; -scout) ;; -wrs) os=-vxworks basic_machine=$1 ;; -chorusos*) os=-chorusos basic_machine=$1 ;; -chorusrdb) os=-chorusrdb basic_machine=$1 ;; -hiux*) os=-hiuxwe2 ;; -sco6) os=-sco5v6 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco5) os=-sco3.2v5 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco4) os=-sco3.2v4 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco3.2.[4-9]*) os=`echo $os | sed -e 's/sco3.2./sco3.2v/'` basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco3.2v[4-9]*) # Don't forget version if it is 3.2v4 or newer. basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco5v6*) # Don't forget version if it is 3.2v4 or newer. basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -sco*) os=-sco3.2v2 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -udk*) basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -isc) os=-isc2.2 basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -clix*) basic_machine=clipper-intergraph ;; -isc*) basic_machine=`echo $1 | sed -e 's/86-.*/86-pc/'` ;; -lynx*) os=-lynxos ;; -ptx*) basic_machine=`echo $1 | sed -e 's/86-.*/86-sequent/'` ;; -windowsnt*) os=`echo $os | sed -e 's/windowsnt/winnt/'` ;; -psos*) os=-psos ;; -mint | -mint[0-9]*) basic_machine=m68k-atari os=-mint ;; esac # Decode aliases for certain CPU-COMPANY combinations. case $basic_machine in # Recognize the basic CPU types without company name. # Some are omitted here because they have special meanings below. 1750a | 580 \ | a29k \ | alpha | alphaev[4-8] | alphaev56 | alphaev6[78] | alphapca5[67] \ | alpha64 | alpha64ev[4-8] | alpha64ev56 | alpha64ev6[78] | alpha64pca5[67] \ | am33_2.0 \ | arc | arm | arm[bl]e | arme[lb] | armv[2345] | armv[345][lb] | avr | avr32 \ | bfin \ | c4x | clipper \ | d10v | d30v | dlx | dsp16xx \ | fido | fr30 | frv \ | h8300 | h8500 | hppa | hppa1.[01] | hppa2.0 | hppa2.0[nw] | hppa64 \ | i370 | i860 | i960 | ia64 \ | ip2k | iq2000 \ | lm32 \ | m32c | m32r | m32rle | m68000 | m68k | m88k \ | maxq | mb | microblaze | mcore | mep | metag \ | mips | mipsbe | mipseb | mipsel | mipsle \ | mips16 \ | mips64 | mips64el \ | mips64octeon | mips64octeonel \ | mips64orion | mips64orionel \ | mips64r5900 | mips64r5900el \ | mips64vr | mips64vrel \ | mips64vr4100 | mips64vr4100el \ | mips64vr4300 | mips64vr4300el \ | mips64vr5000 | mips64vr5000el \ | mips64vr5900 | mips64vr5900el \ | mipsisa32 | mipsisa32el \ | mipsisa32r2 | mipsisa32r2el \ | mipsisa64 | mipsisa64el \ | mipsisa64r2 | mipsisa64r2el \ | mipsisa64sb1 | mipsisa64sb1el \ | mipsisa64sr71k | mipsisa64sr71kel \ | mipstx39 | mipstx39el \ | mn10200 | mn10300 \ | moxie \ | mt \ | msp430 \ | nios | nios2 \ | ns16k | ns32k \ | or32 \ | pdp10 | pdp11 | pj | pjl \ | powerpc | powerpc64 | powerpc64le | powerpcle | ppcbe \ | pyramid \ | rx \ | score \ | sh | sh[1234] | sh[24]a | sh[24]aeb | sh[23]e | sh[34]eb | sheb | shbe | shle | sh[1234]le | sh3ele \ | sh64 | sh64le \ | sparc | sparc64 | sparc64b | sparc64v | sparc86x | sparclet | sparclite \ | sparcv8 | sparcv9 | sparcv9b | sparcv9v \ | spu | strongarm \ | tahoe | thumb | tic4x | tic80 | tron \ | ubicom32 \ | v850 | v850e \ | we32k \ | x86 | xc16x | xscale | xscalee[bl] | xstormy16 | xtensa \ | z8k | z80) basic_machine=$basic_machine-unknown ;; m6811 | m68hc11 | m6812 | m68hc12 | picochip) # Motorola 68HC11/12. basic_machine=$basic_machine-unknown os=-none ;; m88110 | m680[12346]0 | m683?2 | m68360 | m5200 | v70 | w65 | z8k) ;; ms1) basic_machine=mt-unknown ;; # We use `pc' rather than `unknown' # because (1) that's what they normally are, and # (2) the word "unknown" tends to confuse beginning users. i*86 | x86_64) basic_machine=$basic_machine-pc ;; # Object if more than one company name word. *-*-*) echo Invalid configuration \`$1\': machine \`$basic_machine\' not recognized 1>&2 exit 1 ;; # Recognize the basic CPU types with company name. 580-* \ | a29k-* \ | alpha-* | alphaev[4-8]-* | alphaev56-* | alphaev6[78]-* \ | alpha64-* | alpha64ev[4-8]-* | alpha64ev56-* | alpha64ev6[78]-* \ | alphapca5[67]-* | alpha64pca5[67]-* | arc-* \ | arm-* | armbe-* | armle-* | armeb-* | armv*-* \ | avr-* | avr32-* \ | bfin-* | bs2000-* \ | c[123]* | c30-* | [cjt]90-* | c4x-* | c54x-* | c55x-* | c6x-* \ | clipper-* | craynv-* | cydra-* \ | d10v-* | d30v-* | dlx-* \ | elxsi-* \ | f30[01]-* | f700-* | fido-* | fr30-* | frv-* | fx80-* \ | h8300-* | h8500-* \ | hppa-* | hppa1.[01]-* | hppa2.0-* | hppa2.0[nw]-* | hppa64-* \ | i*86-* | i860-* | i960-* | ia64-* \ | ip2k-* | iq2000-* \ | lm32-* \ | m32c-* | m32r-* | m32rle-* \ | m68000-* | m680[012346]0-* | m68360-* | m683?2-* | m68k-* \ | m88110-* | m88k-* | maxq-* | mcore-* | metag-* | microblaze-* \ | mips-* | mipsbe-* | mipseb-* | mipsel-* | mipsle-* \ | mips16-* \ | mips64-* | mips64el-* \ | mips64octeon-* | mips64octeonel-* \ | mips64orion-* | mips64orionel-* \ | mips64r5900-* | mips64r5900el-* \ | mips64vr-* | mips64vrel-* \ | mips64vr4100-* | mips64vr4100el-* \ | mips64vr4300-* | mips64vr4300el-* \ | mips64vr5000-* | mips64vr5000el-* \ | mips64vr5900-* | mips64vr5900el-* \ | mipsisa32-* | mipsisa32el-* \ | mipsisa32r2-* | mipsisa32r2el-* \ | mipsisa64-* | mipsisa64el-* \ | mipsisa64r2-* | mipsisa64r2el-* \ | mipsisa64sb1-* | mipsisa64sb1el-* \ | mipsisa64sr71k-* | mipsisa64sr71kel-* \ | mipstx39-* | mipstx39el-* \ | mmix-* \ | mt-* \ | msp430-* \ | nios-* | nios2-* \ | none-* | np1-* | ns16k-* | ns32k-* \ | orion-* \ | pdp10-* | pdp11-* | pj-* | pjl-* | pn-* | power-* \ | powerpc-* | powerpc64-* | powerpc64le-* | powerpcle-* | ppcbe-* \ | pyramid-* \ | romp-* | rs6000-* | rx-* \ | sh-* | sh[1234]-* | sh[24]a-* | sh[24]aeb-* | sh[23]e-* | sh[34]eb-* | sheb-* | shbe-* \ | shle-* | sh[1234]le-* | sh3ele-* | sh64-* | sh64le-* \ | sparc-* | sparc64-* | sparc64b-* | sparc64v-* | sparc86x-* | sparclet-* \ | sparclite-* \ | sparcv8-* | sparcv9-* | sparcv9b-* | sparcv9v-* | strongarm-* | sv1-* | sx?-* \ | tahoe-* | thumb-* \ | tic30-* | tic4x-* | tic54x-* | tic55x-* | tic6x-* | tic80-* | tile-* \ | tron-* \ | ubicom32-* \ | v850-* | v850e-* | vax-* \ | we32k-* \ | x86-* | x86_64-* | xc16x-* | xps100-* | xscale-* | xscalee[bl]-* \ | xstormy16-* | xtensa*-* \ | ymp-* \ | z8k-* | z80-*) ;; # Recognize the basic CPU types without company name, with glob match. xtensa*) basic_machine=$basic_machine-unknown ;; # Recognize the various machine names and aliases which stand # for a CPU type and a company and sometimes even an OS. 386bsd) basic_machine=i386-unknown os=-bsd ;; 3b1 | 7300 | 7300-att | att-7300 | pc7300 | safari | unixpc) basic_machine=m68000-att ;; 3b*) basic_machine=we32k-att ;; a29khif) basic_machine=a29k-amd os=-udi ;; abacus) basic_machine=abacus-unknown ;; adobe68k) basic_machine=m68010-adobe os=-scout ;; alliant | fx80) basic_machine=fx80-alliant ;; altos | altos3068) basic_machine=m68k-altos ;; am29k) basic_machine=a29k-none os=-bsd ;; amd64) basic_machine=x86_64-pc ;; amd64-*) basic_machine=x86_64-`echo $basic_machine | sed 's/^[^-]*-//'` ;; amdahl) basic_machine=580-amdahl os=-sysv ;; amiga | amiga-*) basic_machine=m68k-unknown ;; amigaos | amigados) basic_machine=m68k-unknown os=-amigaos ;; amigaunix | amix) basic_machine=m68k-unknown os=-sysv4 ;; apollo68) basic_machine=m68k-apollo os=-sysv ;; apollo68bsd) basic_machine=m68k-apollo os=-bsd ;; aros) basic_machine=i386-pc os=-aros ;; aux) basic_machine=m68k-apple os=-aux ;; balance) basic_machine=ns32k-sequent os=-dynix ;; blackfin) basic_machine=bfin-unknown os=-linux ;; blackfin-*) basic_machine=bfin-`echo $basic_machine | sed 's/^[^-]*-//'` os=-linux ;; bluegene*) basic_machine=powerpc-ibm os=-cnk ;; c90) basic_machine=c90-cray os=-unicos ;; cegcc) basic_machine=arm-unknown os=-cegcc ;; convex-c1) basic_machine=c1-convex os=-bsd ;; convex-c2) basic_machine=c2-convex os=-bsd ;; convex-c32) basic_machine=c32-convex os=-bsd ;; convex-c34) basic_machine=c34-convex os=-bsd ;; convex-c38) basic_machine=c38-convex os=-bsd ;; cray | j90) basic_machine=j90-cray os=-unicos ;; craynv) basic_machine=craynv-cray os=-unicosmp ;; cr16) basic_machine=cr16-unknown os=-elf ;; crds | unos) basic_machine=m68k-crds ;; crisv32 | crisv32-* | etraxfs*) basic_machine=crisv32-axis ;; cris | cris-* | etrax*) basic_machine=cris-axis ;; crx) basic_machine=crx-unknown os=-elf ;; da30 | da30-*) basic_machine=m68k-da30 ;; decstation | decstation-3100 | pmax | pmax-* | pmin | dec3100 | decstatn) basic_machine=mips-dec ;; decsystem10* | dec10*) basic_machine=pdp10-dec os=-tops10 ;; decsystem20* | dec20*) basic_machine=pdp10-dec os=-tops20 ;; delta | 3300 | motorola-3300 | motorola-delta \ | 3300-motorola | delta-motorola) basic_machine=m68k-motorola ;; delta88) basic_machine=m88k-motorola os=-sysv3 ;; dicos) basic_machine=i686-pc os=-dicos ;; djgpp) basic_machine=i586-pc os=-msdosdjgpp ;; dpx20 | dpx20-*) basic_machine=rs6000-bull os=-bosx ;; dpx2* | dpx2*-bull) basic_machine=m68k-bull os=-sysv3 ;; ebmon29k) basic_machine=a29k-amd os=-ebmon ;; elxsi) basic_machine=elxsi-elxsi os=-bsd ;; encore | umax | mmax) basic_machine=ns32k-encore ;; es1800 | OSE68k | ose68k | ose | OSE) basic_machine=m68k-ericsson os=-ose ;; fx2800) basic_machine=i860-alliant ;; genix) basic_machine=ns32k-ns ;; gmicro) basic_machine=tron-gmicro os=-sysv ;; go32) basic_machine=i386-pc os=-go32 ;; h3050r* | hiux*) basic_machine=hppa1.1-hitachi os=-hiuxwe2 ;; h8300hms) basic_machine=h8300-hitachi os=-hms ;; h8300xray) basic_machine=h8300-hitachi os=-xray ;; h8500hms) basic_machine=h8500-hitachi os=-hms ;; harris) basic_machine=m88k-harris os=-sysv3 ;; hp300-*) basic_machine=m68k-hp ;; hp300bsd) basic_machine=m68k-hp os=-bsd ;; hp300hpux) basic_machine=m68k-hp os=-hpux ;; hp3k9[0-9][0-9] | hp9[0-9][0-9]) basic_machine=hppa1.0-hp ;; hp9k2[0-9][0-9] | hp9k31[0-9]) basic_machine=m68000-hp ;; hp9k3[2-9][0-9]) basic_machine=m68k-hp ;; hp9k6[0-9][0-9] | hp6[0-9][0-9]) basic_machine=hppa1.0-hp ;; hp9k7[0-79][0-9] | hp7[0-79][0-9]) basic_machine=hppa1.1-hp ;; hp9k78[0-9] | hp78[0-9]) # FIXME: really hppa2.0-hp basic_machine=hppa1.1-hp ;; hp9k8[67]1 | hp8[67]1 | hp9k80[24] | hp80[24] | hp9k8[78]9 | hp8[78]9 | hp9k893 | hp893) # FIXME: really hppa2.0-hp basic_machine=hppa1.1-hp ;; hp9k8[0-9][13679] | hp8[0-9][13679]) basic_machine=hppa1.1-hp ;; hp9k8[0-9][0-9] | hp8[0-9][0-9]) basic_machine=hppa1.0-hp ;; hppa-next) os=-nextstep3 ;; hppaosf) basic_machine=hppa1.1-hp os=-osf ;; hppro) basic_machine=hppa1.1-hp os=-proelf ;; i370-ibm* | ibm*) basic_machine=i370-ibm ;; # I'm not sure what "Sysv32" means. Should this be sysv3.2? i*86v32) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-sysv32 ;; i*86v4*) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-sysv4 ;; i*86v) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-sysv ;; i*86sol2) basic_machine=`echo $1 | sed -e 's/86.*/86-pc/'` os=-solaris2 ;; i386mach) basic_machine=i386-mach os=-mach ;; i386-vsta | vsta) basic_machine=i386-unknown os=-vsta ;; iris | iris4d) basic_machine=mips-sgi case $os in -irix*) ;; *) os=-irix4 ;; esac ;; isi68 | isi) basic_machine=m68k-isi os=-sysv ;; m68knommu) basic_machine=m68k-unknown os=-linux ;; m68knommu-*) basic_machine=m68k-`echo $basic_machine | sed 's/^[^-]*-//'` os=-linux ;; m88k-omron*) basic_machine=m88k-omron ;; magnum | m3230) basic_machine=mips-mips os=-sysv ;; merlin) basic_machine=ns32k-utek os=-sysv ;; microblaze) basic_machine=microblaze-xilinx ;; mingw32) basic_machine=i386-pc os=-mingw32 ;; mingw32ce) basic_machine=arm-unknown os=-mingw32ce ;; miniframe) basic_machine=m68000-convergent ;; *mint | -mint[0-9]* | *MiNT | *MiNT[0-9]*) basic_machine=m68k-atari os=-mint ;; mips3*-*) basic_machine=`echo $basic_machine | sed -e 's/mips3/mips64/'` ;; mips3*) basic_machine=`echo $basic_machine | sed -e 's/mips3/mips64/'`-unknown ;; monitor) basic_machine=m68k-rom68k os=-coff ;; morphos) basic_machine=powerpc-unknown os=-morphos ;; msdos) basic_machine=i386-pc os=-msdos ;; ms1-*) basic_machine=`echo $basic_machine | sed -e 's/ms1-/mt-/'` ;; mvs) basic_machine=i370-ibm os=-mvs ;; ncr3000) basic_machine=i486-ncr os=-sysv4 ;; netbsd386) basic_machine=i386-unknown os=-netbsd ;; netwinder) basic_machine=armv4l-rebel os=-linux ;; news | news700 | news800 | news900) basic_machine=m68k-sony os=-newsos ;; news1000) basic_machine=m68030-sony os=-newsos ;; news-3600 | risc-news) basic_machine=mips-sony os=-newsos ;; necv70) basic_machine=v70-nec os=-sysv ;; next | m*-next ) basic_machine=m68k-next case $os in -nextstep* ) ;; -ns2*) os=-nextstep2 ;; *) os=-nextstep3 ;; esac ;; nh3000) basic_machine=m68k-harris os=-cxux ;; nh[45]000) basic_machine=m88k-harris os=-cxux ;; nindy960) basic_machine=i960-intel os=-nindy ;; mon960) basic_machine=i960-intel os=-mon960 ;; nonstopux) basic_machine=mips-compaq os=-nonstopux ;; np1) basic_machine=np1-gould ;; nsr-tandem) basic_machine=nsr-tandem ;; op50n-* | op60c-*) basic_machine=hppa1.1-oki os=-proelf ;; openrisc | openrisc-*) basic_machine=or32-unknown ;; os400) basic_machine=powerpc-ibm os=-os400 ;; OSE68000 | ose68000) basic_machine=m68000-ericsson os=-ose ;; os68k) basic_machine=m68k-none os=-os68k ;; pa-hitachi) basic_machine=hppa1.1-hitachi os=-hiuxwe2 ;; paragon) basic_machine=i860-intel os=-osf ;; parisc) basic_machine=hppa-unknown os=-linux ;; parisc-*) basic_machine=hppa-`echo $basic_machine | sed 's/^[^-]*-//'` os=-linux ;; pbd) basic_machine=sparc-tti ;; pbb) basic_machine=m68k-tti ;; pc532 | pc532-*) basic_machine=ns32k-pc532 ;; pc98) basic_machine=i386-pc ;; pc98-*) basic_machine=i386-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentium | p5 | k5 | k6 | nexgen | viac3) basic_machine=i586-pc ;; pentiumpro | p6 | 6x86 | athlon | athlon_*) basic_machine=i686-pc ;; pentiumii | pentium2 | pentiumiii | pentium3) basic_machine=i686-pc ;; pentium4) basic_machine=i786-pc ;; pentium-* | p5-* | k5-* | k6-* | nexgen-* | viac3-*) basic_machine=i586-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentiumpro-* | p6-* | 6x86-* | athlon-*) basic_machine=i686-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentiumii-* | pentium2-* | pentiumiii-* | pentium3-*) basic_machine=i686-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pentium4-*) basic_machine=i786-`echo $basic_machine | sed 's/^[^-]*-//'` ;; pn) basic_machine=pn-gould ;; power) basic_machine=power-ibm ;; ppc) basic_machine=powerpc-unknown ;; ppc-*) basic_machine=powerpc-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ppcle | powerpclittle | ppc-le | powerpc-little) basic_machine=powerpcle-unknown ;; ppcle-* | powerpclittle-*) basic_machine=powerpcle-`echo $basic_machine | sed 's/^[^-]*-//'` ;; ppc64) basic_machine=powerpc64-unknown ;; ppc64-*) basic_machine=powerpc64-`echo $basic_machine | sed 's/^[^-]*-//'` ;; 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spur) basic_machine=spur-unknown ;; st2000) basic_machine=m68k-tandem ;; stratus) basic_machine=i860-stratus os=-sysv4 ;; sun2) basic_machine=m68000-sun ;; sun2os3) basic_machine=m68000-sun os=-sunos3 ;; sun2os4) basic_machine=m68000-sun os=-sunos4 ;; sun3os3) basic_machine=m68k-sun os=-sunos3 ;; sun3os4) basic_machine=m68k-sun os=-sunos4 ;; sun4os3) basic_machine=sparc-sun os=-sunos3 ;; sun4os4) basic_machine=sparc-sun os=-sunos4 ;; sun4sol2) basic_machine=sparc-sun os=-solaris2 ;; sun3 | sun3-*) basic_machine=m68k-sun ;; sun4) basic_machine=sparc-sun ;; sun386 | sun386i | roadrunner) basic_machine=i386-sun ;; sv1) basic_machine=sv1-cray os=-unicos ;; symmetry) basic_machine=i386-sequent os=-dynix ;; t3e) basic_machine=alphaev5-cray os=-unicos ;; t90) basic_machine=t90-cray os=-unicos ;; tic54x | c54x*) basic_machine=tic54x-unknown os=-coff ;; tic55x | c55x*) basic_machine=tic55x-unknown os=-coff ;; tic6x | c6x*) basic_machine=tic6x-unknown os=-coff ;; tile*) basic_machine=tile-unknown os=-linux-gnu ;; 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