mpi-ikl-simplemkl-1.0/0000755000335100017570000000000011134124734014364 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/simplemkl.m0000644000335100017570000001204111133661710016534 0ustar pgehlerwebadm% SimpleMKL implementation based on Ipopt and Libsvm % % Usage: % [w, alpha, b] = simplemkl(y, Ks, C, mkl_eps, svm_eps) % % Input: % y - vector of labels (n x 1) % Ks - M Gram matrices (n x n x M) % C - Regularizer % % optional Inputs: % mkl_eps - tolerance for MKL solver (default: 1e-5) % svm_eps - tolerance for SVM solver (default: 1e-6) % Note: it should be svm_eps0,'C<=0'); assert(max(y)>0,'no positive label'); assert(min(y)<0,'no negative label'); assert(~any(isnan(Ks(:))),'Gram matrix contains NaNs'); assert(~any(isinf(Ks(:))),'Gram matrix contains Infs'); if ndims(Ks)==2 nGramMatrices = 1; else nGramMatrices = size(Ks,3); end if nGramMatrices < 1 error(['You must provide at least one kernel to mkltrain.']); end % initialize w0 = ones(nGramMatrices,1)/nGramMatrices; % needed for the Ipopt solver global stored_norm stored_w stored_f global objective_counter retrain_counter gradient_counter if nargin < 4 mkl_eps = 1e-5; end if nargin < 5 svm_eps = 1e-6; end % 0 <= w <= Infty lb = zeros(size(w0)); ub = Inf(size(w0)); % sum(w) = 1 lbc = 1; ubc = 1; % sometimes Ipopt dies, restarting with slightly different w's % solves this problem and we do this at max 5 times nof_max_tries = 5; nof_tries = 0; while nof_tries=nof_max_tries % It did not converge! error('IpOpt died!'); end % ... compute the final SVM parameters kernel.type = 'custom'; [alpha,b] = libsvm(y,Kbeta(Ks,w,1),C,kernel,1,'svm_eps',1e-5); fprintf('MKL: calls F:%d,',objective_counter); fprintf('SVM:%d,',retrain_counter); fprintf('dF:%d\n',gradient_counter); % end of function % % OBJECTIVE FUNCTION % function f = compute_objective(w,y,Ks,C,svm_eps) global stored_w stored_f stored_norm global objective_counter retrain_counter objective_counter = objective_counter + 1; if all(stored_w == w) && ~isinf(stored_f) f = stored_f; return; end retrain_counter = retrain_counter + 1; kernel.type = 'custom'; indx = find(w~=0); if ndims(Ks)==3 [al,b] = libsvm(y,Kbeta(Ks(:,:,indx),w(indx),1),C,kernel,1,'svm_eps',svm_eps); else [al,b] = libsvm(y,Kbeta(Ks,w,1),C,kernel,1,'svm_eps',svm_eps); end ind = find(al~=0); al = al(ind); for i=1:numel(w) norm(i,1) = double(0.5 * al'*Ks(ind,ind,i)*al); end stored_norm = norm; stored_w = w; f = double(sum(abs(al)) - sum(w.*norm)); stored_f = f; assert(~isnan(f),'objective is NaN'); % % GRADIENT OF OBJECTIVE FUNCTION % function df = compute_gradient(w,y,Ks,C,svm_eps) global stored_w stored_f stored_norm gradient_counter global retrain_counter; gradient_counter = gradient_counter +1; if all(stored_w == w) df = - stored_norm; return; end retrain_counter = retrain_counter + 1; kernel.type = 'custom'; indx = find(w~=0); if ndims(Ks)==3 [al,b] = libsvm(y,Kbeta(Ks(:,:,indx),w(indx),1),C,kernel,1,'svm_eps',svm_eps); else [al,b] = libsvm(y,Kbeta(Ks,w,1),C,kernel,1,'svm_eps',svm_eps); end ind = find(al~=0); al = al(ind); for i=1:numel(w) norm(i,1) = double(0.5 * al'*Ks(ind,ind,i)*al); end stored_norm = norm; stored_w = w; f = double(sum(abs(al)) - sum(w.*norm)); stored_f = f; df = -norm; assert(~any(isnan(df)),'derivative is NaN'); % % CONSTRAINT sum(w) =1 % function f = compute_constraints(w) f = sum(w); % % JACOBIAN % function J = compute_jacobian(w,return_structure_only); J = sparse(ones(size(w)))'; mpi-ikl-simplemkl-1.0/rbf.m0000644000335100017570000000032511133661710015312 0ustar pgehlerwebadm% K = rbf(X1,X2,sig) % % computes: % K = exp(-||X1-X2||/(2*sig^2)); function K = rbf(X1,X2,sig) if numel(sig)~=1 error('third argument (sigma) must be a scalar'); end K = exp(-dist_euclid(X1,X2)/(2*sig^2)); mpi-ikl-simplemkl-1.0/run_demo64.sh0000644000335100017570000000024011133661710016675 0ustar pgehlerwebadm#!/bin/bash export LD_LIBRARY_PATH=$PWD/ipopt/ipopt-3.3.5-mumps-amd64/lib:$LD_LIBRARY_PATH matlab -nojvm -r "simplemkl_example" matlab -nojvm -r "ikl_example" mpi-ikl-simplemkl-1.0/subproblem_product_objective.mexglx0000755000335100017570000002460011133661710023562 0ustar pgehlerwebadmELFp4L4 (!4444$4$<@PP&P&Ptd   Qtd     s +  KP0p'<t'{?   ?c)p'"__gmon_start___init_fini__cxa_finalize_Jv_RegisterClassesmexFunctionmxGetNumberOfDimensionsmxGetDimensionsmxGetPrmxGetMmxGetNmxCreateDoubleMatrix__libm_sse2_expmexErrMsgTxt__assert_fail__gxx_personality_v0libimf.solibm.so.6libmex.solibgcc_s.so.1libirc.solibc.so.6libdl.so.2_edata__bss_start_endGLIBC_2.1.3GLIBC_2.0si Aii Mp   G -?Qcjo|h'l'ngB   .B\? :  2DVtH$ H'L'P'`'d'US[8!t X[ hhUS u8t$ ҋuƃ[]Í&US.{ tt $҃[]Ë$ÐUWVSEuEʃE…[ EDڃO # v 66t T$XWT$X 6D$dvD$0vD$0UDى\$pv;_N ;F ӉT$\vT$X; vvjRRU PU3҉T$P)jjT$`RU PBD$PU3҉T$T jT$\RRU PBD$TT$da˃t$X'L$3҃|$ \$t$4D$Z;3ɋD$X\$0T$8ʉD$<L$\$8D$<;dt$H\$d|$Lfɹ3\$(t$\|$`Ӌ\$ًL$X\$\ f(:YY2X^Y0XQ^X΍ڃ;r$d$f\d$$D$lT$$L$p\$  Y YYȋD$P\#D$X|$ht$@3>T$4T$\L$`\$Pt$TT$4YX ΋Ӆ}T$T$D\$L$;|$  t$$Ӄt!ыL$$ Yɹ3fɋt$$fff(fYfXf(\fYfXf(l fYfXf(t0fYfX΃;rfX;f|sT$$ YɃ;XrY\'Ą[^_]3ffՋD$X;t$H|$LD$  D$;\$0D$;Xȋ|$ \$f't$H|$L몋T$X;|$ht$@UfhGh&h`h`h h j'hhhhhpUVS=Êp@t Ћu[^]ÐUS[TdY[void mexFunction(int, mxArray_tag **, int, const mxArray_tag **)Wrong number of input argumentsWrong number of output arguments min 1, max 3Second input argument must be 2Dsubproblem_product_objective.cppFirst argument must be symmetric in first 2 dimensionsSecond argument must be of size [Nfactors,1]Third argument must be of size [Npoints,1]Smoothing variance must be positive??nFactors>0;4zRPL| $   8  "8  "8  "X.. .8  "8  "8  "8  "8  "8  ". . . #.'....  .#.  .$.  .|.  8  "8  "8  ".....+... a<    p W T'`o0oooP&Vfh'H&GCC: (GNU) 4.2.1 (Ubuntu 4.2.1-5ubuntu4)GCC: (GNU) 4.1.3 20070929 (prerelease) (Ubuntu 4.1.2-16ubuntu2)GCC: (GNU) 4.1.3 20070929 (prerelease) (Ubuntu 4.1.2-16ubuntu2)GCC: (GNU) 4.2.1 (Ubuntu 4.2.1-5ubuntu4)$"$< /build/buildd/glibc-2.6.1/build-tree/i386-libc/csu/crti.S/build/buildd/glibc-2.6.1/build-tree/glibc-2.6.1/csuGNU AS 2.18 /build/buildd/glibc-2.6.1/build-tree/i386-libc/csu/crtn.S/build/buildd/glibc-2.6.1/build-tree/glibc-2.6.1/csuGNU AS 2.18U%U%P /build/buildd/glibc-2.6.1/build-tree/i386-libc/csucrti.S!/!=Z!gg//(!/!=Z!zP /build/buildd/glibc-2.6.1/build-tree/i386-libc/csucrtn.S< !!! !!!2<@ .symtab.strtab.shstrtab.hash.dynsym.dynstr.gnu.version.gnu.version_r.rel.dyn.rel.plt.init.text.fini.rodata.eh_frame_hdr.eh_frame.gcc_except_table.ctors.dtors.jcr.dynamic.got.got.plt.data.bss.comment.debug_aranges.debug_info.debug_abbrev.debug_line.debug_ranges! pp@)W1o(>o000M ``V  _0Z@@0epp kq   y  4$4(&(<&<D&DL&LP&PH'H T'Th'hp'ppHP"$ @($t!0 @ &p0`@ p    4$(&<&D&L&P&H'T'h'p' <&%D&3L&@p'Ol'Vp l x@&H&$&L&   $ + `- $ 7 1 A 9! F  S! `. mp  z(&T'h' P&   /PH]p'it'nu   ?p'"initfini.ccrtstuff.c__CTOR_LIST____DTOR_LIST____JCR_LIST__completed.5982p.5980__do_global_dtors_auxframe_dummy__CTOR_END____DTOR_END____FRAME_END____JCR_END____do_global_ctors_aux/tmp/ipo_iccAe1Vup.c_2il0floatpacket.2_2il0floatpacket.1__STRING.8.3__STRING.7.3__STRING.6.3__STRING.5.3__$U0.3__STRING.4.3__STRING.3.3__STRING.2.3__STRING.1.3__STRING.0.3mexFunction$$LSDA_GLOBAL_OFFSET_TABLE___dso_handle__i686.get_pc_thunk.bx_DYNAMICmxGetPrmxGetN__gmon_start___Jv_RegisterClasses_finimxGetNumberOfDimensions__libm_sse2_exp__assert_fail@@GLIBC_2.0__gxx_personality_v0__bss_start_endmxGetMmexFunctionmxCreateDoubleMatrixmexErrMsgTxtmxGetDimensions_edata__cxa_finalize@@GLIBC_2.1.3_initmpi-ikl-simplemkl-1.0/README0000644000335100017570000000202211134124645015241 0ustar pgehlerwebadmMatlab implementation of Infinite Kernel Learning and SimpleMKL 1. Installation =============== Please read the file INSTALL for a step by step guide for installing the software. 2. SimpleMKL ============ There is a matlab script "simplemkl_example" which contains an example of how the code is used. Also try "help simplemkl" and check out the scripts run_demo32.sh and run_demo64.sh (the first one for a 32bit machine, the latter for a 64bit machine) 3. Infinite Kernel Learning =========================== There is a matlab script "ikl_example" with an example run of the algorithm. Also try "help IKLmain" and check out the scripts run_demo32.sh and run_demo64.sh (the first one for a 32bit machine, the latter for a 64bit machine) 4. Contact and References ========================= Email: pgehler@tuebingen.mpg.de Webpage: http://www.kyb.mpg.de/bs/people/pgehler/ikl/index.html 5. License: =========== The software is licensed under the BSD license. See BSD_LICENSE.txt for details. mpi-ikl-simplemkl-1.0/Kbeta.m0000644000335100017570000000100411133661707015570 0ustar pgehlerwebadm% Kbeta % % Computes weighted sum of kernel matrices % % Usage: % K = Kbeta(Ks,w); % K = Kbeta(Ks,w,symmetric); % % Input: % Ks - Gram matrices [n x n x M] % w - weights [M x 1] % symmetric (=false) - optional argument. If 1 the Ks % matrices are assumed to be symmetric which results in a % small speedup. % % Output: % K - weighted kernel matrix equiv with % K=0;for m=1:M, K=K+ w(m)*Ks(:,:,m);end % % Example: % % K = Kbeta(randn(100,100,10),rand(10,1)); % % Peter Gehler 07/2008 pgehler@tuebingen.mpg.de mpi-ikl-simplemkl-1.0/dist_euclid.m0000644000335100017570000000104411133661707017036 0ustar pgehlerwebadmfunction D = dist_euclid(X1,X2,A) if ~exist('A','var') X1X1 = (sum(X1.^2,2)); if ~exist('X2','var') || numel(X2)==0 D = ones(length(X1X1),1)*X1X1' + X1X1*ones(1,length(X1X1)) - 2*(X1*X1'); else X2X2 = (sum(X2.^2,2)); D = ones(length(X1X1),1)*X2X2' + X1X1*ones(1,length(X2X2)) - 2*(X1*X2'); end else X1X1 = (sum((X1*A).^2,2)); if ~exist('X2','var') || numel(X2)==0 X2X2 = X1X1; else X2X2 = (sum((X2*A).^2,2)); end D = ones(length(X1X1),1)*X2X2' + X1X1*ones(1,length(X2X2)) - 2*(X1*A*X2'); end mpi-ikl-simplemkl-1.0/simplemkl_example.m0000644000335100017570000000360511133661710020255 0ustar pgehlerwebadm% % An example script to introduce into the infinite kernel learning algorithm % clear all % first generate some data ... fprintf('generating training and test data...'); nTiles = [2;2]; nPoints = 300; nTestPoints = 500; noisedims = 5; % ... checkerboard data ... [X,Y] = checkerboard(nTiles,nPoints,[1;1]); [Xtest,Ytest] = checkerboard(nTiles,nTestPoints); % ... add noise ... if noisedims >0 X(:,size(X,2)+(1:noisedims)) = randn(size(X,1),noisedims); Xtest(:,size(Xtest,2)+(1:noisedims)) = randn(size(Xtest,1),noisedims); end % ... normalize data ... X = X - ones(size(X,1),1)*mean(X); X = X * diag(1./std(X)); Xtest = Xtest - ones(size(Xtest,1),1)*mean(Xtest); Xtest = Xtest * diag(1./std(Xtest)); % ... and plot figure(3); clf plot(X(Y==1,1),X(Y==1,2),'ro','MarkerSize',5); hold on plot(X(Y==-1,1),X(Y==-1,2),'b*','MarkerSize',5); title('chessboard training data','FontSize',20); axis([-2,2,-2,2]); axis square xlabel('Dimension 1','FontSize',16); ylabel('Dimension 2','FontSize',16); set(gca,'FontSize',16); % and wait for user to acknowledge that fprintf('press key\n'); pause % % Start of MKL algorithm % % some initial guess of the optimal kernel width ... dat = dist_euclid(X,X); dat_test = dist_euclid(Xtest,X); sig0 = 0.5*sqrt(median(dat(:))); % ... and some widhts aroung this guess sigs = sig0 * 10.^[-1:0.25:1]; % ... gram matrix for each width for k=1:numel(sigs) Ks(:,:,k) = exp(-sigs(k)*dat); Ks_test(:,:,k) = exp(-sigs(k)*dat_test); end fprintf('using %d different kernels\n',size(Ks,3)); % train ... C = 10; [w,alpha,b] = simplemkl(Y,Ks,C); % ... and test Ytest_pred = sign(Kbeta(Ks_test,w) * alpha + b); % print some statistics fprintf('Algorithm selected %d kernels\n',sum(w>1e-4)); fprintf('test accuracy: %.5g\n',mean(Ytest==Ytest_pred)); [ignore,ind]= max(w); fprintf('most active kernel (w=%.2g) = %.2f\n',w(ind),sigs(ind)); fprintf('press key to exit\n'); pausempi-ikl-simplemkl-1.0/tests/0000755000335100017570000000000011133662012015521 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/tests/subproblem_product_objective_test.m0000644000335100017570000000640111133661707024715 0ustar pgehlerwebadm% % Test method for the subproblem function, compares C and Matlab code % function subproblem_product_objective_test nFac_list = [1,10]; nPoints_list = [1,100,500]; sig_list = [0.1,1,5]; Ds = randn(50,50,5); al = randn(50,1); fac = randn(5,1); try [f,df,H] = subproblem_product_objective(Ds,fac,al,0.1,0.1); assert(false,'wrong number of input arguments'); end try [f,df,H] = subproblem_product_objective(Ds,fac); assert(false,'wrong number of input arguments'); end try [f,df,H] = subproblem_product_objective(Ds); assert(false,'wrong number of input arguments'); end try [f,df,H,foo] = subproblem_product_objective(Ds,fac,al); assert(false,'wrong number of output arguments'); end try subproblem_product_objective(Ds,fac,al); assert(false,'wrong number of output arguments'); end try [f,df,H] = subproblem_product_objective(Ds,fac,al,-sig); assert(false,'negative variance should fail'); end for ii=1:numel(nFac_list) nFacs = nFac_list(ii); for jj=1:numel(nPoints_list) nPoints = nPoints_list(jj); clear D Ds fac al; for k=1:nFacs D = rand(nPoints,nPoints); D = D+D'; D = D-diag(diag(D)); assert(sum(diag(D))==0); assert(sum(sum(abs(D-D')))==0); Ds(:,:,k) = D; end al = randn(nPoints,1); fac = rand(nFacs,1); [f,df,H] = subproblem_product_objective(Ds,fac,al); [f2,df2] = subproblem_product_objective(Ds,fac,al); [f3] = subproblem_product_objective(Ds,fac,al); [f4,df4,H4] = subprob_obj(Ds,fac,al); assert(f==f2); assert(f==f3); assert(abs(f-f4)<1e-5); assert(all(df==df2)); assert(sum(abs(df-df4))<1e-5); assert(sum(sum(abs(H-H4)))<1e-5); % smoothed variant for kk=1:numel(sig_list) sig = sig_list(kk); [f,df,H] = subproblem_product_objective(Ds,fac,al,sig); [f2,df2] = subproblem_product_objective(Ds,fac,al,sig); [f3] = subproblem_product_objective(Ds,fac,al,sig); [f4,df4,H4] = subprob_obj_sig(Ds,fac,al,sig); assert(f==f2); assert(f==f3); assert(abs((f-f4)/f)<1e-4); assert(all(df==df2)); if sum(df)==0 assert(sum(df2)==0); else assert(sum(abs(df-df2))/sum(abs(df))<1e-4); end if sum(H(:))==0 assert(sum(H4(:))==0); else assert(sum(sum(abs((H-H4))))/sum(abs(H(:)))<1e-4); end [f,df,H] = subprob_obj(Ds,fac,al); [f2,df2,H2] = subprob_obj_sig(Ds,fac,al,0); assert(f==f2); end end end fprintf('Test passed\n'); function [f,df,H] = subprob_obj(Ds,fac,al) D = 0; for k=1:size(Ds,3) D = D + fac(k) * Ds(:,:,k); end K = exp(-D); f = - 0.5*al'*K*al; for k=1:numel(fac) tmp = Ds(:,:,k) .* K; df(k,1) = 0.5 * al' * tmp *al; for k2=1:k H(k,k2) = -0.5*al'*(tmp.*Ds(:,:,k2))*al; end end function [f,df,H] = subprob_obj_sig(Ds,fac,al,sig) assert(sig>=0); if sig==0 scale = 1; else scale = (pi*sig).^(-(size(Ds,3)+1)/2); end D = []; for k=1:size(Ds,3) D(:,:,k) = fac(k) * (Ds(:,:,k)./(1+sig*Ds(:,:,k))); end K = exp(-sum(D,3)); s = (prod(sqrt(1+sig*Ds),3)); tmp = K./s; f = - 0.5*al'*tmp*al; for k=1:numel(fac) tmp = Ds(:,:,k) ./ s .* K; df(k,1) = 0.5 * al' * tmp *al; for k2=1:k H(k,k2) = -0.5*al'*(tmp.*Ds(:,:,k2))*al; end end if (0) f = f*scale; df = df*scale; H = H*scale; end mpi-ikl-simplemkl-1.0/tests/Kbeta_test.m0000644000335100017570000000316411133661707020002 0ustar pgehlerwebadm % % Test the function Kbeta % clear all K = 22; N = 1000; for i=1:K tX = randn(N,N); tX = tX + tX'; X(:,:,i) = tX; end w = rand(K,1); t=cputime; A = Kbeta(X,w,1); t0 = cputime-t; t=cputime; A3 = Kbeta(X,w,0); t2 = cputime-t; t=cputime; A2 = 0; for i=1:K A2 = A2 + X(:,:,i) * w(i); end t1 = cputime-t; fprintf('Kbeta faster fac: %.4f\n',t1/t0); assert(sum(sum(abs(A-A2)))<1e-8); fprintf('using symmetry faster fac: %.4f\n',t2/t0); assert(sum(sum(abs(A3-A2)))<1e-8); disp('Test passed'); % % Test the function Kbeta on non-symmetric output % clear all K = 22; N = 1000; N2 = 2000; for i=1:K tX = randn(N,N2); X(:,:,i) = tX; end w = rand(K,1); t=cputime; A = Kbeta(X,w); t0 = cputime-t; t=cputime; A2 = 0; for i=1:K A2 = A2 + X(:,:,i) * w(i); end t1 = cputime-t; fprintf('Kbeta faster fac: %.4f\n',t1/t0); assert(sum(sum(abs(A-A2)))<1e-8); disp('Test passed'); % % Test the function Kbeta with beta(1,end) = 0 % clear all K = 22; N = 1000; for i=1:K tX = randn(N,N); tX = tX + tX'; X(:,:,i) = tX; end w = rand(K,1); w(1) = 0; w(10) = 0; w(end) = 0; t=cputime; A = Kbeta(X,w); t0 = cputime-t; t=cputime; A2 = 0; for i=1:K A2 = A2 + X(:,:,i) * w(i); end t1 = cputime-t; fprintf('Kbeta faster fac: %.4f\n',t1/t0); assert(sum(sum(abs(A-A2)))<1e-8); disp('Test passed'); % % Test the function Kbeta with beta =1 and only one input dim % clear all N = 1000; tX = randn(N,N); tX = tX + tX'; X(:,:) = tX; w = 1; t=cputime; A = Kbeta(X,w); t0 = cputime-t; t=cputime; A2 = X * w;; t1 = cputime-t; fprintf('Kbeta faster fac: %.4f\n',t1/t0); assert(sum(sum(abs(A-A2)))<1e-8); disp('Test passed'); mpi-ikl-simplemkl-1.0/tests/ipopt_test.m0000644000335100017570000000255011133661707020105 0ustar pgehlerwebadm% % just test whether x^3+x^2 can be optimized over % function stat = ipopt_test % % You need to update the path to IPOPT! % warning('off','MATLAB:dispatcher:pathWarning') switch computer case 'GLNX86' addpath('./ipopt/ipopt-3.3.5-mumps-i686/matlab'); case 'GLNXA64' % cluster addpath('./ipopt/ipopt-3.3.5-mumps-amd64/matlab'); end warning('on','MATLAB:dispatcher:pathWarning') x0 = 7; lb = 2; ub = 10; lbc = []; ubc = []; tolerance = 1e-4; % call MKL with fininte number of kernels Ks [x,multiplier,numiter] = ipopt(x0,lb,ub,lbc,ubc,... @(x) compute_objective(x),... @(x) compute_gradient(x),... @(x) compute_constraints(x),... @(x, rso) compute_jacobian(x, rso),... '', [],'',[],... 'jac_c_constant','yes',... 'hessian_approximation','limited-memory',... 'mu_strategy','adaptive',... 'tol',tolerance,... 'print_level',0,... % 5 for verbosity 'max_iter',1000); fprintf('solution = %g\n',x); assert( abs((x-2))<10*tolerance); fprintf('Test Passed\n'); return; function f = compute_objective(x); f = x.^3 + x.^2; % gradient function function df = compute_gradient(x) df = 3*x.^2 + 2*x; % constraints sum(w) = 1 function f = compute_constraints(x) f = x; % Jacobian function J = compute_jacobian(x,return_structure_only); J = sparse(ones(size(x)))'; mpi-ikl-simplemkl-1.0/tests/test_simplemkl.m0000644000335100017570000000016511133661707020747 0ustar pgehlerwebadmy = sign(randn(100,1)); for d=1:10 X = randn(100,100); Ks(:,:,d) = X'*X; end [w,al,b] = simplemkl(y,Ks,100); mpi-ikl-simplemkl-1.0/run_demo32.sh0000755000335100017570000000025511133661707016707 0ustar pgehlerwebadm#!/bin/bash export LD_LIBRARY_PATH=$PWD/ipopt/ipopt-3.3.5-mumps-i686/lib:$LD_LIBRARY_PATH matlab -nojvm -r "simplemkl_example; exit;" matlab -nojvm -r "ikl_example; exit;" mpi-ikl-simplemkl-1.0/Kbeta.cpp0000644000335100017570000000343211133661710016117 0ustar pgehlerwebadm#include "mex.h" #include /* * This function computes * A = 0; * for i=1:length(beta), A = A + K(:,:,i)*beta(i); end * for symmetric and not-symmetric K */ void mexFunction(int , mxArray *plhs[], int nrhs, const mxArray *prhs[]) { if (nrhs<2 || nrhs>3) mexErrMsgTxt("Wrong number of input arguments"); if (mxGetNumberOfDimensions(prhs[1])!=2) mexErrMsgTxt("Second input argument must be 2D"); unsigned int nDims = mxGetNumberOfDimensions(prhs[0]); const mwSize *dims = mxGetDimensions(prhs[0]); if (nDims<2 || nDims>3) mexErrMsgTxt("First input arg must be 2D or 3D"); double *K = mxGetPr(prhs[0]); double *beta = mxGetPr(prhs[1]); unsigned int n1 = dims[0]; unsigned int n2 = dims[1]; unsigned int m = (nDims==3)?dims[2]:1; unsigned int nn = n1*n2; if (mxGetM(prhs[1]) != m) mexErrMsgTxt("Input dimensions mismatch"); bool symmetric = false; if (n1==n2) if (nrhs>2) symmetric = (bool)(*(mxGetPr(prhs[2]))>0); // output arguments plhs[0]=mxCreateDoubleMatrix(n1,n2,mxREAL); double *Kbeta = mxGetPr(plhs[0]); if (symmetric) { for ( unsigned k=0,knn=0 ; k1,'IpOpt died but #Gram matrices =1'); fprintf('Infkl: IpOpt Died, removing constraint and retrying\n'); % % Ipopt died. Now we try the following heuristic: % remove one constraint and retrain. We remove the % constraint (gram matrix) which is closest in absolute % deviation from the next closest one % nof_ipopt_died = nof_ipopt_died + 1; diff = Inf(size(Ks,3),size(Ks,3)); for ii=1:(size(Ks,3)-1) for jj=(ii+1):size(Ks,3) diff(ii,jj) = sum(sum(abs(Ks(:,:,ii)-Ks(:,:,jj)))); end end assert(size(diff,1)==size(diff,2)) [i1,i2] = ind2sub(size(diff),find(diff==min(diff(:)))); if w0(i1) > w0(i2) remove_ind = i2; else remove_ind = i1; end % now we remove the constraint and restart IpOpt if iscell(Theta) Theta{remove_ind} = []; else Theta(remove_ind,:) = []; end Ks(:,:,remove_ind) = []; w0(remove_ind) = []; w0 = w0/sum(w0); active_set(remove_ind) = []; old_infkl_f = Inf; end end if nof_ipopt_tries == max_ipopt_tries termination_reason = 'ipopt_died'; error('IPOpt died too often'); break; end numiter = numiter + 1; % C->Matlab %assert(numiter==deriv_counter,'#derivative calls != #Ipopt iterations'); infkl_multiplier = multiplier; fprintf('IKL: IpOpt iter %d,',numiter); obj_decrease = (old_infkl_f-infkl_f)./infkl_f; fprintf('Calls F:%d, dF:%d,',svm_counter_iteration,deriv_counter); fprintf('obj: %.05g,',infkl_f); fprintf('rel.decrease: %.05f (thresh %.05f)\n',obj_decrease,opts.objective_eps); svm_counter_total = svm_counter_total + svm_counter_iteration; if (DO_TEST) % it is possible that the objective function increases a % bit due to numerical errors. If two constraints are very % close IpOpt has a hard time to score the two kernel % matrices. Furthermore we are pruning out some weights % which might lead to precision error. assert(opts.maxiter_ipopt<=numiter || obj_decrease >= -1e-2,'Objective function increased'); end old_infkl_f = infkl_f; % check if objective decrease is small if obj_decrease0 left in %w = w./sum(w); fprintf('current w: ');fprintf('%4f ',w); fprintf('\n'); % ... and do assertions ... assert(size(Ks,3)==numel(active_set)); assert(numel(active_set)==numel(unique(active_set))); assert(numel(w)+nof_new_constraints==numel(active_set)); if iscell(Theta),assert(numel(Theta)==numel(w)+nof_new_constraints); else assert(size(Theta,1)==numel(w)+nof_new_constraints);end assert(size(Ks,3)==numel(w0)); assert((opts.decay_eps~=Inf)|(all(active_set==1:numel(active_set)))); end % report why algorithm terminated ... fprintf('IKL: Algorithm terminated beracuse ''%s''\n',get_termination_reason(termination_reason)); % ... prune out decayed w ... decayed_w = find(w0,'no support vectors selected'); al = al(svset); for i=1:size(Ks,3) norm(i,1) = 0.5 * al'*Ks(svset,svset,i)*al; end infkl_f = sum(abs(al)) - sum(w.*norm); infkl_df = -norm; infkl_w = w; end f = infkl_f; df = infkl_df; if ~(numel(f)==1 || C>0 || numel(infkl_b)==1 || numel(infkl_alpha)==numel(y)) fprintf('assert will fail\n'); assert(0); end % sanity checks assert(all(size(infkl_w)==size(infkl_df))); assert(numel(f)==1); assert(C>0); assert(numel(infkl_alpha)==numel(y)); assert(numel(infkl_b)==1); % % Some termination reasons which could occur % function str = get_termination_reason(descr) switch descr case 'obj_tol' str = 'Objective change smaller than threshold'; case 'no_constr' str = 'No violating contraint found by subproblem'; case 'maxiter' str = 'Maximum of iterations reached'; case 'ipopt_died' str = 'IpOpt died unexpectedly'; otherwise str = 'unkown reason of termination'; end mpi-ikl-simplemkl-1.0/INSTALL0000644000335100017570000000332711133661710015421 0ustar pgehlerwebadmA step by step guide to get the code working (for both IKL and MKL) 1. Check if everyhting works already simply type "make test" , if there is no error occuring the code already works. Jump to 5.) 2. Check whether LIBSVM works. Type "make libsvm_test". If this test does NOT end with the message "Test passed" first ckeck whether all shared libraries are present ldd libsvm/libsvm_classifier.mexglx (32bit) ldd libsvm/libsvm_classifier.mexa64 (64bit) You may need to recompile the libsvm file. Go to the libsvm/ directory modify the Makefile to your system. Now type "make". 3. Check whether all mex files for IKL are there If "make Kbeta_test" or "make subprob_test" do NOT end with the message "Test passed" you need to recompile them. Adjust the Makefile and type "make". 4. If point 2. and 3. work it is probably Ipopt which is missing Try "make ipopt_test". If this does NOT end with the message "Test passed" ipopt is not working. We provided 32bit and 64bit precompiled versions of ipopt in the ipopt directory. Make sure the correct lib directories for your machine are in the $LD_LIBRARY_PATH . Make sure there are no missing shared libraries by typeing (on 32bit machine) ldd ipopt/ipopt-3.3.5-mumps-i686/matlab/ipopt.mexglx Install the missing shared libraries and test "make ipopt_test" again. If this does not work you may need to compile your own version of Ipopt which can be found at this website https://projects.coin-or.org/Ipopt (If you comiple a version for Windows with the MUMPS solver please send me the binary so I can include it, Thanks!) 5. Everything works. Now start matlab and try chessboard_example. Have fun mpi-ikl-simplemkl-1.0/subproblem_product_objective.cpp0000644000335100017570000001026411133661707023044 0ustar pgehlerwebadm#include "mex.h" #include /* * This function computes * f = - 0.5 * \sum_{i,j}\alpha_i\alpha_jy_iy_j exp(-\sum_k theta(k)*d_k(x_i,x_j)) * df(l) = 0.5 * \sum_{i,j}\alpha_i\alpha_jy_iy_j d_l(x_i,x_j) *exp(-\sum_k theta(k)*d_k(x_i,x_j)) * H(k1,k2) = -0.5 * \sum_{i,j}\alpha_i\alpha_jy_iy_j d_k2(x_i,x_j) d_k1(x_i,x_j) *exp(-\sum_k theta(k)*d_k(x_i,x_j)) * * input * D : [n,n,nFactors] * fac: [nFactors,1] * alpha: [n,1] * * output * f = [1,1]; * df = [nFactors,1]; * H = [nFactors,nFactors] * * important!!!! This function assumes d_ii = 0 and d_ij=d_ji */ void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) { if ((nrhs!=3) & (nrhs!=4)) mexErrMsgTxt("Wrong number of input arguments"); if ((nlhs==0) |(nlhs>3)) mexErrMsgTxt("Wrong number of output arguments min 1, max 3"); if (mxGetNumberOfDimensions(prhs[1])!=2) mexErrMsgTxt("Second input argument must be 2D"); const unsigned int nDims = mxGetNumberOfDimensions(prhs[0]); const mwSize *dims = mxGetDimensions(prhs[0]); const unsigned int nFactors = (nDims!=3)?1:dims[2]; assert(nFactors>0); const double *Ds = mxGetPr(prhs[0]); const double *factors = mxGetPr(prhs[1]); const double *alpha = mxGetPr(prhs[2]); const bool smoothing = (nrhs==4)?true:false; const unsigned int n = mxGetM(prhs[2]); if ((n != dims[1]) || (n!=dims[0])) mexErrMsgTxt("First argument must be symmetric in first 2 dimensions"); const unsigned int nn = n*n; if (mxGetM(prhs[1]) != nFactors) mexErrMsgTxt("Second argument must be of size [Nfactors,1]"); if (mxGetN(prhs[1]) != 1) mexErrMsgTxt("Second argument must be of size [Nfactors,1]"); if (mxGetN(prhs[2]) != 1) mexErrMsgTxt("Third argument must be of size [Npoints,1]"); // output arguments plhs[0]=mxCreateDoubleMatrix(1,1,mxREAL); double *f = mxGetPr(plhs[0]); // objective value double *df = NULL; // derivative if (nlhs>1) { plhs[1]=mxCreateDoubleMatrix(nFactors,1,mxREAL); df = mxGetPr(plhs[1]); } double *H = NULL; // Hessian if (nlhs>2) { plhs[2]=mxCreateDoubleMatrix(nFactors,nFactors,mxREAL); H = mxGetPr(plhs[2]); } double scale = 1.0; if (!smoothing) { for (unsigned int i=0; ilambda) % w - mixing coefficients (only using 'build') % parm - subproblem parameters, startpoints, points included, % etc. (can differ for any subproblem) % % Input: (user defined, specific to the subproblem) % Ds - distances between training data % parm_range - admissible range for kernel parameters % opts - opts.eps (1e-3) = Tolerance for Ipopt solver (for subproblem) % % Output: % K - generated kernel matrices [n x n x M] or [n x n_test] % for 'build' % new_facs - new kernel parameters % parm - additional data untouched from IKL % % % Peter Gehler 07/2008 pgehler@tuebingen.mpg.de function [K,new_facs,parm] = IKLsubproblem_singledimgaussian(operation,al,lam,Ds,parm,w,parm_range,opts) if ~exist('opts','var') opts = []; end if ~isfield(opts,'constraints_per_iteration') opts.constraints_per_iteration = 1; end if ~isfield(opts,'eps') opts.eps = 1e-3; end if ~isfield(opts,'constraint_eps') opts.constraint_eps = 1e-2; end if ~isfield(opts,'do_plots') opts.do_plots = 0; end DO_TEST = 1; if ndims(Ds) > 1 zeroEntries = squeeze(sum(sum(abs(Ds))))==0; Ds = Ds(:,:,~zeroEntries); end if ~exist('parm_range','var') warning('no range for valid exponents given: setting to [0.1,4]'); parm_range = [0.1;4]; end if parm_range(1) == 0 parm_range(1) = 1e-7; end assert(parm_range(1)Matlab assert(numiter == subprob_deriv_counter,'#derivative calls != #ipopt iterations'); fprintf('SP: IpOpt iter %d, ',numiter); fprintf('Evaluations: F:%d, dF:%d, H:%d, ',subprob_obj_counter,subprob_deriv_counter,subprob_hessian_counter); fprintf('Computations needed: %d\n',subprob_recompute_counter); if (DO_TEST) tmp_f = subprob_f; subprob_fac = -Inf(nFactors,1); f = compute_objective(fac0,al,Ds,svset); assert(tmp_f==f|abs(tmp_f-f)./f<1e-5,'objective differs'); end % check if the found local maxima violates the contraint % pg : to do: criterion relative to absolute value if (-subprob_f - lam) >= opts.constraint_eps % yes it did ... fprintf('found violating constraint...'); % ... already in the list? already_included = any(dist_euclid(parm.facs,fac0')= opts.constraints_per_iteration) fprintf('SP: found maximum of %d constraints, returning\n',nof_violating_constraints); return; end end end % ... no violating constraint has been found, return nothing ... if nof_violating_constraints == 0 K = []; new_facs = []; else fprintf('SP: found only %d constraints returning\n',nof_violating_constraints); end end % Objective function f = compute_objective(fac,al,Ks,svset) global subprob_obj_counter subprob_obj_counter = subprob_obj_counter + 1; f = update_objective(fac,al,Ks,svset); %Gradient function df = compute_gradient(fac,al,Ks,svset) global subprob_deriv_counter subprob_deriv_counter = subprob_deriv_counter +1; [foo,df] = update_objective(fac,al,Ks,svset); % Hessian function H = compute_hessian(fac,ipopt_sigma,ipopt_lambda,return_structure_only,al,D,svset) global subprob_hessian_counter subprob_hessian_counter = subprob_hessian_counter +1; if return_structure_only H = sparse(tril(ones(numel(fac),numel(fac)))); else [foo,foo2,H] = update_objective(fac,al,D,svset); H = ipopt_sigma * sparse(H); end % Constraints function g = compute_constraints(fac) g=fac; % Jacobian function J = compute_jacobian(fac,return_structure_only); J = sparse(numel(fac),1); % % compute the objective, the gradient % function [f,df,H] = update_objective(fac,al,Ds,svset) assert(numel(fac)==size(Ds,3),'number of arguments != number of distances'); %assert(all(fac>=-1e-5),'factor turned negative'); %fac = max(fac,0); global subprob_f subprob_df subprob_fac subprob_H % % All what is following should be computed in a mex file for % more speed % if any(size(fac)~=size(subprob_fac)) | any(fac~=subprob_fac) global subprob_recompute_counter subprob_recompute_counter = subprob_recompute_counter + 1; [subprob_f,subprob_df,subprob_H] = subproblem_product_objective(Ds(svset,svset,:),fac,al); subprob_fac = fac; end f = subprob_f; if nargout>1 df = subprob_df; if nargout >2 H = subprob_H; end end function points = removeDuplicates(points); D = dist_euclid(points'); [i1,i2] = ind2sub(size(D),find(D<1e-6)); indx = find(i1<=i2); i1(indx) = []; i2(indx) = []; indx = []; for i=1:max(i2) indx = [indx;i1(find(i2==i))]; end indx = unique(indx); points(:,indx) = []; D = dist_euclid(points'); assert(sum(D(:)==0)==size(D,1));mpi-ikl-simplemkl-1.0/libsvm/0000755000335100017570000000000011133662012015653 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/libsvm/svm.cpp0000644000335100017570000017111611133661707017205 0ustar pgehlerwebadm#include #include #include #include #include #include #include #include "svm.h" typedef float Qfloat; typedef signed char schar; #ifndef min template inline T min(T x,T y) { return (x inline T max(T x,T y) { return (x>y)?x:y; } #endif template inline void swap(T& x, T& y) { T t=x; x=y; y=t; } template inline void clone(T*& dst, S* src, int n) { dst = new T[n]; memcpy((void *)dst,(void *)src,sizeof(T)*n); } #define INF HUGE_VAL #define TAU 1e-12 #define Malloc(type,n) (type *)malloc((n)*sizeof(type)) #if 0 void info(char *fmt,...) { va_list ap; va_start(ap,fmt); vprintf(fmt,ap); va_end(ap); } void info_flush() { fflush(stdout); } #else void info(char *fmt,...) {} void info_flush() {} #endif // // Kernel Cache // // l is the number of total data items // size is the cache size limit in bytes // class Cache { public: Cache(int l,int size); ~Cache(); // request data [0,len) // return some position p where [p,len) need to be filled // (p >= len if nothing needs to be filled) int get_data(const int index, Qfloat **data, int len); void swap_index(int i, int j); // future_option private: int l; int size; struct head_t { head_t *prev, *next; // a cicular list Qfloat *data; int len; // data[0,len) is cached in this entry }; head_t *head; head_t lru_head; void lru_delete(head_t *h); void lru_insert(head_t *h); }; Cache::Cache(int l_,int size_):l(l_),size(size_) { head = (head_t *)calloc(l,sizeof(head_t)); // initialized to 0 size /= sizeof(Qfloat); size -= l * sizeof(head_t) / sizeof(Qfloat); size = max(size, 2*l); // cache must be large enough for two columns lru_head.next = lru_head.prev = &lru_head; } Cache::~Cache() { for(head_t *h = lru_head.next; h != &lru_head; h=h->next) free(h->data); free(head); } void Cache::lru_delete(head_t *h) { // delete from current location h->prev->next = h->next; h->next->prev = h->prev; } void Cache::lru_insert(head_t *h) { // insert to last position h->next = &lru_head; h->prev = lru_head.prev; h->prev->next = h; h->next->prev = h; } int Cache::get_data(const int index, Qfloat **data, int len) { head_t *h = &head[index]; if(h->len) lru_delete(h); int more = len - h->len; if(more > 0) { // free old space while(size < more) { head_t *old = lru_head.next; lru_delete(old); free(old->data); size += old->len; old->data = 0; old->len = 0; } // allocate new space h->data = (Qfloat *)realloc(h->data,sizeof(Qfloat)*len); size -= more; swap(h->len,len); } lru_insert(h); *data = h->data; return len; } void Cache::swap_index(int i, int j) { if(i==j) return; if(head[i].len) lru_delete(&head[i]); if(head[j].len) lru_delete(&head[j]); swap(head[i].data,head[j].data); swap(head[i].len,head[j].len); if(head[i].len) lru_insert(&head[i]); if(head[j].len) lru_insert(&head[j]); if(i>j) swap(i,j); for(head_t *h = lru_head.next; h!=&lru_head; h=h->next) { if(h->len > i) { if(h->len > j) swap(h->data[i],h->data[j]); else { // give up lru_delete(h); free(h->data); size += h->len; h->data = 0; h->len = 0; } } } } // // Kernel evaluation // // the static method k_function is for doing single kernel evaluation // the constructor of Kernel prepares to calculate the l*l kernel matrix // the member function get_Q is for getting one column from the Q Matrix // class QMatrix { public: virtual Qfloat *get_Q(int column, int len) const = 0; virtual Qfloat *get_QD() const = 0; virtual void swap_index(int i, int j) const = 0; virtual ~QMatrix() {} }; // ======================== extern double *balanced_ridges; // ======================== class Kernel: public QMatrix { public: Kernel(int l, svm_node * const * x, const svm_parameter& param); virtual ~Kernel(); static double k_function(const svm_node *x, const svm_node *y, const svm_parameter& param); virtual Qfloat *get_Q(int column, int len) const = 0; virtual Qfloat *get_QD() const = 0; virtual void swap_index(int i, int j) const // no so const... { swap(x[i],x[j]); if(x_square) swap(x_square[i],x_square[j]); } protected: double (Kernel::*kernel_function)(int i, int j) const; private: const svm_node **x; double *x_square; // svm_parameter const int kernel_type; const double degree; const double gamma; const double coef0; static double dot(const svm_node *px, const svm_node *py); double kernel_linear(int i, int j) const { return dot(x[i],x[j]) +(i==j)*balanced_ridges[i]; } double kernel_poly(int i, int j) const { return pow(gamma*dot(x[i],x[j])+coef0,degree)+(i==j)*balanced_ridges[i]; } double kernel_rbf(int i, int j) const { return exp(-gamma*(x_square[i]+x_square[j]-2*dot(x[i],x[j])))+(i==j)*balanced_ridges[i]; } double kernel_sigmoid(int i, int j) const { return tanh(gamma*dot(x[i],x[j])+coef0)+(i==j)*balanced_ridges[i]; } double kernel_custom(int i, int j) const { extern double *Custom_Kernel; extern int Custom_M,Custom_N; int i2=(int)(x[i]->value)-1; int j2=(int)(x[j]->value)-1; return Custom_Kernel[Custom_M*j2 +i2]+(i==j)*balanced_ridges[i]; } }; Kernel::Kernel(int l, svm_node * const * x_, const svm_parameter& param) :kernel_type(param.kernel_type), degree(param.degree), gamma(param.gamma), coef0(param.coef0) { switch(kernel_type) { case LINEAR: kernel_function = &Kernel::kernel_linear; break; case POLY: kernel_function = &Kernel::kernel_poly; break; case RBF: kernel_function = &Kernel::kernel_rbf; break; case SIGMOID: kernel_function = &Kernel::kernel_sigmoid; break; case CUSTOM: kernel_function = &Kernel::kernel_custom; break; } clone(x,x_,l); if(kernel_type == RBF) { x_square = new double[l]; for(int i=0;iindex != -1 && py->index != -1) { if(px->index == py->index) { sum += px->value * py->value; ++px; ++py; } else { if(px->index > py->index) ++py; else ++px; } } return sum; } double Kernel::k_function(const svm_node *x, const svm_node *y, const svm_parameter& param) { switch(param.kernel_type) { case LINEAR: return dot(x,y); case POLY: return pow(param.gamma*dot(x,y)+param.coef0,param.degree); case RBF: { double sum = 0; while(x->index != -1 && y->index !=-1) { if(x->index == y->index) { double d = x->value - y->value; sum += d*d; ++x; ++y; } else { if(x->index > y->index) { sum += y->value * y->value; ++y; } else { sum += x->value * x->value; ++x; } } } while(x->index != -1) { sum += x->value * x->value; ++x; } while(y->index != -1) { sum += y->value * y->value; ++y; } return exp(-param.gamma*sum); } case SIGMOID: return tanh(param.gamma*dot(x,y)+param.coef0); default: return 0; /* Unreachable */ } } // Generalized SMO+SVMlight algorithm // Solves: // // min 0.5(\alpha^T Q \alpha) + b^T \alpha // // y^T \alpha = \delta // y_i = +1 or -1 // 0 <= alpha_i <= Cp for y_i = 1 // 0 <= alpha_i <= Cn for y_i = -1 // // Given: // // Q, b, y, Cp, Cn, and an initial feasible point \alpha // l is the size of vectors and matrices // eps is the stopping criterion // // solution will be put in \alpha, objective value will be put in obj // class Solver { public: Solver() {}; virtual ~Solver() {}; struct SolutionInfo { double obj; double rho; double upper_bound_p; double upper_bound_n; double r; // for Solver_NU }; void Solve(int l, const QMatrix& Q, const double *b_, const schar *y_, double *alpha_, double Cp, double Cn, double eps, SolutionInfo* si, int shrinking); protected: int active_size; schar *y; double *G; // gradient of objective function enum { LOWER_BOUND, UPPER_BOUND, FREE }; char *alpha_status; // LOWER_BOUND, UPPER_BOUND, FREE double *alpha; const QMatrix *Q; const Qfloat *QD; double eps; double Cp,Cn; double *b; int *active_set; double *G_bar; // gradient, if we treat free variables as 0 int l; bool unshrinked; // XXX double get_C(int i) { return (y[i] > 0)? Cp : Cn; } void update_alpha_status(int i) { if(alpha[i] >= get_C(i)) alpha_status[i] = UPPER_BOUND; else if(alpha[i] <= 0) alpha_status[i] = LOWER_BOUND; else alpha_status[i] = FREE; } bool is_upper_bound(int i) { return alpha_status[i] == UPPER_BOUND; } bool is_lower_bound(int i) { return alpha_status[i] == LOWER_BOUND; } bool is_free(int i) { return alpha_status[i] == FREE; } void swap_index(int i, int j); void reconstruct_gradient(); virtual int select_working_set(int &i, int &j); virtual int max_violating_pair(int &i, int &j); virtual double calculate_rho(); virtual void do_shrinking(); }; void Solver::swap_index(int i, int j) { Q->swap_index(i,j); swap(y[i],y[j]); swap(G[i],G[j]); swap(alpha_status[i],alpha_status[j]); swap(alpha[i],alpha[j]); swap(b[i],b[j]); swap(active_set[i],active_set[j]); swap(G_bar[i],G_bar[j]); } void Solver::reconstruct_gradient() { // reconstruct inactive elements of G from G_bar and free variables if(active_size == l) return; int i; for(i=active_size;iget_Q(i,l); double alpha_i = alpha[i]; for(int j=active_size;jl = l; this->Q = &Q; QD=Q.get_QD(); clone(b, b_,l); clone(y, y_,l); clone(alpha,alpha_,l); this->Cp = Cp; this->Cn = Cn; this->eps = eps; unshrinked = false; // initialize alpha_status { alpha_status = new char[l]; for(int i=0;i 0) { if(alpha[j] < 0) { alpha[j] = 0; alpha[i] = diff; } } else { if(alpha[i] < 0) { alpha[i] = 0; alpha[j] = -diff; } } if(diff > C_i - C_j) { if(alpha[i] > C_i) { alpha[i] = C_i; alpha[j] = C_i - diff; } } else { if(alpha[j] > C_j) { alpha[j] = C_j; alpha[i] = C_j + diff; } } } else { double quad_coef = Q_i[i]+Q_j[j]-2*Q_i[j]; if (quad_coef <= 0) quad_coef = TAU; double delta = (G[i]-G[j])/quad_coef; double sum = alpha[i] + alpha[j]; alpha[i] -= delta; alpha[j] += delta; if(sum > C_i) { if(alpha[i] > C_i) { alpha[i] = C_i; alpha[j] = sum - C_i; } } else { if(alpha[j] < 0) { alpha[j] = 0; alpha[i] = sum; } } if(sum > C_j) { if(alpha[j] > C_j) { alpha[j] = C_j; alpha[i] = sum - C_j; } } else { if(alpha[i] < 0) { alpha[i] = 0; alpha[j] = sum; } } } // update G double delta_alpha_i = alpha[i] - old_alpha_i; double delta_alpha_j = alpha[j] - old_alpha_j; for(int k=0;krho = calculate_rho(); // calculate objective value { double v = 0; int i; for(i=0;iobj = v/2; } // put back the solution { for(int i=0;iupper_bound_p = Cp; si->upper_bound_n = Cn; info("\noptimization finished, #iter = %d\n",iter); delete[] b; delete[] y; delete[] alpha; delete[] alpha_status; delete[] active_set; delete[] G; delete[] G_bar; } // return 1 if already optimal, return 0 otherwise int Solver::select_working_set(int &out_i, int &out_j) { // return i,j such that // i: maximizes -y_i * grad(f)_i, i in I_up(\alpha) // j: minimizes the decrease of obj value // (if quadratic coefficeint <= 0, replace it with tau) // -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha) double Gmax = -INF; double Gmax2 = -INF; int Gmax_idx = -1; int Gmin_idx = -1; double obj_diff_min = INF; for(int t=0;t= Gmax) { Gmax = -G[t]; Gmax_idx = t; } } else { if(!is_lower_bound(t)) if(G[t] >= Gmax) { Gmax = G[t]; Gmax_idx = t; } } int i = Gmax_idx; const Qfloat *Q_i = NULL; if(i != -1) // NULL Q_i not accessed: Gmax=-INF if i=-1 Q_i = Q->get_Q(i,active_size); for(int j=0;j= Gmax2) Gmax2 = G[j]; if (grad_diff > 0) { double obj_diff; double quad_coef=Q_i[i]+QD[j]-2*y[i]*Q_i[j]; if (quad_coef > 0) obj_diff = -(grad_diff*grad_diff)/quad_coef; else obj_diff = -(grad_diff*grad_diff)/TAU; if (obj_diff <= obj_diff_min) { Gmin_idx=j; obj_diff_min = obj_diff; } } } } else { if (!is_upper_bound(j)) { double grad_diff= Gmax-G[j]; if (-G[j] >= Gmax2) Gmax2 = -G[j]; if (grad_diff > 0) { double obj_diff; double quad_coef=Q_i[i]+QD[j]+2*y[i]*Q_i[j]; if (quad_coef > 0) obj_diff = -(grad_diff*grad_diff)/quad_coef; else obj_diff = -(grad_diff*grad_diff)/TAU; if (obj_diff <= obj_diff_min) { Gmin_idx=j; obj_diff_min = obj_diff; } } } } } if(Gmax+Gmax2 < eps) return 1; out_i = Gmax_idx; out_j = Gmin_idx; return 0; } // return 1 if already optimal, return 0 otherwise int Solver::max_violating_pair(int &out_i, int &out_j) { // return i,j: maximal violating pair double Gmax1 = -INF; // max { -y_i * grad(f)_i | i in I_up(\alpha) } int Gmax1_idx = -1; double Gmax2 = -INF; // max { y_i * grad(f)_i | i in I_low(\alpha) } int Gmax2_idx = -1; for(int i=0;i= Gmax1) { Gmax1 = -G[i]; Gmax1_idx = i; } } if(!is_lower_bound(i)) // d = -1 { if(G[i] >= Gmax2) { Gmax2 = G[i]; Gmax2_idx = i; } } } else // y = -1 { if(!is_upper_bound(i)) // d = +1 { if(-G[i] >= Gmax2) { Gmax2 = -G[i]; Gmax2_idx = i; } } if(!is_lower_bound(i)) // d = -1 { if(G[i] >= Gmax1) { Gmax1 = G[i]; Gmax1_idx = i; } } } } if(Gmax1+Gmax2 < eps) return 1; out_i = Gmax1_idx; out_j = Gmax2_idx; return 0; } void Solver::do_shrinking() { int i,j,k; if(max_violating_pair(i,j)!=0) return; double Gm1 = -y[j]*G[j]; double Gm2 = y[i]*G[i]; // shrink for(k=0;k= Gm1) continue; } else if(-G[k] >= Gm2) continue; } else if(is_upper_bound(k)) { if(y[k]==+1) { if(G[k] >= Gm2) continue; } else if(G[k] >= Gm1) continue; } else continue; --active_size; swap_index(k,active_size); --k; // look at the newcomer } // unshrink, check all variables again before final iterations if(unshrinked || -(Gm1 + Gm2) > eps*10) return; unshrinked = true; reconstruct_gradient(); for(k=l-1;k>=active_size;k--) { if(is_lower_bound(k)) { if(y[k]==+1) { if(-G[k] < Gm1) continue; } else if(-G[k] < Gm2) continue; } else if(is_upper_bound(k)) { if(y[k]==+1) { if(G[k] < Gm2) continue; } else if(G[k] < Gm1) continue; } else continue; swap_index(k,active_size); active_size++; ++k; // look at the newcomer } } double Solver::calculate_rho() { double r; int nr_free = 0; double ub = INF, lb = -INF, sum_free = 0; for(int i=0;i 0) ub = min(ub,yG); else lb = max(lb,yG); } else if(is_upper_bound(i)) { if(y[i] < 0) ub = min(ub,yG); else lb = max(lb,yG); } else { ++nr_free; sum_free += yG; } } if(nr_free>0) r = sum_free/nr_free; else r = (ub+lb)/2; return r; } // // Solver for nu-svm classification and regression // // additional constraint: e^T \alpha = constant // class Solver_NU : public Solver { public: Solver_NU() {} void Solve(int l, const QMatrix& Q, const double *b, const schar *y, double *alpha, double Cp, double Cn, double eps, SolutionInfo* si, int shrinking) { this->si = si; Solver::Solve(l,Q,b,y,alpha,Cp,Cn,eps,si,shrinking); } private: SolutionInfo *si; int select_working_set(int &i, int &j); double calculate_rho(); void do_shrinking(); }; // return 1 if already optimal, return 0 otherwise int Solver_NU::select_working_set(int &out_i, int &out_j) { // return i,j such that y_i = y_j and // i: maximizes -y_i * grad(f)_i, i in I_up(\alpha) // j: minimizes the decrease of obj value // (if quadratic coefficeint <= 0, replace it with tau) // -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha) double Gmaxp = -INF; double Gmaxp2 = -INF; int Gmaxp_idx = -1; double Gmaxn = -INF; double Gmaxn2 = -INF; int Gmaxn_idx = -1; int Gmin_idx = -1; double obj_diff_min = INF; for(int t=0;t= Gmaxp) { Gmaxp = -G[t]; Gmaxp_idx = t; } } else { if(!is_lower_bound(t)) if(G[t] >= Gmaxn) { Gmaxn = G[t]; Gmaxn_idx = t; } } int ip = Gmaxp_idx; int in = Gmaxn_idx; const Qfloat *Q_ip = NULL; const Qfloat *Q_in = NULL; if(ip != -1) // NULL Q_ip not accessed: Gmaxp=-INF if ip=-1 Q_ip = Q->get_Q(ip,active_size); if(in != -1) Q_in = Q->get_Q(in,active_size); for(int j=0;j= Gmaxp2) Gmaxp2 = G[j]; if (grad_diff > 0) { double obj_diff; double quad_coef = Q_ip[ip]+QD[j]-2*Q_ip[j]; if (quad_coef > 0) obj_diff = -(grad_diff*grad_diff)/quad_coef; else obj_diff = -(grad_diff*grad_diff)/TAU; if (obj_diff <= obj_diff_min) { Gmin_idx=j; obj_diff_min = obj_diff; } } } } else { if (!is_upper_bound(j)) { double grad_diff=Gmaxn-G[j]; if (-G[j] >= Gmaxn2) Gmaxn2 = -G[j]; if (grad_diff > 0) { double obj_diff; double quad_coef = Q_in[in]+QD[j]-2*Q_in[j]; if (quad_coef > 0) obj_diff = -(grad_diff*grad_diff)/quad_coef; else obj_diff = -(grad_diff*grad_diff)/TAU; if (obj_diff <= obj_diff_min) { Gmin_idx=j; obj_diff_min = obj_diff; } } } } } if(max(Gmaxp+Gmaxp2,Gmaxn+Gmaxn2) < eps) return 1; if (y[Gmin_idx] == +1) out_i = Gmaxp_idx; else out_i = Gmaxn_idx; out_j = Gmin_idx; return 0; } void Solver_NU::do_shrinking() { double Gmax1 = -INF; // max { -y_i * grad(f)_i | y_i = +1, i in I_up(\alpha) } double Gmax2 = -INF; // max { y_i * grad(f)_i | y_i = +1, i in I_low(\alpha) } double Gmax3 = -INF; // max { -y_i * grad(f)_i | y_i = -1, i in I_up(\alpha) } double Gmax4 = -INF; // max { y_i * grad(f)_i | y_i = -1, i in I_low(\alpha) } // find maximal violating pair first int k; for(k=0;k Gmax1) Gmax1 = -G[k]; } else if(-G[k] > Gmax3) Gmax3 = -G[k]; } if(!is_lower_bound(k)) { if(y[k]==+1) { if(G[k] > Gmax2) Gmax2 = G[k]; } else if(G[k] > Gmax4) Gmax4 = G[k]; } } // shrinking double Gm1 = -Gmax2; double Gm2 = -Gmax1; double Gm3 = -Gmax4; double Gm4 = -Gmax3; for(k=0;k= Gm1) continue; } else if(-G[k] >= Gm3) continue; } else if(is_upper_bound(k)) { if(y[k]==+1) { if(G[k] >= Gm2) continue; } else if(G[k] >= Gm4) continue; } else continue; --active_size; swap_index(k,active_size); --k; // look at the newcomer } // unshrink, check all variables again before final iterations if(unshrinked || max(-(Gm1+Gm2),-(Gm3+Gm4)) > eps*10) return; unshrinked = true; reconstruct_gradient(); for(k=l-1;k>=active_size;k--) { if(is_lower_bound(k)) { if(y[k]==+1) { if(-G[k] < Gm1) continue; } else if(-G[k] < Gm3) continue; } else if(is_upper_bound(k)) { if(y[k]==+1) { if(G[k] < Gm2) continue; } else if(G[k] < Gm4) continue; } else continue; swap_index(k,active_size); active_size++; ++k; // look at the newcomer } } double Solver_NU::calculate_rho() { int nr_free1 = 0,nr_free2 = 0; double ub1 = INF, ub2 = INF; double lb1 = -INF, lb2 = -INF; double sum_free1 = 0, sum_free2 = 0; for(int i=0;i 0) r1 = sum_free1/nr_free1; else r1 = (ub1+lb1)/2; if(nr_free2 > 0) r2 = sum_free2/nr_free2; else r2 = (ub2+lb2)/2; si->r = (r1+r2)/2; return (r1-r2)/2; } // // Q matrices for various formulations // class SVC_Q: public Kernel { public: SVC_Q(const svm_problem& prob, const svm_parameter& param, const schar *y_) :Kernel(prob.l, prob.x, param) { clone(y,y_,prob.l); cache = new Cache(prob.l,(int)(param.cache_size*(1<<20))); QD = new Qfloat[prob.l]; for(int i=0;i*kernel_function)(i,i); } Qfloat *get_Q(int i, int len) const { Qfloat *data; int start; if((start = cache->get_data(i,&data,len)) < len) { for(int j=start;j*kernel_function)(i,j)); } return data; } Qfloat *get_QD() const { return QD; } void swap_index(int i, int j) const { cache->swap_index(i,j); Kernel::swap_index(i,j); swap(y[i],y[j]); swap(QD[i],QD[j]); } ~SVC_Q() { delete[] y; delete cache; delete[] QD; } private: schar *y; Cache *cache; Qfloat *QD; }; class ONE_CLASS_Q: public Kernel { public: ONE_CLASS_Q(const svm_problem& prob, const svm_parameter& param) :Kernel(prob.l, prob.x, param) { cache = new Cache(prob.l,(int)(param.cache_size*(1<<20))); QD = new Qfloat[prob.l]; for(int i=0;i*kernel_function)(i,i); } Qfloat *get_Q(int i, int len) const { Qfloat *data; int start; if((start = cache->get_data(i,&data,len)) < len) { for(int j=start;j*kernel_function)(i,j); } return data; } Qfloat *get_QD() const { return QD; } void swap_index(int i, int j) const { cache->swap_index(i,j); Kernel::swap_index(i,j); swap(QD[i],QD[j]); } ~ONE_CLASS_Q() { delete cache; delete[] QD; } private: Cache *cache; Qfloat *QD; }; class SVR_Q: public Kernel { public: SVR_Q(const svm_problem& prob, const svm_parameter& param) :Kernel(prob.l, prob.x, param) { l = prob.l; cache = new Cache(l,(int)(param.cache_size*(1<<20))); QD = new Qfloat[2*l]; sign = new schar[2*l]; index = new int[2*l]; for(int k=0;k*kernel_function)(k,k); QD[k+l]=QD[k]; } buffer[0] = new Qfloat[2*l]; buffer[1] = new Qfloat[2*l]; next_buffer = 0; } void swap_index(int i, int j) const { swap(sign[i],sign[j]); swap(index[i],index[j]); swap(QD[i],QD[j]); } Qfloat *get_Q(int i, int len) const { Qfloat *data; int real_i = index[i]; if(cache->get_data(real_i,&data,l) < l) { for(int j=0;j*kernel_function)(real_i,j); } // reorder and copy Qfloat *buf = buffer[next_buffer]; next_buffer = 1 - next_buffer; schar si = sign[i]; for(int j=0;jl; double *minus_ones = new double[l]; schar *y = new schar[l]; int i; for(i=0;iy[i] > 0) y[i] = +1; else y[i]=-1; } Solver s; s.Solve(l, SVC_Q(*prob,*param,y), minus_ones, y, alpha, Cp, Cn, param->eps, si, param->shrinking); double sum_alpha=0; for(i=0;il)); for(i=0;il; double nu = param->nu; schar *y = new schar[l]; for(i=0;iy[i]>0) y[i] = +1; else y[i] = -1; double sum_pos = nu*l/2; double sum_neg = nu*l/2; for(i=0;ieps, si, param->shrinking); double r = si->r; info("C = %f\n",1/r); for(i=0;irho /= r; si->obj /= (r*r); si->upper_bound_p = 1/r; si->upper_bound_n = 1/r; delete[] y; delete[] zeros; } static void solve_one_class( const svm_problem *prob, const svm_parameter *param, double *alpha, Solver::SolutionInfo* si) { int l = prob->l; double *zeros = new double[l]; schar *ones = new schar[l]; int i; int n = (int)(param->nu*prob->l); // # of alpha's at upper bound for(i=0;il) alpha[n] = param->nu * prob->l - n; for(i=n+1;ieps, si, param->shrinking); delete[] zeros; delete[] ones; } static void solve_epsilon_svr( const svm_problem *prob, const svm_parameter *param, double *alpha, Solver::SolutionInfo* si) { int l = prob->l; double *alpha2 = new double[2*l]; double *linear_term = new double[2*l]; schar *y = new schar[2*l]; int i; for(i=0;ip - prob->y[i]; y[i] = 1; alpha2[i+l] = 0; linear_term[i+l] = param->p + prob->y[i]; y[i+l] = -1; } Solver s; s.Solve(2*l, SVR_Q(*prob,*param), linear_term, y, alpha2, param->C, param->C, param->eps, si, param->shrinking); double sum_alpha = 0; for(i=0;iC*l)); delete[] alpha2; delete[] linear_term; delete[] y; } static void solve_nu_svr( const svm_problem *prob, const svm_parameter *param, double *alpha, Solver::SolutionInfo* si) { int l = prob->l; double C = param->C; double *alpha2 = new double[2*l]; double *linear_term = new double[2*l]; schar *y = new schar[2*l]; int i; double sum = C * param->nu * l / 2; for(i=0;iy[i]; y[i] = 1; linear_term[i+l] = prob->y[i]; y[i+l] = -1; } Solver_NU s; s.Solve(2*l, SVR_Q(*prob,*param), linear_term, y, alpha2, C, C, param->eps, si, param->shrinking); info("epsilon = %f\n",-si->r); for(i=0;il); Solver::SolutionInfo si; switch(param->svm_type) { case C_SVC: solve_c_svc(prob,param,alpha,&si,Cp,Cn); break; case NU_SVC: solve_nu_svc(prob,param,alpha,&si); break; case ONE_CLASS: solve_one_class(prob,param,alpha,&si); break; case EPSILON_SVR: solve_epsilon_svr(prob,param,alpha,&si); break; case NU_SVR: solve_nu_svr(prob,param,alpha,&si); break; } info("obj = %f, rho = %f\n",si.obj,si.rho); // output SVs int nSV = 0; int nBSV = 0; for(int i=0;il;i++) { if(fabs(alpha[i]) > 0) { ++nSV; if(prob->y[i] > 0) { if(fabs(alpha[i]) >= si.upper_bound_p) ++nBSV; } else { if(fabs(alpha[i]) >= si.upper_bound_n) ++nBSV; } } } info("nSV = %d, nBSV = %d\n",nSV,nBSV); decision_function f; f.alpha = alpha; f.rho = si.rho; return f; } // // svm_model // struct svm_model { svm_parameter param; // parameter int nr_class; // number of classes, = 2 in regression/one class svm int l; // total #SV svm_node **SV; // SVs (SV[l]) double **sv_coef; // coefficients for SVs in decision functions (sv_coef[n-1][l]) double *rho; // constants in decision functions (rho[n*(n-1)/2]) double *probA; // pariwise probability information double *probB; // for classification only int *label; // label of each class (label[n]) int *nSV; // number of SVs for each class (nSV[n]) // nSV[0] + nSV[1] + ... + nSV[n-1] = l // XXX int free_sv; // 1 if svm_model is created by svm_load_model // 0 if svm_model is created by svm_train double obj; }; // Platt's binary SVM Probablistic Output: an improvement from Lin et al. void sigmoid_train( int l, const double *dec_values, const double *labels, double& A, double& B) { double prior1=0, prior0 = 0; int i; for (i=0;i 0) prior1+=1; else prior0+=1; int max_iter=100; // Maximal number of iterations double min_step=1e-10; // Minimal step taken in line search double sigma=1e-3; // For numerically strict PD of Hessian double eps=1e-5; double hiTarget=(prior1+1.0)/(prior1+2.0); double loTarget=1/(prior0+2.0); double *t=Malloc(double,l); double fApB,p,q,h11,h22,h21,g1,g2,det,dA,dB,gd,stepsize; double newA,newB,newf,d1,d2; int iter; // Initial Point and Initial Fun Value A=0.0; B=log((prior0+1.0)/(prior1+1.0)); double fval = 0.0; for (i=0;i0) t[i]=hiTarget; else t[i]=loTarget; fApB = dec_values[i]*A+B; if (fApB>=0) fval += t[i]*fApB + log(1+exp(-fApB)); else fval += (t[i] - 1)*fApB +log(1+exp(fApB)); } for (iter=0;iter= 0) { p=exp(-fApB)/(1.0+exp(-fApB)); q=1.0/(1.0+exp(-fApB)); } else { p=1.0/(1.0+exp(fApB)); q=exp(fApB)/(1.0+exp(fApB)); } d2=p*q; h11+=dec_values[i]*dec_values[i]*d2; h22+=d2; h21+=dec_values[i]*d2; d1=t[i]-p; g1+=dec_values[i]*d1; g2+=d1; } // Stopping Criteria if (fabs(g1)= min_step) { newA = A + stepsize * dA; newB = B + stepsize * dB; // New function value newf = 0.0; for (i=0;i= 0) newf += t[i]*fApB + log(1+exp(-fApB)); else newf += (t[i] - 1)*fApB +log(1+exp(fApB)); } // Check sufficient decrease if (newf=max_iter) info("Reaching maximal iterations in two-class probability estimates\n"); free(t); } double sigmoid_predict(double decision_value, double A, double B) { double fApB = decision_value*A+B; if (fApB >= 0) return exp(-fApB)/(1.0+exp(-fApB)); else return 1.0/(1+exp(fApB)) ; } // Method 2 from the multiclass_prob paper by Wu, Lin, and Weng void multiclass_probability(int k, double **r, double *p) { int t,j; int iter = 0, max_iter=max(100,k); double **Q=Malloc(double *,k); double *Qp=Malloc(double,k); double pQp, eps=0.005/k; for (t=0;tmax_error) max_error=error; } if (max_error=max_iter) info("Exceeds max_iter in multiclass_prob\n"); for(t=0;tl); double *dec_values = Malloc(double,prob->l); // random shuffle for(i=0;il;i++) perm[i]=i; for(i=0;il;i++) { int j = i+rand()%(prob->l-i); swap(perm[i],perm[j]); } for(i=0;il/nr_fold; int end = (i+1)*prob->l/nr_fold; int j,k; struct svm_problem subprob; subprob.l = prob->l-(end-begin); subprob.x = Malloc(struct svm_node*,subprob.l); subprob.y = Malloc(double,subprob.l); k=0; for(j=0;jx[perm[j]]; subprob.y[k] = prob->y[perm[j]]; ++k; } for(j=end;jl;j++) { subprob.x[k] = prob->x[perm[j]]; subprob.y[k] = prob->y[perm[j]]; ++k; } int p_count=0,n_count=0; for(j=0;j0) p_count++; else n_count++; if(p_count==0 && n_count==0) for(j=begin;j 0 && n_count == 0) for(j=begin;j 0) for(j=begin;jx[perm[j]],&(dec_values[perm[j]])); // ensure +1 -1 order; reason not using CV subroutine dec_values[perm[j]] *= submodel->label[0]; } svm_destroy_model(submodel); svm_destroy_param(&subparam); free(subprob.x); free(subprob.y); } } sigmoid_train(prob->l,dec_values,prob->y,probA,probB); free(dec_values); free(perm); } // Return parameter of a Laplace distribution double svm_svr_probability( const svm_problem *prob, const svm_parameter *param) { int i; int nr_fold = 5; double *ymv = Malloc(double,prob->l); double mae = 0; svm_parameter newparam = *param; newparam.probability = 0; svm_cross_validation(prob,&newparam,nr_fold,ymv); for(i=0;il;i++) { ymv[i]=prob->y[i]-ymv[i]; mae += fabs(ymv[i]); } mae /= prob->l; double std=sqrt(2*mae*mae); int count=0; mae=0; for(i=0;il;i++) if (fabs(ymv[i]) > 5*std) count=count+1; else mae+=fabs(ymv[i]); mae /= (prob->l-count); info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma= %g\n",mae); free(ymv); return mae; } // label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data // perm, length l, must be allocated before calling this subroutine void svm_group_classes(const svm_problem *prob, int *nr_class_ret, int **label_ret, int **start_ret, int **count_ret, int *perm) { int l = prob->l; int max_nr_class = 16; int nr_class = 0; int *label = Malloc(int,max_nr_class); int *count = Malloc(int,max_nr_class); int *data_label = Malloc(int,l); int i; for(i=0;iy[i]; int j; for(j=0;jparam = *param; model->free_sv = 0; // XXX if(param->svm_type == ONE_CLASS || param->svm_type == EPSILON_SVR || param->svm_type == NU_SVR) { // regression or one-class-svm model->nr_class = 2; model->label = NULL; model->nSV = NULL; model->probA = NULL; model->probB = NULL; model->sv_coef = Malloc(double *,1); if(param->probability && (param->svm_type == EPSILON_SVR || param->svm_type == NU_SVR)) { model->probA = Malloc(double,1); model->probA[0] = svm_svr_probability(prob,param); } decision_function f = svm_train_one(prob,param,0,0); model->rho = Malloc(double,1); model->rho[0] = f.rho; int nSV = 0; int i; for(i=0;il;i++) if(fabs(f.alpha[i]) > 0) ++nSV; model->l = nSV; model->SV = Malloc(svm_node *,nSV); model->sv_coef[0] = Malloc(double,nSV); int j = 0; for(i=0;il;i++) if(fabs(f.alpha[i]) > 0) { model->SV[j] = prob->x[i]; model->sv_coef[0][j] = f.alpha[i]; ++j; } free(f.alpha); } else { // classification int l = prob->l; int nr_class; int *label = NULL; int *start = NULL; int *count = NULL; int *perm = Malloc(int,l); // group training data of the same class svm_group_classes(prob,&nr_class,&label,&start,&count,perm); svm_node **x = Malloc(svm_node *,l); int i; for(i=0;ix[perm[i]]; // calculate weighted C double *weighted_C = Malloc(double, nr_class); for(i=0;iC; for(i=0;inr_weight;i++) { int j; for(j=0;jweight_label[i] == label[j]) break; if(j == nr_class) fprintf(stderr,"warning: class label %d specified in weight is not found\n", param->weight_label[i]); else weighted_C[j] *= param->weight[i]; } // train k*(k-1)/2 models bool *nonzero = Malloc(bool,l); for(i=0;iprobability) { probA=Malloc(double,nr_class*(nr_class-1)/2); probB=Malloc(double,nr_class*(nr_class-1)/2); } int p = 0; for(i=0;iprobability) svm_binary_svc_probability(&sub_prob,param,weighted_C[i],weighted_C[j],probA[p],probB[p]); f[p] = svm_train_one(&sub_prob,param,weighted_C[i],weighted_C[j]); for(k=0;k 0) nonzero[si+k] = true; for(k=0;k 0) nonzero[sj+k] = true; free(sub_prob.x); free(sub_prob.y); ++p; } // build output model->nr_class = nr_class; model->label = Malloc(int,nr_class); for(i=0;ilabel[i] = label[i]; model->rho = Malloc(double,nr_class*(nr_class-1)/2); for(i=0;irho[i] = f[i].rho; if(param->probability) { model->probA = Malloc(double,nr_class*(nr_class-1)/2); model->probB = Malloc(double,nr_class*(nr_class-1)/2); for(i=0;iprobA[i] = probA[i]; model->probB[i] = probB[i]; } } else { model->probA=NULL; model->probB=NULL; } int total_sv = 0; int *nz_count = Malloc(int,nr_class); model->nSV = Malloc(int,nr_class); for(i=0;inSV[i] = nSV; nz_count[i] = nSV; } info("Total nSV = %d\n",total_sv); model->l = total_sv; model->SV = Malloc(svm_node *,total_sv); p = 0; for(i=0;iSV[p++] = x[i]; int *nz_start = Malloc(int,nr_class); nz_start[0] = 0; for(i=1;isv_coef = Malloc(double *,nr_class-1); for(i=0;isv_coef[i] = Malloc(double,total_sv); p = 0; for(i=0;isv_coef[j-1][q++] = f[p].alpha[k]; q = nz_start[j]; for(k=0;ksv_coef[i][q++] = f[p].alpha[ci+k]; ++p; } free(label); free(probA); free(probB); free(count); free(perm); free(start); free(x); free(weighted_C); free(nonzero); for(i=0;il; int *perm = Malloc(int,l); int nr_class; // stratified cv may not give leave-one-out rate // Each class to l folds -> some folds may have zero elements if((param->svm_type == C_SVC || param->svm_type == NU_SVC) && nr_fold < l) { int *start = NULL; int *label = NULL; int *count = NULL; svm_group_classes(prob,&nr_class,&label,&start,&count,perm); // random shuffle and then data grouped by fold using the array perm int *fold_count = Malloc(int,nr_fold); int c; int *index = Malloc(int,l); for(i=0;ix[perm[j]]; subprob.y[k] = prob->y[perm[j]]; ++k; } for(j=end;jx[perm[j]]; subprob.y[k] = prob->y[perm[j]]; ++k; } struct svm_model *submodel = svm_train(&subprob,param); if(param->probability && (param->svm_type == C_SVC || param->svm_type == NU_SVC)) { double *prob_estimates=Malloc(double,svm_get_nr_class(submodel)); for(j=begin;jx[perm[j]],prob_estimates); free(prob_estimates); } else for(j=begin;jx[perm[j]]); svm_destroy_model(submodel); free(subprob.x); free(subprob.y); } free(fold_start); free(perm); } int svm_get_svm_type(const svm_model *model) { return model->param.svm_type; } int svm_get_nr_class(const svm_model *model) { return model->nr_class; } void svm_get_labels(const svm_model *model, int* label) { if (model->label != NULL) for(int i=0;inr_class;i++) label[i] = model->label[i]; } double svm_get_svr_probability(const svm_model *model) { if ((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) && model->probA!=NULL) return model->probA[0]; else { info("Model doesn't contain information for SVR probability inference\n"); return 0; } } void svm_predict_values(const svm_model *model, const svm_node *x, double* dec_values) { if(model->param.svm_type == ONE_CLASS || model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) { double *sv_coef = model->sv_coef[0]; double sum = 0; for(int i=0;il;i++) sum += sv_coef[i] * Kernel::k_function(x,model->SV[i],model->param); sum -= model->rho[0]; *dec_values = sum; } else { int i; int nr_class = model->nr_class; int l = model->l; double *kvalue = Malloc(double,l); for(i=0;iSV[i],model->param); int *start = Malloc(int,nr_class); start[0] = 0; for(i=1;inSV[i-1]; int p=0; int pos=0; for(i=0;inSV[i]; int cj = model->nSV[j]; int k; double *coef1 = model->sv_coef[j-1]; double *coef2 = model->sv_coef[i]; for(k=0;krho[p++]; dec_values[pos++] = sum; } free(kvalue); free(start); } } double svm_predict(const svm_model *model, const svm_node *x) { if(model->param.svm_type == ONE_CLASS || model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) { double res; svm_predict_values(model, x, &res); if(model->param.svm_type == ONE_CLASS) return (res>0)?1:-1; else return res; } else { int i; int nr_class = model->nr_class; double *dec_values = Malloc(double, nr_class*(nr_class-1)/2); svm_predict_values(model, x, dec_values); int *vote = Malloc(int,nr_class); for(i=0;i 0) ++vote[i]; else ++vote[j]; } int vote_max_idx = 0; for(i=1;i vote[vote_max_idx]) vote_max_idx = i; free(vote); free(dec_values); return model->label[vote_max_idx]; } } double svm_predict_probability( const svm_model *model, const svm_node *x, double *prob_estimates) { if ((model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC) && model->probA!=NULL && model->probB!=NULL) { int i; int nr_class = model->nr_class; double *dec_values = Malloc(double, nr_class*(nr_class-1)/2); svm_predict_values(model, x, dec_values); double min_prob=1e-7; double **pairwise_prob=Malloc(double *,nr_class); for(i=0;iprobA[k],model->probB[k]),min_prob),1-min_prob); pairwise_prob[j][i]=1-pairwise_prob[i][j]; k++; } multiclass_probability(nr_class,pairwise_prob,prob_estimates); int prob_max_idx = 0; for(i=1;i prob_estimates[prob_max_idx]) prob_max_idx = i; for(i=0;ilabel[prob_max_idx]; } else return svm_predict(model, x); } const char *svm_type_table[] = { "c_svc","nu_svc","one_class","epsilon_svr","nu_svr",NULL }; const char *kernel_type_table[]= { "linear","polynomial","rbf","sigmoid",NULL }; int svm_save_model(const char *model_file_name, const svm_model *model) { FILE *fp = fopen(model_file_name,"w"); if(fp==NULL) return -1; const svm_parameter& param = model->param; fprintf(fp,"svm_type %s\n", svm_type_table[param.svm_type]); fprintf(fp,"kernel_type %s\n", kernel_type_table[param.kernel_type]); if(param.kernel_type == POLY) fprintf(fp,"degree %g\n", param.degree); if(param.kernel_type == POLY || param.kernel_type == RBF || param.kernel_type == SIGMOID) fprintf(fp,"gamma %g\n", param.gamma); if(param.kernel_type == POLY || param.kernel_type == SIGMOID) fprintf(fp,"coef0 %g\n", param.coef0); int nr_class = model->nr_class; int l = model->l; fprintf(fp, "nr_class %d\n", nr_class); fprintf(fp, "total_sv %d\n",l); { fprintf(fp, "rho"); for(int i=0;irho[i]); fprintf(fp, "\n"); } if(model->label) { fprintf(fp, "label"); for(int i=0;ilabel[i]); fprintf(fp, "\n"); } if(model->probA) // regression has probA only { fprintf(fp, "probA"); for(int i=0;iprobA[i]); fprintf(fp, "\n"); } if(model->probB) { fprintf(fp, "probB"); for(int i=0;iprobB[i]); fprintf(fp, "\n"); } if(model->nSV) { fprintf(fp, "nr_sv"); for(int i=0;inSV[i]); fprintf(fp, "\n"); } fprintf(fp, "SV\n"); const double * const *sv_coef = model->sv_coef; const svm_node * const *SV = model->SV; for(int i=0;iindex != -1) { fprintf(fp,"%d:%.8g ",p->index,p->value); p++; } fprintf(fp, "\n"); } fclose(fp); return 0; } svm_model *svm_load_model(const char *model_file_name) { FILE *fp = fopen(model_file_name,"rb"); if(fp==NULL) return NULL; // read parameters svm_model *model = Malloc(svm_model,1); svm_parameter& param = model->param; model->rho = NULL; model->probA = NULL; model->probB = NULL; model->label = NULL; model->nSV = NULL; char cmd[81]; while(1) { fscanf(fp,"%80s",cmd); if(strcmp(cmd,"svm_type")==0) { fscanf(fp,"%80s",cmd); int i; for(i=0;svm_type_table[i];i++) { if(strcmp(svm_type_table[i],cmd)==0) { param.svm_type=i; break; } } if(svm_type_table[i] == NULL) { fprintf(stderr,"unknown svm type.\n"); free(model->rho); free(model->label); free(model->nSV); free(model); return NULL; } } else if(strcmp(cmd,"kernel_type")==0) { fscanf(fp,"%80s",cmd); int i; for(i=0;kernel_type_table[i];i++) { if(strcmp(kernel_type_table[i],cmd)==0) { param.kernel_type=i; break; } } if(kernel_type_table[i] == NULL) { fprintf(stderr,"unknown kernel function.\n"); free(model->rho); free(model->label); free(model->nSV); free(model); return NULL; } } else if(strcmp(cmd,"degree")==0) fscanf(fp,"%lf",¶m.degree); else if(strcmp(cmd,"gamma")==0) fscanf(fp,"%lf",¶m.gamma); else if(strcmp(cmd,"coef0")==0) fscanf(fp,"%lf",¶m.coef0); else if(strcmp(cmd,"nr_class")==0) fscanf(fp,"%d",&model->nr_class); else if(strcmp(cmd,"total_sv")==0) fscanf(fp,"%d",&model->l); else if(strcmp(cmd,"rho")==0) { int n = model->nr_class * (model->nr_class-1)/2; model->rho = Malloc(double,n); for(int i=0;irho[i]); } else if(strcmp(cmd,"label")==0) { int n = model->nr_class; model->label = Malloc(int,n); for(int i=0;ilabel[i]); } else if(strcmp(cmd,"probA")==0) { int n = model->nr_class * (model->nr_class-1)/2; model->probA = Malloc(double,n); for(int i=0;iprobA[i]); } else if(strcmp(cmd,"probB")==0) { int n = model->nr_class * (model->nr_class-1)/2; model->probB = Malloc(double,n); for(int i=0;iprobB[i]); } else if(strcmp(cmd,"nr_sv")==0) { int n = model->nr_class; model->nSV = Malloc(int,n); for(int i=0;inSV[i]); } else if(strcmp(cmd,"SV")==0) { while(1) { int c = getc(fp); if(c==EOF || c=='\n') break; } break; } else { fprintf(stderr,"unknown text in model file\n"); free(model->rho); free(model->label); free(model->nSV); free(model); return NULL; } } // read sv_coef and SV int elements = 0; long pos = ftell(fp); while(1) { int c = fgetc(fp); switch(c) { case '\n': // count the '-1' element case ':': ++elements; break; case EOF: goto out; default: ; } } out: fseek(fp,pos,SEEK_SET); int m = model->nr_class - 1; int l = model->l; model->sv_coef = Malloc(double *,m); int i; for(i=0;isv_coef[i] = Malloc(double,l); model->SV = Malloc(svm_node*,l); svm_node *x_space=NULL; if(l>0) x_space = Malloc(svm_node,elements); int j=0; for(i=0;iSV[i] = &x_space[j]; for(int k=0;ksv_coef[k][i]); while(1) { int c; do { c = getc(fp); if(c=='\n') goto out2; } while(isspace(c)); ungetc(c,fp); fscanf(fp,"%d:%lf",&(x_space[j].index),&(x_space[j].value)); ++j; } out2: x_space[j++].index = -1; } fclose(fp); model->free_sv = 1; // XXX return model; } void svm_destroy_model(svm_model* model) { if(model->free_sv && model->l > 0) free((void *)(model->SV[0])); for(int i=0;inr_class-1;i++) free(model->sv_coef[i]); free(model->SV); free(model->sv_coef); free(model->rho); free(model->label); free(model->probA); free(model->probB); free(model->nSV); free(model); } void svm_destroy_param(svm_parameter* param) { free(param->weight_label); free(param->weight); } const char *svm_check_parameter(const svm_problem *prob, const svm_parameter *param) { // svm_type int svm_type = param->svm_type; if(svm_type != C_SVC && svm_type != NU_SVC && svm_type != ONE_CLASS && svm_type != EPSILON_SVR && svm_type != NU_SVR) return "unknown svm type"; // kernel_type int kernel_type = param->kernel_type; if(kernel_type != LINEAR && kernel_type != POLY && kernel_type != RBF && kernel_type != SIGMOID) return "unknown kernel type"; // cache_size,eps,C,nu,p,shrinking if(param->cache_size <= 0) return "cache_size <= 0"; if(param->eps <= 0) return "eps <= 0"; if(svm_type == C_SVC || svm_type == EPSILON_SVR || svm_type == NU_SVR) if(param->C <= 0) return "C <= 0"; if(svm_type == NU_SVC || svm_type == ONE_CLASS || svm_type == NU_SVR) if(param->nu <= 0 || param->nu > 1) return "nu <= 0 or nu > 1"; if(svm_type == EPSILON_SVR) if(param->p < 0) return "p < 0"; if(param->shrinking != 0 && param->shrinking != 1) return "shrinking != 0 and shrinking != 1"; if(param->probability != 0 && param->probability != 1) return "probability != 0 and probability != 1"; if(param->probability == 1 && svm_type == ONE_CLASS) return "one-class SVM probability output not supported yet"; // check whether nu-svc is feasible if(svm_type == NU_SVC) { int l = prob->l; int max_nr_class = 16; int nr_class = 0; int *label = Malloc(int,max_nr_class); int *count = Malloc(int,max_nr_class); int i; for(i=0;iy[i]; int j; for(j=0;jnu*(n1+n2)/2 > min(n1,n2)) { free(label); free(count); return "specified nu is infeasible"; } } } free(label); free(count); } return NULL; } int svm_check_probability_model(const svm_model *model) { return ((model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC) && model->probA!=NULL && model->probB!=NULL) || ((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) && model->probA!=NULL); } mpi-ikl-simplemkl-1.0/libsvm/rbf.m0000644000335100017570000000032511133661707016614 0ustar pgehlerwebadm% K = rbf(X1,X2,sig) % % computes: % K = exp(-||X1-X2||/(2*sig^2)); function K = rbf(X1,X2,sig) if numel(sig)~=1 error('third argument (sigma) must be a scalar'); end K = exp(-dist_euclid(X1,X2)/(2*sig^2)); mpi-ikl-simplemkl-1.0/libsvm/svm.h0000644000335100017570000000423011133661707016642 0ustar pgehlerwebadm#ifndef _LIBSVM_H #define _LIBSVM_H #ifdef __cplusplus extern "C" { #endif struct svm_node { int index; double value; }; struct svm_problem { int l; double *y; struct svm_node **x; }; enum { C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR }; /* svm_type */ enum { LINEAR, POLY, RBF, SIGMOID,CUSTOM }; /* kernel_type */ struct svm_parameter { int svm_type; int kernel_type; double degree; /* for poly */ double gamma; /* for poly/rbf/sigmoid */ double coef0; /* for poly/sigmoid */ /* these are for training only */ double cache_size; /* in MB */ double eps; /* stopping criteria */ double C; /* for C_SVC, EPSILON_SVR and NU_SVR */ int nr_weight; /* for C_SVC */ int *weight_label; /* for C_SVC */ double* weight; /* for C_SVC */ double nu; /* for NU_SVC, ONE_CLASS, and NU_SVR */ double p; /* for EPSILON_SVR */ int shrinking; /* use the shrinking heuristics */ int probability; /* do probability estimates */ }; struct svm_model *svm_train(const struct svm_problem *prob, const struct svm_parameter *param); void svm_cross_validation(const struct svm_problem *prob, const struct svm_parameter *param, int nr_fold, double *target); int svm_save_model(const char *model_file_name, const struct svm_model *model); struct svm_model *svm_load_model(const char *model_file_name); int svm_get_svm_type(const struct svm_model *model); int svm_get_nr_class(const struct svm_model *model); void svm_get_labels(const struct svm_model *model, int *label); double svm_get_svr_probability(const struct svm_model *model); void svm_predict_values(const struct svm_model *model, const struct svm_node *x, double* dec_values); double svm_predict(const struct svm_model *model, const struct svm_node *x); double svm_predict_probability(const struct svm_model *model, const struct svm_node *x, double* prob_estimates); void svm_destroy_model(struct svm_model *model); void svm_destroy_param(struct svm_parameter *param); const char *svm_check_parameter(const struct svm_problem *prob, const struct svm_parameter *param); int svm_check_probability_model(const struct svm_model *model); #ifdef __cplusplus } #endif #endif /* _LIBSVM_H */ mpi-ikl-simplemkl-1.0/libsvm/libsvm_classifier.mexa640000755000335100017570000022561211133661707022426 0ustar pgehlerwebadmELF><@H@8@#   h  PtdQtd{FEV^U|, O9i"I+nr#x?-bm$.KT 0'(]>~ovp8!kPte12w;%j)a:cgd/QN_MBR @<=35S 7&`[\L*q6XWZfh zyJu YDsG4l}ACHa1 D 0## AC@K$ C QH *@t< "@@:H($H(HL0  @PUj%1234568:;=?@BCDEGHKLPQRTUVW\]^_adefhijnopqstvz|~--W=ͻ/GێTKqdMro^-XQdtO(diOu3I$.qX;m41o0|}b1mMs5LF1\[ җ U=#lfUw.. 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if ~exist('X2','var') || numel(X2)==0 D = ones(length(X1X1),1)*X1X1' + X1X1*ones(1,length(X1X1)) - 2*(X1*X1'); else X2X2 = (sum(X2.^2,2)); D = ones(length(X1X1),1)*X2X2' + X1X1*ones(1,length(X2X2)) - 2*(X1*X2'); end else X1X1 = (sum((X1*A).^2,2)); if ~exist('X2','var') || numel(X2)==0 X2X2 = X1X1; else X2X2 = (sum((X2*A).^2,2)); end D = ones(length(X1X1),1)*X2X2' + X1X1*ones(1,length(X2X2)) - 2*(X1*A*X2'); end mpi-ikl-simplemkl-1.0/libsvm/libsvm.m0000644000335100017570000001371211133661707017343 0ustar pgehlerwebadm% [alpha,b,obj] = libsvm(y,K,C,kernel,l1) % % Interface to the LIBSVM for matlab % In: % 'y' - labels % 'K' - either Kernel matrix or training data % 'C' - regularization parameter % optional % 'kernel' - struct with kernel defintion % 'l1' - if 1 : l1 penalization, otherwise: l2 penalization % % out: % 'alpha','b' - svm parameter % 'obj' - objective value % % prediction is sign(K*alpha+b) % % Peter Gehler, March 6th 2008 % pgehler@tuebingen.mpg.de function [alpha,b,obj] = libsvm(y,K,C,kernel,l1,varargin) if nargin < 3 error('invalid number of arguments to libsvm'); end % % default values % if ~exist('l1','var') l1 = 1; end if size(y,1) == 1 y = y'; end if size(y,1) ~= size(K,1) error('Dimension mismatch between labels and Kern matrix'); end if size(y,2) ~= 1 error('invalid format of y'); end if (size(K,1) ~= size(K,2)) || ndims(K)~=2 error('invalid format of Kern matrix'); end kernelType = 4; %Custom Kernel cachesize = 40; % in MB gamma = 0; % not needed for custom kernel degree = 3; % degree for polynomial svm_type = 0; %>1 for nu-svm nu = 0; % nu-svm p=0.001; shrinking = 1; balanced_ridge = 0; coef0 = 1; svm_eps = 1e-5; weights = []; if nargin > 5 for i=1:2:nargin-5 switch varargin{i} case 'svm_eps',svm_eps = varargin{i+1}; case 'weights',weights = varargin{i+1}; otherwise error(['unknown argument ''',varargin{i},'''\n']); end end end if ~exist('kernel,','var') || ~numel(kernel) clear kernel kernel.type = 'custom'; end switch kernel.type case 'rbf' gamma = kernel.gamma; case 'poly' % 1 -- polynomial: (gamma*u'*v + coef0)^degree degree = kernel.degree; gamma = kernel.gamma; coef0 = kernel.coef0; degree = kernel.degree; case 'custom' x = repmat( (1:size(K,1))',1,2); otherwise error('unknown kernel'); end number_classes = numel(unique(y)); if number_classes > 2 % Multiclass : 1 vs Rest classes = unique(y); for cl = 1:number_classes trY = y; trY( y~=classes(cl) ) = -1; trY( y==classes(cl) ) = 1; assert( sum((trY==1)|(trY==-1)) == numel(trY)); [alpha(:,cl),b(cl)] = libsvm_train(x,trY,C,svm_eps,kernel,gamma,p,shrinking,degree,coef0,K,l1,weights); if nargout > 2 obj(cl,1) = libsvm_objective(trY,K,alpha(:,cl),b(cl),C,l1); end end assert(size(alpha,2) == number_classes); assert(numel(b) == number_classes); if nargout > 2 assert(numel(obj)==number_classes); end elseif number_classes == 2 % binary if min(y(:)) ~= -1 || max(y(:))~=1 error('labels must be in set {-1,+1}'); end [alpha,b] = libsvm_train(x,y,C,svm_eps,kernel,gamma,p,shrinking,degree,coef0,K,l1,weights); assert(numel(alpha) == size(K,1)); assert(numel(b) == 1); if nargout > 2 obj = libsvm_objective(y,K,alpha,b,C,l1); end else error('invalid number of classes in y'); end function [alpha,b] = libsvm_train(x,y,C,svm_eps,kernel,gamma,p,shrinking,degree,coef0,K,l1,weights) assert(size(x,1)==size(y,1)); assert(size(y,2)==1); assert(size(K,1)==size(K,2)); assert(size(K,1)==numel(y)); assert(isfield(kernel,'type')>0); switch kernel.type case 'rbf' kernelType = 2; case 'poly' % 1 -- polynomial: (gamma*u'*v + coef0)^degree kernelType = 1; case 'custom' kernelType = 4; otherwise error('unknown kernel'); end if numel(weights)==0 switch kernel.type case 'custom' if l1 [al,xSV,bias0]=libsvm_classifier({'X',double(x)},{'Y',y}, ... {'C',C},... {'eps',svm_eps},{'kerneltype',kernelType},{'gamma',gamma},... {'p',p},{'shrinking',shrinking},... {'degree',degree},... {'coef0', coef0},... {'kmatrix',double(K)}); else [al,xSV,bias0]=libsvm_classifier({'X',double(x)},{'Y',y}, ... {'C',1e6},... {'eps',svm_eps},{'kerneltype',kernelType},{'gamma',gamma},... {'p',p},{'shrinking',shrinking},... {'degree',degree},... {'coef0', coef0},... {'kmatrix',double(K)+eye(size(K,1))/C}); end otherwise if l1 [al,xSV,bias0]=libsvm_classifier({'X',double(K)},{'Y',y}, ... {'C',C},... {'eps',svm_eps},{'kerneltype',kernelType},{'gamma',gamma},... {'p',p},{'shrinking',shrinking},... {'degree',degree},... {'coef0', coef0}); else error('currently not supported'); end end else fprintf('using weights!\n'); switch kernel.type case 'custom' if l1 [al,xSV,bias0]=libsvm_classifier_weight({'X',double(x)},{'Y',y}, ... {'C',C},... {'W',weights},... {'eps',svm_eps},{'kerneltype',kernelType},{'gamma',gamma},... {'p',p},{'shrinking',shrinking},... {'degree',degree},... {'coef0', coef0},... {'kmatrix',double(K)}); else [al,xSV,bias0]=libsvm_classifier_weight({'X',double(x)},{'Y',y}, ... {'C',1e6},... {'W',weights},... {'eps',svm_eps},{'kerneltype',kernelType},{'gamma',gamma},... {'p',p},{'shrinking',shrinking},... {'degree',degree},... {'coef0', coef0},... {'kmatrix',double(K)+eye(size(K,1))/C}); end otherwise if l1 [al,xSV,bias0]=libsvm_classifier_weight({'X',double(K)},{'Y',y}, ... {'C',C},... {'W',weights},... {'eps',svm_eps},{'kerneltype',kernelType},{'gamma',gamma},... {'p',p},{'shrinking',shrinking},... {'degree',degree},... {'coef0', coef0}); else error('currently not supported'); end end end assert(max(xSV(:)) <= numel(y)); b = bias0 * y(1); al = al * y(1); alpha = zeros(numel(y),1); alpha(xSV(:,1)) = al; function [obj] = libsvm_objective(y,K,alpha,b,C,l1); if numel(K) == 0 %if ~strcmp(kernel.type,'custom') warning('objective calculation only supported for custom kernel'); obj = Inf; return; end Kalpha = K*alpha; out = Kalpha + b; sv = find(out<1); if l1 obj = 0.5 * (sum(abs(alpha))/C + sum( abs((1-y(sv).*out(sv))))); else obj = 0.5 * (alpha'*Kalpha/C + sum( (1-y(sv).*out(sv)).^2)); end mpi-ikl-simplemkl-1.0/libsvm/libsvm_classifier.mexglx0000755000335100017570000021160411133661707022622 0ustar pgehlerwebadmELF+44 (# EEHHH|||PtdxxxTTQtdwBARZQx3K 5e1E$jn~Ot;'^i#)GPW&Y(:zk"rl4*gLpa-.s7+!]6_`|,MJ[I>fN< 9cH{%\/X  m2T SVbd}vuF8q U@oC0hy=?Da- D #2P# ACKXA  QH(C1T< H@6@((H H!M  @ @Q J4-./0124679;<>?@ACDGHLMNPQRSXYZ[]`abdefjklmoprvxz{|}~--W=ͻ/GێTKqdMro^-XQdtO(diOu3I$.qX;m41o0|}b1mMs5LF1\[ җ U=#lfUw.. 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t$G0t$cw4t4$d4$=dG8t$nc<$e<$d\$t$|$ÉƉ<$d4$f\$g\4|$|$ t$G0t$cw4t4$uc4$cG8t$b<$d\$t$|$ÉƉ<$ld4$Tf,|$$T$0l$(|$8l$4\$gø3t$ |$l$B0$?eD$0P D$4‰D$ T$0BtT$0B4T$4D$\$t$ |$$l$(,Ð,|$$D$4l$(|$8l$0\$gf3t$ |$D$E4$dU D$4L$4 ‰D$ EtL$4E048D$4E8T$4D$\$t$ |$$l$(,Ë $Ð1|$Ðt&UWVS,$LeB2$D$H$PD$e$(1$@t$T$l$$`T$(1T$t&9to$`tD$$"bT$D$T$$_D$T$D$$duD$$aT$1$(e3u ,[^_]ut&UWVS,$Ld"1$D$H$PD$e$(1$@t$T$l$$_T$(1T$t&9$d_tD$$`T$D$T$$^D$T$D$$buD$$`$TT$T$$T$^1$(e3u ,[^_]<UWVS,$LAc/$D$He$(1҉D$$@l$|$$^ut~pD$(1D$&9tV$:^tD$$_T$D$T$$]D$T$D$$au1$(e3vu ,[^_]>&'UWVS,$LAb.$D$H$PD$e$(1$@t$T$l$$]T$(1T$t&9tw$(]tD$$^T$D$T$$\D$T$D$$`uD$$^$H`D$1$(e3u ,[^_] USaÿ-(_UVS`Þ-P@tЋFu[^]US[h-$`Y[CXYepspshrinkingkmatrixbalanced_ridgecoef0nucachesizedegreegammasvm_typeLibsvm interface. Used Libsvm version : %s Usage: [alpha,xSV,bias0]=libsvm_classifier_spider({'X',doublematrix},{'Y',doublematrix}, {'C',scalar},{'kerneltype',scalar}, {'degree',scalar},{'gamma',scalar}, {'coef0',scalar},{'nu',scalar},{'cachesize',scalar}, {'eps',scalar},{'shrinking',scalar},{'p',scalar},{'balanced_ridge', optional scalar},{'kmatrix',K})?Model doesn't contain information for SVR probability inference Exceeds max_iter in multiclass_prob Reaching maximal iterations in two-class probability estimates Line search fails in two-class probability estimates shrinking != 0 and shrinking != 1probability != 0 and probability != 1one-class SVM probability output not supported yet optimization finished, #iter = %d warning: class label %d specified in weight is not found Prob. model for test data: target value = predicted value + z, z: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma= %g rb%80skernel_typenr_classtotal_svrholabelprobAprobBnr_svSVunknown text in model file %d:%lfunknown svm type. unknown kernel function. wsvm_type %s kernel_type %s degree %g gamma %g coef0 %g nr_class %d total_sv %d %g %dSV %.16g %d:%.8g unknown svm typeunknown kernel typecache_size <= 0eps <= 0C <= 0p < 0specified nu is infeasiblenu <= 0 or nu > 1.*nu = %f C = %f epsilon = %f obj = %f, rho = %f nSV = %d, nBSV = %d Total nSV = %d c_svcnu_svcone_classepsilon_svrnu_svrlinearpolynomialrbfsigmoidtttt6tÀՀvMϙ$9Solver_NU6Solver6Kernel7QMatrix5SVC_Q11ONE_CLASS_Q5SVR_Qm{Gzt?MbP?h㈵>-C6?-q=Hz>P?@? 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X is an n x d matrix (n = number of points). % If LINEAR is 0, a global variable K (the n x n kernel matrix) should be defined. % Y is the target vector (+1 or -1, length n). % LAMBDA is the regularization parameter ( = 1/C) % % IF LINEAR is 0, SOL is the expansion of the solution (vector beta of length n). % IF LINEAR is 1, SOL is the hyperplane w (vector of length d). % B is the bias % The outputs on the training points are either K*SOL+B or X*SOL+B % OBJ is the objective function value % % OPT is a structure containing the options (in brackets default values): % cg: Do not use Newton, but nonlinear conjugate gradients [0] % lin_cg: Compute the Newton step with linear CG % [0 unless solving sparse linear SVM] % iter_max_Newton: Maximum number of Newton steps [20] % prec: Stopping criterion % cg_prec and cg_it: stopping criteria for the linear CG. % Copyright Olivier Chapelle, olivier.chapelle@tuebingen.mpg.de % Last modified 25/08/2006 if nargin < 4 % Assign the options to their default values opt = []; end; if ~isfield(opt,'cg'), opt.cg = 0; end; if ~isfield(opt,'lin_cg'), opt.lin_cg = 0; end; if ~isfield(opt,'iter_max_Newton'), opt.iter_max_Newton = 20; end; if ~isfield(opt,'prec'), opt.prec = 1e-6; end; if ~isfield(opt,'cg_prec'), opt.cg_prec = 1e-4; end; if ~isfield(opt,'cg_it'), opt.cg_it = 20; end; % Call the right function depending on problem type and CG / Newton % Also check that X / K exists and that the dimension of Y is correct if linear global X; if isempty(X), error('Global variable X undefined'); end; [n,d] = size(X); if issparse(X), opt.lin_cg = 1; end; if size(Y,1)~=n, error('Dimension error'); end; if ~opt.cg [sol,obj] = primal_svm_linear (Y,lambda,opt); else [sol,obj] = primal_svm_linear_cg(Y,lambda,opt); end; else global K; if isempty(K), error('Global variable K undefined'); end; n = size(Y,1); if any(size(K)~=n), error('Dimension error'); end; if ~opt.cg [sol,obj] = primal_svm_nonlinear (Y,lambda,opt); else [sol,obj] = primal_svm_nonlinear_cg(Y,lambda,opt); end; end; % The last component of the solution is the bias b. b = sol(end); sol = sol(1:end-1); % fprintf('\n'); function [w,obj] = primal_svm_linear(Y,lambda,opt) % ------------------------------- % Train a linear SVM using Newton % ------------------------------- global X; [n,d] = size(X); w = zeros(d+1,1); % The last component of w is b. iter = 0; out = ones(n,1); % Vector containing 1-Y.*(X*w) while 1 iter = iter + 1; if iter > opt.iter_max_Newton; warning(sprintf(['Maximum number of Newton steps reached.' ... 'Try larger lambda'])); break; end; [obj, grad, sv] = obj_fun_linear(w,Y,lambda,out); % Compute the Newton direction either exactly or by linear CG if opt.lin_cg % Advantage of linear CG when using sparse input: the Hessian is never % computed explicitly. [step, foo, relres] = minres(@hess_vect_mult, -grad,... opt.cg_prec,opt.cg_it,[],[],[],sv,lambda); else Xsv = X(sv,:); hess = lambda*diag([ones(d,1); 0]) + ... % Hessian [[Xsv'*Xsv sum(Xsv,1)']; [sum(Xsv,1) length(sv)]]; step = - hess \ grad; % Newton direction end; % Do an exact line search [t,out] = line_search_linear(w,step,out,Y,lambda); w = w + t*step; % fprintf(['Iter = %d, Obj = %f, Nb of sv = %d, Newton decr = %.3f, ' ... % 'Line search = %.3f'],iter,obj,length(sv),-step'*grad/2,t); % if opt.lin_cg % fprintf(', Lin CG acc = %.4f \n',relres); % else % fprintf(' \n'); % end; if -step'*grad < opt.prec * obj % Stop when the Newton decrement is small enough break; end; end; function [w, obj] = primal_svm_linear_cg(Y,lambda,opt) % ----------------------------------------------------- % Train a linear SVM using nonlinear conjugate gradient % ----------------------------------------------------- global X; [n,d] = size(X); w = zeros(d+1,1); % The last component of w is b. iter = 0; out = ones(n,1); % Vector containing 1-Y.*(X*w) go = [X'*Y; sum(Y)]; % -gradient at w=0 s = go; % The first search direction is given by the gradient while 1 iter = iter + 1; if iter > opt.cg_it * min(n,d) warning(sprintf(['Maximum number of CG iterations reached. ' ... 'Try larger lambda'])); break; end; % Do an exact line search [t,out] = line_search_linear(w,s,out,Y,lambda); w = w + t*s; % Compute the new gradient [obj, gn] = obj_fun_linear(w,Y,lambda,out); gn=-gn; % fprintf('Iter = %d, Obj = %f, Norm of grad = %.3f \n',iter,obj,norm(gn)); % Stop when the relative decrease in the objective function is small if t*s'*go < opt.prec*obj, break; end; % Flecher-Reeves update. Change 0 in 1 for Polack-Ribiere be = (gn'*gn - 0*gn'*go) / (go'*go); s = be*s+gn; go = gn; end; function [obj, grad, sv] = obj_fun_linear(w,Y,lambda,out) % Compute the objective function, its gradient and the set of support vectors % Out is supposed to contain 1-Y.*(X*w) global X out = max(0,out); w0 = w; w0(end) = 0; % Do not penalize b obj = sum(out.^2)/2 + lambda*w0'*w0/2; % L2 penalization of the errors grad = lambda*w0 - [((out.*Y)'*X)'; sum(out.*Y)]; % Gradient sv = find(out>0); function y = hess_vect_mult(w,sv,lambda) % Compute the Hessian times a given vector x. % hess = lambda*diag([ones(d-1,1); 0]) + (X(sv,:)'*X(sv,:)); global X y = lambda*w; y(end) = 0; z = (X*w(1:end-1)+w(end)); % Computing X(sv,:)*x takes more time in Matlab :-( zz = zeros(length(z),1); zz(sv)=z(sv); y = y + [(zz'*X)'; sum(zz)]; function [t,out] = line_search_linear(w,d,out,Y,lambda) % From the current solution w, do a line search in the direction d by % 1D Newton minimization global X t = 0; % Precompute some dots products Xd = X*d(1:end-1)+d(end); wd = lambda * w(1:end-1)'*d(1:end-1); dd = lambda * d(1:end-1)'*d(1:end-1); while 1 out2 = out - t*(Y.*Xd); % The new outputs after a step of length t sv = find(out2>0); g = wd + t*dd - (out2(sv).*Y(sv))'*Xd(sv); % The gradient (along the line) h = dd + Xd(sv)'*Xd(sv); % The second derivative (along the line) t = t - g/h; % Take the 1D Newton step. Note that if d was an exact Newton % direction, t is 1 after the first iteration. if g^2/h < 1e-10, break; end; % fprintf('%f %f\n',t,g^2/h) end; out = out2; function [beta,obj] = primal_svm_nonlinear(Y,lambda,opt) % ----------------------------------- % Train a non-linear SVM using Newton % ----------------------------------- global K training = find(Y); % The points with 0 are ignored. n = length(training); % The real number of training points if n>=1000 % Train a subset first perm = randperm(n); ind = training(perm(1:round(.75*n))); % Take a random subset of size n/4 Y2 = Y; Y2(ind) = 0; beta = primal_svm_nonlinear(Y2,lambda,opt); sv = find(beta(1:end-1)~=0); Kb = K(training,sv)*beta(sv); % Kb will always contains K times the current beta else sv = training; beta = zeros(length(Y)+1,1); % The last component of beta is b. Kb = zeros(n,1); end; iter = 0; % If the set of support vectors has changed, we need to reiterate. while 1 old_sv = sv; % Computing the objective function out = 1 - Y(training) .* (Kb+beta(end)); sv = training(out > 0); obj = (lambda*beta(training)'*Kb + sum(max(0,out).^2)) / 2; iter = iter + 1; % If the set of support vectors doesn't change, we can't improve anymore if (iter > 1) & isempty(setxor(sv,old_sv)), break; end; if iter > opt.iter_max_Newton warning(sprintf(['Maximum number of Newton steps reached. ' ... 'Try larger lambda'])); break; end; H = K(sv,sv) + lambda*eye(length(sv)); cte_for_b = mean(diag(K)); H(end+1,:) = cte_for_b; % To take the bias into account H(:,end+1) = cte_for_b; % The actual value of this constant does not matter. H(end,end) = 0; % For numerical reasons, take it of the order of K. % Beta_new would be the new vevtor beta is the full Newton step is taken beta_new = zeros(length(Y)+1,1); if opt.lin_cg [beta_new([sv; end]), foo1, relres] = minres(H,[Y(sv);0],opt.cg_prec,opt.cg_it); else beta_new([sv; end]) = H\[Y(sv);0]; end; beta_new(end) = beta_new(end) * cte_for_b; % Do line search, but with a preference for a full Newton step step = beta_new - beta; [t, Kb] = line_search_nonlinear(step([training; end]),Kb,beta(end),Y,lambda,1); beta = beta + t*step; % fprintf('n = %d, iter = %d, obj = %f, nb of sv = %d, line srch = %.4f',... % [n iter obj length(sv) t]); % if opt.lin_cg % fprintf(', Lin CG acc = %.4f \n',relres); % else % fprintf(' \n'); % end; end; sol = beta; function [beta, obj] = primal_svm_nonlinear_cg(Y,lambda,opt) % ----------------------------------------------------- % Train a linear SVM using nonlinear conjugate gradient % ----------------------------------------------------- global K; n = length(K); beta = zeros(n+1,1); % The last component of beta is b. iter = 0; Kb = zeros(n,1); % Kb will always contains K times the current beta go = [Y; sum(Y)]; % go = -gradient at beta=0 s = go; % Initial search direction Kgo = [K*Y; sum(Y)]; % We use the preconditioner [[K 0]; [0 1]] Ks = Kgo(1:end-1); % Ks will always contain K*s(1:end-1) while 1 iter = iter + 1; if iter > opt.cg_it * n warning(sprintf(['Maximum number of CG iterations reached. ' ... 'Try larger lambda'])); break; end; % Do an exact line search [t,Kb] = line_search_nonlinear(s,Kb,beta(end),Y,lambda,0,Ks); beta = beta + t*s; % Compute new gradient and objective. % Note that the gradient is already "divided" by the preconditioner [obj, grad] = obj_fun_nonlinear(beta,Y,lambda,Kb); gn = -grad; % fprintf('Iter = %d, Obj = %f, Norm grad = %f \n',iter,obj,norm(gn)); % Stop when the relative decrease in the objective function is small if t*s'*Kgo < opt.prec*obj, break; end; Kgn = [K*gn(1:end-1); gn(end)]; % Multiply by the preconditioner % -> Kgn is the real gradient % Flecher-Reeves update. Change 0 in 1 for Polack-Ribiere be = (Kgn'*gn - 0*Kgn'*go) / (Kgo'*go); % be = (gn'*gn - gn'*go) / (go'*go); s = be*s+gn; Ks = be*Ks + Kgn(1:end-1); go = gn; Kgo = Kgn; end; function [t, Kb] = line_search_nonlinear(step,Kb,b,Y,lambda,fullstep,Ks) % Given the current solution (as given by Kb), do a line sesrch in % direction step. First try to take a full step if fullstep = 1. global K; training = find(Y~=0); act = find(step(1:end-1)); % The set of points for which beta change if nargin<7 Ks = K(training,training(act))*step(act); end; Kss = step(act)'*Ks(act); % Precompute some dot products Kbs = step(act)'*Kb(act); t = 0; Y = Y(training); % Compute the objective function for t=1 out = 1-Y.*(Kb+b+Ks+step(end)); sv = out>0; obj1 = (lambda*(2*Kbs+Kss)+sum(out(sv).^2))/2; while 1 out = 1-Y.*(Kb+b+t*(Ks+step(end))); sv = out>0; % The objective function and the first derivative (along the line) obj = (lambda*(2*t*Kbs+t^2*Kss)+sum(out(sv).^2))/2; g = lambda * (Kbs+t*Kss) - (Ks(sv)'+step(end))*(Y(sv).*out(sv)); if fullstep & (t==0) & (obj-obj1 > -0.2*g) % First check t=1: if it works, keep it -> sparser solution t = 1; break; end; % The second derivative (along the line) h = lambda*Kss + norm(Ks(sv)+step(end))^2; % fprintf('%d %f %f %f\n',length(find(sv)),t,obj,g^2/h); % Take the 1D Newton step t = t - g/h; if g^2/h < 1e-10, break; end; end; Kb = Kb + t*Ks; function [obj, grad] = obj_fun_nonlinear(beta,Y,lambda,Kb) global K; out = Kb+beta(end); sv = find(Y.*out < 1); % Objective function... obj = (lambda*beta(1:end-1)'*Kb + sum((1-Y(sv).*out(sv)).^2)) / 2; % ... and preconditioned gradient grad = [lambda*beta(1:end-1); sum(out(sv)-Y(sv))]; grad(sv) = grad(sv) + (out(sv)-Y(sv)); % To compute the real gradient, one would have to execute the following line % grad = [K*grad(1:end-1); grad(end)]; mpi-ikl-simplemkl-1.0/libsvm/libsvm_classifier.cpp0000644000335100017570000001713711133661707022102 0ustar pgehlerwebadm/* $Revision: 1.10 $ */ // Automatically generated by MATLAB Project Wizard version 1.0 // // This is the gateway routine for a MATLAB Math/Graphics Library-based // C MATLAB MEX File. #include #include #include char Versionstring[]="2.81"; #define Malloc(type,n) (type *)malloc((n)*sizeof(type)) #include "svm.h" // struct svm_model { svm_parameter param; // parameter int nr_class; // number of classes, = 2 in regression/one class svm int l; // total #SV svm_node **SV; // SVs (SV[l]) double **sv_coef; // coefficients for SVs in decision functions (sv_coef[n-1][l]) double *rho; // constants in decision functions (rho[n*(n-1)/2]) // for classification only int *label; // label of each class (label[n]) int *nSV; // number of SVs for each class (nSV[n]) // nSV[0] + nSV[1] + ... + nSV[n-1] = l // XXX int free_sv; // 1 if svm_model is created by svm_load_model // 0 if svm_model is created by svm_train }; double *balanced_ridges=NULL; double *Custom_Kernel; int Custom_M,Custom_N; extern "C"{ /* -s svm_type : set type of SVM (default 0) 0 -- C-SVC 1 -- nu-SVC 2 -- one-class SVM 3 -- epsilon-SVR 4 -- nu-SVR -t kernel_type : set type of kernel function (default 2) 0 -- linear: u'*v 1 -- polynomial: (gamma*u'*v + coef0)^degree 2 -- radial basis function: exp(-gamma*|u-v|^2) 3 -- sigmoid: tanh(gamma*u'*v + coef0) -d degree : set degree in kernel function (default 3) -g gamma : set gamma in kernel function (default 1/k) -r coef0 : set coef0 in kernel function (default 0) -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1) -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1) -m cachesize : set cache memory size in MB (default 40) -e epsilon : set tolerance of termination criterion (default 0.001) -h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1) -wi weight: set the parameter C of class i to weight*C, for C-SVC (default 1) -v n: n-fold cross validation mode */ void mexFunction( int nlhs, // Number of left hand side (output) arguments mxArray *plhs[], // Array of left hand side arguments int nrhs, // Number of right hand side (input) arguments const mxArray *prhs[] // Array of right hand side arguments ) /* prhs = [X,Y , s , t , d , g ,r , c , n ,p , m ,e ,h ,wi,v]; */ { int elements,max_index,i,j; struct svm_parameter param; // set by parse_command_line struct svm_problem prob; // set by read_problem struct svm_model *model; struct svm_node *x_space; mexarg arg; if( !(arg.require(nrhs,prhs,"C") & arg.require(nrhs,prhs,"X") & arg.require(nrhs,prhs,"Y") & arg.require(nrhs,prhs,"eps") & arg.require(nrhs,prhs,"p") & arg.require(nrhs,prhs,"shrinking") )) { mexPrintf("Libsvm interface. Used Libsvm version : %s\n",Versionstring); mexPrintf("Usage: [alpha,xSV,bias0]="\ "libsvm_classifier_spider({'X',doublematrix},{'Y',doublematrix},\n"\ "{'C',scalar},{'kerneltype',scalar},\n{'degree',scalar},{'gamma',scalar},\n"\ " {'coef0',scalar},{'nu',scalar},{'cachesize',scalar},\n"\ "{'eps',scalar},{'shrinking',scalar},{'p',scalar},{'balanced_ridge', optional scalar},{'kmatrix',K})"); return; } mxArray *customK; if (arg.getmatrix(nrhs,prhs,"kmatrix",&customK)) { Custom_M = mxGetM( customK); Custom_N = mxGetN( customK); // mexPrintf("Using Custom Kernel %d %d\n",Custom_M,Custom_N); Custom_Kernel = mxGetPr(customK); } mxArray *x,*y; arg.getmatrix(nrhs,prhs,"X",&x); arg.getmatrix(nrhs,prhs,"Y",&y); double *xdata=mxGetPr(x); double *ydata=mxGetPr(y); double svm_type,kernelType,degree,gamma,coef0,nu,cachesize,C,eps,p,shrinking; double balanced_ridge; svm_type = 0; balanced_ridge = 0; coef0 = 0; cachesize = 40; nu = 0; kernelType = 4; degree = 3; gamma= 1; if (arg.require(nrhs,prhs,"balanced_ridge")) arg.getscalar(nrhs,prhs,"balanced_ridge",&balanced_ridge); if (arg.require(nrhs,prhs,"coef0")) arg.getscalar(nrhs,prhs,"coef0",&coef0); if (arg.require(nrhs,prhs,"nu")) arg.getscalar(nrhs,prhs,"nu",&nu); if (arg.require(nrhs,prhs,"cachesize")) arg.getscalar(nrhs,prhs,"cachesize",&cachesize); if (arg.require(nrhs,prhs,"degree")) arg.getscalar(nrhs,prhs,"degree",°ree); if (arg.require(nrhs,prhs,"gamma")) arg.getscalar(nrhs,prhs,"gamma",&gamma); if (arg.require(nrhs,prhs,"svm_type")) arg.getscalar(nrhs,prhs,"svm_type",&svm_type); arg.getscalar(nrhs,prhs,"C",&C); arg.getscalar(nrhs,prhs,"eps",&eps); arg.getscalar(nrhs,prhs,"p",&p); arg.getscalar(nrhs,prhs,"shrinking",&shrinking); balanced_ridges=Malloc(double,mxGetM(x)); memset(balanced_ridges,0,sizeof(double)*mxGetM(x)); if( arg.getscalar(nrhs,prhs,"balanced_ridge",&balanced_ridge)) { double number=mxGetM(x); double ypos=0; double yneg=0; int iter; for(iter=0;iter0)?ypos++:yneg++; for(iter=0;iter0) balanced_ridges[iter]=(ypos/number)*balanced_ridge; else balanced_ridges[iter]=(yneg/number)*balanced_ridge; } memset(¶m,0,sizeof(param)) ; param.cache_size=cachesize; param.C=C; param.eps=eps; param.p=p; param.shrinking=(int)shrinking; param.nr_weight=0; param.nu=nu; param.coef0=coef0; param.gamma=gamma; param.degree=degree; param.kernel_type=(int)kernelType; param.svm_type=(int)svm_type; // prepare libsvm data structure prob.l=mxGetM(x); elements = mxGetN(x); // mexPrintf("%d %d %f\n",prob.l,elements,nu); // alloc memory for data structure prob.y = Malloc(double,prob.l); prob.x = Malloc(struct svm_node *,prob.l); x_space = Malloc(struct svm_node,prob.l*(elements+1)); max_index = 0; j=0; max_index = 0; j=0; for(i=0;i0) { plhs[0]=mxCreateDoubleMatrix(model->l,1,mxREAL); double *p=mxGetPr(plhs[0]); for (int i=0;il;i++) p[i]= model->sv_coef[0][i]; //mexPrintf("alpha copied\n"); } if(nlhs>1) { plhs[1]=mxCreateDoubleMatrix(model->l,elements,mxREAL); double *xsv=mxGetPr(plhs[1]); //mexPrintf("%d %d\n",model->l,elements); for(i=0;il;i++) { for(j=0;jl+i] = model->SV[i][j].value; } } if(nlhs>2) { plhs[2]=mxCreateDoubleMatrix(1,1,mxREAL); double *b=mxGetPr(plhs[2]); *b=-model->rho[0]; //mexPrintf("b0 copied\n"); } if(nlhs>3) { plhs[3]=mxCreateDoubleMatrix(1,1,mxREAL); *mxGetPr(plhs[3]) =param.C; } //mexPrintf("%returning\n"); svm_destroy_model(model); // free memory free(prob.y); free(prob.x); free(x_space); free(balanced_ridges); } } mpi-ikl-simplemkl-1.0/libsvm/test_example_mc.m0000644000335100017570000000150411133661707021214 0ustar pgehlerwebadmclear all nPoints = 1500; gamma = 1; global K if 1 K = randn(nPoints,nPoints); K=K'*K; else X = randn(nPoints,20); K = calc(kernel('rbf',gamma),data(X),data(X)); end Y= round(10 * rand(nPoints,1)); Y = mod(Y,5) +1 ; lambda = 1; opt.prec = 1e-5; tic; [sol2,b2,obj2] = libsvm(Y,K,1/lambda,[],1); t2=toc; classes = unique(Y); number_classes = numel(classes); for i=1:number_classes y = Y; y( Y~=classes(i)) = -1; y( Y==classes(i)) = 1; [sol3(:,i),b3(i),obj3(i,1)] = libsvm(y,K,1/lambda,[],1); [sol(:,i),b(i),obj(i)] = primal_svm(0,y,lambda,opt); end assert(all(size(sol)==size(sol2))); assert(all(size(sol)==size(sol3))); assert(sum(sum(abs(sol3-sol2)))==0); assert(sum(abs(obj3-obj2))==0); assert(sum(abs(b3-b2))==0); assert(mean(mean(abs(sol-sol2)))<1e-5); fprintf('test passed\n'); mpi-ikl-simplemkl-1.0/libsvm/Makefile0000644000335100017570000000173111133661707017327 0ustar pgehlerwebadm# # choose between intel compiler and gcc # CC = gcc-4.2 CFLAGS=-O3 -ffast-math -fomit-frame-pointer -fPIC #-mtune=k8 -march=k8 #CC=/agbs/share/sw/icc/bin/icc #CFLAGS= -xP -fast -fPIC MATLABDIR=/agbs/share/sw/matlab MATLAB_INCLUDE=-I$(MATLABDIR)/extern/include MATLAB_LIB=-L$(MATLABDIR)/bin/glnx86 -lmex -lm #MATLAB_LIB=-L$(MATLABDIR)/bin/glnxa64 -lmex -lm TARGETS=libsvm_classifier INCLUDES=$(MATLAB_INCLUDE) -I. # 32 bit version SUFFIX=mexglx # 64 bit version #SUFFIX=mexa64 LDIRS=$(MATLAB_LIB) all: $(TARGETS:%=%.$(SUFFIX)) svm.o: svm.cpp svm.h $(CC) $(CFLAGS) -c -o svm.o svm.cpp %.o: %.cpp $(CC) $(CFLAGS) $(INCLUDES) $(VERBOSEFLAG) -c -o $@ $*.cpp %.$(SUFFIX): svm.o mexarg.o svm.h mexarg.h %.o $(CC) $*.o $(LDIRS) -shared -o $*.$(SUFFIX) svm.o mexarg.o ### Targets matlab_test: libsvm_classifier.$(SUFFIX) matlab -nojvm -nodisplay -r "test_example; exit"; \ matlab -nojvm -nodisplay -r "test_example_mc; exit"; clean: rm -f *.mexglx *.mexa64 *.o *~ mpi-ikl-simplemkl-1.0/libsvm/create_cvsplit.m0000644000335100017570000000326111133661707021054 0ustar pgehlerwebadm% [tr_split,te_split] = create_cvsplit(Y,nFolds) % % Creates a Cross Validation split for the labels Y % In: % 'Y' - labels % 'nFoldsK' - how many splits to create % % out: % 'tr_split','te_split' - cell array of size [nFolds,1] with indices of % points in training and test splits % % Peter Gehler, August 27th 2008 % pgehler@tuebingen.mpg.de function [tr_split,te_split] = create_cvsplit(Y,nFolds) for f=1:nFolds tr_split{f} = []; te_split{f} = []; end classes = sort(unique(Y)); for c=1:numel(classes) nExamples = sum(Y==classes(c)); if nExamples < nFolds error('less examples than folds'); end nPerFold = floor(nExamples/nFolds); ind = find(Y==classes(c)); ind = ind(randperm(numel(ind))); for f=1:nFolds-1 te_ind = ind( (f-1)*nPerFold + (1:nPerFold))'; tr_ind = setdiff(ind,te_ind); if size(te_ind,1) ==1, te_ind = te_ind'; end if size(tr_ind,1) ==1, tr_ind = tr_ind'; end tr_split{f} = [tr_split{f};tr_ind]; te_split{f} = [te_split{f};te_ind]; end te_ind = ind( (nFolds-1)*nPerFold+1:end)'; tr_ind = setdiff(ind,te_ind); if size(te_ind,1) ==1, te_ind = te_ind'; end if size(tr_ind,1) ==1, tr_ind = tr_ind'; end tr_split{nFolds} = [tr_split{nFolds};tr_ind]; te_split{nFolds} = [te_split{nFolds};te_ind]; if (all(size(tr_split{nFolds})~=1)) error('dimension error'); end if (all(size(te_split{nFolds})~=1)) error('dimension error'); end end for i=1:nFolds if size(tr_split{i},1)==1 tr_split{i} = tr_split{i}'; assert(numel(tr_split{i}) class mexarg { public: int isCellmatrix(int nrhs, const mxArray *prhs[]); int require(int nrhs, const mxArray *prhs[], const char *name); int getstring(int nrhs, const mxArray *prhs[], const char *name,char *target,int len); int getmatrix(int nrhs,const mxArray *prhs[], const char *name,mxArray **target); int getscalar(int nrhs,const mxArray *prhs[], const char *name,double *target); }; #endif mpi-ikl-simplemkl-1.0/libsvm/mexarg.cpp0000644000335100017570000000364311133661707017662 0ustar pgehlerwebadm#include #include #include int mexarg::isCellmatrix(int nrhs, const mxArray *prhs[]) { return nrhs==1; } int mexarg::require(int nrhs, const mxArray *prhs[], const char *name) { if(! isCellmatrix(nrhs,prhs) ) { for(int k=0;k 1 for cind = 1:numel(Cs) for f=1:nFolds if exist('l1','var') [alpha,b] = libsvm(y(tr_split{f}),K(tr_split{f},tr_split{f}),Cs(cind),kernel,l1); elseif exist('kernel','var') [alpha,b] = libsvm(y(tr_split{f}),K(tr_split{f},tr_split{f}),Cs(cind),kernel); else [alpha,b] = libsvm(y(tr_split{f}),K(tr_split{f},tr_split{f}),Cs(cind)); end Ypred = sign(K(te_split{f},tr_split{f})*alpha + b); err(cind,f) = mean(Ypred ~= y(te_split{f})); end end err = err + 1e-9 * randn(size(err)); % break ties err = mean(err,2); assert(numel(err) == numel(Cs)); [minErr,bestInd] = min(err); optimalC = Cs(bestInd); else optimalC = Cs; end if exist('l1','var') [alpha,b,obj] = libsvm(y,K,optimalC,kernel,l1); elseif exist('kernel','var') [alpha,b,obj] = libsvm(y,K,optimalC,kernel); else [alpha,b,obj] = libsvm(y,K,optimalC); end mpi-ikl-simplemkl-1.0/Kbeta.mexa640000755000335100017570000002231311133661710016443 0ustar pgehlerwebadmELF>@@8@!  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__gmon_start___fini__cxa_finalize_Jv_RegisterClasses_ZN5Ipopt13CachedResultsIdED0Ev_ZTVN5Ipopt13CachedResultsIdEE_ZdlPv__gxx_personality_v0_ZN5Ipopt16ReferencedObjectD0Ev_ZTVN5Ipopt16ReferencedObjectE_ZN5Ipopt16ReferencedObjectD1Ev_ZNSt8ios_base4InitC1Ev__cxa_atexit_ZNSt8ios_base4InitD1Ev_ZN5Ipopt13CachedResultsIdED1Ev_ZN5Ipopt8ObserverD0Ev_ZTVN5Ipopt8ObserverEmemmove_ZN5Ipopt8ObserverD1Ev_Unwind_Resume_ZN5Ipopt26RegisterOptions_InterfacesERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZNSsC1EPKcRKSaIcE_ZNSs6assignERKSs_ZNSs4_Rep20_S_empty_rep_storageE_ZN5Ipopt16IpoptApplication15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt11TNLPAdapter15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZNSs4_Rep10_M_destroyERKSaIcE_ZN5Ipopt7SubjectD0Ev_ZTVN5Ipopt7SubjectE_ZN5Ipopt7SubjectD1Ev_ZThn8_N5Ipopt12TaggedObjectD1Ev_ZN5Ipopt12TaggedObjectD1Ev_ZTVN5Ipopt12TaggedObjectE_ZThn8_N5Ipopt12TaggedObjectD0Ev_ZN5Ipopt12TaggedObjectD0Ev_ZThn8_N5Ipopt6VectorD1Ev_ZN5Ipopt6VectorD1Ev_ZTVN5Ipopt6VectorE_ZThn8_N5Ipopt6VectorD0Ev_ZN5Ipopt6VectorD0Ev_ZTIN5Ipopt6VectorE__cxa_pure_virtual_ZN5Ipopt6Vector17AddTwoVectorsImplEdRKS0_dS2_d_ZNK5Ipopt6Vector15FracToBoundImplERKS0_d_ZN5Ipopt6Vector21AddVectorQuotientImplEdRKS0_S2_d_ZNK5Ipopt6Vector19HasValidNumbersImplEv_ZTIN5Ipopt7SubjectE_ZTIN5Ipopt8ObserverE_ZTSN5Ipopt6VectorE_ZTVN10__cxxabiv120__si_class_type_infoE_ZTIN5Ipopt12TaggedObjectE_ZTSN5Ipopt7SubjectE_ZTVN10__cxxabiv117__class_type_infoE_ZTSN5Ipopt8ObserverE_ZTVN10__cxxabiv121__vmi_class_type_infoE_ZTSN5Ipopt12TaggedObjectE_ZTIN5Ipopt16ReferencedObjectE_ZTSN5Ipopt16ReferencedObjectE_ZTIN5Ipopt13CachedResultsIdEE_ZTSN5Ipopt13CachedResultsIdEEpthread_cancel_ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt11OptionsList11OptionValueEESt10_Select1stIS5_ESt4lessISsESaIS5_EE8_M_eraseEPSt13_Rb_tree_nodeIS5_E_ZNSsD1Ev_ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessISsESaIS6_EE8_M_eraseEPSt13_Rb_tree_nodeIS6_E_ZN5Ipopt14IpoptExceptionD0Ev_ZTVN5Ipopt14IpoptExceptionE_ZN5Ipopt14IpoptExceptionD1Ev_ZN5Ipopt16IpoptApplication23RegisterAllIpoptOptionsERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt25RegisterOptions_AlgorithmERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt25RegisterOptions_CGPenaltyERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt29RegisterOptions_LinearSolversERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZStplIcSt11char_traitsIcESaIcEESbIT_T0_T1_EPKS3_RKS6_strlen_ZNSs7reserveEj_ZNSs6appendEPKcj_ZNSs6appendERKSs_ZN5Ipopt17RegisteredOptions23AddBoundedIntegerOptionERKSsS2_iiiS2__ZN5Ipopt17RegisteredOptions16AddStringOption1ERKSsS2_S2_S2_S2_S2__ZN5Ipopt17RegisteredOptions16AddStringOption2ERKSsS2_S2_S2_S2_S2_S2_S2__ZN5Ipopt16IpoptApplication10StatisticsEv_ZN5Ipopt16IpoptApplication14OpenOutputFileESsNS_13EJournalLevelE_ZN5Ipopt10Journalist10GetJournalERKSs_ZN5Ipopt10Journalist14AddFileJournalERKSsS2_NS_13EJournalLevelE_ZN5Ipopt7Journal13SetPrintLevelENS_16EJournalCategoryENS_13EJournalLevelE_ZN5Ipopt16IpoptApplicationD0Ev_ZTVN5Ipopt16IpoptApplicationE_ZN5Ipopt16IpoptApplicationD1Ev_ZN5Ipopt16IpoptApplicationD2Ev_ZN5Ipopt16IpoptApplicationC1ENS_8SmartPtrINS_17RegisteredOptionsEEENS1_INS_11OptionsListEEENS1_INS_10JournalistEEE_ZN5Ipopt16IpoptApplicationC2ENS_8SmartPtrINS_17RegisteredOptionsEEENS1_INS_11OptionsListEEENS1_INS_10JournalistEEE_ZN5Ipopt12TaggedObjectD2Ev_ZN5Ipopt11TOO_FEW_DOFD0Ev_ZN5Ipopt11TOO_FEW_DOFD1Ev_ZN5Ipopt17INVALID_WARMSTARTD1Ev_ZN5Ipopt17INVALID_WARMSTARTD0Ev_ZN5Ipopt23IPOPT_APPLICATION_ERRORD0Ev_ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt11OptionsList11OptionValueEESt10_Select1stIS5_ESt4lessISsESaIS5_EE7_M_copyEPKSt13_Rb_tree_nodeIS5_EPSD__Znwj_ZNSsC1ERKSs__cxa_begin_catch__cxa_rethrow__cxa_end_catch_ZN5Ipopt23IPOPT_APPLICATION_ERRORD1Ev_ZN5Ipopt16IpoptApplication10InitializeERSi_ZN5Ipopt11OptionsList14ReadFromStreamERKNS_10JournalistERSi_ZNK5Ipopt11OptionsList15GetIntegerValueERKSsRiS2__ZN5Ipopt7Journal17SetAllPrintLevelsENS_13EJournalLevelE_ZNK5Ipopt11OptionsList14GetStringValueERKSsRSsS2__ZNKSs7compareEPKc_ZNK5Ipopt11OptionsList12GetBoolValueERKSsRbS2__ZNSt15_List_node_base4hookEPS__ZN5Ipopt17RegisteredOptions30OutputLatexOptionDocumentationERKNS_10JournalistERSt4listISsSaISsEE_ZN5Ipopt17RegisteredOptions25OutputOptionDocumentationERKNS_10JournalistERSt4listISsSaISsEE_ZNK5Ipopt10Journalist6PrintfENS_13EJournalLevelENS_16EJournalCategoryEPKcz__cxa_get_exception_ptr_ZTVSt9bad_alloc_ZNSt9bad_allocD1Ev_ZN5Ipopt16IpoptApplication10InitializeESs_ZNSt8ios_baseC2Ev_ZTTSt14basic_ifstreamIcSt11char_traitsIcEE_ZTVSt14basic_ifstreamIcSt11char_traitsIcEE_ZNSt13basic_filebufIcSt11char_traitsIcEEC1Ev_ZNSt9basic_iosIcSt11char_traitsIcEE4initEPSt15basic_streambufIcS1_E_ZNSt13basic_filebufIcSt11char_traitsIcEE4openEPKcSt13_Ios_Openmode_ZNSt9basic_iosIcSt11char_traitsIcEE5clearESt12_Ios_Iostate_ZTVSt13basic_filebufIcSt11char_traitsIcEE_ZNSt13basic_filebufIcSt11char_traitsIcEE5closeEv_ZNSt12__basic_fileIcED1Ev_ZTVSt15basic_streambufIcSt11char_traitsIcEE_ZNSt6localeD1Ev_ZTVSt9basic_iosIcSt11char_traitsIcEE_ZNSt8ios_baseD2Ev_ZN5Ipopt16IpoptApplicationC1Ebb_ZTVN5Ipopt11OptionsListE_ZN5Ipopt10JournalistC1Ev_ZTVN5Ipopt17RegisteredOptionsE__cxa_allocate_exception_ZTVN5Ipopt23IPOPT_APPLICATION_ERRORE_ZTIN5Ipopt23IPOPT_APPLICATION_ERRORE__cxa_throw__cxa_free_exception_ZN5Ipopt16IpoptApplication5cloneEv_ZN5Ipopt11OptionsListD0Ev_ZN5Ipopt11OptionsListD1Ev_ZN5Ipopt17RegisteredOptionsD0Ev_ZN5Ipopt17RegisteredOptionsD1Ev_ZN5Ipopt16IpoptApplicationC2Ebb_ZN5Ipopt16IpoptApplication13call_optimizeEv_ZN5Ipopt16TimingStatistics10ResetTimesEv_ZNK5Ipopt11OptionsList16PrintUserOptionsERSs_ZNK5Ipopt10Journalist13ProduceOutputENS_13EJournalLevelENS_16EJournalCategoryE_ZNK5Ipopt11OptionsList9PrintListERSs_ZN5Ipopt14IpoptAlgorithm8OptimizeEv_ZN5Ipopt25IpoptCalculatedQuantities15unscaled_curr_fEv_ZN5Ipopt25IpoptCalculatedQuantities6curr_fEv_ZN5Ipopt25IpoptCalculatedQuantities32unscaled_curr_dual_infeasibilityENS_9ENormTypeE_ZN5Ipopt25IpoptCalculatedQuantities23curr_dual_infeasibilityENS_9ENormTypeE_ZN5Ipopt25IpoptCalculatedQuantities38unscaled_curr_nlp_constraint_violationENS_9ENormTypeE_ZN5Ipopt25IpoptCalculatedQuantities29curr_nlp_constraint_violationENS_9ENormTypeE_ZN5Ipopt25IpoptCalculatedQuantities29unscaled_curr_complementarityEdNS_9ENormTypeE_ZN5Ipopt25IpoptCalculatedQuantities20curr_complementarityEdNS_9ENormTypeE_ZN5Ipopt25IpoptCalculatedQuantities23unscaled_curr_nlp_errorEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_nlp_errorEv_ZNK5Ipopt6Vector5PrintERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNK5Ipopt12OrigIpoptNLP30TotalFunctionEvaluationCPUTimeEv_ZTVN5Ipopt11TOO_FEW_DOFE_ZTIN5Ipopt11TOO_FEW_DOFE_ZN5Ipopt25IpoptCalculatedQuantities6curr_dEv_ZN5Ipopt25IpoptCalculatedQuantities6curr_cEv_ZN5Ipopt15SolveStatisticsC1ERKNS_8SmartPtrINS_8IpoptNLPEEERKNS1_INS_9IpoptDataEEERKNS1_INS_25IpoptCalculatedQuantitiesEEE_ZNK5Ipopt10Journalist11FlushBufferEv_ZNK5Ipopt16TimingStatistics24PrintAllTimingStatisticsERNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryE_ZNK5Ipopt12OrigIpoptNLP21PrintTimingStatisticsERNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryE_ZN5Ipopt25IpoptCalculatedQuantities14curr_d_minus_sEv_ZSt9terminatev_ZN5Ipopt16IpoptApplication13ReOptimizeNLPERKNS_8SmartPtrINS_3NLPEEE_ZTVN5Ipopt17INVALID_WARMSTARTE_ZTIN5Ipopt17INVALID_WARMSTARTE_ZN5Ipopt16IpoptApplication14ReOptimizeTNLPERKNS_8SmartPtrINS_4TNLPEEE_ZN5Ipopt16IpoptApplication11OptimizeNLPERKNS_8SmartPtrINS_3NLPEEERNS1_INS_16AlgorithmBuilderEEE_ZN5Ipopt16AlgorithmBuilderC1ENS_8SmartPtrINS_15AugSystemSolverEEE_ZN5Ipopt16IpoptApplication11OptimizeNLPERKNS_8SmartPtrINS_3NLPEEE_ZN5Ipopt16IpoptApplication12OptimizeTNLPERKNS_8SmartPtrINS_4TNLPEEE_ZN5Ipopt11TNLPAdapterC1ENS_8SmartPtrINS_4TNLPEEENS1_IKNS_10JournalistEEE_ZTIN5Ipopt16IpoptApplicationE_ZTSN5Ipopt16IpoptApplicationE_ZTSN5Ipopt11TOO_FEW_DOFE_ZTIN5Ipopt14IpoptExceptionE_ZTSN5Ipopt14IpoptExceptionE_ZTSN5Ipopt17INVALID_WARMSTARTE_ZTSN5Ipopt23IPOPT_APPLICATION_ERRORE_ZTIN5Ipopt11OptionsListE_ZTSN5Ipopt11OptionsListE_ZTIN5Ipopt17RegisteredOptionsE_ZTSN5Ipopt17RegisteredOptionsE_ZTIN5Ipopt14OPTION_INVALIDE_ZTSN5Ipopt14OPTION_INVALIDE_ZTIN5Ipopt30NO_FREE_VARIABLES_BUT_FEASIBLEE_ZTSN5Ipopt30NO_FREE_VARIABLES_BUT_FEASIBLEE_ZTIN5Ipopt32NO_FREE_VARIABLES_AND_INFEASIBLEE_ZTSN5Ipopt32NO_FREE_VARIABLES_AND_INFEASIBLEE_ZTISt9bad_alloc_ZNK5Ipopt15SolveStatistics14IterationCountEv_ZNK5Ipopt15SolveStatistics12TotalCPUTimeEv_ZNK5Ipopt15SolveStatistics19NumberOfEvaluationsERiS1_S1_S1_S1__ZNK5Ipopt15SolveStatistics15InfeasibilitiesERdS1_S1_S1__ZNK5Ipopt15SolveStatistics21ScaledInfeasibilitiesERdS1_S1_S1__ZNK5Ipopt15SolveStatistics14FinalObjectiveEv_ZNK5Ipopt15SolveStatistics20FinalScaledObjectiveEv_ZN5Ipopt15SolveStatisticsD1Ev_ZN5Ipopt15SolveStatisticsD0Ev_ZTVN5Ipopt15SolveStatisticsE_ZN5Ipopt15SolveStatisticsC2ERKNS_8SmartPtrINS_8IpoptNLPEEERKNS1_INS_9IpoptDataEEERKNS1_INS_25IpoptCalculatedQuantitiesEEE_ZTIN5Ipopt15SolveStatisticsE_ZTSN5Ipopt15SolveStatisticsESetIpoptProblemScaling_ZdaPv_ZnajFreeIpoptProblemCreateIpoptProblemAddIpoptNumOption_ZN5Ipopt11OptionsList15SetNumericValueERKSsdbbAddIpoptStrOption_ZN5Ipopt11OptionsList14SetStringValueERKSsS2_bbIpoptSolve_ZN5Ipopt16StdInterfaceTNLPC1EiPKdS2_iS2_S2_iiiS2_S2_S2_S2_PFiiPdiS3_PvEPFiiS3_iiS3_S4_ES6_PFiiS3_iiiPiS9_S3_S4_EPFiiS3_idiS3_iiS9_S9_S3_S4_ES3_S3_S3_S3_S3_S3_S4_dS2_S2_OpenIpoptOutputFileAddIpoptIntOption_ZN5Ipopt11OptionsList15SetIntegerValueERKSsibb_ZTIN5Ipopt24INVALID_STDINTERFACE_NLPE_ZTSN5Ipopt24INVALID_STDINTERFACE_NLPEipsolve_ipfree_ipcreate_putsipaddstroption_strncpyipopenoutputfile_ipaddintoption_ipaddnumoption__ZN5Ipopt4TNLP22get_scaling_parametersERdRbiPdS2_iS3__ZN5Ipopt4TNLP23get_variables_linearityEiPNS0_13LinearityTypeE_ZN5Ipopt4TNLP25get_constraints_linearityEiPNS0_13LinearityTypeE_ZN5Ipopt4TNLP22get_warm_start_iterateERNS_14IteratesVectorE_ZN5Ipopt4TNLP6eval_hEiPKdbdiS2_biPiS3_Pd_ZN5Ipopt4TNLP19set_variable_valuesEiPKdbiS2_b_ZN5Ipopt4TNLP21intermediate_callbackENS_13AlgorithmModeEiddddddddiPKNS_9Ipop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S_14PDSystemSolverEEE_ZN5Ipopt19CGPenaltyLSAcceptorD2Ev_ZN5Ipopt19CGPenaltyLSAcceptor19MultipliersDivergedEv_ZN5Ipopt19CGPenaltyLSAcceptor15RestoredIterateEv_ZN5Ipopt19CGPenaltyLSAcceptor10DoFallbackEv_ZN5Ipopt19CGPenaltyLSAcceptor22UpdatePenaltyParameterEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_compl_x_LEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_compl_x_UEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_compl_s_LEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_compl_s_UEv_ZN5Ipopt19CGPenaltyLSAcceptor22UpdateForNextIterationEd_ZN5Ipopt16PiecewisePenalty5PrintERKNS_10JournalistE_ZN5Ipopt16PiecewisePenalty11UpdateEntryEdd_ZN5Ipopt19CGPenaltyLSAcceptor24TrySecondOrderCorrectionEdRdRNS_8SmartPtrINS_14IteratesVectorEEE_ZN5Ipopt25IpoptCalculatedQuantities7trial_cEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_d_minus_sEv_ZNK5Ipopt14IteratesVector21MakeNewIteratesVectorEb_ZN5Ipopt25IpoptCalculatedQuantities28curr_grad_lag_with_damping_xEv_ZN5Ipopt14CompoundVector7SetCompEiRKNS_6VectorE_ZN5Ipopt25IpoptCalculatedQuantities28curr_grad_lag_with_damping_sEv_ZN5Ipopt25IpoptCalculatedQuantities22curr_relaxed_compl_x_LEv_ZN5Ipopt25IpoptCalculatedQuantities22curr_relaxed_compl_x_UEv_ZN5Ipopt25IpoptCalculatedQuantities22curr_relaxed_compl_s_LEv_ZN5Ipopt25IpoptCalculatedQuantities22curr_relaxed_compl_s_UEv_ZN5Ipopt25IpoptCalculatedQuantities24primal_frac_to_the_boundEdRKNS_6VectorES3__ZN5Ipopt9IpoptData31SetTrialPrimalVariablesFromStepEdRKNS_6VectorES3__ZN5Ipopt19CGPenaltyLSAcceptor30CheckAcceptabilityOfTrialPointEd_ZN5Ipopt19CGPenaltyLSAcceptor14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt22BacktrackingLSAcceptor16ComputeAlphaForYEddRNS_8SmartPtrINS_14IteratesVectorEEE_ZN5Ipopt19CGPenaltyLSAcceptor15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt17RegisteredOptions22AddBoundedNumberOptionERKSsS2_dbdbdS2__ZTIN5Ipopt19CGPenaltyLSAcceptorE_ZTSN5Ipopt19CGPenaltyLSAcceptorE_ZTIN5Ipopt22BacktrackingLSAcceptorE_ZTSN5Ipopt22BacktrackingLSAcceptorE_ZTIN5Ipopt23AlgorithmStrategyObjectE_ZTSN5Ipopt23AlgorithmStrategyObjectE_ZTVN5Ipopt22BacktrackingLSAcceptorE_ZN5Ipopt21CGSearchDirCalculator15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt21CGPerturbationHandler15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt21CGPerturbationHandler19CurrentPerturbationERdS1_S1_S1__ZN5Ipopt21CGPerturbationHandlerC1Ev_ZN5Ipopt21PDPerturbationHandlerC2Ev_ZTVN5Ipopt21CGPerturbationHandlerE_ZN5Ipopt21CGPerturbationHandlerC2Ev_ZN5Ipopt21CGPerturbationHandler8delta_cdEv_ZN5Ipopt21CGPerturbationHandlerD1Ev_ZN5Ipopt21CGPerturbationHandlerD0Ev_ZN5Ipopt21CGPerturbationHandler13finalize_testEv_ZN5Ipopt21CGPerturbationHandler28get_deltas_for_wrong_inertiaERdS1_S1_S1__ZN5Ipopt21CGPerturbationHandler22PerturbForWrongInertiaERdS1_S1_S1__ZN5Ipopt21CGPerturbationHandler17ConsiderNewSystemERdS1_S1_S1__ZN5Ipopt21CGPerturbationHandler21PerturbForSingularityERdS1_S1_S1__ZN5Ipopt21CGPerturbationHandler14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt21PDPerturbationHandler14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt21CGPerturbationHandlerE_ZTSN5Ipopt21CGPerturbationHandlerE_ZTIN5Ipopt21PDPerturbationHandlerE_ZN5Ipopt21CGSearchDirCalculatorC2ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZTVN5Ipopt21CGSearchDirCalculatorE_ZN5Ipopt25SearchDirectionCalculatorD0Ev_ZN5Ipopt25SearchDirectionCalculatorD1Ev_ZN5Ipopt21CGSearchDirCalculatorD1Ev_ZN5Ipopt21CGSearchDirCalculatorC1ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZN5Ipopt21CGSearchDirCalculatorD0Ev_ZN5Ipopt21CGSearchDirCalculatorD2Ev_ZN5Ipopt21CGSearchDirCalculator22ComputeSearchDirectionEv_ZN5Ipopt21CGSearchDirCalculator14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt21CGSearchDirCalculatorE_ZTSN5Ipopt21CGSearchDirCalculatorE_ZTIN5Ipopt25SearchDirectionCalculatorE_ZTSN5Ipopt25SearchDirectionCalculatorE_ZTVN5Ipopt25SearchDirectionCalculatorE_ZN5Ipopt16PiecewisePenaltyC2Ei_ZN5Ipopt16AdaptiveMuUpdate20Compute_tau_monotoneEd_ZN5Ipopt16AdaptiveMuUpdate11max_ref_valEv_ZN5Ipopt16AdaptiveMuUpdate11min_ref_valEv_ZN5Ipopt18TINY_STEP_DETECTEDD0Ev_ZN5Ipopt8MuUpdateD0Ev_ZN5Ipopt8MuUpdateD1Ev_ZN5Ipopt16AdaptiveMuUpdate18lower_mu_safeguardEv_ZN5Ipopt16AdaptiveMuUpdate10NewFixedMuEv_ZN5Ipopt25IpoptCalculatedQuantities15curr_avrg_complEv_ZN5Ipopt16AdaptiveMuUpdate26quality_function_pd_systemEv_ZN5Ipopt25IpoptCalculatedQuantities23curr_centrality_measureEv_ZN5Ipopt16AdaptiveMuUpdate30RememberCurrentPointAsAcceptedEv_ZN5Ipopt6Filter8AddEntryESt6vectorIdSaIdEEi_ZN5Ipopt6Filter5PrintERKNS_10JournalistE_ZN5Ipopt16AdaptiveMuUpdate23CheckSufficientProgressEv_ZNK5Ipopt6Filter10AcceptableESt6vectorIdSaIdEE_ZN5Ipopt16AdaptiveMuUpdateC1ERKNS_8SmartPtrINS_10LineSearchEEERKNS1_INS_8MuOracleEEES9__ZTVN5Ipopt16AdaptiveMuUpdateE_ZN5Ipopt6FilterC1Ei_ZN5Ipopt16AdaptiveMuUpdateC2ERKNS_8SmartPtrINS_10LineSearchEEERKNS1_INS_8MuOracleEEES9__ZN5Ipopt16AdaptiveMuUpdateD1Ev_ZN5Ipopt6Filter5ClearEv_ZN5Ipopt16AdaptiveMuUpdateD0Ev_ZN5Ipopt16AdaptiveMuUpdateD2Ev_ZN5Ipopt18TINY_STEP_DETECTEDD1Ev_ZN5Ipopt16AdaptiveMuUpdate22UpdateBarrierParameterEv_ZN5Ipopt25IpoptCalculatedQuantities18curr_barrier_errorEv_ZN5Ipopt9IpoptData16AcceptTrialPointEv_ZTVN5Ipopt18TINY_STEP_DETECTEDE_ZTIN5Ipopt18TINY_STEP_DETECTEDE_ZN5Ipopt16AdaptiveMuUpdate15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt17RegisteredOptions28AddLowerBoundedIntegerOptionERKSsS2_iiS2__ZN5Ipopt16AdaptiveMuUpdate14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt16AdaptiveMuUpdateE_ZTSN5Ipopt16AdaptiveMuUpdateE_ZTIN5Ipopt8MuUpdateE_ZTSN5Ipopt8MuUpdateE_ZTSN5Ipopt18TINY_STEP_DETECTEDE_ZTVN5Ipopt8MuUpdateE_ZTVN5Ipopt16AlgorithmBuilderE_ZN5Ipopt16AlgorithmBuilderC2ENS_8SmartPtrINS_15AugSystemSolverEEE_ZN5Ipopt16AlgorithmBuilderD1Ev_ZN5Ipopt14HessianUpdaterD1Ev_ZN5Ipopt19StandardScalingBaseD2Ev_ZTVN5Ipopt19StandardScalingBaseE_ZTVN5Ipopt16NLPScalingObjectE_ZN5Ipopt16AlgorithmBuilderD0Ev_ZN5Ipopt14HessianUpdaterD0Ev_ZThn8_N5Ipopt17MultiVectorMatrixD0Ev_ZN5Ipopt17MultiVectorMatrixD0Ev_ZTVN5Ipopt17MultiVectorMatrixE_ZTVN5Ipopt6MatrixE_ZN5Ipopt16AlgorithmBuilder17BuildIpoptObjectsERKNS_10JournalistERKNS_11OptionsListERKSsRKNS_8SmartPtrINS_3NLPEEERNS9_INS_8IpoptNLPEEERNS9_INS_9IpoptDataEEERNS9_INS_25IpoptCalculatedQuantitiesEEE_ZTVN5Ipopt11UserScalingE_ZN5Ipopt12OrigIpoptNLPC1ERKNS_8SmartPtrIKNS_10JournalistEEERKNS1_INS_3NLPEEERKNS1_INS_16NLPScalingObjectEEE_ZN5Ipopt9IpoptDataC1Ev_ZN5Ipopt25IpoptCalculatedQuantitiesC1ERKNS_8SmartPtrINS_8IpoptNLPEEERKNS1_INS_9IpoptDataEEE_ZTVN5Ipopt15GradientScalingE_ZTVN5Ipopt20EquilibrationScalingE_ZTVN5Ipopt18NoNLPScalingObjectE_ZThn8_N5Ipopt17MultiVectorMatrixD1Ev_ZN5Ipopt17MultiVectorMatrixD1Ev_ZN5Ipopt16AlgorithmBuilder19BuildBasicAlgorithmERKNS_10JournalistERKNS_11OptionsListERKSs_ZN5Ipopt31OptimalityErrorConvergenceCheckC1Ev_ZN5Ipopt18StdAugSystemSolverC1ERNS_15SymLinearSolverE_ZN5Ipopt17PDFullSpaceSolverC1ERNS_15AugSystemSolverERNS_21PDPerturbationHandlerE_ZN5Ipopt22LeastSquareMultipliersC1ERNS_15AugSystemSolverE_ZN5Ipopt27WarmStartIterateInitializerC1Ev_ZN5Ipopt25DefaultIterateInitializerC1ERKNS_8SmartPtrINS_22EqMultiplierCalculatorEEERKNS1_INS_18IterateInitializerEEENS1_INS_15AugSystemSolverEEE_ZN5Ipopt16FilterLSAcceptorC1ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZN5Ipopt22BacktrackingLineSearchC1ERKNS_8SmartPtrINS_22BacktrackingLSAcceptorEEERKNS1_INS_16RestorationPhaseEEERKNS1_INS_16ConvergenceCheckEEE_ZN5Ipopt27RestoFilterConvergenceCheck23SetOrigFilterLSAcceptorERKNS_16FilterLSAcceptorE_ZN5Ipopt16MonotoneMuUpdateC1ERKNS_8SmartPtrINS_10LineSearchEEE_ZN5Ipopt19OrigIterationOutputC1Ev_ZTVN5Ipopt19ExactHessianUpdaterE_ZN5Ipopt21PDSearchDirCalculatorC1ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZN5Ipopt14IpoptAlgorithmC1ERKNS_8SmartPtrINS_25SearchDirectionCalculatorEEERKNS1_INS_10LineSearchEEERKNS1_INS_8MuUpdateEEERKNS1_INS_16ConvergenceCheckEEERKNS1_INS_18IterateInitializerEEERKNS1_INS_15IterationOutputEEERKNS1_INS_14HessianUpdaterEEERKNS1_INS_22EqMultiplierCalculatorEEE_ZN5Ipopt21PDPerturbationHandlerC1Ev_ZN5Ipopt24LimMemQuasiNewtonUpdaterC1Eb_ZN5Ipopt20AugRestoSystemSolverC1ERNS_15AugSystemSolverEb_ZN5Ipopt27RestoFilterConvergenceCheckC1Ev_ZN5Ipopt21RestoRestorationPhaseC1Ev_ZN5Ipopt23RestoIterateInitializerC1ERKNS_8SmartPtrINS_22EqMultiplierCalculatorEEE_ZN5Ipopt20RestoIterationOutputC1ERKNS_8SmartPtrINS_19OrigIterationOutputEEE_ZN5Ipopt25MinC_1NrmRestorationPhaseC1ERNS_14IpoptAlgorithmERKNS_8SmartPtrINS_22EqMultiplierCalculatorEEE_ZNSs6assignEPKcj_ZN5Ipopt22LowRankAugSystemSolverC1ERNS_15AugSystemSolverE_ZN5Ipopt12LoqoMuOracleC1Ev_ZN5Ipopt15ProbingMuOracleC1ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZN5Ipopt23QualityFunctionMuOracleC1ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZN5Ipopt16AlgorithmBuilder15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt17RegisteredOptions16AddStringOption7ERKSsS2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2__ZTIN5Ipopt16AlgorithmBuilderE_ZTIN5Ipopt17MultiVectorMatrixE_ZNK5Ipopt6Matrix5PrintENS_8SmartPtrIKNS_10JournalistEEENS_13EJournalLevelENS_16EJournalCategoryERKSsiS8__ZNK5Ipopt6Matrix5PrintERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNK5Ipopt17MultiVectorMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt17MultiVectorMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt6Matrix13AddMSinvZImplEdRKNS_6VectorES3_RS1__ZNK5Ipopt6Matrix18SinvBlrmZMTdBrImplEdRKNS_6VectorES3_S3_S3_RS1__ZNK5Ipopt17MultiVectorMatrix19HasValidNumbersImplEv_ZNK5Ipopt17MultiVectorMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZTSN5Ipopt17MultiVectorMatrixE_ZTIN5Ipopt6MatrixE_ZTSN5Ipopt16AlgorithmBuilderE_ZTVN5Ipopt14HessianUpdaterE_ZTIN5Ipopt14HessianUpdaterE_ZTSN5Ipopt14HessianUpdaterE_ZN5Ipopt25DefaultIterateInitializer15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt22BacktrackingLineSearch15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt16FilterLSAcceptor15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt19StandardScalingBase15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt15GradientScaling15RegisterOptionsERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt20EquilibrationScaling15RegisterOptionsERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt14IpoptAlgorithm15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt9IpoptData15RegisterOptionsERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt25IpoptCalculatedQuantities15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt24LimMemQuasiNewtonUpdater15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt16MonotoneMuUpdate15RegisterOptionsERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt31OptimalityErrorConvergenceCheck15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt12OrigIpoptNLP15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt19OrigIterationOutput15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt21PDSearchDirCalculator15RegisterOptionsERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt17PDFullSpaceSolver15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt21PDPerturbationHandler15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt15ProbingMuOracle15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt23QualityFunctionMuOracle15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt27RestoFilterConvergenceCheck15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt13RestoIpoptNLP15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt25MinC_1NrmRestorationPhase15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt27WarmStartIterateInitializer15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZNK5Ipopt20AugRestoSystemSolver16NumberOfNegEValsEv_ZN5Ipopt8SmartPtrIKNS_6VectorEE15ReleasePointer_Ev_ZNK5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEE19DependentsIdenticalERKSt6vectorIPKNS_12TaggedObjectESaIS8_EERKS5_IdSaIdEE_ZN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEE19RecieveNotificationENS_8Observer10NotifyTypeEPKNS_7SubjectE_ZNSt6vectorIdSaIdEED1Ev_ZN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEED1Ev_ZTVN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEEE_ZN5Ipopt15AugSystemSolver10MultiSolveEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dPKNS_6MatrixES6_dS9_S6_dRSt6vectorINS_8SmartPtrIS5_EESaISC_EESF_SF_SF_RSA_INSB_IS4_EESaISG_EESJ_SJ_SJ_bi_ZN5Ipopt20AugRestoSystemSolver15IncreaseQualityEv_ZNK5Ipopt20AugRestoSystemSolver15ProvidesInertiaEv_ZN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEED0Ev_ZN5Ipopt8Observer19ProcessNotificationENS0_10NotifyTypeEPKNS_7SubjectE_ZN5Ipopt20AugRestoSystemSolver14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt23AlgorithmStrategyObjectD2Ev_ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_6VectorEEESaIS4_EED1Ev_ZN5Ipopt20AugRestoSystemSolverD1Ev_ZTVN5Ipopt20AugRestoSystemSolverE_ZTVN5Ipopt15AugSystemSolverE_ZN5Ipopt20AugRestoSystemSolverD2Ev_ZN5Ipopt20AugRestoSystemSolverC2ERNS_15AugSystemSolverEb_ZN5Ipopt15AugSystemSolverD0Ev_ZN5Ipopt15AugSystemSolverD1Ev_ZSt26__uninitialized_fill_n_auxIPN5Ipopt8SmartPtrINS0_6VectorEEEjS3_EvT_T0_RKT1_St12__false_type_ZNK5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEE15GetCachedResultERS3_RKSt6vectorIPKNS_12TaggedObjectESaIS9_EERKS6_IdSaIdEE_ZSt26__uninitialized_fill_n_auxIPN5Ipopt8SmartPtrIKNS0_6VectorEEEjS4_EvT_T0_RKT1_St12__false_type_ZN5Ipopt15AugSystemSolver5SolveEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dPKNS_6MatrixES6_dS9_S6_dRS5_SA_SA_SA_RS4_SB_SB_SB_bi_ZN5Ipopt20AugRestoSystemSolverD0Ev_ZN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEE19GetCachedResult3DepERS3_PKNS_12TaggedObjectES8_S8__ZN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEED0Ev_ZTVN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEEE_ZN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEED1Ev_ZN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEEC1ERKS3_RKSt6vectorIPKNS_12TaggedObjectESaISA_EERKS7_IdSaIdEE_ZN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEE15AddCachedResultERKS3_RKSt6vectorIPKNS_12TaggedObjectESaISA_EERKS7_IdSaIdEE_ZN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEE19AddCachedResult3DepERKS3_PKNS_12TaggedObjectES9_S9__ZN5Ipopt20AugRestoSystemSolver20Neg_Omega_c_plus_D_cERKNS_8SmartPtrIKNS_6VectorEEES6_PS3_RS3__ZN5Ipopt20AugRestoSystemSolver20Neg_Omega_d_plus_D_dERKNS_6MatrixERKNS_8SmartPtrIKNS_6VectorEEES3_S9_PS6_RS6__ZN5Ipopt20AugRestoSystemSolver6Rhs_dRERKNS_6VectorERKNS_8SmartPtrIS2_EES3_RKNS_6MatrixES7_S3_SA__ZN5Ipopt20AugRestoSystemSolver6Rhs_cRERKNS_6VectorERKNS_8SmartPtrIS2_EES3_S7_S3__ZN5Ipopt20AugRestoSystemSolver13D_x_plus_wr_dERKNS_8SmartPtrIKNS_6VectorEEEdRS3__ZN5Ipopt20AugRestoSystemSolver19Sigma_tilde_p_d_invERKNS_8SmartPtrIKNS_6VectorEEEdRS3__ZN5Ipopt20AugRestoSystemSolver19Sigma_tilde_n_d_invERKNS_8SmartPtrIKNS_6VectorEEEdRS3__ZN5Ipopt20AugRestoSystemSolver19Sigma_tilde_p_c_invERKNS_8SmartPtrIKNS_6VectorEEEdRS3__ZN5Ipopt20AugRestoSystemSolver19Sigma_tilde_n_c_invERKNS_8SmartPtrIKNS_6VectorEEEdRS3__ZN5Ipopt20AugRestoSystemSolver5SolveEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dPKNS_6MatrixES6_dS9_S6_dRS5_SA_SA_SA_RS4_SB_SB_SB_bi_ZTIN5Ipopt12SumSymMatrixE__dynamic_cast_ZNK5Ipopt12SumSymMatrix7GetTermEiRdRNS_8SmartPtrIKNS_9SymMatrixEEE_ZTIN5Ipopt20AugRestoSystemSolverE_ZTSN5Ipopt20AugRestoSystemSolverE_ZTIN5Ipopt15AugSystemSolverE_ZTSN5Ipopt15AugSystemSolverE_ZTIN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEEE_ZTSN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEEE_ZTIN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEEE_ZTSN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEEE_ZN5Ipopt22BacktrackingLineSearch21SetRigorousLineSearchEb_ZN5Ipopt22BacktrackingLineSearch22CheckSkippedLineSearchEv_ZN5Ipopt22BacktrackingLineSearch19CurrentIsAcceptableEv_ZN5Ipopt18RESTORATION_FAILEDD0Ev_ZN5Ipopt22BacktrackingLineSearch5ResetEv_ZN5Ipopt18RESTORATION_FAILEDD1Ev_ZN5Ipopt24ACCEPTABLE_POINT_REACHEDD0Ev_ZN5Ipopt24ACCEPTABLE_POINT_REACHEDD1Ev_ZN5Ipopt10LineSearchD0Ev_ZN5Ipopt10LineSearchD1Ev_ZN5Ipopt22BacktrackingLineSearchC2ERKNS_8SmartPtrINS_22BacktrackingLSAcceptorEEERKNS1_INS_16RestorationPhaseEEERKNS1_INS_16ConvergenceCheckEEE_ZTVN5Ipopt22BacktrackingLineSearchE_ZN5Ipopt22BacktrackingLineSearch22RestoreAcceptablePointEv_ZN5Ipopt22BacktrackingLineSearch20StoreAcceptablePointEv_ZN5Ipopt22BacktrackingLineSearch13StartWatchDogEv_ZN5Ipopt25IpoptCalculatedQuantities29curr_primal_frac_to_the_boundEd_ZN5Ipopt22BacktrackingLineSearchD0Ev_ZN5Ipopt22BacktrackingLineSearchD2Ev_ZN5Ipopt22BacktrackingLineSearch25ActivateFallbackMechanismEv_ZN5Ipopt22BacktrackingLineSearchD1Ev_ZN5Ipopt22BacktrackingLineSearch14DetectTinyStepEv_ZN5Ipopt22BacktrackingLineSearch15PerformDualStepEddRNS_8SmartPtrINS_14IteratesVectorEEE_ZN5Ipopt9IpoptData32SetTrialBoundMultipliersFromStepEdRKNS_6VectorES3_S3_S3__ZN5Ipopt9IpoptData29SetTrialEqMultipliersFromStepEdRKNS_6VectorES3__ZN5Ipopt25IpoptCalculatedQuantities16trial_grad_lag_xEv_ZN5Ipopt25IpoptCalculatedQuantities16trial_grad_lag_sEv_ZN5Ipopt25IpoptCalculatedQuantities22trial_jac_dT_times_vecERKNS_6VectorE_ZN5Ipopt25IpoptCalculatedQuantities22trial_jac_cT_times_vecERKNS_6VectorE_ZN5Ipopt22BacktrackingLineSearch16PerformMagicStepEv_ZN5Ipopt22BacktrackingLineSearch12StopWatchDogERNS_8SmartPtrINS_14IteratesVectorEEE_ZN5Ipopt22BacktrackingLineSearch24DoBacktrackingLineSearchEbRdRbS2_RiS2_RNS_8SmartPtrINS_14IteratesVectorEEEtoupper_ZN5Ipopt22BacktrackingLineSearch16TrySoftRestoStepERNS_8SmartPtrINS_14IteratesVectorEEERb_ZN5Ipopt25IpoptCalculatedQuantities22dual_frac_to_the_boundEdRKNS_6VectorES3_S3_S3__ZN5Ipopt25IpoptCalculatedQuantities30trial_primal_dual_system_errorEd_ZN5Ipopt25IpoptCalculatedQuantities29curr_primal_dual_system_errorEd_ZN5Ipopt22BacktrackingLineSearch24FindAcceptableTrialPointEv_ZNK5Ipopt25IpoptCalculatedQuantities15IsSquareProblemEv_ZTVN5Ipopt18RESTORATION_FAILEDE_ZTIN5Ipopt18RESTORATION_FAILEDE_ZTVN5Ipopt24ACCEPTABLE_POINT_REACHEDE_ZTIN5Ipopt24ACCEPTABLE_POINT_REACHEDE_ZN5Ipopt22BacktrackingLineSearch14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt17RegisteredOptions17AddStringOption10ERKSsS2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2__ZTIN5Ipopt22BacktrackingLineSearchE_ZTSN5Ipopt22BacktrackingLineSearchE_ZTIN5Ipopt10LineSearchE_ZTSN5Ipopt10LineSearchE_ZTSN5Ipopt18RESTORATION_FAILEDE_ZTSN5Ipopt24ACCEPTABLE_POINT_REACHEDE_ZTVN5Ipopt10LineSearchE_ZN5Ipopt25DefaultIterateInitializer14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt18IterateInitializerD0Ev_ZN5Ipopt18IterateInitializerD1Ev_ZN5Ipopt25DefaultIterateInitializer27CalculateLeastSquarePrimalsERNS_6VectorES2__ZN5Ipopt6Vector4ScalEd_ZN5Ipopt14IteratesVector26GetNonConstIterateFromCompEi_ZN5Ipopt25DefaultIterateInitializer18least_square_multsERKNS_10JournalistERNS_8IpoptNLPERNS_9IpoptDataERNS_25IpoptCalculatedQuantitiesERKNS_8SmartPtrINS_22EqMultiplierCalculatorEEEd_ZNK5Ipopt19CompoundVectorSpace12GetCompSpaceEi_ZN5Ipopt14CompoundVector15SetCompNonConstEiRNS_6VectorE_ZTVN5Ipopt25DefaultIterateInitializerE_ZN5Ipopt25DefaultIterateInitializerC2ERKNS_8SmartPtrINS_22EqMultiplierCalculatorEEERKNS1_INS_18IterateInitializerEEENS1_INS_15AugSystemSolverEEE_ZN5Ipopt25DefaultIterateInitializerD0Ev_ZN5Ipopt25DefaultIterateInitializer25CalculateLeastSquareDualsERNS_6VectorES2_S2_S2_S2_S2__ZN5Ipopt25IpoptCalculatedQuantities12trial_grad_fEv_ZN5Ipopt25DefaultIterateInitializerD1Ev_ZN5Ipopt25DefaultIterateInitializer14push_variablesERKNS_10JournalistEddSsRKNS_6VectorERNS_8SmartPtrIS5_EES6_S6_RKNS_6MatrixESC__ZN5Ipopt25DefaultIterateInitializer18SetInitialIteratesEv_ZN5Ipopt9IpoptData24InitializeDataStructuresERNS_8IpoptNLPEbbbbb_ZN5Ipopt25IpoptCalculatedQuantities7trial_dEv_ZTIN5Ipopt25DefaultIterateInitializerE_ZTSN5Ipopt25DefaultIterateInitializerE_ZTIN5Ipopt18IterateInitializerE_ZTSN5Ipopt18IterateInitializerE_ZTVN5Ipopt18IterateInitializerE_ZN5Ipopt20EquilibrationScalingD0Ev_ZN5Ipopt20EquilibrationScalingD1Ev_ZN5Ipopt21FAILED_INITIALIZATIOND0Ev_ZN5Ipopt14PointPerturberD1Ev_ZTVN5Ipopt14PointPerturberE_ZN5Ipopt14PointPerturberD0Ev_ZNK5Ipopt14PointPerturber21MakeNewPerturbedPointEv_ZN5Ipopt13TripletHelper20FillValuesFromVectorEiRKNS_6VectorEPd_ZN5Ipopt13TripletHelper17PutValuesInVectorEiPKdRNS_6VectorE_ZN5Ipopt14PointPerturberC2ERKNS_6VectorEdRKNS_6MatrixES3_S6_S3__ZN5Ipopt14PointPerturberC1ERKNS_6VectorEdRKNS_6MatrixES3_S6_S3__ZN5Ipopt20EquilibrationScaling14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt19StandardScalingBase14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt21FAILED_INITIALIZATIOND1Ev_ZN5Ipopt20EquilibrationScaling30DetermineScalingParametersImplENS_8SmartPtrIKNS_11VectorSpaceEEES4_S4_NS1_IKNS_11MatrixSpaceEEES7_NS1_IKNS_14SymMatrixSpaceEEERKNS_6MatrixERKNS_6VectorESD_SG_RdRNS1_ISE_EESJ_SJ__ZTVN5Ipopt21FAILED_INITIALIZATIONE_ZTIN5Ipopt21FAILED_INITIALIZATIONE_ZN5Ipopt13TripletHelper10FillRowColEiRKNS_6MatrixEPiS4_ii_ZTIN5Ipopt20EquilibrationScalingE_ZN5Ipopt19StandardScalingBase17apply_obj_scalingERKd_ZN5Ipopt19StandardScalingBase19unapply_obj_scalingERKd_ZN5Ipopt19StandardScalingBase31apply_vector_scaling_x_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase22apply_vector_scaling_xERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase33unapply_vector_scaling_x_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase24unapply_vector_scaling_xERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase22apply_vector_scaling_cERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase24unapply_vector_scaling_cERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase31apply_vector_scaling_c_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase33unapply_vector_scaling_c_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase22apply_vector_scaling_dERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase24unapply_vector_scaling_dERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase31apply_vector_scaling_d_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase33unapply_vector_scaling_d_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase19apply_jac_c_scalingENS_8SmartPtrIKNS_6MatrixEEE_ZN5Ipopt19StandardScalingBase19apply_jac_d_scalingENS_8SmartPtrIKNS_6MatrixEEE_ZN5Ipopt19StandardScalingBase21apply_hessian_scalingENS_8SmartPtrIKNS_9SymMatrixEEE_ZN5Ipopt16NLPScalingObject31apply_grad_obj_scaling_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt16NLPScalingObject22apply_grad_obj_scalingERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt16NLPScalingObject33unapply_grad_obj_scaling_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt16NLPScalingObject24unapply_grad_obj_scalingERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase14have_x_scalingEv_ZN5Ipopt19StandardScalingBase14have_c_scalingEv_ZN5Ipopt19StandardScalingBase14have_d_scalingEv_ZN5Ipopt19StandardScalingBase16DetermineScalingENS_8SmartPtrIKNS_11VectorSpaceEEES4_S4_NS1_IKNS_11MatrixSpaceEEES7_NS1_IKNS_14SymMatrixSpaceEEERS7_SB_RSA_RKNS_6MatrixERKNS_6VectorESF_SI__ZTSN5Ipopt20EquilibrationScalingE_ZTIN5Ipopt19StandardScalingBaseE_ZTIN5Ipopt14PointPerturberE_ZTSN5Ipopt14PointPerturberE_ZTSN5Ipopt21FAILED_INITIALIZATIONE_ZN5Ipopt19ExactHessianUpdater14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt19ExactHessianUpdater13UpdateHessianEv_ZN5Ipopt25IpoptCalculatedQuantities18curr_exact_hessianEv_ZN5Ipopt19ExactHessianUpdaterD1Ev_ZN5Ipopt19ExactHessianUpdaterD0Ev_ZTIN5Ipopt19ExactHessianUpdaterE_ZTSN5Ipopt19ExactHessianUpdaterE_ZN5Ipopt11FilterEntryD2Ev_ZN5Ipopt6FilterC2Ei_ZN5Ipopt11FilterEntryC2ESt6vectorIdSaIdEEi_ZN5Ipopt11FilterEntryD1Ev_ZN5Ipopt11FilterEntryC1ESt6vectorIdSaIdEEi_ZN5Ipopt16FilterLSAcceptor12StopWatchDogEv_ZN5Ipopt16FilterLSAcceptor5ResetEv_ZN5Ipopt16FilterLSAcceptor13StartWatchDogEv_ZN5Ipopt25IpoptCalculatedQuantities19curr_gradBarrTDeltaEv_ZN5Ipopt16FilterLSAcceptor18InitThisLineSearchEb_ZN5Ipopt16FilterLSAcceptor7IsFtypeEd_ZN5Ipopt16FilterLSAcceptor17CalculateAlphaMinEv_ZN5Ipopt16FilterLSAcceptor10Compare_leEddd_ZNK5Ipopt16FilterLSAcceptor28IsAcceptableToCurrentIterateEddblog10_ZN5Ipopt16FilterLSAcceptor11ArmijoHoldsEd_ZNK5Ipopt16FilterLSAcceptor27IsAcceptableToCurrentFilterEdd_ZN5Ipopt16FilterLSAcceptor30CheckAcceptabilityOfTrialPointEd_ZN5Ipopt16FilterLSAcceptor13AugmentFilterEv_ZN5Ipopt16FilterLSAcceptor22PrepareRestoPhaseStartEv_ZN5Ipopt16FilterLSAcceptor22UpdateForNextIterationEd_ZN5Ipopt16FilterLSAcceptor14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt16FilterLSAcceptorC2ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZTVN5Ipopt16FilterLSAcceptorE_ZN5Ipopt16FilterLSAcceptorD0Ev_ZN5Ipopt16FilterLSAcceptor24TrySecondOrderCorrectionEdRdRNS_8SmartPtrINS_14IteratesVectorEEE_ZN5Ipopt16FilterLSAcceptorD2Ev_ZN5Ipopt16FilterLSAcceptorD1Ev_ZN5Ipopt16FilterLSAcceptor12TryCorrectorEdRdRNS_8SmartPtrINS_14IteratesVectorEEEgetrusage_ZN5Ipopt25IpoptCalculatedQuantities16trial_avrg_complEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_slack_x_LEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_slack_x_UEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_slack_s_LEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_slack_s_UEv_ZN5Ipopt25IpoptCalculatedQuantities15curr_grad_lag_xEv_ZN5Ipopt25IpoptCalculatedQuantities15curr_grad_lag_sEv_ZTIN5Ipopt16FilterLSAcceptorE_ZTSN5Ipopt16FilterLSAcceptorE_ZN5Ipopt18GenAugSystemSolver22AugmentedSystemChangedEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dRKNS_6MatrixES6_dS9_S6_d_ZN5Ipopt18GenAugSystemSolver10UpdateTagsEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dRKNS_6MatrixES6_dS9_S6_d_ZN5Ipopt18GenAugSystemSolver15IncreaseQualityEv_ZNK5Ipopt18GenAugSystemSolver15ProvidesInertiaEv_ZNK5Ipopt18GenAugSystemSolver16NumberOfNegEValsEv_ZNSt6vectorIN5Ipopt8SmartPtrINS0_6VectorEEESaIS3_EED1Ev_ZN5Ipopt18GenAugSystemSolverC1ERNS_21GenKKTSolverInterfaceE_ZTVN5Ipopt18GenAugSystemSolverE_ZN5Ipopt18GenAugSystemSolverD0Ev_ZN5Ipopt18GenAugSystemSolverC2ERNS_21GenKKTSolverInterfaceE_ZN5Ipopt18GenAugSystemSolver14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt28FATAL_ERROR_IN_LINEAR_SOLVERD1Ev_ZN5Ipopt18GenAugSystemSolver10MultiSolveEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dPKNS_6MatrixES6_dS9_S6_dRSt6vectorINS_8SmartPtrIS5_EESaISC_EESF_SF_SF_RSA_INSB_IS4_EESaISG_EESJ_SJ_SJ_bi_ZTIN5Ipopt11DenseVectorE_ZTVN5Ipopt28FATAL_ERROR_IN_LINEAR_SOLVERE_ZTIN5Ipopt28FATAL_ERROR_IN_LINEAR_SOLVERE_ZN5Ipopt18GenAugSystemSolverD1Ev_ZN5Ipopt18GenAugSystemSolverD2Ev_ZN5Ipopt28FATAL_ERROR_IN_LINEAR_SOLVERD0Ev_ZTIN5Ipopt18GenAugSystemSolverE_ZTSN5Ipopt18GenAugSystemSolverE_ZTSN5Ipopt28FATAL_ERROR_IN_LINEAR_SOLVERE_ZN5Ipopt15GradientScalingD0Ev_ZN5Ipopt15GradientScalingD1Ev_ZN5Ipopt15GradientScaling14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt15GradientScaling30DetermineScalingParametersImplENS_8SmartPtrIKNS_11VectorSpaceEEES4_S4_NS1_IKNS_11MatrixSpaceEEES7_NS1_IKNS_14SymMatrixSpaceEEERKNS_6MatrixERKNS_6VectorESD_SG_RdRNS1_ISE_EESJ_SJ__ZN5Ipopt12IpBlasIdamaxEiPKdi_ZTIN5Ipopt15GradientScalingE_ZTSN5Ipopt15GradientScalingE_ZN5Ipopt14IpoptAlgorithm27ComputeAcceptableTrialPointEv_ZN5Ipopt14IpoptAlgorithm15OutputIterationEv_ZN5Ipopt14IpoptAlgorithm13UpdateHessianEv_ZN5Ipopt14IpoptAlgorithm22UpdateBarrierParameterEv_ZN5Ipopt14IpoptAlgorithmC2ERKNS_8SmartPtrINS_25SearchDirectionCalculatorEEERKNS1_INS_10LineSearchEEERKNS1_INS_8MuUpdateEEERKNS1_INS_16ConvergenceCheckEEERKNS1_INS_18IterateInitializerEEERKNS1_INS_15IterationOutputEEERKNS1_INS_14HessianUpdaterEEERKNS1_INS_22EqMultiplierCalculatorEEE_ZTVN5Ipopt14IpoptAlgorithmE_ZN5Ipopt23STEP_COMPUTATION_FAILEDD0Ev_ZN5Ipopt23STEP_COMPUTATION_FAILEDD1Ev_ZN5Ipopt14IpoptAlgorithmD0Ev_ZN5Ipopt14IpoptAlgorithmD1Ev_ZN5Ipopt14IpoptAlgorithmD2Ev_ZN5Ipopt14IpoptAlgorithm24correct_bound_multiplierERKNS_6VectorES3_S3_RNS_8SmartPtrIS2_EE_ZN5Ipopt14IpoptAlgorithm21calc_number_of_boundsERKNS_6VectorES3_S3_RKNS_6MatrixES6_RiS7_S7_S7__ZN5Ipopt14IpoptAlgorithm22PrintProblemStatisticsEv_ZN5Ipopt14IpoptAlgorithm29ComputeFeasibilityMultipliersEv_ZN5Ipopt14IpoptAlgorithm16AcceptTrialPointEv_ZN5Ipopt25IpoptCalculatedQuantities19AdjustedTrialSlacksEv_ZN5Ipopt25IpoptCalculatedQuantities24ResetAdjustedTrialSlacksEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_slack_x_LEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_slack_x_UEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_slack_s_LEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_slack_s_UEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_compl_x_LEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_compl_x_UEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_compl_s_LEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_compl_s_UEv_ZN5Ipopt14IpoptAlgorithm18InitializeIteratesEv_ZN5Ipopt14IpoptAlgorithm22ComputeSearchDirectionEv_ZNSs9_M_mutateEjjj_ZTVN5Ipopt23STEP_COMPUTATION_FAILEDE_ZTIN5Ipopt23STEP_COMPUTATION_FAILEDE_ZN5Ipopt14IpoptAlgorithm14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt9IpoptData10InitializeERKNS_10JournalistERKNS_11OptionsListERKSs_ZN5Ipopt25IpoptCalculatedQuantities10InitializeERKNS_10JournalistERKNS_11OptionsListERKSs_ZN5Ipopt11OptionsList22SetNumericValueIfUnsetERKSsdbb_ZN5Ipopt11OptionsList21SetStringValueIfUnsetERKSsS2_bb_ZTIN5Ipopt14IpoptAlgorithmE_ZTSN5Ipopt14IpoptAlgorithmE_ZTSN5Ipopt23STEP_COMPUTATION_FAILEDE_ZTIN5Ipopt18LOCALLY_INFEASIBLEE_ZTSN5Ipopt18LOCALLY_INFEASIBLEE_ZTIN5Ipopt39RESTORATION_CONVERGED_TO_FEASIBLE_POINTE_ZTSN5Ipopt39RESTORATION_CONVERGED_TO_FEASIBLE_POINTE_ZTIN5Ipopt28RESTORATION_MAXITER_EXCEEDEDE_ZTSN5Ipopt28RESTORATION_MAXITER_EXCEEDEDE_ZTIN5Ipopt21RESTORATION_USER_STOPE_ZTSN5Ipopt21RESTORATION_USER_STOPE_ZTIN5Ipopt26FEASIBILITY_PROBLEM_SOLVEDE_ZTSN5Ipopt26FEASIBILITY_PROBLEM_SOLVEDE_ZTIN5Ipopt14INTERNAL_ABORTE_ZTSN5Ipopt14INTERNAL_ABORTE_ZNK5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEE19DependentsIdenticalERKSt6vectorIPKNS_12TaggedObjectESaIS9_EERKS6_IdSaIdEE_ZNK5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEE19DependentsIdenticalERKSt6vectorIPKNS_12TaggedObjectESaIS9_EERKS6_IdSaIdEE_ZNK5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEE19DependentsIdenticalERKSt6vectorIPKNS_12TaggedObjectESaIS9_EERKS6_IdSaIdEE_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEE19RecieveNotificationENS_8Observer10NotifyTypeEPKNS_7SubjectE_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEE19RecieveNotificationENS_8Observer10NotifyTypeEPKNS_7SubjectE_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEE19RecieveNotificationENS_8Observer10NotifyTypeEPKNS_7SubjectE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_9SymMatrixEEEED0Ev_ZTVN5Ipopt13CachedResultsINS_8SmartPtrIKNS_9SymMatrixEEEEE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEED0Ev_ZTVN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEEE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEED0Ev_ZTVN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEEE_ZN5Ipopt25IpoptCalculatedQuantities15CalcFracToBoundERKNS_6VectorERS1_RKNS_6MatrixES3_S4_S7_S3_d_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEED1Ev_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEED1Ev_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_9SymMatrixEEEED1Ev_ZN5Ipopt25IpoptCalculatedQuantities29ComputeOptimalityErrorScalingERKNS_6VectorES3_S3_S3_S3_S3_dRdS4__ZN5Ipopt25IpoptCalculatedQuantities21CalcCentralityMeasureERKNS_6VectorES3_S3_S3__ZN5Ipopt25IpoptCalculatedQuantities14CalcNormOfTypeENS_9ENormTypeESt6vectorINS_8SmartPtrIKNS_6VectorEEESaIS6_EE_ZN5Ipopt25IpoptCalculatedQuantities14CalcNormOfTypeENS_9ENormTypeERKNS_6VectorES4__ZN5Ipopt25IpoptCalculatedQuantities11CalcSlack_UERKNS_6MatrixERKNS_6VectorES6__ZN5Ipopt25IpoptCalculatedQuantities11CalcSlack_LERKNS_6MatrixERKNS_6VectorES6__ZN5Ipopt25IpoptCalculatedQuantitiesD0Ev_ZTVN5Ipopt25IpoptCalculatedQuantitiesE_ZN5Ipopt25IpoptCalculatedQuantitiesD1Ev_ZN5Ipopt25IpoptCalculatedQuantitiesD2Ev_ZN5Ipopt25IpoptCalculatedQuantitiesC2ERKNS_8SmartPtrINS_8IpoptNLPEEERKNS1_INS_9IpoptDataEEE_ZN5Ipopt25IpoptCalculatedQuantities9CalcComplERKNS_6VectorES3__ZN5Ipopt25IpoptCalculatedQuantities18CalculateSafeSlackERNS_8SmartPtrINS_6VectorEEERKNS1_IKS2_EES8_S8__ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEEC1ERKS4_RKSt6vectorIPKNS_12TaggedObjectESaISB_EERKS8_IdSaIdEE_ZTVN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEEE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEE15AddCachedResultERKS4_RKSt6vectorIPKNS_12TaggedObjectESaISB_EERKS8_IdSaIdEE_ZNK5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEE15GetCachedResultERS4_RKSt6vectorIPKNS_12TaggedObjectESaISA_EE_ZNK5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEE15GetCachedResultERS3_RKSt6vectorIPKNS_12TaggedObjectESaIS9_EE_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEEC1ERKS4_RKSt6vectorIPKNS_12TaggedObjectESaISB_EERKS8_IdSaIdEE_ZTVN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEEE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_9SymMatrixEEEE15AddCachedResultERKS4_RKSt6vectorIPKNS_12TaggedObjectESaISB_EERKS8_IdSaIdEE_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEED0Ev_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEED1Ev_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEED0Ev_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEED1Ev_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEE19AddCachedResult1DepERKS4_PKNS_12TaggedObjectE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEE19GetCachedResult1DepERS4_PKNS_12TaggedObjectE_ZN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEE19AddCachedResult1DepERKS3_PKNS_12TaggedObjectE_ZN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEE19GetCachedResult1DepERS3_PKNS_12TaggedObjectE_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEEC1ERKS4_RKSt6vectorIPKNS_12TaggedObjectESaISB_EERKS8_IdSaIdEE_ZTVN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEEE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE15AddCachedResultERKS4_RKSt6vectorIPKNS_12TaggedObjectESaISB_EERKS8_IdSaIdEE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE19AddCachedResult3DepERKS4_PKNS_12TaggedObjectESA_SA__ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE19AddCachedResult2DepERKS4_PKNS_12TaggedObjectESA__ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE19AddCachedResult1DepERKS4_PKNS_12TaggedObjectE_ZNK5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE15GetCachedResultERS4_RKSt6vectorIPKNS_12TaggedObjectESaISA_EERKS7_IdSaIdEE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE19GetCachedResult3DepERS4_PKNS_12TaggedObjectES9_S9__ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE19GetCachedResult2DepERS4_PKNS_12TaggedObjectES9__ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE19GetCachedResult1DepERS4_PKNS_12TaggedObjectE_ZN5Ipopt25IpoptCalculatedQuantities11trial_jac_dEv_ZN5Ipopt25IpoptCalculatedQuantities20curr_jac_d_times_vecERKNS_6VectorE_ZN5Ipopt25IpoptCalculatedQuantities20curr_jac_c_times_vecERKNS_6VectorE_ZN5Ipopt25IpoptCalculatedQuantities15unscaled_curr_dEv_ZN5Ipopt25IpoptCalculatedQuantities15unscaled_curr_cEv_ZN5Ipopt25IpoptCalculatedQuantities26curr_jac_cT_times_curr_y_cEv_ZN5Ipopt25IpoptCalculatedQuantities26curr_jac_dT_times_curr_y_dEv_ZN5Ipopt25IpoptCalculatedQuantities7Tmp_x_LEv_ZN5Ipopt25IpoptCalculatedQuantities5Tmp_sEv_ZN5Ipopt25IpoptCalculatedQuantities7Tmp_x_UEv_ZN5Ipopt25IpoptCalculatedQuantities31uncached_dual_frac_to_the_boundEdRKNS_6VectorES3_S3_S3__ZN5Ipopt25IpoptCalculatedQuantities5Tmp_cEv_ZN5Ipopt25IpoptCalculatedQuantities7Tmp_s_LEv_ZN5Ipopt25IpoptCalculatedQuantities27curr_dual_frac_to_the_boundEd_ZN5Ipopt25IpoptCalculatedQuantities5Tmp_dEv_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEED1Ev_ZN5Ipopt25IpoptCalculatedQuantities12curr_sigma_xEv_ZNK5Ipopt6Matrix9AddMSinvZEdRKNS_6VectorES3_RS1__ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEED0Ev_ZN5Ipopt25IpoptCalculatedQuantities7Tmp_s_UEv_ZN5Ipopt25IpoptCalculatedQuantities5Tmp_xEv_ZN5Ipopt25IpoptCalculatedQuantities24ComputeDampingIndicatorsERNS_8SmartPtrIKNS_6VectorEEES5_S5_S5__ZN5Ipopt25IpoptCalculatedQuantities26grad_kappa_times_damping_sEv_ZN5Ipopt25IpoptCalculatedQuantities26grad_kappa_times_damping_xEv_ZN5Ipopt25IpoptCalculatedQuantities15CalcBarrierTermEdRKNS_6VectorES3_S3_S3__ZN5Ipopt25IpoptCalculatedQuantities7trial_fEv_ZN5Ipopt25IpoptCalculatedQuantities16unscaled_trial_fEv_ZN5Ipopt25IpoptCalculatedQuantities28trial_jac_dT_times_trial_y_dEv_ZN5Ipopt25IpoptCalculatedQuantities11trial_jac_cEv_ZN5Ipopt25IpoptCalculatedQuantities28trial_jac_cT_times_trial_y_cEv_ZN5Ipopt25IpoptCalculatedQuantities24trial_dual_infeasibilityENS_9ENormTypeE_ZN5Ipopt25IpoptCalculatedQuantities12curr_sigma_sEv_ZN5Ipopt25IpoptCalculatedQuantities32uncached_slack_frac_to_the_boundEdRKNS_6VectorES3_S3_S3__ZN5Ipopt25IpoptCalculatedQuantities21trial_complementarityEdNS_9ENormTypeE_ZTIN5Ipopt25IpoptCalculatedQuantitiesE_ZTSN5Ipopt25IpoptCalculatedQuantitiesE_ZTIN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEEE_ZTSN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEEE_ZTIN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEEE_ZTSN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEEE_ZTIN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEEE_ZTSN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEEE_ZTIN5Ipopt13CachedResultsINS_8SmartPtrIKNS_9SymMatrixEEEEE_ZTSN5Ipopt13CachedResultsINS_8SmartPtrIKNS_9SymMatrixEEEEE_ZTIN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEEE_ZTSN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEEE_ZTIN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEEE_ZTSN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEEE_ZN5Ipopt16TimingStatisticsC1Ev_ZTVN5Ipopt16TimingStatisticsE_ZN5Ipopt16TimingStatisticsD0Ev_ZN5Ipopt16TimingStatisticsD1Ev_ZN5Ipopt19IteratesVectorSpaceC1ERKNS_11VectorSpaceES3_S3_S3_S3_S3_S3_S3__ZN5Ipopt9IpoptDataC2Ev_ZTVN5Ipopt9IpoptDataE_ZN5Ipopt9IpoptDataD0Ev_ZN5Ipopt9IpoptDataD1Ev_ZN5Ipopt9IpoptDataD2Ev_ZTIN5Ipopt9IpoptDataE_ZTSN5Ipopt9IpoptDataE_ZTIN5Ipopt16TimingStatisticsE_ZTSN5Ipopt16TimingStatisticsE_ZNK5Ipopt19IteratesVectorSpace21MakeNewCompoundVectorEb_ZNK5Ipopt19IteratesVectorSpace7MakeNewEv_ZN5Ipopt19IteratesVectorSpace12SetCompSpaceEiRKNS_11VectorSpaceE_ZThn8_N5Ipopt14IteratesVectorD0Ev_ZN5Ipopt14IteratesVectorD0Ev_ZTVN5Ipopt14IteratesVectorE_ZN5Ipopt14CompoundVectorD2Ev_ZThn8_N5Ipopt14IteratesVectorD1Ev_ZN5Ipopt14IteratesVectorD1Ev_ZN5Ipopt14IteratesVectorD2Ev_ZN5Ipopt14IteratesVectorC1EPKNS_19IteratesVectorSpaceEb_ZN5Ipopt14CompoundVectorC2EPKNS_19CompoundVectorSpaceEb_ZNK5Ipopt19IteratesVectorSpace21MakeNewIteratesVectorEb_ZN5Ipopt14IteratesVectorC2EPKNS_19IteratesVectorSpaceEb_ZN5Ipopt19IteratesVectorSpaceD0Ev_ZTVN5Ipopt19IteratesVectorSpaceE_ZTVN5Ipopt19CompoundVectorSpaceE_ZN5Ipopt19IteratesVectorSpaceD1Ev_ZN5Ipopt19IteratesVectorSpaceD2Ev_ZN5Ipopt19IteratesVectorSpaceC2ERKNS_11VectorSpaceES3_S3_S3_S3_S3_S3_S3__ZN5Ipopt19CompoundVectorSpaceC2Eii_ZN5Ipopt19CompoundVectorSpace12SetCompSpaceEiRKNS_11VectorSpaceE_ZTIN5Ipopt19IteratesVectorSpaceE_ZTIN5Ipopt14IteratesVectorE_ZN5Ipopt14CompoundVector8CopyImplERKNS_6VectorE_ZN5Ipopt14CompoundVector8ScalImplEd_ZN5Ipopt14CompoundVector8AxpyImplEdRKNS_6VectorE_ZNK5Ipopt14CompoundVector7DotImplERKNS_6VectorE_ZNK5Ipopt14CompoundVector8Nrm2ImplEv_ZNK5Ipopt14CompoundVector8AsumImplEv_ZNK5Ipopt14CompoundVector8AmaxImplEv_ZN5Ipopt14CompoundVector7SetImplEd_ZN5Ipopt14CompoundVector21ElementWiseDivideImplERKNS_6VectorE_ZN5Ipopt14CompoundVector23ElementWiseMultiplyImplERKNS_6VectorE_ZN5Ipopt14CompoundVector18ElementWiseMaxImplERKNS_6VectorE_ZN5Ipopt14CompoundVector18ElementWiseMinImplERKNS_6VectorE_ZN5Ipopt14CompoundVector25ElementWiseReciprocalImplEv_ZN5Ipopt14CompoundVector18ElementWiseAbsImplEv_ZN5Ipopt14CompoundVector19ElementWiseSqrtImplEv_ZN5Ipopt14CompoundVector18ElementWiseSgnImplEv_ZN5Ipopt14CompoundVector13AddScalarImplEd_ZNK5Ipopt14CompoundVector7MaxImplEv_ZNK5Ipopt14CompoundVector7MinImplEv_ZNK5Ipopt14CompoundVector7SumImplEv_ZNK5Ipopt14CompoundVector11SumLogsImplEv_ZN5Ipopt14CompoundVector17AddTwoVectorsImplEdRKNS_6VectorEdS3_d_ZNK5Ipopt14CompoundVector15FracToBoundImplERKNS_6VectorEd_ZN5Ipopt14CompoundVector21AddVectorQuotientImplEdRKNS_6VectorES3_d_ZNK5Ipopt14CompoundVector19HasValidNumbersImplEv_ZNK5Ipopt14CompoundVector9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZTSN5Ipopt19IteratesVectorSpaceE_ZTIN5Ipopt19CompoundVectorSpaceE_ZTSN5Ipopt14IteratesVectorE_ZTIN5Ipopt14CompoundVectorE_ZN5Ipopt22LeastSquareMultipliers14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt22LeastSquareMultipliers20CalculateMultipliersERNS_6VectorES2__ZN5Ipopt22EqMultiplierCalculatorD0Ev_ZN5Ipopt22EqMultiplierCalculatorD1Ev_ZN5Ipopt22LeastSquareMultipliersD1Ev_ZTVN5Ipopt22LeastSquareMultipliersE_ZN5Ipopt22LeastSquareMultipliersC2ERNS_15AugSystemSolverE_ZN5Ipopt22LeastSquareMultipliersD0Ev_ZTIN5Ipopt22LeastSquareMultipliersE_ZTSN5Ipopt22LeastSquareMultipliersE_ZTIN5Ipopt22EqMultiplierCalculatorE_ZTSN5Ipopt22EqMultiplierCalculatorE_ZTVN5Ipopt22EqMultiplierCalculatorE_ZN5Ipopt12LoqoMuOracleC2Ev_ZTVN5Ipopt12LoqoMuOracleE_ZN5Ipopt12LoqoMuOracle14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt12LoqoMuOracleD0Ev_ZN5Ipopt12LoqoMuOracle11CalculateMuEddRd_ZN5Ipopt8MuOracleD0Ev_ZN5Ipopt8MuOracleD1Ev_ZN5Ipopt12LoqoMuOracleD1Ev_ZN5Ipopt12LoqoMuOracleD2Ev_ZTIN5Ipopt12LoqoMuOracleE_ZTSN5Ipopt12LoqoMuOracleE_ZTIN5Ipopt8MuOracleE_ZTSN5Ipopt8MuOracleE_ZTVN5Ipopt8MuOracleE_ZNK5Ipopt14SymMatrixSpace7MakeNewEv_ZNK5Ipopt22LowRankAugSystemSolver16NumberOfNegEValsEv_ZNK5Ipopt15DiagMatrixSpace16MakeNewSymMatrixEv_ZN5Ipopt10DiagMatrixC1EPKNS_14SymMatrixSpaceE_ZN5Ipopt22LowRankAugSystemSolver29AugmentedSystemRequiresChangeEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dRKNS_6MatrixES6_dS9_S6_d_ZN5Ipopt22LowRankAugSystemSolver15IncreaseQualityEv_ZNK5Ipopt22LowRankAugSystemSolver15ProvidesInertiaEv_ZN5Ipopt11MatrixSpaceD0Ev_ZN5Ipopt11MatrixSpaceD1Ev_ZN5Ipopt15DiagMatrixSpaceD0Ev_ZN5Ipopt14SymMatrixSpaceD0Ev_ZN5Ipopt14SymMatrixSpaceD1Ev_ZN5Ipopt15DiagMatrixSpaceD1Ev_ZN5Ipopt22LowRankAugSystemSolver14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt22LowRankAugSystemSolverC2ERNS_15AugSystemSolverE_ZTVN5Ipopt22LowRankAugSystemSolverE_ZN5Ipopt22LowRankAugSystemSolverD0Ev_ZN5Ipopt22LowRankAugSystemSolver16SolveMultiVectorEPKNS_6VectorEdS3_dRKNS_6MatrixES3_dS6_S3_dRS2_S7_S7_S7_RKNS_17MultiVectorMatrixERKNS_8SmartPtrIS5_EERNSB_IS8_EESG_SG_bi_ZN5Ipopt22MultiVectorMatrixSpaceC1EiRKNS_11VectorSpaceE_ZN5Ipopt17MultiVectorMatrixC1EPKNS_22MultiVectorMatrixSpaceE_ZN5Ipopt17MultiVectorMatrix9SetVectorEiRKNS_6VectorE_ZN5Ipopt19CompoundVectorSpaceC1Eii_ZN5Ipopt17MultiVectorMatrix17SetVectorNonConstEiRNS_6VectorE_ZN5Ipopt22LowRankAugSystemSolver19UpdateFactorizationEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dRKNS_6MatrixES6_dS9_S6_dRS5_SA_SA_SA_bi_ZN5Ipopt19DenseSymMatrixSpaceC1Ei_ZN5Ipopt14DenseSymMatrixC1EPKNS_19DenseSymMatrixSpaceE_ZN5Ipopt14DenseSymMatrix12FillIdentityEd_ZN5Ipopt14DenseSymMatrix23HighRankUpdateTransposeEdRKNS_17MultiVectorMatrixES3_d_ZN5Ipopt19DenseGenMatrixSpaceC1Eii_ZN5Ipopt14DenseGenMatrixC1EPKNS_19DenseGenMatrixSpaceE_ZN5Ipopt14DenseGenMatrix21ComputeCholeskyFactorERKNS_14DenseSymMatrixE_ZN5Ipopt14DenseGenMatrix23HighRankUpdateTransposeEdRKNS_17MultiVectorMatrixES3_d_ZNK5Ipopt14DenseGenMatrix19CholeskySolveMatrixERS0__ZN5Ipopt17MultiVectorMatrix18AddRightMultMatrixEdRKS0_RKNS_6MatrixEd_ZN5Ipopt22LowRankAugSystemSolver5SolveEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dPKNS_6MatrixES6_dS9_S6_dRS5_SA_SA_SA_RS4_SB_SB_SB_bi_ZTVN5Ipopt15DiagMatrixSpaceE_ZNK5Ipopt14DenseGenMatrix19CholeskySolveVectorERNS_11DenseVectorE_ZN5Ipopt22LowRankAugSystemSolverD1Ev_ZN5Ipopt22LowRankAugSystemSolverD2Ev_ZTIN5Ipopt22LowRankAugSystemSolverE_ZTSN5Ipopt22LowRankAugSystemSolverE_ZTIN5Ipopt15DiagMatrixSpaceE_ZTSN5Ipopt15DiagMatrixSpaceE_ZTIN5Ipopt14SymMatrixSpaceE_ZTSN5Ipopt14SymMatrixSpaceE_ZTIN5Ipopt11MatrixSpaceE_ZTSN5Ipopt11MatrixSpaceE_ZTVN5Ipopt14SymMatrixSpaceE_ZTVN5Ipopt11MatrixSpaceE_ZN5Ipopt16MonotoneMuUpdate14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt16MonotoneMuUpdate15CalcNewMuAndTauERdS1__ZTVN5Ipopt16MonotoneMuUpdateE_ZN5Ipopt16MonotoneMuUpdateC2ERKNS_8SmartPtrINS_10LineSearchEEE_ZN5Ipopt16MonotoneMuUpdateD0Ev_ZN5Ipopt16MonotoneMuUpdateD2Ev_ZN5Ipopt16MonotoneMuUpdateD1Ev_ZN5Ipopt16MonotoneMuUpdate22UpdateBarrierParameterEv_ZTIN5Ipopt16MonotoneMuUpdateE_ZTSN5Ipopt16MonotoneMuUpdateE_ZN5Ipopt8SmartPtrINS_6VectorEE15ReleasePointer_Ev_ZNSt6vectorIPN5Ipopt8ObserverESaIS2_EED1Ev_ZN5Ipopt18NoNLPScalingObject30DetermineScalingParametersImplENS_8SmartPtrIKNS_11VectorSpaceEEES4_S4_NS1_IKNS_11MatrixSpaceEEES7_NS1_IKNS_14SymMatrixSpaceEEERKNS_6MatrixERKNS_6VectorESD_SG_RdRNS1_ISE_EESJ_SJ__ZN5Ipopt16NLPScalingObjectD0Ev_ZN5Ipopt16NLPScalingObjectD1Ev_ZN5Ipopt16NLPScalingObjectD2Ev_ZNK5Ipopt20SymScaledMatrixSpace7MakeNewEv_ZN5Ipopt15SymScaledMatrixC1EPKNS_20SymScaledMatrixSpaceE_ZN5Ipopt12ScaledMatrixC1EPKNS_17ScaledMatrixSpaceE_ZN5Ipopt20SymScaledMatrixSpaceD1Ev_ZTVN5Ipopt20SymScaledMatrixSpaceE_ZN5Ipopt6Vector4CopyERKS0__ZN5Ipopt20SymScaledMatrixSpaceD0Ev_ZN5Ipopt18NoNLPScalingObjectD1Ev_ZNK5Ipopt20SymScaledMatrixSpace16MakeNewSymMatrixEv_ZN5Ipopt18NoNLPScalingObjectD0Ev_ZN5Ipopt19StandardScalingBaseD0Ev_ZN5Ipopt19StandardScalingBaseD1Ev_ZN5Ipopt16NLPScalingObject34apply_vector_scaling_d_LU_NonConstERKNS_6MatrixERKNS_8SmartPtrIKNS_6VectorEEERKNS_11VectorSpaceE_ZN5Ipopt16NLPScalingObject36unapply_vector_scaling_d_LU_NonConstERKNS_6MatrixERKNS_8SmartPtrIKNS_6VectorEEERKNS_11VectorSpaceE_ZN5Ipopt16NLPScalingObject27unapply_vector_scaling_d_LUERKNS_6MatrixERKNS_8SmartPtrIKNS_6VectorEEERKNS_11VectorSpaceE_ZN5Ipopt16NLPScalingObject34apply_vector_scaling_x_LU_NonConstERKNS_6MatrixERKNS_8SmartPtrIKNS_6VectorEEERKNS_11VectorSpaceE_ZN5Ipopt16NLPScalingObject25apply_vector_scaling_x_LUERKNS_6MatrixERKNS_8SmartPtrIKNS_6VectorEEERKNS_11VectorSpaceE_ZN5Ipopt16NLPScalingObject25apply_vector_scaling_d_LUERKNS_6MatrixERKNS_8SmartPtrIKNS_6VectorEEERKNS_11VectorSpaceE_ZN5Ipopt17ScaledMatrixSpaceC1ERKNS_8SmartPtrIKNS_6VectorEEEbRKNS1_IKNS_11MatrixSpaceEEES6_b_ZTIN5Ipopt18NoNLPScalingObjectE_ZTIN5Ipopt16NLPScalingObjectE_ZTSN5Ipopt18NoNLPScalingObjectE_ZTSN5Ipopt19StandardScalingBaseE_ZTSN5Ipopt16NLPScalingObjectE_ZTIN5Ipopt20SymScaledMatrixSpaceE_ZTSN5Ipopt20SymScaledMatrixSpaceE_ZN5Ipopt31OptimalityErrorConvergenceCheckC2Ev_ZTVN5Ipopt31OptimalityErrorConvergenceCheckE_ZN5Ipopt31OptimalityErrorConvergenceCheckD2Ev_ZN5Ipopt31OptimalityErrorConvergenceCheck19CurrentIsAcceptableEv_ZN5Ipopt16ConvergenceCheckD1Ev_ZN5Ipopt31OptimalityErrorConvergenceCheckD0Ev_ZN5Ipopt16ConvergenceCheckD0Ev_ZN5Ipopt31OptimalityErrorConvergenceCheckD1Ev_ZN5Ipopt31OptimalityErrorConvergenceCheck16CheckConvergenceEb_ZN5Ipopt31OptimalityErrorConvergenceCheck14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt31OptimalityErrorConvergenceCheckE_ZTSN5Ipopt31OptimalityErrorConvergenceCheckE_ZTIN5Ipopt16ConvergenceCheckE_ZTSN5Ipopt16ConvergenceCheckE_ZTVN5Ipopt16ConvergenceCheckE_ZNK5Ipopt8IpoptNLP23objective_depends_on_muEv_ZNK5Ipopt12OrigIpoptNLP7f_evalsEv_ZNK5Ipopt12OrigIpoptNLP12grad_f_evalsEv_ZNK5Ipopt12OrigIpoptNLP7c_evalsEv_ZNK5Ipopt12OrigIpoptNLP11jac_c_evalsEv_ZNK5Ipopt12OrigIpoptNLP7d_evalsEv_ZNK5Ipopt12OrigIpoptNLP11jac_d_evalsEv_ZNK5Ipopt12OrigIpoptNLP7h_evalsEv_ZN5Ipopt12OrigIpoptNLP1fERKNS_6VectorEd_ZN5Ipopt12OrigIpoptNLP19GetWarmStartIterateERNS_14IteratesVectorE_ZNK5Ipopt27LowRankUpdateSymMatrixSpace16MakeNewSymMatrixEv_ZN5Ipopt22LowRankUpdateSymMatrixC1EPKNS_27LowRankUpdateSymMatrixSpaceE_ZN5Ipopt12OrigIpoptNLP20IntermediateCallBackENS_13AlgorithmModeEiddddddddiNS_8SmartPtrIKNS_9IpoptDataEEENS2_INS_25IpoptCalculatedQuantitiesEEE_ZNK5Ipopt12OrigIpoptNLP3d_UEv_ZNK5Ipopt12OrigIpoptNLP4Pd_UEv_ZNK5Ipopt12OrigIpoptNLP4Pd_LEv_ZNK5Ipopt12OrigIpoptNLP4Px_UEv_ZN5Ipopt8IpoptNLPD0Ev_ZTVN5Ipopt8IpoptNLPE_ZN5Ipopt8IpoptNLP10InitializeERKNS_10JournalistERKNS_11OptionsListERKSs_ZN5Ipopt8IpoptNLPD1Ev_ZN5Ipopt12OrigIpoptNLP15uninitialized_hEv_ZNK5Ipopt12OrigIpoptNLP3d_LEv_ZNK5Ipopt12OrigIpoptNLP3x_LEv_ZNK5Ipopt12OrigIpoptNLP18HessianMatrixSpaceEv_ZN5Ipopt27LowRankUpdateSymMatrixSpaceD1Ev_ZTVN5Ipopt27LowRankUpdateSymMatrixSpaceE_ZN5Ipopt27LowRankUpdateSymMatrixSpaceD0Ev_ZN5Ipopt12OrigIpoptNLP9GetSpacesERNS_8SmartPtrIKNS_11VectorSpaceEEES5_S5_S5_RNS1_IKNS_11MatrixSpaceEEES5_S9_S5_S9_S5_S9_S9_S9_RNS1_IKNS_14SymMatrixSpaceEEE_ZNK5Ipopt12OrigIpoptNLP4Px_LEv_ZN5Ipopt14INTERNAL_ABORTD0Ev_ZN5Ipopt14INTERNAL_ABORTD1Ev_ZN5Ipopt12OrigIpoptNLP12relax_boundsEdRNS_6VectorE_ZN5Ipopt12OrigIpoptNLPC2ERKNS_8SmartPtrIKNS_10JournalistEEERKNS1_INS_3NLPEEERKNS1_INS_16NLPScalingObjectEEE_ZTVN5Ipopt12OrigIpoptNLPE_ZN5Ipopt13CachedResultsIdE19GetCachedResult1DepERdPKNS_12TaggedObjectE_ZN5Ipopt12OrigIpoptNLP10InitializeERKNS_10JournalistERKNS_11OptionsListERKSs_ZN5Ipopt12OrigIpoptNLPD2Ev_ZN5Ipopt12OrigIpoptNLPD0Ev_ZN5Ipopt12OrigIpoptNLPD1Ev_ZNK5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE15GetCachedResultERS4_RKSt6vectorIPKNS_12TaggedObjectESaISA_EE_ZNK5Ipopt12OrigIpoptNLP3x_UEv_ZN5Ipopt12OrigIpoptNLP14get_unscaled_xERKNS_6VectorE_ZN5Ipopt13CachedResultsIdE19AddCachedResult1DepERKdPKNS_12TaggedObjectE_ZN5Ipopt12OrigIpoptNLP20AdjustVariableBoundsERKNS_6VectorES3_S3_S3__ZN5Ipopt12OrigIpoptNLP16FinalizeSolutionENS_12SolverReturnERKNS_6VectorES4_S4_S4_S4_S4_S4_dPKNS_9IpoptDataEPNS_25IpoptCalculatedQuantitiesE_ZN5Ipopt12OrigIpoptNLP1hERKNS_6VectorEdS3_S3_d_ZTVN5Ipopt14INTERNAL_ABORTE_ZN5Ipopt12OrigIpoptNLP1hERKNS_6VectorEdS3_S3__ZN5Ipopt12OrigIpoptNLP5jac_dERKNS_6VectorE_ZN5Ipopt12OrigIpoptNLP5jac_cERKNS_6VectorE_ZNK5Ipopt6Matrix15HasValidNumbersEv_ZN5Ipopt12OrigIpoptNLP1dERKNS_6VectorE_ZN5Ipopt14IsFiniteNumberEd_ZN5Ipopt12OrigIpoptNLP1cERKNS_6VectorE_ZN5Ipopt12OrigIpoptNLP6grad_fERKNS_6VectorEd_ZN5Ipopt12OrigIpoptNLP6grad_fERKNS_6VectorE_ZN5Ipopt12OrigIpoptNLP1fERKNS_6VectorE_ZN5Ipopt12OrigIpoptNLP20InitializeStructuresERNS_8SmartPtrINS_6VectorEEEbS4_bS4_bS4_bS4_bS4_S4__ZTIN5Ipopt12OrigIpoptNLPE_ZTSN5Ipopt12OrigIpoptNLPE_ZTIN5Ipopt8IpoptNLPE_ZTSN5Ipopt8IpoptNLPE_ZTIN5Ipopt27LowRankUpdateSymMatrixSpaceE_ZTSN5Ipopt27LowRankUpdateSymMatrixSpaceE_ZN5Ipopt19OrigIterationOutputC2Ev_ZTVN5Ipopt19OrigIterationOutputE_ZN5Ipopt19OrigIterationOutputD1Ev_ZN5Ipopt15IterationOutputD0Ev_ZN5Ipopt15IterationOutputD1Ev_ZN5Ipopt19OrigIterationOutputD2Ev_ZN5Ipopt19OrigIterationOutputD0Ev_ZN5Ipopt19OrigIterationOutput11WriteOutputEv_ZN5Ipopt19OrigIterationOutput14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt19OrigIterationOutputE_ZTSN5Ipopt19OrigIterationOutputE_ZTIN5Ipopt15IterationOutputE_ZTSN5Ipopt15IterationOutputE_ZTVN5Ipopt15IterationOutputE_ZN5Ipopt24LimMemQuasiNewtonUpdater7RecalcYEdRKNS_6VectorERNS_17MultiVectorMatrixES5_RNS_8SmartPtrIS4_EE_ZN5Ipopt17MultiVectorMatrix23AddOneMultiVectorMatrixEdRKS0_d_ZN5Ipopt24LimMemQuasiNewtonUpdater23StoreInternalDataBackupEv_ZTVN5Ipopt24LimMemQuasiNewtonUpdaterE_ZN5Ipopt24LimMemQuasiNewtonUpdater17CheckSkippingBFGSERNS_6VectorES2__ZN5Ipopt24LimMemQuasiNewtonUpdater16ShiftMultiVectorERNS_8SmartPtrINS_17MultiVectorMatrixEEERKNS_6VectorE_ZN5Ipopt24LimMemQuasiNewtonUpdater25ReleaseInternalDataBackupEv_ZN5Ipopt24LimMemQuasiNewtonUpdater25RestoreInternalDataBackupEv_ZN5Ipopt24LimMemQuasiNewtonUpdater7RecalcLERNS_17MultiVectorMatrixES2_RNS_8SmartPtrINS_14DenseGenMatrixEEE_ZN5Ipopt24LimMemQuasiNewtonUpdater18AugmentMultiVectorERNS_8SmartPtrINS_17MultiVectorMatrixEEERKNS_6VectorE_ZN5Ipopt24LimMemQuasiNewtonUpdater16ShiftSdotSMatrixERNS_8SmartPtrINS_14DenseSymMatrixEEERKNS_17MultiVectorMatrixE_ZN5Ipopt24LimMemQuasiNewtonUpdater14AugmentLMatrixERNS_8SmartPtrINS_14DenseGenMatrixEEERKNS_17MultiVectorMatrixES7__ZN5Ipopt24LimMemQuasiNewtonUpdater12ShiftLMatrixERNS_8SmartPtrINS_14DenseGenMatrixEEERKNS_17MultiVectorMatrixES7__ZN5Ipopt24LimMemQuasiNewtonUpdater16ShiftSTDRSMatrixERNS_8SmartPtrINS_14DenseSymMatrixEEERKNS_17MultiVectorMatrixES7__ZN5Ipopt24LimMemQuasiNewtonUpdater18AugmentSdotSMatrixERNS_8SmartPtrINS_14DenseSymMatrixEEERKNS_17MultiVectorMatrixE_ZN5Ipopt24LimMemQuasiNewtonUpdater18AugmentSTDRSMatrixERNS_8SmartPtrINS_14DenseSymMatrixEEERKNS_17MultiVectorMatrixES7__ZN5Ipopt24LimMemQuasiNewtonUpdaterD0Ev_ZN5Ipopt24LimMemQuasiNewtonUpdaterD1Ev_ZN5Ipopt24LimMemQuasiNewtonUpdaterC2Eb_ZN5Ipopt24LimMemQuasiNewtonUpdater16SplitEigenvaluesERNS_14DenseGenMatrixERKNS_11DenseVectorERNS_8SmartPtrIS1_EES8__ZN5Ipopt14DenseGenMatrix12ScaleColumnsERKNS_11DenseVectorE_ZN5Ipopt24LimMemQuasiNewtonUpdater4SetWEv_ZNK5Ipopt22CompoundSymMatrixSpace24MakeNewCompoundSymMatrixEv_ZN5Ipopt17CompoundSymMatrix7SetCompEiiRKNS_6MatrixE_ZN5Ipopt24LimMemQuasiNewtonUpdater7RecalcDERNS_17MultiVectorMatrixES2_RNS_8SmartPtrINS_11DenseVectorEEE_ZN5Ipopt24LimMemQuasiNewtonUpdater16ShiftDenseVectorERNS_8SmartPtrINS_11DenseVectorEEEd_ZN5Ipopt24LimMemQuasiNewtonUpdater18AugmentDenseVectorERNS_8SmartPtrINS_11DenseVectorEEEd_ZN5Ipopt24LimMemQuasiNewtonUpdater18UpdateInternalDataERKNS_6VectorES3_NS_8SmartPtrIS1_EE_ZN5Ipopt24LimMemQuasiNewtonUpdater13UpdateHessianEv_ZNK5Ipopt13RestoIpoptNLP3EtaEd_ZN5Ipopt14DenseGenMatrix4CopyERKS0__ZN5Ipopt14DenseSymMatrix14HighRankUpdateEbdRKNS_14DenseGenMatrixEd_ZN5Ipopt14DenseSymMatrix9AddMatrixEdRKS0_d_ZN5Ipopt14DenseSymMatrix18SpecialAddForLMSR1ERKNS_11DenseVectorERKNS_14DenseGenMatrixE_ZN5Ipopt14DenseGenMatrix19ComputeEigenVectorsERKNS_14DenseSymMatrixERNS_11DenseVectorE_ZN5Ipopt14DenseGenMatrix12FillIdentityEd_ZNK5Ipopt14DenseGenMatrix23CholeskyBackSolveMatrixEbdRS0__ZN5Ipopt14DenseGenMatrix16AddMatrixProductEdRKS0_bS2_bd_ZN5Ipopt24LimMemQuasiNewtonUpdater14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt24LimMemQuasiNewtonUpdaterE_ZTSN5Ipopt24LimMemQuasiNewtonUpdaterE_ZNK5Ipopt15DependentResultIPvE19DependentsIdenticalERKSt6vectorIPKNS_12TaggedObjectESaIS6_EERKS3_IdSaIdEE_ZN5Ipopt15DependentResultIPvE19RecieveNotificationENS_8Observer10NotifyTypeEPKNS_7SubjectE_ZN5Ipopt13CachedResultsIPvED0Ev_ZTVN5Ipopt13CachedResultsIPvEE_ZN5Ipopt13CachedResultsIPvED1Ev_ZN5Ipopt17PDFullSpaceSolver20ComputeResidualRatioERKNS_14IteratesVectorES3_S3__ZN5Ipopt17PDFullSpaceSolver14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt14PDSystemSolverD0Ev_ZN5Ipopt14PDSystemSolverD1Ev_ZN5Ipopt17PDFullSpaceSolverD2Ev_ZTVN5Ipopt17PDFullSpaceSolverE_ZTVN5Ipopt14PDSystemSolverE_ZNK5Ipopt13CachedResultsIPvE15GetCachedResultERS1_RKSt6vectorIPKNS_12TaggedObjectESaIS7_EE_ZN5Ipopt15DependentResultIPvEC1ERKS1_RKSt6vectorIPKNS_12TaggedObjectESaIS8_EERKS5_IdSaIdEE_ZTVN5Ipopt15DependentResultIPvEE_ZN5Ipopt13CachedResultsIPvE15AddCachedResultERKS1_RKSt6vectorIPKNS_12TaggedObjectESaIS8_EERKS5_IdSaIdEE_ZN5Ipopt15DependentResultIPvED0Ev_ZN5Ipopt15DependentResultIPvED1Ev_ZN5Ipopt17PDFullSpaceSolverC2ERNS_15AugSystemSolverERNS_21PDPerturbationHandlerE_ZN5Ipopt17PDFullSpaceSolverD0Ev_ZN5Ipopt17PDFullSpaceSolver9SolveOnceEbbRKNS_9SymMatrixERKNS_6MatrixES6_S6_S6_S6_S6_RKNS_6VectorES9_S9_S9_S9_S9_S9_S9_S9_S9_ddRKNS_14IteratesVectorERSA__ZNK5Ipopt6Matrix14SinvBlrmZMTdBrEdRKNS_6VectorES3_S3_S3_RS1__ZN5Ipopt17PDFullSpaceSolver16ComputeResidualsERKNS_9SymMatrixERKNS_6MatrixES6_S6_S6_S6_S6_RKNS_6VectorES9_S9_S9_S9_S9_S9_S9_S9_S9_ddRKNS_14IteratesVectorESC_RSA__ZN5Ipopt17PDFullSpaceSolver5SolveEddRKNS_14IteratesVectorERS1_bb_ZN5Ipopt17PDFullSpaceSolverD1Ev_ZTIN5Ipopt17PDFullSpaceSolverE_ZTSN5Ipopt17PDFullSpaceSolverE_ZTIN5Ipopt14PDSystemSolverE_ZTSN5Ipopt14PDSystemSolverE_ZTIN5Ipopt15DependentResultIPvEE_ZTSN5Ipopt15DependentResultIPvEE_ZTIN5Ipopt13CachedResultsIPvEE_ZTSN5Ipopt13CachedResultsIPvEE_ZTVN5Ipopt21PDPerturbationHandlerE_ZN5Ipopt21PDPerturbationHandler19CurrentPerturbationERdS1_S1_S1__ZN5Ipopt21PDPerturbationHandler8delta_cdEv_ZN5Ipopt21PDPerturbationHandlerD1Ev_ZN5Ipopt21PDPerturbationHandlerD0Ev_ZN5Ipopt21PDPerturbationHandler13finalize_testEv_ZN5Ipopt21PDPerturbationHandler28get_deltas_for_wrong_inertiaERdS1_S1_S1__ZN5Ipopt21PDPerturbationHandler22PerturbForWrongInertiaERdS1_S1_S1__ZN5Ipopt21PDPerturbationHandler21PerturbForSingularityERdS1_S1_S1__ZN5Ipopt21PDPerturbationHandler17ConsiderNewSystemERdS1_S1_S1__ZTSN5Ipopt21PDPerturbationHandlerE_ZTVN5Ipopt21PDSearchDirCalculatorE_ZN5Ipopt21PDSearchDirCalculatorC2ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZN5Ipopt21PDSearchDirCalculatorD1Ev_ZN5Ipopt21PDSearchDirCalculatorD2Ev_ZN5Ipopt21PDSearchDirCalculator14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt21PDSearchDirCalculator22ComputeSearchDirectionEv_ZN5Ipopt21PDSearchDirCalculatorD0Ev_ZTIN5Ipopt21PDSearchDirCalculatorE_ZTSN5Ipopt21PDSearchDirCalculatorE_ZN5Ipopt15ProbingMuOracle14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt15ProbingMuOracleC2ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZTVN5Ipopt15ProbingMuOracleE_ZN5Ipopt15ProbingMuOracleD1Ev_ZN5Ipopt15ProbingMuOracleD2Ev_ZN5Ipopt15ProbingMuOracle17CalculateAffineMuEddRKNS_14IteratesVectorE_ZN5Ipopt15ProbingMuOracle11CalculateMuEddRd_ZN5Ipopt15ProbingMuOracleD0Ev_ZTIN5Ipopt15ProbingMuOracleE_ZTSN5Ipopt15ProbingMuOracleE_ZN5Ipopt23QualityFunctionMuOracle10ScaleSigmaEd_ZN5Ipopt23QualityFunctionMuOracle12UnscaleSigmaEd_ZN5Ipopt23QualityFunctionMuOracleC2ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZTVN5Ipopt23QualityFunctionMuOracleE_ZN5Ipopt23QualityFunctionMuOracleD2Ev_ZN5Ipopt23QualityFunctionMuOracleD0Ev_ZN5Ipopt23QualityFunctionMuOracleD1Ev_ZN5Ipopt23QualityFunctionMuOracle24CalculateQualityFunctionEdRKNS_6VectorES3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3__ZN5Ipopt23QualityFunctionMuOracle20PerformGoldenSectionEddddddRKNS_6VectorES3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3__ZN5Ipopt23QualityFunctionMuOracle11CalculateMuEddRd_ZN5Ipopt23QualityFunctionMuOracle14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt23QualityFunctionMuOracleE_ZTSN5Ipopt23QualityFunctionMuOracleE_ZN5Ipopt27RestoFilterConvergenceCheckD0Ev_ZTVN5Ipopt27RestoFilterConvergenceCheckE_ZN5Ipopt27RestoFilterConvergenceCheckD1Ev_ZN5Ipopt27RestoFilterConvergenceCheckD2Ev_ZN5Ipopt27RestoFilterConvergenceCheckC2Ev_ZN5Ipopt18LOCALLY_INFEASIBLED0Ev_ZN5Ipopt39RESTORATION_CONVERGED_TO_FEASIBLE_POINTD1Ev_ZN5Ipopt18LOCALLY_INFEASIBLED1Ev_ZN5Ipopt39RESTORATION_CONVERGED_TO_FEASIBLE_POINTD0Ev_ZN5Ipopt27RestoFilterConvergenceCheck14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt27RestoFilterConvergenceCheck16CheckConvergenceEb_ZTVN5Ipopt39RESTORATION_CONVERGED_TO_FEASIBLE_POINTE_ZTVN5Ipopt18LOCALLY_INFEASIBLEE_ZTIN5Ipopt27RestoFilterConvergenceCheckE_ZTSN5Ipopt27RestoFilterConvergenceCheckE_ZN5Ipopt13RestoIpoptNLP19GetWarmStartIterateERNS_14IteratesVectorE_ZN5Ipopt13RestoIpoptNLP16FinalizeSolutionENS_12SolverReturnERKNS_6VectorES4_S4_S4_S4_S4_S4_dPKNS_9IpoptDataEPNS_25IpoptCalculatedQuantitiesE_ZNK5Ipopt13RestoIpoptNLP23objective_depends_on_muEv_ZNK5Ipopt13RestoIpoptNLP7f_evalsEv_ZNK5Ipopt13RestoIpoptNLP12grad_f_evalsEv_ZNK5Ipopt13RestoIpoptNLP7c_evalsEv_ZNK5Ipopt13RestoIpoptNLP11jac_c_evalsEv_ZNK5Ipopt13RestoIpoptNLP7d_evalsEv_ZNK5Ipopt13RestoIpoptNLP11jac_d_evalsEv_ZNK5Ipopt13RestoIpoptNLP7h_evalsEv_ZNK5Ipopt19IdentityMatrixSpace16MakeNewSymMatrixEv_ZN5Ipopt14IdentityMatrixC1EPKNS_14SymMatrixSpaceE_ZN5Ipopt13RestoIpoptNLP1hERKNS_6VectorEdS3_S3__ZNK5Ipopt13RestoIpoptNLP18HessianMatrixSpaceEv_ZNK5Ipopt13RestoIpoptNLP4Pd_LEv_ZNK5Ipopt13RestoIpoptNLP3d_UEv_ZNK5Ipopt13RestoIpoptNLP4Px_UEv_ZNK5Ipopt13RestoIpoptNLP4Px_LEv_ZN5Ipopt19IdentityMatrixSpaceD1Ev_ZN5Ipopt13RestoIpoptNLP20IntermediateCallBackENS_13AlgorithmModeEiddddddddiNS_8SmartPtrIKNS_9IpoptDataEEENS2_INS_25IpoptCalculatedQuantitiesEEE_ZN5Ipopt13RestoIpoptNLP15uninitialized_hEv_ZN5Ipopt12SumSymMatrix7SetTermEidRKNS_9SymMatrixE_ZN5Ipopt19IdentityMatrixSpaceD0Ev_ZNK5Ipopt13RestoIpoptNLP4Pd_UEv_ZN5Ipopt13RestoIpoptNLP9GetSpacesERNS_8SmartPtrIKNS_11VectorSpaceEEES5_S5_S5_RNS1_IKNS_11MatrixSpaceEEES5_S9_S5_S9_S5_S9_S9_S9_RNS1_IKNS_14SymMatrixSpaceEEE_ZNK5Ipopt13RestoIpoptNLP3x_UEv_ZNK5Ipopt13RestoIpoptNLP3d_LEv_ZN5Ipopt13RestoIpoptNLPC2ERNS_8IpoptNLPERNS_9IpoptDataERNS_25IpoptCalculatedQuantitiesE_ZTVN5Ipopt13RestoIpoptNLPE_ZNK5Ipopt13RestoIpoptNLP3x_LEv_ZN5Ipopt13RestoIpoptNLPC1ERNS_8IpoptNLPERNS_9IpoptDataERNS_25IpoptCalculatedQuantitiesE_ZN5Ipopt13RestoIpoptNLPD1Ev_ZN5Ipopt13RestoIpoptNLPD0Ev_ZN5Ipopt13RestoIpoptNLPD2Ev_ZN5Ipopt13RestoIpoptNLP5jac_dERKNS_6VectorE_ZNK5Ipopt19CompoundMatrixSpace21MakeNewCompoundMatrixEv_ZN5Ipopt14CompoundMatrix7SetCompEiiRKNS_6MatrixE_ZN5Ipopt13RestoIpoptNLP20AdjustVariableBoundsERKNS_6VectorES3_S3_S3__ZN5Ipopt13RestoIpoptNLP1hERKNS_6VectorEdS3_S3_d_ZN5Ipopt13RestoIpoptNLP5jac_cERKNS_6VectorE_ZN5Ipopt13RestoIpoptNLP1fERKNS_6VectorE_ZN5Ipopt13RestoIpoptNLP10InitializeERKNS_10JournalistERKNS_11OptionsListERKSs_ZN5Ipopt13RestoIpoptNLP6grad_fERKNS_6VectorEd_ZN5Ipopt13RestoIpoptNLP1fERKNS_6VectorEd_ZN5Ipopt13RestoIpoptNLP1cERKNS_6VectorE_ZN5Ipopt13RestoIpoptNLP1dERKNS_6VectorE_ZN5Ipopt13RestoIpoptNLP20InitializeStructuresERNS_8SmartPtrINS_6VectorEEEbS4_bS4_bS4_bS4_bS4_S4__ZN5Ipopt19CompoundMatrixSpaceC1Eiiii_ZN5Ipopt19CompoundMatrixSpace12SetBlockRowsEii_ZN5Ipopt19CompoundMatrixSpace12SetBlockColsEii_ZN5Ipopt19CompoundMatrixSpace12SetCompSpaceEiiRKNS_11MatrixSpaceEb_ZTVN5Ipopt19IdentityMatrixSpaceE_ZN5Ipopt22CompoundSymMatrixSpaceC1Eii_ZN5Ipopt22CompoundSymMatrixSpace11SetBlockDimEii_ZTVN5Ipopt17SumSymMatrixSpaceE_ZN5Ipopt17SumSymMatrixSpace12SetTermSpaceEiRKNS_14SymMatrixSpaceE_ZN5Ipopt22CompoundSymMatrixSpace12SetCompSpaceEiiRKNS_11MatrixSpaceEb_ZN5Ipopt13RestoIpoptNLP6grad_fERKNS_6VectorE_ZTIN5Ipopt13RestoIpoptNLPE_ZTSN5Ipopt13RestoIpoptNLPE_ZTIN5Ipopt19IdentityMatrixSpaceE_ZTSN5Ipopt19IdentityMatrixSpaceE_ZN5Ipopt23RestoIterateInitializer14InitializeImplERKNS_11OptionsListERKSs_ZTVN5Ipopt23RestoIterateInitializerE_ZN5Ipopt23RestoIterateInitializerC2ERKNS_8SmartPtrINS_22EqMultiplierCalculatorEEE_ZN5Ipopt23RestoIterateInitializerD0Ev_ZN5Ipopt23RestoIterateInitializer15solve_quadraticERKNS_6VectorES3_RS1__ZN5Ipopt23RestoIterateInitializerD1Ev_ZN5Ipopt23RestoIterateInitializer18SetInitialIteratesEv_ZTIN5Ipopt23RestoIterateInitializerE_ZTSN5Ipopt23RestoIterateInitializerE_ZTVN5Ipopt20RestoIterationOutputE_ZN5Ipopt20RestoIterationOutputC2ERKNS_8SmartPtrINS_19OrigIterationOutputEEE_ZN5Ipopt20RestoIterationOutputD0Ev_ZN5Ipopt20RestoIterationOutputD2Ev_ZN5Ipopt20RestoIterationOutputD1Ev_ZN5Ipopt20RestoIterationOutput14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt20RestoIterationOutput11WriteOutputEv_ZTIN5Ipopt20RestoIterationOutputE_ZTSN5Ipopt20RestoIterationOutputE_ZN5Ipopt25MinC_1NrmRestorationPhaseC2ERNS_14IpoptAlgorithmERKNS_8SmartPtrINS_22EqMultiplierCalculatorEEE_ZTVN5Ipopt25MinC_1NrmRestorationPhaseE_ZN5Ipopt25MinC_1NrmRestorationPhaseD2Ev_ZTVN5Ipopt16RestorationPhaseE_ZN5Ipopt25MinC_1NrmRestorationPhase14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt16RestorationPhaseD0Ev_ZN5Ipopt16RestorationPhaseD1Ev_ZN5Ipopt25MinC_1NrmRestorationPhaseD0Ev_ZN5Ipopt25MinC_1NrmRestorationPhaseD1Ev_ZN5Ipopt28RESTORATION_MAXITER_EXCEEDEDD0Ev_ZN5Ipopt21RESTORATION_USER_STOPD0Ev_ZN5Ipopt26FEASIBILITY_PROBLEM_SOLVEDD0Ev_ZN5Ipopt26FEASIBILITY_PROBLEM_SOLVEDD1Ev_ZN5Ipopt28RESTORATION_MAXITER_EXCEEDEDD1Ev_ZN5Ipopt21RESTORATION_USER_STOPD1Ev_ZN5Ipopt25MinC_1NrmRestorationPhase26ComputeBoundMultiplierStepERNS_6VectorERKS1_S4_S4__ZN5Ipopt25MinC_1NrmRestorationPhase18PerformRestorationEv_ZTVN5Ipopt21RESTORATION_USER_STOPE_ZTVN5Ipopt28RESTORATION_MAXITER_EXCEEDEDE_ZTVN5Ipopt26FEASIBILITY_PROBLEM_SOLVEDE_ZTIN5Ipopt25MinC_1NrmRestorationPhaseE_ZTSN5Ipopt25MinC_1NrmRestorationPhaseE_ZTIN5Ipopt16RestorationPhaseE_ZTSN5Ipopt16RestorationPhaseE_ZN5Ipopt21RestoRestorationPhaseC2Ev_ZTVN5Ipopt21RestoRestorationPhaseE_ZN5Ipopt21RestoRestorationPhase14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt21RestoRestorationPhaseD0Ev_ZN5Ipopt21RestoRestorationPhaseD1Ev_ZN5Ipopt21RestoRestorationPhase15solve_quadraticERKNS_6VectorES3_RS1__ZN5Ipopt21RestoRestorationPhaseD2Ev_ZN5Ipopt21RestoRestorationPhase18PerformRestorationEv_ZTIN5Ipopt21RestoRestorationPhaseE_ZTSN5Ipopt21RestoRestorationPhaseE_ZNK5Ipopt18StdAugSystemSolver15ProvidesInertiaEv_ZN5Ipopt18StdAugSystemSolver29AugmentedSystemRequiresChangeEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dRKNS_6MatrixES6_dS9_S6_d_ZNK5Ipopt18StdAugSystemSolver16NumberOfNegEValsEv_ZN5Ipopt18StdAugSystemSolver15IncreaseQualityEv_ZN5Ipopt18StdAugSystemSolver20CreateAugmentedSpaceERKNS_9SymMatrixERKNS_6MatrixES6_RKNS_6VectorES9_S9_S9__ZTVN5Ipopt18StdAugSystemSolverE_ZN5Ipopt18StdAugSystemSolverC2ERNS_15SymLinearSolverE_ZN5Ipopt18StdAugSystemSolverD0Ev_ZN5Ipopt18StdAugSystemSolver14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt18StdAugSystemSolver21CreateAugmentedSystemEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dRKNS_6MatrixES6_dS9_S6_dRS5_SA_SA_SA__ZNK5Ipopt17SumSymMatrixSpace19MakeNewSumSymMatrixEv_ZN5Ipopt18StdAugSystemSolver10MultiSolveEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dPKNS_6MatrixES6_dS9_S6_dRSt6vectorINS_8SmartPtrIS5_EESaISC_EESF_SF_SF_RSA_INSB_IS4_EESaISG_EESJ_SJ_SJ_bi_ZN5Ipopt18StdAugSystemSolverD1Ev_ZN5Ipopt18StdAugSystemSolverD2Ev_ZTIN5Ipopt18StdAugSystemSolverE_ZTSN5Ipopt18StdAugSystemSolverE_ZN5Ipopt11UserScalingD0Ev_ZN5Ipopt11UserScalingD1Ev_ZN5Ipopt11UserScaling30DetermineScalingParametersImplENS_8SmartPtrIKNS_11VectorSpaceEEES4_S4_NS1_IKNS_11MatrixSpaceEEES7_NS1_IKNS_14SymMatrixSpaceEEERKNS_6MatrixERKNS_6VectorESD_SG_RdRNS1_ISE_EESJ_SJ__ZTIN5Ipopt11UserScalingE_ZTSN5Ipopt11UserScalingE_ZN5Ipopt27WarmStartIterateInitializerC2Ev_ZTVN5Ipopt27WarmStartIterateInitializerE_ZN5Ipopt27WarmStartIterateInitializer14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt27WarmStartIterateInitializerD0Ev_ZN5Ipopt27WarmStartIterateInitializerD1Ev_ZN5Ipopt27WarmStartIterateInitializer18adapt_to_target_muERNS_6VectorES2_d_ZN5Ipopt27WarmStartIterateInitializer17process_target_muEdRKNS_6VectorES3_S3_RKNS_6MatrixERNS_8SmartPtrIS2_EES9__ZN5Ipopt27WarmStartIterateInitializer18SetInitialIteratesEv_ZTIN5Ipopt27WarmStartIterateInitializerE_ZTSN5Ipopt27WarmStartIterateInitializerE_ZN5Ipopt16TSymLinearSolver15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt20MumpsSolverInterface15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt21TripletToCSRConverterC2Ei_ZTVN5Ipopt21TripletToCSRConverterE_ZN5Ipopt21TripletToCSRConverter13ConvertValuesEiPKdiPd_ZN5Ipopt21TripletToCSRConverterD1Ev_ZN5Ipopt21TripletToCSRConverterC1Ei_ZN5Ipopt21TripletToCSRConverterD2Ev_ZN5Ipopt21TripletToCSRConverterD0Ev_ZNSt4listIN5Ipopt21TripletToCSRConverter12TripletEntryESaIS2_EE4sortEv_ZN5Ipopt21TripletToCSRConverter19InitializeConverterEiiPKiS2__ZTIN5Ipopt21TripletToCSRConverterE_ZTSN5Ipopt21TripletToCSRConverterE_ZN5Ipopt22TSymDependencyDetector15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt22TSymDependencyDetector22DetermineDependentRowsEiiiPdPiS2_RSt4listIiSaIiEE_ZN5Ipopt16TSymLinearSolver22DetermineDependentRowsEiiiPdPiS2_RSt4listIiSaIiEE_ZN5Ipopt19TDependencyDetectorD0Ev_ZN5Ipopt19TDependencyDetectorD1Ev_ZN5Ipopt22TSymDependencyDetectorD1Ev_ZTVN5Ipopt22TSymDependencyDetectorE_ZN5Ipopt22TSymDependencyDetector14InitializeImplERKNS_11OptionsListERKSs_ZNK5Ipopt16TSymLinearSolver27ProvidesDegeneracyDetectionEv_ZN5Ipopt22TSymDependencyDetectorC2ERNS_16TSymLinearSolverE_ZN5Ipopt22TSymDependencyDetectorD0Ev_ZTIN5Ipopt22TSymDependencyDetectorE_ZTSN5Ipopt22TSymDependencyDetectorE_ZTIN5Ipopt19TDependencyDetectorE_ZTSN5Ipopt19TDependencyDetectorE_ZTVN5Ipopt19TDependencyDetectorE_ZNK5Ipopt16TSymLinearSolver15ProvidesInertiaEv_ZNK5Ipopt16TSymLinearSolver16NumberOfNegEValsEv_ZN5Ipopt16TSymLinearSolverC2ENS_8SmartPtrINS_30SparseSymLinearSolverInterfaceEEENS1_INS_17TSymScalingMethodEEE_ZTVN5Ipopt16TSymLinearSolverE_ZN5Ipopt30ERROR_IN_LINEAR_SCALING_METHODD0Ev_ZN5Ipopt30ERROR_IN_LINEAR_SCALING_METHODD1Ev_ZN5Ipopt15SymLinearSolverD0Ev_ZN5Ipopt15SymLinearSolverD1Ev_ZN5Ipopt16TSymLinearSolverD2Ev_ZTVN5Ipopt15SymLinearSolverE_ZN5Ipopt16TSymLinearSolverD0Ev_ZN5Ipopt16TSymLinearSolverD1Ev_ZN5Ipopt16TSymLinearSolver15IncreaseQualityEv_ZN5Ipopt16TSymLinearSolver19InitializeStructureERKNS_9SymMatrixE_ZN5Ipopt16TSymLinearSolver14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt16TSymLinearSolver18GiveMatrixToSolverEbRKNS_9SymMatrixE_ZTVN5Ipopt30ERROR_IN_LINEAR_SCALING_METHODE_ZTIN5Ipopt30ERROR_IN_LINEAR_SCALING_METHODE_ZN5Ipopt16TSymLinearSolver10MultiSolveERKNS_9SymMatrixERSt6vectorINS_8SmartPtrIKNS_6VectorEEESaIS8_EERS4_INS5_IS6_EESaISC_EEbi_ZTIN5Ipopt16TSymLinearSolverE_ZTSN5Ipopt16TSymLinearSolverE_ZTIN5Ipopt15SymLinearSolverE_ZTSN5Ipopt15SymLinearSolverE_ZTSN5Ipopt30ERROR_IN_LINEAR_SCALING_METHODE_ZNK5Ipopt30SparseSymLinearSolverInterface27ProvidesDegeneracyDetectionEv_ZN5Ipopt30SparseSymLinearSolverInterface22DetermineDependentRowsEPKiS2_RSt4listIiSaIiEE_ZNK5Ipopt20MumpsSolverInterface15ProvidesInertiaEv_ZNK5Ipopt20MumpsSolverInterface12MatrixFormatEv_ZN5Ipopt20MumpsSolverInterface17GetValuesArrayPtrEv_ZN5Ipopt11dump_matrixEP14DMUMPS_STRUC_C_ZNK5Ipopt20MumpsSolverInterface16NumberOfNegEValsEv_ZNK5Ipopt20MumpsSolverInterface27ProvidesDegeneracyDetectionEv_ZN5Ipopt20MumpsSolverInterface15IncreaseQualityEv_ZN5Ipopt20MumpsSolverInterface13FactorizationEbi_ZN5Ipopt20MumpsSolverInterface21SymbolicFactorizationEv_ZN5Ipopt20MumpsSolverInterface22DetermineDependentRowsEPKiS2_RSt4listIiSaIiEE_ZN5Ipopt20MumpsSolverInterface5SolveEiPd_ZN5Ipopt20MumpsSolverInterface10MultiSolveEbPKiS2_iPdbi_ZN5Ipopt30SparseSymLinearSolverInterfaceD0Ev_ZN5Ipopt30SparseSymLinearSolverInterfaceD1Ev_ZTVN5Ipopt20MumpsSolverInterfaceEIPOPT_MPI_COMM_WORLD_ZN5Ipopt20MumpsSolverInterfaceC2Ev_ZN5Ipopt20MumpsSolverInterfaceD0Ev_ZN5Ipopt20MumpsSolverInterfaceD2Ev_ZTVN5Ipopt30SparseSymLinearSolverInterfaceE_ZN5Ipopt20MumpsSolverInterfaceD1Ev_ZN5Ipopt20MumpsSolverInterface19InitializeStructureEiiPKiS2__ZN5Ipopt20MumpsSolverInterface14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt20MumpsSolverInterfaceE_ZTSN5Ipopt20MumpsSolverInterfaceE_ZTIN5Ipopt30SparseSymLinearSolverInterfaceE_ZTSN5Ipopt30SparseSymLinearSolverInterfaceE_ZNK5Ipopt7Journal10IsAcceptedENS_16EJournalCategoryENS_13EJournalLevelE_ZN5Ipopt13StreamJournal15SetOutputStreamEPSo_ZN5Ipopt7Journal4NameEv_ZN5Ipopt13StreamJournal15FlushBufferImplEv_ZSt5flushIcSt11char_traitsIcEERSt13basic_ostreamIT_T0_ES6__ZN5Ipopt11FileJournal15FlushBufferImplEvfflush_ZN5Ipopt11FileJournal10PrintfImplENS_16EJournalCategoryENS_13EJournalLevelEPKcPcvfprintf_ZN5Ipopt11FileJournal9PrintImplENS_16EJournalCategoryENS_13EJournalLevelEPKc_ZN5Ipopt11FileJournal4OpenEPKcstdoutstderrfclosefopen_ZNK5Ipopt10Journalist7VPrintfENS_13EJournalLevelENS_16EJournalCategoryEPKcPc_ZNK5Ipopt10Journalist20PrintStringOverLinesENS_13EJournalLevelENS_16EJournalCategoryEiiRKSs_ZNK5Ipopt10Journalist15VPrintfIndentedENS_13EJournalLevelENS_16EJournalCategoryEiPKcPc_ZNK5Ipopt10Journalist14PrintfIndentedENS_13EJournalLevelENS_16EJournalCategoryEiPKcz_ZN5Ipopt13StreamJournal9PrintImplENS_16EJournalCategoryENS_13EJournalLevelEPKc_ZSt16__ostream_insertIcSt11char_traitsIcEERSt13basic_ostreamIT_T0_ES6_PKS3_i_ZN5Ipopt7JournalC1ERKSsNS_13EJournalLevelE_ZTVN5Ipopt7JournalE_ZN5Ipopt7JournalC2ERKSsNS_13EJournalLevelE_ZN5Ipopt13StreamJournalC1ERKSsNS_13EJournalLevelE_ZTVN5Ipopt13StreamJournalE_ZN5Ipopt13StreamJournalC2ERKSsNS_13EJournalLevelE_ZN5Ipopt11FileJournalC1ERKSsNS_13EJournalLevelE_ZTVN5Ipopt11FileJournalE_ZN5Ipopt11FileJournalC2ERKSsNS_13EJournalLevelE_ZN5Ipopt7JournalD0Ev_ZTVN5Ipopt10JournalistE_ZN5Ipopt10JournalistC2Ev_ZN5Ipopt13StreamJournal10PrintfImplENS_16EJournalCategoryENS_13EJournalLevelEPKcPcvsprintf_ZN5Ipopt7JournalD1Ev_ZN5Ipopt7JournalD2Ev_ZN5Ipopt11FileJournalD0Ev_ZN5Ipopt11FileJournalD1Ev_ZN5Ipopt11FileJournalD2Ev_ZN5Ipopt13StreamJournalD0Ev_ZN5Ipopt13StreamJournalD1Ev_ZNSt6vectorIN5Ipopt8SmartPtrINS0_7JournalEEESaIS3_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS3_S5_EERKS3__ZN5Ipopt10Journalist10AddJournalENS_8SmartPtrINS_7JournalEEE_ZN5Ipopt10JournalistD2Ev_ZN5Ipopt10JournalistD0Ev_ZN5Ipopt10JournalistD1Ev_ZTIN5Ipopt13StreamJournalE_ZTIN5Ipopt11FileJournalE_ZTIN5Ipopt7JournalE_ZTIN5Ipopt10JournalistE_ZTSN5Ipopt13StreamJournalE_ZTSN5Ipopt11FileJournalE_ZTSN5Ipopt7JournalE_ZTSN5Ipopt10JournalistE_ZNK5Ipopt11OptionsList9lowercaseESstolower_ZNSs12_M_leak_hardEv_ZN5Ipopt11OptionsList13readnexttokenERSiRSs_ZNSi3getEvisspace_ZNSi6ignoreEii_ZNK5Ipopt11OptionsList18will_allow_clobberERKSs_ZNK5Ipopt11OptionsList8find_tagERKSsS2_RSs_ZSt18_Rb_tree_incrementPKSt18_Rb_tree_node_base_ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt11OptionsList11OptionValueEESt10_Select1stIS5_ESt4lessISsESaIS5_EE9_M_insertEPSt18_Rb_tree_node_baseSD_RKS5__ZSt29_Rb_tree_insert_and_rebalancebPSt18_Rb_tree_node_baseS0_RS__ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt11OptionsList11OptionValueEESt10_Select1stIS5_ESt4lessISsESaIS5_EE16_M_insert_uniqueERKS5__ZSt18_Rb_tree_decrementPSt18_Rb_tree_node_base_ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt11OptionsList11OptionValueEESt10_Select1stIS5_ESt4lessISsESaIS5_EE16_M_insert_uniqueESt17_Rb_tree_iteratorIS5_ERKS5__ZSt18_Rb_tree_incrementPSt18_Rb_tree_node_base_ZN5Ipopt17RegisteredOptions9GetOptionERKSs_ZNK5Ipopt16RegisteredOption17OutputDescriptionERKNS_10JournalistEstrtolstrtod_ZNK5Ipopt16RegisteredOption22MapStringSettingToEnumERKSs_ZNK5Ipopt16RegisteredOption16MapStringSettingERKSs_ZN5Ipopt11OptionsList22SetIntegerValueIfUnsetERKSsibb_ZNK5Ipopt16RegisteredOption20IsValidStringSettingERKSs_ZN5Ipopt17RegisteredOptions25OPTION_ALREADY_REGISTEREDC1ESsSsi_ZTVN5Ipopt17RegisteredOptions25OPTION_ALREADY_REGISTEREDE_ZN5Ipopt16RegisteredOption31ERROR_CONVERTING_STRING_TO_ENUMD1Ev_ZNK5Ipopt16RegisteredOption22OutputShortDescriptionERKNS_10JournalistE_ZNK5Ipopt16RegisteredOption24string_equal_insensitiveERKSsS2__ZN5Ipopt16RegisteredOptionC1ERKSsS2_S2_S2__ZTVN5Ipopt16RegisteredOptionE_ZN5Ipopt16RegisteredOption13next_counter_E_ZNSt8_Rb_treeIiSt4pairIKiN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessIiESaIS6_EE9_M_insertEPSt18_Rb_tree_node_baseSE_RKS6__ZNSt8_Rb_treeIiSt4pairIKiN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessIiESaIS6_EE16_M_insert_uniqueERKS6__ZNSt8_Rb_treeIiSt4pairIKiN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessIiESaIS6_EE16_M_insert_uniqueESt17_Rb_tree_iteratorIS6_ERKS6__ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessISsESaIS6_EE9_M_insertEPSt18_Rb_tree_node_baseSE_RKS6__ZNK5Ipopt16RegisteredOption20MakeValidLatexNumberEd_ZNK5Ipopt16RegisteredOption20MakeValidLatexStringESsRSs_ZNK5Ipopt16RegisteredOption22OutputLatexDescriptionERKNS_10JournalistE_ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessISsESaIS6_EE16_M_insert_uniqueERKS6__ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessISsESaIS6_EE16_M_insert_uniqueESt17_Rb_tree_iteratorIS6_ERKS6__ZNSt8_Rb_treeIiSt4pairIKiN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessIiESaIS6_EE8_M_eraseEPSt13_Rb_tree_nodeIS6_E_ZN5Ipopt16RegisteredOptionD1Ev_ZNKSs5rfindEPKcjj_ZNSsC1ERKSsjj_ZSt20__throw_out_of_rangePKc_ZN5Ipopt17RegisteredOptions25OPTION_ALREADY_REGISTEREDD0Ev_ZN5Ipopt16RegisteredOption31ERROR_CONVERTING_STRING_TO_ENUMD0Ev_ZN5Ipopt17RegisteredOptions25OPTION_ALREADY_REGISTEREDD1Ev_ZNSt6vectorIN5Ipopt16RegisteredOption12string_entryESaIS2_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS2_S4_EERKS2__ZN5Ipopt16RegisteredOption21AddValidStringSettingESsSs_ZN5Ipopt16RegisteredOptionD0Ev_ZTVN5Ipopt16RegisteredOption31ERROR_CONVERTING_STRING_TO_ENUME_ZTIN5Ipopt16RegisteredOption31ERROR_CONVERTING_STRING_TO_ENUME_ZTIN5Ipopt17RegisteredOptions25OPTION_ALREADY_REGISTEREDE_ZN5Ipopt17RegisteredOptions27AddUpperBoundedNumberOptionERKSsS2_dbdS2__ZN5Ipopt17RegisteredOptions28AddUpperBoundedIntegerOptionERKSsS2_iiS2__ZN5Ipopt17RegisteredOptions16AddIntegerOptionERKSsS2_iS2__ZN5Ipopt17RegisteredOptions15AddStringOptionERKSsS2_S2_RKSt6vectorISsSaISsEES7_S2__ZN5Ipopt17RegisteredOptions16AddStringOption8ERKSsS2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2__ZN5Ipopt17RegisteredOptions16AddStringOption5ERKSsS2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2__ZN5Ipopt17RegisteredOptions16AddStringOption9ERKSsS2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2__ZN5Ipopt17RegisteredOptions16AddStringOption6ERKSsS2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2__ZTSN5Ipopt17RegisteredOptions25OPTION_ALREADY_REGISTEREDE_ZTIN5Ipopt16RegisteredOptionE_ZTSN5Ipopt16RegisteredOptionE_ZTSN5Ipopt16RegisteredOption31ERROR_CONVERTING_STRING_TO_ENUMEsrand48drand48finite_ZN5Ipopt11IpBlasDtrsmEbiidPKdiPdi_ZN5Ipopt11IpBlasDsyrkEbiidPKdidPdidsyrk__ZN5Ipopt11IpBlasDgemmEbbiiidPKdiS1_idPdi_ZN5Ipopt11IpBlasDsymvEidPKdiS1_idPdidsymv__ZN5Ipopt11IpBlasDgemvEbiidPKdiS1_idPdi_ZN5Ipopt11IpBlasDscalEidPdi_ZN5Ipopt11IpBlasDaxpyEidPKdiPdi_ZN5Ipopt11IpBlasDasumEiPKdidasum__ZN5Ipopt11IpBlasDnrm2EiPKdi_ZN5Ipopt10IpBlasDdotEiPKdiS1_i_ZNK5Ipopt19CompoundMatrixSpace12GetBlockRowsEi_ZNSt6vectorIiSaIiEED1Ev_ZNK5Ipopt19CompoundMatrixSpace13DimensionsSetEv_ZNK5Ipopt19CompoundMatrixSpace12GetBlockColsEi_ZNSt13_Bvector_baseISaIbEED2Ev_ZNSt6vectorIbSaIbEEC1ERKS1__ZNSt6vectorIN5Ipopt8SmartPtrINS0_6MatrixEEESaIS3_EEC1ERKS5__ZN5Ipopt14CompoundMatrix15SetCompNonConstEiiRNS_6MatrixE_ZNSt6vectorIN5Ipopt8SmartPtrINS0_6MatrixEEESaIS3_EED1Ev_ZNSt6vectorIS_IN5Ipopt8SmartPtrINS0_6MatrixEEESaIS3_EESaIS5_EED1Ev_ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_6MatrixEEESaIS4_EEC1ERKS6__ZSt26__uninitialized_fill_n_auxIPN5Ipopt8SmartPtrIKNS0_6MatrixEEEjS4_EvT_T0_RKT1_St12__false_type_ZNK5Ipopt14CompoundMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNSt6vectorIN5Ipopt8SmartPtrINS0_6MatrixEEESaIS3_EEaSERKS5__ZSt26__uninitialized_fill_n_auxIPN5Ipopt8SmartPtrINS0_6MatrixEEEjS3_EvT_T0_RKT1_St12__false_type_ZNSt6vectorIS_IN5Ipopt8SmartPtrINS0_6MatrixEEESaIS3_EESaIS5_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS5_S7_EERKS5__ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_6MatrixEEESaIS4_EED1Ev_ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_6MatrixEEESaIS4_EEaSERKS6__ZN5Ipopt7SubjectD2Ev_ZN5Ipopt14CompoundMatrix20CreateBlockFromSpaceEii_ZNK5Ipopt14CompoundMatrix13MatricesValidEv_ZNK5Ipopt14CompoundMatrix19HasValidNumbersImplEv_ZNK5Ipopt14CompoundMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_11MatrixSpaceEEESaIS4_EEaSERKS6__ZNK5Ipopt14CompoundMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZSt26__uninitialized_fill_n_auxIPN5Ipopt8SmartPtrIKNS0_11MatrixSpaceEEEjS4_EvT_T0_RKT1_St12__false_type_ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_11MatrixSpaceEEESaIS4_EED1Ev_ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_11MatrixSpaceEEESaIS4_EEC1ERKS6__ZNSt6vectorIS_IN5Ipopt8SmartPtrIKNS0_6MatrixEEESaIS4_EESaIS6_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS6_S8_EERKS6__ZNSt6vectorIS_IN5Ipopt8SmartPtrIKNS0_11MatrixSpaceEEESaIS4_EESaIS6_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS6_S8_EERKS6__ZN5Ipopt6MatrixD2Ev_ZThn8_N5Ipopt14CompoundMatrixD0Ev_ZN5Ipopt14CompoundMatrixD0Ev_ZTVN5Ipopt14CompoundMatrixE_ZThn8_N5Ipopt14CompoundMatrixD1Ev_ZN5Ipopt14CompoundMatrixD1Ev_ZN5Ipopt14CompoundMatrixD2Ev_ZN5Ipopt14CompoundMatrixC2EPKNS_19CompoundMatrixSpaceE_ZN5Ipopt14CompoundMatrixC1EPKNS_19CompoundMatrixSpaceE_ZNK5Ipopt19CompoundMatrixSpace7MakeNewEv_ZNK5Ipopt14CompoundMatrix18SinvBlrmZMTdBrImplEdRKNS_6VectorES3_S3_S3_RS1__ZNK5Ipopt14CompoundMatrix13AddMSinvZImplEdRKNS_6VectorES3_RS1__ZN5Ipopt19CompoundMatrixSpaceD1Ev_ZTVN5Ipopt19CompoundMatrixSpaceE_ZN5Ipopt19CompoundMatrixSpaceD0Ev_ZNSt6vectorIS_IbSaIbEESaIS1_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS1_S3_EERKS1__ZN5Ipopt19CompoundMatrixSpaceC2Eiiii_ZTIN5Ipopt14CompoundMatrixE_ZTSN5Ipopt14CompoundMatrixE_ZTIN5Ipopt19CompoundMatrixSpaceE_ZTSN5Ipopt19CompoundMatrixSpaceE_ZNK5Ipopt17CompoundSymMatrix10NComps_DimEv_ZNK5Ipopt22CompoundSymMatrixSpace11GetBlockDimEi_ZNSt6vectorIbSaIbEE13_M_initializeEj_ZNK5Ipopt22CompoundSymMatrixSpace13DimensionsSetEv_ZN5Ipopt17CompoundSymMatrix15SetCompNonConstEiiRNS_6MatrixE_ZNK5Ipopt17CompoundSymMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNSt6vectorIS_IN5Ipopt8SmartPtrIKNS0_11MatrixSpaceEEESaIS4_EESaIS6_EED1Ev_ZNK5Ipopt17CompoundSymMatrix13MatricesValidEv_ZNK5Ipopt17CompoundSymMatrix19HasValidNumbersImplEv_ZNK5Ipopt17CompoundSymMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZN5Ipopt22CompoundSymMatrixSpaceC2Eii_ZTVN5Ipopt22CompoundSymMatrixSpaceE_ZN5Ipopt22CompoundSymMatrixSpaceD0Ev_ZN5Ipopt22CompoundSymMatrixSpaceD1Ev_ZN5Ipopt17CompoundSymMatrixD2Ev_ZTVN5Ipopt17CompoundSymMatrixE_ZN5Ipopt17CompoundSymMatrixC1EPKNS_22CompoundSymMatrixSpaceE_ZNK5Ipopt22CompoundSymMatrixSpace16MakeNewSymMatrixEv_ZThn8_N5Ipopt17CompoundSymMatrixD0Ev_ZN5Ipopt17CompoundSymMatrixD0Ev_ZThn8_N5Ipopt17CompoundSymMatrixD1Ev_ZN5Ipopt17CompoundSymMatrixD1Ev_ZN5Ipopt17CompoundSymMatrixC2EPKNS_22CompoundSymMatrixSpaceE_ZTIN5Ipopt17CompoundSymMatrixE_ZNK5Ipopt9SymMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZTSN5Ipopt17CompoundSymMatrixE_ZTIN5Ipopt9SymMatrixE_ZTIN5Ipopt22CompoundSymMatrixSpaceE_ZTSN5Ipopt22CompoundSymMatrixSpaceE_ZNK5Ipopt19CompoundVectorSpace7MakeNewEv_ZN5Ipopt14CompoundVector12VectorsValidEv_ZSt26__uninitialized_fill_n_auxIPN5Ipopt8SmartPtrIKNS0_11VectorSpaceEEEjS4_EvT_T0_RKT1_St12__false_type_ZNK5Ipopt6Vector5PrintENS_8SmartPtrIKNS_10JournalistEEENS_13EJournalLevelENS_16EJournalCategoryERKSsiS8__ZN5Ipopt19CompoundVectorSpaceD1Ev_ZN5Ipopt19CompoundVectorSpaceD0Ev_ZTVN5Ipopt14CompoundVectorE_ZN5Ipopt14CompoundVectorC1EPKNS_19CompoundVectorSpaceEb_ZNK5Ipopt19CompoundVectorSpace21MakeNewCompoundVectorEb_ZThn8_N5Ipopt14CompoundVectorD0Ev_ZN5Ipopt14CompoundVectorD0Ev_ZThn8_N5Ipopt14CompoundVectorD1Ev_ZN5Ipopt14CompoundVectorD1Ev_ZTSN5Ipopt19CompoundVectorSpaceE_ZTSN5Ipopt14CompoundVectorE_ZTVN5Ipopt19DenseGenMatrixSpaceE_ZN5Ipopt19DenseGenMatrixSpaceD0Ev_ZNK5Ipopt14DenseGenMatrix19HasValidNumbersImplEv_ZNK5Ipopt14DenseGenMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZN5Ipopt19DenseGenMatrixSpaceC2Eii_ZN5Ipopt19DenseGenMatrixSpaceD1Ev_ZN5Ipopt14IpLapackDpotrsEiiPKdiPdi_ZNK5Ipopt14DenseGenMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZN5Ipopt14IpLapackDpotrfEiPdiRi_ZN5Ipopt13IpLapackDsyevEbiPdiS0_Ri_ZNK5Ipopt14DenseGenMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZThn8_N5Ipopt14DenseGenMatrixD0Ev_ZN5Ipopt14DenseGenMatrixD0Ev_ZTVN5Ipopt14DenseGenMatrixE_ZThn8_N5Ipopt14DenseGenMatrixD1Ev_ZN5Ipopt14DenseGenMatrixD1Ev_ZN5Ipopt14DenseGenMatrixD2Ev_ZNK5Ipopt19DenseGenMatrixSpace7MakeNewEv_ZN5Ipopt14DenseGenMatrixC2EPKNS_19DenseGenMatrixSpaceE_ZTIN5Ipopt14DenseGenMatrixE_ZTSN5Ipopt14DenseGenMatrixE_ZTIN5Ipopt19DenseGenMatrixSpaceE_ZTSN5Ipopt19DenseGenMatrixSpaceE_ZN5Ipopt19DenseSymMatrixSpaceC2Ei_ZTVN5Ipopt19DenseSymMatrixSpaceE_ZNK5Ipopt14DenseSymMatrix19HasValidNumbersImplEv_ZNK5Ipopt14DenseSymMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZN5Ipopt19DenseSymMatrixSpaceD0Ev_ZNK5Ipopt14DenseSymMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZN5Ipopt19DenseSymMatrixSpaceD1Ev_ZThn8_N5Ipopt14DenseSymMatrixD0Ev_ZN5Ipopt14DenseSymMatrixD0Ev_ZTVN5Ipopt14DenseSymMatrixE_ZThn8_N5Ipopt14DenseSymMatrixD1Ev_ZN5Ipopt14DenseSymMatrixD1Ev_ZN5Ipopt14DenseSymMatrixD2Ev_ZNK5Ipopt19DenseSymMatrixSpace16MakeNewSymMatrixEv_ZN5Ipopt14DenseSymMatrixC2EPKNS_19DenseSymMatrixSpaceE_ZTIN5Ipopt14DenseSymMatrixE_ZTSN5Ipopt14DenseSymMatrixE_ZTIN5Ipopt19DenseSymMatrixSpaceE_ZTSN5Ipopt19DenseSymMatrixSpaceE_ZN5Ipopt11DenseVector18ElementWiseSgnImplEv_ZNK5Ipopt11DenseVector11SumLogsImplEv_ZNK5Ipopt11DenseVector7SumImplEv_ZN5Ipopt11DenseVector13AddScalarImplEd_ZN5Ipopt11DenseVector19ElementWiseSqrtImplEv_ZN5Ipopt11DenseVector18ElementWiseAbsImplEv_ZN5Ipopt11DenseVector25ElementWiseReciprocalImplEv_ZN5Ipopt11DenseVector23ElementWiseMultiplyImplERKNS_6VectorE_ZNK5Ipopt11DenseVector8AmaxImplEv_ZNK5Ipopt11DenseVector8AsumImplEv_ZNK5Ipopt11DenseVector8Nrm2ImplEv_ZNK5Ipopt11DenseVector7DotImplERKNS_6VectorE_ZN5Ipopt11DenseVector8ScalImplEd_ZNK5Ipopt11DenseVector15FracToBoundImplERKNS_6VectorEd_ZNK5Ipopt11DenseVector7MinImplEv_ZNK5Ipopt11DenseVector9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNK5Ipopt11DenseVector7MaxImplEv_ZNK5Ipopt11DenseVector14ExpandedValuesEv_ZN5Ipopt11DenseVector7SetImplEd_ZN5Ipopt11DenseVector21AddVectorQuotientImplEdRKNS_6VectorES3_d_ZN5Ipopt11DenseVector21ElementWiseDivideImplERKNS_6VectorE_ZN5Ipopt11DenseVector8AxpyImplEdRKNS_6VectorE_ZN5Ipopt11DenseVector8CopyImplERKNS_6VectorE_ZN5Ipopt11DenseVector17AddTwoVectorsImplEdRKNS_6VectorEdS3_d_ZN5Ipopt11DenseVector18ElementWiseMinImplERKNS_6VectorE_ZN5Ipopt11DenseVector18ElementWiseMaxImplERKNS_6VectorE_ZNK5Ipopt11DenseVector11CopyFromPosEiRNS_6VectorE_ZN5Ipopt11DenseVector9CopyToPosEiRKNS_6VectorE_ZN5Ipopt11DenseVector9SetValuesEPKd_ZTVN5Ipopt11DenseVectorE_ZN5Ipopt11DenseVectorC2EPKNS_16DenseVectorSpaceE_ZThn8_N5Ipopt11DenseVectorD0Ev_ZN5Ipopt11DenseVectorD0Ev_ZThn8_N5Ipopt11DenseVectorD1Ev_ZN5Ipopt11DenseVectorD1Ev_ZN5Ipopt11DenseVectorD2Ev_ZTSN5Ipopt11DenseVectorE_ZNK5Ipopt10DiagMatrix19HasValidNumbersImplEv_ZNK5Ipopt10DiagMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZN5Ipopt10DiagMatrixD2Ev_ZTVN5Ipopt10DiagMatrixE_ZN5Ipopt10DiagMatrixC2EPKNS_14SymMatrixSpaceE_ZThn8_N5Ipopt10DiagMatrixD0Ev_ZN5Ipopt10DiagMatrixD0Ev_ZThn8_N5Ipopt10DiagMatrixD1Ev_ZN5Ipopt10DiagMatrixD1Ev_ZNK5Ipopt10DiagMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZTIN5Ipopt10DiagMatrixE_ZTSN5Ipopt10DiagMatrixE_ZNK5Ipopt6Matrix19HasValidNumbersImplEv_ZNK5Ipopt15ExpansionMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZN5Ipopt20ExpansionMatrixSpaceD1Ev_ZTVN5Ipopt20ExpansionMatrixSpaceE_ZN5Ipopt20ExpansionMatrixSpaceC2EiiPKii_ZN5Ipopt20ExpansionMatrixSpaceD0Ev_ZNK5Ipopt15ExpansionMatrix13AddMSinvZImplEdRKNS_6VectorES3_RS1__ZThn8_N5Ipopt15ExpansionMatrixD0Ev_ZN5Ipopt15ExpansionMatrixD0Ev_ZThn8_N5Ipopt15ExpansionMatrixD1Ev_ZN5Ipopt15ExpansionMatrixD1Ev_ZN5Ipopt15ExpansionMatrixD2Ev_ZN5Ipopt15ExpansionMatrixC2EPKNS_20ExpansionMatrixSpaceE_ZTVN5Ipopt15ExpansionMatrixE_ZNK5Ipopt15ExpansionMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt20ExpansionMatrixSpace7MakeNewEv_ZNK5Ipopt15ExpansionMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt15ExpansionMatrix18SinvBlrmZMTdBrImplEdRKNS_6VectorES3_S3_S3_RS1__ZTIN5Ipopt15ExpansionMatrixE_ZTSN5Ipopt15ExpansionMatrixE_ZTIN5Ipopt20ExpansionMatrixSpaceE_ZTSN5Ipopt20ExpansionMatrixSpaceE_ZNK5Ipopt14IdentityMatrix3DimEv_ZNK5Ipopt14IdentityMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt14IdentityMatrix19HasValidNumbersImplEv_ZNK5Ipopt14IdentityMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNK5Ipopt14IdentityMatrix13AddMSinvZImplEdRKNS_6VectorES3_RS1__ZN5Ipopt14IdentityMatrixD2Ev_ZTVN5Ipopt14IdentityMatrixE_ZThn8_N5Ipopt14IdentityMatrixD0Ev_ZN5Ipopt14IdentityMatrixD0Ev_ZThn8_N5Ipopt14IdentityMatrixD1Ev_ZN5Ipopt14IdentityMatrixD1Ev_ZN5Ipopt14IdentityMatrixC2EPKNS_14SymMatrixSpaceE_ZTIN5Ipopt14IdentityMatrixE_ZTSN5Ipopt14IdentityMatrixEdsyev__ZNK5Ipopt22LowRankUpdateSymMatrix19HasValidNumbersImplEv_ZTVN5Ipopt22LowRankUpdateSymMatrixE_ZNK5Ipopt22LowRankUpdateSymMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZThn8_N5Ipopt22LowRankUpdateSymMatrixD0Ev_ZN5Ipopt22LowRankUpdateSymMatrixD0Ev_ZThn8_N5Ipopt22LowRankUpdateSymMatrixD1Ev_ZN5Ipopt22LowRankUpdateSymMatrixD1Ev_ZTVN5Ipopt9SymMatrixE_ZNK5Ipopt22LowRankUpdateSymMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt17MultiVectorMatrix12LRMultVectorEdRKNS_6VectorEdRS1__ZN5Ipopt22LowRankUpdateSymMatrixD2Ev_ZN5Ipopt22LowRankUpdateSymMatrixC2EPKNS_27LowRankUpdateSymMatrixSpaceE_ZTIN5Ipopt22LowRankUpdateSymMatrixE_ZTSN5Ipopt22LowRankUpdateSymMatrixE_ZThn8_N5Ipopt6MatrixD0Ev_ZN5Ipopt6MatrixD0Ev_ZThn8_N5Ipopt6MatrixD1Ev_ZN5Ipopt6MatrixD1Ev_ZTSN5Ipopt6MatrixE_ZTVN5Ipopt22MultiVectorMatrixSpaceE_ZN5Ipopt22MultiVectorMatrixSpaceC2EiRKNS_11VectorSpaceE_ZN5Ipopt22MultiVectorMatrixSpaceD0Ev_ZN5Ipopt22MultiVectorMatrixSpaceD1Ev_ZN5Ipopt17MultiVectorMatrixC2EPKNS_22MultiVectorMatrixSpaceE_ZNK5Ipopt22MultiVectorMatrixSpace7MakeNewEv_ZN5Ipopt17MultiVectorMatrix12ScaleColumnsERKNS_6VectorE_ZN5Ipopt17MultiVectorMatrix18FillWithNewVectorsEv_ZN5Ipopt17MultiVectorMatrix9ScaleRowsERKNS_6VectorE_ZTIN5Ipopt22MultiVectorMatrixSpaceE_ZTSN5Ipopt22MultiVectorMatrixSpaceE_ZNK5Ipopt12ScaledMatrix19HasValidNumbersImplEv_ZN5Ipopt17ScaledMatrixSpaceD0Ev_ZTVN5Ipopt17ScaledMatrixSpaceE_ZN5Ipopt17ScaledMatrixSpaceC2ERKNS_8SmartPtrIKNS_6VectorEEEbRKNS1_IKNS_11MatrixSpaceEEES6_b_ZN5Ipopt17ScaledMatrixSpaceD1Ev_ZNK5Ipopt12ScaledMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt12ScaledMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZTVN5Ipopt12ScaledMatrixE_ZN5Ipopt12ScaledMatrixC2EPKNS_17ScaledMatrixSpaceE_ZThn8_N5Ipopt12ScaledMatrixD0Ev_ZN5Ipopt12ScaledMatrixD0Ev_ZThn8_N5Ipopt12ScaledMatrixD1Ev_ZN5Ipopt12ScaledMatrixD1Ev_ZN5Ipopt12ScaledMatrixD2Ev_ZNK5Ipopt17ScaledMatrixSpace7MakeNewEv_ZNK5Ipopt12ScaledMatrix13AddMSinvZImplEdRKNS_6VectorES3_RS1__ZNK5Ipopt12ScaledMatrix18SinvBlrmZMTdBrImplEdRKNS_6VectorES3_S3_S3_RS1__ZNK5Ipopt12ScaledMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZTIN5Ipopt12ScaledMatrixE_ZTSN5Ipopt12ScaledMatrixE_ZTIN5Ipopt17ScaledMatrixSpaceE_ZTSN5Ipopt17ScaledMatrixSpaceE_ZNK5Ipopt9SumMatrix6NTermsEv_ZNK5Ipopt9SumMatrix19HasValidNumbersImplEv_ZNK5Ipopt9SumMatrix7GetTermEiRdRNS_8SmartPtrIKNS_6MatrixEEE_ZN5Ipopt9SumMatrix7SetTermEidRKNS_6MatrixE_ZN5Ipopt14SumMatrixSpaceD0Ev_ZTVN5Ipopt14SumMatrixSpaceE_ZNK5Ipopt9SumMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZN5Ipopt14SumMatrixSpaceD1Ev_ZNK5Ipopt9SumMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt14SumMatrixSpace12GetTermSpaceEi_ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_11MatrixSpaceEEESaIS4_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS4_S6_EERKS4__ZN5Ipopt14SumMatrixSpace12SetTermSpaceEiRKNS_11MatrixSpaceE_ZN5Ipopt9SumMatrixC1EPKNS_14SumMatrixSpaceE_ZTVN5Ipopt9SumMatrixE_ZNK5Ipopt14SumMatrixSpace16MakeNewSumMatrixEv_ZNK5Ipopt14SumMatrixSpace7MakeNewEv_ZN5Ipopt9SumMatrixC2EPKNS_14SumMatrixSpaceE_ZThn8_N5Ipopt9SumMatrixD0Ev_ZN5Ipopt9SumMatrixD0Ev_ZThn8_N5Ipopt9SumMatrixD1Ev_ZN5Ipopt9SumMatrixD1Ev_ZN5Ipopt9SumMatrixD2Ev_ZNK5Ipopt9SumMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZTIN5Ipopt14SumMatrixSpaceE_ZTIN5Ipopt9SumMatrixE_ZTSN5Ipopt14SumMatrixSpaceE_ZTSN5Ipopt9SumMatrixE_ZNK5Ipopt12SumSymMatrix6NTermsEv_ZNK5Ipopt12SumSymMatrix19HasValidNumbersImplEv_ZSt26__uninitialized_fill_n_auxIPN5Ipopt8SmartPtrIKNS0_9SymMatrixEEEjS4_EvT_T0_RKT1_St12__false_type_ZNK5Ipopt12SumSymMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZN5Ipopt17SumSymMatrixSpaceD0Ev_ZN5Ipopt17SumSymMatrixSpaceD1Ev_ZNK5Ipopt17SumSymMatrixSpace12GetTermSpaceEi_ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_14SymMatrixSpaceEEESaIS4_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS4_S6_EERKS4__ZNK5Ipopt12SumSymMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZN5Ipopt12SumSymMatrixC1EPKNS_17SumSymMatrixSpaceE_ZTVN5Ipopt12SumSymMatrixE_ZNK5Ipopt17SumSymMatrixSpace16MakeNewSymMatrixEv_ZN5Ipopt12SumSymMatrixC2EPKNS_17SumSymMatrixSpaceE_ZThn8_N5Ipopt12SumSymMatrixD0Ev_ZN5Ipopt12SumSymMatrixD0Ev_ZThn8_N5Ipopt12SumSymMatrixD1Ev_ZN5Ipopt12SumSymMatrixD1Ev_ZN5Ipopt12SumSymMatrixD2Ev_ZTIN5Ipopt17SumSymMatrixSpaceE_ZTSN5Ipopt17SumSymMatrixSpaceE_ZTSN5Ipopt12SumSymMatrixE_ZThn8_N5Ipopt9SymMatrixD1Ev_ZN5Ipopt9SymMatrixD1Ev_ZThn8_N5Ipopt9SymMatrixD0Ev_ZN5Ipopt9SymMatrixD0Ev_ZTSN5Ipopt9SymMatrixE_ZNK5Ipopt15SymScaledMatrix19HasValidNumbersImplEv_ZNK5Ipopt15SymScaledMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZThn8_N5Ipopt15SymScaledMatrixD1Ev_ZN5Ipopt15SymScaledMatrixD1Ev_ZTVN5Ipopt15SymScaledMatrixE_ZN5Ipopt15SymScaledMatrixD2Ev_ZThn8_N5Ipopt15SymScaledMatrixD0Ev_ZN5Ipopt15SymScaledMatrixD0Ev_ZN5Ipopt15SymScaledMatrixC2EPKNS_20SymScaledMatrixSpaceE_ZNK5Ipopt15SymScaledMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZTIN5Ipopt15SymScaledMatrixE_ZTSN5Ipopt15SymScaledMatrixE_ZNK5Ipopt10ZeroMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNK5Ipopt10ZeroMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZN5Ipopt10ZeroMatrixD2Ev_ZN5Ipopt10ZeroMatrixC1EPKNS_11MatrixSpaceE_ZTVN5Ipopt10ZeroMatrixE_ZThn8_N5Ipopt10ZeroMatrixD0Ev_ZN5Ipopt10ZeroMatrixD0Ev_ZThn8_N5Ipopt10ZeroMatrixD1Ev_ZN5Ipopt10ZeroMatrixD1Ev_ZN5Ipopt10ZeroMatrixC2EPKNS_11MatrixSpaceE_ZNK5Ipopt10ZeroMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZTIN5Ipopt10ZeroMatrixE_ZTSN5Ipopt10ZeroMatrixE_ZNK5Ipopt15GenTMatrixSpace19FreeInternalStorageEPd_ZNK5Ipopt15GenTMatrixSpace23AllocateInternalStorageEv_ZNK5Ipopt10GenTMatrix19HasValidNumbersImplEv_ZNK5Ipopt10GenTMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZTVN5Ipopt15GenTMatrixSpaceE_ZN5Ipopt15GenTMatrixSpaceC2EiiiPKiS2__ZN5Ipopt15GenTMatrixSpaceD0Ev_ZN5Ipopt15GenTMatrixSpaceD1Ev_ZThn8_N5Ipopt10GenTMatrixD0Ev_ZN5Ipopt10GenTMatrixD0Ev_ZTVN5Ipopt10GenTMatrixE_ZN5Ipopt10GenTMatrixC2EPKNS_15GenTMatrixSpaceE_ZThn8_N5Ipopt10GenTMatrixD1Ev_ZN5Ipopt10GenTMatrixD1Ev_ZN5Ipopt10GenTMatrixD2Ev_ZN5Ipopt10GenTMatrixC1EPKNS_15GenTMatrixSpaceE_ZNK5Ipopt15GenTMatrixSpace7MakeNewEv_ZN5Ipopt10GenTMatrix9SetValuesEPKd_ZNK5Ipopt10GenTMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt10GenTMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZTIN5Ipopt10GenTMatrixE_ZTSN5Ipopt10GenTMatrixE_ZTIN5Ipopt15GenTMatrixSpaceE_ZTSN5Ipopt15GenTMatrixSpaceE_ZNK5Ipopt10SymTMatrix6ValuesEv_ZNK5Ipopt15SymTMatrixSpace19FreeInternalStorageEPd_ZNK5Ipopt15SymTMatrixSpace23AllocateInternalStorageEv_ZNK5Ipopt10SymTMatrix19HasValidNumbersImplEv_ZNK5Ipopt10SymTMatrix10FillValuesEPd_ZNK5Ipopt10SymTMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNK5Ipopt10SymTMatrix10FillStructEPiS1__ZN5Ipopt15SymTMatrixSpaceD0Ev_ZTVN5Ipopt15SymTMatrixSpaceE_ZN5Ipopt15SymTMatrixSpaceD1Ev_ZN5Ipopt15SymTMatrixSpaceD2Ev_ZN5Ipopt15SymTMatrixSpaceC2EiiPKiS2__ZN5Ipopt10SymTMatrixD2Ev_ZTVN5Ipopt10SymTMatrixE_ZN5Ipopt10SymTMatrixC1EPKNS_15SymTMatrixSpaceE_ZNK5Ipopt15SymTMatrixSpace16MakeNewSymMatrixEv_ZThn8_N5Ipopt10SymTMatrixD0Ev_ZN5Ipopt10SymTMatrixD0Ev_ZThn8_N5Ipopt10SymTMatrixD1Ev_ZN5Ipopt10SymTMatrixD1Ev_ZN5Ipopt10SymTMatrixC2EPKNS_15SymTMatrixSpaceE_ZNK5Ipopt10SymTMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZN5Ipopt10SymTMatrix9SetValuesEPKd_ZTIN5Ipopt15SymTMatrixSpaceE_ZTIN5Ipopt10SymTMatrixE_ZTSN5Ipopt15SymTMatrixSpaceE_ZTSN5Ipopt10SymTMatrixE_ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_10GenTMatrixEiiPiS4__ZN5Ipopt13TripletHelper11FillValues_EiRKNS_10GenTMatrixEPd_ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_10SymTMatrixEiiPiS4__ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_10DiagMatrixEiiPiS4__ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_14IdentityMatrixEiiPiS4__ZN5Ipopt13TripletHelper11FillValues_EiRKNS_14IdentityMatrixEPd_ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_15ExpansionMatrixEiiPiS4__ZN5Ipopt13TripletHelper11FillValues_EiRKNS_15ExpansionMatrixEPd_ZN5Ipopt13TripletHelper11FillValues_EiRKNS_10SymTMatrixEPd_ZN5Ipopt13TripletHelper17GetNumberEntries_ERKNS_9SumMatrixE_ZN5Ipopt13TripletHelper17GetNumberEntries_ERKNS_12SumSymMatrixE_ZN5Ipopt13TripletHelper17GetNumberEntries_ERKNS_14CompoundMatrixE_ZN5Ipopt13TripletHelper17GetNumberEntries_ERKNS_17CompoundSymMatrixE_ZTVN5Ipopt13TripletHelper19UNKNOWN_MATRIX_TYPEE_ZN5Ipopt13TripletHelper19UNKNOWN_MATRIX_TYPED1Ev_ZTIN5Ipopt13TripletHelper19UNKNOWN_MATRIX_TYPEE_ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_9SumMatrixEiiPiS4__ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_12ScaledMatrixEiiPiS4__ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_15SymScaledMatrixEiiPiS4__ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_12SumSymMatrixEiiPiS4__ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_14CompoundMatrixEiiPiS4__ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_17CompoundSymMatrixEiiPiS4__ZN5Ipopt13TripletHelper19UNKNOWN_MATRIX_TYPED0Ev_ZTVN5Ipopt13TripletHelper19UNKNOWN_VECTOR_TYPEE_ZN5Ipopt13TripletHelper19UNKNOWN_VE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4$Pt$DF44$P4$P4$Pv4$PvtFFu4$P<$0ۍэUWV1S9D$4D$$91t$t&;l$t{T$4B0T$uT$4Bnlp()==nlpIsValid(nlp_adapter_)adapter->tnlp()==tnlpSets the default verbosity level for console output. The larger this value the more detailed is the output.NOTE: This option only works when read from the ipopt.opt options file! An output file with this name will be written (leave unset for no file output). The verbosity level is by default set to "print_level", but can be overridden with "file_print_level". The file name is changed to use only small letters.Any acceptable standard file nameFile name of desired output file (leave unset for no file output).NOTE: This option only works when read from the ipopt.opt options file! Determines the verbosity level for the file specified by "output_file". By default it is the same as "print_level".Verbosity level for output file.If selected, the algorithm will print the list of all options set by the user including their values and whether they have been used. In some cases this information might be incorrect, due to the internal program flow.Print all options set by the user.If selected, the algorithm will print the list of all available algorithmic options with some documentation before solving the optimization problem.Switch to print all algorithmic options.If selected, the program will print the CPU usage (user time) for selected tasks.Switch to print timing statistics.quality_function_max_section_stepswatchdog_shortened_iter_triggerexpect_infeasible_problem_ctolsoft_resto_pderror_reduction_factorrequired_infeasibility_reductionevaluate_orig_obj_at_resto_trialException of type: %s in file "%s" at line %d: Exception message: %s Unknown Exception caught in ipoptCaught unknown Ipopt exception List of user-set options: %sstatus != TOO_FEW_DEGREES_OF_FREEDOMToo few degrees of freedom (rethrown)! Number of Iterations....: %d (scaled) (unscaled) Objective...............: %24.16e %24.16e Dual infeasibility......: %24.16e %24.16e Constraint violation....: %24.16e %24.16e Complementarity.........: %24.16e %24.16e Overall NLP error.......: %24.16e %24.16e Number of objective function evaluations = %d Number of objective gradient evaluations = %d Number of equality constraint evaluations = %d Number of inequality constraint evaluations = %d Number of equality constraint Jacobian evaluations = %d Number of inequality constraint Jacobian evaluations = %d Number of Lagrangian Hessian evaluations = %d Total CPU secs in IPOPT (w/o function evaluations) = %10.3f Total CPU secs in NLP function evaluations = %10.3f EXIT: Optimal Solution Found. EXIT: Maximum Number of Iterations Exceeded. EXIT: Search Direction is becoming Too Small. EXIT: Solved To Acceptable Level. EXIT: Iterates divering; problem might be unbounded. EXIT: Error in step computation (regularization becomes too large?)! EXIT: Converged to a point of local infeasibility. Problem may be infeasible. EXIT: Stopping optimization at current point as requested by user. EXIT: Invalid number in NLP function or derivative detected. EXIT: INTERNAL ERROR: Unknown SolverReturn value - Notify IPOPT Authors. EXIT: Problem has too few degrees of freedom. EXIT: Invalid option encountered. EXIT: Problem as only fixed variables and constraints are infeasible. EXIT: Some uncaught Ipopt exception encountered. Unknown Exception caught in IpoptReOptimizeNLP called before OptimizeNLP.ReOptimizeTNLP called for different NLP.ReOptimizeTNLP called before OptimizeTNLP.ReOptimizeTNLP called for different TNLP.N5Ipopt16IpoptApplicationEN5Ipopt11TOO_FEW_DOFEN5Ipopt14IpoptExceptionEN5Ipopt17INVALID_WARMSTARTEN5Ipopt23IPOPT_APPLICATION_ERROREN5Ipopt11OptionsListEN5Ipopt17RegisteredOptionsEN5Ipopt14OPTION_INVALIDEN5Ipopt30NO_FREE_VARIABLES_BUT_FEASIBLEEN5Ipopt32NO_FREE_VARIABLES_AND_INFEASIBLEEN5Ipopt15SolveStatisticsEipopt.optN5Ipopt24INVALID_STDINTERFACE_NLPEError in IpStdFInterface eval_hess!Error in IpStdFInterface eval_jac_g!INVALID_STDINTERFACE_NLPn_var_>0IpStdInterfaceTNLP.cppn_con_>=0x_L_x_U_g_L_ || n_con_==0g_U_ || n_con_==0nele_jac_>=0nele_hess_>=0start_x_eval_f_eval_g_eval_grad_f_eval_jac_g_eval_h_The number of variables must be at least 1.The number of constrains must be non-negative.No lower bounds for variables provided.No upper bounds for variables provided.No lower bounds for constraints provided.No upper bounds for constraints provided.Number of non-zero elements in constraint Jacobian must be non-negative.Number of non-zero elements in Hessian of Lagrangian must be non-negative.index_style_ == 0 || index_style_ == 1Valid index styles are 0 (C style) or 1 (Fortran style)No initial point for the variables provided.No callback function for evaluating the value of objective function provided.No callback function for evaluating the values of constraints provided.No callback function for evaluating the gradient of objective function provided.No callback function for evaluating the Jacobian of the constraints provided.No callback function for evaluating the Hessian of the constraints provided.N5Ipopt16StdInterfaceTNLPEN5Ipopt4TNLPEConstraint violation for ALL constraints is %e. You have set options for user provided scaling, but have not implemented GetScalingParameters in the NLP interfaceThe TNLP passed to TNLPAdapter is NULL. This MUST be a valid TNLP!No dependency_detector_ object available in TNLPAdapter::DetermineDependentConstraintsNo Journalist given to TNLPAdapter. Need Journalist, otherwise can't produce any output in DerivativeChecker! Starting derivative checker. In TNLP derivative test: f could not be evaluated at reference point.In TNLP derivative test: g could not be evaluated at reference point.In TNLP derivative test: grad_f could not be evaluated at reference point.In TNLP derivative test: Jacobian structure could not be evaluated.In TNLP derivative test: Jacobian values could not be evaluated at reference point.In TNLP derivative test: f could not be evaluated at perturbed point.%c grad_f[ %5d] = %23.16e ~ %23.16e [%10.3e] %c jac_g [%5d,%5d] = %23.16e %c ~ %23.16e [%10.3e] In TNLP derivative test: Hessian structure could not be evaluated.In TNLP derivative test: Hessian could not be evaluated at reference point.In TNLP derivative test: grad_f could not be evaluated at perturbed point.%c obj_hess[%5d,%5d] = %23.16e %c ~ %23.16e [%10.3e] %c %5d-th constr_hess[%5d,%5d] = %23.16e %c ~ %23.16e [%10.3e] No errors detected by derivative checker. Derivative checker detected %d error(s). TNLP's get_number_of_nonlinear_variables returns non-negative number, but get_list_of_nonlinear_variables returns false. get_list_of_nonlinear_variables has not been overwrittenOption nlp_scaling_method selected as user-scaling, but no user-scaling available, or it cannot be computed. User scaling chosen, but get_scaling_parameters returned false.warm_start_same_structure chosen, but TNLPAdapter is called for the first time.Reusing previous information for warm start in TNLPAdapter. !warm_start_same_structure_ || (n_full_x == n_full_x_ && n_full_g == n_full_g_ && nz_full_jac_g == nz_full_jac_g_ && nz_full_h == nz_full_h_)warm_start_same_structure chosen, but problem dimensions are different.There are inconsistent bounds on variable %d: lower = %25.16e and upper = %25.16e.There are inconsistent bounds on constraint %d: lower = %25.16e and upper = %25.16e.Too few degrees of freedom (n_x = %d, n_c = %d). Trying fixed_variable_treatment = RELAX_BOUNDS All variables are fixed, but constraints cannot be evaluated at fixed point.All variables are fixed, but objective cannot be evaluated at fixed point.All variables are fixed, and constraint violation is %eAll variables are fixed and constraint violation %e is below tolerance %e. Declaring success. NO_FREE_VARIABLES_BUT_FEASIBLEAll variables are fixed and constraint violation %e is above tolerance %e. Declaring that problem is infeasible. NO_FREE_VARIABLES_AND_INFEASIBLEDependent constraint detector had a problem, assume full rank. Detected %d linearly dependent equality constraints; taking those out. No dependent constraints detected. List of indices of dependent constraints: Option hessian_information is not chosen as limited_memory, but eval_h returns false. eval_h is called but has not been implementedNumber of nonzeros in equality constraint Jacobian...:%9d Number of nonzeros in inequality constraint Jacobian.:%9d Number of nonzeros in Lagrangian Hessian.............:%9d nlp_lower_bound_inf_ < nlp_upper_bound_inf_Option "nlp_lower_bound_inf" must be smaller than "nlp_upper_bound_inf".Ipopt has not been compiled with WSMP. You cannot choose "wsmp" for "dependency_detector".Ipopt has not been compiled with MA28. You cannot choose "ma28" for "dependency_detector".Something internally wrong for "dependency_detector".any bound less or equal this value will be considered -inf (i.e. not lower bounded).any bound greater or this value will be considered +inf (i.e. not upper bounded).The main difference between those options is that the starting point in the "make_constraint" case still has the fixed variables at their given values, whereas in the case "make_parameter" the functions are always evaluated with the fixed values for those variables. Also, for "relax_bounds", the fixing bound constraints are relaxed (according to" bound_relax_factor"). For both "make_constraints" and "relax_bounds", bound multipliers are computed for the fixed variables.Relax fixing bound constraintsAdd equality constraints fixing variablesRemove fixed variable from optimization variablesDetermines how fixed variables should be handled.The default and available choices depend on how Ipopt has been compiled.don't check; no extra work at beginningIndicates which linear solver should be used to detect linearly dependent equality constraints.Indicates if the right hand sides of the constraints should be considered during dependency detectionIf a random perturbation of a points is required, this number indicates the maximal perturbation. Currently, this is only used when we perturb the initial point in order to get a random Jacobian for the linear dependency detection of equality constraints.Maximal perturbation of an evaluation point.If this option is enabled, a (slow) derivative test will be performed before the optimization. The test is performed at the user provided starting point and marks derivative values that seem suspiciousperform test of first and second derivatives at starting pointperform test of first derivatives at starting pointdo not perform derivative testThis determines the relative perturbation of the variable entries.Size of the finite difference perturbation in derivative test.If the relative deviation of the estimated derivative from the given one is larger than this value, the corresponding derivative is marked as wrong.Threshold for indicating wrong derivative.Determines verbosity of derivative checker.Print only suspect derivativesIndicates whether information for all estimated derivatives should be printed.IpNLP.hppUSER_SCALING_NOT_IMPLEMENTEDIsValid(tnlp_)IpTNLPAdapter.cppINVALID_TNLPIsValid(dependency_detector_)OPTION_INVALIDIsValid(jnlst_)ERROR_IN_TNLP_DERIVATIVE_TESTfull_x_Eval_Errorc_dep[%d] = %d warm_start_same_structurepoint_perturbation_radiusdependency_detection_with_rhsdependency_detectornonemumpswsmpma28relax_boundsmake_constraintmake_parameteruse MA28use WSMPuse MUMPSalso consider right hand sideonly look at gradientssecond-orderfirst-orderEnable derivative checkerPrint all derivativesN5Ipopt11TNLPAdapterEN5Ipopt3NLPEN5Ipopt11TNLPAdapter29ERROR_IN_TNLP_DERIVATIVE_TESTEN5Ipopt11TNLPAdapter12INVALID_TNLPEN5Ipopt16DenseVectorSpaceEN5Ipopt11VectorSpaceEN5Ipopt8IpoptNLP10Eval_ErrorEN5Ipopt3NLP28USER_SCALING_NOT_IMPLEMENTEDE:0yE>ư>MUMPS/src/dmumps_bloc2.FError in DMUMPS_47: undef stratError in DMUMPS_BLOC2 undef strat Error in DMUMPS_440: size lastbloc NCOLim1, BLSIZE, NFRONT=Internal error 1 in DMUMPS_503Internal error 2 in DMUMPS_503Internal Error 1 in DMUMPS_50K=U?@@@?MUMPS/src/dmumps_buffer.F** Warning: trying to cancel a request.** This might be problematic on SGI Try_send_cb: SIZE, POSITION = Try_send_master2slave: SIZE, POSITION = Internal error in DMUMPS_62 Buf size (bytes)= Error in DMUMPS_68 : wrong estimated sizeError in TRY_SEND_MAITRE2:Try_send_maitre2, SIZE,POSITION=Error in PARPIV/DMUMPS_67 contniv2: SIZE, POSITION = NBROW, LROW = Error in DMUMPS_71 :Problem in DMUMPS_4: IERR<0 ERROR 1 in TRY_SEND_MAPLIG:Wrong estimated size Error sending blocfacto : size < position Size,position= Error sending blfac slave : size < position Error sending contribution to root:SizeL@?MUMPS/src/dmumps_cv_load.FDMUMPS_513 should be called when K81>0 and K47>2Internal error 1 in DMUMPS_185Unimplemented KEEP(80) StrategyInternal error 3 in DMUMPS_185Internal error 2 in DMUMPS_185PB allocation in DMUMPS_185Internal error in DMUMPS_384Internal error 2 in DMUMPS_499.Internal error 3 in DMUMPS_499.: Internal Error 1 in DMUMPS_504DMUMPS_COMPUTE_PARTI_ACTV_MEM_K10 should be called with KEEP(48) different from 4Internal Error 2 in DMUMPS_504NCB=,SOMME=: Internal Error 3 in DMUMPS_504: Internal error 4 in DMUMPS_504: Internal error 5 in DMUMPS_504:Internal error 6 in DMUMPS_504:Internal error 7 in DMUMPS_504:Internal error 12 in DMUMPS_504Internal error 13 in DMUMPS_504Internal error 14 in DMUMPS_504Internal error 15 in DMUMPS_504: Warning: negative load : Negative load : Internal error 1 in DMUMPS_518: Internal error 2 in DMUMPS_518: Internal error 3 in DMUMPS_518: Internal error 4 in DMUMPS_518: Internal error 5 in DMUMPS_518: Internal error 6 in DMUMPS_518: Internal error 7 in DMUMPS_518: Internal error 8 in DMUMPS_518: Internal error 9 in DMUMPS_518: Internal error 10 in DMUMPS_518: Internal error 11 in DMUMPS_518: Internal error 12 in DMUMPS_518: Internal error 13 in DMUMPS_518: Internal error 14 in DMUMPS_518: Internal error 15 in DMUMPS_518: Internal error 16 in DMUMPS_518: Internal error 17 in DMUMPS_518probleme de partition dans DMUMPS_545problem with partition in DMUMPS_518Strategy 6 not implementedDMUMPS_520 must be called with K47>=2Internal error 1 in DMUMPS_520: I did not find : negative pos_mem or pos_id  :->Internal error 2 in DMUMPS_187Internal error 3 in DMUMPS_187Internal error 7 in DMUMPS_187Internal error 8 in DMUMPS_187problem with NIV2_FLOPS message: Internal error 4 in DMUMPS_187: Internal error 5 in DMUMPS_187: Internal error 6 in DMUMPS_187Internal error 1 in DMUMPS_187Internal error 1 in DMUMPS_467Internal error 2 in DMUMPS_467: Bad value for CHECK_FLOPSInternal Error in DMUMPS_190 Internal Error in DMUMPS_471. NEW_LU must be zero if called from PROCESS_BANDE:Problem with increments in DMUMPS_471Internal Error in DMUMPS_471Internal error in DMUMPS_461Internal Error in DMUMPS_461Error 1 in DMUMPS_461Internal error: Unknown pool management strategyInternal Error in DMUMPS_500Internal Error 1 in DMUMPS_501Internal Error 3 in DMUMPS_501Internal error 1 in PROCESS_NIV2_MEM_MSG: Problem in DMUMPS_512Internal Error in DMUMPS_533PB allocation in COMPUTE_MAX_MEM: has not been found in COMPUTE_MAX_MEM: The first leaf is not ok: Not enough space for allocation???PCJzD$tI2MUMPS/src/dmumps_ooc.F: DMUMPS I/O ERROR : INODE IS TOO BIG TO MATCH ONE HALF-BUFFER : Size of block= Size half-buffer=ENDINGENDED: Internal error (37) in OOC : Internal error (38) in OOC : Problem in DMUMPS_LOW_LEVEL_READ_OCCPB allocation in DMUMPS_INIT_OOC: PB in DMUMPS_LOW_LEVEL_INIT_OOC_CDIM_BUF_IO =: Inernal error (42) in OOC : Inernal error (43) in OOC : Internal error (39) in OOC Invalid Flag Value in DMUMPS_597: Internal error (40) in OOC : Internal error (41) in OOC LRLUS_SOLVE must be (1) > 0: Internal error (1) in OOC : Internal error (2) in OOC : Internal error (3) in OOC : Internal error (4) in OOC DMUMPS_597 : Internal error (10) in OOC DMUMPS_604: Internal error (11) in OOC : Internal error (12) in OOC : Internal error (14) in OOC DMUMPS_605: Internal error (15) in OOC : Internal error (16) in OOC : Internal error (18) in OOC : Internal error (19) in OOC DMUMPS_605: Internal error (20) in OOC Problem avec debut (2): Internal error (21) in OOC Problem with CURRENT_POS_T: Internal error (22) in OOC DMUMPS_607: Internal error (23) in OOC : Internal error (23b) in OOC : Internal error (25) in OOC : There are no free blocks in DMUMPS_608: Internal error (26) in OOC DMUMPS_608: Internal error (27) in OOC : Internal error (28) in OOC LRLUS_SOLVE must be (4) > 0: Internal error (29) in OOC : Internal error (30) in OOC Problem avec debut POSFAC_SOLVE: Internal error (31) in OOC : Internal error (8) in OOC Not enough space for Solve: Internal error (9) in OOC LRLUS_SOLVE must be (3) > 0: Internal error (32) in OOC DMUMPS_609: Internal error (33) in OOC LRLUS_SOLVE must be (5) ++ > 0: Internal error (34) in OOC LRLUS_SOLVE must be (5) > 0: Internal error (7) in OOC : Internal error (35) in OOC Unknown Flag value in DMUMPS_602: Internal error (5) in OOC Problem in DMUMPS_598: Internal error (6) in OOC : LRLUS_SOLVE must be (2) > 0PB allocation in DMUMPS_613: More space needed for solution step in DMUMPS_586 PB allocation in DMUMPS_586PB allocation in DMUMPS_INIT_OOC_SOLVEfff?L> '(/'******INTERNAL VALUE OF PARAMETERS (ICNTL/KEEP)****'/)( 'ICNTL(1) Output stream for error messages =',I10/ 'ICNTL(2) Output stream for diagnostic messages =',I10/ 'ICNTL(3) Output stream for global information =',I10/ 'ICNTL(4) Level of printing =',I10)( 'ICNTL(5) Matrix format ( keep(55) ) =',I10/ 'ICNTL(6) Maximum transversal ( keep(23) ) =',I10/ 'ICNTL(7) Ordering =',I10/ 'ICNTL(12) LDLT ordering strat ( keep(95) ) =',I10/ 'ICNTL(13) Parallel root (0=on, 1=off) =',I10/ 'ICNTL(18) Distributed matrix ( keep(54) ) =',I10/ 'ICNTL(19) Schur option ( keep(60) 0=off,else=on ) =',I10/ 'ICNTL(22) Out-off-core option (0=Off, >0=ON) =',I10)( 'ICNTL(8) Scaling strategy ( keep(52) ) =',I10)( 'ICNTL(14) Percent of memory increase ( keep(12) ) =',I10)( 'ICNTL(9) Solve A x=b (1) or A''x = b (else) =',I10/ 'ICNTL(10) Max steps iterative refinement =',I10/ 'ICNTL(11) Error analysis ( 0= off, else=on) =',I10/ 'ICNTL(20) Dense (0) or sparse (1) RHS =',I10/ 'ICNTL(21) Gathered (0) or distributed(1) solution =',I10)DON-MJJHD(/'***********CONTROL PARAMETERS (ICNTL)**************'/)( 'ICNTL(8) Scaling strategy =',I10)( ' Size of SCHUR matrix (SIZE_SHUR) =',I10)( 'ICNTL(14) Percent of memory increase =',I10)( 'ICNTL(9) Solve A x=b (1) or A''x = b (else) =',I10/ 'ICNTL(10) Max steps iterative refinement =',I10/ 'ICNTL(11) Error analysis ( 0= off, else=on) =',I10)dU\`^]\YU Error return from MPI_TEST in DMUMPS_329 RECEPTION BUF TOO SMALL, Msgtag/len=Internal error 3 in DMUMPS_322: Internal error, routine DMUMPS_322.FAILURE, WORKSPACE TOO SMALL DURING FAILURE IN INTEGER ALLOCATION DURING FAILURE IN DYNAMIC ALLOCATION DURING PB compress ass..process_contribLRLU,LRLUS= FAILURE, WORKSPACE TOO SMALL DURING DMUMPS_264PB compress ass..blocfacto: LRLU,LRLUS= FAILURE IN INTEGER ALLOCATION DURING DMUMPS_264: Internal ERROR 1 in DMUMPS_264 INODE = MAITRE_DESC_BANDE not yet received from ?NL ERROR 1 **** IN BLACFACTO  ERR: ERROR : NBROWS > NBROWF ERR: INODE = ERR: NBROW=NBROWF= ERR: ROW_LIST=PB compress ass..mpi51f_niv2 ERROR 2 during ass_niv2 FAILURE IN INTEGER DYNAMIC ALLOCATION DURING DMUMPS_253 FAILURE ALLOCATING TMP_SLAVES_LIST DURING DMUMPS_253 FAILURE IN INTEGER ALLOCATION DURING DMUMPS_253 FAILURE, WORKSPACE TOO SMALL DURING DMUMPS_253 FAILURE, SEND BUFFER TOO SMALL (1) DURING DMUMPS_253 FAILURE, RECV BUFFER TOO SMALL (1) DURING DMUMPS_253 FAILURE, SEND BUFFER TOO SMALL (2) DURING DMUMPS_253 FAILURE, RECV BUFFER TOO SMALL (2) DURING DMUMPS_253Error1 in mpi51f_niv1 PB compress ass..mpi51f_niv1.F ERROR 1 during ass_niv1 FAILURE IN INTEGER ALLOCATION DURING DMUMPS_252 FAILURE, WORKSPACE TOO SMALL DURING DMUMPS_252 FAILURE, SEND BUFFER TOO SMALL DURING DMUMPS_252 FAILURE IN INTEGER DYNAMIC ALLOCATION DURING DMUMPS_252error in process_bande: Could not allocate BUFI: goto 500: Could not allocate BUFR: goto 500: Could not allocate IW4: goto 500Error allocating IW4Error allocating BUFIError allocating BUFR:INTERNAL Error: root arrowhead :is not belonging to me. IARR,JARR=Error 1 in anal_arrowheadsError 2 in anal_arrowheads&(A,I4,I12,I15)Entering driver (DMUMPS) WITH JOB, N, NZ =Entering driver (DMUMPS) WITH JOB, N, NELT =(' Error in DMUMPS initialization: MPI is not running.')(' ** Allocation error: could not permute JCN.')Problem in allocating work array for analysis.Problem in allocating work array PROCNODEProblem in allocating work array ELTPROCError in analysis: IRN/JCN badly allocated.Error in analysis: ELTPTR/ELTVAR badly allocated.(A) SCHUR not associated SCHUR allocated but too small ** WARNING : SCALING ** scaling already computed during analysis ** column permutation applied: ** column scaling has to be permuted ** Warning: Scaling not applied. ** (incompatibility with null space) ** (incompatibility with Schur) ** (incompatibility with dist entry) ** Warning: Scaling option n.a. for symmetric matrix ** (only user scaling av. for elt. entry)Problems in allocations before facto SCHUR leading dimension SCHUR_LLD too small with respect to : Size Schur= SCHUR_LLD= SCHUR_MLOC= SCHUR_NLOC=(' ** ERROR RETURN ** FROM DMUMPS INFO(1)=', I3)(' ** INFO(2)=', I10)(A,I12) On return from DMUMPS, INFOG(1)= On return from DMUMPS, INFOG(2)=MUMPS/src/dmumps_part1.F%MUMPS/src/dmumps_part2.F(/'*** Warning message from analysis routine ***')(I8,A,I8,A,I8,A)th entry (in row and column) ignoredst entry (in rownd entry (in rowrd entry (in row(A,I5) ... Structural symmetry (in percent)=(A,3I5) Density: NBdense, Average, Median =(' *** WARNING MESSAGE FROM DMUMPS_198 ***' )(I6, ' NON-ZERO (IN ROW, I6, 11H AND COLUMN ', I6, ') IGNORED')(' ****** Error in DMUMPS_443. INFO(1) = ',I2, ' because ',(A),' = ',I10)JOBMNNE(' ****** Error in DMUMPS_443. INFO(1) = ',I2/ ' LIW too small, must be at least ',I8)(' ****** Error in DMUMPS_443. INFO(1) = ',I2/ ' LDW too small, must be at least ',I8)(' ****** Error in DMUMPS_443. INFO(1) = ',I2/ ' Column ',I8, ' contains an entry with invalid row index ',I8)(' ****** Error in DMUMPS_443. INFO(1) = ',I2/ ' Column ',I8, ' contains two or more entries with row index ',I8)(' ****** Input parameters for DMUMPS_443:'/ ' JOB =',I10/' M =',I10/' N =',I10/' NE =',I10)(' IP(1:N+1) = ',8I8/(15X,8I8))(' IRN(1:NE) = ',8I8/(15X,8I8))(' A(1:NE) = ',4(1PD14.4)/(15X,4(1PD14.4)))(' ICNTL(1:10) = ',8I8/(15X,2I8))(' CNTL(1:10) = ',4(1PD14.4)/(15X,4(1PD14.4)))(' ****** Warning from DMUMPS_443. INFO(1) = ',I2)(' - The matrix is structurally singular.')(' - Some scaling factors may be too large.')(' - CNTL(1) is negative and was treated as zero.')(' ****** Output parameters for DMUMPS_443:'/ ' INFO(1:2) = ',2I8)(' NUM = ',I8)(' PERM(1:M) = ',8I8/(15X,8I8))(' DW(1:M) = ',5(F11.3)/(15X,5(F11.3)))(' DW(M+1:M+N) = ',5(F11.3)/(15X,5(F11.3))) ERROR in DMUMPS_422 quasi dense rows remains Internal error 2 in QAMD : Schur size expected:Real:Internal ERROR 2 detected in QAMD NEL not equal to N: N, NEL = Internal ERROR quasi dense rows remains(A,I11) Order of root node after splitting : Error: INODE_FATH < 0 ERROR 2 in SPLIT NODE Error 1 in HALO_AMD error 1 in DMUMPS_162 wrong principal var for Schur !! Error 2 in DMUMPS_162 NEL, N=  (/'Leaving analysis phase with ...'/ 'INFOG(1) =',I12/ 'INFOG(2) =',I12/ ' -- (20) Number of entries in factors (estim.) =',I12/ ' -- (3) Storage of factors (REAL, estimated) =',I12/ ' -- (4) Storage of factors (INT , estimated) =',I12/ ' -- (5) Maximum frontal size (estimated) =',I12/ ' -- (6) Number of nodes in the tree =',I12/ ' -- (7) Ordering option effectively used =',I12/ 'ICNTL(6) Maximum transversal option =',I12/ 'ICNTL(7) Pivot order option =',I12/ 'Percentage of memory relaxation (effective) =',I12/ 'Number of level 2 nodes =',I12/ 'Number of splitted nodes =',I12/ 'RINFO(1) Operations during elimination (estim) = ',1PD10.3)('Ordering compressed/constrained (ICNTL(12)) =',I12)('Distributed matrix entry format (ICNTL(18)) =',I12)('Effective Schur option (ICNTL(19)) =',I12)A root of estimated size has been selected for Scalapack Largest root node of size (/'****** Preprocessing of original matrix '/)Analysis: auto scaling set OFFScaling will be computed during analysisWARNING scaling required: set MTRANS option to 5(A,I3)Compute maximum matching (Maximum Transversal): ... JOB =(A,I3,A): BOTTLENECK THESIS: BOTTLENECK SIMAX: MAXIMIZE SUM DIAGIONAL: MAXIMIZE PRODUCT DIAGONAL AND SCALE INTERNAL ERROR in MAXTRANS INFO(1)= WARNING in MAXTRANS INFO(1)=(A) ... Apply column permutation(/A) WARNING input matrix data modified** Error in DMUMPS_203** Failure during allocation of COLSCA** Failure during allocation of ROWSCA ... Column permutation not used** Error: Matrix is structurally singular(A,I9)** Failure during allocation of INTEGER array of size ** Failure during allocation of S2(/'Entering analysis phase with ...'/ ' N NZ LIW INFO(1)'/, 9X, I8, I11, I12, I14)('Matrix entries: IRN() ICN()'/ (I12, I7, I12, I7, I12, I7))('IKEEP(.,1)=', 10I6/(12X, 10I6))Compressed/constrained ordering set OFFWARNING: METIS not available. Ordering set to default.WARNING: PORD not available. Ordering set to default.WARNING: SCOTCH not available. Ordering set to default.WARNING: ANAL_F constrained ordering not available with selected orderingWARNING: ANAL_F AMD not available with compressed ordering -> move to QAMDSetting static pivoting ON, COMPRESS = ... Apply column permutation (already computed) ... No column permutation Ordering based on AMF Ordering based on QAMD Ordering based on AMD Constrained Ordering based on AMF Ordering given is used('IKEEP(.,2)=', 10I6/(12X, 10I6))('IKEEP(.,3)=', 10I6/(12X, 10I6))('PTRAR(.,1)=', 10I6/(12X, 10I6))('PTRAR(.,2)=', 10I6/(12X, 10I6))('PTRAR(.,3)=', 10I6/(12X, 10I6))('NFSIZ(.) =', 10I6/(12X, 10I6))('FILS (.) =', 10I6/(12X, 10I6))('FRERE(.) =', 10I6/(12X, 10I6))(/'** Error return ** from Analysis * INFO(1)=', I3)('Error in permutation array KEEP INFO(2)=', I3)cError : bad argument to DMUMPS_242Error in PROCESS_MAITRE2: error detected by DMUMPS_73 error 1 in process_last_rtnelind : FACTO_NIV2 :failed to allocate integersProcess_factor: error1. Wrong number of slavesINODE,NFRONT,NROW,NSLAVES=Process_factor: error2. wrong block sizeProcess_factor: error3Process_factor: error4O@FHC@F9B.?9B.@MUMPS/src/dmumps_part3.F(/'*** Warning message from subroutine DMUMPS_258 ***')(A,I8,A,I8,A)Element variable ignored. ** Allocation Error 1 in DMUMPS_F77.** Allocation Error 2 in DMUMPS_F77. ** Instance Error 1 in DMUMPS_F77 Instance Error 2 in DMUMPS_F77 ** Warning: instance already freed this should normally not happen. ** Warning : For element distrib NBRECORDS set to because one element is large ** ERROR ELT DIST: RELPTR - 1 / id%NA_ELT= ERR: ERROR : NBROWS > NBROWF ERR: INODE = ERR: NBROW=NBROWF=PB compress ass..mpi51f_niv2LRLU,LRLUS= ERROR 2 during ass_niv2 FAILURE IN INTEGER DYNAMIC ALLOCATION during assembly FAILURE ALLOCATING TMP_SLAVES_LIST FAILURE IN INTEGER ALLOCATION DURING DMUMPS_37 FAILURE, WORKSPACE TOO SMALL DURING DMUMPS_37 FAILURE, SEND BUFFER TOO SMALL (1) DURING DMUMPS_37 FAILURE, RECV BUFFER TOO SMALL (1) DURING DMUMPS_37 FAILURE, SENDBUFFER TOO SMALL (2) DURING DMUMPS_37 FAILURE, RECVBUFFER TOO SMALL (2) DURING DMUMPS_37Error1 in mpi51f_niv1 PB compress ass..mpi51f_niv1.F ERROR 1 during ass_niv1_ELT FAILURE IN INTEGER ALLOCATION DURING DMUMPS_36 FAILURE, WORKSPACE TOO SMALL DURING DMUMPS_36 FAILURE, SEND BUFFER TOO SMALL DURING DMUMPS_36 FAILURE, RECV BUFFER TOO SMALL DURING DMUMPS_36 DYNAMIC ALLOCATION DURING DMUMPS_36(/3X,'Error message from DMUMPS_315: INFO(1) = ',I2)(3X,'LIW is insufficient. Upper bound on required work', 'space is ',I8)Error return from DMUMPS_315. INFO(1) = ERROR 1 in file DMUMPS_153 (/'Entering analysis phase with ...'/ ' N NELT LIW INFO(1)'/, 9X, I8, I11, I12, I14)('Element pointers: ELTPTR() '/(9X, 7I10))('Element variables: ELTVAR() '/(9X, 7I10))('IKEEP(.,1)=', 10I6/(12X, 10I6))Internal error in DMUMPS_128('IKEEP(.,2)=', 10I6/(12X, 10I6))('IKEEP(.,3)=', 10I6/(12X, 10I6))('NFSIZ(.) =', 10I6/(12X, 10I6))('FILS (.) =', 10I6/(12X, 10I6))('FRERE(.) =', 10I6/(12X, 10I6))(/'** Error return ** from Analysis * INFO(1)=', I3)('Error in permutation array KEEP INFO(2)=', I3): Extracting from a subtree for helping: Extracting from top inode=for helping: selecting from subtree: I must search for a task to save My friendError 2 in DMUMPS_509: unknown strategyError 1 in DMUMPS_509Error 3 in DMUMPS_509: ca a change pour moiError 5 in DMUMPS_509Internal error 1 in DMUMPS_270PB1 compress root2slave:LRLU,LRLUS=PB2 compress root2slave:LRLU,LRLUS=error 1 in PROCESS_ROOT2SLAVEbefore alloc_cb:IPTRLU = after alloc_cb:IPTRLU = (/' LEAVING FACTORIZATION PHASE WITH ...'/ ' INFO (1) =',I12/ ' --- (2) =',I12/ ' NUMBER OF NODES IN THE TREE =',I12/ ' INFO (9) REAL SPACE FOR FACTORS =',I12/ ' --- (10) INTEGER SPACE FOR FACTORS =',I12/ ' --- (11) MAXIMUM SIZE OF FRONTAL MATRICES =',I12/ ' --- (12) NUMBER OF OFF DIAGONAL PIVOTS =',I12/ ' --- (13) NUMBER OF DELAYED PIVOTS =',I12/ ' --- (14) NUMBER OF MEMORY COMPRESSES =',I12/ ' RINFO(2) OPERATIONS DURING NODE ASSEMBLY =',1PD10.3/ ' -----(3) OPERATIONS DURING NODE ELIMINATION =',1PD10.3)Internal error in DMUMPS_22Internal error 2 in DMUMPS_22PB compress... alloc_cbInternal error 3 in DMUMPS_22 Error in DMUMPS_243PB compress Stack_band:LRLU,LRLUS=: Internal error in DMUMPS_576 ERROR 1 compressLU:Should not point to a band. ERROR 2 compressLU:Stack not performed yet ERROR 3 compressLU: problem with level of inode error 2 in maplig_fils_niv1 : PB allocation NBROW in DMUMPS_211 : PB allocation SLAVES_PERE in DMUMPS_211 : PB allocation LMAP in DMUMPS_211: PB allocation PERM in DMUMPS_211: PB allocation SIZE_SLV_PERE in DMUMPS_211Error 1 in MAPLIG_FILS_NIV1: Error 2 in DMUMPS_211 : PB allocation MAX_ARRAY during DMUMPS_211: FAILURE, SEND BUFFER TOO SMALL DURING DMUMPS_211: FAILURE, RECV BUFFER TOO SMALL DURING DMUMPS_211: FAILURE, MAX_ARRAY ALLOC FAILED DURING DMUMPS_211error 3 in DMUMPS_211 : PB allocation SLAVES_PERE in DMUMPS_210 : PB allocation NBROW in DMUMPS_210 : PB allocation LMAP in DMUMPS_210: PB allocation PERM in DMUMPS_210: PB allocation SIZE_SLV_PERE in DMUMPS_210Error 1 in PARPIV/DMUMPS_210MAX_ARRAY allocation failed: Internal error in Maplig: PTRIST(STEP(ISON))=: I, NBROW(I)=: NSLAVES_PERE=: ISON, INODE_PERE=: Son header=FAILURE: SEND BUFFER TOO SMALL IN DMUMPS_210FAILURE: RECV BUFFER TOO SMALL IN DMUMPS_210FAILURE: MAX_ARRAY allocation failed IN DMUMPS_210)\(?MUMPS/src/dmumps_part4.F(A) END OF ROW SCALING END OF COLUMN SCALING**** STAT. OF MATRIX PRIOR ROW&COL SCALING MAXIMUM NORM-MAX OF COLUMNS: MINIMUM NORM-MAX OF COLUMNS: MINIMUM NORM-MAX OF ROWS : END OF SCALING BY MAX IN ROW AND COLMemory allocation error in DMUMPS_363Memory allocation error in DMUMPS_363Internal Error in reordertree: problem with perm parameter in reordertreeInternal Error in reordertree INODE < 0 !Probleme dans reorder!!!!INODE=RANKCOST=Internal Error in DMUMPS_363 Failure in int space allocation in CB area during assembly of root : DMUMPS_273 size required was : NELIM= NSLAVES= ERROR 1 in DMUMPS_294 NIV2:Detected 0 pivot, INODE,NPIV= Error in DMUMPS_272 . Internal error 1 IN DMUMPS_627Internal error 2 in DMUMPS_627Internal error 3 in DMUMPS_627Internal error 2 in DMUMPS_94Internal error 1 in DMUMPS_94?UNRLDetected a null pivot, INODE/NPIV=(/A/A,I3) **** Error return from DMUMPS_216 **** IFAIL = END OF DIAGONAL SCALING END OF SCALING USING MC29(/' ****** SCALING OF ORIGINAL MATRIX '/) DIAGONAL SCALING SCALING BASED ON (MC29) COLUMN SCALING ROW AND COLUMN SCALING (1 Pass) MC29 FOLLOWED BY ROW &COL SCALING MC29 FOLLOWED BY COLUMN SCALING*** ERROR: Not enough space to scale matrix ERROR 1 in DMUMPS_271 :Process root2son: INODE=Header= ERROR 5 in process_root2son : IOLDPS,PTLUST_S(STEP(INODE))=: ERROR 2 in DMUMPS_271 : INODE,LCONT, NROW, NPIV, NASS, NELIM=: IOLDPS=: internal error in DMUMPS_271 ERROR 1 in DMUMPS_246 : ERROR 2 in DMUMPS_246. ITOP = ERROR 3 in DMUMPS_246ERROR 4 in DMUMPS_246  MAX-NORM of computed solut. is zero(/' RESIDUAL IS ............ (MAX-NORM) =',1PD9.2/ ' .. (2-NORM) =',1PD9.2/ ' RINFOG(4):NORM OF input Matrix (MAX-NORM)=',1PD9.2/ ' RINFOG(5):NORM OF Computed SOLUT (MAX-NORM)=',1PD9.2/ ' RINFOG(6):SCALED RESIDUAL ...... (MAX-NORM)=',1PD9.2) MAX-NORM of exact solution is zero(/' ERROR IS ............ (MAX-NORM) =',1PD9.2/ ' ............ (2-NORM) =',1PD9.2/ ' RELATIVE ERROR........... (MAX-NORM) =',1PD9.2/ ' Comp. Wise ERROR......... (MAX-NORM) =',1PD9.2/ ' AND RESIDUAL IS ......... (MAX-NORM) =',1PD9.2/ ' .. (2-NORM) =',1PD9.2/ ' NORM OF input MATRIX ... (MAX-NORM) =',1PD9.2/ ' NORM of computed SOLUT... (MAX-NORM) =',1PD9.2/ ' SCALED RESIDUAL ......... (MAX-NORM) =',1PD9.2):INTERNAL Error: recvd root arrowhead :not belonging to me. IARR,JARR=:IROW_GRID,JCOL_GRID=:MYROW, MYCOL=:IPOSROOT,JPOSROOT=  ** Error allocating int buffer for matrix distribution** Error allocating real buffer for matrix distribution** Error allocating int recv buffer for matrix distribution** Error allocating IW4 for matrix distribution(A9,A42,I12) Maximum Average (/' ****** FACTORIZATION STEP ********'/)(/A,I3) Null space option :(A,I10) Max deficiency : Min deficiency : (A) ** Warning : SPD solver called, resetting CNTL(1) to 0.0D0(/' GLOBAL STATISTICS PRIOR NUMERICAL FACTORIZATION ...'/ ' NUMBER OF WORKING PROCESSES =',I12/ ' REAL SPACE FOR FACTORS =',I12/ ' INTEGER SPACE FOR FACTORS =',I12/ ' MAXIMUM FRONTAL SIZE (ESTIMATED) =',I12/ ' NUMBER OF NODES IN THE TREE =',I12) ABSOLUTE PIVOT THRESHOLD for rank revealing = ZERO PIVOT DETECTION ON, THRESHOLD = FIXATION FOR NULL PIVOTS =INFINITE FIXATION effective relaxed value of MAXS =: Warning: DIM_BUF_IO might be too big for Filesystem:Error allocating DBLARR : IERR = (' GLOBAL TIME FOR MATRIX DISTRIBUTION =',F12.4)(/' LOCAL REDISTRIB: DATA LOCAL/SENT =',I12,I12)(/' REDISTRIB: TOTAL DATA LOCAL/SENT =',I12,I12)( /,' Allocated buffers',/,' ------------------',/, ' Size of reception buffer in bytes ...... = ', I10, /, ' Size of async. emission buffer (bytes).. = ', I10,/, ' Small emission buffer (bytes) .......... = ', I10):Error allocating small Send buffer:IERR=:Error allocating Send buffer:IERR=:Error allocating BUFR:IERR=:Error allocating IS:IERR=:Error allocatingPTLUST:IERR = :Error allocatingPTRFAC:IERR = :Error allocating IWK : IERR = (/' STATISTICS PRIOR NUMERICAL FACTORIZATION ...'/ ' MAXS =',I12/ ' MAXIS =',I12/ ' MINIMUM (ICNTL(14)=0) VALUE OF MAXS =',I12/ ' MINIMUM (ICNTL(14)=0) VALUE OF MAXIS =',I12/ ' REAL SPACE FOR ORIGINAL MATRIX =',I12/ ' INTEGER SPACE FOR ORIGINAL MATRIX =',I12/ ' REAL SPACE FOR FACTORS =',I12/ ' INTEGER SPACE FOR FACTORS =',I12/ ' MAXIMUM FRONTAL SIZE (ESTIMATED) =',I12)(A,I10) ** Space in MBYTES used during factorization : ** Memory relaxation parameter ( ICNTL(14) ) : ** Rank of processor needing largest memory in facto : ** Space in MBYTES used by this processor for facto : ** Avg. Space in MBYTES per working proc during facto :(/' NUMBER OF ENTRIES FOR // ROOT =',I12)&(/' ELAPSED TIME FOR FACTORIZATION =',F12.4) ** Effective minimum Space in BYTES for facto :effective space used in S (KEEP(67) = ** EFF Min: Rank of processor needing largest memory : ** EFF Min: Space in MBYTES used by this processor : ** EFF Min: Avg. Space in MBYTES per working proc :((/' GLOBAL STATISTICS '/ ' RINFOG(2) OPERATIONS DURING NODE ASSEMBLY =',1PD10.3/ ' ------(3) OPERATIONS DURING NODE ELIMINATION =',1PD10.3/ ' INFOG (9) REAL SPACE FOR FACTORS =',I12/ ' INFOG(10) INTEGER SPACE FOR FACTORS =',I12/ ' INFOG(11) MAXIMUM FRONT SIZE =',I12)( ' INFOG(12) NEGATIVE PIVOTS =',I12)( ' INFOG(13) NUMBER OF OFF DIAGONAL PIVOTS =',I12)( ' INFOG(12) NUMBER OF DELAYED PIVOTS =',I12)( ' INFOG(25) NUMBER OF TINY PIVOTS =',I12)( ' NUMBER OF 2x2 PIVOTS in type 1 nodes =',I12)( ' NUMBER OF 2x2 PIVOTS in type 2 nodes =',I12)( ' NUMBER OF ZERO PIVOTS =',I12)( ' NUMBER OF ZERO PIVOTS WT ROOT =',I12)( ' DEFICIENCY ON LAST ROOT NODE =',I12)( ' INFOG(28) ESTIMATED DEFICIENCY =',I12)( ' INFOG(14) NUMBER OF MEMORY COMPRESS =',I12) ** Warning Static pivoting was necessary ** to factor interior variables with Schur ONError 1 in Gerror: more rows than columnsInternal error in DMUMPS_254 ERROR 1 in 51G : IOLDPS,PTLUST_S(STEP(INODE))= *** CHG_HEADER ERROR 1 : *** CHG_HEADER ERROR 2 : *** CHG_HEADER ERROR 3 : not rootDERROR 2 in DMUMPS_251Error .. in DMUMPS_251: INODE == KEEP(38) ERROR 1 in DMUMPS_251: NBFIN= NBROOT= Error allocating, real array of size before DMUMPS_146: Internal error in DMUMPS_576( /' DMUMPS Version ',A6,' -- Jan 2007' )L U Solver for unsymmetric matricesL D L^T Solver for symmetric positive definite matricesL D L^T Solver for general symmetric matricesType of parallelism: Working hostType of parallelism: Host not working(/' ****** ANALYSIS STEP ********'/) Resetting candidate strategy to 0 because NSLAVES=1 Resetting candidate strategy to 8 ** Ignoring user-ordering, because incompatible with Schur. ** ICNTL(7) treated as 0. Out-of-range value for ICNTL(18). Used 0 ie matrix not distributed Out-of-range value for ICNTL(5). Used 0 ie matrix is assembled ** Max-trans not compatible with LLT factorization ** Max-trans not allowed because of Schur ** Scaling not allowed because of Schur ** Max-trans not allowed because ordering is given ** Max-trans not allowed because matrix is distributed ** Scaling not allowed because matrix is distributed ** Max-trans not allowed for element matrix ** Scaling not allowed for element matrix ** Distributed entry not available for element matrixWARNING: ANAL_0 constrained ordering not available with selected ordering( /' ** FAILURE DURING DMUMPS_26, DYNAMIC ALLOCATION OF', A30)MEM_DISTREQPTRIRNJCN#$%NAME_NOT_INITIALIZED%%MatrixMarket matrix coordinate .rhs%%MatrixMarket matrix array general(I7)***********************************MAXIS1 and MAXIS1_CHECK are different !!MAXIS1, MAXIS1_CHECK=This might cause problems ...PB allocating an array of size 1 in Schur IWtempXNODELNODELSSARBR(A,F10.0) ** Peak of sequential stack size (number of real entries) : ** Peak of sequential total memory (number of real entries) : Internal error in DMUMPS_369FRTPTRPAR2_NODESInternal Error 2 in DMUMPS_26CANDIDATESnid%STEPid%PROCNODE_STEPSid%NE_STEPSid%ND_STEPSid%FRERE_STEPSid%DAD_STEPSid%SYM_PERMid%FILSid%PTRARid%FRTPTR/FRTELTid%UNS_PERMid%NAInternal error 2 in DMUMPS_26Internal error 3 in DMUMPS_26TEMP_MEMTEMP_LEAFTEMP_SIZETEMP_ROOTDEPTH_FIRSTCOST_TRAV_TMPid%DEPTH_FIRSTid%COST_TRAVid%COST_TRAV(1)id%MEM_SUBTREEid%MY_ROOT_SBTRid%MY_FIRST_LEAFid%MY_NB_LEAFMY_FIRST_LEAFMY_NB_LEAFid%MEM_SUBTREE(1)id%MY_ROOT_SBTR(1)id%MY_FIRST_LEAF(1)id%MY_NB_LEAF(1)PAR2_NODES/id%CANDIDATESid%ISTEP_TO_INIV2id%TAB_POS_IN_PEREFUTURE_NIV2Problem with your version of the BLACS.Try using a BLACS version from netlib. estimated INTEGER space for factors : estimated REAL space for factors : Minimum value of MAXIS for factorization : Minimum value of MAXS for IC factorization : Minimum value of MAXS for OOC factorization: Current value of space relaxation parameter : Relaxed Value of MAXIS for factorization : Relaxed Value of MAXS for IC factorization : Relaxed Value of MAXS for OOC factorization : Estimated maximum SENT message size (bytes) : Estimated maximum RECV message size (bytes) :id%MAPPINGIWtemp(N) Estimated space in MBYTES for IC factorization : ** Rank of proc needing largest memory in IC facto : ** Estimated corresponding MBYTES for IC facto : ** Estimated avg. MBYTES per work. proc at facto (IC) : ** TOTAL space in MBYTES for IC factorization : ** Rank of proc needing largest memory for OOC facto : ** Estimated corresponding MBYTES for OOC facto : ** Estimated avg. MBYTES per work. proc at facto (OOC) : ** TOTAL space in MBYTES for OOC factorization : Error 1 in mc51d NTOTPVTOT= Error 2 in mc51d NTOTPVTOT=MUMPS/src/dmumps_part5.F|=dA'PG R : MEMORY ALLOCATION FAILURE in DMUMPS_80error: myrow,mycol= MYID,PDEST=: Error in b&scbroot: pb compress: LRLU, LRLUS=FAILURE, SEND BUFFER TOO SMALL DURING DMUMPS_80FAILURE, RECV BUFFER TOO SMALL DURING DMUMPS_80 :error, received negative NPIV in BLFACPB compress ass..blocfacto: LRLU,LRLUS= : PB allocation U in blfac_slave ?NT! Internal error in PROCESS_BLFAC_SLAVE.UWARNING matrix may be singular : FACTO_NIV2 :failed to allocate integers Internal error related to null pivot row detection: FAILURE IN DMUMPS_274, REAL WORKSPACE TOO SMALL: FAILURE IN DMUMPS_274, INTEGER WORKSPACE TOO SMALL: Internal ERROR 1 in DMUMPS_274 INODE = MAITRE_DESC_BANDE not yet received from SymBLFC Error: NPIV1 + NCOL .NE. NASS1 :: ALLOCATION FAILURE FOR UIP21K IN DMUMPS_274: ALLOCATION FAILURE FOR LIST_SLAVES_FOLLOW IN DMUMPS_274L: FAILURE, SEND BUFFER TOO SMALL DURING DMUMPS_274: FAILURE, RECV BUFFER TOO SMALL DURING DMUMPS_274 Internal error in PROCESS_SYM_BLOCFACTO.: Error in calling transdiag:unsym:Error in CHECK_ORDER_LIST: not sorted:List = :Permuted list =:Error in MUMP_CHECK_ORDER_LIST: bad ITLOC:ITLOC( IW ) =: Bad First ITLOC ITLOC(IW(1)),NASS1+1=MUMPS/src/dmumps_part6.FP90yE^#B ;MUMPS/src/dmumps_part7.F(/'*** Warning message from analysis routine ***')(I8,A,I8,A,I8,A)th entry (in row and column) ignoredst entry (in rownd entry (in rowrd entry (in row(A,I5) ... Structural symmetry (in percent)=(A,3I5) Density: NBdense, Average, Median =(A)** Warning: ICNTL(25) option disabled because** null space was not required during factorization** it is not available for the transposed systemERROR: WRONG VALUE FOR ICNTL(2) = ERROR: WRONG VALUE FOR ICNTL(1) = : problem allocating IPIV() in root Error: symmetrization only works for square block sizes, MBLOCK/NBLOCK=Not enough workspace for symmetrization.L Problem during solve of the root. Reduce number of right hand sides.NTL Problem during solve of the root Error1 in 41S : NROW L/RECU=?Error 2 in usedbympima41s.FU Internal Error type 2 node with no CB c Internal error in SOLVE_NODE for Root node Internal error 41r2 : Pool is too small.INTERNAL Error 41r: Pool is too small.Internal error in DMUMPS_63967 warning solve: lpool.NE.nsteps+1 :: ** ERROR RETURN FROM DMUMPS_248,INFO(1:2)=After DESCINIT, IERR = (//' LEAVING SOLVE (MPI41C) WITH')(' RHS (first column)'/(1X,1P,5D14.6))(' RHS (2 nd column)'/(1X,1P,5D14.6))(//' ****** SOLVE & CHECK STEP ********'/) Problem reallocation of MAXS (/' STATISTICS PRIOR SOLVE PHASE ...........'/ ' NUMBER OF RIGHT-HAND-SIDES =',I12/ ' BLOCKING FACTOR FOR MULTIPLE RHS =',I12/ ' ICNTL (9) =',I12/ ' --- (10) =',I12/ ' --- (11) =',I12/ ' --- (20) =',I12/ ' --- (21) =',I12)(' --- (25) =',I12)(' --- (26) =',I12)(A) WARNING: ICNTL(20) treated as if set to 0 (null space) WARNING: ICNTL(10) treated as if set to 0 (null space) WARNING: ICNTL(11) treated as if set to 0 (null space) WARNING: ICNTL(10) treated as if set to 0 (reduced RHS)) WARNING: ICNTL(11) treated as if set to 0 (reduced RHS) WARNING: ICNTL(11) treated as if set to zero WARNING: ICNTL(10) treated as if set to zero Problem in solve: error allocating SAVERHS Problem in solve: error allocating BUFR:Error allocating small Send buffer:IERR=:Error allocating Send buffer:IERR=ERREUR while allocating RHS on a slaveERROR in DMUMPS_301: LIWW<0 ERROR in DMUMPS_301: allocating POSINRHSCOMP_NError allocating temporary scaling arrayError allocating local scaling array:Error allocating RW1 in DMUMPS_SOLVE_DRIVE WARNING : -11 error code obtained in solve WARNING : -14 error code obtained in solveFIXME: to be implemented(//' ERROR ANALYSIS' )(//' BEGIN ITERATIVE REFINEMENT' )(1X, A42,I4)MAXIMUM NUMBER OF STEPS =(/' STATISTICS AFTER ITERATIVE REFINEMENT ')NUMBER OF STEPS OF ITERATIVE REFINEMENTS =(/' END ITERATIVE REFINEMENT ')(//' ERROR ANALYSIS AFTER ITERATIVE REFINEMENT')(1X, A44,1P,D9.2)RINFOG(7):COMPONENTWISE SCALED RESIDUAL(W1)=------(8):---------------------------- (W2)=------(9):Upper bound ERROR ...............=-----(10):CONDITION NUMBER (1) ............=-----(11):CONDITION NUMBER (2) ............=could not allocate integers.(//' LEAVING SOLVER WITH: INFO(1) ............ =',I12/ ' INFO(2) ............ =',I12)(//' VECTOR SOLUTION FOR COLUMN ',I12)(' RHS'/(1X,1P,5D14.6))MUMPS/src/dmumps_part8.FL= ?Problem with low level writeProblem with low level readNAME_NOT_INITIALIZEDMUMPS_OOC_TMPDIR/tmpMUMPS_OOC_PREFIX_%s%d_XXXXXX/%s%s%s%s%s%s%d_XXXXXXUnable to open OOC fileProblem while opening OOC fileProblem while closing OOC fileInternal (1) error in low-level I/O operation %lfdmumps_NUnable to remove OOC file%d: Volume of read i/o = %lf %d: Total i/o volume = %lf Error: unknown I/O strategy : %d %d: total time spent in i/o mode = %lf %d: Volume of write i/o = %lf Error: prefix not initialized Error: tmpdir not initialized dmumps_low_level_init_ooc_c should not be called with strategy %d dmumps_wait_all_requests should not be called with strategy %d dmumps_wait_request should not be called with strategy %d dmumps_test_request should not be called with strategy %d dmumps_clean_request should not be called with strategy %d dmumps_is_there_finished_request should not be called with strategy %d %s: %sInternal error in OOC Management layer (dmumps_clean_request_th) Internal error in OOC Management layer (dmumps_test_request_th (1)) Internal error in OOC Management layer (dmumps_test_request_th (2)) Internal error in OOC Management layer (dmumps_async_read_th) Internal error: dmumps_low_level_init_ooc_c_th should not to be called with strat_IO=%d Error : Mumps_IO : Operation %d is neither READ nor WRITE Internal error in _dmumps_async_thread_functionUnable to create I/O threadMUMPS/libseq/mpi.fError. DESCSET should not be called.Error. PXERBLA should not be called.Error. PCHK2MAT should not be called.Error. CHK1MAT should not be called.Error. PDORMQR should not be called.Error. PSORMQR should not be called.Error. PZUNMQR should not be called.Error. PCUNMQR should not be called.Error. PDTRSM should not be called.Error. PSTRSM should not be called.Error. PZTRSM should not be called.Error. PCTRSM should not be called.Error. PDAXPY should not be called.Error. PSAXPY should not be called.Error. PZAXPY should not be called.Error. PCAXPY should not be called.Error. PDGEQPF should not be called.Error. PSGEQPF should not be called.Error. PZGEQPF should not be called.Error. PCGEQPF should not be called.Error. PDGECON should not be called.Error. PSGECON should not be called.Error. PZGECON should not be called.Error. PCGECON should not be called.Error. PDLANGE should not be called.Error. PSLANGE should not be called.Error. PZLANGE should not be called.Error. PCLANGE should not be called.Error. PDNRM2 should not be called.Error. PSNRM2 should not be called.Error. PZNRM2 should not be called.Error. PCNRM2 should not be called.Error. PDPOTRS should not be called.Error. PSPOTRS should not be called.Error. PZPOTRS should not be called.Error. PCPOTRS should not be called.Error. PDGETRS should not be called.Error. PSGETRS should not be called.Error. PZGETRS should not be called.Error. PCGETRS should not be called.Error. DGEBR2D should not be called.Error. SGEBR2D should not be called.Error. CGEBR2D should not be called.Error. ZGEBR2D should not be called.Error. DGEBS2D should not be called.Error. SGEBS2D should not be called.Error. CGEBS2D should not be called.Error. ZGEBS2D should not be called.Error. PSDOT should not be called.Error. PDDOT should not be called.Error. PZDOT should not be called.Error. PCDOT should not be called.Error. PSSCAL should not be called.Error. PDSCAL should not be called.Error. PZSCAL should not be called.Error. PCSCAL should not be called.Error. INFOG2L should not be called.Error. PDTRTRS should not be called.Error. PDGETRF should not be called.Error. PDPOTRF should not be called.Error. PSTRTRS should not be called.Error. PSGETRF should not be called.Error. PSPOTRF should not be called.Error. PZTRTRS should not be called.Error. PZGETRF should not be called.Error. PZPOTRF should not be called.Error. PCTRTRS should not be called.Error. PCGETRF should not be called.Error. PCPOTRF should not be called.Error. Last parameter from NUMROC should be 1Error. IPROC should be 0 in NUMROC.Error. DESCINIT should not be called.Error. BLACS_GRIDEXIT should not be called.Error. BLACS_GRIDINFO should not be called.Error. BLACS_GRIDINIT should not be called.** Abort calledError. MPI_WAITANY should not be called.Error. MPI_WAIT should not be called.Error. MPI_UNPACK should not be called.Error. MPI_SSEND should not be called.Error. MPI_SEND should not be called.Error. MPI_RECV should not be called.Error. MPI_PROBE should not be called.Error. MPI_PACK_SIZE should not be called.Error. MPI_PACKED should not be called.Error. MPI_ISEND should not be called.Error. MPI_GET_COUNT should not be called.ERROR in MPI_REDUCE. DATATYPE=ERROR in MPI_ALLREDUCE. DATATYPE=ERROR in MPI_GATHERV=ERROR in MPI_GATHER=vector::_M_insert_auxN5Ipopt11CGPenaltyCqEN5Ipopt15DependentResultIdEE@0B{Gz?(knNN5Ipopt13CGPenaltyDataEChecking acceptability for trial step size alpha_primal_test=%13.6e: New values of penalty function = %23.16e (reference %23.16e): curr_barr = %23.16e curr_inf = %23.16e trial_barr = %23.16e trial_inf = %23.16e Starting tests for penalty parameter update: Penalty parameter becomes too large.Trying second order correction number %d Warning: SOC step rejected due to evaluation error Second order correction step accepted with %d corrections. trial_infeasi = %e is larger than theta_max = %e never_use_piecewise_penalty_lspenalty_update_infeasibility_tolpiecewisepenalty_gamma_infeasiOption "max_soc": This option is non-negative, but no linear solver for computing the SOC given to FilterLSAcceptor object.Value "acceptor" for option "alpha_for_y" not valid for this line search../../../Algorithm/IpBacktrackingLSAcceptor.hppnever use the piecewise penalty methodalways use the piecewise penalty methodToggle to switch off the piecewise penalty methodRelaxation factor in the Armijo condition for the penalty function.If the new constraint violation is smaller than this tolerance, the penalty parameter is not increased.Threshold for infeasibility in penalty parameter update test.The algorithmic parameter theta_max is determined as theta_max_fact times the maximum of 1 and the constraint violation at initial point. Any point with a constraint violation larger than theta_max is unacceptable to the filter (see Eqn. (21) in implementation paper).Determines upper bound for constraint violation in the filter.tolerance for deciding if the multipliers are diverginghelptrial infeasibility = %8.2 Max norm of step = %8.2 Minimal compl = %8.2 Maximal compl = %8.2 omega_test for c = %8.2 omega_test for d = %8.2 Updating eta to = %8.2 IpCGPenaltyLSAcceptor.cppjumpeta_penaltypenalty_update_compl_tolchi_hatchi_tildechi_cupgamma_hatgamma_tildeepsilon_cpiecewisepenalty_gamma_objpen_theta_max_factmin_alpha_primaltheta_minmult_diverg_feasibility_tolmult_diverg_y_tolpenalty_maxIsValid(pd_solver_)kappa_socLIFENG WRITES THIS.N5Ipopt19CGPenaltyLSAcceptorEN5Ipopt22BacktrackingLSAcceptorEN5Ipopt23AlgorithmStrategyObjectE '@{Gz?-C6?h㈵>Nhj Nh Nj Dj Dh Dhj dxperturb_always_cddelta_x perturbation is becoming too large: %e Degeneracy test for hess_degenerate_ = %d and jac_degenerate_ = %d test_status_ = %d Can't get_deltas_for_wrong_inertia for delta_x_curr_ = %e and delta_c_curr_ = %e jacobian_regularization_exponentN5Ipopt21CGPerturbationHandlerE(z*penalty_init_maxpenalty_init_minkappa_x_diskappa_y_disvarthetadelta_y_maxfast_des_factpen_des_factpen_init_facInitializing penalty parameter for KKT matrix... Max(||y_c||_inf,||y_d||_inf = %8.2e Initial value of the penalty parameter for line search = %8.2e Initial value of the kkt penalty parameter for scaling the linear system = %8.2e Testing if fast direction can be used. diff_dx_nrm = %8.2e tilde_dx_norm = %8.2e Testing if fast direction can be used. tilde_dy_nrm = %8.2e bar_y_nrm = %8.2e dT_times_BarH_times_d = %23.15e fast_direct_deriv = %23.15e never_use_fact_cgpen_directionMaximal value for the intial penalty parameter (for Chen-Goldfarb line search).Minimal value for the intial penalty parameter for line search(for Chen-Goldfarb line search).Maximal value for the penalty parameter (for Chen-Goldfarb line search).a parameter used in penalty parameter computation (for Chen-Goldfarb line search).a parameter used to check if the fast direction can be used asthe line search direction (for Chen-Goldfarb line search).a parameter used to choose initial penalty parameterswhen the regularized Newton method is used.never compute the fast directionalways compute the fast directionToggle to switch off the fast Chen-Goldfarb directionN5Ipopt21CGSearchDirCalculatorEN5Ipopt25SearchDirectionCalculatorE AThe current piecewise penalty has %d entries. The min piecewise penalty is %d . pen_r barrier_obj infeasi We only allow %d entries. %5d %23.16e %23.16e %23.16e New fixed value for mu could not be computed from the mu_oracle. KKT error in barrier update check: primal infeasibility: %15.6e dual infeasibility: %15.6e complementarity: %15.6e centrality: %15.6e kkt error: %15.6e pd system reference[%2d] = %.6e Switching back to free mu mode. Problem solved to best possible numerical accuracyReducing mu to %24.16e in fixed mu mode. Tau becomes %24.16e Restoring most recent accepted point. The mu oracle could not compute a new value of the barrier parameter. mu = %e smaller than safeguard = %e. Increasing mu. Barrier parameter mu computed by oracle is %e Barrier parameter mu after safeguards is %e Switching to fixed mu mode with mu = %24.16e and tau = %24.16e. This option determines the upper bound on the barrier parameter. This upper bound is computed as the average complementarity at the initial point times the value of this option. (Only used if option "mu_strategy" is chosen as "adaptive".)Factor for initialization of maximum value for barrier parameter.This option specifies an upper bound on the barrier parameter in the adaptive mu selection mode. If this option is set, it overwrites the effect of mu_max_fact. (Only used if option "mu_strategy" is chosen as "adaptive".)Maximum value for barrier parameter.This option specifies the lower bound on the barrier parameter in the adaptive mu selection mode. By default, it is set to the minimum of 1e-11 and min("tol","compl_inf_tol")/("barrier_tol_factor"+1), which should be a reasonable value. (Only used if option "mu_strategy" is chosen as "adaptive".)Minimum value for barrier parameter.To achieve global convergence of the adaptive version, the algorithm has to switch to the monotone mode (Fiacco-McCormick approach) when convergence does not seem to appear. This option sets the criterion used to decide when to do this switch. (Only used if option "mu_strategy" is chosen as "adaptive".)2-dim filter for objective and constraint violationnonmonotone decrease of kkt-errorGlobalization strategy for the adaptive mu selection mode.For the "kkt-error" based globalization strategy, sufficient progress must be made for "adaptive_mu_kkterror_red_iters" iterations. If this number of iterations is exceeded, the globalization strategy switches to the monotone mode.Maximum number of iterations requiring sufficient progress.adaptive_mu_kkterror_red_itersFor the "kkt-error" based globalization strategy, the error must decrease by this factor to be deemed sufficient decrease.Sufficient decrease factor for "kkt-error" globalization strategy.When using the adaptive globalization strategy, "obj-constr-filter", sufficient progress for a filter entry is defined as follows: (new obj) < (filter obj) - filter_margin_fact*(new constr-viol) OR (new constr-viol) < (filter constr-viol) - filter_margin_fact*(new constr-viol). For the description of the "kkt-error-filter" option see "filter_max_margin".Factor determining width of margin for obj-constr-filter adaptive globalization strategy.Maximum width of margin in obj-constr-filter adaptive globalization strategy.When the globalization strategy for the adaptive barrier algorithm switches to the monotone mode, it can either start from the most recent iterate (no), or from the last iterate that was accepted (yes).don't restore accepted iterateIndicates if the previous iterate should be restored if the monotone mode is entered.adaptive_mu_restore_previous_iterateWhen the globalization strategy for the adaptive barrier algorithm switches to the monotone mode and fixed_mu_oracle is chosen as "average_compl", the barrier parameter is set to the current average complementarity times the value of "adaptive_mu_monotone_init_factor".Determines the initial value of the barrier parameter when switching to the monotone mode.adaptive_mu_monotone_init_factorWhen computing the KKT error for the globalization strategies, the norm to be used is specified with this option. Note, this options is also used in the QualityFunctionMuOracle.use the 2-norm squared (sum of squares)Norm used for the KKT error in the adaptive mu globalization strategies.quality_function_balancing_termSetting mu_max to %e. Remaining in fixed mu mode. IpAdaptiveMuUpdate.cppTINY_STEP_DETECTEDStaying in free mu mode. adaptive_mu_safeguard_factordisables globalizationnever-monotone-modeobj-constr-filterkkt-erroradaptive_mu_globalizationadaptive_mu_kkterror_red_factfilter_margin_factfilter_max_marginrestore accepted iterateuse 2-normuse the infinity norm (max)max-norm2-norm-squareduse the 1-norm (abs sum)adaptive_mu_kkt_norm_typetau_minquality_function_norm_typequality_function_centralityresto.N5Ipopt16AdaptiveMuUpdateEN5Ipopt8MuUpdateEN5Ipopt18TINY_STEP_DETECTEDE=`XCuser-scalinggradient-basedequilibration-basedma27IpAlgBuilder.cppma57pardisotaucscustomIsValid(custom_solver_)mc19line_search_methodadaptivemonotoneloqoprobingquality-functionuse custom linear solveruse MUMPS packageuse WSMP packageuse the Pardiso packageuse the Harwell routine MA57use the Harwell routine MA27use the Harwell routine MC19no scaling will be performedminimize a quality functionLOQO's centrality ruleMehrotra's probing heuristicaverage_complFilter methodSelected linear solver MA27 not available.Selected linear solver MA57 not available.Selected linear solver Pardiso not available.Selected linear solver TAUCS not available.Selected linear solver WSMP not available.Selected linear solver CUSTOM not available.Selected linear system scaling method MC19 not available.!mehrotra_algorithm || smuupdate=="adaptive"If mehrotra_algorithm=yes, mu_strategy must be "adaptive".!mehrotra_algorithm || smuoracle=="probing"If mehrotra_algorithm=yes, mu_oracle must be "probing".Determines which linear algebra package is to be used for the solution of the augmented linear system (for obtaining the search directions). Note, the code must have been compiled with the linear solver you want to choose. Depending on your Ipopt installation, not all options are available.use TAUCS package (not yet working)Linear solver used for step computations.Determines the method used to compute symmetric scaling factors for the augmented system (see also the "linear_scaling_on_demand" option). This scaling is independentof the NLP problem scaling. By default, MC19 is only used if MA27 or MA57 are selected as linear solvers. This option is only available if Ipopt has been compiled with MC19.Method for scaling the linear system.Selects the technique used for scaling the problem internally before it is solved. For user-scaling, the parameters come from the NLP. If you are using AMPL, they can be specified through suffixes ("scaling_factor")scale the problem so that first derivatives are of order 1 at random points (only available with MC19)scale the problem so the maximum gradient at the starting point is scaling_max_gradientscaling parameters will come from the userno problem scaling will be performedSelect the technique used for scaling the NLP.Determines which barrier parameter update strategy is to be used.use the adaptive update strategyuse the monotone (Fiacco-McCormick) strategyUpdate strategy for barrier parameter.Determines how a new barrier parameter is computed in each "free-mode" iteration of the adaptive barrier parameter strategy. (Only considered if "adaptive" is selected for option "mu_strategy").Oracle for a new barrier parameter in the adaptive strategy.Determines how the first value of the barrier parameter should be computed when switching to the "monotone mode" in the adaptive strategy. (Only considered if "adaptive" is selected for option "mu_strategy".)base on current average complementarityOracle for the barrier parameter when switching to fixed mode.Chen-Goldfarb penalty functionGlobalization method used in backtracking line searchN5Ipopt17MultiVectorMatrixEN5Ipopt16AlgorithmBuilderEN5Ipopt14HessianUpdaterEMain AlgorithmN5Ipopt20AugRestoSystemSolverEN5Ipopt15AugSystemSolverEN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEEEN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEEEStarting Watch Dog Mdelta_s_magicStopping Watch Dog C Trial step accepted. Trial step rejected. IpBacktrackingLineSearch.cppRESTORATION_FAILEDACCEPTABLE_POINT_REACHEDFailed restoration phase!!!alpha_red_factormagic_stepsaccept_every_trial_stepalpha_for_y_toltiny_step_toltiny_step_y_tolmax_soft_resto_itersDOESN'T REALLY WORK YET!don't take magic stepsEnables magic steps.always accept the full stepacceptordual-and-fullprimal-and-fullsafe_min_dual_infeastake a full step of size onebound_multuse primal step sizeFallback option activated in BacktrackingLineSearch! Relative step size for delta_x = %e Relative step size for delta_s = %e Tiny step of relative size %e detected. Magic step with max-norm %.6e taken. minimal step size ALPHA_MIN = %E Starting checks for alpha (primal) = %8.2e Warning: Cutting back alpha due to evaluation error Trying soft restoration phase step with step length %13.6e Warning: Evaluation error during soft restoration phase step. Trial step acceptable with respect to original backtracking globalization. Primal-dual error at current point: %23.16e Primal-dual error at trial point : %23.16e --> Starting filter line search in iteration %d <-- Mu has changed in line search - resetting watchdog counters. Storing current iterate as backup acceptable point. We are in an emergency mode, but not restoration phase or other fall back is available.Tiny step detected. Use step size alpha = %e unchecked Watch dog procedure successful! Skipping call of restoration phase... --> Starting soft restoration phase <-- Restoration phase called at acceptable point.No Restoration Phase given to this Filter Line Search Object!Restoration phase is called at almost feasible point, but acceptable point from iteration %d could be restored. Restoration phase called at almost feasible point, but acceptable point could be restored. Restoration phase is called at point that is almost feasible, with constraint violation %e. Abort. Restoration phase called, but point is almost feasible.Restoration phase failed, but acceptable point could be restore. Constraint violation is with %e less than expect_infeasible_problem_ctol. Disable expect_infeasible_problem_heuristic. At every step of the backtracking line search, the trial step size is reduced by this factor.Fractional reduction of the trial step size in the backtracking line search.Setting this option to "yes" essentially disables the line search and makes the algorithm take aggressive steps, without global convergence guarantees.don't arbitrarily accept the full stepAlways accept the first trial step.This option determines how the step size (alpha_y) will be calculated when updating the constraint multipliers.Call LSAcceptor to get step size for yuse the dual step size, and full step if delta_x <= alpga_for_y_toluse the primal step size, and full step if delta_x <= alpga_for_y_tollike "min_dual_infeas", but safeguarded by "min" and "max"choose step size minimizing new dual infeasibilityuse the max of primal and bound multipliersuse the min of primal and bound multipliersuse step size for the bound multipliers (good for LPs)Method to determine the step size for constraint multipliers.This is only relevant if "alpha_for_y" is chosen "primal-and-full" or "dual-and-full". The step size for the equality constraint multipliers is taken to be one if the max-norm of the primal step is less than this tolerance.Tolerance for switching to full equality multiplier steps.If the search direction in the primal variables (x and s) is, in relative terms for each component, less than this value, the algorithm accepts the full step without line search. If this happens repeatedly, the algorithm will terminate with a corresponding exit message. The default value is 10 times machine precision.Tolerance for detecting numerically insignificant steps.If the search direction in the primal variables (x and s) is, in relative terms for each component, repeatedly less than tiny_step_tol, and the step in the y variables is smaller than this threshold, the algorithm will terminate.Tolerance for quitting because of numerically insignificant steps.If the number of successive iterations in which the backtracking line search did not accept the first trial point exceeds this number, the watchdog procedure is activated. Choosing "0" here disables the watchdog procedure.Number of shortened iterations that trigger the watchdog.This option determines the number of trial iterations allowed before the watchdog procedure is aborted and the algorithm returns to the stored point.Maximum number of watchdog iterations.This options is meant to activate heuristics that may speed up the infeasibility determination if you expect that there is a good chance for the problem to be infeasible. In the filter line search procedure, the restoration phase is called more quickly than usually, and more reduction in the constraint violation is enforced before the restoration phase is left. If the problem is square, this option is enabled automatically.the problem has a good chance to be infeasiblethe problem probably be feasibleEnable heuristics to quickly detect an infeasible problem.If the constraint violation becomes smaller than this threshold, the "expect_infeasible_problem" heuristics in the filter line search are disabled. If the problem is square, this options is set to 0.Threshold for disabling "expect_infeasible_problem" option.Setting this option to "yes" forces the algorithm to switch to the feasibility restoration phase in the first iteration. If the initial point is feasible, the algorithm will abort with a failure.force start in restoration phasedon't force start in restoration phaseTells algorithm to switch to restoration phase in first iteration.The soft restoration phase attempts to reduce the primal-dual error with regular steps. If the damped primal-dual step (damped only to satisfy the fraction-to-the-boundary rule) is not decreasing the primal-dual error by at least this factor, then the regular restoration phase is called. Choosing "0" here disables the soft restoration phase.Required reduction in primal-dual error in the soft restoration phase.If the soft restoration phase is performed for more than so many iterations in a row, the regular restoration phase is called.Maximum number of iterations performed successively in soft restoration phase.#G`N5Ipopt22BacktrackingLineSearchEN5Ipopt10LineSearchEN5Ipopt18RESTORATION_FAILEDEN5Ipopt24ACCEPTABLE_POINT_REACHEDE LDetermines how much the initial point might have to be modified in order to be sufficiently inside the bounds (together with "bound_frac"). (This is kappa_1 in Section 3.6 of implementation paper.)Desired minimum absolute distance from the initial point to bound.Determines how much the initial point might have to be modified in order to be sufficiently inside the bounds (together with "bound_push"). (This is kappa_2 in Section 3.6 of implementation paper.)Desired minimum relative distance from the initial point to bound.Determines how much the initial slack variables might have to be modified in order to be sufficiently inside the inequality bounds (together with "slack_bound_frac"). (This is kappa_1 in Section 3.6 of implementation paper.)Desired minimum absolute distance from the initial slack to bound.Determines how much the initial slack variables might have to be modified in order to be sufficiently inside the inequality bounds (together with "slack_bound_push"). (This is kappa_2 in Section 3.6 of implementation paper.)Desired minimum relative distance from the initial slack to bound.Determines how large the initial least-square guesses of the constraint multipliers are allowed to be (in max-norm). If the guess is larger than this value, it is discarded and all constraint multipliers are set to zero. This options is also used when initializing the restoration phase. By default, "resto.constr_mult_init_max" (the one used in RestoIterateInitializer) is set to zero.Maximum allowed least-square guess of constraint multipliers.All dual variables corresponding to bound constraints are initialized to this value.Initial value for the bound multipliers.If set to yes, Ipopt ignores the user provided point and solves a least square problem for the primal variables (x and s), to fit the linearize equality and inequality constraints. This might be useful if the user doesn't know anything about the starting point, or for solving an LP or QP.overwrite user-provided point with least-square estimatesLeast square initialization of the primal variablesIf set to yes, Ipopt tries to compute least-square multipliers (considering ALL dual variables). If successful, the bound multipliers are possibly corrected to be at least bound_mult_init_val. This might be useful if the user doesn't know anything about the starting point, or for solving an LP or QP.use bound_mult_init_val and least-square equality constraint multipliersLeast square initialization of all dual variablesIndicates whether this optimization should use a warm start initialization, where values of primal and dual variables are given (e.g., from a previous optimization of a related problem.)use the warm start initializationdo not use the warm start initialization!least_square_init_primal_ || IsValid(aug_system_solver_)The least_square_init_primal can only be chosen if the DefaultInitializer object has an AugSystemSolver. IpDefaultIterateInitializer.cpp!least_square_init_duals_ || IsValid(aug_system_solver_)The least_square_init_duals can only be chosen if the DefaultInitializer object has an AugSystemSolver. Least square estimates max(y_c) = %e, max(y_d) = %e Moved initial values of %s sufficiently inside the bounds. Initial values of %s sufficiently inside the bounds. Least square intial values for x and s computed. Least square initialization of x and s failed! Least square intial values for z_L, z_U,v_L, v_U, y_c, y_d computed. Least square initialization of z_L, z_U,v_L, v_U, y_c, y_d failed! take user-provided pointleast_square_init_primalleast_square_init_dualsWarm-start for initial pointoriginal varsnew varsx_lss_lszL_newzU_newvL_newvU_newyc_newyd_newN5Ipopt25DefaultIterateInitializerEN5Ipopt18IterateInitializerEIpEquilibrationScaling.cppFAILED_INITIALIZATIONError getting initial point from NLP in EquilibrationScaling. Too many evaluation failures during equilibiration-based scaling.Currently cannot do equilibration-based NLP scaling if MC19 is not available.Error evaluating first derivatives as at perturbed point for equilibration-based scaling. N5Ipopt20EquilibrationScalingEN5Ipopt14PointPerturberEN5Ipopt21FAILED_INITIALIZATIONE>N5Ipopt19ExactHessianUpdaterEThe current filter has %d entries. phi theta iter %23.16e %5d reference_theta = %e reference_gradBarrTDelta = %e reference_theta is slightly positive at feasible point. Setting it to %e The algorithmic parameter theta_max is determined as theta_max_fact times the maximum of 1 and the constraint violation at initial point. Any point with a constraint violation larger than theta_max is unacceptable to the filter (see Eqn. (21) in the implementation paper).The algorithmic parameter theta_min is determined as theta_min_fact times the maximum of 1 and the constraint violation at initial point. The switching rules treats an iteration as an h-type iteration whenever the current constraint violation is larger than theta_min (see paragraph before Eqn. (19) in the implementation paper).Determines constraint violation threshold in the switching rule.(See Eqn. (20) in the implementation paper)Relaxation factor in the Armijo condition.(See Eqn. (19) in the implementation paper.)Multiplier for constraint violation in the switching rule.Exponent for linear barrier function model in the switching rule.Exponent for current constraint violation in the switching rule.(See Eqn. (18a) in the implementation paper.)Relaxation factor in the filter margin for the barrier function.(See Eqn. (18b) in the implementation paper.)Relaxation factor in the filter margin for the constraint violation.(This is gamma_alpha in Eqn. (20) in the implementation paper.)Safety factor for the minimal step size (before switching to restoration phase).Choosing 0 disables the second order corrections. (This is p^{max} of Step A-5.9 of Algorithm A in the implementation paper.)Maximum number of second order correction trial steps at each iteration.This option determines how much a second order correction step must reduce the constraint violation so that further correction steps are attempted. (See Step A-5.9 of Algorithm A in the implementation paper.)Factor in the sufficient reduction rule for second order correction.Trial points are rejected if they lead to an increase in the barrier objective function by more than obj_max_inc orders of magnitude.Determines the upper bound on the acceptable increase of barrier objective function.A positive number enables a heuristic that resets the filter, whenever in more than "filter_reset_trigger" successive iterations the last rejected trial steps size was rejected because of the filter. This option determine the maximal number of resets that are allowed to take place.Maximal allowed number of filter resetsIf the filter reset heuristic is active and the number of successive iterations in which the last rejected trial step size was rejected because of the filter, the filter is reset.Number of iterations that trigger the filter reset.If "mu_strategy" is "adaptive", this option determines what kind of corrector steps should be tried.corrector step towards current muThe type of corrector steps that should be taken (unsupported!).The corrector step is not tried if negative curvature has been encountered during the computation of the search direction in the current iteration. This option is only used if "mu_strategy" is "adaptive".Skip the corrector step in negative curvature iteration (unsupported!).The corrector step is not tried if the algorithm is currently in the monotone mode (see also option "barrier_strategy").This option is only used if "mu_strategy" is "adaptive".Skip the corrector step during monotone barrier parameter mode (unsupported!).This option determines the factor by which complementarity is allowed to increase for a corrector step to be accepted.Complementarity tolerance factor for accepting corrector step (unsupported!).Rejecting trial point because barrier objective function increasing too rapidly (from %27.15e to %27.15e) trial_max is initialized to %e trial_min is initialized to %e trial_theta = %e is larger than theta_max = %e New values of barrier function = %23.16e (reference %23.16e): New values of constraint violation = %23.16e (reference %23.16e): Checking filter acceptability... Resetting filter because in %d iterations last rejection was due to filterFilter should be reset, but maximal number of resets already exceeded. Checking sufficient reduction... theta_min_fact_ < theta_max_fact_Option "theta_min_fact": This value must be larger than 0 and less than theta_max_fact.The linear system could not be solved for the corrector step. Solving the Primal Dual System for the affine step avrg_compl(curr) = %e, avrg_compl(trial) = %e Rejecting corrector step, because trial complementarity is too large. Warning: Corrector step rejected due to evaluation error Corrector step accepted with alpha_primal = %e *** Accepted corrector for Iteration: %d theta_min_facteta_phideltas_phis_thetagamma_phigamma_thetaalpha_min_fracobj_max_incmax_filter_resetsfilter_reset_triggerprimal-dualcorrector step towards mu=0affineno correctordon't skipskip_corr_if_neg_curvskip_corr_in_monotone_modecorrector_compl_avrg_red_factTmaxChecking Armijo Condition... Failed... Succeeded... F+F-IpFilterLSAcceptor.cppIpBacktrackingLSAcceptor.hppdelta_corrN5Ipopt16FilterLSAcceptorEIpGenAugSystemSolver.cppFATAL_ERROR_IN_LINEAR_SOLVERA fatal error occured in the linear solver.Factorization failed with retval = %d N5Ipopt18GenAugSystemSolverEN5Ipopt28FATAL_ERROR_IN_LINEAR_SOLVEREIpGradientScaling.cppnlp_scaling_obj_target_gradientnlp_scaling_constr_target_gradientThis is the gradient scaling cut-off. If the maximum gradient is above this value, then gradient based scaling will be performed. Scaling parameters are calculated to scale the maximum gradient back to this value. (This is g_max in Section 3.8 of the implementation paper.) Note: This option is only used if "nlp_scaling_method" is chosen as "gradient-based".Maximum gradient after NLP scaling.If a positive number is chosen, the scaling factor the objective function is computed so that the gradient has the max norm of the given size at the starting point. This overrides nlp_scaling_max_gradient for the objective function.Target value for objective function gradient size.If a positive number is chosen, the scaling factor the constraint functions is computed so that the gradient has the max norm of the given size at the starting point. This overrides nlp_scaling_max_gradient for the constraint functions.Target value for constraint function gradient size.Error getting initial point from NLP in GradientScaling. Gradient of objective function is zero at starting point. Cannot determine scaling factor based on scaling_obj_target_gradient option. Scaling parameter for objective function = %e Error evaluating objective gradient at user provided starting point. No scaling factor for objective function computed! Equality constraints are scaled with smallest scaling parameter is %e Equality constraints are not scaled. Equality constraints are scaled uniformly by %e Error evaluating Jacobian of equality constraints at user provided starting point. No scaling factors for equality constraints computed! Inequality constraints are scaled with smallest scaling parameter is %e Inequality constraints are not scaled. Inequality constraints are scaled uniformly by %e Error evaluating Jacobian of inequality constraints at user provided starting point. No scaling factors for inequality constraints computed! N5Ipopt15GradientScalingE ************************************************** *** Finding Acceptable Trial Point for Iteration %d: ************************************************** *** Update HessianMatrix for Iteration %d:*** Update Barrier Parameter for Iteration %d:Barrier parameter could not be updated! Total number of variables............................: %8d variables with only lower bounds: %8d variables with lower and upper bounds: %8d variables with only upper bounds: %8d Total number of equality constraints.................: %8d Total number of inequality constraints...............: %8d inequality constraints with only lower bounds: %8d inequality constraints with lower and upper bounds: %8d inequality constraints with only upper bounds: %8d This is a square problem, but multipliers cannot be recomputed at solution, since no eq_mult_calculator object is available in IpoptAlgorithm Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator If the dual variables deviate from their primal estimates, a correction is performed. (See Eqn. (16) in the implementation paper.) Setting the value to less than 1 disables the correction.Factor limiting the deviation of dual variables from primal estimates.This asks the algorithm to recompute the multipliers, whenever the current infeasibility is less than recalc_y_feas_tol. Choosing yes might be helpful in the quasi-Newton option. However, each recalculation requires an extra factorization of the linear system. If a limited memory quasi-Newton option is chosen, this is used by default.use least-square multiplier estimatesuse the Newton step to update the multipliersTells the algorithm to recalculate the equality and inequality multipliers as least square estimates.If recalc_y is chosen and the current infeasibility is less than this value, then the multipliers are recomputed.Feasibility threshold for recomputation of multipliers.If set to yes, Ipopt runs as Mehrotra's predictor-corrector algorithm. This works usually very well for LPs and convex QPs. This automatically disables the line search, and chooses the (unglobalized) adaptive mu strategy with the "probing" oracle, and uses "corrector_type=affine" without any safeguards; you should not set any of those options explicitly in addition. Also, unlessotherwise specified, the values of "bound_push", "bound_frac", and "bound_mult_init_val" are set more aggressive, and sets "alpha_for_y=bound_mult".Do Mehrotra's predictor-corrector algorithm.Indicates if we want to do Mehrotra's algorithm.Line search didn't find acceptable trial point. In iteration %d, %d Slack too small, adjusting variable bound In iteration %d, %d Slacks too small, adjusting variable bounds Some value in z_L becomes too large - maximal correction = %8.2e Some value in z_U becomes too large - maximal correction = %8.2e Some value in v_L becomes too large - maximal correction = %8.2e Some value in v_U becomes too large - maximal correction = %8.2e dual infeasisibility before least square multiplier update = %e Recalculation of y multipliers skipped because eq_mult_calc returned false. Error while obtaining initial iterates.*** Solving the Primal Dual System for Iteration %d:*** Step Calculated for Iteration: %d *** Step could not be computed in iteration %d! ****************************************************************************** This program contains Ipopt, a library for large-scale nonlinear optimization. Ipopt is released as open source code under the Common Public License (CPL). For more information visit http://projects.coin-or.org/Ipopt ****************************************************************************** NOTE: You are using Ipopt only with the MUMPS linear solver. Other linear solvers might be more efficient (see Ipopt documentation). WARNING: Problem in step computation; switching to emergency mode. ERROR: Problem in step computation, but emergency mode cannot be activated. string_option=="never-monotone-mode"If mehrotra_algorithm=yes, adaptive_mu_globalization must be "never-monotone-mode".If mehrotra_algorithm=yes, corrector_type must be "afnone".If mehrotra_algorithm=yes, accept_every_trial_step must be "yes".the IpIpoptData object failed to initialize.the IpIpoptCalculatedQuantities object failed to initialize.the IpIpoptNLP object failed to initialize.the iterate_initializer strategy failed to initialize.the mu_update strategy failed to initialize.the search_direction_calculator strategy failed to initialize.the line_search strategy failed to initialize.the conv_check strategy failed to initialize.the iter_output strategy failed to initialize.the hessian_updater strategy failed to initialize.Barrier Parameter: %e kappa_sigmaDo the usual Ipopt algorithm.sbold_x_Lold_x_Uold_d_Lold_d_Unew_x_Lnew_x_Unew_d_Lnew_d_Uzy IpIpoptAlg.cppStep computation failed.STEP_COMPUTATION_FAILEDstring_option=="none"string_option=="yes"retvalueN5Ipopt14IpoptAlgorithmEN5Ipopt23STEP_COMPUTATION_FAILEDEN5Ipopt18LOCALLY_INFEASIBLEEN5Ipopt39RESTORATION_CONVERGED_TO_FEASIBLE_POINTEN5Ipopt28RESTORATION_MAXITER_EXCEEDEDEN5Ipopt21RESTORATION_USER_STOPEN5Ipopt26FEASIBILITY_PROBLEM_SOLVEDEN5Ipopt14INTERNAL_ABORTEs_maxkappa_dslack_moveLine searchuse the infinity normuse the 2-normuse the 1-normconstraint_violation_norm_type(See paragraph after Eqn. (6) in the implementation paper.)Scaling threshold for the NLP error.(see Section 3.7 in implementation paper.)Weight for linear damping term (to handle one-sided bounds).Due to numerical issues or the lack of an interior, the slack variables might become very small. If a slack becomes very small compared to machine precision, the corresponding bound is moved slightly. This parameter determines how large the move should be. Its default value is mach_eps^{3/4}. (See also end of Section 3.5 in implementation paper - but actual implementation might be somewhat different.)Correction size for very small slacks.Determines which norm should be used when the algorithm computes the constraint violation in the line search.Norm to be used for the constraint violation in the line search.N5Ipopt25IpoptCalculatedQuantitiesEN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEEEN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEEEN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEEEN5Ipopt13CachedResultsINS_8SmartPtrIKNS_9SymMatrixEEEEEN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEEEN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEEEresto.tolDetermines the convergence tolerance for the algorithm. The algorithm terminates successfully, if the (scaled) NLP error becomes smaller than this value, and if the (absolute) criteria according to "dual_inf_tol", "primal_inf_tol", and "cmpl_inf_tol" are met. (This is epsilon_tol in Eqn. (6) in implementation paper). See also "acceptable_tol" as a second termination criterion. Note, some other algorithmic features also use this quantity to determine thresholds etc.Desired convergence tolerance (relative).N5Ipopt9IpoptDataEN5Ipopt16TimingStatisticsEN5Ipopt19IteratesVectorSpaceEN5Ipopt14IteratesVectorEN5Ipopt22LeastSquareMultipliersEN5Ipopt22EqMultiplierCalculatorE Average complemantarity is %lf Xi (distance from uniformity) is %lf Barrier parameter proposed by LOQO rule is %lf sigma=%8.2e xi=%8.2e N5Ipopt12LoqoMuOracleEN5Ipopt8MuOracleE?LowRankAugSystemSolver: SolveMultiVector returned retval = %d for V. LowRankAugSystemSolver: Cholesky for M1 returned error! LowRankAugSystemSolver: SolveMultiVector returned retval = %d for U. LowRankAugSystemSolver: Cholesky for M2 returned error. LowRankAugSystemSolver: AugSystemSolver returned retval = %d for right hand side. N5Ipopt22LowRankAugSystemSolverEN5Ipopt15DiagMatrixSpaceEN5Ipopt14SymMatrixSpaceEN5Ipopt11MatrixSpaceEIpMonotoneMuUpdate.cppmu_allow_fast_monotone_decreaseThis option determines the initial value for the barrier parameter (mu). It is only relevant in the monotone, Fiacco-McCormick version of the algorithm. (i.e., if "mu_strategy" is chosen as "monotone")Initial value for the barrier parameter.The convergence tolerance for each barrier problem in the monotone mode is the value of the barrier parameter times "barrier_tol_factor". This option is also used in the adaptive mu strategy during the monotone mode. (This is kappa_epsilon in implementation paper).Factor for mu in barrier stop test.For the Fiacco-McCormick update procedure the new barrier parameter mu is obtained by taking the minimum of mu*"mu_linear_decrease_factor" and mu^"superlinear_decrease_power". (This is kappa_mu in implementation paper.) This option is also used in the adaptive mu strategy during the monotone mode.Determines linear decrease rate of barrier parameter.For the Fiacco-McCormick update procedure the new barrier parameter mu is obtained by taking the minimum of mu*"mu_linear_decrease_factor" and mu^"superlinear_decrease_power". (This is theta_mu in implementation paper.) This option is also used in the adaptive mu strategy during the monotone mode.Determines superlinear decrease rate of barrier parameter.If set to "no", the algorithm enforces at least one iteration per barrier problem, even if the barrier test is already met for the updated barrier parameter.Allow fast decrease of mu if barrier test it metTake at least one iteration per barrier problemAllow skipping of barrier problem if barrier test is already met.(This is tau_min in the implementation paper.) This option is also used in the adaptive mu strategy during the monotone mode.Lower bound on fraction-to-the-boundary parameter tau.Optimality Error for Barrier Sub-problem = %e sub_problem_error < kappa_eps * mu (%e) Updating mu=%25.16e and tau=%25.16e to new_mu=%25.16e and new_tau=%25.16e N5Ipopt16MonotoneMuUpdateEx scaling vectorNo x scaling provided c scaling vectorNo c scaling provided d scaling vectorNo d scaling provided This option sets a scaling factor for the objective function. The scaling is seen internally by Ipopt but the unscaled objective is reported in the console output. If additional scaling parameters are computed (e.g. user-scaling or gradient-based), both factors are multiplied. If this value is chosen to be negative, Ipopt will maximize the objective function instead of minimizing it.Scaling factor for the objective function.objective scaling factor = %g N5Ipopt18NoNLPScalingObjectEN5Ipopt19StandardScalingBaseEN5Ipopt16NLPScalingObjectEN5Ipopt20SymScaledMatrixSpaceEAacceptable_iterMaximum number of iterations.The algorithm terminates with an error message if the number of iterations exceeded this number.Absolute tolerance on the dual infeasibility. Successful termination requires that the max-norm of the (unscaled) dual infeasibility is less than this threshold.Desired threshold for the dual infeasibility.Absolute tolerance on the constraint violation. Successful termination requires that the max-norm of the (unscaled) constraint violation is less than this threshold.Desired threshold for the constraint violation.Absolute tolerance on the complementarity. Successful termination requires that the max-norm of the (unscaled) complementarity is less than this threshold.Desired threshold for the complementarity conditions.Determines which (scaled) overall optimality error is considered to be "acceptable." There are two levels of termination criteria. If the usual "desired" tolerances (see tol, dual_inf_tol etc) are satisfied at an iteration, the algorithm immediately terminates with a success message. On the other hand, if the algorithm encounters "acceptable_iter" many iterations in a row that are considered "acceptable", it will terminate before the desired convergence tolerance is met. This is useful in cases where the algorithm might not be able to achieve the "desired" level of accuracy."Acceptable" convergence tolerance (relative).If the algorithm encounters this many successive "acceptable" iterates (see "acceptable_tol"), it terminates, assuming that the problem has been solved to best possible accuracy given round-off. If it is set to zero, this heuristic is disabled.Number of "acceptable" iterates before triggering termination.Absolute tolerance on the dual infeasibility. "Acceptable" termination requires that the (max-norm of the unscaled) dual infeasibility is less than this threshold; see also acceptable_tol."Acceptance" threshold for the dual infeasibility.Absolute tolerance on the constraint violation. "Acceptable" termination requires that the max-norm of the (unscaled) constraint violation is less than this threshold; see also acceptable_tol."Acceptance" threshold for the constraint violation.Absolute tolerance on the complementarity. "Acceptable" termination requires that the max-norm of the (unscaled) complementarity is less than this threshold; see also acceptable_tol."Acceptance" threshold for the complementarity conditions.If any component of the primal iterates exceeded this value (in absolute terms), the optimization is aborted with the exit message that the iterates seem to be diverging.Threshold for maximal value of primal iterates.N5Ipopt31OptimalityErrorConvergenceCheckEN5Ipopt16ConvergenceCheckEu<7~Function Evaluations................: %10.3f Objective function.................: %10.3f Equality constraints...............: %10.3f Inequality constraints.............: %10.3f Equality constraint Jacobian.......: %10.3f Inequality constraint Jacobian.....: %10.3f Lagrangian Hessian.................: %10.3f Before start of the optimization, the bounds given by the user are relaxed. This option sets the factor for this relaxation. If it is set to zero, then then bounds relaxation is disabled. (See Eqn.(35) in implementation paper.)Factor for initial relaxation of the bounds.Ipopt might relax the bounds during the optimization (see, e.g., option "bound_relax_factor"). This option determines whether the final point should be projected back into the user-provide original bounds after the optimization.Project final point back into original boundsIndicates whether final points should be projected into original bounds.If "yes" is chosen, then the algorithm assumes that an NLP is now to be solved, whose strcture is identical to one that already was considered (with the same NLP object).Assume this is problem has known structureIndicates whether a problem with a structure identical to the previous one is to be solved.Activating this option will cause an error if an invalid number is detected in the constraint Jacobians or the Lagrangian Hessian. If this is not activated, the test is skipped, and the algorithm might proceed with invalid numbers and fail.Check Jacobians and Hessian for Nan and Inf.Indicates whether it is desired to check for Nan/Inf in derivative matricesActivating this option will cause Ipopt to ask for the Jacobian of the equality constraints only once from the NLP and reuse this information later.Assume that equality constraints Jacobian are constantDon't assume that all equality constraints are linearIndicates whether all equality constraints are linearActivating this option will cause Ipopt to ask for the Jacobian of the inequality constraints only once from the NLP and reuse this information later.Don't assume that all inequality constraints are linearIndicates whether all inequality constraints are linearActivating this option will cause Ipopt to ask for the Hessian of the Lagrangian function only once from the NLP and reuse this information later.Assume that Hessian is constantIndicates whether the problem is a quadratic problemThis determines which kind of information for the Hessian of the Lagrangian function is used by the algorithm.Perform a limited-memory quasi-Newton approximationUse second derivatives provided by the NLP.Indicates what Hessian information is to be used.in space of all variables (without slacks)only in space of nonlinear variables.Indicates in which subspace the Hessian information is to be approximated.ERROR: This method is only a for h(mu) and should not be calledError evaluating the hessian of the lagrangianError evaluating the jacobian of the inequality constraintsError evaluating the jacobian of the equality constraintsThe Jacobian for the equality constraints contains an invalid number The Jacobian for the equality constraints contains an invalid numbersuccess && IsFiniteNumber(unscaled_d->Nrm2())Error evaluating the inequality constraintssuccess && IsFiniteNumber(unscaled_c->Nrm2())Error evaluating the equality constraintsERROR: This method is only a placeholder for grad_f(mu) and should not be calledsuccess && IsFiniteNumber(unscaled_grad_f->Nrm2())Error evaluating the gradient of the objective functionsuccess && IsFiniteNumber(ret)Error evaluating the objective functionGetSpaces method for the NLP returns false. Hessian approximation will be done in smaller space of dimension %d (instead of %d) Hessian approximation will be done in the space of all %d x variables. Too few degrees of freedom: %d equality constriants but only %d variablesToo few degrees of freedom (no free variables)!OrigIpoptNLP called with warm_start_same_structure, but the problem is solved for the first time.hessian_approximation_spaceLeave final point unchangedAssume this is a new problem.Don't check (faster).Assume that Hessian changeslimited-memoryexactall-variablesnonlinear-variablesfinal x unscaledfinal y_c unscaledfinal y_d unscaledfinal z_L unscaledfinal z_U unscaledIpOrigIpoptNLP.cppINTERNAL_ABORTsuccessx_space_->Dim() > 0IsValid(x_space_)original x_L unscaledoriginal x_U unscaledoriginal d_L unscaledoriginal d_U unscaledmodified x_L scaledmodified x_U scaledmodified d_L scaledmodified d_U scaledinitial x unscaledinitial y_c unscaledinitial y_d unscaledinitial z_L unscaledinitial z_U unscaledN5Ipopt12OrigIpoptNLPEN5Ipopt8IpoptNLPEN5Ipopt27LowRankUpdateSymMatrixSpaceEiter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls ************************************************** %4d%c%14.7e %7.2e %7.2e %5.1f %7.2e %5s %7.2e %7.2e%c%3d*** Beginning Iteration %d from the following point:Current barrier parameter mu = %21.16e Current fraction-to-the-boundary parameter tau = %21.16e No search direction has been computed yet. ***Current NLP Values for Iteration %d: This string contains some insider information about the current iteration.print string at end of each iteration outputEnables printing of additional info string at end of iteration output.*** Summary of Iteration: %d:%5.1f||curr_x||_inf = %.16e ||curr_s||_inf = %.16e ||curr_y_c||_inf = %.16e ||curr_y_d||_inf = %.16e ||curr_z_L||_inf = %.16e ||curr_z_U||_inf = %.16e ||curr_v_L||_inf = %.16e ||curr_v_U||_inf = %.16e ||delta_x||_inf = %.16e ||delta_s||_inf = %.16e ||delta_y_c||_inf = %.16e ||delta_y_d||_inf = %.16e ||delta_z_L||_inf = %.16e ||delta_z_U||_inf = %.16e ||delta_v_L||_inf = %.16e ||delta_v_U||_inf = %.16e curr_xcurr_y_ccurr_y_dcurr_slack_x_Lcurr_slack_x_Ucurr_z_Lcurr_z_Ucurr_slack_s_Lcurr_slack_s_Ucurr_v_Lcurr_v_Ucurr_grad_lag_xcurr_grad_lag_sgrad_fcurr_dcurr_d - curr_sjac_cjac_d - print_info_stringdon't print stringN5Ipopt19OrigIterationOutputEN5Ipopt15IterationOutputE Skip the update. Perform the update. IsValid(h_space_)curr_eta (for B0) is %e WrWSWesigma (for B0) is %e WpWbWslimited_memory_update_typelimited_memory_initializationlimited_memory_init_valSR1 (not working well)sr1BFGS update (with skipping)bfgssigma = y^Ty/s^Tyscalar2sigma = s^Ty/s^Tsscalar1Limited-Memory test for skipping: s^Ty = %e snrm = %e ynrm = %e Eigenvalues in SR1 update: emin=%e emax=%e ratio=%e Limited-memory quasi-Newton option chosen, but NLP doesn't provide LowRankUpdateSymMatrixSpace.IpLimMemQuasiNewtonUpdater.cppLimited-Memory approximation started; store data at current iterate. Resetting Limited-Memory Update. In limited-memory update, s_new_max is %e Skipping BFGS update in restoration phase because Dmin is %e Projecting sigma into safeguards to be %e! Cholesky factorization failed for LBFGS update! Skipping update. Undoing most recent SR1 update. Eigenvalue decomposition failed for limited-memory SR1 update.Number of successive iterations with skipping: %d This option determines the number of most recent iterations that are taken into account for the limited-memory quasi-Newton approximation.Maximum size of the history for the limited quasi-Newton Hessian approximation.Determines which update formula is to be used for the limited-memory quasi-Newton approximation.Quasi-Newton update formula for the limited memory approximation.Determines how the diagonal Matrix B_0 as the first term in the limited memory approximation should be computed.sigma = limited_memory_init_valInitialization strategy for the limited memory quasi-Newton approximation.The starting matrix in the low rank update, B0, is chosen to be this multiple of the identity in the first iteration (when no updates have been performed yet), and is constantly chosen as this value, if "limited_memory_initialization" is "constant".Value for B0 in low-rank update.If the update is skipped more than this number of successive iterations, we quasi-Newton approximation is reset.Threshold for successive iterations where update is skipped.N5Ipopt24LimMemQuasiNewtonUpdaterE-q=Iterative refinement (on the full unsymmetric system) is performed for each right hand side. This option determines the minimum number of iterative refinements (i.e. at least "min_refinement_steps" iterative refinement steps are enforced per right hand side.)Minimum number of iterative refinement steps per linear system solve.Iterative refinement (on the full unsymmetric system) is performed for each right hand side. This option determines the maximum number of iterative refinement steps.Maximum number of iterative refinement steps per linear system solve.Iterative refinement is performed until the residual test ratio is less than this tolerance (or until "max_refinement_steps" refinement steps are performed).Iterative refinement toleranceIf the residual test ratio is larger than this value after failed iterative refinement, the algorithm pretends that the linear system is singular.Threshold for declaring linear system singular after failed iterative refinement.If the improvement of the residual test ratio made by one iterative refinement step is not better than this factor, iterative refinement is aborted.Minimal required reduction of residual test ratio in iterative refinement.If positive, incorrect inertia in the augmented system is ignored, and we test if the direction is a direction of positive curvature. This tolerance determines when the direction is considered to be sufficiently positive.Tolerance for heurstic to ignore wrong inertia.nrm_rhs = %8.2e nrm_sol = %8.2e nrm_resid = %8.2e max_refinement_steps_ >= min_refinement_steps_Option "max_refinement_steps": This value must be larger than or equal to min_refinement_steps (default 1)residual_ratio_singular_ >= residual_ratio_max_Option "residual_ratio_singular": This value must be not smaller than residual_ratio_max.Solving system with delta_x=%e delta_s=%e delta_c=%e delta_d=%e PerturbForSingularity can't be done Number of negative eigenvalues too small! Asking augmented system solver to improve quality of its solutions. Quality could not be improved PerturbForSingularity can't be done for assume singular. PerturbForWrongInertia can't be done for wrong interia or singular. In inertia heuristic: xWx = %e xx = %e -> Redo with modified matrix. PerturbForWrongInertia can't be done for inertia heuristic. Number of trial factorizations performed: %d Perturbation parameters: delta_x=%e delta_s=%e delta_c=%e delta_d=%e SolveOnce returns false during iterative refinement.Iterative refinement failed with residual_ratio = %e Just accept current solution. Pretend that the current system (including modifications) is singular. residual_ratio_maxresidual_ratio_singularresidual_improvement_factorneg_curv_test_tolIpPDFullSpaceSolver.cppqresidmax-norm resid_x %e max-norm resid_s %e max-norm resid_c %e max-norm resid_d %e max-norm resid_zL %e max-norm resid_zU %e max-norm resid_vL %e max-norm resid_vU %e residual_ratio = %e solve_retvalN5Ipopt17PDFullSpaceSolverEN5Ipopt14PDSystemSolverEN5Ipopt15DependentResultIPvEEN5Ipopt13CachedResultsIPvEEalways use perturbationIn order to guarantee that the search directions are indeed proper descent directions, Ipopt requires that the inertia of the (augmented) linear system for the step computation has the correct number of negative and positive eigenvalues. The idea is that this guides the algorithm away from maximizers and makes Ipopt more likely converge to first order optimal points that are minimizers. If the inertia is not correct, a multiple of the identity matrix is added to the Hessian of the Lagrangian in the augmented system. This parameter gives the maximum value of the regularization parameter. If a regularization of that size is not enough, the algorithm skips this iteration and goes to the restoration phase. (This is delta_w^max in the implementation paper.)Maximum value of regularization parameter for handling negative curvature.The size of the perturbation of the Hessian block is never selected smaller than this value, unless no perturbation is necessary. (This is delta_w^min in implementation paper.)Smallest perturbation of the Hessian block.The factor by which the perturbation is increased when a trial value was not sufficient - this value is used for the computation of the very first perturbation and allows a different value for for the first perturbation than that used for the remaining perturbations. (This is bar_kappa_w^+ in the implementation paper.)Increase factor for x-s perturbation for very first perturbation.The factor by which the perturbation is increased when a trial value was not sufficient - this value is used for the computation of all perturbations except for the first. (This is kappa_w^+ in the implementation paper.)Increase factor for x-s perturbation.The factor by which the perturbation is decreased when a trial value is deduced from the size of the most recent successful perturbation. (This is kappa_w^- in the implementation paper.)Decrease factor for x-s perturbation.The first value tried for the x-s perturbation in the inertia correction scheme.(This is delta_0 in the implementation paper.)Size of first x-s perturbation tried.(This is bar delta_c in the implementation paper.)Size of the regularization for rank-deficient constraint Jacobians.(This is kappa_c in the implementation paper.)Exponent for mu in the regularization for rank-deficient constraint Jacobians.This options makes the delta_c and delta_d perturbation be used for the computation of every search direction. Usually, it is only used when the iteration matrix is singular.perturbation only used when requiredActive permanent perturbation of constraint linearization.N5Ipopt21PDPerturbationHandlerEfast_step_computationIf set to yes, the algorithm assumes that the linear system that is solved to obtain the search direction, is solved sufficiently well. In that case, no residuals are computed, and the computation of the search direction is a little faster.Trust that linear systems are solved well.Verify solution of linear system by computing residuals.Indicates if the linear system should be solved quickly.N5Ipopt21PDSearchDirCalculatorEsigma_maxThe linear system could not be solved for the affine step! The affine maximal step sizes are alpha_primal_aff = %23.16e alpha_dual_aff = %23.16e The average complementariy at the affine step is %23.16e The average complementariy at the current point is %23.16e N5Ipopt15ProbingMuOracleEsigma = %8.2e d_inf = %18.12e p_inf = %18.12e cmpl = %18.12e q = %18.12e a_pri = %8.2e a_dual = %8.2e xi = %8.2e Solving the Primal Dual System for the centering step The linear system could not be solved for the centering step! quality_function_section_sigma_tolquality_function_section_qf_tolThis is the upper bound for the centering parameter chosen by the quality function based barrier parameter update. (Only used if option "mu_oracle" is set to "quality-function".)Maximum value of the centering parameter.This is the lower bound for the centering parameter chosen by the quality function based barrier parameter update. (Only used if option "mu_oracle" is set to "quality-function".)Minimum value of the centering parameter.Norm used for components of the quality function.This determines whether a term is added to the quality function to penalize deviation from centrality with respect to complementarity. The complementarity measure here is the xi in the Loqo update rule. (Only used if option "mu_oracle" is set to "quality-function".)complementarity * the reciprocal of the centrality measure cubedcomplementarity * the reciprocal of the centrality measurecomplementarity * the log of the centrality measureThe penalty term for centrality that is included in quality function.This determines whether a term is added to the quality function that penalizes situations where the complementarity is much smaller than dual and primal infeasibilities. (Only used if option "mu_oracle" is set to "quality-function".)Max(0,Max(dual_inf,primal_inf)-compl)^3The balancing term included in the quality function for centrality.The golden section search is performed for the quality function based mu oracle. (Only used if option "mu_oracle" is set to "quality-function".)Maximum number of search steps during direct search procedure determining the optimal centering parameter.Tolerance for the section search procedure determining the optimal centering parameter (in sigma space).Tolerance for the golden section search procedure determining the optimal centering parameter (in the function value space).qf_tol Sigma = %e qf=%dsigma_mincubed-reciprocallogno penalty term is addedcubicno balancing term is addedN5Ipopt23QualityFunctionMuOracleE`-!r?P/7?@The restoration phase algorithm is performed, until a point is found that is acceptable to the filter and the infeasibility has been reduced by at least the fraction given by this option.Required reduction of infeasibility before leaving restoration phase.The algorithm terminates with an error message if the number of iterations successively taken in the restoration phase exceeds this number.Maximum number of successive iterations in restoration phase.More than %d successive iterations taken in restoration phase. orig_curr_theta = %8.2e, orig_trial_theta = %8.2e This is the first iteration - continue to take at least one step. Point does not provide sufficient reduction w.r.t the original theta (orig_theta_max=%e). Restoration phase found points satisfying feasibility tolerance in square problem. Point is not acceptable to the original filter. Point is not acceptable to the original current point. Restoration found a point that provides sufficient reduction in theta and is acceptable to the current filter. Tightening restoration phase tolerance to %e. Restoration phase converged to a feasible point that is unacceptable to the filter for the original problem. Restoration phase converged to a feasible point that is unacceptable to the filter for the original problem.RESTORATION_CONVERGED_TO_FEASIBLE_POINTRestoration phase converged to a point of local infeasibilitymax_resto_iterorig_trial_barr = %8.2e IpRestoFilterConvCheck.cppLOCALLY_INFEASIBLEN5Ipopt27RestoFilterConvergenceCheckEERROR: In RestoIpoptNLP f() is called without mu!Setting this option to "yes" makes the restoration phase algorithm evaluate the objective function of the original problem at every trial point encountered during the restoration phase, even if this value is not required. In this way, it is guaranteed that the original objective function can be evaluated without error at all accepted iterates; otherwise the algorithm might fail at a point where the restoration phase accepts an iterate that is good for the restoration phase problem, but not the original problem. On the other hand, if the evaluation of the original objective is expensive, this might be costly.Determines if the original objective function should be evaluated at restoration phase trial points.ERROR: In RestoIpoptNLP grad_f() is called without mu!IpRestoIpoptNLP.cppevaluate at every trial pointskip evaluationN5Ipopt13RestoIpoptNLPEN5Ipopt19IdentityMatrixSpaceEInitial barrier parameter resto_mu = %e N5Ipopt23RestoIterateInitializerE*** Summary of Iteration %d for original NLP:Primal infeasibility for restoration phase problem = %.16e Dual infeasibility for restoration phase problem = %.16e ***Current NLP Values for Iteration (Restoration phase problem) %d: N5Ipopt20RestoIterationOutputEAfter returning from the restoration phase, the bound multipliers are updated with a Newton step for complementarity. Here, the change in the primal variables during the entire restoration phase is taken to be the corresponding primal Newton step. However, if after the update the largest bound multiplier exceeds the threshold specified by this option, the multipliers are all reset to 1.Threshold for resetting bound multipliers after the restoration phase.After returning from the restoration phase, the constraint multipliers are recomputed by a least square estimate. This option triggers when those least-square estimates should be ignored.Threshold for resetting equality and inequality multipliers after restoration phase.Starting Restoration Phase for the %d. time resto.required_infeasibility_reductionOptimal Objective Value = %.16E Restoration phase converged to a point with small primal infeasibility. Restoration phase converged to a point with small primal infeasibilityMaximal number of iterations exceeded in restoration phase.Restoration phase in the restoration phase failed. Restoration phase in the restoration phase failed.User requested stop during restoration phaseRecursive restoration phase algorithm termined successfully for square problem. Restoration phase converged to sufficiently feasible point of original square problem.Step size for bound multipliers: %8.2e Bound multipliers after restoration phase too large (max=%8.2e). Set all to 1. resto.start_with_restoresto.theta_max_fact RESTORATION PHASE RESULTS Optimal solution found! Number of Iterations = %d currIpRestoMinC_1Nrm.cppRESTORATION_MAXITER_EXCEEDEDRESTORATION_USER_STOPSorry, things failed ?!?! FEASIBILITY_PROBLEM_SOLVEDN5Ipopt25MinC_1NrmRestorationPhaseEN5Ipopt16RestorationPhaseEMbP?Performing second level restoration phase for current constriant violation %8.2e N5Ipopt21RestoRestorationPhaseEIsValid(augmented_system_)IpStdAugSystemSolver.cppRHS[%2d]KKT******* KKT SYSTEM ******* (%d) KKT[%d][%d] = %23.15e Factorization successful. SOL[%2d]StdAugSystemSolver called with warm_start_same_structure, but augmented system is not initialized.N5Ipopt18StdAugSystemSolverEOverallAlgorithm....................: %10.3f PrintProblemStatistics.............: %10.3f InitializeIterates.................: %10.3f UpdateHessian......................: %10.3f OutputIteration....................: %10.3f UpdateBarrierParameter.............: %10.3f ComputeSearchDirection.............: %10.3f ComputeAcceptableTrialPoint........: %10.3f AcceptTrialPoint...................: %10.3f CheckConvergence...................: %10.3f PDSystemSolverTotal.................: %10.3f PDSystemSolverSolveOnce............: %10.3f ComputeResiduals...................: %10.3f LinearSystemScaling................: %10.3f LinearSystemSymbolicFactorization..: %10.3f LinearSystemFactorization..........: %10.3f LinearSystemBackSolve..............: %10.3f LinearSystemStructureConverter.....: %10.3f LinearSystemStructureConverterInit: %10.3f QualityFunctionSearch...............: %10.3f TryCorrector........................: %10.3f Task1...............................: %10.3f Task2...............................: %10.3f Task3...............................: %10.3f Task4...............................: %10.3f Task5...............................: %10.3f N5Ipopt11UserScalingEwarm_start_target_muwarm_start_entire_iterateUnsupported!NWuser-provided y_cuser-provided y_duser-provided z_Luser-provided z_Ud at user-provided xinitial xinitial sinitial y_cinitial y_dinitial z_Linitial z_Uinitial v_Linitial v_Uinitial slack_x_Linitial slack_x_Uinitial slack_s_Linitial slack_s_Usame as bound_push for the regular initializer.same as bound_frac for the regular initializer.same as slack_bound_push for the regular initializer.same as slack_bound_frac for the regular initializer.same as mult_bound_push for the regular initializer.Maximum initial value for the equality multipliers.call GetWarmStartIterate in the NLPcall GetStartingPoint in the NLPTells algorithm whether to use the GetWarmStartIterate method in the NLP.Tried to obtain entire warm start iterate from NLP, but it returned false. N5Ipopt27WarmStartIterateInitializerEN5Ipopt21TripletToCSRConverterEtsym_linear_solver_->ProvidesDegeneracyDetection()Selected linear solver does not support dependency detectionIpTSymDependencyDetector.cppN5Ipopt22TSymDependencyDetectorEN5Ipopt19TDependencyDetectorESwitching on scaling of the linear system (on demand). TSymLinearSolver called with warm_start_same_structure, but the problem is solved for the first time.TSymLinearSolver called with warm_start_same_structure, but the internal structures are not initialized.This option is only important if a linear scaling method (e.g., mc19) is used. If you choose "no", then the scaling factors are computed for every linear system from the start. This can be quite expensive. Choosing "yes" means that the algorithm will start the scaling method only when the solutions to the linear system seem not good, and then use it until the end.Start using linear system scaling if solutions seem not good.Always scale the linear system.Flag indicating that linear scaling is only done if it seems required.Error during computation of scaling factors. scaling_method_->ComputeSymTScalingFactors returned false.ERROR_IN_LINEAR_SCALING_METHODscaling factor[%6d] = %22.17e Right hand side %d in TSymLinearSolver: Solution %d in TSymLinearSolver: Mcdim_==sym_A.Dim()IpTSymLinearSolver.cpphave_structure_Trhs[%5d,%5d] = %23.16e Tsol[%5d,%5d] = %23.16e N5Ipopt16TSymLinearSolverEN5Ipopt15SymLinearSolverEN5Ipopt30ERROR_IN_LINEAR_SCALING_METHODEIncreasing pivot tolerance for MUMPS from %7.2e MUMPS returned INFO(1) = %d and requires more memory, reallocating. Attempt %d Increasing icntl[13] from %d to MUMPS was not able to obtain enough memory. MUMPS returned INFO(1) = %d matrix is singular. In MumpsSolverInterface::Factorization: negevals_ = %d, but numberOfNegEVals = %d MUMPS returned INFO(1) =%d - out or memory. MUMPS returned INFO(1) =%d MUMPS failure. MUMPS used permuting_scaling %d and pivot_order %d. scaling will be %d. Error=%d returned from MUMPS in Factorization. Error=%d returned from MUMPS in Solve. mumps_->n==dim && mumps_->nz==nonzerosMumpsSolverInterface called with warm_start_same_structure, but the problem size has changed.Option "mumps_pivtolmax": This value must be between mumps_pivtol and 1.MumpsSolverInterface called with warm_start_same_structure, but the problem is solved for the first time.A smaller number pivots for sparsity, a larger number pivots for stability. This option is only available if Ipopt has been compiled with MUMPS.Pivot tolerance for the linear solver MUMPS.Ipopt may increase pivtol as high as pivtolmax to get a more accurate solution to the linear system. This option is only available if Ipopt has been compiled with MUMPS.Maximum pivot tolerance for the linear solver MUMPS.In MUMPS when significant extra fill-in is caused by numerical pivoting, larger values of mumps_mem_percent may help use the workspace more efficiently.Percentage increase in the estimated working space for MUMPS.Controls permuting and scaling in MUMPSWhen MUMPS is used to determine linearly dependent constraints, this is determines the threshold for a pivot to be considered zero. This is CNTL(3) in MUMPS.Pivot threshold for detection of linearly dependent constraints in MUMPS.to %7.2e. %d. IpMumpsSolverInterface.cpppivtolmax_>=pivtol_mumps_dep_tolmumps_->n>0 && mumps_->nz>0This is ICTL(6) in MUMPS.This is ICTL(7) in MUMPS.Controls pivot order in MUMPSThis is ICTL(8) in MUMPS.Controls scaling in MUMPSN5Ipopt20MumpsSolverInterfaceEN5Ipopt30SparseSymLinearSolverInterfaceEstderrw+N5Ipopt13StreamJournalEN5Ipopt11FileJournalEN5Ipopt7JournalEN5Ipopt10JournalistE# times usedValueName%40s %-20s %s %40s = %-20s %6d %40s = %-20s %4s IpOptionsList.cpp String UnknownOption "" found. IntegerfalseofftrueTried to set Option: Setting: " to a value of "%gRead Option: resultIPOPT tried to get the value of Option: . It is not a valid registered option.. It is a valid option, but it is of type , not of type Integer. Please check the documentation for options.": Integer value expected, but non-integer value ", not of type Number. Please check the documentation for options.": Double value expected, but non-numeric value ", not of type String. Please check the documentation for options.Tried to get a boolean from an option and failed.. It is not a valid option. Please check the list of available options. , not of type Integer. Please check the documentation for options. is not a valid setting for Option: . Check the option documentation. WARNING: Tried to set option "", but the previous value is set to disallow clobbering. The setting will remain as: ", not of type Number. Please check the documentation for options. , not of type String. Please check the documentation for options. Start reading options from stream. Finished reading options from file. Error reading value for tag %s from file. Adding option "%s" with value "%s" to OptionsList. . It is not a valid option. Check the list of available options.Error setting string value read from option file.": Double value expected, but non-numeric option value "Error setting numeric value read from file.": Integer value expected, but non-integer option value "Error setting integer value read from option file.Error setting value read from option file.OPTION_ALREADY_REGISTERED%-30s%10g-inf%10s(%11g)%-10g +inf%10d <= %10s < (%11d) <= %-10d < %-10s ("%s") Possible values: - %-23s [] Real Number < (%g)%g +inf <= (%d) <= Valid Settings: %s (%s) Default: "%s" \cdot 10^{\_\^\paragraph{%s:} %s $\;$ \\ {\tt -inf}{\tt %s }{\tt +inf}%d \le %s < \le %d < %s\\ Possible values: \begin{itemize} \item %s: \end{itemize} basic_string::substr ### %s ### i->value_ != "*"IpRegOptions.cppmatched_setting != -1 in option: \subsection{%s} The option: ### %s (%s) ### Category: %s Description: %s The valid range for this real option is $$ and its default value is $%s$. The valid range for this integer option is $$ and its default value is $%d$. The default value for this string option is "%s". Cannot map a wildcard setting to an enumerationCould not find a match for setting ERROR_CONVERTING_STRING_TO_ENUMregistered_options_.find(name) == registered_options_.end() has already been registered by someone elseN5Ipopt17RegisteredOptions25OPTION_ALREADY_REGISTEREDEN5Ipopt16RegisteredOptionEN5Ipopt16RegisteredOption31ERROR_CONVERTING_STRING_TO_ENUME%s[%2d][%2d]%sCompoundMatrix "%s" with %d row and %d columns components: %sComponent for row %d and column %d: %sComponent has not been set. N5Ipopt14CompoundMatrixEN5Ipopt19CompoundMatrixSpaceE%s[%d][%d]%sCompoundSymMatrix "%s" with %d rows and columns components: %sThis component has not been set. N5Ipopt17CompoundSymMatrixEN5Ipopt22CompoundSymMatrixSpaceE%sComponent %d: %s[%2d]%sCompoundVector "%s" with %d components: %sComponent %d is not yet set! N5Ipopt19CompoundVectorSpaceEN5Ipopt14CompoundVectorE%s%s[%5d,%5d]=%23.16e %sDenseGenMatrix "%s" with %d rows and %d columns: The matrix has not yet been initialized! N5Ipopt14DenseGenMatrixEN5Ipopt19DenseGenMatrixSpaceE%sDenseSymMatrix "%s" of dimension %d (only lower triangular part printed): N5Ipopt14DenseSymMatrixEN5Ipopt19DenseSymMatrixSpaceE%sDenseVector "%s" with %d elements: %sHomogeneous vector, all elements have value %23.16e %s%s[%5d]=%23.16e %sUninitialized! N5Ipopt11DenseVectorE%sDiagonal elements not set! %sDiagMatrix "%s" with %d rows and columns, and with diagonal elements: N5Ipopt10DiagMatrixE%s%s[%5d,%5d]=%23.16e (%d) %sExpansionMatrix "%s" with %d nonzero elements: N5Ipopt15ExpansionMatrixEN5Ipopt20ExpansionMatrixSpaceE%sIdentityMatrix "%s" with %d rows and columns and the factor %23.16e. N5Ipopt14IdentityMatrixE%sDiagonal matrix: -D%sDiagonal matrix not set! -V%sV matrix not set! -U%sU matrix not set! %sLowRankUpdateSymMatrix "%s" with %d rows and columns: %sThis matrix has reduced diagonal. %sThis matrix has full diagonal. %sMultiVectorMatrix V for positive update: %sMultiVectorMatrix U for positive update: N5Ipopt22LowRankUpdateSymMatrixEN5Ipopt6MatrixE%sMultiVectorMatrix "%s" with %d columns: %sVector in column %d is not yet set! N5Ipopt22MultiVectorMatrixSpaceE_row_scalingRowScaling is NULL _unscaled_matrix%sunscaled matrix is NULL _column_scaling%sColumnScaling is NULL %sScaledMatrix "%s" of dimension %d x %d: N5Ipopt12ScaledMatrixEN5Ipopt17ScaledMatrixSpaceETerm: %d%sSumMatrix "%s" of dimension %d x %d with %d terms: %sTerm %d with factor %23.16e and the following matrix: N5Ipopt14SumMatrixSpaceEN5Ipopt9SumMatrixE%sSumSymMatrix "%s" of dimension %d with %d terms: N5Ipopt17SumSymMatrixSpaceEN5Ipopt12SumSymMatrixEN5Ipopt9SymMatrixE_row_col_scaling%sSymScaledMatrix "%s" of dimension %d x %d: N5Ipopt15SymScaledMatrixE%sZeroMatrix "%s" with %d row and %d columns components: N5Ipopt10ZeroMatrixE%sGenTMatrix "%s" of dimension %d by %d with %d nonzero elements: N5Ipopt10GenTMatrixEN5Ipopt15GenTMatrixSpaceE%sSymTMatrix "%s" of dimension %d with %d nonzero elements: N5Ipopt15SymTMatrixSpaceEN5Ipopt10SymTMatrixEUnknown matrix type passed to TripletHelper::GetNumberEntriesUnknown matrix type passed to TripletHelper::FillRowColUnknown vector type passed to TripletHelper::FillValuesUnknown matrix type passed to TripletHelper::FillValuesUnknown vector type passed to TripletHelper::PutValuesInVectorIpTripletHelper.cppUNKNOWN_MATRIX_TYPEUNKNOWN_VECTOR_TYPEN5Ipopt13TripletHelper19UNKNOWN_VECTOR_TYPEEN5Ipopt13TripletHelper19UNKNOWN_MATRIX_TYPEE;3_d3303$3p3440t44P445t5@6660L777`78P8880p::0:@1<; 4L<J<<=P\=@==P5Й5`46h6p7880,9`9099P9<:|;;P;`<`>p>0>H>`> x>0>@>>>8?Эx?@?p?`@@8@вl@@@p@ A`8A dA`|AApA`BLBB0BB0,C``CDp8DhDDE(C(C`)D,TEp,|E,E0-F-0FP.LF.hF/F0F 3G34G4hG4G 9Gp;HI@IHIpQISI@UJPWHJ0aJ`lJwUFU0TPUQVLQZR\R^S_Xp?pq`BrE0sJpsQsXs\4tPattftkt0q4uPmlmm$np\nxnn0nPLoТoPpФpp,q@q@q r@rrPrPxuuu vPt"@"D"G$#@I#I#`J $J@$PO$PP$Q*Q*R+@R8+0S\+ T+PU+ V+W+pW,W4,@XX,X|,X,Y,`Y,Y-Y-Y8-ZP- Z-[(.\(/`0`0@b 10c`1h1@l2m3n3`o4pX4@t4x4@z@55І6`6h-`-h...l///0,0l0 1$2`d22$3d35056D67(7@7707 7(8l88 90`99@d: :P:$;;; <0X78 9$:d;h<<P<=`H===>PH>>> ?H?? ? @D@@@@@AAAB@BBB@C@CC C4DptDD`D4EptEEEL<@ |@ A!C "4FP"LF"tF$F@$F$ G &LG'Gp(G) H *LH +H-H@. 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pUD$D$ ED$ED$E$h$[]Ít&'USE HpD$D$ ED$ED$E$h[]USEpūPHT$L$$`[]Ð&USEoÅHPL$T$$S`[]Í'USEoE$1j[]ÍUSEXo$j[]Ít&'USE(o$h[]ÍUSEnõ$h[]ÐUE E]ÐUE]Ít&UE]@Ðt&UE]@Ðt&UEE s ]Í]ÍUE U@Z]É'UE U@Z]É'UE Ez]Ð&USE mõD$E$xb[]fUE ؉‹Et]USEme$h[]Ít&'USExm5$\[]Ít&'USEHm$][]Ít&'USEmը$[[]Ít&'USElå$ac[]Ít&'USElu$![[]Ít&'U8E U]l@uu\$@\$ $K_EE^]u]ÍvUU ]uu0l$BD$"gV]u]ÍvUSEkõ$a[]Ít&'U8E U]kÀuu\$@\$ $[`EE^]u]ÍvUU ]uupk-$BD$hV]u]ÍvUSE8k$f[]Ít&'USEkŦ$1b[]Ít&'USEjÕ$i[]Ít&'USEje$Y[]Ít&'USExj5$^[]Ít&'USEHj$b[]Ít&'USEjե$A\[]Ít&'U8E U]iàuu\$@\$ $ _EE^]u]ÍvUU ]uuiM$BD$RWV]u]ÍvUE]Ít&USEHi$a[]Ít&'U8E U]iФuu\$@\$ $`EE^]u]ÍvUU ]uuh}$BD$]V]u]ÍvUSEhE$!g[]Ít&'U8E U]Shuu\$@\$ $[YEE^]u]ÍvUU ]uuhý$BD$b[V]u]ÍvUSEgÅ$qU[]Ít&'USUgU$BD$ZY[]ÐUY[ + %s addr2line-e-f-s Backtrace for this error: *_gfortranmain_start_gfortrani_handlerlibgfortran.solibgfortran.dyliblibgfortran.aMAIN__ + in the main program + function %s (0x%s) from file %s at line %d of file %s pstack** Something went wrong while running addr2line. ** ** Falling back to a simpler backtrace scheme. ** Invalid value for maximum subrecord lengthInvalid value for record marker Program received signal %d (%s): %s. Illegal instructionSIGILLBus errorSIGBUSFloating-point exceptionSIGFPE Program received signal %d. Segmentation faultSIGSEGVGNU Fortran 95 runtime library version UNKNOWN --help Print this list Unit number that will be preconnected to standard input (No preconnection if negative)Unit number that will be preconnected to standard output (No preconnection if negative)Unit number that will be preconnected to standard error (No preconnection if negative)Sends library output to standard error instead of standard output.Directory for scratch files. Overrides the TMP environment variable If TMP is not set /tmp is used.If TRUE, all output is unbuffered. This will slow down large writes but can be useful for forcing data to be displayed immediately.GFORTRAN_UNBUFFERED_PRECONNECTEDIf TRUE, output to preconnected units is unbuffered.If TRUE, print filename and line number where runtime errors happen.Print optional plus signs in numbers where permitted. Default FALSE.Default maximum record length for sequential files. Most useful for adjusting line length of preconnected units. Default 1073741824Separator to use when writing list output. May contain any number of spaces and at most one comma. Default is a single space.Set format for unformatted filesDump a core file (if possible) on runtime errorPrint out a backtrace (if possible) on runtime errorEnvironment variables: ---------------------- Integer Boolean String %s Runtime error codes: -------------------- %d %s Command line arguments: DefaultSet Bad %s "%s" NoYes%s %s %s %d big_endianlittle_endiannativeswapGFORTRAN_STDIN_UNITGFORTRAN_STDOUT_UNITGFORTRAN_STDERR_UNITGFORTRAN_USE_STDERRGFORTRAN_TMPDIRGFORTRAN_UNBUFFERED_ALLGFORTRAN_SHOW_LOCUSGFORTRAN_OPTIONAL_PLUSGFORTRAN_DEFAULT_RECLGFORTRAN_LIST_SEPARATORGFORTRAN_CONVERT_UNITGFORTRAN_ERROR_DUMPCOREGFORTRAN_ERROR_BACKTRACE4DT4DTEnd of fileEnd of recordSuccessful returnOperating system errorBad statement optionMissing statement optionConflicting statement optionsUnattached unitFORMAT errorIncorrect ACTION specifiedRead past ENDFILE recordBad value during readNumeric overflow on readInternal unit I/O errorUnknown error codeAt line %d of file %s Fortran runtime error: %s Fortran runtime warning: %s Internal Error: %s Fortran runtime error: File already opened in another unitCorrupt unformatted sequential fileInternal error in run-time libraryWrite exceeds length of DIRECT access recordI/O past end of record on unformatted fileUnformatted file structure has been corruptedAt line %d of file %s (unit = %d, file = '%s') ../../../src/libgfortran/runtime/error.clen >= (sizeof (GFC_UINTEGER_8) * 2 + 1)len >= (sizeof (GFC_INTEGER_8) * 3 + 2)** Warning: a core dump was requested, but the core sizelimit ** is currently zero. Operating system error: %s %s xtoagfc_itoaFortran runtime warning: IEEE 'denormal number' exception not supported. Unable to determine machine endianness%s%c%sMemory allocation failedTo resume execution, type go. Other input will terminate the job. go RESUMED PAUSE %cPAUSE PAUSE %d STOP STOP %d rank of return array incorrect in ALL intrinsic: is %d, should be %dIncorrect extent in return value of ALL intrinsic in dimension %d: is %ld, should be %ldFunny sized logical array in ALL intrinsicrank of return array incorrect in ANY intrinsic: is %d, should be %dIncorrect extent in return value of ANY intrinsic in dimension %d: is %ld, should be %ldFunny sized logical array in ANY intrinsicrank of return array incorrect in COUNT intrinsic: is %d, should be %dIncorrect extent in return value of COUNT intrinsic in dimension %d: is %ld, should be %ldFunny sized logical array in COUNT intrinsicRank of array needs to be > 0Funny sized logical arrayrank of return array in MAXLOC intrinsic should be 1, is %ldIncorrect extent in return value of MAXLOC intrnisic: is %ld, should be %ldrank of MASK argument in MAXLOC intrnisicshould be %ld, is %ldIncorrect extent in MASK argument of MAXLOC intrinsic in dimension %ld: is %ld, should be %lddimension of return array incorrectrank of return array incorrect in MAXLOC intrinsicIncorrect extent in return value of MAXLOC intrinsic in dimension %ld: is %ld, should be %ldrank of return array incorrect in MAXLOC intrinsic: is %ld, should be %ldrank of return array incorrect in MAXVAL intrinsicIncorrect extent in return value of MAXVAL intrinsic in dimension %ld: is %ld, should be %ldIncorrect extent in MASK argument of MAXVAL intrinsic in dimension %ld: is %ld, should be %ldrank of return array incorrect in MAXVAL intrinsic: is %ld, should be %ldrank of return array in MINLOC intrinsic should be 1, is %ldIncorrect extent in return value of MINLOC intrnisic: is %ld, should be %ldrank of MASK argument in MINLOC intrnisicshould be %ld, is %ldIncorrect extent in MASK argument of MINLOC intrinsic in dimension %ld: is %ld, should be %ldrank of return array incorrect in MINLOC intrinsicIncorrect extent in return value of MINLOC intrinsic in dimension %ld: is %ld, should be %ldrank of return array incorrect in MINLOC intrinsic: is %ld, should be %ldrank of return array incorrect in MINVAL intrinsicIncorrect extent in return value of MINVAL intrinsic in dimension %ld: is %ld, should be %ldIncorrect extent in MASK argument of MINVAL intrinsic in dimension %ld: is %ld, should be %ldrank of return array incorrect in MINVAL intrinsic: is %ld, should be %ldrank of return array incorrect in PRODUCT intrinsicIncorrect extent in return value of PRODUCT intrinsic in dimension %ld: is %ld, should be %ldIncorrect extent in MASK argument of PRODUCT intrinsic in dimension %ld: is %ld, should be %ldrank of return array incorrect in PRODUCT intrinsic: is %ld, should be %ld???rank of return array incorrect in SUM intrinsicIncorrect extent in return value of SUM intrinsic in dimension %ld: is %ld, should be %ldIncorrect extent in MASK argument of SUM intrinsic in dimension %ld: is %ld, should be %ldrank of return array incorrect in SUM intrinsic: is %ld, should be %ld../../../src/libgfortran/generated/matmul_i1.c((a)->dtype & 0x07) == 2 || ((b)->dtype & 0x07) == 2dimension of array B incorrect in MATMUL intrinsicgemm != ((void *)0)matmul_i1../../../src/libgfortran/generated/matmul_i2.cmatmul_i2../../../src/libgfortran/generated/matmul_i4.cmatmul_i4../../../src/libgfortran/generated/matmul_i8.cmatmul_i8../../../src/libgfortran/generated/matmul_r4.cmatmul_r4../../../src/libgfortran/generated/matmul_r8.cmatmul_r8../../../src/libgfortran/generated/matmul_r10.cmatmul_r10../../../src/libgfortran/generated/matmul_c4.c?matmul_c4../../../src/libgfortran/generated/matmul_c8.c?matmul_c8../../../src/libgfortran/generated/matmul_c10.c?matmul_c10../../../src/libgfortran/generated/matmul_l4.ccount == b->dim[0].ubound + 1 - b->dim[0].lboundmatmul_l4../../../src/libgfortran/generated/matmul_l8.cmatmul_l8../../../src/libgfortran/generated/transpose_i4.c((source)->dtype & 0x07) == 2((ret)->dtype & 0x07) == 2ret->dtype == source->dtypetranspose_i4../../../src/libgfortran/generated/transpose_i8.ctranspose_i8../../../src/libgfortran/generated/transpose_r4.ctranspose_r4../../../src/libgfortran/generated/transpose_r8.ctranspose_r8../../../src/libgfortran/generated/transpose_r10.ctranspose_r10../../../src/libgfortran/generated/transpose_c4.ctranspose_c4../../../src/libgfortran/generated/transpose_c8.ctranspose_c8../../../src/libgfortran/generated/transpose_c10.ctranspose_c10Argument 'DIM' is out of range in call to 'CSHIFT'shape and target do not conform?Bad STATUS parameter in CLOSE statementCan't KEEP a scratch file on CLOSEkeepdeleteSpecified UNIT in FLUSH is not connectedCannot REWIND a file opened for DIRECT accessCannot perform ENDFILE on a file opened for DIRECT accessCannot BACKSPACE a file opened for DIRECT accessCannot BACKSPACE an unformatted stream fileIllegal value for record marker?<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<|>r>+>+>+>+>+>+>+>+>+>+>!><<<<<<>=<====y=i=<= 0LLLL,LNOYESUNKNOWNmem_alloc_w_at../../../src/libgfortran/io/unix.cbad integer kindUnspecified precisionprintf is brokenUnexpected format token%+0*d%+-#31.*e%+-#31.*Lebad real kind = %d*Bad type for namelist write,& / ]\L\dYXXX\x|||`|}zy~t~\Dotoabtoalist_formatted_write(): Bad type../../../src/libgfortran/io/write.clen >= (sizeof (GFC_INTEGER_8) * 3 + 1)len >= (sizeof (GFC_INTEGER_8) * 8 + 1)? A?Missing argument to get_command_argument/bin/chmodư>75$$tT../../../src/libgfortran/intrinsics/date_and_time.clen >= 9len >= 3%04d%02d%02d%02d%02d%02d.%03d%+03d%02d__zone_len >= 5__time_len >= 10__date_len >= 8ltime_i8ltime_i4gmtime_i8gmtime_i4idate_i8idate_i4itime_i8itime_i4date_and_timeaEpBGMbP?Insufficient number of elements in TARRAY.Name is required for get_environment_variable.Zero-length string passed as name to get_environment_variable.Zero-length string passed as value to get_environment_variable.Both arguments to getenv are mandatory.Zero length string(s) passed to getenv.Incorrect extent in return value of PACK intrinsic; is %ld, should be %ldincorrect destination rank in spread()dim outside of rank in spread()return array missing descriptor in spread()return rank too large in spread()Incorrect extent in return value of SPREAD intrinsic in dimension %ld: is %ld, should be %lddim too large in spread()rank mismatch in spread()MINMAXFirst argument of '%s' intrinsic should be presentSecond argument of '%s' intrinsic should be present0RANDOM_SEED should have at most one argument present.Array size of PUT is too small.Array size of GET is too small.Array rank of PUT is not 1.Array rank of GET is not 1.[U;]h:Vɹ"MS/_ %3 CArray rank of SARRAY is not 1.Array size of SARRAY is too small.../../../src/libgfortran/intrinsics/transpose_generic.c((source)->dtype & 0x07) == 2 && ((ret)->dtype & 0x07) == 2transpose_internal;( DdzR| ԝAB E<DAB E\-AB E|EAB E@o t    H              !      MM5M ` x o|tn1 M  phoooHoo*of &6FVfv&6FVfv&6FVfv&6FVfv&6FVfv&6FVfv&6FVfv&6FVfv&6FVfv&6FVfv&6FVfv&6FVfv&6FVfv&6FVfv&6FVfv&6FVfv&6FVfv&6FVfv$  / \ ' / < / Q / d e u /  /  /  / < @/  έ 0 P /   /  stdinstdoutstderr[U;]h:Vɹ"MSlibgfortran.so.3.0.0?ǘ.shstrtab.gnu.hash.dynsym.dynstr.gnu.version.gnu.version_d.gnu.version_r.rel.dyn.rel.plt.init.text.fini.rodata.eh_frame_hdr.eh_frame.ctors.dtors.jcr.data.rel.ro.dynamic.got.got.plt.data.bss.gnu_debuglink o|| 11p=tntnM%o**2opAoHHP hY pp b``0]h nx x t o[ |  ,< <       @ @        $   &  d   mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/0000755000335100017570000000000011133662012022402 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/ipopt.mexglx0000755000335100017570000022152611133661704025005 0ustar pgehlerwebadmELF44 ($!```88Ptd```QtdCUT;(Q1IJLPHFS)0E&.4A?N9,/'#>M%OCB35GK@7 R  $ "2* 68<-D!:+=R RT 5?r UWmWwGW {  9I\'fM`RG3dW7 oI7#'R M ,,F W*pV0$15YFHN 38YL ^ c,FHt#,jdfGz#iD?M3 a"pq @ 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sparse matrixMatlab array must be an N x N matrixUnable to copy sparse matrix; source has a different height than destinationUnable to copy sparse matrix; source has a different width than destinationsource.size = %d, dest.size = %d Unable to copy sparse matrix; source has more non-zero elements than destinationError copying sparse matrix; the collection of non-zero entries in the source matrix is not a subset of the non-zero entries present in the destinationMatlab array must be in double precisionThe rows in the sparse matrix are not in increasing order, as requiredfeval Call to MATLAB iterative callback routineEvaluation of Hessian of the Lagrangian failed in call to MATLAB routineInvalid Hessian of the Lagrangian passed back from Matlab routineMatlab array must be a lower triangular matrix; type HELP TRILEvaluation of constraint Jacobian failed in call to MATLAB routineInvalid constraint Jacobian passed back from Matlab routineEvaluation of objective function failed in call to MATLAB routineEvaluation of objective gradient failed in call to MATLAB routineInvalid gradient passed back from MATLAB routineEvaluation of constraint equations failed in call to MATLAB routineInvalid constraint values passed back from Matlab routineInitialization of Lagrange multipliers requested but initial values are not provided13MatlabProgramN5Ipopt4TNLPEN5Ipopt16ReferencedObjectEMatlab array must be a structure arrayMATLAB multipliers input does not have the correct fieldszlzulambdafunction_handleMatlab array must be a function handleIncorrect number of input argumentsIncorrect number of output argumentsInput arguments LB and UB must have the same structure as XInput arguments CONSTRAINTLB and CONSTRAINTUB should have the same number of elementsIPOPT solver initialization failedIPOPT solver terminated unexpectedlynlp_lower_bound_infnlp_upper_bound_infnowarm_start_init_pointyesNonexistent IPOPT optionInvalid IPOPT option 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5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/examplehs071.m0000644000335100017570000000511311133661704026623 0ustar pgehlerwebadm% Test the "ipopt" Matlab interface on the Hock & Schittkowski test problem % #71. See: Willi Hock and Klaus Schittkowski. (1981) Test Examples for % Nonlinear Programming Codes. Lecture Notes in Economics and Mathematical % Systems Vol. 187, Springer-Verlag. % % Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function [x, multipliers] = examplehs071 % The starting point. x0 = [1 5 5 1]; % The starting point. lb = [1 1 1 1]; % Lower bound on the variables. ub = [5 5 5 5]; % Upper bound on the variables. lbc = [25 40]; % Lower bounds on the constraint functions. ubc = [inf 40]; % Upper bounds on the constraint functions. % Initial dual point. multipliers.zl = [1 1 1 1]; multipliers.zu = [1 1 1 1]; multipliers.lambda = [1 1]; [x multipliers] = ipopt(x0,lb,ub,lbc,ubc,@computeObjective,... @computeGradient,@computeConstraints,... @computeJacobian,@computeHessian,[],'',... multipliers,'mu_strategy','adaptive','tol',1e-7); % ---------------------------------------------------------------------- function f = computeObjective (x) f = x(1)*x(4)*sum(x(1:3)) + x(3); % ---------------------------------------------------------------------- function c = computeConstraints (x) c(1) = prod(x); c(2) = sum(x.^2); % ---------------------------------------------------------------------- function g = computeGradient (x) g(1) = x(1)*x(4) + x(4)*sum(x(1:3)); g(2) = x(1)*x(4); g(3) = x(1)*x(4) + 1; g(4) = x(1)*sum(x(1:3)); % ---------------------------------------------------------------------- function J = computeJacobian (x, returnStructureOnly) if returnStructureOnly J = sparse(ones(2,4)); else J = sparse([prod(x) ./ x; 2*x]); end % ---------------------------------------------------------------------- function H = computeHessian (x, sigma, lambda, returnStructureOnly) if returnStructureOnly H = sparse(tril(ones(4))); else H = sigma*[ 2*x(4) 0 0 0; x(4) 0 0 0; x(4) 0 0 0; 2*x(1)+x(2)+x(3) x(1) x(1) 0 ]; H = H + lambda(1)*[ 0 0 0 0; x(3)*x(4) 0 0 0; x(2)*x(4) x(1)*x(4) 0 0; x(2)*x(3) x(1)*x(3) x(1)*x(2) 0 ]; H = H + lambda(2)*diag([2 2 2 2]); H = sparse(H); end mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/examplehs038.m0000644000335100017570000000444311133661704026633 0ustar pgehlerwebadm% Test the "ipopt" Matlab interface on the Hock & Schittkowski test problem % #38. See: Willi Hock and Klaus Schittkowski. (1981) Test Examples for % Nonlinear Programming Codes. Lecture Notes in Economics and Mathematical % Systems Vol. 187, Springer-Verlag. % % Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function x = examplehs038 % The starting point. x0 = [-3 -1 -3 -1]; % The starting point. lb = [-10 -10 -10 -10]; % Lower bound on the variables. ub = [+10 +10 +10 +10]; % Upper bound on the variables. x = ipopt(x0,lb,ub,[],[],@computeObjective,@computeGradient,'','',... @computeHessian,[],@callback,[],'mu_strategy','adaptive',... 'tol',1e-7,'max_iter',100); % ---------------------------------------------------------------------- function f = computeObjective (x) x1 = x(1); x2 = x(2); x3 = x(3); x4 = x(4); f = 100*(x2-x1^2)^2 + (1-x1)^2 + 90*(x4-x3^2)^2 + (1-x3)^2 + ... 10.1*(x2-1)^2 + 10.1*(x4-1)^2 + 19.8*(x2-1)*(x4-1); % ---------------------------------------------------------------------- function g = computeGradient (x) x1 = x(1); x2 = x(2); x3 = x(3); x4 = x(4); g(1) = -400*x1*(x2-x1^2) - 2*(1-x1); g(2) = 200*(x2-x1^2) + 20.2*(x2-1) + 19.8*(x4-1); g(3) = -360*x3*(x4-x3^2) -2*(1-x3); g(4) = 180*(x4-x3^2) + 20.2*(x4-1) + 19.8*(x2-1); % ---------------------------------------------------------------------- function H = computeHessian (x, sigma, lambda, returnStructureOnly) if returnStructureOnly H = sparse([1 0 0 0; 1 1 0 0; 0 0 1 0; 0 1 1 1]); else x1 = x(1); x2 = x(2); x3 = x(3); x4 = x(4); H = sigma*[ 1200*x1^2 - 400*x2 + 2 0 0 0; -400*x1 220.2 0 0; 0 0 1080*x3^2 - 360*x4 + 2 0; 0 19.8 -360*x3 200.2]; H = sparse(H); end % ---------------------------------------------------------------------- function callback (t, f, x) fprintf('%3d %0.3g \n', t, f); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/examplehs051.m0000644000335100017570000000327611133661704026631 0ustar pgehlerwebadm% Test the "ipopt" Matlab interface on the Hock & Schittkowski test problem % #51. See: Willi Hock and Klaus Schittkowski. (1981) Test Examples for % Nonlinear Programming Codes. Lecture Notes in Economics and Mathematical % Systems Vol. 187, Springer-Verlag. % % Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function x = examplehs051 x0 = [ +2.5 +0.5 +2.0 -1.0 +0.5 ]; lb = [-inf -inf -inf -inf -inf]; ub = [+inf +inf +inf +inf +inf]; lbc = [4 0 0]; ubc = [4 0 0]; x = ipopt(x0,lb,ub,lbc,ubc,@computeObjective,@computeGradient,... @computeConstraints,@computeJacobian,'',[],'',[],... 'jac_c_constant','yes','hessian_approximation','limited-memory',... 'mu_strategy','adaptive','tol',1e-7); % ---------------------------------------------------------------------- function f = computeObjective (x) f = (x(1) - x(2))^2 + ... (x(2) + x(3) - 2)^2 + ... (x(4) - 1)^2 + (x(5) - 1)^2; % ---------------------------------------------------------------------- function g = computeGradient (x) g = 2*[ x(1) - x(2); x(2) + x(3) - 2 - x(1) + x(2); x(2) + x(3) - 2; x(4) - 1; x(5) - 1 ]; % ---------------------------------------------------------------------- function c = computeConstraints (x) c = [ x(1) + 3*x(2); x(3) + x(4) - 2*x(5); x(2) - x(5) ]; % ---------------------------------------------------------------------- function J = computeJacobian (x, returnStructureOnly) J = sparse([ 1 3 0 0 0; 0 0 1 1 -2; 0 1 0 0 -1 ]); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/bayesnet/0000755000335100017570000000000011133662012026032 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/bayesnet/examplelauritzen.m0000644000335100017570000000657411133661704031624 0ustar pgehlerwebadm% This is a small script to compute the Bethe approximation of a small % factor graph. The factor graph is generated by taking the Bayes net of % Lauritzen and Spiegelhalter (1988) and setting the following random % variables as evidence % % visit to Asia = false % positive X-ray = true % smoking = true % % For this particular set of evidence, the Bethe approximation is exact % because the corresponding factor graph does not have any cycles. % % For more information, see: Lauritzen and Speigelhalter (1988). "Local % computations with probabilities on graphical structures and their % application to expert systems." Journal of the Royal Statistical % Society, Series B, Vol. 50, No. 2, pp. 157-194. % % Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 verbose = true; % The remaining random variables can either be true or false. They are: % 1. tuberculosis % 2. lung cancer % 3. bronchitis % 4. tuberculosis or lung cancer % 5. dysponoea nv = 5; % The number of random variables. nf = 6; % The number of factors. K = 2*ones(nv,1); % The number of possible discrete assignments to the % random variable at each site. C = cell(nf,1); % The factor neighbourhoods. f = cell(nf,1); % The factors. % Set up the factors. % ------------------ % This is p(tuberculosis | visit to Asia = false). C{1} = 1; f{1} = [ 0.01 0.99 ]'; % This is p(lung cancer | smoking = true). C{2} = 2; f{2} = [ 0.1 0.9 ]'; % This is p(bronchitis | smoking = true). C{3} = 3; f{3} = [ 0.6 0.4 ]'; % This is p(tuberculosis or lung cancer | tuberculosis, lung cancer). C{4} = [4 1 2]; p = zeros(2,2,2); p(:,1,1) = [ 0.99 0.01 ]'; p(:,1,2) = [ 0.99 0.01 ]'; p(:,2,1) = [ 0.99 0.01 ]'; p(:,2,2) = [ 0.99 0.01 ]'; f{4} = p; % This is p(positive X-ray = true | tuberculosis or lung cancer). C{5} = 4; f{5} = [ 0.98 0.5 ]'; % This is p(dyspnoea | tuberculosis or lung cancer, bronchitis). C{6} = [5 4 3]; p = zeros(2,2,2); p(:,1,1) = [ 0.9 0.1 ]'; p(:,1,2) = [ 0.7 0.3 ]'; p(:,2,1) = [ 0.8 0.2 ]'; p(:,2,2) = [ 0.1 0.9 ]'; f{6} = p; clear p % Set up the junction graph. % ------------------------- % These are the large regions of the junction graph. Rv{1} = [1 2 4]; Rf{1} = [1 2 4]; NR{1} = [1]; Rv{2} = [3 4 5]; Rf{2} = [3 5 6]; NR{2} = [1]; % These are the small regions of the junction graph. Sv{1} = [4]; Sf{1} = []; NS{1} = [1 2]; nr = length(Rv); % The number of large regions. ns = length(Sv); % The number of small regions. % Infer the region marginals. % -------------------------- % Compute the junction graph approximation to the variational free % energy. Since the junction graph is a tree in this case (trivially, % because there are only two large regions), the junction graph % approximation is exact. qR = bopt(K,C,f,Rv,Rf,Sv,Sf,NS,verbose); % Output the marginal probabilities. % --------------------------------- p = ndsum(qR{1},[2 3]); fprintf('Pr(tuberculosis | evidence) = %0.2f \n', p(1)); p = ndsum(qR{1},[1 3]); fprintf('Pr(lung cancer | evidence) = %0.2f \n', p(1)); p = ndsum(qR{2},[2 3]); fprintf('Pr(bronchitis | evidence) = %0.2f \n', p(1)); p = ndsum(qR{2},[1 2]); fprintf('Pr(dysponoea | evidence) = %0.2f \n', p(1)); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/bayesnet/margfactor.m0000644000335100017570000000230311133661704030342 0ustar pgehlerwebadm% MARGFACTOR(F,R,S) takes as input a factor F defined on set R, and % another set S which must be a (non-strict and non-empty) subset of % R. The return value is a factor defined on the set S. % % Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function f = margfactor (f, R, S) nr = length(R); % The size of set R. ns = length(S); % The size of set S. % Get the intersection of sets S and R. [ans RI SI] = intersect(R,S); % Get the set of elements that belong to R but don't belong to S. [ans RD] = setdiff(R,S); % Reorder the factor table so that the elements belonging to the % intersection of R and S supersede the elements that are not in the % intersection. if nr > 1 f = permute(f,[RI RD]); end % Sum over the dimensions in R - S. if nr > ns f = ndsum(f,ns+1:nr); end if ns > 1 % This is the reverse mapping to recover a factor table on S. SR = zeros(1,ns); SR(SI) = 1:ns; % Reorder the final result. f = permute(f,SR); endmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/bayesnet/computeJGConstraints.m0000644000335100017570000000305011133661704032342 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function g = computeJGConstraints (qR, qS, auxdata) [K C f Rv Rf Sv Sf NS d] = deal(auxdata{:}); nr = length(Rv); % The number of large regions. ns = length(Sv); % The number of small regions. % Reshape the input vectors. qR = reshapemarginals(qR,Rv,K); qS = reshapemarginals(qS,Sv,K); % Compute the normalization constraints for the large regions. Repeat % for each large region. gR = zeros(nr,1); for r = 1:nr gR(r) = sum(qR{r}(:)); end % Compute the normalization constraints for the small regions. Repeat % for each small region. gS = zeros(ns,1); for s = 1:ns gS(s) = sum(qS{s}(:)); end % These are the consistency constraints. There is a constraint for % every small region S, then again for every neighbouring large % region R, and then for every table entry of the marginal qS(xS). % These constraints are stored in the cell array "cRS". It has the % same structure as the collection of sum-product messages "mRS" in % the MATLAB function "bp". cRS = cell(1,ns); for s = 1:ns is = Sv{s}; % The variable nodes in the small region. rs = NS{s}; % The set of neighbouring large regions. for i = 1:length(rs) r = rs(i); cRS{s}{i} = margfactor(qR{r},Rv{r},is) - qS{s}; end end g = [gR; gS; vectorize(cRS)]; mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/bayesnet/vectorize.m0000644000335100017570000000101111133661704030222 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function v = vectorize (cells) n = length(cells); % The number of cells in the cell array. v = []; % The return value. % Repeat for each cell. for i = 1:n c = cells{i}; if iscell(c) c = vectorize(c); end v = [v; c(:)]; end mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/bayesnet/ndsum.m0000644000335100017570000000033011133661704027341 0ustar pgehlerwebadmfunction X = ndsum (X, dims) % Sum over requested dimensions. s = size(X); for i = dims X = sum(X,i); end % Remove singleton dimensions. s(dims) = []; X = reshape(X,[s 1 1]); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/bayesnet/computeJGJacobian.m0000644000335100017570000001217711133661704031553 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function J = computeJGJacobian (qR, qS, returnStructureOnly, auxdata) [K C f Rv Rf Sv Sf NS d] = deal(auxdata{:}); nr = length(Rv); % The number of large regions. ns = length(Sv); % The number of small regions. nqr = length(qR); % The number of variables associated with the % marginals defined on the large regions. nqs = length(qS); % The number of variables associated with the % marginals defined on the small regions. % Reshape the input vectors. qR = reshapemarginals(qR,Rv,K); qS = reshapemarginals(qS,Sv,K); % This block is the part of the Jacobian that represents the % first-order partial derivatives of the normalization constraints on % the large regions. We have one constraint function for every small % region. rows = zeros(1,nqr); is = 0; for r = 1:nr tablesize = numel(qR{r}); is = is(end) + (1:tablesize); rows(is) = r; end JR = sparse(rows,1:nqr,ones(1,nqr),nr,nqr); % This block is the part of the Jacobian that represents the % first-order partial derivatives of the normalization constraints on % the small regions. We have one constraint function for every small % region. rows = zeros(1,nqs); is = 0; for s = 1:ns tablesize = numel(qS{s}); is = is(end) + (1:tablesize); rows(is) = s; end JS = sparse(rows,1:nqs,ones(1,nqs),ns,nqs); % This block is the part of the Jacobian that represents the % first-order partial derivatives of the consistency constraints with % respect to the variables qS(xS). What we will do is form subblocks of % this block. We have one constraint function for each small region, % then again for each neighbouring large region, and then for every % possible configuration on the small region. JCS = cell(1,ns); for s = 1:ns w = numel(qS{s}); % The width of the Jacobian subblock. h = d(s) * w; % The height of the Jacobian subblock. cols = repmat(1:w,1,d(s)); JCS{s} = sparse(1:h,cols,-ones(1,h),h,w); end % This block is the part of the Jacobian that represents the % first-order partial derivatives of the consistency constraints with % respect to the variables qR(xR). What we will do is form subblocks of % this block. We have one constraint function for each small region, % then again for each neighbouring large region, and then for every % possible configuration on the small region. JCR = cell(sum(d),1); t = 1; % The row of the current subblock. for s = 1:ns for r = NS{s} h = numel(qS{s}); % The height of the Jacobian subblock. w = numel(qR{r}); % The width of the Jacobian subblock. rows = multiplyfactors(ones([K(Rv{r}) 1]),Rv{r},... reshape(1:h,[K(Sv{s}) 1]),Sv{s}); JCR{t} = sparse(rows(:)',1:w,ones(1,w),h,w); t = t + 1; % Move to the next row. end end % Construct the full Jacobian from its blocks. J = [ JR spzeros(nr,nqs); spzeros(ns,nqr) JS; spblockrows(JCR,vectorize(NS)) spblockdiag(JCS) ]; % ------------------------------------------------------------------ function A = spblockdiag (blocks) n = length(blocks); % The number of blocks. h = 0; % The height of the full matrix. w = 0; % The width of the full matrix. rows = []; cols = []; entries = []; % Repeat for each block. for i = 1:n [hb wb] = size(blocks{i}); % The height and width of the block. [is js xs] = find(blocks{i}); rows = [rows h + is']; cols = [cols w + js']; entries = [entries xs']; h = h + hb; w = w + wb; end % Construct the full matrix from the diagonal subblocks. A = sparse(rows,cols,entries,h,w); % ------------------------------------------------------------------ function A = spblockrows (blocks, c) n = length(blocks); % The number of blocks. nc = max(c); % The number of block columns. h = 0; % The height of the full matrix. rows = []; % The rows of the individual entries. cols = []; % The columns of the individual entries. entries = []; % Compute the width of every subblock. bw = zeros(1,nc); for i = 1:n bw(c(i)) = size(blocks{i},2); end % Compute the starting column index (minus 1) for every subblock. Also, % compute the width of the full matrix, w. bw = cumsum(bw); w = bw(nc); bw = [0 bw(1:nc-1)]; % Repeat for each block. for i = 1:n hb = size(blocks{i},1); % The height of the block. [is js xs] = find(blocks{i}); rows = [rows h + is']; cols = [cols bw(c(i)) + js']; entries = [entries xs']; h = h + hb; end % Construct the full matrix from the subblocks. A = sparse(rows,cols,entries,h,w); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/bayesnet/multiplyfactors.m0000644000335100017570000000345711133661704031471 0ustar pgehlerwebadm% MULTIPLYFACTORS(FR,R,FS,S) returns the pointwise product of factors FR % and FS defined on sets R and S, respectively. We require that S be a % (non-strict and non-empty) subset of R. The resulting factor is defined % on set R. % % Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function fR = multiplyfactors (fR, R, fS, S) nr = length(R); % The size of set R. ns = length(S); % The size of set S. K = size(fR); % The number of possible discrete assignments to each % random variable in R. if nr == 1 fR = fR .* fS; else % Get the intersection of sets S and R. [ans RI SI] = intersect(R,S); % Get the set of elements that belong to R but don't belong to C. [ans RD] = setdiff(R,S); % Check whether or not the intersection is the same size as S. If not, % it means that R is not a superset of S. if length(SI) ~= length(S) error('R is not a superset of S'); end % Reorder the factor tables so that the elements belonging to the % intersection of R and S supersede the elements that are not in the % intersection. RT = [RI RD]; fR = permute(fR,RT); if ns > 1 fS = permute(fS,SI); end % This is the reverse mapping to recover the original factor table. RR = zeros(1,nr); RR(RT) = 1:nr; % Expand the table fS so that it is of the same size as fR. fS = repmat(fS,[ones(1,ns) K(RD)]); % Multiply fR with the expanded version of fS. fR = fR .* fS; % Finally, we need to return the factor table fR back to its original % ordering. fR = permute(fR,RR); end mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/bayesnet/computeJGGradient.m0000644000335100017570000000266311133661704031601 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function [dR, dS] = computeJGGradient (qR, qS, auxdata) [K C f Rv Rf Sv Sf NS d] = deal(auxdata{:}); nr = length(Rv); % The number of large regions. ns = length(Sv); % The number of small regions. % Reshape the input vectors. qR = reshapemarginals(qR,Rv,K); qS = reshapemarginals(qS,Sv,K); % Compute the gradient terms for the large regions. Repeat for each % large region. dR = cell(1,nr); for r = 1:nr is = Rv{r}; % The variable nodes in the large region. table = 1 ./ qR{r}; % Multiply the table by all the factors in the large region. for j = Rf{r} table = multiplyfactors(table,is,f{j},C{j}); end dR{r} = -log(table) + 1; end % Compute the gradient terms for the small regions. Repeat for each % small region. dS = cell(1,ns); for s = 1:ns is = Sv{s}; % The variable nodes in the small region. table = 1 ./ qS{s}; % Multiply the table by all the factors in the small region. for j = Sf{s} table = multiplyfactors(table,is,f{j},C{j}); end dS{s} = (1 - d(s)) * (-log(table) + 1); end % Convert the computed gradients into vectors. dR = vectorize(dR); dS = vectorize(dS); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/bayesnet/computeJGHessian.m0000644000335100017570000000327311133661704031434 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function H = computeJGHessian (qR, qS, sigma, lambda, ... returnStructureOnly, auxdata) [K C f Rv Rf Sv Sf NS d] = deal(auxdata{:}); ns = length(Sv); % The number of small regions. nqr = length(qR); % The number of variables associated with the % marginals defined on the large regions. nqs = length(qS); % The number of variables associated with the % marginals defined on the small regions. if returnStructureOnly H = speye(nqr + nqs); else % Compute the second-order partial derivatives on the subblock HR. HR = spdiag(1 ./ qR); % Compute the second-order partial derivatives on the subblock % HS. Repeat for each small region. entries = zeros(1,nqs); is = 0; for s = 1:ns tablesize = prod(K(Sv{s})); % The number of entries necessary to % store the entire marginal. is = is(end) + (1:tablesize); entries(is) = (1-d(s)) ./ qS(is); end HS = spdiag(entries); % Construct the full Hessian from its blocks. H = sigma * [ HR spzeros(nqr,nqs); spzeros(nqs,nqr) HS ]; end % ------------------------------------------------------------------ function A = spdiag (xs) n = length(xs); % The height and width of the sparse matrix. % Produce the sparse, diagonal matrix. A = sparse(1:n,1:n,xs,n,n); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/bayesnet/reshapemarginals.m0000644000335100017570000000140311133661704031542 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function qR = reshapemarginals (q, R, K) nr = length(R); % The number of regions. qR = cell(1,nr); % The cell array to return. pos = 0; % Current entry of the vector q. % Repeat for each region. for r = 1:nr is = R{r}; % The variable nodes in region r. tablesize = prod(K(is)); % The size of the table. table = q(pos + (1:tablesize)); qR{r} = reshape(table,[K(is) 1]); % Move to the next location in the table. pos = pos + tablesize; end mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/bayesnet/spzeros.m0000644000335100017570000000046411133661704027730 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function A = spzeros (m, n) A = sparse([],[],[],m,n,0); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/bayesnet/computeJGObjective.m0000644000335100017570000000327711133661704031760 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function F = computeJGObjective (qR, qS, auxdata) [K C f Rv Rf Sv Sf NS d] = deal(auxdata{:}); % Reshape the input vectors. qR = reshapemarginals(qR,Rv,K); qS = reshapemarginals(qS,Sv,K); % The objective function is the junction graph approximation to the % variational free energy. F = computefreeenergy(C,f,Rv,Rf,Sv,Sf,qR,qS,d); % ------------------------------------------------------------------ function F = computefreeenergy (C, f, Rv, Rf, Sv, Sf, qR, qS, d) nr = length(Rv); % The number of large regions. ns = length(Sv); % The number of small regions. F = 0; % The return value. % Compute the average energy of each large region. Repeat for each % large region, then for each factor within the large region. for r = 1:nr for j = Rf{r} table = multiplyfactors(qR{r},Rv{r},log(f{j}),C{j}); F = F - sum(table(:)); end end % Compute the average energy of each small region. Repeat for each % small region, then for each factor within the small region. for s = 1:ns for j = Sf{s} table = multiplyfactors(qS{s},Sv{s},log(f{j}),C{j}); F = F - (1 - d(s)) * sum(table(:)); end end % Compute the entropy of each large region. for r = 1:nr table = qR{r} .* log(qR{r}); F = F + sum(table(:)); end % Compute the entropy of each small region. for s = 1:ns table = qS{s} .* log(qS{s}); F = F + (1 - d(s)) * sum(table(:)); end mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/bayesnet/genericcallback.m0000644000335100017570000000047511133661704031316 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function callbackHS038 (t, f, x) fprintf('%3d %0.3g \n', t, f); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/examples/bayesnet/bopt.m0000644000335100017570000000537011133661704027170 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function qR = bopt (K, C, f, Rv, Rf, Sv, Sf, NS, verbose) maxiter = 100; % The maximum number of iterations. tolerance = 1e-6; % The convergence criterion. nr = length(Rv); % The number of large regions. ns = length(Sv); % The number of small regions. % Convert the input K to a row vector. K = K(:)'; % Get the degrees of the separators. d = degrees(NS); % Initialize the marginals on the large and small regions to uniform % probability tables. qR = initmarginals(K,Rv); qS = initmarginals(K,Sv); % Compute the total number consistency constaints. We have one % constraint for every pair (R,S), then again for every possible % configuration xS. nc = numconsistencyconstraints(qS,d); % Run the IPOPT solver. qR = vectorize(qR); qS = vectorize(qS); nqr = length(qR); nqs = length(qS); [qR qS] = ipopt({qR qS},{ repmat(eps,nqr,1) repmat(eps,nqs,1) },... { repmat(inf,nqr,1) repmat(inf,nqs,1) },... [ ones(1,nr) ones(1,ns) zeros(1,nc) ],... [ ones(1,nr) ones(1,ns) zeros(1,nc) ],... @computeJGObjective,@computeJGGradient,... @computeJGConstraints,@computeJGJacobian,... @computeJGHessian,{ K C f Rv Rf Sv Sf NS d },'',... [],'mu_strategy','adaptive','max_iter',maxiter,... 'tol',tolerance,'jac_c_constant','yes',... 'jac_d_constant','yes','print_level',verbose*5); % Reshape the solution. qR = reshapemarginals(qR,Rv,K); qS = reshapemarginals(qS,Sv,K); % ------------------------------------------------------------------ function d = degrees (NS) ns = length(NS); % The number of separators. d = zeros(1,ns); % Repeat for each separator. for s = 1:ns d(s) = length(NS{s}); end % ---------------------------------------------------------------- function qR = initmarginals (K, Rv) nr = length(Rv); % The number of regions. qR = cell(1,nr); % The return value. % Repeat for each region. for r = 1:nr is = Rv{r}; % The variables nodes in the large region. table = ones([K(is) 1]); qR{r} = table / sum(table(:)); end % ---------------------------------------------------------------- function nc = numconsistencyconstraints (qS, d) ns = length(qS); % The number of small regions. nc = 0; % The return value. % Repeat for each small region. for s = 1:ns tablesize = numel(qS{s}); nc = nc + d(s) * tablesize; end mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/matlab/ipopt.m0000644000335100017570000002356711133661704023737 0ustar pgehlerwebadm% IPOPT Call the IPOPT constrained, nonlinear solver. % The basic function call is % % x = IPOPT(x0,lb,ub,constraintlb,constraintub,objfunc,gradfunc,... % constraintfunc,jacobianfunc,hessianfunc) % % The first input argument x0 is either a matrix or a cell array of % matrices. It declares the starting point for the solver. % % The second and third input arguments lb and ub must be of the same % structure as x0. They declare the lower and upper bounds on the % variables, respectively. Set an entry of lb to Inf to declare no lower % bound, and likewise for the upper bounds. % % The fourth and fifth input arguments constraintlb and constraintub set % the lower and upper bounds on the constraints, respectively. Each should % be a vector of length equal to the number of constraints. As before, a % bound is removed by setting the entry to Inf. An equality constraint is % achieved by setting constraintlb(i) = constraintub(i). % % If the solver successfully converges to a stationary point or terminated % without an unrecoverable error, the function IPOPT outputs the candidate % solution. In all other cases, an error is thrown. The number of outputs % is equal to the number of cell entries in x0. If x0 is a matrix, IPOPT % produces a single output. % % EXAMPLE. Here is an example of a basic call to IPOPT, in which the % optimization variables are specified by an array of length n and another % array of length m: % % [r s] = ipopt({r s},{ zeros(n,1) zeros(m,1) },... % { repmat(inf,n,1) repmat(inf,m,1) },lb,ub,... % @computeobjective,@computegradient,@computeconstraints',... % @computejacobian,@computeJGHessian,auxdata); % % The optimization variables are bounded below by 0. The bounds on the % equality and inequality constraints are specified by the MATLAB % variables lb and ub. Upon convergence to a feasible, stationary point, % the routine will return the values of the primal variables r and % s. Also, auxiliary data ('auxdata') is passed to all the callback % routines. For more information, continue reading below. % % The remaining input arguments must be function handles for various % MATLAB routines (M-files or subfunctions): % % objfunc Calculates the objective function at the current % point. The routine must accept as many inputs as cell % entries in x0 (or one input if x0 is a matrix). The % output must be a scalar representing the objective % evaluated at the current point. % % gradfunc Computes the gradient of the objective at the current % point. The input is the same as objfunc, but it must % return as many outputs as there are inputs, and each % of the outputs must have the same matrix structure % as its corresponding input. % % constraintfunc Evaluates the constraint functions at the current % point. The MATLAB routine must accept the same inputs % as objfunc. The return value is a vector of length % equal to the number of constraints (it must be of the % same length as constraintlb and constraintub). If % there are no constraints, constraintfunc may be set % to the empty string because the MATLAB callback % routine will never be called. % % jacobianfunc Evaluates the Jacobian of the constraints at the % current point. The inputs are the same as the % previously described callback functions, except that % it has an additional input "returnStructureOnly" which % will either true or false. If returnStructureOnly is % false, return the Jacobian matrix evaluated at the % specified point. If returnStructureOnly is true, % return a sparse matrix whereby an entry is zero if and % only if the Jacobian of the constraints is zero at % EVERY possible (feasible or non-feasible) point. The % output must always be an M x N sparse matrix, where M % is the number of constraints and N is the number of % variables. Type HELP SPARSE for more information on % constructing sparse matrices in MATLAB. If there % are no constraints, jacobianfunc may be set to the % empty string because the MATLAB callback routine % will never be called. % % hessianfunc Evaluates the Hessian of the Lagrangian at the current % point. Its inputs are the same as the objective and % gradient callback functions, but it also has three % additional inputs. The first additional input is % "sigma". It is a scalar factor on the % objective. The second additional input is "lambda", % which is a vector of length equal to the number of % constraints. The remaining additional input is % returnStructureOnly (see the description of % jacobianfunc above). The function should compute % % sigma*H + lambda(1)*G1 + ... + lambda(M)*GM % % where M is the number of constraints, H is the Hessian % of the objective and the G's are the Hessians of the % constraint functions. The output must always be an N x % N sparse, lower triangular matrix, where N is the % number of variables. In other words, if X is the % output value, then X must be the same as TRIL(X). % % For more information on using functions and function handles in MATLAB, % type HELP FUNCTION and HELP FUNCTION_HANDLE in the MATLAB prompt. % % Optionally, one may choose to pass additional auxiliary data to the % MATLAB callback routines listed above through the function call % IPOPT(...,auxdata). If auxdata is the empty matrix, no extra information % is passed. It is important to observe that the auxiliary data MAY NOT % CHANGE through the course of the IPOPT optimization! The auxiliary data % keep the same values as they possessed in the initial call. If you need % variables that change over time, you may want to consider global % variables (type HELP GLOBAL). % % EXAMPLE (continued). Returning to the above example, the 5 callback % routines would be of the following form: % % function F = computeobjective (r,s,auxdata) % function [dr, ds] = computegradient (r,s,auxdata) % function g = computeconstraints (r,s,auxdata) % function J = computejacobian (r,s,returnStructOnly,auxdata) % function H = computehessian (r,s,sigma,lambda,... % returnStructOnly,auxdata) % % IPOPT(...,auxdata,iterfunc) specifies a a function handle to an % additional callback routine which is called once per algorithm % iteration. The callback routine must take the form ITERFUNC(T,F,AUXDATA). % T is the current iteration of the algorithm. F is the current value of % the objective. Finally, extra information may be passed through the % input AUXDATA. No outputs are expected from iterfunc. Also note that the % value of the optimization variables is not passed to this callback % routine. If iterfunc is the empty string, no routine is called. % % By default, the print level is set so that IPOPT displays the % progress of the algorithm. However, if the user implements an % iterative callback routine, the print level is automatically set so % that IPOPT does not generate output to the MATLAB console. % % IPOPT(...,auxdata,iterfunc,multipliers) specifies the initial values for % the Lagrange multipliers. The input "multipliers" must have three % fields. The field "zl" must be an array of length equal to the total % number of optimization variables. It contains the initial values for the % Lagrange multipliers associated with the lower bounds on the % optimization variables. The field "zu" is an array of the same % length. It contains the initial values for the upper bounds Lagrange % multipliers. The field "lambda" must be an array of length equal to the % number of constraints. It specifies the initial values for the Lagrange % multipliers associated with the equality and inequality constraints. % % These inputs may be followed by parameter/value pairs to modify the % default IPOPT algorithm options. See the IPOPT documentation for more % information. If the IPOPT option 'hessian_approximation' is set to % 'limited-memory', the Hessian callback routine will never be called % so it may be set to the empty string. % % [..., multipliers, numiter] = IPOPT(...) returns some additional % information to the user: the values of the Lagrange multipliers at the % solution (the multipliers corresponding to the lower bounds on the % variables, the upper bounds on the variables, and the equality and % inequality constraints), and the number of iterations needed to converge % to the stationary point (provided it has converged at all). % % For more information, please consult: A. Wachter and L. T. Biegler. "On % the Implementation of a Primal-Dual Interior Point Filter Line Search % Algorithm for Large-Scale Nonlinear Programming." Mathematical % Programming 106(1), pp. 25-57, 2006. % % Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % October 13, 2007 mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/0000755000335100017570000000000011133662012022565 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/0000755000335100017570000000000011133662012023515 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/config_ipopt.h0000644000335100017570000001226611133661705026365 0ustar pgehlerwebadm/* inc/config_ipopt.h. Generated by configure. */ /* inc/config_ipopt.h.in. Generated from configure.ac by autoheader. */ /* If defined, the Ampl Solver Library is available. */ /* #undef COIN_HAS_ASL */ /* If defined, the BLAS Library is available. */ #define COIN_HAS_BLAS 1 /* Define to 1 if the Ipopt package is used */ #define COIN_HAS_IPOPT 1 /* If defined, the LAPACK Library is available. */ #define COIN_HAS_LAPACK 1 /* If defined, the MUMPS Library is available. */ #define COIN_HAS_MUMPS 1 /* Define to the debug sanity check level (0 is no test) */ #define COIN_IPOPT_CHECKLEVEL 0 /* Define to the debug verbosity level (0 is no output) */ #define COIN_IPOPT_VERBOSITY 0 /* Define to dummy `main' function (if any) required to link to the Fortran libraries. */ /* #undef F77_DUMMY_MAIN */ /* Define to a macro mangling the given C identifier (in lower and upper case), which must not contain underscores, for linking with Fortran. */ #define F77_FUNC(name,NAME) name ## _ /* As F77_FUNC, but for C identifiers containing underscores. */ #define F77_FUNC_(name,NAME) name ## _ /* Define if F77 and FC dummy `main' functions are identical. */ /* #undef FC_DUMMY_MAIN_EQ_F77 */ /* Define to the C type corresponding to Fortran INTEGER */ #define FORTRAN_INTEGER_TYPE int /* Define to 1 if you have the header file. */ /* #undef HAVE_ASSERT_H */ /* Define to 1 if you have the header file. */ #define HAVE_CASSERT 1 /* Define to 1 if you have the header file. */ #define HAVE_CCTYPE 1 /* Define to 1 if you have the header file. */ #define HAVE_CFLOAT 1 /* Define to 1 if you have the header file. */ /* #undef HAVE_CIEEEFP */ /* Define to 1 if you have the header file. */ #define HAVE_CMATH 1 /* Define to 1 if you have the header file. */ #define HAVE_CSTDARG 1 /* Define to 1 if you have the header file. */ #define HAVE_CSTDIO 1 /* Define to 1 if you have the header file. */ #define HAVE_CSTDLIB 1 /* Define to 1 if you have the header file. */ #define HAVE_CSTRING 1 /* Define to 1 if you have the header file. */ #define HAVE_CTIME 1 /* Define to 1 if you have the header file. */ /* #undef HAVE_CTYPE_H */ /* Define to 1 if you have the header file. */ #define HAVE_DLFCN_H 1 /* Define to 1 if function drand48 is available */ #define HAVE_DRAND48 1 /* Define to 1 if you have the header file. */ /* #undef HAVE_FLOAT_H */ /* Define to 1 if you have the header file. */ /* #undef HAVE_IEEEFP_H */ /* Define to 1 if you have the header file. */ #define HAVE_INTTYPES_H 1 /* Define to 1 if MA27 is available */ /* #undef HAVE_MA27 */ /* Define to 1 if MA28 is available */ /* #undef HAVE_MA28 */ /* Define to 1 if MA57 is available */ /* #undef HAVE_MA57 */ /* Define to 1 if you have the header file. */ /* #undef HAVE_MATH_H */ /* Define to 1 if MC19 is available */ /* #undef HAVE_MC19 */ /* Define to 1 if you have the header file. */ #define HAVE_MEMORY_H 1 /* Define to 1 if Pardiso is available */ /* #undef HAVE_PARDISO */ /* Define to 1 if you are using the parallel version of Pardiso */ /* #undef HAVE_PARDISO_PARALLEL */ /* Define to 1 if function rand is available */ #define HAVE_RAND 1 /* Define to 1 if you have the header file. */ /* #undef HAVE_STDARG_H */ /* Define to 1 if you have the header file. */ #define HAVE_STDINT_H 1 /* Define to 1 if you have the header file. */ /* #undef HAVE_STDIO_H */ /* Define to 1 if you have the header file. */ #define HAVE_STDLIB_H 1 /* Define to 1 if function std::rand is available */ #define HAVE_STD__RAND 1 /* Define to 1 if you have the header file. */ #define HAVE_STRINGS_H 1 /* Define to 1 if you have the header file. */ #define HAVE_STRING_H 1 /* Define to 1 if you have the header file. */ #define HAVE_SYS_STAT_H 1 /* Define to 1 if you have the header file. */ #define HAVE_SYS_TYPES_H 1 /* Define to 1 if TAUCS is available */ /* #undef HAVE_TAUCS */ /* Define to 1 if you have the header file. */ /* #undef HAVE_TIME_H */ /* Define to 1 if you have the header file. */ #define HAVE_UNISTD_H 1 /* Define to 1 if va_copy is avaliable */ #define HAVE_VA_COPY 1 /* Define to 1 if WSMP is available */ /* #undef HAVE_WSMP */ /* Define to be the name of C-function for NaNInf check */ #define MY_C_FINITE finite /* Name of package */ #define PACKAGE "ipopt" /* Define to the address where bug reports for this package should be sent. */ #define PACKAGE_BUGREPORT "http://projects.coin-or.org/Ipopt/newticket" /* Define to the full name of this package. */ #define PACKAGE_NAME "Ipopt" /* Define to the full name and version of this package. */ #define PACKAGE_STRING "Ipopt 3.3.5" /* Define to the one symbol short name of this package. */ #define PACKAGE_TARNAME "ipopt" /* Define to the version of this package. */ #define PACKAGE_VERSION "3.3.5" /* The size of a `int *', as computed by sizeof. */ #define SIZEOF_INT_P 4 /* Define to 1 if you have the ANSI C header files. */ #define STDC_HEADERS 1 /* Version number of package */ #define VERSION "3.3.5" mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpoptConfig.h0000644000335100017570000000531411133661705026122 0ustar pgehlerwebadm#ifndef HAVE_CONFIG_H #define HAVE_CONFIG_H #endif /* * Include file for the configuration of Ipopt. * * On systems where the code is configured with the configure script * (i.e., compilation is always done with HAVE_CONFIG_H defined), this * header file includes the automatically generated header file, and * undefines macros that might configure with other Config.h files. * * On systems that are compiled in other ways (e.g., with the * Developer Studio), a header files is included to define those * macros that depend on the operating system and the compiler. The * macros that define the configuration of the particular user setting * (e.g., presence of other COIN packages or third party code) are set * here. The project maintainer needs to remember to update this file * and choose reasonable defines. A user can modify the default * setting by editing this file here. * */ #ifndef __IPOPTCONFIG_H__ #define __IPOPTCONFIG_H__ #ifdef HAVE_CONFIG_H #include "config_ipopt.h" /* undefine macros that could conflict with those in other config.h files */ #undef PACKAGE #undef PACKAGE_BUGREPORT #undef PACKAGE_NAME #undef PACKAGE_STRING #undef PACKAGE_TARNAME #undef PACKAGE_VERSION #undef VERSION #else /* HAVE_CONFIG_H */ /* include the COIN-wide system specific configure header */ #include "configall_system.h" /***************************************************************************/ /* HERE DEFINE THE CONFIGURATION SPECIFIC MACROS */ /***************************************************************************/ /* Define to the debug sanity check level (0 is no test) */ #define COIN_IPOPT_CHECKLEVEL 0 /* Define to the debug verbosity level (0 is no output) */ #define COIN_IPOPT_VERBOSITY 0 /* If defined, the Ampl Solver Library is available. */ #define COIN_HAS_ASL 1 /* If defined, the BLAS Library is available. */ #define COIN_HAS_BLAS 1 /* Define to 1 if the Ipopt package is used */ #define COIN_HAS_IPOPT 1 /* If defined, the LAPACK Library is available. */ #define COIN_HAS_LAPACK 1 /* Define to 1 if MA27 is available */ #define HAVE_MA27 1 /* Define to 1 if MA57 is available */ /* #undef HAVE_MA57 */ /* Define to 1 if MC19 is available */ #define HAVE_MC19 1 /* Define to 1 if MUMPS is available */ /* #undef HAVE_MUMPS */ /* Define to 1 if Pardiso is available */ /* #undef HAVE_PARDISO */ /* Define to 1 if you are using the parallel version of Pardiso */ /* #undef HAVE_PARDISO_PARALLEL */ /* Define to 1 if TAUCS is available */ /* #undef HAVE_TAUCS */ /* Define to 1 if WSMP is available */ /* #undef HAVE_WSMP */ #endif /* HAVE_CONFIG_H */ /* Type of Fortran integer translated into C */ typedef FORTRAN_INTEGER_TYPE ipfint; #endif /*__IPOPTCONFIG_H__*/ mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpTNLP.hpp0000644000335100017570000002534211133661705025312 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpTNLP.hpp 1010 2007-06-21 15:54:36Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPTNLP_HPP__ #define __IPTNLP_HPP__ #include "IpUtils.hpp" #include "IpReferenced.hpp" #include "IpException.hpp" #include "IpAlgTypes.hpp" #include "IpReturnCodes.hpp" namespace Ipopt { // forward declarations class IpoptData; class IpoptCalculatedQuantities; class IteratesVector; /** Base class for all NLP's that use standard triplet matrix form * and dense vectors. This is the standard base class for all * NLP's that use the standard triplet matrix form (as for Harwell * routines) and dense vectors. The class TNLPAdapter then converts * this interface to an interface that can be used directly by * ipopt. * * This interface presents the problem form: * * min f(x) * * s.t. gL <= g(x) <= gU * * xL <= x <= xU * * In order to specify an equality constraint, set gL_i = gU_i = * rhs. The value that indicates "infinity" for the bounds * (i.e. the variable or constraint has no lower bound (-infinity) * or upper bound (+infinity)) is set through the option * nlp_lower_bound_inf and nlp_upper_bound_inf. To indicate that a * variable has no upper or lower bound, set the bound to * -ipopt_inf or +ipopt_inf respectively */ class TNLP : public ReferencedObject { public: /** Type of the constraints*/ enum LinearityType { LINEAR/** Constraint/Variable is linear.*/, NON_LINEAR/**Constraint/Varaible is non-linear.*/ }; /**@name Constructors/Destructors */ //@{ TNLP() {} /** Default destructor */ virtual ~TNLP() {} //@} DECLARE_STD_EXCEPTION(INVALID_TNLP); /**@name methods to gather information about the NLP */ //@{ /** overload this method to return the number of variables * and constraints, and the number of non-zeros in the jacobian and * the hessian. The index_style parameter lets you specify C or Fortran * style indexing for the sparse matrix iRow and jCol parameters. * C_STYLE is 0-based, and FORTRAN_STYLE is 1-based. */ enum IndexStyleEnum { C_STYLE=0, FORTRAN_STYLE=1 }; virtual bool get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, Index& nnz_h_lag, IndexStyleEnum& index_style)=0; /** overload this method to return the information about the bound * on the variables and constraints. The value that indicates * that a bound does not exist is specified in the parameters * nlp_lower_bound_inf and nlp_upper_bound_inf. By default, * nlp_lower_bound_inf is -1e19 and nlp_upper_bound_inf is * 1e19. (see TNLPAdapter) */ virtual bool get_bounds_info(Index n, Number* x_l, Number* x_u, Index m, Number* g_l, Number* g_u)=0; /** overload this method to return scaling parameters. This is * only called if the options are set to retrieve user scaling. * There, use_x_scaling (or use_g_scaling) should get set to true * only if the variables (or constraints) are to be scaled. This * method should return true only if the scaling parameters could * be provided. */ virtual bool get_scaling_parameters(Number& obj_scaling, bool& use_x_scaling, Index n, Number* x_scaling, bool& use_g_scaling, Index m, Number* g_scaling) { return false; } /** overload this method to return the variables linearity * (TNLP::Linear or TNLP::NonLinear). The var_types * array should be allocated with length at least n. (default implementation * just return false and does not fill the array).*/ virtual bool get_variables_linearity(Index n, LinearityType* var_types) { return false; } /** overload this method to return the constraint linearity. * array should be alocated with length at least n. (default implementation * just return false and does not fill the array).*/ virtual bool get_constraints_linearity(Index m, LinearityType* const_types) { return false; } /** overload this method to return the starting point. The bools * init_x and init_lambda are both inputs and outputs. As inputs, * they indicate whether or not the algorithm wants you to * initialize x and lambda respectively. If, for some reason, the * algorithm wants you to initialize these and you cannot, set * the respective bool to false. */ virtual bool get_starting_point(Index n, bool init_x, Number* x, bool init_z, Number* z_L, Number* z_U, Index m, bool init_lambda, Number* lambda)=0; /** overload this method to provide an Ipopt iterate (already in * the form Ipopt requires it internally) for a warm start. * Since this is only for expert users, a default dummy * implementation is provided and returns false. */ virtual bool get_warm_start_iterate(IteratesVector& warm_start_iterate) { return false; } /** overload this method to return the value of the objective function */ virtual bool eval_f(Index n, const Number* x, bool new_x, Number& obj_value)=0; /** overload this method to return the vector of the gradient of * the objective w.r.t. x */ virtual bool eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f)=0; /** overload this method to return the vector of constraint values */ virtual bool eval_g(Index n, const Number* x, bool new_x, Index m, Number* g)=0; /** overload this method to return the jacobian of the * constraints. The vectors iRow and jCol only need to be set * once. The first call is used to set the structure only (iRow * and jCol will be non-NULL, and values will be NULL) For * subsequent calls, iRow and jCol will be NULL. */ virtual bool eval_jac_g(Index n, const Number* x, bool new_x, Index m, Index nele_jac, Index* iRow, Index *jCol, Number* values)=0; /** overload this method to return the hessian of the * lagrangian. The vectors iRow and jCol only need to be set once * (during the first call). The first call is used to set the * structure only (iRow and jCol will be non-NULL, and values * will be NULL) For subsequent calls, iRow and jCol will be * NULL. This matrix is symmetric - specify the lower diagonal * only. A default implementation is provided, in case the user * wants to se quasi-Newton approximations to estimate the second * derivatives and doesn't not neet to implement this method. */ virtual bool eval_h(Index n, const Number* x, bool new_x, Number obj_factor, Index m, const Number* lambda, bool new_lambda, Index nele_hess, Index* iRow, Index* jCol, Number* values) { return false; } /** this method is called if option ??? is set. In that case, the algorithm calls this method with the values of variables and multipliers that will be used for the next calls of any evaluation method. */ virtual bool set_variable_values(Index n, const Number* x, bool new_x, Index m, const Number* lambda, bool new_lambda) { return false; } //@} /** @name Solution Methods */ //@{ /** This method is called when the algorithm is complete so the TNLP can store/write the solution */ virtual void finalize_solution(SolverReturn status, Index n, const Number* x, const Number* z_L, const Number* z_U, Index m, const Number* g, const Number* lambda, Number obj_value, const IpoptData* ip_data, IpoptCalculatedQuantities* ip_cq)=0; /** Intermediate Callback method for the user. Providing dummy * default implementation. For details see IntermediateCallBack * in IpNLP.hpp. */ virtual bool intermediate_callback(AlgorithmMode mode, Index iter, Number obj_value, Number inf_pr, Number inf_du, Number mu, Number d_norm, Number regularization_size, Number alpha_du, Number alpha_pr, Index ls_trials, const IpoptData* ip_data, IpoptCalculatedQuantities* ip_cq) { return true; } //@} /** @name Methods for quasi-Newton approximation. If the second * derivatives are approximated by Ipopt, it is better to do this * only in the space of nonlinear variables. The following * methods are call by Ipopt if the quasi-Newton approximation is * selected. If -1 is returned as number of nonlinear variables, * Ipopt assumes that all variables are nonlinear. Otherwise, it * calls get_list_of_nonlinear_variables with an array into which * the indices of the nonlinear variables should be written - the * array has the lengths num_nonlin_vars, which is identical with * the return value of get_number_of_nonlinear_variables(). It * is assumed that the indices are counted starting with 1 in the * FORTRAN_STYLE, and 0 for the C_STYLE. */ //@{ virtual Index get_number_of_nonlinear_variables() { return -1; } virtual bool get_list_of_nonlinear_variables(Index num_nonlin_vars, Index* pos_nonlin_vars) { return false; } //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ //TNLP(); /** Copy Constructor */ TNLP(const TNLP&); /** Overloaded Equals Operator */ void operator=(const TNLP&); //@} }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpRegOptions.hpp0000644000335100017570000006163211133661705026630 0ustar pgehlerwebadm// Copyright (C) 2004, 2007 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpRegOptions.hpp 1091 2007-09-25 12:16:28Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2005-06-18 #ifndef __IPREGOPTIONS_HPP__ #define __IPREGOPTIONS_HPP__ #include "IpUtils.hpp" #include "IpReferenced.hpp" #include "IpException.hpp" #include "IpSmartPtr.hpp" #include namespace Ipopt { enum RegisteredOptionType { OT_Number, OT_Integer, OT_String, OT_Unknown }; /** Base class for registered options. The derived types are more * specific to a string option or a Number (real) option, etc. */ class RegisteredOption : public ReferencedObject { public: /** Constructors / Destructors */ //@{ RegisteredOption() : type_(OT_Unknown), has_lower_(false), has_upper_(false), counter_(0) {} RegisteredOption(const std::string& name, const std::string& short_description, const std::string& long_description, const std::string& registering_category) : name_(name), short_description_(short_description), long_description_(long_description), registering_category_(registering_category), type_(OT_Unknown), has_lower_(false), has_upper_(false), counter_(next_counter_++) {} RegisteredOption(const RegisteredOption& copy) : name_(copy.name_), short_description_(copy.short_description_), long_description_(copy.long_description_), registering_category_(copy.registering_category_), type_(copy.type_), has_lower_(copy.has_lower_), lower_(copy.lower_), has_upper_(copy.has_upper_), upper_(copy.upper_), valid_strings_(copy.valid_strings_), counter_(copy.counter_) {} virtual ~RegisteredOption() {} //@} DECLARE_STD_EXCEPTION(ERROR_CONVERTING_STRING_TO_ENUM); /** Standard Get / Set Methods */ //@{ /** Get the option's name (tag in the input file) */ const std::string& Name() const { return name_; } /** Set the option's name (tag in the input file) */ void SetName(const std::string& name) { name_ = name; } /** Get the short description */ const std::string& ShortDescription() const { return short_description_; } /** Get the long description */ const std::string& LongDescription() const { return long_description_; } /** Set the short description */ void SetShortDescription(const std::string& short_description) { short_description_ = short_description; } /** Set the long description */ void SetLongDescription(const std::string& long_description) { long_description_ = long_description; } /** Get the registering class */ const std::string& RegisteringCategory() const { return registering_category_; } /** Set the registering class */ void SetRegisteringCategory(const std::string& registering_category) { registering_category_ = registering_category; } /** Get the Option's type */ const RegisteredOptionType& Type() const { return type_; } /** Get the Option's type */ void SetType(const RegisteredOptionType& type) { type_ = type; } /** Counter */ Index Counter() const { return counter_; } //@} /** @name Get / Set methods valid for specific types - NOTE: the Type * must be set before calling these methods. */ //@{ /** check if the option has a lower bound - can be called for * OT_Number & OT_Integer*/ const bool& HasLower() const { DBG_ASSERT(type_ == OT_Number || type_ == OT_Integer); return has_lower_; } /** check if the lower bound is strict - can be called for OT_Number */ const bool& LowerStrict() const { DBG_ASSERT(type_ == OT_Number && has_lower_ == true); return lower_strict_; } /** get the Number version of the lower bound - can be called for * OT_Number */ Number LowerNumber() const { DBG_ASSERT(has_lower_ == true && type_ == OT_Number); return lower_; } /** set the Number version of the lower bound - can be called for * OT_Number */ void SetLowerNumber(const Number& lower, const bool& strict) { DBG_ASSERT(type_ == OT_Number); lower_ = lower; lower_strict_ = strict, has_lower_ = true; } /** get the Integer version of the lower bound can be called for * OT_Integer*/ Index LowerInteger() const { DBG_ASSERT(has_lower_ == true && type_ == OT_Integer); return (Index)lower_; } /** set the Integer version of the lower bound - can be called for * OT_Integer */ void SetLowerInteger(const Index& lower) { DBG_ASSERT(type_ == OT_Integer); lower_ = (Number)lower; has_lower_ = true; } /** check if the option has an upper bound - can be called for * OT_Number & OT_Integer*/ const bool& HasUpper() const { DBG_ASSERT(type_ == OT_Number || type_ == OT_Integer); return has_upper_; } /** check if the upper bound is strict - can be called for * OT_Number */ const bool& UpperStrict() const { DBG_ASSERT(type_ == OT_Number && has_upper_ == true); return upper_strict_; } /** get the Number version of the upper bound - can be called for * OT_Number */ Number UpperNumber() const { DBG_ASSERT(has_upper_ == true && type_ == OT_Number); return upper_; } /** set the Number version of the upper bound - can be called for * OT_Number */ void SetUpperNumber(const Number& upper, const bool& strict) { DBG_ASSERT(type_ == OT_Number); upper_ = upper; upper_strict_ = strict; has_upper_ = true; } /** get the Integer version of the upper bound - can be called for * OT_Integer*/ Index UpperInteger() const { DBG_ASSERT(has_upper_ == true && type_ == OT_Integer); return (Index)upper_; } /** set the Integer version of the upper bound - can be called for * OT_Integer */ void SetUpperInteger(const Index& upper) { DBG_ASSERT(type_ == OT_Integer); upper_ = (Number)upper; has_upper_ = true; } /** method to add valid string entries - can be called for * OT_String */ void AddValidStringSetting(const std::string value, const std::string description) { DBG_ASSERT(type_ == OT_String); valid_strings_.push_back(string_entry(value, description)); } /** get the default as a Number - can be called for OT_Number */ Number DefaultNumber() const { DBG_ASSERT(type_ == OT_Number); return default_number_; } /** Set the default as a Number - can be called for OT_Number */ void SetDefaultNumber(const Number& default_value) { DBG_ASSERT(type_ == OT_Number); default_number_ = default_value; } /** get the default as an Integer - can be called for OT_Integer*/ Index DefaultInteger() const { DBG_ASSERT(type_ == OT_Integer); return (Index)default_number_; } /** Set the default as an Integer - can be called for OT_Integer */ void SetDefaultInteger(const Index& default_value) { DBG_ASSERT(type_ == OT_Integer); default_number_ = (Number)default_value; } /** get the default as a string - can be called for OT_String */ std::string DefaultString() const { DBG_ASSERT(type_ == OT_String); return default_string_; } /** get the default as a string, but as the index of the string in * the list - helps map from a string to an enum- can be called * for OT_String */ Index DefaultStringAsEnum() const { DBG_ASSERT(type_ == OT_String); return MapStringSettingToEnum(default_string_); } /** Set the default as a string - can be called for OT_String */ void SetDefaultString(const std::string& default_value) { DBG_ASSERT(type_ == OT_String); default_string_ = default_value; } /** Check if the Number value is a valid setting - can be called * for OT_Number */ bool IsValidNumberSetting(const Number& value) const { DBG_ASSERT(type_ == OT_Number); if (has_lower_ && ((lower_strict_ == true && value <= lower_) || (lower_strict_ == false && value < lower_))) { return false; } if (has_upper_ && ((upper_strict_ == true && value >= upper_) || (upper_strict_ == false && value > upper_))) { return false; } return true; } /** Check if the Integer value is a valid setting - can be called * for OT_Integer */ bool IsValidIntegerSetting(const Index& value) const { DBG_ASSERT(type_ == OT_Integer); if (has_lower_ && value < lower_) { return false; } if (has_upper_ && value > upper_) { return false; } return true; } /** Check if the String value is a valid setting - can be called * for OT_String */ bool IsValidStringSetting(const std::string& value) const; /** Map a user setting (allowing any case) to the case used when * the setting was registered. */ std::string MapStringSetting(const std::string& value) const; /** Map a user setting (allowing any case) to the index of the * matched setting in the list of string settings. Helps map a * string setting to an enumeration. */ Index MapStringSettingToEnum(const std::string& value) const; //@} /** output a description of the option */ void OutputDescription(const Journalist& jnlst) const; /** output a more concise version */ void OutputShortDescription(const Journalist& jnlst) const; /** output a latex version */ void OutputLatexDescription(const Journalist& jnlst) const; private: std::string name_; std::string short_description_; std::string long_description_; std::string registering_category_; RegisteredOptionType type_; bool has_lower_; bool lower_strict_; Number lower_; bool has_upper_; bool upper_strict_; Number upper_; Number default_number_; void MakeValidLatexString(std::string source, std::string& dest) const; std::string MakeValidLatexNumber(Number value) const; /** Compare two strings and return true if they are equal (case insensitive comparison) */ bool string_equal_insensitive(const std::string& s1, const std::string& s2) const; /** class to hold the valid string settings for a string option */ class string_entry { public: string_entry(const std::string& value, const std::string& description) : value_(value), description_(description) {} std::string value_; std::string description_; }; std::vector valid_strings_; std::string default_string_; /** Has the information as how many-th option this one was * registered. */ const Index counter_; static Index next_counter_; }; /** Class for storing registered options. Used for validation and * documentation. */ class RegisteredOptions : public ReferencedObject { public: /** Constructors / Destructors */ //@{ /** Standard Constructor */ RegisteredOptions() : current_registering_category_("Uncategorized") {} /** Standard Destructor */ ~RegisteredOptions() {} //@} DECLARE_STD_EXCEPTION(OPTION_ALREADY_REGISTERED); /** Methods to interact with registered options */ //@{ /** set the registering class. All subsequent options will be * added with the registered class */ void SetRegisteringCategory(const std::string& registering_category) { current_registering_category_ = registering_category; } /** retrieve the value of the current registering category */ std::string RegisteringCategory() { return current_registering_category_; } /** Add a Number option (with no restrictions) */ void AddNumberOption(const std::string& name, const std::string& short_description, Number default_value, const std::string& long_description=""); /** Add a Number option (with a lower bound) */ void AddLowerBoundedNumberOption(const std::string& name, const std::string& short_description, Number lower, bool strict, Number default_value, const std::string& long_description=""); /** Add a Number option (with a upper bound) */ void AddUpperBoundedNumberOption(const std::string& name, const std::string& short_description, Number upper, bool strict, Number default_value, const std::string& long_description=""); /** Add a Number option (with a both bounds) */ void AddBoundedNumberOption(const std::string& name, const std::string& short_description, Number lower, bool lower_strict, Number upper, bool upper_strict, Number default_value, const std::string& long_description=""); /** Add a Integer option (with no restrictions) */ void AddIntegerOption(const std::string& name, const std::string& short_description, Index default_value, const std::string& long_description=""); /** Add a Integer option (with a lower bound) */ void AddLowerBoundedIntegerOption(const std::string& name, const std::string& short_description, Index lower, Index default_value, const std::string& long_description=""); /** Add a Integer option (with a upper bound) */ void AddUpperBoundedIntegerOption(const std::string& name, const std::string& short_description, Index upper, Index default_value, const std::string& long_description=""); /** Add a Integer option (with a both bounds) */ void AddBoundedIntegerOption(const std::string& name, const std::string& short_description, Index lower, Index upper, Index default_value, const std::string& long_description=""); /** Add a String option (with no restrictions) */ void AddStringOption(const std::string& name, const std::string& short_description, const std::string& default_value, const std::vector& settings, const std::vector& descriptions, const std::string& long_description=""); /** Methods that make adding string options with only a few * entries easier */ void AddStringOption1(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& long_description=""); void AddStringOption2(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& long_description=""); void AddStringOption3(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& long_description=""); void AddStringOption4(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& setting4, const std::string& description4, const std::string& long_description=""); void AddStringOption5(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& setting4, const std::string& description4, const std::string& setting5, const std::string& description5, const std::string& long_description=""); void AddStringOption6(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& setting4, const std::string& description4, const std::string& setting5, const std::string& description5, const std::string& setting6, const std::string& description6, const std::string& long_description=""); void AddStringOption7(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& setting4, const std::string& description4, const std::string& setting5, const std::string& description5, const std::string& setting6, const std::string& description6, const std::string& setting7, const std::string& description7, const std::string& long_description=""); void AddStringOption8(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& setting4, const std::string& description4, const std::string& setting5, const std::string& description5, const std::string& setting6, const std::string& description6, const std::string& setting7, const std::string& description7, const std::string& setting8, const std::string& description8, const std::string& long_description=""); void AddStringOption9(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& setting4, const std::string& description4, const std::string& setting5, const std::string& description5, const std::string& setting6, const std::string& description6, const std::string& setting7, const std::string& description7, const std::string& setting8, const std::string& description8, const std::string& setting9, const std::string& description9, const std::string& long_description=""); void AddStringOption10(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& setting4, const std::string& description4, const std::string& setting5, const std::string& description5, const std::string& setting6, const std::string& description6, const std::string& setting7, const std::string& description7, const std::string& setting8, const std::string& description8, const std::string& setting9, const std::string& description9, const std::string& setting10, const std::string& description10, const std::string& long_description=""); /** Get a registered option - this will return NULL if the option * does not exist */ SmartPtr GetOption(const std::string& name); /** Output documentation for the options - gives a description, * etc. */ void OutputOptionDocumentation(const Journalist& jnlst, std::list& categories); /** Output documentation in Latex format to include in a latex file */ void OutputLatexOptionDocumentation(const Journalist& jnlst, std::list& categories); //@} typedef std::map > RegOptionsList; /** Giving access to iteratable representation of the registered * options */ const RegOptionsList& RegisteredOptionsList () const { return registered_options_; } private: std::string current_registering_category_; std::map > registered_options_; }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpReturnCodes.inc0000644000335100017570000000356611133661705026760 0ustar pgehlerwebadmC Copyright (C) 2005, 2006 International Business Machines and others. C All Rights Reserved. C This code is published under the Common Public License. C C $Id: IpReturnCodes.inc 1010 2007-06-21 15:54:36Z andreasw $ C C Author: Andreas Waechter IBM 2005-08-11 C INTEGER IP_SOLVE_SUCCEEDED PARAMETER( IP_SOLVE_SUCCEEDED = 0 ) INTEGER IP_ACCEPTABLE_LEVEL PARAMETER( IP_ACCEPTABLE_LEVEL = 1 ) INTEGER IP_INFEASIBLE_PROBLEM PARAMETER( IP_INFEASIBLE_PROBLEM = 2 ) INTEGER IP_SEARCH_DIRECTION_TOO_SMALL PARAMETER( IP_SEARCH_DIRECTION_TOO_SMALL = 3 ) INTEGER IP_DIVERGING_ITERATES PARAMETER( IP_DIVERGING_ITERATES = 4 ) INTEGER IP_USER_REQUESTED_STOP PARAMETER( IP_USER_REQUESTED_STOP = 5 ) INTEGER IP_ITERATION_EXCEEDED PARAMETER( IP_ITERATION_EXCEEDED = -1 ) INTEGER IP_RESTORATION_FAILED PARAMETER( IP_RESTORATION_FAILED = -2 ) INTEGER IP_ERROR_IN_STEP_COMPUTATION PARAMETER( IP_ERROR_IN_STEP_COMPUTATION = -3 ) INTEGER IP_NOT_ENOUGH_DEGREES_OF_FRE PARAMETER( IP_NOT_ENOUGH_DEGREES_OF_FRE = -10 ) INTEGER IP_INVALID_PROBLEM_DEFINITION PARAMETER( IP_INVALID_PROBLEM_DEFINITION = -11) INTEGER IP_INVALID_OPTION PARAMETER( IP_INVALID_OPTION = -12 ) INTEGER IP_INVALID_NUMBER_DETECTED PARAMETER( IP_INVALID_NUMBER_DETECTED = -13 ) INTEGER IP_UNRECOVERABLE_EXCEPTION PARAMETER( IP_UNRECOVERABLE_EXCEPTION = -100 ) INTEGER IP_NON_IPOPT_EXCEPTION PARAMETER( IP_NON_IPOPT_EXCEPTION = -101 ) INTEGER IP_INSUFFICIENT_MEMORY PARAMETER( IP_INSUFFICIENT_MEMORY = -102 ) INTEGER IP_INTERNAL_ERROR PARAMETER( IP_INTERNAL_ERROR = -199 ) INTEGER IP_REGULAR_MODE PARAMETER( IP_REGULAR_MODE = 0 ) INTEGER IP_RESTORATION_PHASE_MODE PARAMETER( IP_RESTORATION_PHASE_MODE = 1 ) mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpUtils.hpp0000644000335100017570000000417411133661705025635 0ustar pgehlerwebadm// Copyright (C) 2004, 2007 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpUtils.hpp 1010 2007-06-21 15:54:36Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPUTILS_HPP__ #define __IPUTILS_HPP__ // Standard Ip Include Files #include "IpTypes.hpp" #include "IpDebug.hpp" namespace Ipopt { inline Index Max(Index a, Index b) { return ((a) > (b) ? (a) : (b)); } inline Index Max(Index a, Index b, Index c) { Index max = Max(a,b); max = Max(max, c); return max; } inline Index Max(Index a, Index b, Index c, Index d) { Index max = Max(a, b, c); max = Max(max, d); return max; } inline Index Min(Index a, Index b) { return ((a) < (b) ? (a) : (b)); } inline Index Min(Index a, Index b, Index c) { Index min = Min(a,b); min = Min(min, c); return min; } inline Index Min(Index a, Index b, Index c, Index d) { Index min = Min(a, b, c); min = Min(min, d); return min; } /////////////////////////////////////////// inline Number Max(Number a, Number b) { return ((a) > (b) ? (a) : (b)); } inline Number Max(Number a, Number b, Number c) { Number max = Max(a,b); max = Max(max, c); return max; } inline Number Max(Number a, Number b, Number c, Number d) { Number max = Max(a, b, c); max = Max(max, d); return max; } inline Number Min(Number a, Number b) { return ((a) < (b) ? (a) : (b)); } inline Number Min(Number a, Number b, Number c) { Number min = Min(a,b); min = Min(min, c); return min; } inline Number Min(Number a, Number b, Number c, Number d) { Number min = Min(a, b, c); min = Min(min, d); return min; } /** Function returning true iff the argument is a valid double number * (not NaN or Inf). */ bool IsFiniteNumber(Number val); /** Function returning a random number between 0 and 1 */ Number IpRandom01(); /** Function resetting the random number generator */ void IpResetRandom01(); } //namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpTypes.hpp0000644000335100017570000000106011133661705025630 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpTypes.hpp 759 2006-07-07 03:07:08Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPTYPES_HPP__ #define __IPTYPES_HPP__ namespace Ipopt { /** Type of all numbers */ typedef double Number; /** Type of all indices of vectors, matrices etc */ typedef int Index; /** Type of default integer */ typedef int Int; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpReturnCodes.hpp0000644000335100017570000000111511133661705026762 0ustar pgehlerwebadm/*********************************************************************** // Copyright (C) 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpReturnCodes.hpp 759 2006-07-07 03:07:08Z andreasw $ // // Authors: Andreas Waechter IBM 2006-03-01 ************************************************************************/ #ifndef __IPRETURNCODES_HPP__ #define __IPRETURNCODES_HPP__ /* include from a common include file */ namespace Ipopt { #include "IpReturnCodes_inc.h" } #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpReturnCodes_inc.h0000644000335100017570000000264411133661705027263 0ustar pgehlerwebadm/*********************************************************************** // Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpReturnCodes_inc.h 759 2006-07-07 03:07:08Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 ************************************************************************/ /* !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! */ /* !!!!!!!!!!!!!!!! REMEMBER TO UPDATE IpReturnCodes.inc !!!!!!!!!!!!!!!! */ /* !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! */ /** Return codes for the Optimize call for an application */ enum ApplicationReturnStatus { Solve_Succeeded=0, Solved_To_Acceptable_Level=1, Infeasible_Problem_Detected=2, Search_Direction_Becomes_Too_Small=3, Diverging_Iterates=4, User_Requested_Stop=5, Maximum_Iterations_Exceeded=-1, Restoration_Failed=-2, Error_In_Step_Computation=-3, Not_Enough_Degrees_Of_Freedom=-10, Invalid_Problem_Definition=-11, Invalid_Option=-12, Invalid_Number_Detected=-13, Unrecoverable_Exception=-100, NonIpopt_Exception_Thrown=-101, Insufficient_Memory=-102, Internal_Error=-199 }; /** enum to indicate the mode in which the algorithm is */ enum AlgorithmMode { RegularMode=0, RestorationPhaseMode=1 }; mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpOptionsList.hpp0000644000335100017570000002176011133661705027024 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpOptionsList.hpp 1010 2007-06-21 15:54:36Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPOPTLIST_HPP__ #define __IPOPTLIST_HPP__ #include "IpUtils.hpp" #include "IpReferenced.hpp" #include "IpException.hpp" #include "IpRegOptions.hpp" #include #include namespace Ipopt { /** Exception that can be used to indicate errors with options */ DECLARE_STD_EXCEPTION(OPTION_INVALID); /** This class stores a list of user set options. Each options is * identified by a case-insensitive keyword (tag). Its value is * stored internally as a string (always lower case), but for * convenience set and get methods are provided to obtain Index and * Number type values. For each keyword we also keep track of how * often the value of an option has been requested by a get method. */ class OptionsList : public ReferencedObject { /** Class for storing the value and counter for each option in * OptionsList. */ class OptionValue { public: /**@name Constructors/Destructors */ //@{ /** Default constructor (needed for the map) */ OptionValue() : initialized_(false) {} /** Constructor given the value */ OptionValue(std::string value, bool allow_clobber, bool dont_print) : value_(value), counter_(0), initialized_(true), allow_clobber_(allow_clobber), dont_print_(dont_print) {} /** Copy Constructor */ OptionValue(const OptionValue& copy) : value_(copy.value_), counter_(copy.counter_), initialized_(copy.initialized_), allow_clobber_(copy.allow_clobber_), dont_print_(copy.dont_print_) {} /** Equals operator */ void operator=(const OptionValue& copy) { value_=copy.value_; counter_=copy.counter_; initialized_=copy.initialized_; allow_clobber_=copy.allow_clobber_; dont_print_=copy.dont_print_; } /** Default Destructor */ ~OptionValue() {} //@} /** Method for retrieving the value of an option. Calling this * method will increase the counter by one. */ std::string GetValue() const { DBG_ASSERT(initialized_); counter_++; return value_; } /** Method for retrieving the value without increasing the * counter */ std::string Value() const { DBG_ASSERT(initialized_); return value_; } /** Method for accessing current value of the request counter */ Index Counter() const { DBG_ASSERT(initialized_); return counter_; } /** True if the option can be overwritten */ bool AllowClobber() const { DBG_ASSERT(initialized_); return allow_clobber_; } /** True if this option is not to show up in the * print_user_options output */ bool DontPrint() const { DBG_ASSERT(initialized_); return dont_print_; } private: /** Value for this option */ std::string value_; /** Counter for requests */ mutable Index counter_; /** for debugging */ bool initialized_; /** True if the option can be overwritten */ bool allow_clobber_; /** True if this option is not to show up in the * print_user_options output */ bool dont_print_; }; public: /**@name Constructors/Destructors */ //@{ OptionsList(SmartPtr reg_options, SmartPtr jnlst) : reg_options_(reg_options), jnlst_(jnlst) {} OptionsList() {} /** Copy Constructor */ OptionsList(const OptionsList& copy) { // copy all the option strings and values options_ = copy.options_; // copy the registered options pointer reg_options_ = copy.reg_options_; } /** Default destructor */ virtual ~OptionsList() {} /** Overloaded Equals Operator */ void operator=(const OptionsList& source) { options_ = source.options_; reg_options_ = source.reg_options_; jnlst_ = source.jnlst_; } //@} /** Method for clearing all previously set options */ void clear() { options_.clear(); } /** @name Get / Set Methods */ //@{ void SetRegisteredOptions(const SmartPtr reg_options) { reg_options_ = reg_options; } void SetJournalist(const SmartPtr jnlst) { jnlst_ = jnlst; } //@} /** @name Methods for setting options */ //@{ bool SetStringValue(const std::string& tag, const std::string& value, bool allow_clobber = true, bool dont_print = false); bool SetNumericValue(const std::string& tag, Number value, bool allow_clobber = true, bool dont_print = false); bool SetIntegerValue(const std::string& tag, Index value, bool allow_clobber = true, bool dont_print = false); //@} /** @name Methods for setting options only if they have not been * set before*/ //@{ bool SetStringValueIfUnset(const std::string& tag, const std::string& value, bool allow_clobber = true, bool dont_print = false); bool SetNumericValueIfUnset(const std::string& tag, Number value, bool allow_clobber = true, bool dont_print = false); bool SetIntegerValueIfUnset(const std::string& tag, Index value, bool allow_clobber = true, bool dont_print = false); //@} /** @name Methods for retrieving values from the options list. If * a tag is not found, the methods return false, and value is set * to the default value defined in the registered options. */ //@{ bool GetStringValue(const std::string& tag, std::string& value, const std::string& prefix) const; bool GetEnumValue(const std::string& tag, Index& value, const std::string& prefix) const; bool GetBoolValue(const std::string& tag, bool& value, const std::string& prefix) const; bool GetNumericValue(const std::string& tag, Number& value, const std::string& prefix) const; bool GetIntegerValue(const std::string& tag, Index& value, const std::string& prefix) const; //@} /** Get a string with the list of all options (tag, value, counter) */ void PrintList(std::string& list) const; /** Get a string with the list of all options set by the user * (tag, value, use/notused). Here, options with dont_print flag * set to true are not printed. */ void PrintUserOptions(std::string& list) const; /** Read options from the stream is. Returns false if * an error was encountered. */ bool ReadFromStream(const Journalist& jnlst, std::istream& is); private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ // OptionsList(); //@} /** map for storing the options */ std::map< std::string, OptionValue > options_; /** list of all the registered options to validate against */ SmartPtr reg_options_; /** Journalist for writing error messages, etc. */ SmartPtr jnlst_; /** auxilliary method for converting sting to all lower-case * letters */ const std::string& lowercase(const std::string tag) const; /** auxilliary method for finding the value for a tag in the * options list. This method first looks for the concatenated * string prefix+tag (if prefix is not ""), and if this is not * found, it looks for tag. The return value is true iff * prefix+tag or tag is found. In that case, the corresponding * string value is copied into value. */ bool find_tag(const std::string& tag, const std::string& prefix, std::string& value) const; /** tells whether or not we can clobber a particular option. * returns true if the option does not already exist, or if * the option exists but is set to allow_clobber */ bool will_allow_clobber(const std::string& tag) const; /** read the next token from stream is. Returns false, if EOF was * reached before a tokens was ecountered. */ bool readnexttoken(std::istream& is, std::string& token); /** auxilliary string set by lowercase method */ mutable std::string lowercase_buffer_; }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpAlgTypes.hpp0000644000335100017570000000323711133661705026264 0ustar pgehlerwebadm// Copyright (C) 2005, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpAlgTypes.hpp 1054 2007-07-17 23:09:07Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2005-07-19 #ifndef __IPALGTYPES_HPP__ #define __IPALGTYPES_HPP__ #include "IpTypes.hpp" #include "IpException.hpp" namespace Ipopt { /**@name Enumerations */ //@{ /** enum for the return from the optimize algorithm * (obviously we need to add more) */ enum SolverReturn { SUCCESS, MAXITER_EXCEEDED, STOP_AT_TINY_STEP, STOP_AT_ACCEPTABLE_POINT, LOCAL_INFEASIBILITY, USER_REQUESTED_STOP, DIVERGING_ITERATES, RESTORATION_FAILURE, ERROR_IN_STEP_COMPUTATION, INVALID_NUMBER_DETECTED, TOO_FEW_DEGREES_OF_FREEDOM, INTERNAL_ERROR }; //@} /** @name Some exceptions used in multiple places */ //@{ DECLARE_STD_EXCEPTION(LOCALLY_INFEASIBLE); DECLARE_STD_EXCEPTION(TOO_FEW_DOF); DECLARE_STD_EXCEPTION(TINY_STEP_DETECTED); DECLARE_STD_EXCEPTION(ACCEPTABLE_POINT_REACHED); DECLARE_STD_EXCEPTION(FEASIBILITY_PROBLEM_SOLVED); DECLARE_STD_EXCEPTION(INVALID_WARMSTART); DECLARE_STD_EXCEPTION(INTERNAL_ABORT); DECLARE_STD_EXCEPTION(NO_FREE_VARIABLES_BUT_FEASIBLE); DECLARE_STD_EXCEPTION(NO_FREE_VARIABLES_AND_INFEASIBLE); /** Exception FAILED_INITIALIZATION for problem during * initialization of a strategy object (or other problems). This * is thrown by a strategy object, if a problem arises during * initialization, such as a value out of a feasible range. */ DECLARE_STD_EXCEPTION(FAILED_INITIALIZATION); //@} } #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpReferenced.hpp0000644000335100017570000002124011133661705026570 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpReferenced.hpp 1020 2007-06-24 04:06:59Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPREFERENCED_HPP__ #define __IPREFERENCED_HPP__ #include "IpTypes.hpp" #include "IpDebug.hpp" #include namespace Ipopt { /** Psydo-class, from which everything has to inherit that wants to * use be registered as a Referencer for a ReferencedObject. */ class Referencer {} ; /** ReferencedObject class. * This is part of the implementation of an intrusive smart pointer * design. This class stores the reference count of all the smart * pointers that currently reference it. See the documentation for * the SmartPtr class for more details. * * A SmartPtr behaves much like a raw pointer, but manages the lifetime * of an object, deleting the object automatically. This class implements * a reference-counting, intrusive smart pointer design, where all * objects pointed to must inherit off of ReferencedObject, which * stores the reference count. Although this is intrusive (native types * and externally authored classes require wrappers to be referenced * by smart pointers), it is a safer design. A more detailed discussion of * these issues follows after the usage information. * * Usage Example: * Note: to use the SmartPtr, all objects to which you point MUST * inherit off of ReferencedObject. * * \verbatim * * In MyClass.hpp... * * #include "IpReferenced.hpp" * namespace Ipopt { * * class MyClass : public ReferencedObject // must derive from ReferencedObject * { * ... * } * } // namespace Ipopt * * * In my_usage.cpp... * * #include "IpSmartPtr.hpp" * #include "MyClass.hpp" * * void func(AnyObject& obj) * { * SmartPtr ptr_to_myclass = new MyClass(...); * // ptr_to_myclass now points to a new MyClass, * // and the reference count is 1 * * ... * * obj.SetMyClass(ptr_to_myclass); * // Here, let's assume that AnyObject uses a * // SmartPtr internally here. * // Now, both ptr_to_myclass and the internal * // SmartPtr in obj point to the same MyClass object * // and its reference count is 2. * * ... * * // No need to delete ptr_to_myclass, this * // will be done automatically when the * // reference count drops to zero. * * } * * \endverbatim * * Other Notes: * The SmartPtr implements both dereference operators -> & *. * The SmartPtr does NOT implement a conversion operator to * the raw pointer. Use the GetRawPtr() method when this * is necessary. Make sure that the raw pointer is NOT * deleted. * The SmartPtr implements the comparison operators == & != * for a variety of types. Use these instead of * \verbatim * if (GetRawPtr(smrt_ptr) == ptr) // Don't use this * \endverbatim * SmartPtr's, as currently implemented, do NOT handle circular references. * For example: consider a higher level object using SmartPtrs to point to * A and B, but A and B also point to each other (i.e. A has a SmartPtr * to B and B has a SmartPtr to A). In this scenario, when the higher * level object is finished with A and B, their reference counts will * never drop to zero (since they reference each other) and they * will not be deleted. This can be detected by memory leak tools like * valgrind. If the circular reference is necessary, the problem can be * overcome by a number of techniques: * * 1) A and B can have a method that "releases" each other, that is * they set their internal SmartPtrs to NULL. * \verbatim * void AClass::ReleaseCircularReferences() * { * smart_ptr_to_B = NULL; * } * \endverbatim * Then, the higher level class can call these methods before * it is done using A & B. * * 2) Raw pointers can be used in A and B to reference each other. * Here, an implicit assumption is made that the lifetime is * controlled by the higher level object and that A and B will * both exist in a controlled manner. Although this seems * dangerous, in many situations, this type of referencing * is very controlled and this is reasonably safe. * * 3) This SmartPtr class could be redesigned with the Weak/Strong * design concept. Here, the SmartPtr is identified as being * Strong (controls lifetime of the object) or Weak (merely * referencing the object). The Strong SmartPtr increments * (and decrements) the reference count in ReferencedObject * but the Weak SmartPtr does not. In the example above, * the higher level object would have Strong SmartPtrs to * A and B, but A and B would have Weak SmartPtrs to each * other. Then, when the higher level object was done with * A and B, they would be deleted. The Weak SmartPtrs in A * and B would not decrement the reference count and would, * of course, not delete the object. This idea is very similar * to item (2), where it is implied that the sequence of events * is controlled such that A and B will not call anything using * their pointers following the higher level delete (i.e. in * their destructors!). This is somehow safer, however, because * code can be written (however expensive) to perform run-time * detection of this situation. For example, the ReferencedObject * could store pointers to all Weak SmartPtrs that are referencing * it and, in its destructor, tell these pointers that it is * dying. They could then set themselves to NULL, or set an * internal flag to detect usage past this point. * * Comments on Non-Intrusive Design: * In a non-intrusive design, the reference count is stored somewhere other * than the object being referenced. This means, unless the reference * counting pointer is the first referencer, it must get a pointer to the * referenced object from another smart pointer (so it has access to the * reference count location). In this non-intrusive design, if we are * pointing to an object with a smart pointer (or a number of smart * pointers), and we then give another smart pointer the address through * a RAW pointer, we will have two independent, AND INCORRECT, reference * counts. To avoid this pitfall, we use an intrusive reference counting * technique where the reference count is stored in the object being * referenced. */ class ReferencedObject { public: ReferencedObject() : reference_count_(0) {} virtual ~ReferencedObject() { DBG_ASSERT(reference_count_ == 0); } Index ReferenceCount() const; void AddRef(const Referencer* referencer) const; void ReleaseRef(const Referencer* referencer) const; private: mutable Index reference_count_; # ifdef REF_DEBUG mutable std::list referencers_; # endif }; /* inline methods */ inline Index ReferencedObject::ReferenceCount() const { // DBG_START_METH("ReferencedObject::ReferenceCount()", 0); // DBG_PRINT((1,"Returning reference_count_ = %d\n", reference_count_)); return reference_count_; } inline void ReferencedObject::AddRef(const Referencer* referencer) const { // DBG_START_METH("ReferencedObject::AddRef(const Referencer* referencer)", 0); reference_count_++; // DBG_PRINT((1, "New reference_count_ = %d\n", reference_count_)); # ifdef REF_DEBUG referencers_.push_back(referencer); # endif } inline void ReferencedObject::ReleaseRef(const Referencer* referencer) const { // DBG_START_METH("ReferencedObject::ReleaseRef(const Referencer* referencer)", // 0); reference_count_--; // DBG_PRINT((1, "New reference_count_ = %d\n", reference_count_)); # ifdef REF_DEBUG bool found = false; std::list::iterator iter; for (iter = referencers_.begin(); iter != referencers_.end(); iter++) { if ((*iter) == referencer) { found = true; break; } } // cannot call release on a reference that was never added... DBG_ASSERT(found); if (found) { referencers_.erase(iter); } # endif } } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpTaggedObject.hpp0000644000335100017570000001126411133661705027055 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpTaggedObject.hpp 1020 2007-06-24 04:06:59Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPTAGGEDOBJECT_HPP__ #define __IPTAGGEDOBJECT_HPP__ #include "IpUtils.hpp" #include "IpDebug.hpp" #include "IpReferenced.hpp" #include "IpObserver.hpp" #include namespace Ipopt { /** TaggedObject class. * Often, certain calculations or operations are expensive, * and it can be very inefficient to perform these calculations * again if the input to the calculation has not changed * since the result was last stored. * This base class provides an efficient mechanism to update * a tag, indicating that the object has changed. * Users of a TaggedObject class, need their own Tag data * member to keep track of the state of the TaggedObject, the * last time they performed a calculation. A basic use case for * users of a class inheriting off of TaggedObject follows like * this: * * Initialize your own Tag to zero in constructor. * * * * Before an expensive calculation, * check if the TaggedObject has changed, passing in * your own Tag, indicating the last time you used * the object for the calculation. If it has changed, * perform the calculation again, and store the result. * If it has not changed, simply return the stored result. * * Here is a simple example: \verbatim if (vector.HasChanged(my_vector_tag_)) { my_vector_tag_ = vector.GetTag(); result = PerformExpensiveCalculation(vector); return result; } else { return result; } \endverbatim * * Objects derived from TaggedObject: * Objects derived from TaggedObject must indicate that they have changed to * the base class using the protected member function ObjectChanged(). For * example, a Vector class, inside its own set method, MUST call * ObjectChanged() to update the internally stored tag for comparison. */ class TaggedObject : public ReferencedObject, public Subject { public: /** Type for the Tag values */ typedef unsigned int Tag; /** Constructor. */ TaggedObject() : Subject() { ObjectChanged(); } /** Destructor. */ virtual ~TaggedObject() {} /** Users of TaggedObjects call this to * update their own internal tags every time * they perform the expensive operation. */ Tag GetTag() const { return tag_; } /** Users of TaggedObjects call this to * check if the object HasChanged since * they last updated their own internal * tag. */ bool HasChanged(const Tag comparison_tag) const { return (comparison_tag == tag_) ? false : true; } protected: /** Objects derived from TaggedObject MUST call this * method every time their internal state changes to * update the internal tag for comparison */ void ObjectChanged() { DBG_START_METH("TaggedObject::ObjectChanged()", 0); tag_ = unique_tag_; unique_tag_++; DBG_ASSERT(unique_tag_ < std::numeric_limits::max()); // The Notify method from the Subject base class notifies all // registed Observers that this subject has changed. Notify(Observer::NT_Changed); } private: /**@name Default Compiler Generated Methods (Hidden to avoid * implicit creation/calling). These methods are not implemented * and we do not want the compiler to implement them for us, so we * declare them private and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Copy Constructor */ TaggedObject(const TaggedObject&); /** Overloaded Equals Operator */ void operator=(const TaggedObject&); //@} /** static data member that is incremented every * time ANY TaggedObject changes. This allows us * to obtain a unique Tag when the object changes */ static Tag unique_tag_; /** The tag indicating the current state of the object. * We use this to compare against the comparison_tag * in the HasChanged method. This member is updated * from the unique_tag_ every time the object changes. */ Tag tag_; /** The index indicating the cache priority for this * TaggedObject. If a result that depended on this * TaggedObject is cached, it will be cached with this * priority */ Index cache_priority_; }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpVector.hpp0000644000335100017570000004751411133661705026004 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpVector.hpp 1054 2007-07-17 23:09:07Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPVECTOR_HPP__ #define __IPVECTOR_HPP__ #include "IpTypes.hpp" #include "IpTaggedObject.hpp" #include "IpCachedResults.hpp" #include "IpSmartPtr.hpp" #include "IpJournalist.hpp" #include namespace Ipopt { /* forward declarations */ class VectorSpace; /** Vector Base Class. * This is the base class for all derived vector types. Those vectors * are meant to store entities like iterates, Lagrangian multipliers, * constraint values etc. The implementation of a vector type depends * on the computational environment (e.g. just a double array on a shared * memory machine, or distributed double arrays for a distributed * memory machine.) * * Deriving from Vector: This class inherits from tagged object to * implement an advanced caching scheme. Because of this, the * TaggedObject method ObjectChanged() must be called each time the * Vector changes. If you overload the XXXX_Impl protected methods, * this taken care of (along with caching if possible) for you. If * you have additional methods in your derived class that change the * underlying data (vector values), you MUST remember to call * ObjectChanged() AFTER making the change! */ class Vector : public TaggedObject { public: /** @name Constructor/Destructor */ //@{ /** Constructor. It has to be given a pointer to the * corresponding VectorSpace. */ Vector(const VectorSpace* owner_space); /** Destructor */ virtual ~Vector(); //@} /** Create new Vector of the same type with uninitialized data */ Vector* MakeNew() const; /** Create new Vector of the same type and copy the data over */ Vector* MakeNewCopy() const; /**@name Standard BLAS-1 Operations * (derived classes do NOT overload these * methods, instead, overload the * protected versions of these methods). */ //@{ /** Copy the data of the vector x into this vector (DCOPY). */ void Copy(const Vector& x); /** Scales the vector by scalar alpha (DSCAL) */ void Scal(Number alpha); /** Add the multiple alpha of vector x to this vector (DAXPY) */ void Axpy(Number alpha, const Vector &x); /** Computes inner product of vector x with this (DDOT) */ Number Dot(const Vector &x) const; /** Computes the 2-norm of this vector (DNRM2) */ Number Nrm2() const; /** Computes the 1-norm of this vector (DASUM) */ Number Asum() const; /** Computes the max-norm of this vector (based on IDAMAX) */ Number Amax() const; //@} /** @name Additional (Non-BLAS) Vector Methods * (derived classes do NOT overload these * methods, instead, overload the * protected versions of these methods). */ //@{ /** Set each element in the vector to the scalar alpha. */ void Set(Number alpha); /** Element-wise division \f$y_i \gets y_i/x_i\f$*/ void ElementWiseDivide(const Vector& x); /** Element-wise multiplication \f$y_i \gets y_i*x_i\f$ */ void ElementWiseMultiply(const Vector& x); /** Element-wise max against entries in x */ void ElementWiseMax(const Vector& x); /** Element-wise min against entries in x */ void ElementWiseMin(const Vector& x); /** Reciprocates the entries in the vector */ void ElementWiseReciprocal(); /** Absolute values of the entries in the vector */ void ElementWiseAbs(); /** Element-wise square root of the entries in the vector */ void ElementWiseSqrt(); /** Replaces the vector values with their sgn values ( -1 if x_i < 0, 0 if x_i == 0, and 1 if x_i > 0) */ void ElementWiseSgn(); /** Add scalar to every vector component */ void AddScalar(Number scalar); /** Returns the maximum value in the vector */ Number Max() const; /** Returns the minimum value in the vector */ Number Min() const; /** Returns the sum of the vector entries */ Number Sum() const; /** Returns the sum of the logs of each vector entry */ Number SumLogs() const; //@} /** @name Methods for specialized operations. A prototype * implementation is provided, but for efficient implementation * those should be specially implemented. */ //@{ /** Add one vector, y = a * v1 + c * y. This is automatically * reduced to call AddTwoVectors. */ void AddOneVector(Number a, const Vector& v1, Number c); /** Add two vectors, y = a * v1 + b * v2 + c * y. Here, this * vector is y */ void AddTwoVectors(Number a, const Vector& v1, Number b, const Vector& v2, Number c); /** Fraction to the boundary parameter. Computes \f$\alpha = * \max\{\bar\alpha\in(0,1] : x + \bar\alpha \Delta \geq (1-\tau)x\}\f$ */ Number FracToBound(const Vector& delta, Number tau) const; /** Add the quotient of two vectors, y = a * z/s + c * y. */ void AddVectorQuotient(Number a, const Vector& z, const Vector& s, Number c); //@} /** Method for determining if all stored numbers are valid (i.e., * no Inf or Nan). */ bool HasValidNumbers() const; /** @name Accessor methods */ //@{ /** Dimension of the Vector */ Index Dim() const; /** Return the owner VectorSpace*/ SmartPtr OwnerSpace() const; //@} /** @name Output methods * (derived classes do NOT overload these * methods, instead, overload the * protected versions of these methods). */ //@{ /** Print the entire vector */ void Print(SmartPtr jnlst, EJournalLevel level, EJournalCategory category, const std::string& name, Index indent=0, const std::string& prefix="") const; void Print(const Journalist& jnlst, EJournalLevel level, EJournalCategory category, const std::string& name, Index indent=0, const std::string& prefix="") const; //@} protected: /** @name implementation methods (derived classes MUST * overload these pure virtual protected methods.) */ //@{ /** Copy the data of the vector x into this vector (DCOPY). */ virtual void CopyImpl(const Vector& x)=0; /** Scales the vector by scalar alpha (DSCAL) */ virtual void ScalImpl(Number alpha)=0; /** Add the multiple alpha of vector x to this vector (DAXPY) */ virtual void AxpyImpl(Number alpha, const Vector &x)=0; /** Computes inner product of vector x with this (DDOT) */ virtual Number DotImpl(const Vector &x) const =0; /** Computes the 2-norm of this vector (DNRM2) */ virtual Number Nrm2Impl() const =0; /** Computes the 1-norm of this vector (DASUM) */ virtual Number AsumImpl() const =0; /** Computes the max-norm of this vector (based on IDAMAX) */ virtual Number AmaxImpl() const =0; /** Set each element in the vector to the scalar alpha. */ virtual void SetImpl(Number alpha)=0; /** Element-wise division \f$y_i \gets y_i/x_i\f$*/ virtual void ElementWiseDivideImpl(const Vector& x)=0; /** Element-wise multiplication \f$y_i \gets y_i*x_i\f$ */ virtual void ElementWiseMultiplyImpl(const Vector& x)=0; /** Element-wise max against entries in x */ virtual void ElementWiseMaxImpl(const Vector& x)=0; /** Element-wise min against entries in x */ virtual void ElementWiseMinImpl(const Vector& x)=0; /** Reciprocates the elements of the vector */ virtual void ElementWiseReciprocalImpl()=0; /** Take elementwise absolute values of the elements of the vector */ virtual void ElementWiseAbsImpl()=0; /** Take elementwise square-root of the elements of the vector */ virtual void ElementWiseSqrtImpl()=0; /** Replaces entries with sgn of the entry */ virtual void ElementWiseSgnImpl()=0; /** Add scalar to every component of vector */ virtual void AddScalarImpl(Number scalar)=0; /** Max value in the vector */ virtual Number MaxImpl() const=0; /** Min number in the vector */ virtual Number MinImpl() const=0; /** Sum of entries in the vector */ virtual Number SumImpl() const=0; /** Sum of logs of entries in the vector */ virtual Number SumLogsImpl() const=0; /** Add two vectors (a * v1 + b * v2). Result is stored in this vector. */ virtual void AddTwoVectorsImpl(Number a, const Vector& v1, Number b, const Vector& v2, Number c); /** Fraction to boundary parameter. */ virtual Number FracToBoundImpl(const Vector& delta, Number tau) const; /** Add the quotient of two vectors */ virtual void AddVectorQuotientImpl(Number a, const Vector& z, const Vector& s, Number c); /** Method for determining if all stored numbers are valid (i.e., * no Inf or Nan). A default implementation using Asum is * provided. */ virtual bool HasValidNumbersImpl() const; /** Print the entire vector */ virtual void PrintImpl(const Journalist& jnlst, EJournalLevel level, EJournalCategory category, const std::string& name, Index indent, const std::string& prefix) const =0; //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default constructor */ Vector(); /** Copy constructor */ Vector(const Vector&); /** Overloaded Equals Operator */ Vector& operator=(const Vector&); //@} /** Vector Space */ const SmartPtr owner_space_; /**@name CachedResults data members */ //@{ /** Cache for dot products */ mutable CachedResults dot_cache_; mutable TaggedObject::Tag nrm2_cache_tag_; mutable Number cached_nrm2_; mutable TaggedObject::Tag asum_cache_tag_; mutable Number cached_asum_; mutable TaggedObject::Tag amax_cache_tag_; mutable Number cached_amax_; mutable TaggedObject::Tag max_cache_tag_; mutable Number cached_max_; mutable TaggedObject::Tag min_cache_tag_; mutable Number cached_min_; mutable TaggedObject::Tag sum_cache_tag_; mutable Number cached_sum_; mutable TaggedObject::Tag sumlogs_cache_tag_; mutable Number cached_sumlogs_; mutable TaggedObject::Tag valid_cache_tag_; mutable bool cached_valid_; // AW: I removed this cache since it gets in the way for the // quality function search // /** Cache for FracToBound */ // mutable CachedResults frac_to_bound_cache_; //@} }; /** VectorSpace base class, corresponding to the Vector base class. * For each Vector implementation, a corresponding VectorSpace has * to be implemented. A VectorSpace is able to create new Vectors * of a specific type. The VectorSpace should also store * information that is common to all Vectors of that type. For * example, the dimension of a Vector is stored in the VectorSpace * base class. */ class VectorSpace : public ReferencedObject { public: /** @name Constructors/Destructors */ //@{ /** Constructor, given the dimension of all vectors generated by * this VectorSpace. */ VectorSpace(Index dim); /** Destructor */ virtual ~VectorSpace() {} //@} /** Pure virtual method for creating a new Vector of the * corresponding type. */ virtual Vector* MakeNew() const=0; /** Accessor function for the dimension of the vectors of this type.*/ Index Dim() const { return dim_; } private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** default constructor */ VectorSpace(); /** Copy constructor */ VectorSpace(const VectorSpace&); /** Overloaded Equals Operator */ VectorSpace& operator=(const VectorSpace&); //@} /** Dimension of the vectors in this vector space. */ const Index dim_; }; /* inline methods */ inline Vector::~Vector() {} inline Vector::Vector(const VectorSpace* owner_space) : TaggedObject(), owner_space_(owner_space), dot_cache_(10), nrm2_cache_tag_(0), asum_cache_tag_(0), amax_cache_tag_(0), max_cache_tag_(0), min_cache_tag_(0), sum_cache_tag_(0), sumlogs_cache_tag_(0), cached_valid_(0) { DBG_ASSERT(IsValid(owner_space_)); } inline Vector* Vector::MakeNew() const { return owner_space_->MakeNew(); } inline Vector* Vector::MakeNewCopy() const { // ToDo: We can probably copy also the cached values for Norms etc here Vector* copy = MakeNew(); copy->Copy(*this); return copy; } inline void Vector::Copy(const Vector& x) { CopyImpl(x); ObjectChanged(); // Also copy any cached scalar values from the original vector // ToDo: Check if that is too much overhead TaggedObject::Tag x_tag = x.GetTag(); if (x_tag == x.nrm2_cache_tag_) { nrm2_cache_tag_ = GetTag(); cached_nrm2_ = x.cached_nrm2_; } if (x_tag == x.asum_cache_tag_) { asum_cache_tag_ = GetTag(); cached_asum_ = x.cached_asum_; } if (x_tag == x.amax_cache_tag_) { amax_cache_tag_ = GetTag(); cached_amax_ = x.cached_amax_; } if (x_tag == x.max_cache_tag_) { max_cache_tag_ = GetTag(); cached_max_ = x.cached_max_; } if (x_tag == x.min_cache_tag_) { min_cache_tag_ = GetTag(); cached_min_ = x.cached_min_; } if (x_tag == x.sum_cache_tag_) { sum_cache_tag_ = GetTag(); cached_sum_ = x.cached_sum_; } if (x_tag == x.sumlogs_cache_tag_) { sumlogs_cache_tag_ = GetTag(); cached_sumlogs_ = x.cached_sumlogs_; } } inline void Vector::Axpy(Number alpha, const Vector &x) { AxpyImpl(alpha, x); ObjectChanged(); } inline Number Vector::Dot(const Vector &x) const { // The current implementation of the caching doesn't allow to have // a dependency of something with itself. Therefore, we use the // Nrm2 method if the dot product is to be taken with the vector // itself. Might be more efficient anyway. if (this==&x) { Number nrm2 = Nrm2(); return nrm2*nrm2; } Number retValue; if (!dot_cache_.GetCachedResult2Dep(retValue, this, &x)) { retValue = DotImpl(x); dot_cache_.AddCachedResult2Dep(retValue, this, &x); } return retValue; } inline Number Vector::Nrm2() const { if (nrm2_cache_tag_ != GetTag()) { cached_nrm2_ = Nrm2Impl(); nrm2_cache_tag_ = GetTag(); } return cached_nrm2_; } inline Number Vector::Asum() const { if (asum_cache_tag_ != GetTag()) { cached_asum_ = AsumImpl(); asum_cache_tag_ = GetTag(); } return cached_asum_; } inline Number Vector::Amax() const { if (amax_cache_tag_ != GetTag()) { cached_amax_ = AmaxImpl(); amax_cache_tag_ = GetTag(); } return cached_amax_; } inline Number Vector::Sum() const { if (sum_cache_tag_ != GetTag()) { cached_sum_ = SumImpl(); sum_cache_tag_ = GetTag(); } return cached_sum_; } inline Number Vector::SumLogs() const { if (sumlogs_cache_tag_ != GetTag()) { cached_sumlogs_ = SumLogsImpl(); sumlogs_cache_tag_ = GetTag(); } return cached_sumlogs_; } inline void Vector::ElementWiseSgn() { ElementWiseSgnImpl(); ObjectChanged(); } inline void Vector::Set(Number alpha) { // Could initialize caches here SetImpl(alpha); ObjectChanged(); } inline void Vector::ElementWiseDivide(const Vector& x) { ElementWiseDivideImpl(x); ObjectChanged(); } inline void Vector::ElementWiseMultiply(const Vector& x) { ElementWiseMultiplyImpl(x); ObjectChanged(); } inline void Vector::ElementWiseReciprocal() { ElementWiseReciprocalImpl(); ObjectChanged(); } inline void Vector::ElementWiseMax(const Vector& x) { // Could initialize some caches here ElementWiseMaxImpl(x); ObjectChanged(); } inline void Vector::ElementWiseMin(const Vector& x) { // Could initialize some caches here ElementWiseMinImpl(x); ObjectChanged(); } inline void Vector::ElementWiseAbs() { // Could initialize some caches here ElementWiseAbsImpl(); ObjectChanged(); } inline void Vector::ElementWiseSqrt() { ElementWiseSqrtImpl(); ObjectChanged(); } inline void Vector::AddScalar(Number scalar) { // Could initialize some caches here AddScalarImpl(scalar); ObjectChanged(); } inline Number Vector::Max() const { if (max_cache_tag_ != GetTag()) { cached_max_ = MaxImpl(); max_cache_tag_ = GetTag(); } return cached_max_; } inline Number Vector::Min() const { if (min_cache_tag_ != GetTag()) { cached_min_ = MinImpl(); min_cache_tag_ = GetTag(); } return cached_min_; } inline void Vector::AddOneVector(Number a, const Vector& v1, Number c) { AddTwoVectorsImpl(a, v1, 0., v1, c); } inline void Vector::AddTwoVectors(Number a, const Vector& v1, Number b, const Vector& v2, Number c) { AddTwoVectorsImpl(a, v1, b, v2, c); ObjectChanged(); } inline Number Vector::FracToBound(const Vector& delta, Number tau) const { /* AW: I avoid the caching here, since it leads to overhead in the quality function search. Caches for this are in CalculatedQuantities. Number retValue; std::vector tdeps(1); tdeps[0] = δ std::vector sdeps(1); sdeps[0] = tau; if (!frac_to_bound_cache_.GetCachedResult(retValue, tdeps, sdeps)) { retValue = FracToBoundImpl(delta, tau); frac_to_bound_cache_.AddCachedResult(retValue, tdeps, sdeps); } return retValue; */ return FracToBoundImpl(delta, tau); } inline void Vector::AddVectorQuotient(Number a, const Vector& z, const Vector& s, Number c) { AddVectorQuotientImpl(a, z, s, c); ObjectChanged(); } inline bool Vector::HasValidNumbers() const { if (valid_cache_tag_ != GetTag()) { cached_valid_ = HasValidNumbersImpl(); valid_cache_tag_ = GetTag(); } return cached_valid_; } inline Index Vector::Dim() const { return owner_space_->Dim(); } inline SmartPtr Vector::OwnerSpace() const { return owner_space_; } inline VectorSpace::VectorSpace(Index dim) : dim_(dim) {} } // namespace Ipopt // Macro definitions for debugging vectors #if COIN_IPOPT_VERBOSITY == 0 # define DBG_PRINT_VECTOR(__verbose_level, __vec_name, __vec) #else # define DBG_PRINT_VECTOR(__verbose_level, __vec_name, __vec) \ if (dbg_jrnl.Verbosity() >= (__verbose_level)) { \ if (dbg_jrnl.Jnlst()!=NULL) { \ (__vec).Print(dbg_jrnl.Jnlst(), \ J_ERROR, J_DBG, \ __vec_name, \ dbg_jrnl.IndentationLevel()*2, \ "# "); \ } \ } #endif //if COIN_IPOPT_VERBOSITY == 0 #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpMatrix.hpp0000644000335100017570000002212211133661705025772 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpMatrix.hpp 1054 2007-07-17 23:09:07Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPMATRIX_HPP__ #define __IPMATRIX_HPP__ #include "IpVector.hpp" namespace Ipopt { /* forward declarations */ class MatrixSpace; /** Matrix Base Class. This is the base class for all derived matrix * types. All Matrices, such as Jacobian and Hessian matrices, as * well as possibly the iteration matrices needed for the step * computation, are of this type. * * Deriving from Matrix: Overload the protected XXX_Impl method. */ class Matrix : public TaggedObject { public: /** @name Constructor/Destructor */ //@{ /** Constructor. It has to be given a pointer to the * corresponding MatrixSpace. */ Matrix(const MatrixSpace* owner_space) : TaggedObject(), owner_space_(owner_space), valid_cache_tag_(0) {} /** Destructor */ virtual ~Matrix() {} //@} /**@name Operations of the Matrix on a Vector */ //@{ /** Matrix-vector multiply. Computes y = alpha * Matrix * x + * beta * y. Do not overload. Overload MultVectorImpl instead. */ void MultVector(Number alpha, const Vector& x, Number beta, Vector& y) const { MultVectorImpl(alpha, x, beta, y); } /** Matrix(transpose) vector multiply. Computes y = alpha * * Matrix^T * x + beta * y. Do not overload. Overload * TransMultVectorImpl instead. */ void TransMultVector(Number alpha, const Vector& x, Number beta, Vector& y) const { TransMultVectorImpl(alpha, x, beta, y); } //@} /** @name Methods for specialized operations. A prototype * implementation is provided, but for efficient implementation * those should be specially implemented. */ //@{ /** X = X + alpha*(Matrix S^{-1} Z). Should be implemented * efficiently for the ExansionMatrix */ void AddMSinvZ(Number alpha, const Vector& S, const Vector& Z, Vector& X) const; /** X = S^{-1} (r + alpha*Z*M^Td). Should be implemented * efficiently for the ExansionMatrix */ void SinvBlrmZMTdBr(Number alpha, const Vector& S, const Vector& R, const Vector& Z, const Vector& D, Vector& X) const; //@} /** Method for determining if all stored numbers are valid (i.e., * no Inf or Nan). */ bool HasValidNumbers() const; //* @name Information about the size of the matrix */ //@{ /** Number of rows */ Index NRows() const; /** Number of columns */ Index NCols() const; //@} /** Print detailed information about the matrix. Do not overload. * Overload PrintImpl instead. */ //@{ virtual void Print(SmartPtr jnlst, EJournalLevel level, EJournalCategory category, const std::string& name, Index indent=0, const std::string& prefix="") const; virtual void Print(const Journalist& jnlst, EJournalLevel level, EJournalCategory category, const std::string& name, Index indent=0, const std::string& prefix="") const; //@} /** Return the owner MatrixSpace*/ SmartPtr OwnerSpace() const; protected: /** @name implementation methods (derived classes MUST * overload these pure virtual protected methods. */ //@{ /** Matrix-vector multiply. Computes y = alpha * Matrix * x + * beta * y */ virtual void MultVectorImpl(Number alpha, const Vector& x, Number beta, Vector& y) const =0; /** Matrix(transpose) vector multiply. * Computes y = alpha * Matrix^T * x + beta * y */ virtual void TransMultVectorImpl(Number alpha, const Vector& x, Number beta, Vector& y) const =0; /** X = X + alpha*(Matrix S^{-1} Z). Prototype for this * specialize method is provided, but for efficient * implementation it should be overloaded for the expansion matrix. */ virtual void AddMSinvZImpl(Number alpha, const Vector& S, const Vector& Z, Vector& X) const; /** X = S^{-1} (r + alpha*Z*M^Td). Should be implemented * efficiently for the ExpansionMatrix. */ virtual void SinvBlrmZMTdBrImpl(Number alpha, const Vector& S, const Vector& R, const Vector& Z, const Vector& D, Vector& X) const; /** Method for determining if all stored numbers are valid (i.e., * no Inf or Nan). A default implementation always returning true * is provided, but if possible it should be implemented. */ virtual bool HasValidNumbersImpl() const { return true; } /** Print detailed information about the matrix. */ virtual void PrintImpl(const Journalist& jnlst, EJournalLevel level, EJournalCategory category, const std::string& name, Index indent, const std::string& prefix) const =0; //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** default constructor */ Matrix(); /** Copy constructor */ Matrix(const Matrix&); /** Overloaded Equals Operator */ Matrix& operator=(const Matrix&); //@} const SmartPtr owner_space_; /**@name CachedResults data members */ //@{ mutable TaggedObject::Tag valid_cache_tag_; mutable bool cached_valid_; //@} }; /** MatrixSpace base class, corresponding to the Matrix base class. * For each Matrix implementation, a corresponding MatrixSpace has * to be implemented. A MatrixSpace is able to create new Matrices * of a specific type. The MatrixSpace should also store * information that is common to all Matrices of that type. For * example, the dimensions of a Matrix is stored in the MatrixSpace * base class. */ class MatrixSpace : public ReferencedObject { public: /** @name Constructors/Destructors */ //@{ /** Constructor, given the number rows and columns of all matrices * generated by this MatrixSpace. */ MatrixSpace(Index nRows, Index nCols) : nRows_(nRows), nCols_(nCols) {} /** Destructor */ virtual ~MatrixSpace() {} //@} /** Pure virtual method for creating a new Matrix of the * corresponding type. */ virtual Matrix* MakeNew() const=0; /** Accessor function for the number of rows. */ Index NRows() const { return nRows_; } /** Accessor function for the number of columns. */ Index NCols() const { return nCols_; } /** Method to test if a given matrix belongs to a particular * matrix space. */ bool IsMatrixFromSpace(const Matrix& matrix) const { return (matrix.OwnerSpace() == this); } private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** default constructor */ MatrixSpace(); /** Copy constructor */ MatrixSpace(const MatrixSpace&); /** Overloaded Equals Operator */ MatrixSpace& operator=(const MatrixSpace&); //@} /** Number of rows for all matrices of this type. */ const Index nRows_; /** Number of columns for all matrices of this type. */ const Index nCols_; }; /* Inline Methods */ inline Index Matrix::NRows() const { return owner_space_->NRows(); } inline Index Matrix::NCols() const { return owner_space_->NCols(); } inline SmartPtr Matrix::OwnerSpace() const { return owner_space_; } } // namespace Ipopt // Macro definitions for debugging matrices #if COIN_IPOPT_VERBOSITY == 0 # define DBG_PRINT_MATRIX(__verbose_level, __mat_name, __mat) #else # define DBG_PRINT_MATRIX(__verbose_level, __mat_name, __mat) \ if (dbg_jrnl.Verbosity() >= (__verbose_level)) { \ if (dbg_jrnl.Jnlst()!=NULL) { \ (__mat).Print(dbg_jrnl.Jnlst(), \ J_ERROR, J_DBG, \ __mat_name, \ dbg_jrnl.IndentationLevel()*2, \ "# "); \ } \ } #endif // #if COIN_IPOPT_VERBOSITY == 0 #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpSolveStatistics.hpp0000644000335100017570000001123011133661705027667 0ustar pgehlerwebadm// Copyright (C) 2005, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpSolveStatistics.hpp 759 2006-07-07 03:07:08Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2005-08-15 #ifndef __IPSOLVESTATISTICS_HPP__ #define __IPSOLVESTATISTICS_HPP__ #include "IpReferenced.hpp" #include "IpSmartPtr.hpp" namespace Ipopt { // forward declaration (to avoid inclusion of too many header files) class IpoptNLP; class IpoptData; class IpoptCalculatedQuantities; /** This class collects statistics about an optimziation run, such * as iteration count, final infeasibilities etc. It is meant to * provide such information to a user of Ipopt during the * finalize_solution call. */ class SolveStatistics : public ReferencedObject { public: /**@name Constructors/Destructors */ //@{ /** Default constructor. It takes in those collecting Ipopt * objects that can provide the statistics information. Those * statistics are retrieved at the time of the constructor * call. */ SolveStatistics(const SmartPtr& ip_nlp, const SmartPtr& ip_data, const SmartPtr& ip_cq); /** Default destructor */ virtual ~SolveStatistics() {} //@} /** @name Accessor methods for retrieving different kind of solver * statistics information */ //@{ /** Iteration counts. */ Index IterationCount() const; /** Total CPU time, including function evaluations. */ Number TotalCPUTime() const; /** Number of NLP function evaluations. */ void NumberOfEvaluations(Index& num_obj_evals, Index& num_constr_evals, Index& num_obj_grad_evals, Index& num_constr_jac_evals, Index& num_hess_evals) const; /** Unscaled solution infeasibilities */ void Infeasibilities(Number& dual_inf, Number& constr_viol, Number& complementarity, Number& kkt_error) const; /** Scaled solution infeasibilities */ void ScaledInfeasibilities(Number& scaled_dual_inf, Number& scaled_constr_viol, Number& scaled_complementarity, Number& scaled_kkt_error) const; /** Final value of objective function */ Number FinalObjective() const; /** Final scaled value of objective function */ Number FinalScaledObjective() const; //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ SolveStatistics(); /** Copy Constructor */ SolveStatistics(const SolveStatistics&); /** Overloaded Equals Operator */ void operator=(const SolveStatistics&); //@} /** @name Fields for storing the statistics data */ //@{ /** Number of iterations. */ Index num_iters_; /* Total CPU time */ Number total_cpu_time_; /** Number of objective function evaluations. */ Index num_obj_evals_; /** Number of constraints evaluations (max of equality and * inequality) */ Index num_constr_evals_; /** Number of objective gradient evaluations. */ Index num_obj_grad_evals_; /** Number of constraint Jacobian evaluations. */ Index num_constr_jac_evals_; /** Number of Lagrangian Hessian evaluations. */ Index num_hess_evals_; /** Final scaled value of objective function */ Number scaled_obj_val_; /** Final unscaled value of objective function */ Number obj_val_; /** Final scaled dual infeasibility (max-norm) */ Number scaled_dual_inf_; /** Final unscaled dual infeasibility (max-norm) */ Number dual_inf_; /** Final scaled constraint violation (max-norm) */ Number scaled_constr_viol_; /** Final unscaled constraint violation (max-norm) */ Number constr_viol_; /** Final scaled complementarity error (max-norm) */ Number scaled_compl_; /** Final unscaled complementarity error (max-norm) */ Number compl_; /** Final overall scaled KKT error (max-norm) */ Number scaled_kkt_error_; /** Final overall unscaled KKT error (max-norm) */ Number kkt_error_; //@} }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpDebug.hpp0000644000335100017570000000740111133661705025557 0ustar pgehlerwebadm// Copyright (C) 2004, 2007 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpDebug.hpp 1010 2007-06-21 15:54:36Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPDEBUG_HPP__ #define __IPDEBUG_HPP__ #include "IpoptConfig.h" #include "IpTypes.hpp" #ifdef HAVE_CASSERT # include #else # ifdef HAVE_ASSERT_H # include # else # error "don't have header file for assert" # endif #endif #if COIN_IPOPT_CHECKLEVEL > 0 # ifdef NDEBUG # undef NDEBUG # endif # define DBG_ASSERT(test) assert(test) # define DBG_ASSERT_EXCEPTION(__condition, __except_type, __msg) \ ASSERT_EXCEPTION( (__condition), __except_type, __msg); # define DBG_DO(__cmd) __cmd #else # define DBG_ASSERT(test) # define DBG_ASSERT_EXCEPTION(__condition, __except_type, __msg) # define DBG_DO(__cmd) #endif #if COIN_IPOPT_VERBOSITY < 1 # define DBG_START_FUN(__func_name, __verbose_level) # define DBG_START_METH(__func_name, __verbose_level) # define DBG_PRINT(__printf_args) # define DBG_PRINT_VECTOR(__verbose_level, __vec_name, __vec) # define DBG_PRINT_MATRIX(__verbose_level, __mat_name, __mat) # define DBG_EXEC(__verbosity, __cmd) # define DBG_VERBOSITY() 0 #else #include namespace Ipopt { // forward definition class Journalist; /** Class that lives throughout the execution of a method or * function for which debug output is to be generated. The output * is sent to the unique debug journalist that is set with * SetJournalist at the beginning of program execution. */ class DebugJournalistWrapper { public: /** @name Constructors/Destructors. */ //@{ DebugJournalistWrapper(std::string func_name, Index verbose_level); DebugJournalistWrapper(std::string func_name, Index verbose_level, const void* const method_owner); ~DebugJournalistWrapper(); //@} /** @name accessor methods */ //@{ Index Verbosity() { return verbose_level_; } const Journalist* Jnlst() { return jrnl_; } Index IndentationLevel() { return indentation_level_; } //@} /** Printing */ void DebugPrintf(Index verbosity, const char* pformat, ...); /* Method for initialization of the static GLOBAL journalist, * through with all debug printout is to be written. This needs * to be set before any debug printout can be done. */ static void SetJournalist(Journalist* jrnl); private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** default constructor */ DebugJournalistWrapper(); /** copy contructor */ DebugJournalistWrapper(const DebugJournalistWrapper&); /** Overloaded Equals Operator */ DebugJournalistWrapper& operator=(const DebugJournalistWrapper&); //@} static Index indentation_level_; std::string func_name_; Index verbose_level_; const void* method_owner_; static Journalist* jrnl_; }; } # define DBG_START_FUN(__func_name, __verbose_level) \ DebugJournalistWrapper dbg_jrnl((__func_name), (__verbose_level)); \ # define DBG_START_METH(__func_name, __verbose_level) \ DebugJournalistWrapper dbg_jrnl((__func_name), (__verbose_level), this); # define DBG_PRINT(__args) \ dbg_jrnl.DebugPrintf __args; # define DBG_EXEC(__verbose_level, __cmd) \ if (dbg_jrnl.Verbosity() >= (__verbose_level)) { \ (__cmd); \ } # define DBG_VERBOSITY() \ dbg_jrnl.Verbosity() #endif #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpNLP.hpp0000644000335100017570000002214711133661705025166 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpNLP.hpp 1010 2007-06-21 15:54:36Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPNLP_HPP__ #define __IPNLP_HPP__ #include "IpUtils.hpp" #include "IpVector.hpp" #include "IpSmartPtr.hpp" #include "IpMatrix.hpp" #include "IpSymMatrix.hpp" #include "IpOptionsList.hpp" #include "IpAlgTypes.hpp" #include "IpReturnCodes.hpp" namespace Ipopt { // forward declarations class IpoptData; class IpoptCalculatedQuantities; class IteratesVector; /** Brief Class Description. * Detailed Class Description. */ class NLP : public ReferencedObject { public: /**@name Constructors/Destructors */ //@{ /** Default constructor */ NLP() {} /** Default destructor */ virtual ~NLP() {} //@} /** Exceptions */ //@{ DECLARE_STD_EXCEPTION(USER_SCALING_NOT_IMPLEMENTED); //@} /** @name NLP Initialization (overload in * derived classes).*/ //@{ /** Overload if you want the chance to process options or parameters that * may be specific to the NLP */ virtual bool ProcessOptions(const OptionsList& options, const std::string& prefix) { return true; } /** Method for creating the derived vector / matrix types. The * Hess_lagrangian_space pointer can be NULL if a quasi-Newton * options is chosen. */ virtual bool GetSpaces(SmartPtr& x_space, SmartPtr& c_space, SmartPtr& d_space, SmartPtr& x_l_space, SmartPtr& px_l_space, SmartPtr& x_u_space, SmartPtr& px_u_space, SmartPtr& d_l_space, SmartPtr& pd_l_space, SmartPtr& d_u_space, SmartPtr& pd_u_space, SmartPtr& Jac_c_space, SmartPtr& Jac_d_space, SmartPtr& Hess_lagrangian_space)=0; /** Method for obtaining the bounds information */ virtual bool GetBoundsInformation(const Matrix& Px_L, Vector& x_L, const Matrix& Px_U, Vector& x_U, const Matrix& Pd_L, Vector& d_L, const Matrix& Pd_U, Vector& d_U)=0; /** Method for obtaining the starting point for all the * iterates. ToDo it might not make sense to ask for initial * values for v_L and v_U? */ virtual bool GetStartingPoint( SmartPtr x, bool need_x, SmartPtr y_c, bool need_y_c, SmartPtr y_d, bool need_y_d, SmartPtr z_L, bool need_z_L, SmartPtr z_U, bool need_z_U )=0; /** Method for obtaining an entire iterate as a warmstart point. * The incoming IteratesVector has to be filled. The default * dummy implementation returns false. */ virtual bool GetWarmStartIterate(IteratesVector& warm_start_iterate) { return false; } //@} /** @name NLP evaluation routines (overload * in derived classes. */ //@{ virtual bool Eval_f(const Vector& x, Number& f) = 0; virtual bool Eval_grad_f(const Vector& x, Vector& g_f) = 0; virtual bool Eval_c(const Vector& x, Vector& c) = 0; virtual bool Eval_jac_c(const Vector& x, Matrix& jac_c) = 0; virtual bool Eval_d(const Vector& x, Vector& d) = 0; virtual bool Eval_jac_d(const Vector& x, Matrix& jac_d) = 0; virtual bool Eval_h(const Vector& x, Number obj_factor, const Vector& yc, const Vector& yd, SymMatrix& h) = 0; //@} /** @name NLP solution routines. Have default dummy * implementations that can be overloaded. */ //@{ /** This method is called at the very end of the optimization. It * provides the final iterate to the user, so that it can be * stored as the solution. The status flag indicates the outcome * of the optimization, where SolverReturn is defined in * IpAlgTypes.hpp. */ virtual void FinalizeSolution(SolverReturn status, const Vector& x, const Vector& z_L, const Vector& z_U, const Vector& c, const Vector& d, const Vector& y_c, const Vector& y_d, Number obj_value, const IpoptData* ip_data, IpoptCalculatedQuantities* ip_cq) {} /** This method is called once per iteration, after the iteration * summary output has been printed. It provides the current * information to the user to do with it anything she wants. It * also allows the user to ask for a premature termination of the * optimization by returning false, in which case Ipopt will * terminate with a corresponding return status. The basic * information provided in the argument list has the quantities * values printed in the iteration summary line. If more * information is required, a user can obtain it from the IpData * and IpCalculatedQuantities objects. However, note that the * provided quantities are all for the problem that Ipopt sees, * i.e., the quantities might be scaled, fixed variables might be * sorted out, etc. The status indicates things like whether the * algorithm is in the restoration phase... In the restoration * phase, the dual variables are probably not not changing. */ virtual bool IntermediateCallBack(AlgorithmMode mode, Index iter, Number obj_value, Number inf_pr, Number inf_du, Number mu, Number d_norm, Number regularization_size, Number alpha_du, Number alpha_pr, Index ls_trials, const IpoptData* ip_data, IpoptCalculatedQuantities* ip_cq) { return true; } //@} /** Routines to get the scaling parameters. These do not need to * be overloaded unless the options are set for User scaling */ //@{ virtual void GetScalingParameters( const SmartPtr x_space, const SmartPtr c_space, const SmartPtr d_space, Number& obj_scaling, SmartPtr& x_scaling, SmartPtr& c_scaling, SmartPtr& d_scaling) const { THROW_EXCEPTION(USER_SCALING_NOT_IMPLEMENTED, "You have set options for user provided scaling, but have" " not implemented GetScalingParameters in the NLP interface"); } //@} /** Method for obtaining the subspace in which the limited-memory * Hessian approximation should be done. This is only called if * the limited-memory Hessian approximation is chosen. Since the * Hessian is zero in the space of all variables that appear in * the problem functions only linearly, this allows the user to * provide a VectorSpace for all nonlinear variables, and an * ExpansionMatrix to lift from this VectorSpace to the * VectorSpace of the primal variables x. If the returned values * are NULL, it is assumed that the Hessian is to be approximated * in the space of all x variables. The default instantiation of * this method returns NULL, and a user only has to overwrite * this method if the approximation is to be done only in a * subspace. */ virtual void GetQuasiNewtonApproximationSpaces(SmartPtr& approx_space, SmartPtr& P_approx) { approx_space = NULL; P_approx = NULL; } private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Copy Constructor */ NLP(const NLP&); /** Overloaded Equals Operator */ void operator=(const NLP&); //@} }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpSmartPtr.hpp0000644000335100017570000004630311133661705026311 0ustar pgehlerwebadm// Copyright (C) 2004, 2007 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpSmartPtr.hpp 1020 2007-06-24 04:06:59Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPSMARTPTR_HPP__ #define __IPSMARTPTR_HPP__ #include "IpReferenced.hpp" #include "IpDebug.hpp" #if COIN_IPOPT_CHECKLEVEL > 2 # define IP_DEBUG_SMARTPTR #endif namespace Ipopt { /** Template class for Smart Pointers. * A SmartPtr behaves much like a raw pointer, but manages the lifetime * of an object, deleting the object automatically. This class implements * a reference-counting, intrusive smart pointer design, where all * objects pointed to must inherit off of ReferencedObject, which * stores the reference count. Although this is intrusive (native types * and externally authored classes require wrappers to be referenced * by smart pointers), it is a safer design. A more detailed discussion of * these issues follows after the usage information. * * Usage Example: * Note: to use the SmartPtr, all objects to which you point MUST * inherit off of ReferencedObject. * * \verbatim * * In MyClass.hpp... * * #include "IpReferenced.hpp" * namespace Ipopt { * * class MyClass : public ReferencedObject // must derive from ReferencedObject * { * ... * } * } // namespace Ipopt * * * In my_usage.cpp... * * #include "IpSmartPtr.hpp" * #include "MyClass.hpp" * * void func(AnyObject& obj) * { * SmartPtr ptr_to_myclass = new MyClass(...); * // ptr_to_myclass now points to a new MyClass, * // and the reference count is 1 * * ... * * obj.SetMyClass(ptr_to_myclass); * // Here, let's assume that AnyObject uses a * // SmartPtr internally here. * // Now, both ptr_to_myclass and the internal * // SmartPtr in obj point to the same MyClass object * // and its reference count is 2. * * ... * * // No need to delete ptr_to_myclass, this * // will be done automatically when the * // reference count drops to zero. * * } * * \endverbatim * * It is not necessary to use SmartPtr's in all cases where an * object is used that has been allocated "into" a SmartPtr. It is * possible to just pass objects by reference or regular pointers, * even if lower down in the stack a SmartPtr is to be held on to. * Everything should work fine as long as a pointer created by "new" * is immediately passed into a SmartPtr, and if SmartPtr's are used * to hold on to objects. * * Other Notes: * The SmartPtr implements both dereference operators -> & *. * The SmartPtr does NOT implement a conversion operator to * the raw pointer. Use the GetRawPtr() method when this * is necessary. Make sure that the raw pointer is NOT * deleted. * The SmartPtr implements the comparison operators == & != * for a variety of types. Use these instead of * \verbatim * if (GetRawPtr(smrt_ptr) == ptr) // Don't use this * \endverbatim * SmartPtr's, as currently implemented, do NOT handle circular references. * For example: consider a higher level object using SmartPtrs to point to * A and B, but A and B also point to each other (i.e. A has a SmartPtr * to B and B has a SmartPtr to A). In this scenario, when the higher * level object is finished with A and B, their reference counts will * never drop to zero (since they reference each other) and they * will not be deleted. This can be detected by memory leak tools like * valgrind. If the circular reference is necessary, the problem can be * overcome by a number of techniques: * * 1) A and B can have a method that "releases" each other, that is * they set their internal SmartPtrs to NULL. * \verbatim * void AClass::ReleaseCircularReferences() * { * smart_ptr_to_B = NULL; * } * \endverbatim * Then, the higher level class can call these methods before * it is done using A & B. * * 2) Raw pointers can be used in A and B to reference each other. * Here, an implicit assumption is made that the lifetime is * controlled by the higher level object and that A and B will * both exist in a controlled manner. Although this seems * dangerous, in many situations, this type of referencing * is very controlled and this is reasonably safe. * * 3) This SmartPtr class could be redesigned with the Weak/Strong * design concept. Here, the SmartPtr is identified as being * Strong (controls lifetime of the object) or Weak (merely * referencing the object). The Strong SmartPtr increments * (and decrements) the reference count in ReferencedObject * but the Weak SmartPtr does not. In the example above, * the higher level object would have Strong SmartPtrs to * A and B, but A and B would have Weak SmartPtrs to each * other. Then, when the higher level object was done with * A and B, they would be deleted. The Weak SmartPtrs in A * and B would not decrement the reference count and would, * of course, not delete the object. This idea is very similar * to item (2), where it is implied that the sequence of events * is controlled such that A and B will not call anything using * their pointers following the higher level delete (i.e. in * their destructors!). This is somehow safer, however, because * code can be written (however expensive) to perform run-time * detection of this situation. For example, the ReferencedObject * could store pointers to all Weak SmartPtrs that are referencing * it and, in its destructor, tell these pointers that it is * dying. They could then set themselves to NULL, or set an * internal flag to detect usage past this point. * * Comments on Non-Intrusive Design: * In a non-intrusive design, the reference count is stored somewhere other * than the object being referenced. This means, unless the reference * counting pointer is the first referencer, it must get a pointer to the * referenced object from another smart pointer (so it has access to the * reference count location). In this non-intrusive design, if we are * pointing to an object with a smart pointer (or a number of smart * pointers), and we then give another smart pointer the address through * a RAW pointer, we will have two independent, AND INCORRECT, reference * counts. To avoid this pitfall, we use an intrusive reference counting * technique where the reference count is stored in the object being * referenced. */ template class SmartPtr : public Referencer { public: #define dbg_smartptr_verbosity 0 /**@name Constructors/Destructors */ //@{ /** Default constructor, initialized to NULL */ SmartPtr(); /** Copy constructor, initialized from copy */ SmartPtr(const SmartPtr& copy); /** Constructor, initialized from T* ptr */ SmartPtr(T* ptr); /** Destructor, automatically decrements the * reference count, deletes the object if * necessary.*/ ~SmartPtr(); //@} /**@name Overloaded operators. */ //@{ /** Overloaded arrow operator, allows the user to call * methods using the contained pointer. */ T* operator->() const; /** Overloaded dereference operator, allows the user * to dereference the contained pointer. */ T& operator*() const; /** Overloaded equals operator, allows the user to * set the value of the SmartPtr from a raw pointer */ SmartPtr& operator=(T* rhs); /** Overloaded equals operator, allows the user to * set the value of the SmartPtr from another * SmartPtr */ SmartPtr& operator=(const SmartPtr& rhs); /** Overloaded equality comparison operator, allows the * user to compare the value of two SmartPtrs */ template friend bool operator==(const SmartPtr& lhs, const SmartPtr& rhs); /** Overloaded equality comparison operator, allows the * user to compare the value of a SmartPtr with a raw pointer. */ template friend bool operator==(const SmartPtr& lhs, U2* raw_rhs); /** Overloaded equality comparison operator, allows the * user to compare the value of a raw pointer with a SmartPtr. */ template friend bool operator==(U1* lhs, const SmartPtr& raw_rhs); /** Overloaded in-equality comparison operator, allows the * user to compare the value of two SmartPtrs */ template friend bool operator!=(const SmartPtr& lhs, const SmartPtr& rhs); /** Overloaded in-equality comparison operator, allows the * user to compare the value of a SmartPtr with a raw pointer. */ template friend bool operator!=(const SmartPtr& lhs, U2* raw_rhs); /** Overloaded in-equality comparison operator, allows the * user to compare the value of a SmartPtr with a raw pointer. */ template friend bool operator!=(U1* lhs, const SmartPtr& raw_rhs); //@} /**@name friend method declarations. */ //@{ /** Returns the raw pointer contained. * Use to get the value of * the raw ptr (i.e. to pass to other * methods/functions, etc.) * Note: This method does NOT copy, * therefore, modifications using this * value modify the underlying object * contained by the SmartPtr, * NEVER delete this returned value. */ template friend U* GetRawPtr(const SmartPtr& smart_ptr); /** Returns a const pointer */ template friend SmartPtr ConstPtr(const SmartPtr& smart_ptr); /** Returns true if the SmartPtr is NOT NULL. * Use this to check if the SmartPtr is not null * This is preferred to if(GetRawPtr(sp) != NULL) */ template friend bool IsValid(const SmartPtr& smart_ptr); /** Returns true if the SmartPtr is NULL. * Use this to check if the SmartPtr IsNull. * This is preferred to if(GetRawPtr(sp) == NULL) */ template friend bool IsNull(const SmartPtr& smart_ptr); //@} private: /**@name Private Data/Methods */ //@{ /** Actual raw pointer to the object. */ T* ptr_; /** Set the value of the internal raw pointer * from another raw pointer, releasing the * previously referenced object if necessary. */ SmartPtr& SetFromRawPtr_(T* rhs); /** Set the value of the internal raw pointer * from a SmartPtr, releasing the previously referenced * object if necessary. */ SmartPtr& SetFromSmartPtr_(const SmartPtr& rhs); /** Release the currently referenced object. */ void ReleasePointer_(); //@} }; /**@name SmartPtr friend function declarations.*/ //@{ template U* GetRawPtr(const SmartPtr& smart_ptr); template SmartPtr ConstPtr(const SmartPtr& smart_ptr); template bool IsNull(const SmartPtr& smart_ptr); template bool IsValid(const SmartPtr& smart_ptr); template bool operator==(const SmartPtr& lhs, const SmartPtr& rhs); template bool operator==(const SmartPtr& lhs, U2* raw_rhs); template bool operator==(U1* lhs, const SmartPtr& raw_rhs); template bool operator!=(const SmartPtr& lhs, const SmartPtr& rhs); template bool operator!=(const SmartPtr& lhs, U2* raw_rhs); template bool operator!=(U1* lhs, const SmartPtr& raw_rhs); //@} template SmartPtr::SmartPtr() : ptr_(NULL) { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH("SmartPtr::SmartPtr()", dbg_smartptr_verbosity); #endif #ifdef CHECK_SMARTPTR const ReferencedObject* trying_to_use_SmartPtr_with_an_object_that_does_not_inherit_from_ReferencedObject_ = ptr_; trying_to_use_SmartPtr_with_an_object_that_does_not_inherit_from_ReferencedObject_ = NULL; #endif } template SmartPtr::SmartPtr(const SmartPtr& copy) : ptr_(NULL) { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH("SmartPtr::SmartPtr(const SmartPtr& copy)", dbg_smartptr_verbosity); #endif #ifdef CHECK_SMARTPTR const ReferencedObject* trying_to_use_SmartPtr_with_an_object_that_does_not_inherit_from_ReferencedObject_ = ptr_; trying_to_use_SmartPtr_with_an_object_that_does_not_inherit_from_ReferencedObject_ = NULL; #endif (void) SetFromSmartPtr_(copy); } template SmartPtr::SmartPtr(T* ptr) : ptr_(NULL) { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH("SmartPtr::SmartPtr(T* ptr)", dbg_smartptr_verbosity); #endif #ifdef CHECK_SMARTPTR const ReferencedObject* trying_to_use_SmartPtr_with_an_object_that_does_not_inherit_from_ReferencedObject_ = ptr_; trying_to_use_SmartPtr_with_an_object_that_does_not_inherit_from_ReferencedObject_ = NULL; #endif (void) SetFromRawPtr_(ptr); } template SmartPtr::~SmartPtr() { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH("SmartPtr::~SmartPtr(T* ptr)", dbg_smartptr_verbosity); #endif ReleasePointer_(); } template T* SmartPtr::operator->() const { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH("T* SmartPtr::operator->()", dbg_smartptr_verbosity); #endif // cannot deref a null pointer #if COIN_IPOPT_CHECKLEVEL > 0 assert(ptr_); #endif return ptr_; } template T& SmartPtr::operator*() const { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH("T& SmartPtr::operator*()", dbg_smartptr_verbosity); #endif // cannot dereference a null pointer #if COIN_IPOPT_CHECKLEVEL > 0 assert(ptr_); #endif return *ptr_; } template SmartPtr& SmartPtr::operator=(T* rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH("SmartPtr& SmartPtr::operator=(T* rhs)", dbg_smartptr_verbosity); #endif return SetFromRawPtr_(rhs); } template SmartPtr& SmartPtr::operator=(const SmartPtr& rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH( "SmartPtr& SmartPtr::operator=(const SmartPtr& rhs)", dbg_smartptr_verbosity); #endif return SetFromSmartPtr_(rhs); } template SmartPtr& SmartPtr::SetFromRawPtr_(T* rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH( "SmartPtr& SmartPtr::SetFromRawPtr_(T* rhs)", dbg_smartptr_verbosity); #endif // Release any old pointer ReleasePointer_(); if (rhs != NULL) { rhs->AddRef(this); ptr_ = rhs; } return *this; } template SmartPtr& SmartPtr::SetFromSmartPtr_(const SmartPtr& rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH( "SmartPtr& SmartPtr::SetFromSmartPtr_(const SmartPtr& rhs)", dbg_smartptr_verbosity); #endif T* ptr = GetRawPtr(rhs); /* AW: I changed this so that NULL is correctly copied from the right hand side */ // if (ptr != NULL) { // SetFromRawPtr_(ptr); // } SetFromRawPtr_(ptr); return (*this); } template void SmartPtr::ReleasePointer_() { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH( "void SmartPtr::ReleasePointer()", dbg_smartptr_verbosity); #endif if (ptr_) { ptr_->ReleaseRef(this); if (ptr_->ReferenceCount() == 0) { delete ptr_; } ptr_ = NULL; } } template U* GetRawPtr(const SmartPtr& smart_ptr) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "T* GetRawPtr(const SmartPtr& smart_ptr)", 0); #endif return smart_ptr.ptr_; } template SmartPtr ConstPtr(const SmartPtr& smart_ptr) { // compiler should implicitly cast return GetRawPtr(smart_ptr); } template bool IsValid(const SmartPtr& smart_ptr) { return !IsNull(smart_ptr); } template bool IsNull(const SmartPtr& smart_ptr) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool IsNull(const SmartPtr& smart_ptr)", 0); #endif return (smart_ptr.ptr_ == NULL); } template bool ComparePointers(const U1* lhs, const U2* rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool ComparePtrs(const U1* lhs, const U2* rhs)", dbg_smartptr_verbosity); #endif if (lhs == rhs) { return true; } // Even if lhs and rhs point to the same object // with different interfaces U1 and U2, we cannot guarantee that // the value of the pointers will be equivalent. We can // guarantee this if we convert to void* const void* v_lhs = static_cast(lhs); const void* v_rhs = static_cast(rhs); if (v_lhs == v_rhs) { return true; } // They must not be the same return false; } template bool operator==(const SmartPtr& lhs, const SmartPtr& rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool operator==(const SmartPtr& lhs, const SmartPtr& rhs)", dbg_smartptr_verbosity); #endif U1* raw_lhs = GetRawPtr(lhs); U2* raw_rhs = GetRawPtr(rhs); return ComparePointers(raw_lhs, raw_rhs); } template bool operator==(const SmartPtr& lhs, U2* raw_rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool operator==(SmartPtr& lhs, U2* rhs)", dbg_smartptr_verbosity); #endif U1* raw_lhs = GetRawPtr(lhs); return ComparePointers(raw_lhs, raw_rhs); } template bool operator==(U1* raw_lhs, const SmartPtr& rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool operator==(U1* raw_lhs, SmartPtr& rhs)", dbg_smartptr_verbosity); #endif const U2* raw_rhs = GetRawPtr(rhs); return ComparePointers(raw_lhs, raw_rhs); } template bool operator!=(const SmartPtr& lhs, const SmartPtr& rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool operator!=(const SmartPtr& lhs, const SmartPtr& rhs)", dbg_smartptr_verbosity); #endif bool retValue = operator==(lhs, rhs); return !retValue; } template bool operator!=(const SmartPtr& lhs, U2* raw_rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool operator!=(SmartPtr& lhs, U2* rhs)", dbg_smartptr_verbosity); #endif bool retValue = operator==(lhs, raw_rhs); return !retValue; } template bool operator!=(U1* raw_lhs, const SmartPtr& rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool operator!=(U1* raw_lhs, SmartPtr& rhs)", dbg_smartptr_verbosity); #endif bool retValue = operator==(raw_lhs, rhs); return !retValue; } } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpTimedTask.hpp0000644000335100017570000000756411133661705026430 0ustar pgehlerwebadm// Copyright (C) 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpTimedTask.hpp 759 2006-07-07 03:07:08Z andreasw $ // // Authors: Andreas Waechter IBM 2005-09-19 #ifndef __IPTIMEDTASK_HPP__ #define __IPTIMEDTASK_HPP__ #ifdef HAVE_CTIME # include #else # ifdef HAVE_TIME_H # include # else # error "don't have header file for time" # endif #endif // The following lines are copied from CoinTime.hpp // We should probably make some more tests here #if defined(_MSC_VER) // Turn off compiler warning about long names # pragma warning(disable:4786) #else // MacOS-X and FreeBSD needs sys/time.h # if defined(__MACH__) || defined (__FreeBSD__) # include # endif # if !defined(__MSVCRT__) # include # endif #endif namespace Ipopt { /** This class is used to collect timing information for a * particular task. */ class TimedTask { public: /**@name Constructors/Destructors */ //@{ /** Default constructor. */ TimedTask() : total_time_(0.), start_called_(false), end_called_(true) {} /** Default destructor */ ~TimedTask() {} //@} /** Method for resetting time to zero. */ void Reset() { total_time_ = 0.; start_called_ = false; end_called_ = true; } /** Method that is called before execution of the task. */ void Start() { DBG_ASSERT(end_called_); DBG_ASSERT(!start_called_); end_called_ = false; start_called_ = true; start_time_ = CpuTime(); } /** Method that is called after execution of the task. */ void End() { DBG_ASSERT(!end_called_); DBG_ASSERT(start_called_); end_called_ = true; start_called_ = false; total_time_ += CpuTime() - start_time_; } /** Method that is called after execution of the task for which * timing might have been started. This only updates the timing * if the timing has indeed been conducted. This is useful to * stop timing after catching exceptions. */ void EndIfStarted() { if (start_called_) { end_called_ = true; start_called_ = false; total_time_ += CpuTime() - start_time_; } DBG_ASSERT(end_called_); } /** Method returning total time spend for task so far. */ Number TotalTime() const { DBG_ASSERT(end_called_); return total_time_; } // The following lines were taken from CoinTime.hpp in COIN/Coin /** method determining CPU executed since start of program */ static inline Number CpuTime() { double cpu_temp; #if defined(_MSC_VER) || defined(__MSVCRT__) unsigned int ticksnow; /* clock_t is same as int */ ticksnow = (unsigned int)clock(); cpu_temp = (double)((double)ticksnow/CLOCKS_PER_SEC); #else struct rusage usage; getrusage(RUSAGE_SELF,&usage); cpu_temp = usage.ru_utime.tv_sec; cpu_temp += 1.0e-6*((double) usage.ru_utime.tv_usec); #endif return cpu_temp; } private: /**@name Default Compiler Generated Methods (Hidden to avoid * implicit creation/calling). These methods are not * implemented and we do not want the compiler to implement them * for us, so we declare them private and do not define * them. This ensures that they will not be implicitly * created/called. */ //@{ /** Copy Constructor */ TimedTask(const TimedTask&); /** Overloaded Equals Operator */ void operator=(const TimedTask&); //@} /** Time at beginning of task. */ Number start_time_; /** Total time for task measured so far. */ Number total_time_; /** @name fields for debugging */ //@{ bool start_called_; bool end_called_; //@} }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpJournalist.hpp0000644000335100017570000003641711133661705026674 0ustar pgehlerwebadm// Copyright (C) 2004, 2007 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpJournalist.hpp 1136 2007-12-27 19:36:21Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPJOURNALIST_HPP__ #define __IPJOURNALIST_HPP__ #include "IpoptConfig.h" #include "IpTypes.hpp" #include "IpReferenced.hpp" #include "IpSmartPtr.hpp" #ifdef HAVE_CSTDARG # include #else # ifdef HAVE_STDARG_H # include # else # error "don't have header file for stdarg" # endif #endif #include #include #include namespace Ipopt { // forward declarations class Journal; class FileJournal; /**@name Journalist Enumerations. */ //@{ /** Print Level Enum. */ enum EJournalLevel { J_INSUPPRESSIBLE=-1, J_NONE=0, J_ERROR, J_STRONGWARNING, J_SUMMARY, J_WARNING, J_ITERSUMMARY, J_DETAILED, J_MOREDETAILED, J_VECTOR, J_MOREVECTOR, J_MATRIX, J_MOREMATRIX, J_ALL, J_LAST_LEVEL }; /** Category Selection Enum. */ enum EJournalCategory { J_DBG=0, J_STATISTICS, J_MAIN, J_INITIALIZATION, J_BARRIER_UPDATE, J_SOLVE_PD_SYSTEM, J_FRAC_TO_BOUND, J_LINEAR_ALGEBRA, J_LINE_SEARCH, J_HESSIAN_APPROXIMATION, J_SOLUTION, J_DOCUMENTATION, J_NLP, J_TIMING_STATISTICS, J_USER_APPLICATION /** This can be used by the user's application*/ , J_USER1 /** This can be used by the user's application*/ , J_USER2 /** This can be used by the user's application*/ , J_USER3 /** This can be used by the user's application*/ , J_USER4 /** This can be used by the user's application*/ , J_USER5 /** This can be used by the user's application*/ , J_USER6 /** This can be used by the user's application*/ , J_USER7 /** This can be used by the user's application*/ , J_USER8 /** This can be used by the user's application*/ , J_USER9 /** This can be used by the user's application*/ , J_USER10 /** This can be used by the user's application*/ , J_USER11 /** This can be used by the user's application*/ , J_USER12 /** This can be used by the user's application*/ , J_USER13 /** This can be used by the user's application*/ , J_USER14 /** This can be used by the user's application*/ , J_USER15 /** This can be used by the user's application*/ , J_USER16 /** This can be used by the user's application*/ , J_USER17 /** This can be used by the user's application*/ , J_LAST_CATEGORY }; //@} /** Class responsible for all message output. * This class is responsible for all messaging and output. * The "printing" code or "author" should send ALL messages to the * Journalist, indicating an appropriate category and print level. * The journalist then decides, based on reader specified * acceptance criteria, which message is actually printed in which * journals. * This allows the printing code to send everything, while the * "reader" can decide what they really want to see. * * Authors: * Authors use the * Journals: You can add as many Journals as you like to the * Journalist with the AddJournal or the AddFileJournal methods. * Each one represents a different printing location (or file). * Then, you can call the "print" methods of the Journalist to output * information to each of the journals. * * Acceptance Criteria: Each print message should be flagged * appropriately with an EJournalCategory and EJournalLevel. * * The AddFileJournal * method returns a pointer to the newly created Journal object * (if successful) so you can set Acceptance criteria for that * particular location. * */ class Journalist : public ReferencedObject { public: /**@name Constructor / Desructor. */ //@{ /** Constructor. */ Journalist(); /** Destructor... */ virtual ~Journalist(); //@} /**@name Author Methods. * These methods are used by authoring code, or code that wants * to report some information. */ //@{ /** Method to print a formatted string */ void Printf(EJournalLevel level, EJournalCategory category, const char* format, ...) const; /** Method to print a long string including indentation. The * string is printed starting at the current position. If the * position (counting started at the current position) exceeds * max_length, a new line is inserted, and indent_spaces many * spaces are printed before the string is continued. This is * for example used during the printing of the option * documentation. */ void PrintStringOverLines(EJournalLevel level, EJournalCategory category, Index indent_spaces, Index max_length, const std::string& line) const; /** Method to print a formatted string with indentation */ void PrintfIndented(EJournalLevel level, EJournalCategory category, Index indent_level, const char* format, ...) const; /** Method to print a formatted string * using the va_list argument. */ void VPrintf(EJournalLevel level, EJournalCategory category, const char* pformat, va_list ap) const; /** Method to print a formatted string with indentation, * using the va_list argument. */ void VPrintfIndented(EJournalLevel level, EJournalCategory category, Index indent_level, const char* pformat, va_list ap) const; /** Method that returns true if there is a Journal that would * write output for the given JournalLevel and JournalCategory. * This is useful if expensive computation would be required for * a particular output. The author code can check with this * method if the computations are indeed required. */ bool ProduceOutput(EJournalLevel level, EJournalCategory category) const; /** Method that flushes the current buffer for all Journalists. Calling this method after one optimization run helps to avoid cluttering output with that produced by other parts of the program (e.g. written in Fortran) */ void FlushBuffer() const; //@} /**@name Reader Methods. * These methods are used by the reader. The reader will setup the * journalist with each output file and the acceptance * criteria for that file. * * Use these methods to setup the journals (files or other output). * These are the internal objects that keep track of the print levels * for each category. Then use the internal Journal objects to * set specific print levels for each category (or keep defaults). * */ //@{ /** Add a new journal. The location_name is a string identifier, * which can be used to obtain the pointer to the new Journal at * a later point using the GetJournal method. * The default_level is * used to initialize the * printing level for all categories. */ bool AddJournal(const SmartPtr jrnl); /** Add a new FileJournal. fname is the name * of the * file to which this Journal corresponds. Use * fname="stdout" * for stdout, and use fname="stderr" for * stderr. This method * returns the Journal pointer so you can * set specific acceptance criteria. It returns NULL if there * was a problem creating a new Journal. */ SmartPtr AddFileJournal( const std::string& location_name, /** identifier */ const std::string& fname, EJournalLevel default_level = J_WARNING ); /** Get an existing journal. You can use this method to change * the acceptance criteria at runtime. */ SmartPtr GetJournal(const std::string& location_name); //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Copy Constructor */ Journalist(const Journalist&); /** Overloaded Equals Operator */ void operator=(const Journalist&); //@} //** Private Data Members. */ //@{ std::vector< SmartPtr > journals_; //@} }; /** Journal class (part of the Journalist implementation.). This * class is the base class for all Journals. It controls the * acceptance criteria for print statements etc. Derived classes * like the FileJournal - output those messages to specific locations */ class Journal : public ReferencedObject { public: /** Constructor. */ Journal(const std::string& name, EJournalLevel default_level); /** Destructor. */ virtual ~Journal(); /** Get the name of the Journal */ std::string Name(); /** Set the print level for a particular category. */ void SetPrintLevel( EJournalCategory category, EJournalLevel level ); /** Set the print level for all category. */ void SetAllPrintLevels( EJournalLevel level ); /**@name Journal Output Methods. These methods are called by the * Journalist who first checks if the output print level and category * are acceptable. * Calling the Print methods explicitly (instead of through the * Journalist will output the message regardless of print level * and category. You should use the Journalist to print & flush instead */ //@{ /** Ask if a particular print level/category is accepted by the * journal. */ bool IsAccepted( EJournalCategory category, EJournalLevel level ) const; /** Print to the designated output location */ void Print(EJournalCategory category, EJournalLevel level, const char* str) { PrintImpl(category, level, str); } /** Printf to the designated output location */ void Printf(EJournalCategory category, EJournalLevel level, const char* pformat, va_list ap) { PrintfImpl(category, level, pformat, ap); } /** Flush output buffer.*/ void FlushBuffer() { FlushBufferImpl(); } //@} protected: /**@name Implementation version of Print methods. Derived classes * should overload the Impl methods. */ //@{ /** Print to the designated output location */ virtual void PrintImpl(EJournalCategory category, EJournalLevel level, const char* str)=0; /** Printf to the designated output location */ virtual void PrintfImpl(EJournalCategory category, EJournalLevel level, const char* pformat, va_list ap)=0; /** Flush output buffer.*/ virtual void FlushBufferImpl()=0; //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ Journal(); /** Copy Constructor */ Journal(const Journal&); /** Overloaded Equals Operator */ void operator=(const Journal&); //@} /** Name of the output location */ std::string name_; /** vector of integers indicating the level for each category */ Index print_levels_[J_LAST_CATEGORY]; }; /** FileJournal class. This is a particular Journal implementation that * writes to a file for output. It can write to (stdout, stderr, or disk) * by using "stdout" and "stderr" as filenames. */ class FileJournal : public Journal { public: /** Constructor. */ FileJournal(const std::string& name, EJournalLevel default_level); /** Destructor. */ virtual ~FileJournal(); /** Open a new file for the output location. * Special Names: stdout means stdout, * : stderr means stderr. * * Return code is false only if the file with the given name * could not be opened. */ bool Open(const char* fname); protected: /**@name Implementation version of Print methods - Overloaded from * Journal base class. */ //@{ /** Print to the designated output location */ virtual void PrintImpl(EJournalCategory category, EJournalLevel level, const char* str); /** Printf to the designated output location */ virtual void PrintfImpl(EJournalCategory category, EJournalLevel level, const char* pformat, va_list ap); /** Flush output buffer.*/ virtual void FlushBufferImpl(); //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ FileJournal(); /** Copy Constructor */ FileJournal(const FileJournal&); /** Overloaded Equals Operator */ void operator=(const FileJournal&); //@} /** FILE pointer for the output destination */ FILE* file_; }; /** StreamJournal class. This is a particular Journal implementation that * writes to a stream for output. */ class StreamJournal : public Journal { public: /** Constructor. */ StreamJournal(const std::string& name, EJournalLevel default_level); /** Destructor. */ virtual ~StreamJournal() {} /** Setting the output stream pointer */ void SetOutputStream(std::ostream* os); protected: /**@name Implementation version of Print methods - Overloaded from * Journal base class. */ //@{ /** Print to the designated output location */ virtual void PrintImpl(EJournalCategory category, EJournalLevel level, const char* str); /** Printf to the designated output location */ virtual void PrintfImpl(EJournalCategory category, EJournalLevel level, const char* pformat, va_list ap); /** Flush output buffer.*/ virtual void FlushBufferImpl(); //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ StreamJournal(); /** Copy Constructor */ StreamJournal(const StreamJournal&); /** Overloaded Equals Operator */ void operator=(const StreamJournal&); //@} /** pointer to output stream for the output destination */ std::ostream* os_; /** buffer for sprintf. Being generous in size here... */ char buffer_[32768]; }; } #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpException.hpp0000644000335100017570000001011711133661705026465 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpException.hpp 1020 2007-06-24 04:06:59Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPEXCEPTION_HPP__ #define __IPEXCEPTION_HPP__ #include "IpUtils.hpp" #include "IpJournalist.hpp" /* This file contains a base class for all exceptions * and a set of macros to help with exceptions */ namespace Ipopt { /** This is the base class for all exceptions. The easiest way to * use this class is by means of the following macros: * * \verbatim DECLARE_STD_EXCEPTION(ExceptionType); \endverbatim * * This macro defines a new class with the name ExceptionType, * inherited from the base class IpoptException. After this, * exceptions of this type can be thrown using * * \verbatim THROW_EXCEPTION(ExceptionType, Message); \endverbatim * * where Message is a std::string with a message that gives an * indication of what caused the exception. Exceptions can also be * thrown using the macro * * \verbatim ASSERT_EXCEPTION(Condition, ExceptionType, Message); \endverbatim * * where Conditions is an expression. If Condition evaluates to * false, then the exception of the type ExceptionType is thrown * with Message. * * When an exception is caught, the method ReportException can be * used to write the information about the exception to the * Journalist, using the level J_ERROR and the category J_MAIN. * */ class IpoptException { public: /**@name Constructors/Destructors */ //@{ /** Constructor */ IpoptException(std::string msg, std::string file_name, Index line_number, std::string type="IpoptException") : msg_(msg), file_name_(file_name), line_number_(line_number), type_(type) {} /** Copy Constructor */ IpoptException(const IpoptException& copy) : msg_(copy.msg_), file_name_(copy.file_name_), line_number_(copy.line_number_), type_(copy.type_) {} /** Default destructor */ virtual ~IpoptException() {} //@} /** Method to report the exception to a journalist */ void ReportException(const Journalist& jnlst, EJournalLevel level = J_ERROR) const { jnlst.Printf(level, J_MAIN, "Exception of type: %s in file \"%s\" at line %d:\n Exception message: %s\n", type_.c_str(), file_name_.c_str(), line_number_, msg_.c_str()); } const std::string& Message() const { return msg_; } private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ IpoptException(); /** Overloaded Equals Operator */ void operator=(const IpoptException&); //@} std::string msg_; std::string file_name_; Index line_number_; std::string type_; }; #define THROW_EXCEPTION(__except_type, __msg) \ throw __except_type( (__msg), (__FILE__), (__LINE__) ); #define ASSERT_EXCEPTION(__condition, __except_type, __msg) \ if (! (__condition) ) { \ std::string newmsg = #__condition; \ newmsg += " evaluated false: "; \ newmsg += __msg; \ throw __except_type( (newmsg), (__FILE__), (__LINE__) ); \ } #define DECLARE_STD_EXCEPTION(__except_type) \ class __except_type : public IpoptException \ { \ public: \ __except_type(std::string msg, std::string fname, Index line) \ : IpoptException(msg,fname,line, #__except_type) {} \ __except_type(const __except_type& copy) \ : IpoptException(copy) {} \ private: \ __except_type(); \ void operator=(const __except_type&); \ } } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpCachedResults.hpp0000644000335100017570000005727311133661705027276 0ustar pgehlerwebadm// Copyright (C) 2004, 2007 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpCachedResults.hpp 1010 2007-06-21 15:54:36Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPCACHEDRESULTS_HPP__ #define __IPCACHEDRESULTS_HPP__ #include "IpTaggedObject.hpp" #include "IpObserver.hpp" #include #include #include namespace Ipopt //#define IP_DEBUG_CACHE #if COIN_IPOPT_CHECKLEVEL > 2 # define IP_DEBUG_CACHE #endif #ifdef IP_DEBUG_CACHE # include "IpDebug.hpp" #endif { // Forward Declarations template class DependentResult; // AW: I'm taking this out, since this is by far the most used // class. We should keep it as simple as possible. // /** Cache Priority Enum */ // enum CachePriority // { // CP_Lowest, // CP_Standard, // CP_Trial, // CP_Iterate // }; /** Templated class for Chached Results. This class stores up to a * given number of "results", entities that are stored here * together with identifies, that can be used to later retrieve the * information again. * * Typically, T is a SmartPtr for some calculated quantity that * should be stored (such as a Vector). The identifiers (or * dependencies) are a (possibly varying) number of Tags from * TaggedObjects, and a number of Numbers. Results are added to * the cache using the AddCachedResults methods, and the can be * retrieved with the GetCachedResults methods. The second set of * methods checks whether a result has been cached for the given * identifiers. If a corresponding results is found, a copy of it * is returned and the method evaluates to true, otherwise it * evaluates to false. * * Note that cached results can become "stale", namely when a * TaggedObject that is used to identify this CachedResult is * changed. When this happens, the cached result can never be * asked for again, so that there is no point in storing it any * longer. For this purpose, a cached result, which is stored as a * DependentResult, inherits off an Observer. This Observer * retrieves notification whenever a TaggedObject dependency has * changed. Stale results are later removed from the cache. */ template class CachedResults { public: #ifdef IP_DEBUG_CACHE /** (Only if compiled in DEBUG mode): debug verbosity level */ static const Index dbg_verbosity; #endif /** @name Constructors and Destructors. */ //@{ /** Constructor, where max_cache_size is the maximal number of * results that should be cached. If max_cache_size is negative, * we allow an infinite abount of cache. */ CachedResults(Int max_cache_size); /** Destructor */ virtual ~CachedResults(); //@} /** @name Generic methods for adding and retrieving cached results. */ //@{ /** Generic method for adding a result to the cache, given a * std::vector of TaggesObjects and a std::vector of Numbers. */ void AddCachedResult(const T& result, const std::vector& dependents, const std::vector& scalar_dependents); /** Generic method for retrieving a cached results, given the * dependencies as a std::vector of TaggesObjects and a * std::vector of Numbers. */ bool GetCachedResult(T& retResult, const std::vector& dependents, const std::vector& scalar_dependents) const; /** Method for adding a result, providing only a std::vector of * TaggedObjects. */ void AddCachedResult(const T& result, const std::vector& dependents); /** Method for retrieving a cached result, providing only a * std::vector of TaggedObjects. */ bool GetCachedResult(T& retResult, const std::vector& dependents) const; //@} /** @name Pointer-based methods for adding and retrieving cached * results, providing dependencies explicitly. */ //@{ /** Method for adding a result to the cache, proving one * dependency as a TaggedObject explicitly. */ void AddCachedResult1Dep(const T& result, const TaggedObject* dependent1); /** Method for retrieving a cached result, proving one dependency * as a TaggedObject explicitly. */ bool GetCachedResult1Dep(T& retResult, const TaggedObject* dependent1); /** Method for adding a result to the cache, proving two * dependencies as a TaggedObject explicitly. */ void AddCachedResult2Dep(const T& result, const TaggedObject* dependent1, const TaggedObject* dependent2); /** Method for retrieving a cached result, proving two * dependencies as a TaggedObject explicitly. */ bool GetCachedResult2Dep(T& retResult, const TaggedObject* dependent1, const TaggedObject* dependent2); /** Method for adding a result to the cache, proving three * dependencies as a TaggedObject explicitly. */ void AddCachedResult3Dep(const T& result, const TaggedObject* dependent1, const TaggedObject* dependent2, const TaggedObject* dependent3); /** Method for retrieving a cached result, proving three * dependencies as a TaggedObject explicitly. */ bool GetCachedResult3Dep(T& retResult, const TaggedObject* dependent1, const TaggedObject* dependent2, const TaggedObject* dependent3); /** @name Pointer-free version of the Add and Get methods */ //@{ bool GetCachedResult1Dep(T& retResult, const TaggedObject& dependent1) { return GetCachedResult1Dep(retResult, &dependent1); } bool GetCachedResult2Dep(T& retResult, const TaggedObject& dependent1, const TaggedObject& dependent2) { return GetCachedResult2Dep(retResult, &dependent1, &dependent2); } bool GetCachedResult3Dep(T& retResult, const TaggedObject& dependent1, const TaggedObject& dependent2, const TaggedObject& dependent3) { return GetCachedResult3Dep(retResult, &dependent1, &dependent2, &dependent3); } void AddCachedResult1Dep(const T& result, const TaggedObject& dependent1) { AddCachedResult1Dep(result, &dependent1); } void AddCachedResult2Dep(const T& result, const TaggedObject& dependent1, const TaggedObject& dependent2) { AddCachedResult2Dep(result, &dependent1, &dependent2); } void AddCachedResult3Dep(const T& result, const TaggedObject& dependent1, const TaggedObject& dependent2, const TaggedObject& dependent3) { AddCachedResult3Dep(result, &dependent1, &dependent2, &dependent3); } //@} /** Invalidates the result for given dependecies. Sets the stale * flag for the corresponding cached result to true if it is * found. Returns true, if the result was found. */ bool InvalidateResult(const std::vector& dependents, const std::vector& scalar_dependents); /** Invalidates all cached results */ void Clear(); /** Invalidate all cached results and changes max_cache_size */ void Clear(Int max_cache_size); private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ CachedResults(); /** Copy Constructor */ CachedResults(const CachedResults&); /** Overloaded Equals Operator */ void operator=(const CachedResults&); //@} /** maximum number of cached results */ Int max_cache_size_; /** list of currently cached results. */ mutable std::list*>* cached_results_; /** internal method for removing stale DependentResults from the * list. It is called at the beginning of every * GetDependentResult method. */ void CleanupInvalidatedResults() const; /** Print list of currently cached results */ void DebugPrintCachedResults() const; }; /** Templated class which stores one entry for the CachedResult * class. It stores the result (of type T), together with its * dependencies (vector of TaggedObjects and vector of Numbers). * It also stores a priority. */ template class DependentResult : public Observer { public: #ifdef IP_DEBUG_CACHE static const Index dbg_verbosity; #endif /** @name Constructor, Destructors */ //@{ /** Constructor, given all information about the result. */ DependentResult(const T& result, const std::vector& dependents, const std::vector& scalar_dependents); /** Destructor. */ ~DependentResult(); //@} /** @name Accessor method. */ //@{ /** This returns true, if the DependentResult is no longer valid. */ bool IsStale() const; /** Invalidates the cached result. */ void Invalidate(); /** Returns the cached result. */ const T& GetResult() const; //@} /** This method returns true if the dependencies provided to this * function are identical to the ones stored with the * DependentResult. */ bool DependentsIdentical(const std::vector& dependents, const std::vector& scalar_dependents) const; /** Print information about this DependentResults. */ void DebugPrint() const; protected: /** This method is overloading the pure virtual method from the * Observer base class. This method is called when a Subject * registered for this Observer sends a notification. In this * particular case, if this method is called with * notify_type==NT_Changed or NT_BeingDeleted, then this results * is marked as stale. */ virtual void RecieveNotification(NotifyType notify_type, const Subject* subject); private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ DependentResult(); /** Copy Constructor */ DependentResult(const DependentResult&); /** Overloaded Equals Operator */ void operator=(const DependentResult&); //@} /** Flag indicating, if the cached result is still valid. A result becomes invalid, if the RecieveNotification method is called with NT_Changed */ bool stale_; /** The value of the dependent results */ const T result_; /** Dependencies in form of TaggedObjects */ std::vector dependent_tags_; /** Dependencies in form a Numbers */ std::vector scalar_dependents_; }; #ifdef IP_DEBUG_CACHE template const Index CachedResults::dbg_verbosity = 0; template const Index DependentResult::dbg_verbosity = 0; #endif template DependentResult::DependentResult( const T& result, const std::vector& dependents, const std::vector& scalar_dependents) : stale_(false), result_(result), dependent_tags_(dependents.size()), scalar_dependents_(scalar_dependents) { #ifdef IP_DEBUG_CACHE DBG_START_METH("DependentResult::DependentResult()", dbg_verbosity); #endif for (Index i=0; i<(Index)dependents.size(); i++) { if (dependents[i]) { // Call the RequestAttach method of the Observer base class. // This will add this dependent result in the Observer list // for the Subject dependents[i]. As a consequence, the // RecieveNotification method of this DependentResult will be // called with notify_type=NT_Changed, whenever the // TaggedResult dependents[i] is changed (i.e. its HasChanged // method is called). RequestAttach(NT_Changed, dependents[i]); dependent_tags_[i] = dependents[i]->GetTag(); } else { dependent_tags_[i] = 0; } } } template DependentResult::~DependentResult() { #ifdef IP_DEBUG_CACHE DBG_START_METH("DependentResult::~DependentResult()", dbg_verbosity); //DBG_ASSERT(stale_ == true); #endif // Nothing to be done here, destructor // of T should sufficiently remove // any memory, etc. } template bool DependentResult::IsStale() const { return stale_; } template void DependentResult::Invalidate() { stale_ = true; } template void DependentResult::RecieveNotification(NotifyType notify_type, const Subject* subject) { #ifdef IP_DEBUG_CACHE DBG_START_METH("DependentResult::RecieveNotification", dbg_verbosity); #endif if (notify_type == NT_Changed || notify_type==NT_BeingDestroyed) { stale_ = true; // technically, I could unregister the notifications here, but they // aren't really hurting anything } } template bool DependentResult::DependentsIdentical(const std::vector& dependents, const std::vector& scalar_dependents) const { #ifdef IP_DEBUG_CACHE DBG_START_METH("DependentResult::DependentsIdentical", dbg_verbosity); DBG_ASSERT(stale_ == false); DBG_ASSERT(dependents.size() == dependent_tags_.size()); #endif bool retVal = true; if (dependents.size() != dependent_tags_.size() || scalar_dependents.size() != scalar_dependents_.size()) { retVal = false; } else { for (Index i=0; i<(Index)dependents.size(); i++) { if ( (dependents[i] && dependents[i]->GetTag() != dependent_tags_[i]) || (!dependents[i] && dependent_tags_[i] != 0) ) { retVal = false; break; } } if (retVal) { for (Index i=0; i<(Index)scalar_dependents.size(); i++) { if (scalar_dependents[i] != scalar_dependents_[i]) { retVal = false; break; } } } } return retVal; } template const T& DependentResult::GetResult() const { #ifdef IP_DEBUG_CACHE DBG_START_METH("DependentResult::GetResult()", dbg_verbosity); DBG_ASSERT(stale_ == false); #endif return result_; } template void DependentResult::DebugPrint() const { #ifdef IP_DEBUG_CACHE DBG_START_METH("DependentResult::DebugPrint", dbg_verbosity); #endif } template CachedResults::CachedResults(Int max_cache_size) : max_cache_size_(max_cache_size), cached_results_(NULL) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::CachedResults", dbg_verbosity); #endif } template CachedResults::~CachedResults() { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::!CachedResults()", dbg_verbosity); #endif if (cached_results_) { for (typename std::list< DependentResult* >::iterator iter = cached_results_-> begin(); iter != cached_results_->end(); iter++) { delete *iter; } delete cached_results_; } /* while (!cached_results_.empty()) { DependentResult* result = cached_results_.back(); cached_results_.pop_back(); delete result; } */ } template void CachedResults::AddCachedResult(const T& result, const std::vector& dependents, const std::vector& scalar_dependents) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::AddCachedResult", dbg_verbosity); #endif CleanupInvalidatedResults(); // insert the new one here DependentResult* newResult = new DependentResult(result, dependents, scalar_dependents); if (!cached_results_) { cached_results_ = new std::list*>; } cached_results_->push_front(newResult); // keep the list small enough if (max_cache_size_ >= 0) { // if negative, allow infinite cache // non-negative - limit size of list to max_cache_size DBG_ASSERT((Int)cached_results_->size()<=max_cache_size_+1); if ((Int)cached_results_->size() > max_cache_size_) { delete cached_results_->back(); cached_results_->pop_back(); } } #ifdef IP_DEBUG_CACHE DBG_EXEC(2, DebugPrintCachedResults()); #endif } template void CachedResults::AddCachedResult(const T& result, const std::vector& dependents) { std::vector scalar_dependents; AddCachedResult(result, dependents, scalar_dependents); } template bool CachedResults::GetCachedResult(T& retResult, const std::vector& dependents, const std::vector& scalar_dependents) const { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::GetCachedResult", dbg_verbosity); #endif if (!cached_results_) return false; CleanupInvalidatedResults(); bool retValue = false; typename std::list< DependentResult* >::const_iterator iter; for (iter = cached_results_->begin(); iter != cached_results_->end(); iter++) { if ((*iter)->DependentsIdentical(dependents, scalar_dependents)) { retResult = (*iter)->GetResult(); retValue = true; break; } } #ifdef IP_DEBUG_CACHE DBG_EXEC(2, DebugPrintCachedResults()); #endif return retValue; } template bool CachedResults::GetCachedResult( T& retResult, const std::vector& dependents) const { std::vector scalar_dependents; return GetCachedResult(retResult, dependents, scalar_dependents); } template void CachedResults::AddCachedResult1Dep(const T& result, const TaggedObject* dependent1) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::AddCachedResult1Dep", dbg_verbosity); #endif std::vector dependents(1); dependents[0] = dependent1; AddCachedResult(result, dependents); } template bool CachedResults::GetCachedResult1Dep(T& retResult, const TaggedObject* dependent1) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::GetCachedResult1Dep", dbg_verbosity); #endif std::vector dependents(1); dependents[0] = dependent1; return GetCachedResult(retResult, dependents); } template void CachedResults::AddCachedResult2Dep(const T& result, const TaggedObject* dependent1, const TaggedObject* dependent2) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::AddCachedResult2dDep", dbg_verbosity); #endif std::vector dependents(2); dependents[0] = dependent1; dependents[1] = dependent2; AddCachedResult(result, dependents); } template bool CachedResults::GetCachedResult2Dep(T& retResult, const TaggedObject* dependent1, const TaggedObject* dependent2) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::GetCachedResult2Dep", dbg_verbosity); #endif std::vector dependents(2); dependents[0] = dependent1; dependents[1] = dependent2; return GetCachedResult(retResult, dependents); } template void CachedResults::AddCachedResult3Dep(const T& result, const TaggedObject* dependent1, const TaggedObject* dependent2, const TaggedObject* dependent3) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::AddCachedResult2dDep", dbg_verbosity); #endif std::vector dependents(3); dependents[0] = dependent1; dependents[1] = dependent2; dependents[2] = dependent3; AddCachedResult(result, dependents); } template bool CachedResults::GetCachedResult3Dep(T& retResult, const TaggedObject* dependent1, const TaggedObject* dependent2, const TaggedObject* dependent3) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::GetCachedResult2Dep", dbg_verbosity); #endif std::vector dependents(3); dependents[0] = dependent1; dependents[1] = dependent2; dependents[2] = dependent3; return GetCachedResult(retResult, dependents); } template bool CachedResults::InvalidateResult(const std::vector& dependents, const std::vector& scalar_dependents) { if (!cached_results_) return false; CleanupInvalidatedResults(); bool retValue = false; typename std::list< DependentResult* >::const_iterator iter; for (iter = cached_results_->begin(); iter != cached_results_->end(); iter++) { if ((*iter)->DependentsIdentical(dependents, scalar_dependents)) { (*iter)->Invalidate(); retValue = true; break; } } return retValue; } template void CachedResults::Clear() { if (!cached_results_) return; typename std::list< DependentResult* >::const_iterator iter; for (iter = cached_results_->begin(); iter != cached_results_->end(); iter++) { (*iter)->Invalidate(); } CleanupInvalidatedResults(); } template void CachedResults::Clear(Int max_cache_size) { Clear(); max_cache_size_ = max_cache_size; } template void CachedResults::CleanupInvalidatedResults() const { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::CleanupInvalidatedResults", dbg_verbosity); #endif if (!cached_results_) return; typename std::list< DependentResult* >::iterator iter; iter = cached_results_->begin(); while (iter != cached_results_->end()) { if ((*iter)->IsStale()) { typename std::list< DependentResult* >::iterator iter_to_remove = iter; iter++; DependentResult* result_to_delete = (*iter_to_remove); cached_results_->erase(iter_to_remove); delete result_to_delete; } else { iter++; } } } template void CachedResults::DebugPrintCachedResults() const { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::DebugPrintCachedResults", dbg_verbosity); if (DBG_VERBOSITY()>=2 ) { if (!chached_results_) { DBG_PRINT((2," DependentResult:0x%x\n", (*iter))); } else { typename std::list< DependentResult* >::const_iterator iter; DBG_PRINT((2,"Current set of cached results:\n")); for (iter = cached_results_->begin(); iter != cached_results_->end(); iter++) { DBG_PRINT((2," DependentResult:0x%x\n", (*iter))); } } } #endif } } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpObserver.hpp0000644000335100017570000002405411133661705026323 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpObserver.hpp 1020 2007-06-24 04:06:59Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPOBSERVER_HPP__ #define __IPOBSERVER_HPP__ #include "IpUtils.hpp" #include #include //#define IP_DEBUG_OBSERVER #if COIN_IPOPT_CHECKLEVEL > 2 # define IP_DEBUG_OBSERVER #endif #ifdef IP_DEBUG_OBSERVER # include "IpDebug.hpp" #endif namespace Ipopt { /** Forward declarations */ class Subject; /** Slight Variation of the Observer Design Pattern. * This class implements the Observer class of the * Observer Design Pattern. An Observer "Attach"es * to a Subject, indicating that it would like to * be notified of changes in the Subject. * Any derived class wishing to recieve notifications * from a Subject should inherit off of * Observer and overload the protected method, * RecieveNotification_(...). */ class Observer { public: #ifdef IP_DEBUG_OBSERVER static const Index dbg_verbosity; #endif /**@name Constructors/Destructors */ //@{ /** Default Constructor */ Observer() {} /** Default destructor */ virtual ~Observer(); //@} /** Enumeration specifying the type of notification */ enum NotifyType { NT_All, NT_BeingDestroyed, NT_Changed }; protected: /** Derived classes should call this method * to request an "Attach" to a Subject. Do * not call "Attach" explicitly on the Subject * since further processing is done here */ void RequestAttach(NotifyType notify_type, const Subject* subject); /** Derived classes should call this method * to request a "Detach" to a Subject. Do * not call "Detach" explicitly on the Subject * since further processing is done here */ void RequestDetach(NotifyType notify_type, const Subject* subject); /** Derived classes should overload this method to * recieve the requested notification from * attached Subjects */ virtual void RecieveNotification(NotifyType notify_type, const Subject* subject)=0; private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Copy Constructor */ Observer(const Observer&); /** Overloaded Equals Operator */ void operator=(const Observer&); //@} /** A list of the subjects currently being * observed. */ std::vector subjects_; /** Private Method for Recieving Notification * should only be called by the friend class * Subject. This method will, in turn, call * the overloaded RecieveNotification method * for the derived class to process. */ void ProcessNotification(NotifyType notify_type, const Subject* subject); friend class Subject; }; /** Slight Variation of the Observer Design Pattern (Subject part). * This class implements the Subject class of the Observer Design * Pattern. An Observer "Attach"es to a Subject, indicating that it * would like to be notified of changes in the Subject. Any * derived class that is to be observed has to inherit off the * Subject base class. If the subject needs to notify the * Observer, it calls the Notify method. */ class Subject { public: #ifdef IP_DEBUG_OBSERVER static const Index dbg_verbosity; #endif /**@name Constructors/Destructors */ //@{ /** Default Constructor */ Subject() {} /** Default destructor */ virtual ~Subject(); //@} /**@name Methods to Add and Remove Observers. * Currently, the notify_type flags are not used, * and Observers are attached in general and will * recieve all notifications (of the type requested * and possibly of types not requested). It is * up to the observer to ignore the types they * are not interested in. The NotifyType in the * parameter list is so a more efficient mechanism * depending on type could be implemented later if * necessary.*/ //@{ /** Attach the specified observer * (i.e., begin recieving notifications). */ void AttachObserver(Observer::NotifyType notify_type, Observer* observer) const; /** Detach the specified observer * (i.e., no longer recieve notifications). */ void DetachObserver(Observer::NotifyType notify_type, Observer* observer) const; //@} protected: void Notify(Observer::NotifyType notify_type) const; private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Copy Constructor */ Subject(const Subject&); /** Overloaded Equals Operator */ void operator=(const Subject&); //@} mutable std::vector observers_; }; /* inline methods */ inline Observer::~Observer() { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Observer::~Observer", dbg_verbosity); if (DBG_VERBOSITY()>=1) { for (Index i=0; i<(Index)subjects_.size(); i++) { DBG_PRINT((1,"subjects_[%d] = 0x%x\n", i, subjects_[i])); } } #endif // Detach all subjects for (Int i=(Int)(subjects_.size()-1); i>=0; i--) { #ifdef IP_DEBUG_OBSERVER DBG_PRINT((1,"About to detach subjects_[%d] = 0x%x\n", i, subjects_[i])); #endif RequestDetach(NT_All, subjects_[i]); } } inline void Observer::RequestAttach(NotifyType notify_type, const Subject* subject) { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Observer::RequestAttach", dbg_verbosity); // Add the subject to the list if it does not already exist std::vector::iterator attached_subject; attached_subject = std::find(subjects_.begin(), subjects_.end(), subject); DBG_ASSERT(attached_subject == subjects_.end()); DBG_ASSERT(subject); #endif // add the subject to the list subjects_.push_back(subject); // Attach the observer to the subject subject->AttachObserver(notify_type, this); } inline void Observer::RequestDetach(NotifyType notify_type, const Subject* subject) { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Observer::RequestDetach", dbg_verbosity); DBG_PRINT((1, "Requesting detach of subject: 0x%x\n", subject)); DBG_ASSERT(subject); #endif if (subject) { std::vector::iterator attached_subject; attached_subject = std::find(subjects_.begin(), subjects_.end(), subject); #ifdef IP_DEBUG_OBSERVER DBG_ASSERT(attached_subject != subjects_.end()); #endif if (attached_subject != subjects_.end()) { #ifdef IP_DEBUG_OBSERVER DBG_PRINT((1, "Removing subject: 0x%x from the list\n", subject)); #endif subjects_.erase(attached_subject); } // Detach the observer from the subject subject->DetachObserver(notify_type, this); } } inline void Observer::ProcessNotification(NotifyType notify_type, const Subject* subject) { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Observer::ProcessNotification", dbg_verbosity); DBG_ASSERT(subject); #endif if (subject) { std::vector::iterator attached_subject; attached_subject = std::find(subjects_.begin(), subjects_.end(), subject); // We must be processing a notification for a // subject that was previously attached. #ifdef IP_DEBUG_OBSERVER DBG_ASSERT(attached_subject != subjects_.end()); #endif this->RecieveNotification(notify_type, subject); if (notify_type == NT_BeingDestroyed) { // the subject is going away, remove it from our list subjects_.erase(attached_subject); } } } inline Subject::~Subject() { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Subject::~Subject", dbg_verbosity); #endif std::vector::iterator iter; for (iter = observers_.begin(); iter != observers_.end(); iter++) { (*iter)->ProcessNotification(Observer::NT_BeingDestroyed, this); } } inline void Subject::AttachObserver(Observer::NotifyType notify_type, Observer* observer) const { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Subject::AttachObserver", dbg_verbosity); // current implementation notifies all observers of everything // they must filter the notifications that they are not interested // in (i.e. a hub, not a router) DBG_ASSERT(observer); std::vector::iterator attached_observer; attached_observer = std::find(observers_.begin(), observers_.end(), observer); DBG_ASSERT(attached_observer == observers_.end()); DBG_ASSERT(observer); #endif observers_.push_back(observer); } inline void Subject::DetachObserver(Observer::NotifyType notify_type, Observer* observer) const { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Subject::DetachObserver", dbg_verbosity); DBG_ASSERT(observer); #endif if (observer) { std::vector::iterator attached_observer; attached_observer = std::find(observers_.begin(), observers_.end(), observer); #ifdef IP_DEBUG_OBSERVER DBG_ASSERT(attached_observer != observers_.end()); #endif if (attached_observer != observers_.end()) { observers_.erase(attached_observer); } } } inline void Subject::Notify(Observer::NotifyType notify_type) const { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Subject::Notify", dbg_verbosity); #endif std::vector::iterator iter; for (iter = observers_.begin(); iter != observers_.end(); iter++) { (*iter)->ProcessNotification(notify_type, this); } } } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpIpoptApplication.hpp0000644000335100017570000001475511133661705030022 0ustar pgehlerwebadm// Copyright (C) 2004, 2007 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpIpoptApplication.hpp 1091 2007-09-25 12:16:28Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPIPOPTAPPLICATION_HPP__ #define __IPIPOPTAPPLICATION_HPP__ #include #include "IpJournalist.hpp" #include "IpTNLP.hpp" #include "IpNLP.hpp" /* Return codes for the Optimize call for an application */ #include "IpReturnCodes.hpp" namespace Ipopt { DECLARE_STD_EXCEPTION(IPOPT_APPLICATION_ERROR); /* forward declarations */ class IpoptAlgorithm; class IpoptNLP; class IpoptData; class IpoptCalculatedQuantities; class AlgorithmBuilder; class RegisteredOptions; class OptionsList; class SolveStatistics; /** This is the main application class for making calls to Ipopt. */ class IpoptApplication : public ReferencedObject { public: IpoptApplication(bool create_console_out = true, bool create_empty = false); /** Another constructor that assumes that the code in the * (default) constructor has already been executed */ IpoptApplication(SmartPtr reg_options, SmartPtr options, SmartPtr jnlst); virtual ~IpoptApplication(); /** Method for creating a new IpoptApplication that uses the same * journalist and registered options, and a copy of the options list. */ SmartPtr clone(); /** Initialize method. This method reads the params file and * initializes the journalists. You should call this method at * some point before the first optimize call. Note: you can skip * the processing of a params file by setting params_file to "". * It returns something other than Solve_Succeeded if there was a * problem in the initialization (such as an invalid option). */ ApplicationReturnStatus Initialize(std::string params_file = "ipopt.opt"); ApplicationReturnStatus Initialize(std::istream& is); /**@name Solve methods */ //@{ /** Solve a problem that inherits from TNLP */ ApplicationReturnStatus OptimizeTNLP(const SmartPtr& tnlp); /** Solve a problem that inherits from NLP */ ApplicationReturnStatus OptimizeNLP(const SmartPtr& nlp); /** Solve a problem that inherits from NLP */ ApplicationReturnStatus OptimizeNLP(const SmartPtr& nlp, SmartPtr& alg_builder); /** Solve a problem (that inherits from TNLP) for a repeated time. * The OptimizeTNLP method must have been called before. The * TNLP must be the same object, and the structure (number of * variables and constraints and position of nonzeros in Jacobian * and Hessian must be the same). */ ApplicationReturnStatus ReOptimizeTNLP(const SmartPtr& tnlp); /** Solve a problem (that inherits from NLP) for a repeated time. * The OptimizeNLP method must have been called before. The * NLP must be the same object, and the structure (number of * variables and constraints and position of nonzeros in Jacobian * and Hessian must be the same). */ ApplicationReturnStatus ReOptimizeNLP(const SmartPtr& nlp); //@} /** Method for opening an output file with given print_level. * Returns false if there was a problem. */ bool OpenOutputFile(std::string file_name, EJournalLevel print_level); /**@name Accessor methods */ //@{ /** Get the Journalist for printing output */ SmartPtr Jnlst() { return jnlst_; } /** Get a pointer to RegisteredOptions object to * add new options */ SmartPtr RegOptions() { return reg_options_; } /** Get the options list for setting options */ SmartPtr Options() { return options_; } /** Get the options list for setting options (const version) */ SmartPtr Options() const { return ConstPtr(options_); } /** Get the object with the statistics about the most recent * optimization run. */ SmartPtr Statistics(); //@} /** @name Methods for IpoptTypeInfo */ //@{ static void RegisterOptions(SmartPtr roptions); //@} /** Method to registering all Ipopt options. */ static void RegisterAllIpoptOptions(const SmartPtr& roptions); private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ // IpoptApplication(); /** Copy Constructor */ IpoptApplication(const IpoptApplication&); /** Overloaded Equals Operator */ void operator=(const IpoptApplication&); //@} /** Method for the actual optimize call of the Ipopt algorithm. * This is used both for Optimize and ReOptimize */ ApplicationReturnStatus call_optimize(); /**@name Variables that customize the application behavior */ //@{ /** Decide whether or not the ipopt.opt file should be read */ bool read_params_dat_; //@} /** Journalist for reporting output */ SmartPtr jnlst_; /** RegisteredOptions */ SmartPtr reg_options_; /** OptionsList used for the application */ SmartPtr options_; /** Object for storing statistics about the most recent * optimization run. */ SmartPtr statistics_; /** Object with the algorithm sceleton. */ SmartPtr alg_; /** IpoptNLP Object for the NLP. We keep this around for a * ReOptimize warm start. */ SmartPtr ip_nlp_; /** IpoptData Object for the NLP. We keep this around for a * ReOptimize warm start. */ SmartPtr ip_data_; /** IpoptCalculatedQuantities Object for the NLP. We keep this * around for a ReOptimize warm start. */ SmartPtr ip_cq_; /** Pointer to the TNLPAdapter used to convert the TNLP to an NLP. * We keep this around for the ReOptimizerTNLP call. */ SmartPtr nlp_adapter_; }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpSymMatrix.hpp0000644000335100017570000000701611133661705026470 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpSymMatrix.hpp 1010 2007-06-21 15:54:36Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPSYMMATRIX_HPP__ #define __IPSYMMATRIX_HPP__ #include "IpUtils.hpp" #include "IpMatrix.hpp" namespace Ipopt { /* forward declarations */ class SymMatrixSpace; /** This is the base class for all derived symmetric matrix types. */ class SymMatrix : public Matrix { public: /** @name Constructor/Destructor */ //@{ /** Constructor, taking the owner_space. */ SymMatrix(const SymMatrixSpace* owner_space); /** Destructor */ virtual ~SymMatrix() {} //@} /** @name Information about the size of the matrix */ //@{ /** Dimension of the matrix (number of rows and columns) */ Index Dim() const; //@} SmartPtr OwnerSymMatrixSpace() const; protected: /** @name Overloaded methods from Matrix. Since the matrix is * symmetric, it is only necessary to implement the * MultVectorImpl method in a class that inherits from this base * class. If the TransMultVectorImpl is called, this base class * automatically calls MultVectorImpl instead. */ //@{ virtual void TransMultVectorImpl(Number alpha, const Vector& x, Number beta, Vector& y) const; //@} private: /** Copy of the owner space ptr as a SymMatrixSpace instead * of a MatrixSpace */ const SymMatrixSpace* owner_space_; }; /** SymMatrixSpace base class, corresponding to the SymMatrix base * class. */ class SymMatrixSpace : public MatrixSpace { public: /** @name Constructors/Destructors */ //@{ /** Constructor, given the dimension (identical to the number of * rows and columns). */ SymMatrixSpace(Index dim) : MatrixSpace(dim,dim) {} /** Destructor */ virtual ~SymMatrixSpace() {} //@} /** Pure virtual method for creating a new matrix of this specific * type. */ virtual SymMatrix* MakeNewSymMatrix() const=0; /** Overloaded MakeNew method for the MatrixSpace base class. */ virtual Matrix* MakeNew() const { return MakeNewSymMatrix(); } /** Accessor method for the dimension of the matrices in this * matrix space. */ Index Dim() const { DBG_ASSERT(NRows() == NCols()); return NRows(); } private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** default constructor */ SymMatrixSpace(); /* Copy constructor */ SymMatrixSpace(const SymMatrixSpace&); /** Overloaded Equals Operator */ SymMatrixSpace& operator=(const SymMatrixSpace&); //@} }; /* inline methods */ inline SymMatrix::SymMatrix(const SymMatrixSpace* owner_space) : Matrix(owner_space), owner_space_(owner_space) {} inline Index SymMatrix::Dim() const { return owner_space_->Dim(); } inline SmartPtr SymMatrix::OwnerSymMatrixSpace() const { return owner_space_; } } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpReturnCodes.h0000644000335100017570000000107311133661705026425 0ustar pgehlerwebadm/*********************************************************************** // Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpReturnCodes.h 759 2006-07-07 03:07:08Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 ************************************************************************/ #ifndef __IPRETURNCODES_H__ #define __IPRETURNCODES_H__ /* include from a common include file */ #include "IpReturnCodes_inc.h" #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/include/coin/IpStdCInterface.h0000644000335100017570000002441411133661705026652 0ustar pgehlerwebadm/************************************************************************* Copyright (C) 2004, 2006 International Business Machines and others. All Rights Reserved. This code is published under the Common Public License. $Id: IpStdCInterface.h 820 2006-12-14 04:54:14Z andreasw $ Authors: Carl Laird, Andreas Waechter IBM 2004-09-02 *************************************************************************/ #ifndef __IPSTDCINTERFACE_H__ #define __IPSTDCINTERFACE_H__ #ifdef __cplusplus extern "C" { #endif /** Type for all number. We need to make sure that this is identical with what is defined in Common/IpTypes.hpp */ typedef double Number; /** Type for all incides. We need to make sure that this is identical with what is defined in Common/IpTypes.hpp */ typedef int Index; /** Type for all integers. We need to make sure that this is identical with what is defined in Common/IpTypes.hpp */ typedef int Int; /* This includes the SolverReturn enum type */ #include "IpReturnCodes.h" /** Structure collecting all information about the problem * definition and solve statistics etc. This is defined in the * source file. */ struct IpoptProblemInfo; /** Pointer to a Ipopt Problem. */ typedef struct IpoptProblemInfo* IpoptProblem; /** define a boolean type for C */ typedef int Bool; #ifndef TRUE # define TRUE (1) #endif #ifndef FALSE # define FALSE (0) #endif /** A pointer for anything that is to be passed between the called * and individual callback function */ typedef void * UserDataPtr; /** Type defining the callback function for evaluating the value of * the objective function. Return value should be set to false if * there was a problem doing the evaluation. */ typedef Bool (*Eval_F_CB)(Index n, Number* x, Bool new_x, Number* obj_value, UserDataPtr user_data); /** Type defining the callback function for evaluating the gradient of * the objective function. Return value should be set to false if * there was a problem doing the evaluation. */ typedef Bool (*Eval_Grad_F_CB)(Index n, Number* x, Bool new_x, Number* grad_f, UserDataPtr user_data); /** Type defining the callback function for evaluating the value of * the constraint functions. Return value should be set to false if * there was a problem doing the evaluation. */ typedef Bool (*Eval_G_CB)(Index n, Number* x, Bool new_x, Index m, Number* g, UserDataPtr user_data); /** Type defining the callback function for evaluating the Jacobian of * the constrant functions. Return value should be set to false if * there was a problem doing the evaluation. */ typedef Bool (*Eval_Jac_G_CB)(Index n, Number *x, Bool new_x, Index m, Index nele_jac, Index *iRow, Index *jCol, Number *values, UserDataPtr user_data); /** Type defining the callback function for evaluating the Hessian of * the Lagrangian function. Return value should be set to false if * there was a problem doing the evaluation. */ typedef Bool (*Eval_H_CB)(Index n, Number *x, Bool new_x, Number obj_factor, Index m, Number *lambda, Bool new_lambda, Index nele_hess, Index *iRow, Index *jCol, Number *values, UserDataPtr user_data); /** Function for creating a new Ipopt Problem object. This function * returns an object that can be passed to the IpoptSolve call. It * contains the basic definition of the optimization problem, such * as number of variables and constraints, bounds on variables and * constraints, information about the derivatives, and the callback * function for the computation of the optimization problem * functions and derivatives. During this call, the options file * PARAMS.DAT is read as well. * * If NULL is returned, there was a problem with one of the inputs * or reading the options file. */ IpoptProblem CreateIpoptProblem( Index n /** Number of optimization variables */ , Number* x_L /** Lower bounds on variables. This array of size n is copied internally, so that the caller can change the incoming data after return without that IpoptProblem is modified. Any value less or equal than the number specified by option 'nlp_lower_bound_inf' is interpreted to be minus infinity. */ , Number* x_U /** Upper bounds on variables. This array of size n is copied internally, so that the caller can change the incoming data after return without that IpoptProblem is modified. Any value greater or equal than the number specified by option 'nlp_upper_bound_inf' is interpreted to be plus infinity. */ , Index m /** Number of constraints. */ , Number* g_L /** Lower bounds on constraints. This array of size m is copied internally, so that the caller can change the incoming data after return without that IpoptProblem is modified. Any value less or equal than the number specified by option 'nlp_lower_bound_inf' is interpreted to be minus infinity. */ , Number* g_U /** Upper bounds on constraints. This array of size m is copied internally, so that the caller can change the incoming data after return without that IpoptProblem is modified. Any value greater or equal than the number specified by option 'nlp_upper_bound_inf' is interpreted to be plus infinity. */ , Index nele_jac /** Number of non-zero elements in constraint Jacobian. */ , Index nele_hess /** Number of non-zero elements in Hessian of Lagrangian. */ , Index index_style /** indexing style for iRow & jCol, 0 for C style, 1 for Fortran style */ , Eval_F_CB eval_f /** Callback function for evaluating objective function */ , Eval_G_CB eval_g /** Callback function for evaluating constraint functions */ , Eval_Grad_F_CB eval_grad_f /** Callback function for evaluating gradient of objective function */ , Eval_Jac_G_CB eval_jac_g /** Callback function for evaluating Jacobian of constraint functions */ , Eval_H_CB eval_h /** Callback function for evaluating Hessian of Lagrangian function */ ); /** Method for freeing a previously created IpoptProblem. After freeing an IpoptProblem, it cannot be used anymore. */ void FreeIpoptProblem(IpoptProblem ipopt_problem); /** Function for adding a string option. Returns FALSE the option * could not be set (e.g., if keyword is unknown) */ Bool AddIpoptStrOption(IpoptProblem ipopt_problem, char* keyword, char* val); /** Function for adding a Number option. Returns FALSE the option * could not be set (e.g., if keyword is unknown) */ Bool AddIpoptNumOption(IpoptProblem ipopt_problem, char* keyword, Number val); /** Function for adding an Int option. Returns FALSE the option * could not be set (e.g., if keyword is unknown) */ Bool AddIpoptIntOption(IpoptProblem ipopt_problem, char* keyword, Int val); /** Function for opening an output file for a given name with given * printlevel. Returns false, if there was a problem opening the * file. */ Bool OpenIpoptOutputFile(IpoptProblem ipopt_problem, char* file_name, Int print_level); /** Optional function for setting scaling parameter for the NLP. * This corresponds to the get_scaling_parameters method in TNLP. * If the pointers x_scaling or g_scaling are NULL, then no scaling * for x resp. g is done. */ Bool SetIpoptProblemScaling(IpoptProblem ipopt_problem, Number obj_scaling, Number* x_scaling, Number* g_scaling); /** Function calling the Ipopt optimization algorithm for a problem previously defined with CreateIpoptProblem. The return specified outcome of the optimization procedure (e.g., success, failure etc). */ enum ApplicationReturnStatus IpoptSolve( IpoptProblem ipopt_problem /** Problem that is to be optimized. Ipopt will use the options previously specified with AddIpoptOption (etc) for this problem. */ , Number* x /** Input: Starting point Output: Optimal solution */ , Number* g /** Values of constraint at final point (output only - ignored if set to NULL) */ , Number* obj_val /** Final value of objective function (output only - ignored if set to NULL) */ , Number* mult_g /** Final multipliers for constraints (output only - ignored if set to NULL) */ , Number* mult_x_L /** Final multipliers for lower variable bounds (output only - ignored if set to NULL) */ , Number* mult_x_U /** Final multipliers for upper variable bounds (output only - ignored if set to NULL) */ , UserDataPtr user_data /** Pointer to user data. This will be passed unmodified to the callback functions. */ ); /** void IpoptStatisticsCounts; void IpoptStatisticsInfeasibilities; */ #ifdef __cplusplus } /* extern "C" { */ #endif #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/share/0000755000335100017570000000000011133662012022244 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/share/doc/0000755000335100017570000000000011133662012023011 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/share/doc/coin/0000755000335100017570000000000011133662012023741 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/share/doc/coin/Ipopt/0000755000335100017570000000000011133662012025034 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/share/doc/coin/Ipopt/LICENSE0000644000335100017570000002653511133661704026063 0ustar pgehlerwebadmCommon Public License Version 1.0 THE ACCOMPANYING PROGRAM IS PROVIDED UNDER THE TERMS OF THIS COMMON PUBLIC LICENSE ("AGREEMENT"). 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Each party waives its rights to a jury trial in any resulting litigation. mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/share/doc/coin/Ipopt/ipopt_addlibs_f.txt0000644000335100017570000000004011133661704030720 0ustar pgehlerwebadm-lpthread -llapack -lblas -lm mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/share/doc/coin/Ipopt/README0000644000335100017570000000044011133661704025721 0ustar pgehlerwebadmThese directories contain the C++ reimplementation of the interior point NLP solver Ipopt. For more inforamation, see Docs/documentation.pdf or see the online version of it at http://www.coin-or.org/Ipopt/documentation/ The project's website is at http://projects.coin-or.org/Ipopt mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/share/doc/coin/Ipopt/ipopt_addlibs_cpp.txt0000644000335100017570000000035511133661704031266 0ustar pgehlerwebadm-lpthread -llapack -lblas -lm -L/usr/lib/gcc/i486-linux-gnu/4.2.4 -L/usr/lib/gcc/i486-linux-gnu/4.2.4/../../../../lib -L/lib/../lib -L/usr/lib/../lib -L/usr/lib/gcc/i486-linux-gnu/4.2.4/../../.. -lgfortranbegin -lgfortran -lm -lgcc_s mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-i686/share/doc/coin/Ipopt/AUTHORS0000644000335100017570000000371511133661704026121 0ustar pgehlerwebadmMain authors: Andreas Waechter, project leader (IBM) Carl Laird (IBM, Carnegie Mellon University) Contributors: - Yifan Hu (Wolfram): Contributed TAUCS interface, implemented in the files PDSystemImpl/PDFullSpace/IpTAUCSSolverInterface.hpp PDSystemImpl/PDFullSpace/IpTAUCSSolverInterface.cpp - Yoshiaki Kawajiri (Carnegie Mellon Univeristy): First version of Latex documentation file Docs/documentation.tex - Olaf Schenk (Universirty of Basel): Modifications to the PARDISO interface in the files Algorithm/LinearSolvers/IpPardisoSolverInterface.cpp Algorithm/LinearSolvers/IpPardisoSolverInterface.hpp - Michael Hagemann (Universirty of Basel): MA57 interface Algorithm/LinearSolvers/IpMa57SolverInterface.cpp Algorithm/LinearSolvers/IpMa57SolverInterface.hpp - Damien Hocking (KBC Advanced Technologies) MUMPS interface Algorithm/LinearSolvers/IpMumpsSolverInterface.cpp Algorithm/LinearSolvers/IpMumpsSolverInterface.hpp - Jon Lee (IBM Research) example nl file (writting by hand) for unitTest Test/mytoy.nl - Peter Carbonetto (University of British Columbia) Matlab interface files in directory (and subdirctories of) contrib/MatlabInterface - Lifeng Chen/Zaiwen Wen (Columbia University) Changes and additions to the implementation of the Chen-Goldfarb penalty function algorithm src/contrib/CGPenalty/IpCGPenaltyCq.cpp src/contrib/CGPenalty/IpCGPenaltyCq.hpp src/contrib/CGPenalty/IpCGPenaltyData.cpp src/contrib/CGPenalty/IpCGPenaltyData.hpp src/contrib/CGPenalty/IpCGPenaltyLSAcceptor.cpp src/contrib/CGPenalty/IpCGPenaltyLSAcceptor.hpp src/contrib/CGPenalty/IpCGPenaltyRegOp.cpp src/contrib/CGPenalty/IpCGPenaltyRegOp.hpp src/contrib/CGPenalty/IpCGPerturbationHandler.cpp src/contrib/CGPenalty/IpCGPerturbationHandler.hpp src/contrib/CGPenalty/IpCGSearchDirCalc.cpp src/contrib/CGPenalty/IpCGSearchDirCalc.hpp src/contrib/CGPenalty/IpPiecewisePenalty.cpp src/contrib/CGPenalty/IpPiecewisePenalty.hpp mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/0000755000335100017570000000000011133662012021361 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/lib/0000755000335100017570000000000011133662012022127 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/lib/libipopt.so.00000777000335100017570000000000011134124727027275 2libipopt.so.0.0.0ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/lib/libipopt.so.0.0.00000755000335100017570001677140211133661703025001 0ustar pgehlerwebadmELF>0@(98@8@# 77 7WW `7`W`WPtd838383NNQtd 8 R q j  M # ,I t ! p y {& 1  >L  Jd  N * G   ; g v(E  Q p pK  C D RI + \ |/HXnR e B Yxi X   {BM s > p m'\  -m O X 0& C u # S $y1% r  5 c u  s) [  c ]\uF Kb [ =P > 2  bzS o %y NS B 5I uY 8 4  ,jy O)  iA6 e & %n2 A7v  6; &A HkWx  e Z  -  # , t  R+ P6 l $ , sC- x w ,D    oG!dz q3 /=K  OE  ;GtN V X [ 4f }  U J  x1 w6F  %Mh ]         Y r  [? z ) ] F Zg a!=b 9 X[ E, 7 c 0 G  F jSl  % B   3]  ;1 If e ;D h &   6Z ' , q U-=) X>  l}/   j; a * CI_( / 5 O 42P?A Y VF  (  /K  2 }U T  |P  8J    N?WWJ 9(+ ? 2k H E   Ql@z{ Py  zpBm_ ]. / v h| 0K4H  % _ Zg QY" l y   v~ "^ ! 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t__gmon_start___fini__cxa_finalize_Jv_RegisterClasses_ZN5Ipopt13CachedResultsIdED0Ev_ZTVN5Ipopt13CachedResultsIdEE_ZdlPv__gxx_personality_v0_ZN5Ipopt16ReferencedObjectD0Ev_ZTVN5Ipopt16ReferencedObjectE_ZN5Ipopt16ReferencedObjectD1Ev_ZNSt8ios_base4InitC1Ev__cxa_atexit_ZNSt8ios_base4InitD1Ev_ZN5Ipopt13CachedResultsIdED1Ev_ZN5Ipopt8ObserverD0Ev_ZTVN5Ipopt8ObserverEmemmove_ZN5Ipopt8ObserverD1Ev_Unwind_Resume_ZN5Ipopt26RegisterOptions_InterfacesERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZNSsC1EPKcRKSaIcE_ZNSs6assignERKSs_ZNSs4_Rep20_S_empty_rep_storageE_ZN5Ipopt16IpoptApplication15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt11TNLPAdapter15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZNSs4_Rep10_M_destroyERKSaIcE_ZThn16_N5Ipopt12TaggedObjectD1Ev_ZN5Ipopt12TaggedObjectD1Ev_ZTVN5Ipopt12TaggedObjectE_ZTVN5Ipopt7SubjectE_ZN5Ipopt7SubjectD0Ev_ZN5Ipopt7SubjectD1Ev_ZThn16_N5Ipopt12TaggedObjectD0Ev_ZN5Ipopt12TaggedObjectD0Ev_ZThn16_N5Ipopt6VectorD1Ev_ZN5Ipopt6VectorD1Ev_ZTVN5Ipopt6VectorE_ZThn16_N5Ipopt6VectorD0Ev_ZN5Ipopt6VectorD0Ev_ZTIN5Ipopt6VectorE__cxa_pure_virtual_ZN5Ipopt6Vector17AddTwoVectorsImplEdRKS0_dS2_d_ZNK5Ipopt6Vector15FracToBoundImplERKS0_d_ZN5Ipopt6Vector21AddVectorQuotientImplEdRKS0_S2_d_ZNK5Ipopt6Vector19HasValidNumbersImplEv_ZTIN5Ipopt7SubjectE_ZTIN5Ipopt8ObserverE_ZTSN5Ipopt6VectorE_ZTVN10__cxxabiv120__si_class_type_infoE_ZTIN5Ipopt12TaggedObjectE_ZTSN5Ipopt7SubjectE_ZTVN10__cxxabiv117__class_type_infoE_ZTSN5Ipopt8ObserverE_ZTVN10__cxxabiv121__vmi_class_type_infoE_ZTSN5Ipopt12TaggedObjectE_ZTIN5Ipopt16ReferencedObjectE_ZTSN5Ipopt16ReferencedObjectE_ZTIN5Ipopt13CachedResultsIdEE_ZTSN5Ipopt13CachedResultsIdEEpthread_cancel_ZN5Ipopt16IpoptApplication23RegisterAllIpoptOptionsERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt25RegisterOptions_AlgorithmERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt25RegisterOptions_CGPenaltyERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt29RegisterOptions_LinearSolversERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZNSsD1Ev_ZN5Ipopt17RegisteredOptions23AddBoundedIntegerOptionERKSsS2_iiiS2__ZN5Ipopt17RegisteredOptions16AddStringOption1ERKSsS2_S2_S2_S2_S2__ZN5Ipopt17RegisteredOptions16AddStringOption2ERKSsS2_S2_S2_S2_S2_S2_S2__ZN5Ipopt16IpoptApplication10StatisticsEv_ZN5Ipopt16IpoptApplicationC1ENS_8SmartPtrINS_17RegisteredOptionsEEENS1_INS_11OptionsListEEENS1_INS_10JournalistEEE_ZTVN5Ipopt16IpoptApplicationE_ZN5Ipopt16IpoptApplicationD0Ev_ZN5Ipopt16IpoptApplicationD1Ev_ZN5Ipopt16IpoptApplicationD2Ev_ZN5Ipopt16IpoptApplicationC2ENS_8SmartPtrINS_17RegisteredOptionsEEENS1_INS_11OptionsListEEENS1_INS_10JournalistEEE_ZN5Ipopt11TOO_FEW_DOFD0Ev_ZTVN5Ipopt14IpoptExceptionE_ZN5Ipopt11TOO_FEW_DOFD1Ev_ZN5Ipopt23IPOPT_APPLICATION_ERRORD1Ev_ZN5Ipopt17INVALID_WARMSTARTD1Ev_ZN5Ipopt17INVALID_WARMSTARTD0Ev_ZN5Ipopt16IpoptApplicationC1Ebb_Znwm_ZTVN5Ipopt11OptionsListE_ZN5Ipopt10JournalistC1Ev_ZN5Ipopt10Journalist14AddFileJournalERKSsS2_NS_13EJournalLevelE_ZN5Ipopt7Journal13SetPrintLevelENS_16EJournalCategoryENS_13EJournalLevelE_ZTVN5Ipopt17RegisteredOptionsE__cxa_begin_catch_ZNSsC1ERKSs_ZNK5Ipopt10Journalist6PrintfENS_13EJournalLevelENS_16EJournalCategoryEPKcz__cxa_allocate_exception_ZTVN5Ipopt23IPOPT_APPLICATION_ERRORE_ZTIN5Ipopt23IPOPT_APPLICATION_ERRORE__cxa_throw__cxa_end_catch_ZSt9terminatev__cxa_free_exception__cxa_get_exception_ptr_ZTVSt9bad_alloc_ZNSt9bad_allocD1Ev_ZN5Ipopt14IpoptExceptionD1Ev_ZN5Ipopt14IpoptExceptionD0Ev_ZN5Ipopt23IPOPT_APPLICATION_ERRORD0Ev_ZN5Ipopt16IpoptApplicationC2Ebb_ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt11OptionsList11OptionValueEESt10_Select1stIS5_ESt4lessISsESaIS5_EE8_M_eraseEPSt13_Rb_tree_nodeIS5_E_ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt11OptionsList11OptionValueEESt10_Select1stIS5_ESt4lessISsESaIS5_EE7_M_copyEPKSt13_Rb_tree_nodeIS5_EPSD___cxa_rethrow_ZN5Ipopt16IpoptApplication5cloneEv_ZN5Ipopt11OptionsListD0Ev_ZN5Ipopt11OptionsListD1Ev_ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessISsESaIS6_EE8_M_eraseEPSt13_Rb_tree_nodeIS6_E_ZN5Ipopt17RegisteredOptionsD1Ev_ZN5Ipopt17RegisteredOptionsD0Ev_ZStplIcSt11char_traitsIcESaIcEESbIT_T0_T1_EPKS3_RKS6_strlen_ZNSs7reserveEm_ZNSs6appendEPKcm_ZNSs6appendERKSs_ZN5Ipopt16IpoptApplication14OpenOutputFileESsNS_13EJournalLevelE_ZN5Ipopt10Journalist10GetJournalERKSs_ZN5Ipopt16IpoptApplication10InitializeERSi_ZN5Ipopt11OptionsList14ReadFromStreamERKNS_10JournalistERSi_ZNK5Ipopt11OptionsList15GetIntegerValueERKSsRiS2__ZN5Ipopt7Journal17SetAllPrintLevelsENS_13EJournalLevelE_ZNK5Ipopt11OptionsList14GetStringValueERKSsRSsS2__ZNKSs7compareEPKc_ZNK5Ipopt11OptionsList12GetBoolValueERKSsRbS2__ZNSt15_List_node_base4hookEPS__ZN5Ipopt17RegisteredOptions30OutputLatexOptionDocumentationERKNS_10JournalistERSt4listISsSaISsEE_ZN5Ipopt17RegisteredOptions25OutputOptionDocumentationERKNS_10JournalistERSt4listISsSaISsEE_ZN5Ipopt16IpoptApplication10InitializeESs_ZNSt8ios_baseC2Ev_ZTTSt14basic_ifstreamIcSt11char_traitsIcEE_ZTVSt14basic_ifstreamIcSt11char_traitsIcEE_ZNSt13basic_filebufIcSt11char_traitsIcEEC1Ev_ZNSt9basic_iosIcSt11char_traitsIcEE4initEPSt15basic_streambufIcS1_E_ZNSt13basic_filebufIcSt11char_traitsIcEE4openEPKcSt13_Ios_Openmode_ZNSt9basic_iosIcSt11char_traitsIcEE5clearESt12_Ios_Iostate_ZTVSt13basic_filebufIcSt11char_traitsIcEE_ZNSt13basic_filebufIcSt11char_traitsIcEE5closeEv_ZNSt12__basic_fileIcED1Ev_ZTVSt15basic_streambufIcSt11char_traitsIcEE_ZNSt6localeD1Ev_ZTVSt9basic_iosIcSt11char_traitsIcEE_ZNSt8ios_baseD2Ev__stack_chk_fail_ZN5Ipopt16IpoptApplication13call_optimizeEv_ZN5Ipopt16TimingStatistics10ResetTimesEv_ZNK5Ipopt11OptionsList16PrintUserOptionsERSs_ZNK5Ipopt10Journalist13ProduceOutputENS_13EJournalLevelENS_16EJournalCategoryE_ZNK5Ipopt11OptionsList9PrintListERSs_ZN5Ipopt14IpoptAlgorithm8OptimizeEv_ZN5Ipopt25IpoptCalculatedQuantities15unscaled_curr_fEv_ZN5Ipopt25IpoptCalculatedQuantities6curr_fEv_ZN5Ipopt25IpoptCalculatedQuantities32unscaled_curr_dual_infeasibilityENS_9ENormTypeE_ZN5Ipopt25IpoptCalculatedQuantities23curr_dual_infeasibilityENS_9ENormTypeE_ZN5Ipopt25IpoptCalculatedQuantities38unscaled_curr_nlp_constraint_violationENS_9ENormTypeE_ZN5Ipopt25IpoptCalculatedQuantities29curr_nlp_constraint_violationENS_9ENormTypeE_ZN5Ipopt25IpoptCalculatedQuantities29unscaled_curr_complementarityEdNS_9ENormTypeE_ZN5Ipopt25IpoptCalculatedQuantities20curr_complementarityEdNS_9ENormTypeE_ZN5Ipopt25IpoptCalculatedQuantities23unscaled_curr_nlp_errorEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_nlp_errorEv_ZNK5Ipopt6Vector5PrintERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNK5Ipopt12OrigIpoptNLP30TotalFunctionEvaluationCPUTimeEv_ZTIN5Ipopt11TOO_FEW_DOFE_ZN5Ipopt25IpoptCalculatedQuantities6curr_dEv_ZN5Ipopt25IpoptCalculatedQuantities6curr_cEv_ZN5Ipopt15SolveStatisticsC1ERKNS_8SmartPtrINS_8IpoptNLPEEERKNS1_INS_9IpoptDataEEERKNS1_INS_25IpoptCalculatedQuantitiesEEE_ZNK5Ipopt10Journalist11FlushBufferEv_ZNK5Ipopt16TimingStatistics24PrintAllTimingStatisticsERNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryE_ZNK5Ipopt12OrigIpoptNLP21PrintTimingStatisticsERNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryE_ZN5Ipopt25IpoptCalculatedQuantities14curr_d_minus_sEv_ZN5Ipopt16IpoptApplication13ReOptimizeNLPERKNS_8SmartPtrINS_3NLPEEE_ZTVN5Ipopt17INVALID_WARMSTARTE_ZTIN5Ipopt17INVALID_WARMSTARTE_ZN5Ipopt16IpoptApplication14ReOptimizeTNLPERKNS_8SmartPtrINS_4TNLPEEE_ZN5Ipopt16IpoptApplication11OptimizeNLPERKNS_8SmartPtrINS_3NLPEEERNS1_INS_16AlgorithmBuilderEEE_ZN5Ipopt16AlgorithmBuilderC1ENS_8SmartPtrINS_15AugSystemSolverEEE_ZN5Ipopt16IpoptApplication11OptimizeNLPERKNS_8SmartPtrINS_3NLPEEE_ZN5Ipopt16IpoptApplication12OptimizeTNLPERKNS_8SmartPtrINS_4TNLPEEE_ZN5Ipopt11TNLPAdapterC1ENS_8SmartPtrINS_4TNLPEEENS1_IKNS_10JournalistEEE_ZTIN5Ipopt16IpoptApplicationE_ZTSN5Ipopt16IpoptApplicationE_ZTSN5Ipopt11TOO_FEW_DOFE_ZTIN5Ipopt14IpoptExceptionE_ZTSN5Ipopt14IpoptExceptionE_ZTVN5Ipopt11TOO_FEW_DOFE_ZTSN5Ipopt17INVALID_WARMSTARTE_ZTSN5Ipopt23IPOPT_APPLICATION_ERRORE_ZTIN5Ipopt11OptionsListE_ZTSN5Ipopt11OptionsListE_ZTIN5Ipopt17RegisteredOptionsE_ZTSN5Ipopt17RegisteredOptionsE_ZTIN5Ipopt14OPTION_INVALIDE_ZTSN5Ipopt14OPTION_INVALIDE_ZTIN5Ipopt30NO_FREE_VARIABLES_BUT_FEASIBLEE_ZTSN5Ipopt30NO_FREE_VARIABLES_BUT_FEASIBLEE_ZTIN5Ipopt32NO_FREE_VARIABLES_AND_INFEASIBLEE_ZTSN5Ipopt32NO_FREE_VARIABLES_AND_INFEASIBLEE_ZTISt9bad_alloc_ZNK5Ipopt15SolveStatistics14IterationCountEv_ZNK5Ipopt15SolveStatistics12TotalCPUTimeEv_ZNK5Ipopt15SolveStatistics19NumberOfEvaluationsERiS1_S1_S1_S1__ZNK5Ipopt15SolveStatistics15InfeasibilitiesERdS1_S1_S1__ZNK5Ipopt15SolveStatistics21ScaledInfeasibilitiesERdS1_S1_S1__ZNK5Ipopt15SolveStatistics14FinalObjectiveEv_ZNK5Ipopt15SolveStatistics20FinalScaledObjectiveEv_ZN5Ipopt15SolveStatisticsD0Ev_ZN5Ipopt15SolveStatisticsC2ERKNS_8SmartPtrINS_8IpoptNLPEEERKNS1_INS_9IpoptDataEEERKNS1_INS_25IpoptCalculatedQuantitiesEEE_ZTVN5Ipopt15SolveStatisticsE_ZN5Ipopt15SolveStatisticsD1Ev_ZTIN5Ipopt15SolveStatisticsE_ZTSN5Ipopt15SolveStatisticsESetIpoptProblemScaling_ZdaPv_ZnamCreateIpoptProblemFreeIpoptProblem_ZN5Ipopt12TaggedObjectD2EvAddIpoptNumOption_ZN5Ipopt11OptionsList15SetNumericValueERKSsdbbAddIpoptStrOption_ZN5Ipopt11OptionsList14SetStringValueERKSsS2_bbIpoptSolve_ZN5Ipopt16StdInterfaceTNLPC1EiPKdS2_iS2_S2_iiiS2_S2_S2_S2_PFiiPdiS3_PvEPFiiS3_iiS3_S4_ES6_PFiiS3_iiiPiS9_S3_S4_EPFiiS3_idiS3_iiS9_S9_S3_S4_ES3_S3_S3_S3_S3_S3_S4_dS2_S2_OpenIpoptOutputFileAddIpoptIntOption_ZN5Ipopt11OptionsList15SetIntegerValueERKSsibb_ZTIN5Ipopt24INVALID_STDINTERFACE_NLPE_ZTSN5Ipopt24INVALID_STDINTERFACE_NLPEipsolve_ipfree_ipcreate_putsipaddstroption_strncpyipopenoutputfile_ipaddintoption_ipaddnumoption__ZN5Ipopt4TNLP22get_scaling_parametersERdRbiPdS2_iS3__ZN5Ipopt4TNLP23get_variables_linearityEiPNS0_13LinearityTypeE_ZN5Ipopt4TNLP25get_constraints_linearityEiPNS0_13LinearityTypeE_ZN5Ipopt4TNLP22get_warm_start_iterateERNS_14IteratesVectorE_ZN5Ipopt4TNLP6eval_hEiPKdbdiS2_biPiS3_Pd_ZN5Ipopt4TNLP19set_variable_valuesEiPKdbiS2_b_ZN5Ipopt4TNLP21intermediate_callbackENS_13AlgorithmModeEiddddddddiPKNS_9IpoptDataEPNS_25IpoptCalculatedQuantitiesE_ZN5Ipopt4TNLP33get_number_of_nonlinear_variablesEv_ZN5Ipopt4TNLP31get_list_of_nonlinear_variablesEiPi_ZN5Ipopt16StdInterfaceTNLP12get_nlp_infoERiS1_S1_S1_RNS_4TNLP14IndexStyleEnumE_ZN5Ipopt16StdInterfaceTNLP15get_bounds_infoEiPdS1_iS1_S1__ZN5Ipopt16StdInterfaceTNLP22get_scaling_parametersERdRbiPdS2_iS3__ZN5Ipopt16StdInterfaceTNLP18get_starting_pointEibPdbS1_S1_ibS1__ZN5Ipopt16StdInterfaceTNLP11apply_new_xEbiPKd_ZN5Ipopt16StdInterfaceTNLP10eval_jac_gEiPKdbiiPiS3_Pd_ZN5Ipopt16StdInterfaceTNLP6eval_gEiPKdbiPd_ZN5Ipopt16StdInterfaceTNLP11eval_grad_fEiPKdbPd_ZN5Ipopt16StdInterfaceTNLP6eval_fEiPKdbRd_ZN5Ipopt16StdInterfaceTNLP17finalize_solutionENS_12SolverReturnEiPKdS3_S3_iS3_S3_dPKNS_9IpoptDataEPNS_25IpoptCalculatedQuantitiesE_ZN5Ipopt11IpBlasDcopyEiPKdiPdi_ZN5Ipopt16StdInterfaceTNLP6eval_hEiPKdbdiS2_biPiS3_Pd_ZN5Ipopt24INVALID_STDINTERFACE_NLPD1Ev_ZN5Ipopt4TNLPD1Ev_ZN5Ipopt4TNLPD0Ev_ZN5Ipopt24INVALID_STDINTERFACE_NLPD0Ev_ZN5Ipopt16StdInterfaceTNLPD2Ev_ZTVN5Ipopt16StdInterfaceTNLPE_ZN5Ipopt16StdInterfaceTNLPD0Ev_ZN5Ipopt16StdInterfaceTNLPD1Ev_ZN5Ipopt24INVALID_STDINTERFACE_NLPC1ESsSsi_ZTVN5Ipopt24INVALID_STDINTERFACE_NLPE_ZN5Ipopt16StdInterfaceTNLPC2EiPKdS2_iS2_S2_iiiS2_S2_S2_S2_PFiiPdiS3_PvEPFiiS3_iiS3_S4_ES6_PFiiS3_iiiPiS9_S3_S4_EPFiiS3_idiS3_iiS9_S9_S3_S4_ES3_S3_S3_S3_S3_S3_S4_dS2_S2__ZTIN5Ipopt16StdInterfaceTNLPE_ZTSN5Ipopt16StdInterfaceTNLPE_ZTIN5Ipopt4TNLPE_ZTSN5Ipopt4TNLPE_ZTVN5Ipopt4TNLPE_ZN5Ipopt3NLP14ProcessOptionsERKNS_11OptionsListERKSs_ZN5Ipopt3NLP19GetWarmStartIterateERNS_14IteratesVectorE_ZN5Ipopt3NLP16FinalizeSolutionENS_12SolverReturnERKNS_6VectorES4_S4_S4_S4_S4_S4_dPKNS_9IpoptDataEPNS_25IpoptCalculatedQuantitiesE_ZN5Ipopt3NLP20IntermediateCallBackENS_13AlgorithmModeEiddddddddiPKNS_9IpoptDataEPNS_25IpoptCalculatedQuantitiesE_ZNK5Ipopt16DenseVectorSpace7MakeNewEv_ZN5Ipopt11DenseVectorC1EPKNS_16DenseVectorSpaceE_ZNSt4listIiSaIiEE5mergeERS1__ZNSt15_List_node_base8transferEPS_S0__ZN5Ipopt11TNLPAdapter19GetWarmStartIterateERNS_14IteratesVectorE_ZN5Ipopt11TNLPAdapter19internal_eval_jac_gEb_ZN5Ipopt11TNLPAdapter15internal_eval_gEb_ZN5Ipopt11TNLPAdapter20IntermediateCallBackENS_13AlgorithmModeEiddddddddiPKNS_9IpoptDataEPNS_25IpoptCalculatedQuantitiesE_ZN5Ipopt11TNLPAdapter7ResortXERKNS_6VectorEPd_ZN5Ipopt11TNLPAdapter10ResortBndsERKNS_6VectorEPdS3_S4__ZN5Ipopt11TNLPAdapter7ResortGERKNS_6VectorES3_Pd_ZN5Ipopt11TNLPAdapter19update_local_lambdaERKNS_6VectorES3__ZN5Ipopt11TNLPAdapter14update_local_xERKNS_6VectorE_ZN5Ipopt11TNLPAdapter6Eval_fERKNS_6VectorERd_ZN5Ipopt11TNLPAdapter16FinalizeSolutionENS_12SolverReturnERKNS_6VectorES4_S4_S4_S4_S4_S4_dPKNS_9IpoptDataEPNS_25IpoptCalculatedQuantitiesE_ZN5Ipopt3NLP33GetQuasiNewtonApproximationSpacesERNS_8SmartPtrINS_11VectorSpaceEEERNS1_INS_6MatrixEEE_ZN5Ipopt11TNLPAdapter6Eval_hERKNS_6VectorEdS3_S3_RNS_9SymMatrixE_ZN5Ipopt10SymTMatrix6ValuesEv_ZN5Ipopt16DenseVectorSpaceD0Ev_ZN5Ipopt3NLPD0Ev_ZN5Ipopt3NLPD1Ev_ZN5Ipopt11VectorSpaceD0Ev_ZN5Ipopt11VectorSpaceD1Ev_ZN5Ipopt16DenseVectorSpaceD1Ev_ZN5Ipopt11TNLPAdapter10Eval_jac_cERKNS_6VectorERNS_6MatrixE_ZN5Ipopt12TaggedObject11unique_tag_E_ZNSt4listIiSaIiEE4sortEv_ZNSt15_List_node_base4swapERS_S0__ZN5Ipopt11TNLPAdapter10Eval_jac_dERKNS_6VectorERNS_6MatrixE_ZN5Ipopt11TNLPAdapterD1Ev_ZTVN5Ipopt11TNLPAdapterE_ZN5Ipopt11TNLPAdapterD2Ev_ZN5Ipopt11TNLPAdapter6Eval_dERKNS_6VectorERS1__ZN5Ipopt11DenseVector22set_values_from_scalarEv_ZN5Ipopt11TNLPAdapter6Eval_cERKNS_6VectorERS1__ZN5Ipopt11TNLPAdapter11Eval_grad_fERKNS_6VectorERS1__ZN5Ipopt11TNLPAdapter16GetStartingPointENS_8SmartPtrINS_6VectorEEEbS3_bS3_bS3_bS3_b_ZN5Ipopt11TNLPAdapter20GetBoundsInformationERKNS_6MatrixERNS_6VectorES3_S5_S3_S5_S3_S5__ZN5Ipopt11TNLPAdapterD0Ev_ZN5Ipopt11TNLPAdapterC2ENS_8SmartPtrINS_4TNLPEEENS1_IKNS_10JournalistEEE_ZTVN5Ipopt11TNLPAdapter12INVALID_TNLPE_ZN5Ipopt11TNLPAdapter12INVALID_TNLPD1Ev_ZTIN5Ipopt11TNLPAdapter12INVALID_TNLPE_ZNK5Ipopt3NLP20GetScalingParametersENS_8SmartPtrIKNS_11VectorSpaceEEES4_S4_RdRNS1_INS_6VectorEEES8_S8__ZTVN5Ipopt3NLP28USER_SCALING_NOT_IMPLEMENTEDE_ZN5Ipopt3NLP28USER_SCALING_NOT_IMPLEMENTEDD1Ev_ZTIN5Ipopt3NLP28USER_SCALING_NOT_IMPLEMENTEDE_ZN5Ipopt11TNLPAdapter29ERROR_IN_TNLP_DERIVATIVE_TESTD0Ev_ZN5Ipopt3NLP28USER_SCALING_NOT_IMPLEMENTEDD0Ev_ZN5Ipopt30NO_FREE_VARIABLES_BUT_FEASIBLED1Ev_ZN5Ipopt14OPTION_INVALIDD1Ev_ZN5Ipopt14OPTION_INVALIDD0Ev_ZN5Ipopt11TNLPAdapter12INVALID_TNLPD0Ev_ZN5Ipopt11TNLPAdapter29ERROR_IN_TNLP_DERIVATIVE_TESTD1Ev_ZN5Ipopt8IpoptNLP10Eval_ErrorD1Ev_ZN5Ipopt32NO_FREE_VARIABLES_AND_INFEASIBLED1Ev_ZN5Ipopt8IpoptNLP10Eval_ErrorD0Ev_ZN5Ipopt32NO_FREE_VARIABLES_AND_INFEASIBLED0Ev_ZN5Ipopt11TNLPAdapter29DetermineDependentConstraintsEiPKiPKdS4_S4_S4_iS2_RSt4listIiSaIiEE_ZN5Ipopt20ExpansionMatrixSpaceC1EiiPKii_ZN5Ipopt15ExpansionMatrixC1EPKNS_20ExpansionMatrixSpaceE_ZN5Ipopt15IpResetRandom01Ev_ZN5Ipopt10IpRandom01Ev_ZTVN5Ipopt14OPTION_INVALIDE_ZN5Ipopt30NO_FREE_VARIABLES_BUT_FEASIBLED0Ev_ZN5Ipopt11TNLPAdapter16CheckDerivativesENS0_18DerivativeTe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altyLSAcceptor19MultipliersDivergedEv_ZN5Ipopt19CGPenaltyLSAcceptor15RestoredIterateEv_ZN5Ipopt19CGPenaltyLSAcceptorD2Ev_ZN5Ipopt19CGPenaltyLSAcceptorC1ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZN5Ipopt19CGPenaltyLSAcceptor10DoFallbackEv_ZN5Ipopt19CGPenaltyLSAcceptor22UpdatePenaltyParameterEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_compl_x_LEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_compl_x_UEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_compl_s_LEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_compl_s_UEv_ZN5Ipopt19CGPenaltyLSAcceptor22UpdateForNextIterationEd_ZN5Ipopt16PiecewisePenalty5PrintERKNS_10JournalistE_ZN5Ipopt16PiecewisePenalty11UpdateEntryEdd_ZN5Ipopt19CGPenaltyLSAcceptor24TrySecondOrderCorrectionEdRdRNS_8SmartPtrINS_14IteratesVectorEEE_ZN5Ipopt25IpoptCalculatedQuantities7trial_cEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_d_minus_sEv_ZNK5Ipopt14IteratesVector21MakeNewIteratesVectorEb_ZN5Ipopt25IpoptCalculatedQuantities28curr_grad_lag_with_damping_xEv_ZN5Ipopt14CompoundVector7SetCompEiRKNS_6VectorE_ZN5Ipopt25IpoptCalculatedQuantities28curr_grad_lag_with_damping_sEv_ZN5Ipopt25IpoptCalculatedQuantities22curr_relaxed_compl_x_LEv_ZN5Ipopt25IpoptCalculatedQuantities22curr_relaxed_compl_x_UEv_ZN5Ipopt25IpoptCalculatedQuantities22curr_relaxed_compl_s_LEv_ZN5Ipopt25IpoptCalculatedQuantities22curr_relaxed_compl_s_UEv_ZN5Ipopt25IpoptCalculatedQuantities24primal_frac_to_the_boundEdRKNS_6VectorES3__ZN5Ipopt9IpoptData31SetTrialPrimalVariablesFromStepEdRKNS_6VectorES3__ZN5Ipopt19CGPenaltyLSAcceptor30CheckAcceptabilityOfTrialPointEd_ZN5Ipopt22BacktrackingLSAcceptor16ComputeAlphaForYEddRNS_8SmartPtrINS_14IteratesVectorEEE_ZN5Ipopt19CGPenaltyLSAcceptor14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt19CGPenaltyLSAcceptor15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt17RegisteredOptions22AddBoundedNumberOptionERKSsS2_dbdbdS2__ZTIN5Ipopt19CGPenaltyLSAcceptorE_ZTSN5Ipopt19CGPenaltyLSAcceptorE_ZTIN5Ipopt22BacktrackingLSAcceptorE_ZTSN5Ipopt22BacktrackingLSAcceptorE_ZTIN5Ipopt23AlgorithmStrategyObjectE_ZTSN5Ipopt23AlgorithmStrategyObjectE_ZTVN5Ipopt22BacktrackingLSAcceptorE_ZN5Ipopt21CGSearchDirCalculator15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt21CGPerturbationHandler15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt21CGPerturbationHandler19CurrentPerturbationERdS1_S1_S1__ZN5Ipopt21CGPerturbationHandlerC1Ev_ZN5Ipopt21PDPerturbationHandlerC2Ev_ZTVN5Ipopt21CGPerturbationHandlerE_ZN5Ipopt21CGPerturbationHandlerC2Ev_ZN5Ipopt21CGPerturbationHandler8delta_cdEv_ZN5Ipopt21CGPerturbationHandlerD1Ev_ZN5Ipopt21CGPerturbationHandlerD0Ev_ZN5Ipopt21CGPerturbationHandler13finalize_testEv_ZN5Ipopt21CGPerturbationHandler28get_deltas_for_wrong_inertiaERdS1_S1_S1__ZN5Ipopt21CGPerturbationHandler22PerturbForWrongInertiaERdS1_S1_S1__ZN5Ipopt21CGPerturbationHandler17ConsiderNewSystemERdS1_S1_S1__ZN5Ipopt21CGPerturbationHandler21PerturbForSingularityERdS1_S1_S1__ZN5Ipopt21CGPerturbationHandler14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt21PDPerturbationHandler14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt21CGPerturbationHandlerE_ZTSN5Ipopt21CGPerturbationHandlerE_ZTIN5Ipopt21PDPerturbationHandlerE_ZN5Ipopt23AlgorithmStrategyObjectD2Ev_ZN5Ipopt25SearchDirectionCalculatorD0Ev_ZN5Ipopt25SearchDirectionCalculatorD1Ev_ZN5Ipopt21CGSearchDirCalculatorC2ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZTVN5Ipopt21CGSearchDirCalculatorE_ZN5Ipopt21CGSearchDirCalculatorD0Ev_ZN5Ipopt21CGSearchDirCalculatorD2Ev_ZTVN5Ipopt25SearchDirectionCalculatorE_ZN5Ipopt21CGSearchDirCalculatorC1ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZN5Ipopt21CGSearchDirCalculatorD1Ev_ZN5Ipopt21CGSearchDirCalculator22ComputeSearchDirectionEv_ZN5Ipopt21CGSearchDirCalculator14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt21CGSearchDirCalculatorE_ZTSN5Ipopt21CGSearchDirCalculatorE_ZTIN5Ipopt25SearchDirectionCalculatorE_ZTSN5Ipopt25SearchDirectionCalculatorE_ZN5Ipopt16PiecewisePenaltyC2Ei_ZN5Ipopt16AdaptiveMuUpdate20Compute_tau_monotoneEd_ZN5Ipopt16AdaptiveMuUpdate11max_ref_valEv_ZN5Ipopt16AdaptiveMuUpdate11min_ref_valEv_ZN5Ipopt18TINY_STEP_DETECTEDD0Ev_ZN5Ipopt16AdaptiveMuUpdateC2ERKNS_8SmartPtrINS_10LineSearchEEERKNS1_INS_8MuOracleEEES9__ZTVN5Ipopt16AdaptiveMuUpdateE_ZN5Ipopt6FilterC1Ei_ZN5Ipopt16AdaptiveMuUpdate14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt6Filter5ClearEv_ZN5Ipopt8MuUpdateD0Ev_ZN5Ipopt8MuUpdateD1Ev_ZN5Ipopt16AdaptiveMuUpdate26quality_function_pd_systemEv_ZN5Ipopt25IpoptCalculatedQuantities23curr_centrality_measureEv_ZN5Ipopt16AdaptiveMuUpdate23CheckSufficientProgressEv_ZNK5Ipopt6Filter10AcceptableESt6vectorIdSaIdEE_ZN5Ipopt16AdaptiveMuUpdate30RememberCurrentPointAsAcceptedEv_ZN5Ipopt6Filter8AddEntryESt6vectorIdSaIdEEi_ZN5Ipopt6Filter5PrintERKNS_10JournalistE_ZN5Ipopt16AdaptiveMuUpdate18lower_mu_safeguardEv_ZN5Ipopt16AdaptiveMuUpdate10NewFixedMuEv_ZN5Ipopt25IpoptCalculatedQuantities15curr_avrg_complEv_ZN5Ipopt16AdaptiveMuUpdateD1Ev_ZN5Ipopt16AdaptiveMuUpdateD2Ev_ZN5Ipopt16AdaptiveMuUpdateC1ERKNS_8SmartPtrINS_10LineSearchEEERKNS1_INS_8MuOracleEEES9__ZN5Ipopt16AdaptiveMuUpdateD0Ev_ZN5Ipopt18TINY_STEP_DETECTEDD1Ev_ZN5Ipopt16AdaptiveMuUpdate22UpdateBarrierParameterEv_ZN5Ipopt25IpoptCalculatedQuantities18curr_barrier_errorEv_ZN5Ipopt9IpoptData16AcceptTrialPointEv_ZTIN5Ipopt18TINY_STEP_DETECTEDE_ZN5Ipopt16AdaptiveMuUpdate15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt17RegisteredOptions28AddLowerBoundedIntegerOptionERKSsS2_iiS2__ZTIN5Ipopt16AdaptiveMuUpdateE_ZTSN5Ipopt16AdaptiveMuUpdateE_ZTIN5Ipopt8MuUpdateE_ZTSN5Ipopt8MuUpdateE_ZTSN5Ipopt18TINY_STEP_DETECTEDE_ZTVN5Ipopt18TINY_STEP_DETECTEDE_ZTVN5Ipopt8MuUpdateE_ZTVN5Ipopt16AlgorithmBuilderE_ZN5Ipopt16AlgorithmBuilderC2ENS_8SmartPtrINS_15AugSystemSolverEEE_ZN5Ipopt14HessianUpdaterD1Ev_ZN5Ipopt16AlgorithmBuilderD0Ev_ZN5Ipopt16AlgorithmBuilderD1Ev_ZN5Ipopt19StandardScalingBaseD2Ev_ZTVN5Ipopt19StandardScalingBaseE_ZTVN5Ipopt16NLPScalingObjectE_ZN5Ipopt14HessianUpdaterD0Ev_ZThn16_N5Ipopt17MultiVectorMatrixD0Ev_ZN5Ipopt17MultiVectorMatrixD0Ev_ZTVN5Ipopt17MultiVectorMatrixE_ZTVN5Ipopt6MatrixE_ZN5Ipopt16AlgorithmBuilder17BuildIpoptObjectsERKNS_10JournalistERKNS_11OptionsListERKSsRKNS_8SmartPtrINS_3NLPEEERNS9_INS_8IpoptNLPEEERNS9_INS_9IpoptDataEEERNS9_INS_25IpoptCalculatedQuantitiesEEE_ZTVN5Ipopt11UserScalingE_ZN5Ipopt12OrigIpoptNLPC1ERKNS_8SmartPtrIKNS_10JournalistEEERKNS1_INS_3NLPEEERKNS1_INS_16NLPScalingObjectEEE_ZN5Ipopt9IpoptDataC1Ev_ZN5Ipopt25IpoptCalculatedQuantitiesC1ERKNS_8SmartPtrINS_8IpoptNLPEEERKNS1_INS_9IpoptDataEEE_ZTVN5Ipopt15GradientScalingE_ZTVN5Ipopt20EquilibrationScalingE_ZTVN5Ipopt18NoNLPScalingObjectE_ZThn16_N5Ipopt17MultiVectorMatrixD1Ev_ZN5Ipopt17MultiVectorMatrixD1Ev_ZN5Ipopt16AlgorithmBuilder19BuildBasicAlgorithmERKNS_10JournalistERKNS_11OptionsListERKSs_ZN5Ipopt31OptimalityErrorConvergenceCheckC1Ev_ZN5Ipopt18StdAugSystemSolverC1ERNS_15SymLinearSolverE_ZN5Ipopt17PDFullSpaceSolverC1ERNS_15AugSystemSolverERNS_21PDPerturbationHandlerE_ZN5Ipopt22LeastSquareMultipliersC1ERNS_15AugSystemSolverE_ZN5Ipopt27WarmStartIterateInitializerC1Ev_ZN5Ipopt25DefaultIterateInitializerC1ERKNS_8SmartPtrINS_22EqMultiplierCalculatorEEERKNS1_INS_18IterateInitializerEEENS1_INS_15AugSystemSolverEEE_ZN5Ipopt16FilterLSAcceptorC1ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZN5Ipopt22BacktrackingLineSearchC1ERKNS_8SmartPtrINS_22BacktrackingLSAcceptorEEERKNS1_INS_16RestorationPhaseEEERKNS1_INS_16ConvergenceCheckEEE_ZN5Ipopt27RestoFilterConvergenceCheck23SetOrigFilterLSAcceptorERKNS_16FilterLSAcceptorE_ZN5Ipopt16MonotoneMuUpdateC1ERKNS_8SmartPtrINS_10LineSearchEEE_ZN5Ipopt19OrigIterationOutputC1Ev_ZTVN5Ipopt19ExactHessianUpdaterE_ZN5Ipopt21PDSearchDirCalculatorC1ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZN5Ipopt14IpoptAlgorithmC1ERKNS_8SmartPtrINS_25SearchDirectionCalculatorEEERKNS1_INS_10LineSearchEEERKNS1_INS_8MuUpdateEEERKNS1_INS_16ConvergenceCheckEEERKNS1_INS_18IterateInitializerEEERKNS1_INS_15IterationOutputEEERKNS1_INS_14HessianUpdaterEEERKNS1_INS_22EqMultiplierCalculatorEEE_ZN5Ipopt21PDPerturbationHandlerC1Ev_ZN5Ipopt24LimMemQuasiNewtonUpdaterC1Eb_ZN5Ipopt20AugRestoSystemSolverC1ERNS_15AugSystemSolverEb_ZN5Ipopt27RestoFilterConvergenceCheckC1Ev_ZN5Ipopt21RestoRestorationPhaseC1Ev_ZN5Ipopt23RestoIterateInitializerC1ERKNS_8SmartPtrINS_22EqMultiplierCalculatorEEE_ZN5Ipopt20RestoIterationOutputC1ERKNS_8SmartPtrINS_19OrigIterationOutputEEE_ZN5Ipopt25MinC_1NrmRestorationPhaseC1ERNS_14IpoptAlgorithmERKNS_8SmartPtrINS_22EqMultiplierCalculatorEEE_ZNSs6assignEPKcm_ZN5Ipopt22LowRankAugSystemSolverC1ERNS_15AugSystemSolverE_ZN5Ipopt12LoqoMuOracleC1Ev_ZN5Ipopt15ProbingMuOracleC1ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZN5Ipopt23QualityFunctionMuOracleC1ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZN5Ipopt16AlgorithmBuilder15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt17RegisteredOptions16AddStringOption7ERKSsS2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2__ZTIN5Ipopt16AlgorithmBuilderE_ZTIN5Ipopt17MultiVectorMatrixE_ZNK5Ipopt6Matrix5PrintENS_8SmartPtrIKNS_10JournalistEEENS_13EJournalLevelENS_16EJournalCategoryERKSsiS8__ZNK5Ipopt6Matrix5PrintERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNK5Ipopt17MultiVectorMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt17MultiVectorMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt6Matrix13AddMSinvZImplEdRKNS_6VectorES3_RS1__ZNK5Ipopt6Matrix18SinvBlrmZMTdBrImplEdRKNS_6VectorES3_S3_S3_RS1__ZNK5Ipopt17MultiVectorMatrix19HasValidNumbersImplEv_ZNK5Ipopt17MultiVectorMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZTSN5Ipopt17MultiVectorMatrixE_ZTIN5Ipopt6MatrixE_ZTSN5Ipopt16AlgorithmBuilderE_ZTVN5Ipopt14HessianUpdaterE_ZTIN5Ipopt14HessianUpdaterE_ZTSN5Ipopt14HessianUpdaterE_ZNSt6vectorIPN5Ipopt8ObserverESaIS2_EED1Ev_ZN5Ipopt25DefaultIterateInitializer15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt22BacktrackingLineSearch15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt16FilterLSAcceptor15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt19StandardScalingBase15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt15GradientScaling15RegisterOptionsERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt20EquilibrationScaling15RegisterOptionsERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt14IpoptAlgorithm15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt9IpoptData15RegisterOptionsERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt25IpoptCalculatedQuantities15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt24LimMemQuasiNewtonUpdater15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt16MonotoneMuUpdate15RegisterOptionsERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt31OptimalityErrorConvergenceCheck15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt12OrigIpoptNLP15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt19OrigIterationOutput15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt21PDSearchDirCalculator15RegisterOptionsERKNS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt17PDFullSpaceSolver15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt21PDPerturbationHandler15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt15ProbingMuOracle15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt23QualityFunctionMuOracle15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt27RestoFilterConvergenceCheck15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt13RestoIpoptNLP15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt25MinC_1NrmRestorationPhase15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt27WarmStartIterateInitializer15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZNK5Ipopt20AugRestoSystemSolver16NumberOfNegEValsEv_ZN5Ipopt8SmartPtrIKNS_6VectorEE15ReleasePointer_Ev_ZNK5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEE19DependentsIdenticalERKSt6vectorIPKNS_12TaggedObjectESaIS8_EERKS5_IdSaIdEE_ZN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEE19RecieveNotificationENS_8Observer10NotifyTypeEPKNS_7SubjectE_ZNSt6vectorIdSaIdEED1Ev_ZN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEED0Ev_ZTVN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEEE_ZN5Ipopt15AugSystemSolver10MultiSolveEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dPKNS_6MatrixES6_dS9_S6_dRSt6vectorINS_8SmartPtrIS5_EESaISC_EESF_SF_SF_RSA_INSB_IS4_EESaISG_EESJ_SJ_SJ_bi_ZN5Ipopt20AugRestoSystemSolver15IncreaseQualityEv_ZNK5Ipopt20AugRestoSystemSolver15ProvidesInertiaEv_ZN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEED1Ev_ZNK5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEE25CleanupInvalidatedResultsEv_ZN5Ipopt20AugRestoSystemSolver14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt8Observer19ProcessNotificationENS0_10NotifyTypeEPKNS_7SubjectE_ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_6VectorEEESaIS4_EED1Ev_ZN5Ipopt20AugRestoSystemSolverC2ERNS_15AugSystemSolverEb_ZTVN5Ipopt20AugRestoSystemSolverE_ZN5Ipopt20AugRestoSystemSolverD0Ev_ZTVN5Ipopt15AugSystemSolverE_ZN5Ipopt20AugRestoSystemSolverD1Ev_ZN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEED1Ev_ZTVN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEEE_ZN5Ipopt15AugSystemSolverD0Ev_ZN5Ipopt15AugSystemSolverD1Ev_ZN5Ipopt20AugRestoSystemSolverD2Ev_ZSt26__uninitialized_fill_n_auxIPN5Ipopt8SmartPtrINS0_6VectorEEEmS3_EvT_T0_RKT1_St12__false_type_ZNK5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEE15GetCachedResultERS3_RKSt6vectorIPKNS_12TaggedObjectESaIS9_EERKS6_IdSaIdEE_ZSt26__uninitialized_fill_n_auxIPN5Ipopt8SmartPtrIKNS0_6VectorEEEmS4_EvT_T0_RKT1_St12__false_type_ZN5Ipopt15AugSystemSolver5SolveEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dPKNS_6MatrixES6_dS9_S6_dRS5_SA_SA_SA_RS4_SB_SB_SB_bi_ZN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEE19GetCachedResult3DepERS3_PKNS_12TaggedObjectES8_S8__ZN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEED0Ev_ZN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEEC1ERKS3_RKSt6vectorIPKNS_12TaggedObjectESaISA_EERKS7_IdSaIdEE_ZN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEE15AddCachedResultERKS3_RKSt6vectorIPKNS_12TaggedObjectESaISA_EERKS7_IdSaIdEE_ZN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEE19AddCachedResult3DepERKS3_PKNS_12TaggedObjectES9_S9__ZN5Ipopt20AugRestoSystemSolver20Neg_Omega_c_plus_D_cERKNS_8SmartPtrIKNS_6VectorEEES6_PS3_RS3__ZN5Ipopt20AugRestoSystemSolver6Rhs_dRERKNS_6VectorERKNS_8SmartPtrIS2_EES3_RKNS_6MatrixES7_S3_SA__ZN5Ipopt20AugRestoSystemSolver6Rhs_cRERKNS_6VectorERKNS_8SmartPtrIS2_EES3_S7_S3__ZN5Ipopt20AugRestoSystemSolver13D_x_plus_wr_dERKNS_8SmartPtrIKNS_6VectorEEEdRS3__ZN5Ipopt20AugRestoSystemSolver19Sigma_tilde_p_d_invERKNS_8SmartPtrIKNS_6VectorEEEdRS3__ZN5Ipopt20AugRestoSystemSolver19Sigma_tilde_n_d_invERKNS_8SmartPtrIKNS_6VectorEEEdRS3__ZN5Ipopt20AugRestoSystemSolver19Sigma_tilde_p_c_invERKNS_8SmartPtrIKNS_6VectorEEEdRS3__ZN5Ipopt20AugRestoSystemSolver19Sigma_tilde_n_c_invERKNS_8SmartPtrIKNS_6VectorEEEdRS3__ZN5Ipopt20AugRestoSystemSolver20Neg_Omega_d_plus_D_dERKNS_6MatrixERKNS_8SmartPtrIKNS_6VectorEEES3_S9_PS6_RS6__ZN5Ipopt20AugRestoSystemSolver5SolveEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dPKNS_6MatrixES6_dS9_S6_dRS5_SA_SA_SA_RS4_SB_SB_SB_bi_ZTIN5Ipopt12SumSymMatrixE__dynamic_cast_ZNK5Ipopt12SumSymMatrix7GetTermEiRdRNS_8SmartPtrIKNS_9SymMatrixEEE_ZTIN5Ipopt20AugRestoSystemSolverE_ZTSN5Ipopt20AugRestoSystemSolverE_ZTIN5Ipopt15AugSystemSolverE_ZTSN5Ipopt15AugSystemSolverE_ZTIN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEEE_ZTSN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEEE_ZTIN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEEE_ZTSN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEEE_ZN5Ipopt22BacktrackingLineSearch21SetRigorousLineSearchEb_ZN5Ipopt22BacktrackingLineSearch22CheckSkippedLineSearchEv_ZN5Ipopt22BacktrackingLineSearch19CurrentIsAcceptableEv_ZN5Ipopt24ACCEPTABLE_POINT_REACHEDD0Ev_ZN5Ipopt22BacktrackingLineSearch5ResetEv_ZN5Ipopt18RESTORATION_FAILEDD1Ev_ZN5Ipopt24ACCEPTABLE_POINT_REACHEDD1Ev_ZN5Ipopt18RESTORATION_FAILEDD0Ev_ZN5Ipopt10LineSearchD0Ev_ZN5Ipopt10LineSearchD1Ev_ZN5Ipopt22BacktrackingLineSearchC2ERKNS_8SmartPtrINS_22BacktrackingLSAcceptorEEERKNS1_INS_16RestorationPhaseEEERKNS1_INS_16ConvergenceCheckEEE_ZTVN5Ipopt22BacktrackingLineSearchE_ZN5Ipopt22BacktrackingLineSearch20StoreAcceptablePointEv_ZN5Ipopt22BacktrackingLineSearch22RestoreAcceptablePointEv_ZN5Ipopt22BacktrackingLineSearch13StartWatchDogEv_ZN5Ipopt25IpoptCalculatedQuantities29curr_primal_frac_to_the_boundEd_ZN5Ipopt22BacktrackingLineSearchD0Ev_ZN5Ipopt22BacktrackingLineSearchD2Ev_ZN5Ipopt22BacktrackingLineSearch25ActivateFallbackMechanismEv_ZN5Ipopt22BacktrackingLineSearchD1Ev_ZN5Ipopt22BacktrackingLineSearch14DetectTinyStepEv_ZN5Ipopt22BacktrackingLineSearch15PerformDualStepEddRNS_8SmartPtrINS_14IteratesVectorEEE_ZN5Ipopt9IpoptData32SetTrialBoundMultipliersFromStepEdRKNS_6VectorES3_S3_S3__ZN5Ipopt9IpoptData29SetTrialEqMultipliersFromStepEdRKNS_6VectorES3__ZN5Ipopt25IpoptCalculatedQuantities16trial_grad_lag_xEv_ZN5Ipopt25IpoptCalculatedQuantities16trial_grad_lag_sEv_ZN5Ipopt25IpoptCalculatedQuantities22trial_jac_dT_times_vecERKNS_6VectorE_ZN5Ipopt25IpoptCalculatedQuantities22trial_jac_cT_times_vecERKNS_6VectorE_ZN5Ipopt22BacktrackingLineSearch16TrySoftRestoStepERNS_8SmartPtrINS_14IteratesVectorEEERb_ZN5Ipopt25IpoptCalculatedQuantities22dual_frac_to_the_boundEdRKNS_6VectorES3_S3_S3__ZN5Ipopt25IpoptCalculatedQuantities30trial_primal_dual_system_errorEd_ZN5Ipopt25IpoptCalculatedQuantities29curr_primal_dual_system_errorEd_ZN5Ipopt22BacktrackingLineSearch16PerformMagicStepEv_ZN5Ipopt22BacktrackingLineSearch12StopWatchDogERNS_8SmartPtrINS_14IteratesVectorEEE_ZN5Ipopt22BacktrackingLineSearch24DoBacktrackingLineSearchEbRdRbS2_RiS2_RNS_8SmartPtrINS_14IteratesVectorEEEtoupper_ZN5Ipopt22BacktrackingLineSearch24FindAcceptableTrialPointEv_ZNK5Ipopt25IpoptCalculatedQuantities15IsSquareProblemEv_ZTVN5Ipopt18RESTORATION_FAILEDE_ZTIN5Ipopt18RESTORATION_FAILEDE_ZTIN5Ipopt24ACCEPTABLE_POINT_REACHEDE_ZTVN5Ipopt24ACCEPTABLE_POINT_REACHEDE_ZN5Ipopt22BacktrackingLineSearch14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt17RegisteredOptions17AddStringOption10ERKSsS2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2__ZTIN5Ipopt22BacktrackingLineSearchE_ZTSN5Ipopt22BacktrackingLineSearchE_ZTIN5Ipopt10LineSearchE_ZTSN5Ipopt10LineSearchE_ZTSN5Ipopt18RESTORATION_FAILEDE_ZTSN5Ipopt24ACCEPTABLE_POINT_REACHEDE_ZTVN5Ipopt10LineSearchE_ZTVN5Ipopt25DefaultIterateInitializerE_ZN5Ipopt25DefaultIterateInitializerC2ERKNS_8SmartPtrINS_22EqMultiplierCalculatorEEERKNS1_INS_18IterateInitializerEEENS1_INS_15AugSystemSolverEEE_ZN5Ipopt18IterateInitializerD0Ev_ZN5Ipopt18IterateInitializerD1Ev_ZN5Ipopt14IteratesVector26GetNonConstIterateFromCompEi_ZN5Ipopt25DefaultIterateInitializer18least_square_multsERKNS_10JournalistERNS_8IpoptNLPERNS_9IpoptDataERNS_25IpoptCalculatedQuantitiesERKNS_8SmartPtrINS_22EqMultiplierCalculatorEEEd_ZNK5Ipopt19CompoundVectorSpace12GetCompSpaceEi_ZN5Ipopt14CompoundVector15SetCompNonConstEiRNS_6VectorE_ZN5Ipopt25DefaultIterateInitializer27CalculateLeastSquarePrimalsERNS_6VectorES2__ZN5Ipopt6Vector4ScalEd_ZN5Ipopt25DefaultIterateInitializerD0Ev_ZN5Ipopt25DefaultIterateInitializer25CalculateLeastSquareDualsERNS_6VectorES2_S2_S2_S2_S2__ZN5Ipopt25IpoptCalculatedQuantities12trial_grad_fEv_ZN5Ipopt25DefaultIterateInitializerD1Ev_ZN5Ipopt25DefaultIterateInitializer14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt25DefaultIterateInitializer14push_variablesERKNS_10JournalistEddSsRKNS_6VectorERNS_8SmartPtrIS5_EES6_S6_RKNS_6MatrixESC__ZN5Ipopt25DefaultIterateInitializer18SetInitialIteratesEv_ZN5Ipopt9IpoptData24InitializeDataStructuresERNS_8IpoptNLPEbbbbb_ZN5Ipopt25IpoptCalculatedQuantities7trial_dEv_ZTIN5Ipopt25DefaultIterateInitializerE_ZTSN5Ipopt25DefaultIterateInitializerE_ZTIN5Ipopt18IterateInitializerE_ZTSN5Ipopt18IterateInitializerE_ZTVN5Ipopt18IterateInitializerE_ZN5Ipopt20EquilibrationScaling14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt19StandardScalingBase14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt20EquilibrationScalingD0Ev_ZN5Ipopt20EquilibrationScalingD1Ev_ZN5Ipopt14PointPerturberD1Ev_ZTVN5Ipopt14PointPerturberE_ZN5Ipopt21FAILED_INITIALIZATIOND0Ev_ZN5Ipopt21FAILED_INITIALIZATIOND1Ev_ZN5Ipopt14PointPerturberD0Ev_ZNK5Ipopt14PointPerturber21MakeNewPerturbedPointEv_ZN5Ipopt13TripletHelper20FillValuesFromVectorEiRKNS_6VectorEPd_ZN5Ipopt13TripletHelper17PutValuesInVectorEiPKdRNS_6VectorE_ZN5Ipopt14PointPerturberC2ERKNS_6VectorEdRKNS_6MatrixES3_S6_S3__ZN5Ipopt14PointPerturberC1ERKNS_6VectorEdRKNS_6MatrixES3_S6_S3__ZN5Ipopt20EquilibrationScaling30DetermineScalingParametersImplENS_8SmartPtrIKNS_11VectorSpaceEEES4_S4_NS1_IKNS_11MatrixSpaceEEES7_NS1_IKNS_14SymMatrixSpaceEEERKNS_6MatrixERKNS_6VectorESD_SG_RdRNS1_ISE_EESJ_SJ__ZTVN5Ipopt21FAILED_INITIALIZATIONE_ZTIN5Ipopt21FAILED_INITIALIZATIONE_ZN5Ipopt13TripletHelper10FillRowColEiRKNS_6MatrixEPiS4_ii_ZTIN5Ipopt20EquilibrationScalingE_ZN5Ipopt19StandardScalingBase17apply_obj_scalingERKd_ZN5Ipopt19StandardScalingBase19unapply_obj_scalingERKd_ZN5Ipopt19StandardScalingBase31apply_vector_scaling_x_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase22apply_vector_scaling_xERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase33unapply_vector_scaling_x_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase24unapply_vector_scaling_xERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase22apply_vector_scaling_cERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase24unapply_vector_scaling_cERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase31apply_vector_scaling_c_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase33unapply_vector_scaling_c_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase22apply_vector_scaling_dERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase24unapply_vector_scaling_dERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase31apply_vector_scaling_d_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase33unapply_vector_scaling_d_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase19apply_jac_c_scalingENS_8SmartPtrIKNS_6MatrixEEE_ZN5Ipopt19StandardScalingBase19apply_jac_d_scalingENS_8SmartPtrIKNS_6MatrixEEE_ZN5Ipopt19StandardScalingBase21apply_hessian_scalingENS_8SmartPtrIKNS_9SymMatrixEEE_ZN5Ipopt16NLPScalingObject31apply_grad_obj_scaling_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt16NLPScalingObject22apply_grad_obj_scalingERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt16NLPScalingObject33unapply_grad_obj_scaling_NonConstERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt16NLPScalingObject24unapply_grad_obj_scalingERKNS_8SmartPtrIKNS_6VectorEEE_ZN5Ipopt19StandardScalingBase14have_x_scalingEv_ZN5Ipopt19StandardScalingBase14have_c_scalingEv_ZN5Ipopt19StandardScalingBase14have_d_scalingEv_ZN5Ipopt19StandardScalingBase16DetermineScalingENS_8SmartPtrIKNS_11VectorSpaceEEES4_S4_NS1_IKNS_11MatrixSpaceEEES7_NS1_IKNS_14SymMatrixSpaceEEERS7_SB_RSA_RKNS_6MatrixERKNS_6VectorESF_SI__ZTSN5Ipopt20EquilibrationScalingE_ZTIN5Ipopt19StandardScalingBaseE_ZTIN5Ipopt14PointPerturberE_ZTSN5Ipopt14PointPerturberE_ZTSN5Ipopt21FAILED_INITIALIZATIONE_ZN5Ipopt19ExactHessianUpdater14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt19ExactHessianUpdater13UpdateHessianEv_ZN5Ipopt25IpoptCalculatedQuantities18curr_exact_hessianEv_ZN5Ipopt19ExactHessianUpdaterD0Ev_ZN5Ipopt19ExactHessianUpdaterD1Ev_ZTIN5Ipopt19ExactHessianUpdaterE_ZTSN5Ipopt19ExactHessianUpdaterE_ZN5Ipopt11FilterEntryD2Ev_ZN5Ipopt6FilterC2Ei_ZN5Ipopt11FilterEntryC2ESt6vectorIdSaIdEEi_ZN5Ipopt11FilterEntryD1Ev_ZN5Ipopt11FilterEntryC1ESt6vectorIdSaIdEEi_ZN5Ipopt16FilterLSAcceptor12StopWatchDogEv_ZN5Ipopt16FilterLSAcceptor5ResetEv_ZN5Ipopt16FilterLSAcceptor13StartWatchDogEv_ZN5Ipopt25IpoptCalculatedQuantities19curr_gradBarrTDeltaEv_ZN5Ipopt16FilterLSAcceptor18InitThisLineSearchEb_ZN5Ipopt16FilterLSAcceptor7IsFtypeEd_ZN5Ipopt16FilterLSAcceptor17CalculateAlphaMinEv_ZN5Ipopt16FilterLSAcceptor10Compare_leEddd_ZNK5Ipopt16FilterLSAcceptor28IsAcceptableToCurrentIterateEddblog10_ZN5Ipopt16FilterLSAcceptor11ArmijoHoldsEd_ZN5Ipopt16FilterLSAcceptor13AugmentFilterEv_ZN5Ipopt16FilterLSAcceptor22PrepareRestoPhaseStartEv_ZN5Ipopt16FilterLSAcceptor22UpdateForNextIterationEd_ZNK5Ipopt16FilterLSAcceptor27IsAcceptableToCurrentFilterEdd_ZN5Ipopt16FilterLSAcceptor30CheckAcceptabilityOfTrialPointEd_ZTVN5Ipopt16FilterLSAcceptorE_ZN5Ipopt16FilterLSAcceptor14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt16FilterLSAcceptorC2ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZN5Ipopt16FilterLSAcceptor24TrySecondOrderCorrectionEdRdRNS_8SmartPtrINS_14IteratesVectorEEE_ZN5Ipopt16FilterLSAcceptorD0Ev_ZN5Ipopt16FilterLSAcceptorD2Ev_ZN5Ipopt16FilterLSAcceptor12TryCorrectorEdRdRNS_8SmartPtrINS_14IteratesVectorEEEgetrusage_ZN5Ipopt25IpoptCalculatedQuantities16trial_avrg_complEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_slack_x_LEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_slack_x_UEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_slack_s_LEv_ZN5Ipopt25IpoptCalculatedQuantities14curr_slack_s_UEv_ZN5Ipopt25IpoptCalculatedQuantities15curr_grad_lag_xEv_ZN5Ipopt25IpoptCalculatedQuantities15curr_grad_lag_sEv_ZN5Ipopt16FilterLSAcceptorD1Ev_ZTIN5Ipopt16FilterLSAcceptorE_ZTSN5Ipopt16FilterLSAcceptorE_ZN5Ipopt18GenAugSystemSolver22AugmentedSystemChangedEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dRKNS_6MatrixES6_dS9_S6_d_ZN5Ipopt18GenAugSystemSolver10UpdateTagsEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dRKNS_6MatrixES6_dS9_S6_d_ZN5Ipopt18GenAugSystemSolver15IncreaseQualityEv_ZNK5Ipopt18GenAugSystemSolver15ProvidesInertiaEv_ZNK5Ipopt18GenAugSystemSolver16NumberOfNegEValsEv_ZNSt6vectorIN5Ipopt8SmartPtrINS0_6VectorEEESaIS3_EED1Ev_ZN5Ipopt28FATAL_ERROR_IN_LINEAR_SOLVERD0Ev_ZN5Ipopt18GenAugSystemSolverC1ERNS_21GenKKTSolverInterfaceE_ZN5Ipopt18GenAugSystemSolverD1Ev_ZTVN5Ipopt18GenAugSystemSolverE_ZN5Ipopt18GenAugSystemSolverD2Ev_ZN5Ipopt28FATAL_ERROR_IN_LINEAR_SOLVERD1Ev_ZN5Ipopt18GenAugSystemSolver14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt18GenAugSystemSolver10MultiSolveEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dPKNS_6MatrixES6_dS9_S6_dRSt6vectorINS_8SmartPtrIS5_EESaISC_EESF_SF_SF_RSA_INSB_IS4_EESaISG_EESJ_SJ_SJ_bi_ZTIN5Ipopt11DenseVectorE_ZTIN5Ipopt28FATAL_ERROR_IN_LINEAR_SOLVERE_ZN5Ipopt18GenAugSystemSolverC2ERNS_21GenKKTSolverInterfaceE_ZN5Ipopt18GenAugSystemSolverD0Ev_ZTIN5Ipopt18GenAugSystemSolverE_ZTSN5Ipopt18GenAugSystemSolverE_ZTSN5Ipopt28FATAL_ERROR_IN_LINEAR_SOLVERE_ZTVN5Ipopt28FATAL_ERROR_IN_LINEAR_SOLVERE_ZN5Ipopt15GradientScalingD0Ev_ZN5Ipopt15GradientScalingD1Ev_ZN5Ipopt15GradientScaling30DetermineScalingParametersImplENS_8SmartPtrIKNS_11VectorSpaceEEES4_S4_NS1_IKNS_11MatrixSpaceEEES7_NS1_IKNS_14SymMatrixSpaceEEERKNS_6MatrixERKNS_6VectorESD_SG_RdRNS1_ISE_EESJ_SJ__ZN5Ipopt12IpBlasIdamaxEiPKdi_ZN5Ipopt15GradientScaling14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt15GradientScalingE_ZTSN5Ipopt15GradientScalingE_ZN5Ipopt14IpoptAlgorithm22UpdateBarrierParameterEv_ZN5Ipopt14IpoptAlgorithm27ComputeAcceptableTrialPointEv_ZN5Ipopt14IpoptAlgorithm15OutputIterationEv_ZN5Ipopt14IpoptAlgorithm13UpdateHessianEv_ZN5Ipopt23STEP_COMPUTATION_FAILEDD0Ev_ZN5Ipopt23STEP_COMPUTATION_FAILEDD1Ev_ZN5Ipopt14IpoptAlgorithmD0Ev_ZTVN5Ipopt14IpoptAlgorithmE_ZN5Ipopt14IpoptAlgorithmC2ERKNS_8SmartPtrINS_25SearchDirectionCalculatorEEERKNS1_INS_10LineSearchEEERKNS1_INS_8MuUpdateEEERKNS1_INS_16ConvergenceCheckEEERKNS1_INS_18IterateInitializerEEERKNS1_INS_15IterationOutputEEERKNS1_INS_14HessianUpdaterEEERKNS1_INS_22EqMultiplierCalculatorEEE_ZN5Ipopt14IpoptAlgorithmD1Ev_ZN5Ipopt14IpoptAlgorithmD2Ev_ZN5Ipopt14IpoptAlgorithm24correct_bound_multiplierERKNS_6VectorES3_S3_RNS_8SmartPtrIS2_EE_ZN5Ipopt14IpoptAlgorithm21calc_number_of_boundsERKNS_6VectorES3_S3_RKNS_6MatrixES6_RiS7_S7_S7__ZN5Ipopt14IpoptAlgorithm22PrintProblemStatisticsEv_ZN5Ipopt14IpoptAlgorithm29ComputeFeasibilityMultipliersEv_ZN5Ipopt14IpoptAlgorithm18InitializeIteratesEv_ZN5Ipopt14IpoptAlgorithm22ComputeSearchDirectionEv_ZN5Ipopt14IpoptAlgorithm16AcceptTrialPointEv_ZN5Ipopt25IpoptCalculatedQuantities19AdjustedTrialSlacksEv_ZN5Ipopt25IpoptCalculatedQuantities24ResetAdjustedTrialSlacksEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_slack_x_LEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_slack_x_UEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_slack_s_LEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_slack_s_UEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_compl_x_LEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_compl_x_UEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_compl_s_LEv_ZN5Ipopt25IpoptCalculatedQuantities15trial_compl_s_UEv_ZNSs9_M_mutateEmmm_ZTIN5Ipopt23STEP_COMPUTATION_FAILEDE_ZN5Ipopt14IpoptAlgorithm14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt9IpoptData10InitializeERKNS_10JournalistERKNS_11OptionsListERKSs_ZN5Ipopt25IpoptCalculatedQuantities10InitializeERKNS_10JournalistERKNS_11OptionsListERKSs_ZN5Ipopt11OptionsList22SetNumericValueIfUnsetERKSsdbb_ZN5Ipopt11OptionsList21SetStringValueIfUnsetERKSsS2_bb_ZTIN5Ipopt14IpoptAlgorithmE_ZTSN5Ipopt14IpoptAlgorithmE_ZTSN5Ipopt23STEP_COMPUTATION_FAILEDE_ZTVN5Ipopt23STEP_COMPUTATION_FAILEDE_ZTIN5Ipopt18LOCALLY_INFEASIBLEE_ZTSN5Ipopt18LOCALLY_INFEASIBLEE_ZTIN5Ipopt39RESTORATION_CONVERGED_TO_FEASIBLE_POINTE_ZTSN5Ipopt39RESTORATION_CONVERGED_TO_FEASIBLE_POINTE_ZTIN5Ipopt28RESTORATION_MAXITER_EXCEEDEDE_ZTSN5Ipopt28RESTORATION_MAXITER_EXCEEDEDE_ZTIN5Ipopt21RESTORATION_USER_STOPE_ZTSN5Ipopt21RESTORATION_USER_STOPE_ZTIN5Ipopt26FEASIBILITY_PROBLEM_SOLVEDE_ZTSN5Ipopt26FEASIBILITY_PROBLEM_SOLVEDE_ZTIN5Ipopt14INTERNAL_ABORTE_ZTSN5Ipopt14INTERNAL_ABORTE_ZN5Ipopt8SmartPtrIKNS_14IteratesVectorEE15ReleasePointer_Ev_ZNK5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEE19DependentsIdenticalERKSt6vectorIPKNS_12TaggedObjectESaIS9_EERKS6_IdSaIdEE_ZNK5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEE19DependentsIdenticalERKSt6vectorIPKNS_12TaggedObjectESaIS9_EERKS6_IdSaIdEE_ZNK5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEE19DependentsIdenticalERKSt6vectorIPKNS_12TaggedObjectESaIS9_EERKS6_IdSaIdEE_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEE19RecieveNotificationENS_8Observer10NotifyTypeEPKNS_7SubjectE_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEE19RecieveNotificationENS_8Observer10NotifyTypeEPKNS_7SubjectE_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEE19RecieveNotificationENS_8Observer10NotifyTypeEPKNS_7SubjectE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_9SymMatrixEEEED1Ev_ZTVN5Ipopt13CachedResultsINS_8SmartPtrIKNS_9SymMatrixEEEEE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEED0Ev_ZTVN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEEE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEED0Ev_ZTVN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEEE_ZN5Ipopt25IpoptCalculatedQuantities15CalcFracToBoundERKNS_6VectorERS1_RKNS_6MatrixES3_S4_S7_S3_d_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEED1Ev_ZNK5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE25CleanupInvalidatedResultsEv_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_9SymMatrixEEEED0Ev_ZNK5Ipopt13CachedResultsINS_8SmartPtrIKNS_9SymMatrixEEEE25CleanupInvalidatedResultsEv_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEED1Ev_ZNK5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEE25CleanupInvalidatedResultsEv_ZN5Ipopt25IpoptCalculatedQuantities21CalcCentralityMeasureERKNS_6VectorES3_S3_S3__ZN5Ipopt25IpoptCalculatedQuantities14CalcNormOfTypeENS_9ENormTypeESt6vectorINS_8SmartPtrIKNS_6VectorEEESaIS6_EE_ZN5Ipopt25IpoptCalculatedQuantities14CalcNormOfTypeENS_9ENormTypeERKNS_6VectorES4__ZN5Ipopt25IpoptCalculatedQuantities29ComputeOptimalityErrorScalingERKNS_6VectorES3_S3_S3_S3_S3_dRdS4__ZN5Ipopt25IpoptCalculatedQuantities11CalcSlack_UERKNS_6MatrixERKNS_6VectorES6__ZN5Ipopt25IpoptCalculatedQuantities11CalcSlack_LERKNS_6MatrixERKNS_6VectorES6__ZN5Ipopt25IpoptCalculatedQuantitiesD1Ev_ZTVN5Ipopt25IpoptCalculatedQuantitiesE_ZN5Ipopt25IpoptCalculatedQuantitiesD2Ev_ZN5Ipopt25IpoptCalculatedQuantitiesD0Ev_ZN5Ipopt25IpoptCalculatedQuantitiesC2ERKNS_8SmartPtrINS_8IpoptNLPEEERKNS1_INS_9IpoptDataEEE_ZN5Ipopt25IpoptCalculatedQuantities7Tmp_s_LEv_ZN5Ipopt25IpoptCalculatedQuantities7Tmp_s_UEv_ZN5Ipopt25IpoptCalculatedQuantities18CalculateSafeSlackERNS_8SmartPtrINS_6VectorEEERKNS1_IKS2_EES8_S8__ZN5Ipopt25IpoptCalculatedQuantities7Tmp_x_LEv_ZNK5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEE15GetCachedResultERS3_RKSt6vectorIPKNS_12TaggedObjectESaIS9_EE_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEEC1ERKS4_RKSt6vectorIPKNS_12TaggedObjectESaISB_EERKS8_IdSaIdEE_ZTVN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEEE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_9SymMatrixEEEE15AddCachedResultERKS4_RKSt6vectorIPKNS_12TaggedObjectESaISB_EERKS8_IdSaIdEE_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEED0Ev_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEED1Ev_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEED0Ev_ZTVN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEEE_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEED1Ev_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEED0Ev_ZTVN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEEE_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEED1Ev_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEEC1ERKS4_RKSt6vectorIPKNS_12TaggedObjectESaISB_EERKS8_IdSaIdEE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEE15AddCachedResultERKS4_RKSt6vectorIPKNS_12TaggedObjectESaISB_EERKS8_IdSaIdEE_ZNK5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEE15GetCachedResultERS4_RKSt6vectorIPKNS_12TaggedObjectESaISA_EE_ZN5Ipopt25IpoptCalculatedQuantities7Tmp_x_UEv_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEE19GetCachedResult1DepERS4_PKNS_12TaggedObjectE_ZN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEE19AddCachedResult1DepERKS3_PKNS_12TaggedObjectE_ZN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEE19GetCachedResult1DepERS3_PKNS_12TaggedObjectE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEE19AddCachedResult1DepERKS4_PKNS_12TaggedObjectE_ZN5Ipopt25IpoptCalculatedQuantities9CalcComplERKNS_6VectorES3__ZN5Ipopt25IpoptCalculatedQuantities5Tmp_cEv_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE19GetCachedResult3DepERS4_PKNS_12TaggedObjectES9_S9__ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE19GetCachedResult2DepERS4_PKNS_12TaggedObjectES9__ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE19GetCachedResult1DepERS4_PKNS_12TaggedObjectE_ZN5Ipopt25IpoptCalculatedQuantities5Tmp_dEv_ZN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEEC1ERKS4_RKSt6vectorIPKNS_12TaggedObjectESaISB_EERKS8_IdSaIdEE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE15AddCachedResultERKS4_RKSt6vectorIPKNS_12TaggedObjectESaISB_EERKS8_IdSaIdEE_ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE19AddCachedResult3DepERKS4_PKNS_12TaggedObjectESA_SA__ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE19AddCachedResult2DepERKS4_PKNS_12TaggedObjectESA__ZN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE19AddCachedResult1DepERKS4_PKNS_12TaggedObjectE_ZN5Ipopt25IpoptCalculatedQuantities5Tmp_sEv_ZN5Ipopt25IpoptCalculatedQuantities5Tmp_xEv_ZN5Ipopt25IpoptCalculatedQuantities15unscaled_curr_cEv_ZN5Ipopt25IpoptCalculatedQuantities7trial_fEv_ZN5Ipopt25IpoptCalculatedQuantities16unscaled_trial_fEv_ZN5Ipopt25IpoptCalculatedQuantities27curr_dual_frac_to_the_boundEd_ZN5Ipopt25IpoptCalculatedQuantities26curr_jac_cT_times_curr_y_cEv_ZN5Ipopt25IpoptCalculatedQuantities24ComputeDampingIndicatorsERNS_8SmartPtrIKNS_6VectorEEES5_S5_S5__ZN5Ipopt25IpoptCalculatedQuantities15CalcBarrierTermEdRKNS_6VectorES3_S3_S3__ZN5Ipopt25IpoptCalculatedQuantities11trial_jac_dEv_ZN5Ipopt25IpoptCalculatedQuantities28trial_jac_dT_times_trial_y_dEv_ZN5Ipopt25IpoptCalculatedQuantities11trial_jac_cEv_ZN5Ipopt25IpoptCalculatedQuantities28trial_jac_cT_times_trial_y_cEv_ZN5Ipopt25IpoptCalculatedQuantities24trial_dual_infeasibilityENS_9ENormTypeE_ZN5Ipopt25IpoptCalculatedQuantities26grad_kappa_times_damping_sEv_ZN5Ipopt25IpoptCalculatedQuantities12curr_sigma_xEv_ZNK5Ipopt6Matrix9AddMSinvZEdRKNS_6VectorES3_RS1__ZN5Ipopt25IpoptCalculatedQuantities31uncached_dual_frac_to_the_boundEdRKNS_6VectorES3_S3_S3__ZN5Ipopt25IpoptCalculatedQuantities26curr_jac_dT_times_curr_y_dEv_ZN5Ipopt25IpoptCalculatedQuantities21trial_complementarityEdNS_9ENormTypeE_ZN5Ipopt25IpoptCalculatedQuantities26grad_kappa_times_damping_xEv_ZN5Ipopt25IpoptCalculatedQuantities12curr_sigma_sEv_ZN5Ipopt25IpoptCalculatedQuantities32uncached_slack_frac_to_the_boundEdRKNS_6VectorES3_S3_S3__ZN5Ipopt25IpoptCalculatedQuantities15unscaled_curr_dEv_ZN5Ipopt25IpoptCalculatedQuantities20curr_jac_d_times_vecERKNS_6VectorE_ZN5Ipopt25IpoptCalculatedQuantities20curr_jac_c_times_vecERKNS_6VectorE_ZTIN5Ipopt25IpoptCalculatedQuantitiesE_ZTSN5Ipopt25IpoptCalculatedQuantitiesE_ZTIN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEEE_ZTSN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEEE_ZTIN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEEE_ZTSN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEEE_ZTIN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEEE_ZTSN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEEE_ZTIN5Ipopt13CachedResultsINS_8SmartPtrIKNS_9SymMatrixEEEEE_ZTSN5Ipopt13CachedResultsINS_8SmartPtrIKNS_9SymMatrixEEEEE_ZTIN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEEE_ZTSN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEEE_ZTIN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEEE_ZTSN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEEE_ZN5Ipopt16TimingStatisticsC1Ev_ZTVN5Ipopt16TimingStatisticsE_ZN5Ipopt16TimingStatisticsD0Ev_ZN5Ipopt16TimingStatisticsD1Ev_ZN5Ipopt19IteratesVectorSpaceC1ERKNS_11VectorSpaceES3_S3_S3_S3_S3_S3_S3__ZN5Ipopt9IpoptDataD0Ev_ZTVN5Ipopt9IpoptDataE_ZN5Ipopt9IpoptDataD1Ev_ZN5Ipopt9IpoptDataD2Ev_ZN5Ipopt9IpoptDataC2Ev_ZTIN5Ipopt9IpoptDataE_ZTSN5Ipopt9IpoptDataE_ZTIN5Ipopt16TimingStatisticsE_ZTSN5Ipopt16TimingStatisticsE_ZNK5Ipopt19IteratesVectorSpace21MakeNewCompoundVectorEb_ZNK5Ipopt19IteratesVectorSpace7MakeNewEv_ZN5Ipopt19IteratesVectorSpace12SetCompSpaceEiRKNS_11VectorSpaceE_ZThn16_N5Ipopt14IteratesVectorD0Ev_ZN5Ipopt14IteratesVectorD0Ev_ZTVN5Ipopt14IteratesVectorE_ZN5Ipopt14CompoundVectorD2Ev_ZThn16_N5Ipopt14IteratesVectorD1Ev_ZN5Ipopt14IteratesVectorD1Ev_ZN5Ipopt14IteratesVectorD2Ev_ZN5Ipopt14IteratesVectorC1EPKNS_19IteratesVectorSpaceEb_ZN5Ipopt14CompoundVectorC2EPKNS_19CompoundVectorSpaceEb_ZNK5Ipopt19IteratesVectorSpace21MakeNewIteratesVectorEb_ZN5Ipopt14IteratesVectorC2EPKNS_19IteratesVectorSpaceEb_ZN5Ipopt19IteratesVectorSpaceD0Ev_ZTVN5Ipopt19IteratesVectorSpaceE_ZTVN5Ipopt19CompoundVectorSpaceE_ZN5Ipopt19IteratesVectorSpaceC2ERKNS_11VectorSpaceES3_S3_S3_S3_S3_S3_S3__ZN5Ipopt19CompoundVectorSpaceC2Eii_ZN5Ipopt19CompoundVectorSpace12SetCompSpaceEiRKNS_11VectorSpaceE_ZN5Ipopt19IteratesVectorSpaceD2Ev_ZN5Ipopt19IteratesVectorSpaceD1Ev_ZTIN5Ipopt19IteratesVectorSpaceE_ZTIN5Ipopt14IteratesVectorE_ZN5Ipopt14CompoundVector8CopyImplERKNS_6VectorE_ZN5Ipopt14CompoundVector8ScalImplEd_ZN5Ipopt14CompoundVector8AxpyImplEdRKNS_6VectorE_ZNK5Ipopt14CompoundVector7DotImplERKNS_6VectorE_ZNK5Ipopt14CompoundVector8Nrm2ImplEv_ZNK5Ipopt14CompoundVector8AsumImplEv_ZNK5Ipopt14CompoundVector8AmaxImplEv_ZN5Ipopt14CompoundVector7SetImplEd_ZN5Ipopt14CompoundVector21ElementWiseDivideImplERKNS_6VectorE_ZN5Ipopt14CompoundVector23ElementWiseMultiplyImplERKNS_6VectorE_ZN5Ipopt14CompoundVector18ElementWiseMaxImplERKNS_6VectorE_ZN5Ipopt14CompoundVector18ElementWiseMinImplERKNS_6VectorE_ZN5Ipopt14CompoundVector25ElementWiseReciprocalImplEv_ZN5Ipopt14CompoundVector18ElementWiseAbsImplEv_ZN5Ipopt14CompoundVector19ElementWiseSqrtImplEv_ZN5Ipopt14CompoundVector18ElementWiseSgnImplEv_ZN5Ipopt14CompoundVector13AddScalarImplEd_ZNK5Ipopt14CompoundVector7MaxImplEv_ZNK5Ipopt14CompoundVector7MinImplEv_ZNK5Ipopt14CompoundVector7SumImplEv_ZNK5Ipopt14CompoundVector11SumLogsImplEv_ZN5Ipopt14CompoundVector17AddTwoVectorsImplEdRKNS_6VectorEdS3_d_ZNK5Ipopt14CompoundVector15FracToBoundImplERKNS_6VectorEd_ZN5Ipopt14CompoundVector21AddVectorQuotientImplEdRKNS_6VectorES3_d_ZNK5Ipopt14CompoundVector19HasValidNumbersImplEv_ZNK5Ipopt14CompoundVector9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZTSN5Ipopt19IteratesVectorSpaceE_ZTIN5Ipopt19CompoundVectorSpaceE_ZTSN5Ipopt14IteratesVectorE_ZTIN5Ipopt14CompoundVectorE_ZN5Ipopt22LeastSquareMultipliers14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt22LeastSquareMultipliers20CalculateMultipliersERNS_6VectorES2__ZN5Ipopt22EqMultiplierCalculatorD0Ev_ZN5Ipopt22EqMultiplierCalculatorD1Ev_ZTVN5Ipopt22LeastSquareMultipliersE_ZN5Ipopt22LeastSquareMultipliersC2ERNS_15AugSystemSolverE_ZN5Ipopt22LeastSquareMultipliersD1Ev_ZN5Ipopt22LeastSquareMultipliersD0Ev_ZTIN5Ipopt22LeastSquareMultipliersE_ZTSN5Ipopt22LeastSquareMultipliersE_ZTIN5Ipopt22EqMultiplierCalculatorE_ZTSN5Ipopt22EqMultiplierCalculatorE_ZTVN5Ipopt22EqMultiplierCalculatorE_ZN5Ipopt12LoqoMuOracleC2Ev_ZTVN5Ipopt12LoqoMuOracleE_ZN5Ipopt12LoqoMuOracle14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt12LoqoMuOracleD0Ev_ZN5Ipopt12LoqoMuOracle11CalculateMuEddRd_ZN5Ipopt8MuOracleD0Ev_ZN5Ipopt8MuOracleD1Ev_ZN5Ipopt12LoqoMuOracleD1Ev_ZN5Ipopt12LoqoMuOracleD2Ev_ZTIN5Ipopt12LoqoMuOracleE_ZTSN5Ipopt12LoqoMuOracleE_ZTIN5Ipopt8MuOracleE_ZTSN5Ipopt8MuOracleE_ZTVN5Ipopt8MuOracleE_ZNK5Ipopt14SymMatrixSpace7MakeNewEv_ZNK5Ipopt22LowRankAugSystemSolver16NumberOfNegEValsEv_ZNK5Ipopt15DiagMatrixSpace16MakeNewSymMatrixEv_ZN5Ipopt10DiagMatrixC1EPKNS_14SymMatrixSpaceE_ZN5Ipopt22LowRankAugSystemSolver29AugmentedSystemRequiresChangeEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dRKNS_6MatrixES6_dS9_S6_d_ZN5Ipopt22LowRankAugSystemSolver15IncreaseQualityEv_ZNK5Ipopt22LowRankAugSystemSolver15ProvidesInertiaEv_ZN5Ipopt11MatrixSpaceD0Ev_ZN5Ipopt11MatrixSpaceD1Ev_ZN5Ipopt15DiagMatrixSpaceD0Ev_ZN5Ipopt15DiagMatrixSpaceD1Ev_ZN5Ipopt14SymMatrixSpaceD0Ev_ZN5Ipopt14SymMatrixSpaceD1Ev_ZN5Ipopt22LowRankAugSystemSolver14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt22LowRankAugSystemSolverD1Ev_ZTVN5Ipopt22LowRankAugSystemSolverE_ZN5Ipopt22LowRankAugSystemSolverD2Ev_ZN5Ipopt22LowRankAugSystemSolverC2ERNS_15AugSystemSolverE_ZN5Ipopt22LowRankAugSystemSolverD0Ev_ZN5Ipopt22LowRankAugSystemSolver16SolveMultiVectorEPKNS_6VectorEdS3_dRKNS_6MatrixES3_dS6_S3_dRS2_S7_S7_S7_RKNS_17MultiVectorMatrixERKNS_8SmartPtrIS5_EERNSB_IS8_EESG_SG_bi_ZN5Ipopt22MultiVectorMatrixSpaceC1EiRKNS_11VectorSpaceE_ZN5Ipopt17MultiVectorMatrixC1EPKNS_22MultiVectorMatrixSpaceE_ZN5Ipopt17MultiVectorMatrix9SetVectorEiRKNS_6VectorE_ZN5Ipopt19CompoundVectorSpaceC1Eii_ZN5Ipopt17MultiVectorMatrix17SetVectorNonConstEiRNS_6VectorE_ZN5Ipopt22LowRankAugSystemSolver19UpdateFactorizationEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dRKNS_6MatrixES6_dS9_S6_dRS5_SA_SA_SA_bi_ZN5Ipopt19DenseSymMatrixSpaceC1Ei_ZN5Ipopt14DenseSymMatrixC1EPKNS_19DenseSymMatrixSpaceE_ZN5Ipopt14DenseSymMatrix12FillIdentityEd_ZN5Ipopt14DenseSymMatrix23HighRankUpdateTransposeEdRKNS_17MultiVectorMatrixES3_d_ZN5Ipopt19DenseGenMatrixSpaceC1Eii_ZN5Ipopt14DenseGenMatrixC1EPKNS_19DenseGenMatrixSpaceE_ZN5Ipopt14DenseGenMatrix21ComputeCholeskyFactorERKNS_14DenseSymMatrixE_ZN5Ipopt14DenseGenMatrix23HighRankUpdateTransposeEdRKNS_17MultiVectorMatrixES3_d_ZNK5Ipopt14DenseGenMatrix19CholeskySolveMatrixERS0__ZN5Ipopt17MultiVectorMatrix18AddRightMultMatrixEdRKS0_RKNS_6MatrixEd_ZN5Ipopt22LowRankAugSystemSolver5SolveEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dPKNS_6MatrixES6_dS9_S6_dRS5_SA_SA_SA_RS4_SB_SB_SB_bi_ZTVN5Ipopt15DiagMatrixSpaceE_ZNK5Ipopt14DenseGenMatrix19CholeskySolveVectorERNS_11DenseVectorE_ZTIN5Ipopt22LowRankAugSystemSolverE_ZTSN5Ipopt22LowRankAugSystemSolverE_ZTIN5Ipopt15DiagMatrixSpaceE_ZTSN5Ipopt15DiagMatrixSpaceE_ZTIN5Ipopt14SymMatrixSpaceE_ZTSN5Ipopt14SymMatrixSpaceE_ZTIN5Ipopt11MatrixSpaceE_ZTSN5Ipopt11MatrixSpaceE_ZTVN5Ipopt14SymMatrixSpaceE_ZTVN5Ipopt11MatrixSpaceE_ZN5Ipopt16MonotoneMuUpdate14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt16MonotoneMuUpdate15CalcNewMuAndTauERdS1__ZTVN5Ipopt16MonotoneMuUpdateE_ZN5Ipopt16MonotoneMuUpdateC2ERKNS_8SmartPtrINS_10LineSearchEEE_ZN5Ipopt16MonotoneMuUpdateD0Ev_ZN5Ipopt16MonotoneMuUpdateD2Ev_ZN5Ipopt16MonotoneMuUpdateD1Ev_ZN5Ipopt16MonotoneMuUpdate22UpdateBarrierParameterEv_ZTIN5Ipopt16MonotoneMuUpdateE_ZTSN5Ipopt16MonotoneMuUpdateE_ZN5Ipopt8SmartPtrINS_6VectorEE15ReleasePointer_Ev_ZN5Ipopt16NLPScalingObjectD0Ev_ZN5Ipopt16NLPScalingObjectD1Ev_ZN5Ipopt12ScaledMatrixC1EPKNS_17ScaledMatrixSpaceE_ZN5Ipopt20SymScaledMatrixSpaceD1Ev_ZTVN5Ipopt20SymScaledMatrixSpaceE_ZN5Ipopt20SymScaledMatrixSpaceD0Ev_ZNK5Ipopt20SymScaledMatrixSpace7MakeNewEv_ZN5Ipopt15SymScaledMatrixC1EPKNS_20SymScaledMatrixSpaceE_ZN5Ipopt19StandardScalingBaseD1Ev_ZN5Ipopt18NoNLPScalingObjectD0Ev_ZNK5Ipopt20SymScaledMatrixSpace16MakeNewSymMatrixEv_ZN5Ipopt19StandardScalingBaseD0Ev_ZN5Ipopt18NoNLPScalingObjectD1Ev_ZN5Ipopt17ScaledMatrixSpaceC1ERKNS_8SmartPtrIKNS_6VectorEEEbRKNS1_IKNS_11MatrixSpaceEEES6_b_ZN5Ipopt16NLPScalingObject34apply_vector_scaling_x_LU_NonConstERKNS_6MatrixERKNS_8SmartPtrIKNS_6VectorEEERKNS_11VectorSpaceE_ZN5Ipopt16NLPScalingObject25apply_vector_scaling_x_LUERKNS_6MatrixERKNS_8SmartPtrIKNS_6VectorEEERKNS_11VectorSpaceE_ZN5Ipopt16NLPScalingObject36unapply_vector_scaling_d_LU_NonConstERKNS_6MatrixERKNS_8SmartPtrIKNS_6VectorEEERKNS_11VectorSpaceE_ZN5Ipopt16NLPScalingObject27unapply_vector_scaling_d_LUERKNS_6MatrixERKNS_8SmartPtrIKNS_6VectorEEERKNS_11VectorSpaceE_ZN5Ipopt16NLPScalingObject34apply_vector_scaling_d_LU_NonConstERKNS_6MatrixERKNS_8SmartPtrIKNS_6VectorEEERKNS_11VectorSpaceE_ZN5Ipopt16NLPScalingObject25apply_vector_scaling_d_LUERKNS_6MatrixERKNS_8SmartPtrIKNS_6VectorEEERKNS_11VectorSpaceE_ZN5Ipopt18NoNLPScalingObject30DetermineScalingParametersImplENS_8SmartPtrIKNS_11VectorSpaceEEES4_S4_NS1_IKNS_11MatrixSpaceEEES7_NS1_IKNS_14SymMatrixSpaceEEERKNS_6MatrixERKNS_6VectorESD_SG_RdRNS1_ISE_EESJ_SJ__ZTIN5Ipopt18NoNLPScalingObjectE_ZTIN5Ipopt16NLPScalingObjectE_ZTSN5Ipopt18NoNLPScalingObjectE_ZTSN5Ipopt19StandardScalingBaseE_ZTSN5Ipopt16NLPScalingObjectE_ZTIN5Ipopt20SymScaledMatrixSpaceE_ZTSN5Ipopt20SymScaledMatrixSpaceE_ZN5Ipopt31OptimalityErrorConvergenceCheckC2Ev_ZTVN5Ipopt31OptimalityErrorConvergenceCheckE_ZN5Ipopt31OptimalityErrorConvergenceCheckD1Ev_ZN5Ipopt31OptimalityErrorConvergenceCheck19CurrentIsAcceptableEv_ZN5Ipopt16ConvergenceCheckD0Ev_ZN5Ipopt16ConvergenceCheckD1Ev_ZN5Ipopt31OptimalityErrorConvergenceCheckD2Ev_ZN5Ipopt31OptimalityErrorConvergenceCheckD0Ev_ZN5Ipopt31OptimalityErrorConvergenceCheck16CheckConvergenceEb_ZN5Ipopt31OptimalityErrorConvergenceCheck14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt31OptimalityErrorConvergenceCheckE_ZTSN5Ipopt31OptimalityErrorConvergenceCheckE_ZTIN5Ipopt16ConvergenceCheckE_ZTSN5Ipopt16ConvergenceCheckE_ZTVN5Ipopt16ConvergenceCheckE_ZNK5Ipopt8IpoptNLP23objective_depends_on_muEv_ZNK5Ipopt12OrigIpoptNLP7f_evalsEv_ZNK5Ipopt12OrigIpoptNLP12grad_f_evalsEv_ZNK5Ipopt12OrigIpoptNLP7c_evalsEv_ZNK5Ipopt12OrigIpoptNLP11jac_c_evalsEv_ZNK5Ipopt12OrigIpoptNLP7d_evalsEv_ZNK5Ipopt12OrigIpoptNLP11jac_d_evalsEv_ZNK5Ipopt12OrigIpoptNLP7h_evalsEv_ZN5Ipopt12OrigIpoptNLP1fERKNS_6VectorEd_ZN5Ipopt12OrigIpoptNLP20IntermediateCallBackENS_13AlgorithmModeEiddddddddiNS_8SmartPtrIKNS_9IpoptDataEEENS2_INS_25IpoptCalculatedQuantitiesEEE_ZNK5Ipopt27LowRankUpdateSymMatrixSpace16MakeNewSymMatrixEv_ZN5Ipopt22LowRankUpdateSymMatrixC1EPKNS_27LowRankUpdateSymMatrixSpaceE_ZN5Ipopt12OrigIpoptNLP19GetWarmStartIterateERNS_14IteratesVectorE_ZNK5Ipopt12OrigIpoptNLP18HessianMatrixSpaceEv_ZNK5Ipopt12OrigIpoptNLP3d_UEv_ZNK5Ipopt12OrigIpoptNLP4Pd_UEv_ZNK5Ipopt12OrigIpoptNLP4Pd_LEv_ZNK5Ipopt12OrigIpoptNLP4Px_LEv_ZN5Ipopt8IpoptNLPD0Ev_ZTVN5Ipopt8IpoptNLPE_ZN5Ipopt8IpoptNLP10InitializeERKNS_10JournalistERKNS_11OptionsListERKSs_ZN5Ipopt12OrigIpoptNLP15uninitialized_hEv_ZNK5Ipopt12OrigIpoptNLP3x_LEv_ZNK5Ipopt12OrigIpoptNLP3d_LEv_ZN5Ipopt8IpoptNLPD1Ev_ZN5Ipopt27LowRankUpdateSymMatrixSpaceD0Ev_ZTVN5Ipopt27LowRankUpdateSymMatrixSpaceE_ZN5Ipopt27LowRankUpdateSymMatrixSpaceD1Ev_ZN5Ipopt12OrigIpoptNLP9GetSpacesERNS_8SmartPtrIKNS_11VectorSpaceEEES5_S5_S5_RNS1_IKNS_11MatrixSpaceEEES5_S9_S5_S9_S5_S9_S9_S9_RNS1_IKNS_14SymMatrixSpaceEEE_ZNK5Ipopt12OrigIpoptNLP4Px_UEv_ZN5Ipopt14INTERNAL_ABORTD0Ev_ZN5Ipopt14INTERNAL_ABORTD1Ev_ZN5Ipopt12OrigIpoptNLP12relax_boundsEdRNS_6VectorE_ZN5Ipopt13CachedResultsIdE19GetCachedResult1DepERdPKNS_12TaggedObjectE_ZN5Ipopt12OrigIpoptNLP10InitializeERKNS_10JournalistERKNS_11OptionsListERKSs_ZN5Ipopt13CachedResultsIdE19AddCachedResult1DepERKdPKNS_12TaggedObjectE_ZN5Ipopt12OrigIpoptNLPC2ERKNS_8SmartPtrIKNS_10JournalistEEERKNS1_INS_3NLPEEERKNS1_INS_16NLPScalingObjectEEE_ZN5Ipopt12OrigIpoptNLPD2Ev_ZTVN5Ipopt12OrigIpoptNLPE_ZN5Ipopt12OrigIpoptNLPD1Ev_ZN5Ipopt12OrigIpoptNLPD0Ev_ZNK5Ipopt12OrigIpoptNLP3x_UEv_ZNK5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEE15GetCachedResultERS4_RKSt6vectorIPKNS_12TaggedObjectESaISA_EE_ZN5Ipopt12OrigIpoptNLP14get_unscaled_xERKNS_6VectorE_ZN5Ipopt12OrigIpoptNLP20AdjustVariableBoundsERKNS_6VectorES3_S3_S3__ZN5Ipopt12OrigIpoptNLP16FinalizeSolutionENS_12SolverReturnERKNS_6VectorES4_S4_S4_S4_S4_S4_dPKNS_9IpoptDataEPNS_25IpoptCalculatedQuantitiesE_ZN5Ipopt12OrigIpoptNLP1hERKNS_6VectorEdS3_S3_d_ZTVN5Ipopt14INTERNAL_ABORTE_ZN5Ipopt12OrigIpoptNLP1hERKNS_6VectorEdS3_S3__ZN5Ipopt12OrigIpoptNLP5jac_dERKNS_6VectorE_ZN5Ipopt12OrigIpoptNLP5jac_cERKNS_6VectorE_ZNK5Ipopt6Matrix15HasValidNumbersEv_ZN5Ipopt12OrigIpoptNLP1dERKNS_6VectorE_ZN5Ipopt14IsFiniteNumberEd_ZN5Ipopt12OrigIpoptNLP1cERKNS_6VectorE_ZN5Ipopt12OrigIpoptNLP6grad_fERKNS_6VectorEd_ZN5Ipopt12OrigIpoptNLP6grad_fERKNS_6VectorE_ZN5Ipopt12OrigIpoptNLP1fERKNS_6VectorE_ZN5Ipopt12OrigIpoptNLP20InitializeStructuresERNS_8SmartPtrINS_6VectorEEEbS4_bS4_bS4_bS4_bS4_S4__ZTIN5Ipopt12OrigIpoptNLPE_ZTSN5Ipopt12OrigIpoptNLPE_ZTIN5Ipopt8IpoptNLPE_ZTSN5Ipopt8IpoptNLPE_ZTIN5Ipopt27LowRankUpdateSymMatrixSpaceE_ZTSN5Ipopt27LowRankUpdateSymMatrixSpaceE_ZN5Ipopt19OrigIterationOutputC2Ev_ZTVN5Ipopt19OrigIterationOutputE_ZN5Ipopt19OrigIterationOutputD2Ev_ZN5Ipopt15IterationOutputD0Ev_ZN5Ipopt15IterationOutputD1Ev_ZN5Ipopt19OrigIterationOutputD0Ev_ZN5Ipopt19OrigIterationOutputD1Ev_ZN5Ipopt19OrigIterationOutput11WriteOutputEv_ZN5Ipopt19OrigIterationOutput14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt19OrigIterationOutputE_ZTSN5Ipopt19OrigIterationOutputE_ZTIN5Ipopt15IterationOutputE_ZTSN5Ipopt15IterationOutputE_ZTVN5Ipopt15IterationOutputE_ZN5Ipopt24LimMemQuasiNewtonUpdater7RecalcYEdRKNS_6VectorERNS_17MultiVectorMatrixES5_RNS_8SmartPtrIS4_EE_ZN5Ipopt17MultiVectorMatrix23AddOneMultiVectorMatrixEdRKS0_d_ZN5Ipopt24LimMemQuasiNewtonUpdater25ReleaseInternalDataBackupEv_ZN5Ipopt24LimMemQuasiNewtonUpdater23StoreInternalDataBackupEv_ZN5Ipopt24LimMemQuasiNewtonUpdater16ShiftDenseVectorERNS_8SmartPtrINS_11DenseVectorEEEd_ZN5Ipopt24LimMemQuasiNewtonUpdater18AugmentMultiVectorERNS_8SmartPtrINS_17MultiVectorMatrixEEERKNS_6VectorE_ZN5Ipopt24LimMemQuasiNewtonUpdater7RecalcDERNS_17MultiVectorMatrixES2_RNS_8SmartPtrINS_11DenseVectorEEE_ZN5Ipopt24LimMemQuasiNewtonUpdater16ShiftSTDRSMatrixERNS_8SmartPtrINS_14DenseSymMatrixEEERKNS_17MultiVectorMatrixES7__ZN5Ipopt24LimMemQuasiNewtonUpdater7RecalcLERNS_17MultiVectorMatrixES2_RNS_8SmartPtrINS_14DenseGenMatrixEEE_ZN5Ipopt24LimMemQuasiNewtonUpdater16ShiftSdotSMatrixERNS_8SmartPtrINS_14DenseSymMatrixEEERKNS_17MultiVectorMatrixE_ZN5Ipopt24LimMemQuasiNewtonUpdater12ShiftLMatrixERNS_8SmartPtrINS_14DenseGenMatrixEEERKNS_17MultiVectorMatrixES7__ZN5Ipopt24LimMemQuasiNewtonUpdater18AugmentSTDRSMatrixERNS_8SmartPtrINS_14DenseSymMatrixEEERKNS_17MultiVectorMatrixES7__ZN5Ipopt24LimMemQuasiNewtonUpdater14AugmentLMatrixERNS_8SmartPtrINS_14DenseGenMatrixEEERKNS_17MultiVectorMatrixES7__ZN5Ipopt24LimMemQuasiNewtonUpdater17CheckSkippingBFGSERNS_6VectorES2__ZN5Ipopt24LimMemQuasiNewtonUpdater25RestoreInternalDataBackupEv_ZN5Ipopt24LimMemQuasiNewtonUpdater16ShiftMultiVectorERNS_8SmartPtrINS_17MultiVectorMatrixEEERKNS_6VectorE_ZN5Ipopt24LimMemQuasiNewtonUpdater16SplitEigenvaluesERNS_14DenseGenMatrixERKNS_11DenseVectorERNS_8SmartPtrIS1_EES8__ZN5Ipopt14DenseGenMatrix12ScaleColumnsERKNS_11DenseVectorE_ZN5Ipopt24LimMemQuasiNewtonUpdater18AugmentSdotSMatrixERNS_8SmartPtrINS_14DenseSymMatrixEEERKNS_17MultiVectorMatrixE_ZTVN5Ipopt24LimMemQuasiNewtonUpdaterE_ZN5Ipopt24LimMemQuasiNewtonUpdaterD0Ev_ZN5Ipopt24LimMemQuasiNewtonUpdaterC2Eb_ZN5Ipopt24LimMemQuasiNewtonUpdater18AugmentDenseVectorERNS_8SmartPtrINS_11DenseVectorEEEd_ZN5Ipopt24LimMemQuasiNewtonUpdaterD1Ev_ZN5Ipopt24LimMemQuasiNewtonUpdater18UpdateInternalDataERKNS_6VectorES3_NS_8SmartPtrIS1_EE_ZN5Ipopt24LimMemQuasiNewtonUpdater4SetWEv_ZNK5Ipopt22CompoundSymMatrixSpace24MakeNewCompoundSymMatrixEv_ZN5Ipopt17CompoundSymMatrix7SetCompEiiRKNS_6MatrixE_ZN5Ipopt24LimMemQuasiNewtonUpdater13UpdateHessianEv_ZNK5Ipopt13RestoIpoptNLP3EtaEd_ZN5Ipopt14DenseGenMatrix4CopyERKS0__ZN5Ipopt14DenseSymMatrix14HighRankUpdateEbdRKNS_14DenseGenMatrixEd_ZN5Ipopt14DenseSymMatrix9AddMatrixEdRKS0_d_ZN5Ipopt14DenseSymMatrix18SpecialAddForLMSR1ERKNS_11DenseVectorERKNS_14DenseGenMatrixE_ZN5Ipopt14DenseGenMatrix19ComputeEigenVectorsERKNS_14DenseSymMatrixERNS_11DenseVectorE_ZN5Ipopt14DenseGenMatrix12FillIdentityEd_ZNK5Ipopt14DenseGenMatrix23CholeskyBackSolveMatrixEbdRS0__ZN5Ipopt14DenseGenMatrix16AddMatrixProductEdRKS0_bS2_bd_ZN5Ipopt24LimMemQuasiNewtonUpdater14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt24LimMemQuasiNewtonUpdaterE_ZTSN5Ipopt24LimMemQuasiNewtonUpdaterE_ZNK5Ipopt15DependentResultIPvE19DependentsIdenticalERKSt6vectorIPKNS_12TaggedObjectESaIS6_EERKS3_IdSaIdEE_ZN5Ipopt15DependentResultIPvE19RecieveNotificationENS_8Observer10NotifyTypeEPKNS_7SubjectE_ZN5Ipopt13CachedResultsIPvED0Ev_ZTVN5Ipopt13CachedResultsIPvEE_ZN5Ipopt13CachedResultsIPvED1Ev_ZNK5Ipopt13CachedResultsIPvE25CleanupInvalidatedResultsEv_ZN5Ipopt17PDFullSpaceSolver20ComputeResidualRatioERKNS_14IteratesVectorES3_S3__ZTVN5Ipopt17PDFullSpaceSolverE_ZN5Ipopt17PDFullSpaceSolverD2Ev_ZTVN5Ipopt14PDSystemSolverE_ZN5Ipopt17PDFullSpaceSolver14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt14PDSystemSolverD0Ev_ZN5Ipopt14PDSystemSolverD1Ev_ZN5Ipopt17PDFullSpaceSolverC2ERNS_15AugSystemSolverERNS_21PDPerturbationHandlerE_ZN5Ipopt15DependentResultIPvEC1ERKS1_RKSt6vectorIPKNS_12TaggedObjectESaIS8_EERKS5_IdSaIdEE_ZTVN5Ipopt15DependentResultIPvEE_ZN5Ipopt13CachedResultsIPvE15AddCachedResultERKS1_RKSt6vectorIPKNS_12TaggedObjectESaIS8_EERKS5_IdSaIdEE_ZN5Ipopt15DependentResultIPvED0Ev_ZN5Ipopt15DependentResultIPvED1Ev_ZNK5Ipopt13CachedResultsIPvE15GetCachedResultERS1_RKSt6vectorIPKNS_12TaggedObjectESaIS7_EE_ZN5Ipopt17PDFullSpaceSolverD0Ev_ZN5Ipopt17PDFullSpaceSolver9SolveOnceEbbRKNS_9SymMatrixERKNS_6MatrixES6_S6_S6_S6_S6_RKNS_6VectorES9_S9_S9_S9_S9_S9_S9_S9_S9_ddRKNS_14IteratesVectorERSA__ZNK5Ipopt6Matrix14SinvBlrmZMTdBrEdRKNS_6VectorES3_S3_S3_RS1__ZN5Ipopt17PDFullSpaceSolverD1Ev_ZN5Ipopt17PDFullSpaceSolver16ComputeResidualsERKNS_9SymMatrixERKNS_6MatrixES6_S6_S6_S6_S6_RKNS_6VectorES9_S9_S9_S9_S9_S9_S9_S9_S9_ddRKNS_14IteratesVectorESC_RSA__ZN5Ipopt17PDFullSpaceSolver5SolveEddRKNS_14IteratesVectorERS1_bb_ZTIN5Ipopt17PDFullSpaceSolverE_ZTSN5Ipopt17PDFullSpaceSolverE_ZTIN5Ipopt14PDSystemSolverE_ZTSN5Ipopt14PDSystemSolverE_ZTIN5Ipopt15DependentResultIPvEE_ZTSN5Ipopt15DependentResultIPvEE_ZTIN5Ipopt13CachedResultsIPvEE_ZTSN5Ipopt13CachedResultsIPvEE_ZTVN5Ipopt21PDPerturbationHandlerE_ZN5Ipopt21PDPerturbationHandler19CurrentPerturbationERdS1_S1_S1__ZN5Ipopt21PDPerturbationHandler8delta_cdEv_ZN5Ipopt21PDPerturbationHandlerD1Ev_ZN5Ipopt21PDPerturbationHandlerD0Ev_ZN5Ipopt21PDPerturbationHandler13finalize_testEv_ZN5Ipopt21PDPerturbationHandler28get_deltas_for_wrong_inertiaERdS1_S1_S1__ZN5Ipopt21PDPerturbationHandler22PerturbForWrongInertiaERdS1_S1_S1__ZN5Ipopt21PDPerturbationHandler21PerturbForSingularityERdS1_S1_S1__ZN5Ipopt21PDPerturbationHandler17ConsiderNewSystemERdS1_S1_S1__ZTSN5Ipopt21PDPerturbationHandlerE_ZN5Ipopt21PDSearchDirCalculatorD0Ev_ZTVN5Ipopt21PDSearchDirCalculatorE_ZN5Ipopt21PDSearchDirCalculatorD2Ev_ZN5Ipopt21PDSearchDirCalculator14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt21PDSearchDirCalculatorC2ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZN5Ipopt21PDSearchDirCalculator22ComputeSearchDirectionEv_ZN5Ipopt21PDSearchDirCalculatorD1Ev_ZTIN5Ipopt21PDSearchDirCalculatorE_ZTSN5Ipopt21PDSearchDirCalculatorE_ZN5Ipopt15ProbingMuOracle14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt15ProbingMuOracleD1Ev_ZTVN5Ipopt15ProbingMuOracleE_ZN5Ipopt15ProbingMuOracleD2Ev_ZN5Ipopt15ProbingMuOracleC2ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZN5Ipopt15ProbingMuOracle17CalculateAffineMuEddRKNS_14IteratesVectorE_ZN5Ipopt15ProbingMuOracle11CalculateMuEddRd_ZN5Ipopt15ProbingMuOracleD0Ev_ZTIN5Ipopt15ProbingMuOracleE_ZTSN5Ipopt15ProbingMuOracleE_ZN5Ipopt23QualityFunctionMuOracle10ScaleSigmaEd_ZN5Ipopt23QualityFunctionMuOracle12UnscaleSigmaEd_ZN5Ipopt23QualityFunctionMuOracle24CalculateQualityFunctionEdRKNS_6VectorES3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3__ZN5Ipopt23QualityFunctionMuOracleC2ERKNS_8SmartPtrINS_14PDSystemSolverEEE_ZTVN5Ipopt23QualityFunctionMuOracleE_ZN5Ipopt23QualityFunctionMuOracleD0Ev_ZN5Ipopt23QualityFunctionMuOracleD1Ev_ZN5Ipopt23QualityFunctionMuOracleD2Ev_ZN5Ipopt23QualityFunctionMuOracle20PerformGoldenSectionEddddddRKNS_6VectorES3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3_S3__ZN5Ipopt23QualityFunctionMuOracle11CalculateMuEddRd_ZN5Ipopt23QualityFunctionMuOracle14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt23QualityFunctionMuOracleE_ZTSN5Ipopt23QualityFunctionMuOracleE_ZN5Ipopt27RestoFilterConvergenceCheckD0Ev_ZTVN5Ipopt27RestoFilterConvergenceCheckE_ZN5Ipopt27RestoFilterConvergenceCheckD1Ev_ZN5Ipopt27RestoFilterConvergenceCheckD2Ev_ZN5Ipopt27RestoFilterConvergenceCheckC2Ev_ZN5Ipopt39RESTORATION_CONVERGED_TO_FEASIBLE_POINTD1Ev_ZN5Ipopt18LOCALLY_INFEASIBLED0Ev_ZN5Ipopt18LOCALLY_INFEASIBLED1Ev_ZN5Ipopt39RESTORATION_CONVERGED_TO_FEASIBLE_POINTD0Ev_ZN5Ipopt27RestoFilterConvergenceCheck14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt27RestoFilterConvergenceCheck16CheckConvergenceEb_ZTVN5Ipopt39RESTORATION_CONVERGED_TO_FEASIBLE_POINTE_ZTVN5Ipopt18LOCALLY_INFEASIBLEE_ZTIN5Ipopt27RestoFilterConvergenceCheckE_ZTSN5Ipopt27RestoFilterConvergenceCheckE_ZN5Ipopt13RestoIpoptNLP19GetWarmStartIterateERNS_14IteratesVectorE_ZN5Ipopt13RestoIpoptNLP16FinalizeSolutionENS_12SolverReturnERKNS_6VectorES4_S4_S4_S4_S4_S4_dPKNS_9IpoptDataEPNS_25IpoptCalculatedQuantitiesE_ZNK5Ipopt13RestoIpoptNLP23objective_depends_on_muEv_ZNK5Ipopt13RestoIpoptNLP7f_evalsEv_ZNK5Ipopt13RestoIpoptNLP12grad_f_evalsEv_ZNK5Ipopt13RestoIpoptNLP7c_evalsEv_ZNK5Ipopt13RestoIpoptNLP11jac_c_evalsEv_ZNK5Ipopt13RestoIpoptNLP7d_evalsEv_ZNK5Ipopt13RestoIpoptNLP11jac_d_evalsEv_ZNK5Ipopt13RestoIpoptNLP7h_evalsEv_ZNK5Ipopt19IdentityMatrixSpace16MakeNewSymMatrixEv_ZN5Ipopt14IdentityMatrixC1EPKNS_14SymMatrixSpaceE_ZNK5Ipopt13RestoIpoptNLP18HessianMatrixSpaceEv_ZN5Ipopt13RestoIpoptNLP1hERKNS_6VectorEdS3_S3__ZNK5Ipopt13RestoIpoptNLP4Px_UEv_ZNK5Ipopt13RestoIpoptNLP4Pd_LEv_ZNK5Ipopt13RestoIpoptNLP3x_UEv_ZNK5Ipopt13RestoIpoptNLP4Px_LEv_ZNK5Ipopt13RestoIpoptNLP3x_LEv_ZNK5Ipopt13RestoIpoptNLP4Pd_UEv_ZNK5Ipopt13RestoIpoptNLP3d_LEv_ZNK5Ipopt13RestoIpoptNLP3d_UEv_ZN5Ipopt13RestoIpoptNLP20IntermediateCallBackENS_13AlgorithmModeEiddddddddiNS_8SmartPtrIKNS_9IpoptDataEEENS2_INS_25IpoptCalculatedQuantitiesEEE_ZN5Ipopt19IdentityMatrixSpaceD0Ev_ZN5Ipopt19IdentityMatrixSpaceD1Ev_ZN5Ipopt13RestoIpoptNLP15uninitialized_hEv_ZN5Ipopt12SumSymMatrix7SetTermEidRKNS_9SymMatrixE_ZN5Ipopt13RestoIpoptNLP9GetSpacesERNS_8SmartPtrIKNS_11VectorSpaceEEES5_S5_S5_RNS1_IKNS_11MatrixSpaceEEES5_S9_S5_S9_S5_S9_S9_S9_RNS1_IKNS_14SymMatrixSpaceEEE_ZN5Ipopt13RestoIpoptNLPD0Ev_ZTVN5Ipopt13RestoIpoptNLPE_ZN5Ipopt13RestoIpoptNLP1hERKNS_6VectorEdS3_S3_d_ZN5Ipopt13RestoIpoptNLPC1ERNS_8IpoptNLPERNS_9IpoptDataERNS_25IpoptCalculatedQuantitiesE_ZN5Ipopt13RestoIpoptNLP20AdjustVariableBoundsERKNS_6VectorES3_S3_S3__ZN5Ipopt13RestoIpoptNLPC2ERNS_8IpoptNLPERNS_9IpoptDataERNS_25IpoptCalculatedQuantitiesE_ZN5Ipopt13RestoIpoptNLPD1Ev_ZN5Ipopt13RestoIpoptNLP5jac_dERKNS_6VectorE_ZNK5Ipopt19CompoundMatrixSpace21MakeNewCompoundMatrixEv_ZN5Ipopt14CompoundMatrix7SetCompEiiRKNS_6MatrixE_ZN5Ipopt13RestoIpoptNLPD2Ev_ZN5Ipopt13RestoIpoptNLP5jac_cERKNS_6VectorE_ZN5Ipopt13RestoIpoptNLP1fERKNS_6VectorE_ZN5Ipopt13RestoIpoptNLP10InitializeERKNS_10JournalistERKNS_11OptionsListERKSs_ZN5Ipopt13RestoIpoptNLP1fERKNS_6VectorEd_ZN5Ipopt13RestoIpoptNLP1cERKNS_6VectorE_ZN5Ipopt13RestoIpoptNLP6grad_fERKNS_6VectorEd_ZN5Ipopt13RestoIpoptNLP1dERKNS_6VectorE_ZN5Ipopt13RestoIpoptNLP6grad_fERKNS_6VectorE_ZN5Ipopt13RestoIpoptNLP20InitializeStructuresERNS_8SmartPtrINS_6VectorEEEbS4_bS4_bS4_bS4_bS4_S4__ZN5Ipopt19CompoundMatrixSpaceC1Eiiii_ZN5Ipopt19CompoundMatrixSpace12SetBlockRowsEii_ZN5Ipopt19CompoundMatrixSpace12SetBlockColsEii_ZN5Ipopt19CompoundMatrixSpace12SetCompSpaceEiiRKNS_11MatrixSpaceEb_ZTVN5Ipopt19IdentityMatrixSpaceE_ZN5Ipopt22CompoundSymMatrixSpaceC1Eii_ZN5Ipopt22CompoundSymMatrixSpace11SetBlockDimEii_ZTVN5Ipopt17SumSymMatrixSpaceE_ZN5Ipopt17SumSymMatrixSpace12SetTermSpaceEiRKNS_14SymMatrixSpaceE_ZN5Ipopt22CompoundSymMatrixSpace12SetCompSpaceEiiRKNS_11MatrixSpaceEb_ZTIN5Ipopt13RestoIpoptNLPE_ZTSN5Ipopt13RestoIpoptNLPE_ZTIN5Ipopt19IdentityMatrixSpaceE_ZTSN5Ipopt19IdentityMatrixSpaceE_ZN5Ipopt23RestoIterateInitializer14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt23RestoIterateInitializerD1Ev_ZTVN5Ipopt23RestoIterateInitializerE_ZN5Ipopt23RestoIterateInitializerC2ERKNS_8SmartPtrINS_22EqMultiplierCalculatorEEE_ZN5Ipopt23RestoIterateInitializer15solve_quadraticERKNS_6VectorES3_RS1__ZN5Ipopt23RestoIterateInitializerD0Ev_ZN5Ipopt23RestoIterateInitializer18SetInitialIteratesEv_ZTIN5Ipopt23RestoIterateInitializerE_ZTSN5Ipopt23RestoIterateInitializerE_ZTVN5Ipopt20RestoIterationOutputE_ZN5Ipopt20RestoIterationOutputC2ERKNS_8SmartPtrINS_19OrigIterationOutputEEE_ZN5Ipopt20RestoIterationOutputD0Ev_ZN5Ipopt20RestoIterationOutputD1Ev_ZN5Ipopt20RestoIterationOutputD2Ev_ZN5Ipopt20RestoIterationOutput14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt20RestoIterationOutput11WriteOutputEv_ZTIN5Ipopt20RestoIterationOutputE_ZTSN5Ipopt20RestoIterationOutputE_ZN5Ipopt25MinC_1NrmRestorationPhase14InitializeImplERKNS_11OptionsListERKSs_ZTVN5Ipopt25MinC_1NrmRestorationPhaseE_ZN5Ipopt25MinC_1NrmRestorationPhaseC2ERNS_14IpoptAlgorithmERKNS_8SmartPtrINS_22EqMultiplierCalculatorEEE_ZN5Ipopt25MinC_1NrmRestorationPhaseD0Ev_ZTVN5Ipopt16RestorationPhaseE_ZN5Ipopt16RestorationPhaseD0Ev_ZN5Ipopt16RestorationPhaseD1Ev_ZN5Ipopt25MinC_1NrmRestorationPhase26ComputeBoundMultiplierStepERNS_6VectorERKS1_S4_S4__ZN5Ipopt25MinC_1NrmRestorationPhase18PerformRestorationEv_ZTVN5Ipopt21RESTORATION_USER_STOPE_ZN5Ipopt21RESTORATION_USER_STOPD1Ev_ZTVN5Ipopt28RESTORATION_MAXITER_EXCEEDEDE_ZN5Ipopt28RESTORATION_MAXITER_EXCEEDEDD1Ev_ZTVN5Ipopt26FEASIBILITY_PROBLEM_SOLVEDE_ZN5Ipopt26FEASIBILITY_PROBLEM_SOLVEDD1Ev_ZN5Ipopt25MinC_1NrmRestorationPhaseD1Ev_ZN5Ipopt28RESTORATION_MAXITER_EXCEEDEDD0Ev_ZN5Ipopt21RESTORATION_USER_STOPD0Ev_ZN5Ipopt26FEASIBILITY_PROBLEM_SOLVEDD0Ev_ZN5Ipopt25MinC_1NrmRestorationPhaseD2Ev_ZTIN5Ipopt25MinC_1NrmRestorationPhaseE_ZTSN5Ipopt25MinC_1NrmRestorationPhaseE_ZTIN5Ipopt16RestorationPhaseE_ZTSN5Ipopt16RestorationPhaseE_ZN5Ipopt21RestoRestorationPhaseC2Ev_ZTVN5Ipopt21RestoRestorationPhaseE_ZN5Ipopt21RestoRestorationPhase14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt21RestoRestorationPhaseD2Ev_ZN5Ipopt21RestoRestorationPhaseD0Ev_ZN5Ipopt21RestoRestorationPhaseD1Ev_ZN5Ipopt21RestoRestorationPhase15solve_quadraticERKNS_6VectorES3_RS1__ZN5Ipopt21RestoRestorationPhase18PerformRestorationEv_ZTIN5Ipopt21RestoRestorationPhaseE_ZTSN5Ipopt21RestoRestorationPhaseE_ZN5Ipopt18StdAugSystemSolver29AugmentedSystemRequiresChangeEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dRKNS_6MatrixES6_dS9_S6_d_ZN5Ipopt18StdAugSystemSolver15IncreaseQualityEv_ZNK5Ipopt18StdAugSystemSolver15ProvidesInertiaEv_ZNK5Ipopt18StdAugSystemSolver16NumberOfNegEValsEv_ZN5Ipopt18StdAugSystemSolverD1Ev_ZTVN5Ipopt18StdAugSystemSolverE_ZN5Ipopt18StdAugSystemSolverD0Ev_ZN5Ipopt18StdAugSystemSolver20CreateAugmentedSpaceERKNS_9SymMatrixERKNS_6MatrixES6_RKNS_6VectorES9_S9_S9__ZN5Ipopt18StdAugSystemSolverC2ERNS_15SymLinearSolverE_ZN5Ipopt18StdAugSystemSolverD2Ev_ZN5Ipopt18StdAugSystemSolver21CreateAugmentedSystemEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dRKNS_6MatrixES6_dS9_S6_dRS5_SA_SA_SA__ZNK5Ipopt17SumSymMatrixSpace19MakeNewSumSymMatrixEv_ZN5Ipopt18StdAugSystemSolver14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt18StdAugSystemSolver10MultiSolveEPKNS_9SymMatrixEdPKNS_6VectorEdS6_dPKNS_6MatrixES6_dS9_S6_dRSt6vectorINS_8SmartPtrIS5_EESaISC_EESF_SF_SF_RSA_INSB_IS4_EESaISG_EESJ_SJ_SJ_bi_ZTIN5Ipopt18StdAugSystemSolverE_ZTSN5Ipopt18StdAugSystemSolverE_ZN5Ipopt11UserScalingD0Ev_ZN5Ipopt11UserScalingD1Ev_ZN5Ipopt11UserScaling30DetermineScalingParametersImplENS_8SmartPtrIKNS_11VectorSpaceEEES4_S4_NS1_IKNS_11MatrixSpaceEEES7_NS1_IKNS_14SymMatrixSpaceEEERKNS_6MatrixERKNS_6VectorESD_SG_RdRNS1_ISE_EESJ_SJ__ZTIN5Ipopt11UserScalingE_ZTSN5Ipopt11UserScalingE_ZN5Ipopt27WarmStartIterateInitializerC2Ev_ZTVN5Ipopt27WarmStartIterateInitializerE_ZN5Ipopt27WarmStartIterateInitializer14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt27WarmStartIterateInitializerD0Ev_ZN5Ipopt6Vector4CopyERKS0__ZN5Ipopt27WarmStartIterateInitializer18adapt_to_target_muERNS_6VectorES2_d_ZN5Ipopt27WarmStartIterateInitializerD1Ev_ZN5Ipopt27WarmStartIterateInitializer17process_target_muEdRKNS_6VectorES3_S3_RKNS_6MatrixERNS_8SmartPtrIS2_EES9__ZN5Ipopt27WarmStartIterateInitializer18SetInitialIteratesEv_ZTIN5Ipopt27WarmStartIterateInitializerE_ZTSN5Ipopt27WarmStartIterateInitializerE_ZN5Ipopt16TSymLinearSolver15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt20MumpsSolverInterface15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt21TripletToCSRConverterC2Ei_ZTVN5Ipopt21TripletToCSRConverterE_ZN5Ipopt21TripletToCSRConverter13ConvertValuesEiPKdiPd_ZN5Ipopt21TripletToCSRConverterD1Ev_ZNSt4listIN5Ipopt21TripletToCSRConverter12TripletEntryESaIS2_EE5mergeERS4__ZN5Ipopt21TripletToCSRConverterC1Ei_ZN5Ipopt21TripletToCSRConverterD2Ev_ZN5Ipopt21TripletToCSRConverterD0Ev_ZNSt4listIN5Ipopt21TripletToCSRConverter12TripletEntryESaIS2_EE4sortEv_ZN5Ipopt21TripletToCSRConverter19InitializeConverterEiiPKiS2__ZTIN5Ipopt21TripletToCSRConverterE_ZTSN5Ipopt21TripletToCSRConverterE_ZN5Ipopt22TSymDependencyDetector15RegisterOptionsENS_8SmartPtrINS_17RegisteredOptionsEEE_ZN5Ipopt22TSymDependencyDetector22DetermineDependentRowsEiiiPdPiS2_RSt4listIiSaIiEE_ZN5Ipopt16TSymLinearSolver22DetermineDependentRowsEiiiPdPiS2_RSt4listIiSaIiEE_ZN5Ipopt19TDependencyDetectorD0Ev_ZN5Ipopt19TDependencyDetectorD1Ev_ZN5Ipopt22TSymDependencyDetector14InitializeImplERKNS_11OptionsListERKSs_ZNK5Ipopt16TSymLinearSolver27ProvidesDegeneracyDetectionEv_ZTVN5Ipopt22TSymDependencyDetectorE_ZN5Ipopt22TSymDependencyDetectorC2ERNS_16TSymLinearSolverE_ZN5Ipopt22TSymDependencyDetectorD0Ev_ZTVN5Ipopt19TDependencyDetectorE_ZN5Ipopt22TSymDependencyDetectorD1Ev_ZTIN5Ipopt22TSymDependencyDetectorE_ZTSN5Ipopt22TSymDependencyDetectorE_ZTIN5Ipopt19TDependencyDetectorE_ZTSN5Ipopt19TDependencyDetectorE_ZNK5Ipopt16TSymLinearSolver15ProvidesInertiaEv_ZNK5Ipopt16TSymLinearSolver16NumberOfNegEValsEv_ZTVN5Ipopt16TSymLinearSolverE_ZN5Ipopt16TSymLinearSolverD1Ev_ZTVN5Ipopt15SymLinearSolverE_ZN5Ipopt30ERROR_IN_LINEAR_SCALING_METHODD1Ev_ZN5Ipopt15SymLinearSolverD1Ev_ZN5Ipopt16TSymLinearSolverC2ENS_8SmartPtrINS_30SparseSymLinearSolverInterfaceEEENS1_INS_17TSymScalingMethodEEE_ZN5Ipopt15SymLinearSolverD0Ev_ZN5Ipopt16TSymLinearSolverD0Ev_ZN5Ipopt16TSymLinearSolverD2Ev_ZN5Ipopt30ERROR_IN_LINEAR_SCALING_METHODD0Ev_ZTVN5Ipopt30ERROR_IN_LINEAR_SCALING_METHODE_ZTIN5Ipopt30ERROR_IN_LINEAR_SCALING_METHODE_ZN5Ipopt16TSymLinearSolver18GiveMatrixToSolverEbRKNS_9SymMatrixE_ZN5Ipopt16TSymLinearSolver14InitializeImplERKNS_11OptionsListERKSs_ZN5Ipopt16TSymLinearSolver15IncreaseQualityEv_ZN5Ipopt16TSymLinearSolver19InitializeStructureERKNS_9SymMatrixE_ZN5Ipopt16TSymLinearSolver10MultiSolveERKNS_9SymMatrixERSt6vectorINS_8SmartPtrIKNS_6VectorEEESaIS8_EERS4_INS5_IS6_EESaISC_EEbi_ZTIN5Ipopt16TSymLinearSolverE_ZTSN5Ipopt16TSymLinearSolverE_ZTIN5Ipopt15SymLinearSolverE_ZTSN5Ipopt15SymLinearSolverE_ZTSN5Ipopt30ERROR_IN_LINEAR_SCALING_METHODE_ZNK5Ipopt30SparseSymLinearSolverInterface27ProvidesDegeneracyDetectionEv_ZN5Ipopt30SparseSymLinearSolverInterface22DetermineDependentRowsEPKiS2_RSt4listIiSaIiEE_ZNK5Ipopt20MumpsSolverInterface15ProvidesInertiaEv_ZNK5Ipopt20MumpsSolverInterface12MatrixFormatEv_ZN5Ipopt20MumpsSolverInterface17GetValuesArrayPtrEv_ZN5Ipopt11dump_matrixEP14DMUMPS_STRUC_C_ZNK5Ipopt20MumpsSolverInterface16NumberOfNegEValsEv_ZNK5Ipopt20MumpsSolverInterface27ProvidesDegeneracyDetectionEv_ZN5Ipopt20MumpsSolverInterface15IncreaseQualityEv_ZN5Ipopt20MumpsSolverInterface13FactorizationEbi_ZN5Ipopt20MumpsSolverInterface21SymbolicFactorizationEv_ZN5Ipopt20MumpsSolverInterface22DetermineDependentRowsEPKiS2_RSt4listIiSaIiEE_ZN5Ipopt20MumpsSolverInterface5SolveEiPd_ZN5Ipopt20MumpsSolverInterface10MultiSolveEbPKiS2_iPdbi_ZN5Ipopt30SparseSymLinearSolverInterfaceD0Ev_ZN5Ipopt30SparseSymLinearSolverInterfaceD1Ev_ZTVN5Ipopt20MumpsSolverInterfaceEIPOPT_MPI_COMM_WORLD_ZN5Ipopt20MumpsSolverInterfaceD1Ev_ZN5Ipopt20MumpsSolverInterfaceD0Ev_ZN5Ipopt20MumpsSolverInterfaceD2Ev_ZTVN5Ipopt30SparseSymLinearSolverInterfaceE_ZN5Ipopt20MumpsSolverInterfaceC2Ev_ZN5Ipopt20MumpsSolverInterface19InitializeStructureEiiPKiS2__ZN5Ipopt20MumpsSolverInterface14InitializeImplERKNS_11OptionsListERKSs_ZTIN5Ipopt20MumpsSolverInterfaceE_ZTSN5Ipopt20MumpsSolverInterfaceE_ZTIN5Ipopt30SparseSymLinearSolverInterfaceE_ZTSN5Ipopt30SparseSymLinearSolverInterfaceE_ZNK5Ipopt7Journal10IsAcceptedENS_16EJournalCategoryENS_13EJournalLevelE_ZN5Ipopt13StreamJournal15SetOutputStreamEPSo_ZN5Ipopt7Journal4NameEv_ZN5Ipopt13StreamJournal15FlushBufferImplEv_ZSt5flushIcSt11char_traitsIcEERSt13basic_ostreamIT_T0_ES6__ZN5Ipopt11FileJournal15FlushBufferImplEvfflush_ZN5Ipopt11FileJournal10PrintfImplENS_16EJournalCategoryENS_13EJournalLevelEPKcP13__va_list_tagvfprintf_ZN5Ipopt11FileJournal9PrintImplENS_16EJournalCategoryENS_13EJournalLevelEPKc_ZN5Ipopt11FileJournal4OpenEPKcstdoutstderrfclosefopen_ZNK5Ipopt10Journalist7VPrintfENS_13EJournalLevelENS_16EJournalCategoryEPKcP13__va_list_tag_ZNK5Ipopt10Journalist20PrintStringOverLinesENS_13EJournalLevelENS_16EJournalCategoryEiiRKSs_ZNK5Ipopt10Journalist15VPrintfIndentedENS_13EJournalLevelENS_16EJournalCategoryEiPKcP13__va_list_tag_ZNK5Ipopt10Journalist14PrintfIndentedENS_13EJournalLevelENS_16EJournalCategoryEiPKcz_ZN5Ipopt13StreamJournal9PrintImplENS_16EJournalCategoryENS_13EJournalLevelEPKc_ZSt16__ostream_insertIcSt11char_traitsIcEERSt13basic_ostreamIT_T0_ES6_PKS3_l_ZTVN5Ipopt10JournalistE_ZN5Ipopt10JournalistC2Ev_ZN5Ipopt7JournalC1ERKSsNS_13EJournalLevelE_ZTVN5Ipopt7JournalE_ZN5Ipopt7JournalC2ERKSsNS_13EJournalLevelE_ZN5Ipopt13StreamJournalC1ERKSsNS_13EJournalLevelE_ZTVN5Ipopt13StreamJournalE_ZN5Ipopt13StreamJournalC2ERKSsNS_13EJournalLevelE_ZN5Ipopt11FileJournalC1ERKSsNS_13EJournalLevelE_ZTVN5Ipopt11FileJournalE_ZN5Ipopt11FileJournalC2ERKSsNS_13EJournalLevelE_ZN5Ipopt13StreamJournal10PrintfImplENS_16EJournalCategoryENS_13EJournalLevelEPKcP13__va_list_tagvsprintf_ZN5Ipopt7JournalD0Ev_ZN5Ipopt7JournalD1Ev_ZN5Ipopt7JournalD2Ev_ZN5Ipopt11FileJournalD0Ev_ZN5Ipopt11FileJournalD1Ev_ZN5Ipopt11FileJournalD2Ev_ZN5Ipopt13StreamJournalD0Ev_ZN5Ipopt13StreamJournalD1Ev_ZNSt6vectorIN5Ipopt8SmartPtrINS0_7JournalEEESaIS3_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS3_S5_EERKS3__ZN5Ipopt10Journalist10AddJournalENS_8SmartPtrINS_7JournalEEE_ZN5Ipopt10JournalistD2Ev_ZN5Ipopt10JournalistD0Ev_ZN5Ipopt10JournalistD1Ev_ZTIN5Ipopt13StreamJournalE_ZTIN5Ipopt11FileJournalE_ZTIN5Ipopt7JournalE_ZTIN5Ipopt10JournalistE_ZTSN5Ipopt13StreamJournalE_ZTSN5Ipopt11FileJournalE_ZTSN5Ipopt7JournalE_ZTSN5Ipopt10JournalistE_ZNK5Ipopt11OptionsList9lowercaseESstolower_ZNSs12_M_leak_hardEv_ZN5Ipopt11OptionsList13readnexttokenERSiRSs_ZNSi3getEvisspace_ZNSi6ignoreEli_ZSt18_Rb_tree_incrementPKSt18_Rb_tree_node_base_ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt11OptionsList11OptionValueEESt10_Select1stIS5_ESt4lessISsESaIS5_EE9_M_insertEPSt18_Rb_tree_node_baseSD_RKS5__ZSt29_Rb_tree_insert_and_rebalancebPSt18_Rb_tree_node_baseS0_RS__ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt11OptionsList11OptionValueEESt10_Select1stIS5_ESt4lessISsESaIS5_EE16_M_insert_uniqueERKS5__ZSt18_Rb_tree_decrementPSt18_Rb_tree_node_base_ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt11OptionsList11OptionValueEESt10_Select1stIS5_ESt4lessISsESaIS5_EE16_M_insert_uniqueESt17_Rb_tree_iteratorIS5_ERKS5__ZSt18_Rb_tree_incrementPSt18_Rb_tree_node_base_ZNK5Ipopt11OptionsList18will_allow_clobberERKSs_ZN5Ipopt17RegisteredOptions9GetOptionERKSs_ZNK5Ipopt16RegisteredOption17OutputDescriptionERKNS_10JournalistE_ZNK5Ipopt16RegisteredOption20IsValidStringSettingERKSs_ZNK5Ipopt11OptionsList8find_tagERKSsS2_RSsstrtol_ZN5Ipopt11OptionsList22SetIntegerValueIfUnsetERKSsibbstrtod_ZNK5Ipopt16RegisteredOption22MapStringSettingToEnumERKSs_ZNK5Ipopt16RegisteredOption16MapStringSettingERKSs_ZN5Ipopt16RegisteredOption31ERROR_CONVERTING_STRING_TO_ENUMD1Ev_ZNK5Ipopt16RegisteredOption22OutputShortDescriptionERKNS_10JournalistE_ZNK5Ipopt16RegisteredOption24string_equal_insensitiveERKSsS2__ZN5Ipopt16RegisteredOptionC1ERKSsS2_S2_S2__ZTVN5Ipopt16RegisteredOptionE_ZN5Ipopt16RegisteredOption13next_counter_E_ZNSt8_Rb_treeIiSt4pairIKiN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessIiESaIS6_EE9_M_insertEPSt18_Rb_tree_node_baseSE_RKS6__ZNSt8_Rb_treeIiSt4pairIKiN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessIiESaIS6_EE16_M_insert_uniqueERKS6__ZNSt8_Rb_treeIiSt4pairIKiN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessIiESaIS6_EE16_M_insert_uniqueESt17_Rb_tree_iteratorIS6_ERKS6__ZN5Ipopt17RegisteredOptions25OPTION_ALREADY_REGISTEREDC1ESsSsi_ZTVN5Ipopt17RegisteredOptions25OPTION_ALREADY_REGISTEREDE_ZNK5Ipopt16RegisteredOption20MakeValidLatexStringESsRSs_ZNK5Ipopt16RegisteredOption20MakeValidLatexNumberEd_ZNK5Ipopt16RegisteredOption22OutputLatexDescriptionERKNS_10JournalistE_ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessISsESaIS6_EE9_M_insertEPSt18_Rb_tree_node_baseSE_RKS6__ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessISsESaIS6_EE16_M_insert_uniqueERKS6__ZNSt8_Rb_treeISsSt4pairIKSsN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessISsESaIS6_EE16_M_insert_uniqueESt17_Rb_tree_iteratorIS6_ERKS6__ZNSt8_Rb_treeIiSt4pairIKiN5Ipopt8SmartPtrINS2_16RegisteredOptionEEEESt10_Select1stIS6_ESt4lessIiESaIS6_EE8_M_eraseEPSt13_Rb_tree_nodeIS6_E_ZNKSs5rfindEPKcmm_ZNSsC1ERKSsmm_ZSt20__throw_out_of_rangePKc_ZNSt6vectorIN5Ipopt16RegisteredOption12string_entryESaIS2_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS2_S4_EERKS2__ZN5Ipopt16RegisteredOption21AddValidStringSettingESsSs_ZN5Ipopt17RegisteredOptions25OPTION_ALREADY_REGISTEREDD0Ev_ZN5Ipopt17RegisteredOptions25OPTION_ALREADY_REGISTEREDD1Ev_ZN5Ipopt16RegisteredOption31ERROR_CONVERTING_STRING_TO_ENUMD0Ev_ZN5Ipopt16RegisteredOptionD1Ev_ZN5Ipopt16RegisteredOptionD0Ev_ZNSt3mapISsN5Ipopt8SmartPtrINS0_16RegisteredOptionEEESt4lessISsESaISt4pairIKSsS3_EEEixERS7__ZTIN5Ipopt17RegisteredOptions25OPTION_ALREADY_REGISTEREDE_ZTVN5Ipopt16RegisteredOption31ERROR_CONVERTING_STRING_TO_ENUME_ZTIN5Ipopt16RegisteredOption31ERROR_CONVERTING_STRING_TO_ENUME_ZN5Ipopt17RegisteredOptions16AddIntegerOptionERKSsS2_iS2__ZN5Ipopt17RegisteredOptions28AddUpperBoundedIntegerOptionERKSsS2_iiS2__ZN5Ipopt17RegisteredOptions27AddUpperBoundedNumberOptionERKSsS2_dbdS2__ZN5Ipopt17RegisteredOptions15AddStringOptionERKSsS2_S2_RKSt6vectorISsSaISsEES7_S2__ZN5Ipopt17RegisteredOptions16AddStringOption9ERKSsS2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2__ZN5Ipopt17RegisteredOptions16AddStringOption8ERKSsS2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2__ZN5Ipopt17RegisteredOptions16AddStringOption6ERKSsS2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2__ZN5Ipopt17RegisteredOptions16AddStringOption5ERKSsS2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2_S2__ZTSN5Ipopt17RegisteredOptions25OPTION_ALREADY_REGISTEREDE_ZTIN5Ipopt16RegisteredOptionE_ZTSN5Ipopt16RegisteredOptionE_ZTSN5Ipopt16RegisteredOption31ERROR_CONVERTING_STRING_TO_ENUMEsrand48drand48finite_ZN5Ipopt11IpBlasDtrsmEbiidPKdiPdi_ZN5Ipopt11IpBlasDsyrkEbiidPKdidPdidsyrk__ZN5Ipopt11IpBlasDgemmEbbiiidPKdiS1_idPdi_ZN5Ipopt11IpBlasDsymvEidPKdiS1_idPdidsymv__ZN5Ipopt11IpBlasDgemvEbiidPKdiS1_idPdi_ZN5Ipopt11IpBlasDscalEidPdi_ZN5Ipopt11IpBlasDaxpyEidPKdiPdi_ZN5Ipopt11IpBlasDasumEiPKdidasum__ZN5Ipopt11IpBlasDnrm2EiPKdi_ZN5Ipopt10IpBlasDdotEiPKdiS1_i_ZNK5Ipopt19CompoundMatrixSpace12GetBlockRowsEi_ZNSt13_Bvector_baseISaIbEED2Ev_ZNSt6vectorIbSaIbEE13_M_initializeEm_ZNK5Ipopt19CompoundMatrixSpace13DimensionsSetEv_ZNK5Ipopt19CompoundMatrixSpace12GetBlockColsEi_ZNSt6vectorIiSaIiEED1Ev_ZN5Ipopt14CompoundMatrix15SetCompNonConstEiiRNS_6MatrixE_ZNSt6vectorIN5Ipopt8SmartPtrINS0_6MatrixEEESaIS3_EEC1ERKS5__ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_6MatrixEEESaIS4_EED1Ev_ZSt26__uninitialized_fill_n_auxIPN5Ipopt8SmartPtrINS0_6MatrixEEEmS3_EvT_T0_RKT1_St12__false_type_ZNSt6vectorIbSaIbEEC1ERKS1__ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_6MatrixEEESaIS4_EEC1ERKS6__ZNK5Ipopt14CompoundMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNSt6vectorIN5Ipopt8SmartPtrINS0_6MatrixEEESaIS3_EEaSERKS5__ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_6MatrixEEESaIS4_EEaSERKS6__ZNSt6vectorIS_IN5Ipopt8SmartPtrIKNS0_6MatrixEEESaIS4_EESaIS6_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS6_S8_EERKS6__ZNSt6vectorIN5Ipopt8SmartPtrINS0_6MatrixEEESaIS3_EED1Ev_ZNSt6vectorIS_IN5Ipopt8SmartPtrINS0_6MatrixEEESaIS3_EESaIS5_EED1Ev_ZNSt6vectorIS_IN5Ipopt8SmartPtrINS0_6MatrixEEESaIS3_EESaIS5_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS5_S7_EERKS5__ZSt26__uninitialized_fill_n_auxIPN5Ipopt8SmartPtrIKNS0_6MatrixEEEmS4_EvT_T0_RKT1_St12__false_type_ZN5Ipopt7SubjectD2Ev_ZNSt6vectorIS_IbSaIbEESaIS1_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS1_S3_EERKS1__ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_11MatrixSpaceEEESaIS4_EED1Ev_ZNK5Ipopt14CompoundMatrix13MatricesValidEv_ZNK5Ipopt14CompoundMatrix19HasValidNumbersImplEv_ZSt26__uninitialized_fill_n_auxIPN5Ipopt8SmartPtrIKNS0_11MatrixSpaceEEEmS4_EvT_T0_RKT1_St12__false_type_ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_11MatrixSpaceEEESaIS4_EEC1ERKS6__ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_11MatrixSpaceEEESaIS4_EEaSERKS6__ZN5Ipopt14CompoundMatrix20CreateBlockFromSpaceEii_ZNK5Ipopt14CompoundMatrix18SinvBlrmZMTdBrImplEdRKNS_6VectorES3_S3_S3_RS1__ZNSt6vectorIS_IN5Ipopt8SmartPtrIKNS0_11MatrixSpaceEEESaIS4_EESaIS6_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS6_S8_EERKS6__ZNK5Ipopt14CompoundMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt14CompoundMatrix13AddMSinvZImplEdRKNS_6VectorES3_RS1__ZNK5Ipopt14CompoundMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZTVN5Ipopt19CompoundMatrixSpaceE_ZN5Ipopt19CompoundMatrixSpaceD0Ev_ZN5Ipopt19CompoundMatrixSpaceD1Ev_ZN5Ipopt19CompoundMatrixSpaceC2Eiiii_ZN5Ipopt6MatrixD2Ev_ZThn16_N5Ipopt14CompoundMatrixD0Ev_ZN5Ipopt14CompoundMatrixD0Ev_ZTVN5Ipopt14CompoundMatrixE_ZThn16_N5Ipopt14CompoundMatrixD1Ev_ZN5Ipopt14CompoundMatrixD1Ev_ZN5Ipopt14CompoundMatrixD2Ev_ZN5Ipopt14CompoundMatrixC2EPKNS_19CompoundMatrixSpaceE_ZN5Ipopt14CompoundMatrixC1EPKNS_19CompoundMatrixSpaceE_ZNK5Ipopt19CompoundMatrixSpace7MakeNewEv_ZTIN5Ipopt14CompoundMatrixE_ZTSN5Ipopt14CompoundMatrixE_ZTIN5Ipopt19CompoundMatrixSpaceE_ZTSN5Ipopt19CompoundMatrixSpaceE_ZNK5Ipopt17CompoundSymMatrix10NComps_DimEv_ZNK5Ipopt22CompoundSymMatrixSpace11GetBlockDimEi_ZNK5Ipopt22CompoundSymMatrixSpace13DimensionsSetEv_ZN5Ipopt17CompoundSymMatrix15SetCompNonConstEiiRNS_6MatrixE_ZNK5Ipopt17CompoundSymMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNSt6vectorIS_IN5Ipopt8SmartPtrIKNS0_11MatrixSpaceEEESaIS4_EESaIS6_EED1Ev_ZNK5Ipopt17CompoundSymMatrix13MatricesValidEv_ZNK5Ipopt17CompoundSymMatrix19HasValidNumbersImplEv_ZNK5Ipopt17CompoundSymMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZN5Ipopt22CompoundSymMatrixSpaceC2Eii_ZTVN5Ipopt22CompoundSymMatrixSpaceE_ZThn16_N5Ipopt17CompoundSymMatrixD0Ev_ZN5Ipopt17CompoundSymMatrixD0Ev_ZTVN5Ipopt17CompoundSymMatrixE_ZN5Ipopt17CompoundSymMatrixD2Ev_ZThn16_N5Ipopt17CompoundSymMatrixD1Ev_ZN5Ipopt17CompoundSymMatrixD1Ev_ZN5Ipopt17CompoundSymMatrixC1EPKNS_22CompoundSymMatrixSpaceE_ZNK5Ipopt22CompoundSymMatrixSpace16MakeNewSymMatrixEv_ZN5Ipopt17CompoundSymMatrixC2EPKNS_22CompoundSymMatrixSpaceE_ZN5Ipopt22CompoundSymMatrixSpaceD1Ev_ZN5Ipopt22CompoundSymMatrixSpaceD0Ev_ZTIN5Ipopt17CompoundSymMatrixE_ZNK5Ipopt9SymMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZTSN5Ipopt17CompoundSymMatrixE_ZTIN5Ipopt9SymMatrixE_ZTIN5Ipopt22CompoundSymMatrixSpaceE_ZTSN5Ipopt22CompoundSymMatrixSpaceE_ZNK5Ipopt19CompoundVectorSpace7MakeNewEv_ZN5Ipopt14CompoundVector12VectorsValidEv_ZNK5Ipopt6Vector5PrintENS_8SmartPtrIKNS_10JournalistEEENS_13EJournalLevelENS_16EJournalCategoryERKSsiS8__ZN5Ipopt19CompoundVectorSpaceD0Ev_ZSt26__uninitialized_fill_n_auxIPN5Ipopt8SmartPtrIKNS0_11VectorSpaceEEEmS4_EvT_T0_RKT1_St12__false_type_ZN5Ipopt19CompoundVectorSpaceD1Ev_ZTVN5Ipopt14CompoundVectorE_ZThn16_N5Ipopt14CompoundVectorD0Ev_ZN5Ipopt14CompoundVectorD0Ev_ZThn16_N5Ipopt14CompoundVectorD1Ev_ZN5Ipopt14CompoundVectorD1Ev_ZN5Ipopt14CompoundVectorC1EPKNS_19CompoundVectorSpaceEb_ZNK5Ipopt19CompoundVectorSpace21MakeNewCompoundVectorEb_ZTSN5Ipopt19CompoundVectorSpaceE_ZTSN5Ipopt14CompoundVectorE_ZTVN5Ipopt19DenseGenMatrixSpaceE_ZNK5Ipopt14DenseGenMatrix19HasValidNumbersImplEv_ZNK5Ipopt14DenseGenMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZN5Ipopt19DenseGenMatrixSpaceC2Eii_ZN5Ipopt19DenseGenMatrixSpaceD0Ev_ZN5Ipopt19DenseGenMatrixSpaceD1Ev_ZN5Ipopt14IpLapackDpotrsEiiPKdiPdi_ZN5Ipopt14IpLapackDpotrfEiPdiRi_ZNK5Ipopt14DenseGenMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt14DenseGenMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZN5Ipopt13IpLapackDsyevEbiPdiS0_Ri_ZThn16_N5Ipopt14DenseGenMatrixD0Ev_ZN5Ipopt14DenseGenMatrixD0Ev_ZTVN5Ipopt14DenseGenMatrixE_ZThn16_N5Ipopt14DenseGenMatrixD1Ev_ZN5Ipopt14DenseGenMatrixD1Ev_ZNK5Ipopt19DenseGenMatrixSpace7MakeNewEv_ZN5Ipopt14DenseGenMatrixC2EPKNS_19DenseGenMatrixSpaceE_ZN5Ipopt14DenseGenMatrixD2Ev_ZTIN5Ipopt14DenseGenMatrixE_ZTSN5Ipopt14DenseGenMatrixE_ZTIN5Ipopt19DenseGenMatrixSpaceE_ZTSN5Ipopt19DenseGenMatrixSpaceE_ZN5Ipopt19DenseSymMatrixSpaceC2Ei_ZTVN5Ipopt19DenseSymMatrixSpaceE_ZNK5Ipopt14DenseSymMatrix19HasValidNumbersImplEv_ZNK5Ipopt14DenseSymMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZN5Ipopt19DenseSymMatrixSpaceD1Ev_ZNK5Ipopt14DenseSymMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZN5Ipopt19DenseSymMatrixSpaceD0Ev_ZN5Ipopt14DenseSymMatrixD2Ev_ZTVN5Ipopt14DenseSymMatrixE_ZTVN5Ipopt9SymMatrixE_ZThn16_N5Ipopt14DenseSymMatrixD0Ev_ZN5Ipopt14DenseSymMatrixD0Ev_ZN5Ipopt14DenseSymMatrixC2EPKNS_19DenseSymMatrixSpaceE_ZNK5Ipopt19DenseSymMatrixSpace16MakeNewSymMatrixEv_ZThn16_N5Ipopt14DenseSymMatrixD1Ev_ZN5Ipopt14DenseSymMatrixD1Ev_ZTIN5Ipopt14DenseSymMatrixE_ZTSN5Ipopt14DenseSymMatrixE_ZTIN5Ipopt19DenseSymMatrixSpaceE_ZTSN5Ipopt19DenseSymMatrixSpaceE_ZN5Ipopt11DenseVector18ElementWiseSgnImplEv_ZNK5Ipopt11DenseVector11SumLogsImplEv_ZNK5Ipopt11DenseVector7SumImplEv_ZN5Ipopt11DenseVector13AddScalarImplEd_ZN5Ipopt11DenseVector19ElementWiseSqrtImplEv_ZN5Ipopt11DenseVector18ElementWiseAbsImplEv_ZN5Ipopt11DenseVector25ElementWiseReciprocalImplEv_ZNK5Ipopt11DenseVector8AmaxImplEv_ZNK5Ipopt11DenseVector8AsumImplEv_ZNK5Ipopt11DenseVector8Nrm2ImplEv_ZNK5Ipopt11DenseVector7DotImplERKNS_6VectorE_ZN5Ipopt11DenseVector8ScalImplEd_ZNK5Ipopt11DenseVector15FracToBoundImplERKNS_6VectorEd_ZNK5Ipopt11DenseVector7MinImplEv_ZNK5Ipopt11DenseVector9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNK5Ipopt11DenseVector7MaxImplEv_ZNK5Ipopt11DenseVector14ExpandedValuesEv_ZN5Ipopt11DenseVector7SetImplEd_ZN5Ipopt11DenseVector21AddVectorQuotientImplEdRKNS_6VectorES3_d_ZN5Ipopt11DenseVector17AddTwoVectorsImplEdRKNS_6VectorEdS3_d_ZN5Ipopt11DenseVector18ElementWiseMinImplERKNS_6VectorE_ZN5Ipopt11DenseVector18ElementWiseMaxImplERKNS_6VectorE_ZN5Ipopt11DenseVector23ElementWiseMultiplyImplERKNS_6VectorE_ZN5Ipopt11DenseVector21ElementWiseDivideImplERKNS_6VectorE_ZN5Ipopt11DenseVector8AxpyImplEdRKNS_6VectorE_ZN5Ipopt11DenseVector8CopyImplERKNS_6VectorE_ZNK5Ipopt11DenseVector11CopyFromPosEiRNS_6VectorE_ZN5Ipopt11DenseVector9CopyToPosEiRKNS_6VectorE_ZN5Ipopt11DenseVector9SetValuesEPKd_ZTVN5Ipopt11DenseVectorE_ZN5Ipopt11DenseVectorC2EPKNS_16DenseVectorSpaceE_ZThn16_N5Ipopt11DenseVectorD0Ev_ZN5Ipopt11DenseVectorD0Ev_ZThn16_N5Ipopt11DenseVectorD1Ev_ZN5Ipopt11DenseVectorD1Ev_ZN5Ipopt11DenseVectorD2Ev_ZTSN5Ipopt11DenseVectorE_ZNK5Ipopt10DiagMatrix19HasValidNumbersImplEv_ZNK5Ipopt10DiagMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZN5Ipopt10DiagMatrixC2EPKNS_14SymMatrixSpaceE_ZTVN5Ipopt10DiagMatrixE_ZN5Ipopt10DiagMatrixD2Ev_ZThn16_N5Ipopt10DiagMatrixD0Ev_ZN5Ipopt10DiagMatrixD0Ev_ZThn16_N5Ipopt10DiagMatrixD1Ev_ZN5Ipopt10DiagMatrixD1Ev_ZNK5Ipopt10DiagMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZTIN5Ipopt10DiagMatrixE_ZTSN5Ipopt10DiagMatrixE_ZNK5Ipopt6Matrix19HasValidNumbersImplEv_ZNK5Ipopt15ExpansionMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZN5Ipopt20ExpansionMatrixSpaceD1Ev_ZTVN5Ipopt20ExpansionMatrixSpaceE_ZN5Ipopt20ExpansionMatrixSpaceC2EiiPKii_ZN5Ipopt20ExpansionMatrixSpaceD0Ev_ZNK5Ipopt15ExpansionMatrix13AddMSinvZImplEdRKNS_6VectorES3_RS1__ZThn16_N5Ipopt15ExpansionMatrixD0Ev_ZN5Ipopt15ExpansionMatrixD0Ev_ZThn16_N5Ipopt15ExpansionMatrixD1Ev_ZN5Ipopt15ExpansionMatrixD1Ev_ZN5Ipopt15ExpansionMatrixD2Ev_ZN5Ipopt15ExpansionMatrixC2EPKNS_20ExpansionMatrixSpaceE_ZTVN5Ipopt15ExpansionMatrixE_ZNK5Ipopt15ExpansionMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt20ExpansionMatrixSpace7MakeNewEv_ZNK5Ipopt15ExpansionMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt15ExpansionMatrix18SinvBlrmZMTdBrImplEdRKNS_6VectorES3_S3_S3_RS1__ZTIN5Ipopt15ExpansionMatrixE_ZTSN5Ipopt15ExpansionMatrixE_ZTIN5Ipopt20ExpansionMatrixSpaceE_ZTSN5Ipopt20ExpansionMatrixSpaceE_ZNK5Ipopt14IdentityMatrix3DimEv_ZNK5Ipopt14IdentityMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt14IdentityMatrix19HasValidNumbersImplEv_ZNK5Ipopt14IdentityMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNK5Ipopt14IdentityMatrix13AddMSinvZImplEdRKNS_6VectorES3_RS1__ZThn16_N5Ipopt14IdentityMatrixD1Ev_ZN5Ipopt14IdentityMatrixD1Ev_ZN5Ipopt14IdentityMatrixD2Ev_ZThn16_N5Ipopt14IdentityMatrixD0Ev_ZN5Ipopt14IdentityMatrixD0Ev_ZN5Ipopt14IdentityMatrixC2EPKNS_14SymMatrixSpaceE_ZTVN5Ipopt14IdentityMatrixE_ZTIN5Ipopt14IdentityMatrixE_ZTSN5Ipopt14IdentityMatrixEdsyev__ZNK5Ipopt22LowRankUpdateSymMatrix19HasValidNumbersImplEv_ZThn16_N5Ipopt22LowRankUpdateSymMatrixD1Ev_ZN5Ipopt22LowRankUpdateSymMatrixD1Ev_ZTVN5Ipopt22LowRankUpdateSymMatrixE_ZN5Ipopt22LowRankUpdateSymMatrixD2Ev_ZNK5Ipopt22LowRankUpdateSymMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNK5Ipopt22LowRankUpdateSymMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt17MultiVectorMatrix12LRMultVectorEdRKNS_6VectorEdRS1__ZThn16_N5Ipopt22LowRankUpdateSymMatrixD0Ev_ZN5Ipopt22LowRankUpdateSymMatrixD0Ev_ZN5Ipopt22LowRankUpdateSymMatrixC2EPKNS_27LowRankUpdateSymMatrixSpaceE_ZTIN5Ipopt22LowRankUpdateSymMatrixE_ZTSN5Ipopt22LowRankUpdateSymMatrixE_ZThn16_N5Ipopt6MatrixD0Ev_ZN5Ipopt6MatrixD0Ev_ZThn16_N5Ipopt6MatrixD1Ev_ZN5Ipopt6MatrixD1Ev_ZTSN5Ipopt6MatrixE_ZN5Ipopt22MultiVectorMatrixSpaceC2EiRKNS_11VectorSpaceE_ZTVN5Ipopt22MultiVectorMatrixSpaceE_ZN5Ipopt22MultiVectorMatrixSpaceD0Ev_ZN5Ipopt22MultiVectorMatrixSpaceD1Ev_ZNK5Ipopt22MultiVectorMatrixSpace7MakeNewEv_ZN5Ipopt17MultiVectorMatrixC2EPKNS_22MultiVectorMatrixSpaceE_ZN5Ipopt17MultiVectorMatrix12ScaleColumnsERKNS_6VectorE_ZN5Ipopt17MultiVectorMatrix9ScaleRowsERKNS_6VectorE_ZN5Ipopt17MultiVectorMatrix18FillWithNewVectorsEv_ZTIN5Ipopt22MultiVectorMatrixSpaceE_ZTSN5Ipopt22MultiVectorMatrixSpaceE_ZNK5Ipopt12ScaledMatrix19HasValidNumbersImplEv_ZNK5Ipopt12ScaledMatrix18SinvBlrmZMTdBrImplEdRKNS_6VectorES3_S3_S3_RS1__ZN5Ipopt17ScaledMatrixSpaceD0Ev_ZTVN5Ipopt17ScaledMatrixSpaceE_ZN5Ipopt17ScaledMatrixSpaceC2ERKNS_8SmartPtrIKNS_6VectorEEEbRKNS1_IKNS_11MatrixSpaceEEES6_b_ZNK5Ipopt12ScaledMatrix13AddMSinvZImplEdRKNS_6VectorES3_RS1__ZN5Ipopt17ScaledMatrixSpaceD1Ev_ZTVN5Ipopt12ScaledMatrixE_ZNK5Ipopt17ScaledMatrixSpace7MakeNewEv_ZNK5Ipopt12ScaledMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt12ScaledMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNK5Ipopt12ScaledMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZN5Ipopt12ScaledMatrixC2EPKNS_17ScaledMatrixSpaceE_ZThn16_N5Ipopt12ScaledMatrixD0Ev_ZN5Ipopt12ScaledMatrixD0Ev_ZThn16_N5Ipopt12ScaledMatrixD1Ev_ZN5Ipopt12ScaledMatrixD1Ev_ZN5Ipopt12ScaledMatrixD2Ev_ZTIN5Ipopt12ScaledMatrixE_ZTSN5Ipopt12ScaledMatrixE_ZTIN5Ipopt17ScaledMatrixSpaceE_ZTSN5Ipopt17ScaledMatrixSpaceE_ZNK5Ipopt9SumMatrix6NTermsEv_ZNK5Ipopt9SumMatrix19HasValidNumbersImplEv_ZN5Ipopt9SumMatrix7SetTermEidRKNS_6MatrixE_ZN5Ipopt14SumMatrixSpaceD0Ev_ZTVN5Ipopt14SumMatrixSpaceE_ZNK5Ipopt9SumMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNK5Ipopt9SumMatrix7GetTermEiRdRNS_8SmartPtrIKNS_6MatrixEEE_ZN5Ipopt14SumMatrixSpaceD1Ev_ZNK5Ipopt14SumMatrixSpace12GetTermSpaceEi_ZNK5Ipopt9SumMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_11MatrixSpaceEEESaIS4_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS4_S6_EERKS4__ZN5Ipopt14SumMatrixSpace12SetTermSpaceEiRKNS_11MatrixSpaceE_ZN5Ipopt9SumMatrixC2EPKNS_14SumMatrixSpaceE_ZTVN5Ipopt9SumMatrixE_ZN5Ipopt9SumMatrixD2Ev_ZN5Ipopt9SumMatrixC1EPKNS_14SumMatrixSpaceE_ZNK5Ipopt14SumMatrixSpace16MakeNewSumMatrixEv_ZNK5Ipopt14SumMatrixSpace7MakeNewEv_ZThn16_N5Ipopt9SumMatrixD0Ev_ZN5Ipopt9SumMatrixD0Ev_ZThn16_N5Ipopt9SumMatrixD1Ev_ZN5Ipopt9SumMatrixD1Ev_ZNK5Ipopt9SumMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZTIN5Ipopt14SumMatrixSpaceE_ZTIN5Ipopt9SumMatrixE_ZTSN5Ipopt14SumMatrixSpaceE_ZTSN5Ipopt9SumMatrixE_ZNK5Ipopt12SumSymMatrix6NTermsEv_ZNK5Ipopt12SumSymMatrix19HasValidNumbersImplEv_ZSt26__uninitialized_fill_n_auxIPN5Ipopt8SmartPtrIKNS0_9SymMatrixEEEmS4_EvT_T0_RKT1_St12__false_type_ZNK5Ipopt12SumSymMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZN5Ipopt17SumSymMatrixSpaceD0Ev_ZNK5Ipopt17SumSymMatrixSpace12GetTermSpaceEi_ZN5Ipopt17SumSymMatrixSpaceD1Ev_ZNSt6vectorIN5Ipopt8SmartPtrIKNS0_14SymMatrixSpaceEEESaIS4_EE13_M_insert_auxEN9__gnu_cxx17__normal_iteratorIPS4_S6_EERKS4__ZNK5Ipopt12SumSymMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZN5Ipopt12SumSymMatrixC1EPKNS_17SumSymMatrixSpaceE_ZTVN5Ipopt12SumSymMatrixE_ZNK5Ipopt17SumSymMatrixSpace16MakeNewSymMatrixEv_ZThn16_N5Ipopt12SumSymMatrixD0Ev_ZN5Ipopt12SumSymMatrixD0Ev_ZThn16_N5Ipopt12SumSymMatrixD1Ev_ZN5Ipopt12SumSymMatrixD1Ev_ZN5Ipopt12SumSymMatrixD2Ev_ZN5Ipopt12SumSymMatrixC2EPKNS_17SumSymMatrixSpaceE_ZTIN5Ipopt17SumSymMatrixSpaceE_ZTSN5Ipopt17SumSymMatrixSpaceE_ZTSN5Ipopt12SumSymMatrixE_ZThn16_N5Ipopt9SymMatrixD1Ev_ZN5Ipopt9SymMatrixD1Ev_ZThn16_N5Ipopt9SymMatrixD0Ev_ZN5Ipopt9SymMatrixD0Ev_ZTSN5Ipopt9SymMatrixE_ZNK5Ipopt15SymScaledMatrix19HasValidNumbersImplEv_ZNK5Ipopt15SymScaledMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZThn16_N5Ipopt15SymScaledMatrixD0Ev_ZN5Ipopt15SymScaledMatrixD0Ev_ZTVN5Ipopt15SymScaledMatrixE_ZN5Ipopt15SymScaledMatrixD2Ev_ZThn16_N5Ipopt15SymScaledMatrixD1Ev_ZN5Ipopt15SymScaledMatrixD1Ev_ZNK5Ipopt15SymScaledMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZN5Ipopt15SymScaledMatrixC2EPKNS_20SymScaledMatrixSpaceE_ZTIN5Ipopt15SymScaledMatrixE_ZTSN5Ipopt15SymScaledMatrixE_ZNK5Ipopt10ZeroMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNK5Ipopt10ZeroMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZThn16_N5Ipopt10ZeroMatrixD1Ev_ZN5Ipopt10ZeroMatrixD1Ev_ZN5Ipopt10ZeroMatrixC1EPKNS_11MatrixSpaceE_ZTVN5Ipopt10ZeroMatrixE_ZN5Ipopt10ZeroMatrixD2Ev_ZThn16_N5Ipopt10ZeroMatrixD0Ev_ZN5Ipopt10ZeroMatrixD0Ev_ZN5Ipopt10ZeroMatrixC2EPKNS_11MatrixSpaceE_ZNK5Ipopt10ZeroMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZTIN5Ipopt10ZeroMatrixE_ZTSN5Ipopt10ZeroMatrixE_ZNK5Ipopt15GenTMatrixSpace19FreeInternalStorageEPd_ZNK5Ipopt15GenTMatrixSpace23AllocateInternalStorageEv_ZNK5Ipopt10GenTMatrix19HasValidNumbersImplEv_ZNK5Ipopt10GenTMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZN5Ipopt15GenTMatrixSpaceD0Ev_ZTVN5Ipopt15GenTMatrixSpaceE_ZN5Ipopt15GenTMatrixSpaceD1Ev_ZN5Ipopt15GenTMatrixSpaceC2EiiiPKiS2__ZThn16_N5Ipopt10GenTMatrixD1Ev_ZN5Ipopt10GenTMatrixD1Ev_ZTVN5Ipopt10GenTMatrixE_ZN5Ipopt10GenTMatrixD2Ev_ZN5Ipopt10GenTMatrixC1EPKNS_15GenTMatrixSpaceE_ZNK5Ipopt15GenTMatrixSpace7MakeNewEv_ZN5Ipopt10GenTMatrixC2EPKNS_15GenTMatrixSpaceE_ZThn16_N5Ipopt10GenTMatrixD0Ev_ZN5Ipopt10GenTMatrixD0Ev_ZN5Ipopt10GenTMatrix9SetValuesEPKd_ZNK5Ipopt10GenTMatrix19TransMultVectorImplEdRKNS_6VectorEdRS1__ZNK5Ipopt10GenTMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZTIN5Ipopt10GenTMatrixE_ZTSN5Ipopt10GenTMatrixE_ZTIN5Ipopt15GenTMatrixSpaceE_ZTSN5Ipopt15GenTMatrixSpaceE_ZNK5Ipopt10SymTMatrix6ValuesEv_ZNK5Ipopt15SymTMatrixSpace19FreeInternalStorageEPd_ZNK5Ipopt15SymTMatrixSpace23AllocateInternalStorageEv_ZNK5Ipopt10SymTMatrix19HasValidNumbersImplEv_ZNK5Ipopt10SymTMatrix10FillValuesEPd_ZNK5Ipopt10SymTMatrix9PrintImplERKNS_10JournalistENS_13EJournalLevelENS_16EJournalCategoryERKSsiS7__ZNK5Ipopt10SymTMatrix10FillStructEPiS1__ZN5Ipopt15SymTMatrixSpaceD2Ev_ZTVN5Ipopt15SymTMatrixSpaceE_ZN5Ipopt15SymTMatrixSpaceD0Ev_ZN5Ipopt15SymTMatrixSpaceC2EiiPKiS2__ZN5Ipopt15SymTMatrixSpaceD1Ev_ZN5Ipopt10SymTMatrixD2Ev_ZTVN5Ipopt10SymTMatrixE_ZN5Ipopt10SymTMatrixC1EPKNS_15SymTMatrixSpaceE_ZNK5Ipopt15SymTMatrixSpace16MakeNewSymMatrixEv_ZThn16_N5Ipopt10SymTMatrixD0Ev_ZN5Ipopt10SymTMatrixD0Ev_ZThn16_N5Ipopt10SymTMatrixD1Ev_ZN5Ipopt10SymTMatrixD1Ev_ZN5Ipopt10SymTMatrixC2EPKNS_15SymTMatrixSpaceE_ZNK5Ipopt10SymTMatrix14MultVectorImplEdRKNS_6VectorEdRS1__ZN5Ipopt10SymTMatrix9SetValuesEPKd_ZTIN5Ipopt15SymTMatrixSpaceE_ZTIN5Ipopt10SymTMatrixE_ZTSN5Ipopt15SymTMatrixSpaceE_ZTSN5Ipopt10SymTMatrixE_ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_10GenTMatrixEiiPiS4__ZN5Ipopt13TripletHelper11FillValues_EiRKNS_10GenTMatrixEPd_ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_10SymTMatrixEiiPiS4__ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_10DiagMatrixEiiPiS4__ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_14IdentityMatrixEiiPiS4__ZN5Ipopt13TripletHelper11FillValues_EiRKNS_14IdentityMatrixEPd_ZN5Ipopt13TripletHelper11FillRowCol_EiRKNS_15ExpansionMatrixEiiPiS4__ZN5Ipopt13TripletHelper11FillValues_EiRKNS_15ExpansionMatrixEPd_ZN5Ipopt13TripletHelper11FillValues_EiRKNS_10SymTMatrixEPd_ZN5Ipopt13TripletHelper19UNKNOWN_VECTOR_TYPED1Ev_ZN5Ipopt13TripletHelper19UNKNOWN_MATRIX_TYPED0Ev_ZN5Ipopt13TripletHelper19UNKNOWN_VECTOR_TYPED0Ev_ZN5Ipopt13TripletHelper17GetNumberEntries_ERKNS_9SumMatrixE_ZN5Ipopt13TripletHelper17GetNumberEntries_ERKNS_12SumSymMatrixE_ZN5Ipopt13TripletHelper17GetNumberEntries_ERKNS_14CompoundMatrixE_ZN5Ipopt13T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H,HzHH $)c $6I*AIDD9l$<\$Ht$H|$Hf(D $=l $Eu#D$4HT$(|$<D$8HT$ ؉L5і>IITp )*f.2f."s-H,1f(H\H,H1\HH,H1D$8HL$ tf. uIzGH9>IcHL$ HHPH@HT$(1HH[]A\A]A^A_fW1f.ztH=z>f(H5Mu >~)~>H=*>12:H9ÅÐ N>~)B>H=>12:H9ÅÐHAHffffff.SHHĔx[GH >8HAr[֍fDH>1SHHHy>H1?xYH=>t<t%H5pH=>1s;[rHU;x@Hy>[Ð[HH>SHu [Ðt#H=>H51s;[<[H>SLEHH4>D e[H\$Hl$HLd$H(ID$ Xx%Ht$ A$HNH\$Hl$Ld$ H(A$dfSHHHHHHL$ D$ jxH[Ð)H[HHHHL$HD$|Hfff.HHt$D$jtHDH\$Ld$H=ύLl$HH_>HА>38OL-I>3H=2AEOL%1>3H=A$NHa>3H=AXEAX$H$Ld$Ll$HNH\$Hl$HLd$HL%>HA$tjH)>tKt4H=g>H51;q;Hl$H$Ld$H颊Vy萊H1FA$H$Hl$Ld$Hff.H\$Hl$HH>HL̓8v0H!>H >*XH\$Hl$HfYyljEH\$Hl$Hffffff.H\$Hl$HLd$Ll$MLt$L|$HxH|$0I1IILYH>;tRH$H=>H5\1o[H\$HHl$PLd$XLl$`Lt$hL|$pHxH$H$IMLLHHD$H$HT$LH$KH|$ 1XH*D$(H*L$ H>H*T$0H>H m>^XH*D$8^XX\AE*XL$L$LLLLIXx)H$OH$lj19H$ljfH\$Hl$HLd$Ll$MLt$L|$HxH|$0I1IILWH>0;tRH$H=C>H51n苇H\$HHl$PLd$XLl$`Lt$hL|$pHxH$H$IMLLHHD$H$HT$LH$苚H|$ 1WH*D$(H*L$ IH">H*T$0Hԏ>H >^XH*D$8^XX\AE*XL$L$LLLL9Y^H$ljpHff.H\$Hl$LLd$Ll$ILt$L|$H8HA>E1=>IIHL8HZ>Hi>A=>IH >H>H5i>H=>nLLL>>H>L8EtIt3H\$Hl$Ld$Ll$ Lt$(L|$0H8NHHM;xHV>H\$Hl$Ld$Ll$ Lt$(L|$0H8H=>H5w1HstH=z>H531tHOffffff.SH1H H|$TH>ufH=x11/HH1TH*D$H* $H>H*T$^XH*D$^XX\H [Ãt+H=Ő>H51jH [1bi\ff.UH1SHH(H|$S;tmH>ugH=φ11FGH1SH*D$H* $H>H*T$^XH*D$^XX\H([]Ãt-H=ۏ>H5$1iE$H([]HUEdXfffff.H\$Hl$HLd$H8HH|$1IRH >u{H=11KFH1RH*D$H* $H>H*T$^XH*D$^XX\H\$ Hl$(Ld$0H8t+H=̎>H51hA$fHHVA$Nf@ff.UH1SHH(H|$QH >ukH=T11KEH1QH*D$H* $H>H*T$^XH*D$^XX\H([]@t-H=܍>H5%1gE%H([]HV_E`Tffffff.UH1SHH(H|$PH>ukH=11[DH1PH*D$H* $H>H*T$^XH*D$^XX\H([]@t-H=>H551fE5H([]HVE`TH>fDH>H0H>H8Hu>fffff.H=у>LJ>1SLH! t€DHAHDHL)9HJ>H?H__1[BH2HHH=>1HfHH=e>1X1HHH=>8t1HH=2>轒1HfDHH >8t1HH=>1HfDH\$Hl$1Ld$Ll$Lt$H(AHImL- >AEu"kH5|HHL1dEu1]H$Hl$1Ld$Ll$Lt$ H(ÐH\$Hl$HH->HMt7HR>Eu H=p>諑H\$Hl$1HH=S>fff.H\$Ld$HHl$HH+>I8H>HInternalI error iIn OOC MaInagementI layer (H_dmumps_Hpost_semHLXLPLHL@ Hx(Hp0f@8) @:H$Hl$Ld$HH-N>H.tH誐1fDLȎfDHl$Ld$HH\$HH;>I8H>IInternalH error iHn OOC MaHnagementH layer (I_dmumps_H_get_semLHxHpHHHP L`(Hh0f@8) @:H$Hl$Ld$HH^>H>EHA$迏1ff.ATHo>IUHS8t|HK>I error iHn OOC MaHnagementHInternalI layer (I_dmumps_Iwait_semHL`HhHXLX LP(LH0f@8) @:[]A\H>HEuHL-~EtH߉E[]A\1ÐHH>H>DE~^H~>?1LHcH@HI9xu4Agfff))HcH@HI9xtD9u1HHp0Hx`A1Hff.USHH>H->EH|>Hh~>1H8&H1@EtH- ~>H}YHy}>H8YHb~>H8rYH1[]H-Z>}uH-}>hH}H0<HH}HhH0H貇Hӄ>H-!H5с>H=R>H5>H=>H{>H=>ZNB6躉#DH\$Hl$1Ld$HHUL%}>A$L>H (|>H=>IcHH4H1E;AgfffA4$)H>()хAt8H}>ttAHl$H$Ld$HH=z>>SH=z>轋H~}>uH5g>H=>{HJ>Hn OOC MaIInternalH error iInagementI layer (Idmumps_cIlean_reqHuest_th)HXL HhLXLP LH(L@0Hx8f@@ $AUE1ATUSHL%4|>A$t~Hl$H7vD$t.H\$H vD$tH UtH[]A\A]A$u!H=by>蝊A$H1[]A\A]1EuH[]A\A]H=/y>AׄA$ff.H\$Hl$1Ld$Ll$H(HIKStH\$Hl$Ld$Ll$ H(L-x>LkHt>;;8Hk~>DEH>gfffDHy>G\LDD)ʍA)IcA;<uH\|>DANH>DHy>LDD1)D)HH@HA;|u:B gfff))HcHIHA;|D9uH>Hest_requHInternalH error iHn OOC MaHnagementI layer (Idmumps_tHest_th (Hh0H8HpHHHPLh L`(HX8@@1)) @DKA9A$Hy>1<L6 /A$fE~DD1)Dȍ)HA;IInternalI error iIn OOC MaInagementH layer (Hdmumps_tHest_requHest_th (LLPLHL@Hx Hp(HH0HP8@@2)) @D fffff.USHHHx>D$8t%Hl$HHGu DL$EtH[]Hl$HGuDD$EuH OHHGfffff.Ld$Lt$1L|$H\$IHl$Ll$HHHT$HL$ML$OAHD$`ED L-x>AE1:A}H-t>H苀Hx>3&H=>H;~>HcVH=t>H\$H@HH v>H4HAыL|TH$D(DHD$H\ HDH\$XD,A}H\$PBHeDH\$Hl$ Ld$(Ll$0Lt$8L|$@HHH-s>HHav>1|9Hw>;fDH ^HV;tH=\>gfffDADD)ʍA)DHD$PD`H5+z>BH= x>`xH54|>H= z>89H}>In OOC MaIInternalI error iHnagementH layer (Idmumps_aIsync_reaL`AL0LhHhHX LX(LP0@8d_thf@<) @>zffffff.Ld$Lt$1L|$H\$IHl$Ll$HHHT$HL$ML$LAHD$`ED L-@u>A]17A}H-r>H}Hv>36H=>Hk{>HcVH=*r>H\$H@HHH4HAыL|TH$D(DHD$H\ HDH\$XD,A}HD$PBH蕂AEt @DH\$Hl$ Ld$(Ll$0Lt$8L|$@HHH-q>H|H|s>16Ht>;H [Hv|;tH=|>gfffDADD)ʍA)DH5Xw>H=Au>u/D`H5[y>H=4w>/50H;z>IInternalI error iIn OOC MaInagementH layer (Hdmumps_aHsync_wriHte_th) ALLPLHL@Hx Hp(HH0HP8fATUSHHo>Hl$ HT{L%s>H3KuKH7{A$u H@u>t$H誀HkD$ u 0ZH膀1H[]A\ff.AUI1ATUSHHro>L%r>H=p>Hx>A$H}>Hr>HXw>Hcw>H~t>Hiu>Hq>H_q>HH9s>;AUH=bn>1kE1\ ?A<$H-o>HEb>H#o>H>H HHp>HDHH(uA$t'AUH=w>H5Eo1Q@Har>H=s>1Ht>Hy>(HIt>MH=/t>1MH=Iv>1LH=;y>1LH=w>16DHs>H=n>11VUHo>H1H[]A\A]Ht>H=n>11dVHx01hrLHE@`HH}1H0ULHED`HhH uSH=xv>H5 n1RPrH Yv>HnAXNHfffff.AWAVAUATUSHXL-#o>L=p>HD$0L5n>HD$HD$ HD$HD$HHD$HD$LH$H|$1:9A}dH|$1#9AhH*D$(^QH*L$ H*T$0A}XH*D$8^+AXX\An|$HL%t>Ic$H@HHl>HHs(PHK HSHsH{L $LC,#;pA}H-j>HvH5s>Hk>HcHCHl>HcHgfff)Hnp>Hn>)щ(A$A}A$H{H5as>H=:q>uojH*D$(^H*l>H*T$ A}AH*XH*@^X\AHt$H=iq>f|tH=k1*S5HK HSHsH{L $LC,61V@A}A$ Hs0H{`nH5

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The larger this value the more detailed is the output.NOTE: This option only works when read from the ipopt.opt options file! An output file with this name will be written (leave unset for no file output). The verbosity level is by default set to "print_level", but can be overridden with "file_print_level". The file name is changed to use only small letters.Any acceptable standard file nameFile name of desired output file (leave unset for no file output).NOTE: This option only works when read from the ipopt.opt options file! Determines the verbosity level for the file specified by "output_file". By default it is the same as "print_level".Verbosity level for output file.If selected, the algorithm will print the list of all options set by the user including their values and whether they have been used. In some cases this information might be incorrect, due to the internal program flow.Print all options set by the user.If selected, the algorithm will print the list of all available algorithmic options with some documentation before solving the optimization problem.Switch to print all algorithmic options.If selected, the program will print the CPU usage (user time) for selected tasks.Switch to print timing statistics.Exception of type: %s in file "%s" at line %d: Exception message: %s Caught unknown Ipopt exceptionUnknown Exception caught in ipoptquality_function_max_section_stepswatchdog_shortened_iter_triggerexpect_infeasible_problem_ctolsoft_resto_pderror_reduction_factorrequired_infeasibility_reductionevaluate_orig_obj_at_resto_trial List of user-set options: %sstatus != TOO_FEW_DEGREES_OF_FREEDOMToo few degrees of freedom (rethrown)! Number of Iterations....: %d (scaled) (unscaled) Objective...............: %24.16e %24.16e Dual infeasibility......: %24.16e %24.16e Constraint violation....: %24.16e %24.16e Complementarity.........: %24.16e %24.16e Overall NLP error.......: %24.16e %24.16e Number of objective function evaluations = %d Number of objective gradient evaluations = %d Number of equality constraint evaluations = %d Number of inequality constraint evaluations = %d Number of equality constraint Jacobian evaluations = %d Number of inequality constraint Jacobian evaluations = %d Number of Lagrangian Hessian evaluations = %d Total CPU secs in IPOPT (w/o function evaluations) = %10.3f Total CPU secs in NLP function evaluations = %10.3f EXIT: Optimal Solution Found. EXIT: Maximum Number of Iterations Exceeded. EXIT: Search Direction is becoming Too Small. EXIT: Solved To Acceptable Level. EXIT: Iterates divering; problem might be unbounded. EXIT: Error in step computation (regularization becomes too large?)! EXIT: Converged to a point of local infeasibility. Problem may be infeasible. EXIT: Stopping optimization at current point as requested by user. EXIT: Invalid number in NLP function or derivative detected. EXIT: INTERNAL ERROR: Unknown SolverReturn value - Notify IPOPT Authors. EXIT: Problem has too few degrees of freedom. EXIT: Invalid option encountered. EXIT: Problem as only fixed variables and constraints are infeasible. EXIT: Some uncaught Ipopt exception encountered. Unknown Exception caught in IpoptReOptimizeNLP called before OptimizeNLP.ReOptimizeTNLP called for different NLP.ReOptimizeTNLP called before OptimizeTNLP.ReOptimizeTNLP called for different TNLP.N5Ipopt16IpoptApplicationEN5Ipopt11TOO_FEW_DOFEN5Ipopt14IpoptExceptionEN5Ipopt17INVALID_WARMSTARTEN5Ipopt23IPOPT_APPLICATION_ERROREN5Ipopt11OptionsListEN5Ipopt17RegisteredOptionsEN5Ipopt14OPTION_INVALIDEN5Ipopt30NO_FREE_VARIABLES_BUT_FEASIBLEEN5Ipopt32NO_FREE_VARIABLES_AND_INFEASIBLEEN5Ipopt15SolveStatisticsEipopt.optN5Ipopt24INVALID_STDINTERFACE_NLPEError in IpStdFInterface eval_hess!Error in IpStdFInterface eval_jac_g!INVALID_STDINTERFACE_NLPn_var_>0IpStdInterfaceTNLP.cppn_con_>=0x_L_x_U_g_L_ || n_con_==0g_U_ || n_con_==0nele_jac_>=0nele_hess_>=0start_x_eval_f_eval_g_eval_grad_f_eval_jac_g_eval_h_The number of variables must be at least 1.The number of constrains must be non-negative.No lower bounds for variables provided.No upper bounds for variables provided.No lower bounds for constraints provided.No upper bounds for constraints provided.Number of non-zero elements in constraint Jacobian must be non-negative.Number of non-zero elements in Hessian of Lagrangian must be non-negative.index_style_ == 0 || index_style_ == 1Valid index styles are 0 (C style) or 1 (Fortran style)No initial point for the variables provided.No callback function for evaluating the value of objective function provided.No callback function for evaluating the values of constraints provided.No callback function for evaluating the gradient of objective function provided.No callback function for evaluating the Jacobian of the constraints provided.No callback function for evaluating the Hessian of the constraints provided.N5Ipopt16StdInterfaceTNLPEN5Ipopt4TNLPEConstraint violation for ALL constraints is %e. The TNLP passed to TNLPAdapter is NULL. This MUST be a valid TNLP!You have set options for user provided scaling, but have not implemented GetScalingParameters in the NLP interfaceNo dependency_detector_ object available in TNLPAdapter::DetermineDependentConstraintsNo Journalist given to TNLPAdapter. Need Journalist, otherwise can't produce any output in DerivativeChecker! Starting derivative checker. In TNLP derivative test: f could not be evaluated at reference point.In TNLP derivative test: g could not be evaluated at reference point.In TNLP derivative test: grad_f could not be evaluated at reference point.In TNLP derivative test: Jacobian structure could not be evaluated.In TNLP derivative test: Jacobian values could not be evaluated at reference point.In TNLP derivative test: f could not be evaluated at perturbed point.%c grad_f[ %5d] = %23.16e ~ %23.16e [%10.3e] %c jac_g [%5d,%5d] = %23.16e %c ~ %23.16e [%10.3e] In TNLP derivative test: Hessian structure could not be evaluated.In TNLP derivative test: Hessian could not be evaluated at reference point.In TNLP derivative test: grad_f could not be evaluated at perturbed point.%c obj_hess[%5d,%5d] = %23.16e %c ~ %23.16e [%10.3e] %c %5d-th constr_hess[%5d,%5d] = %23.16e %c ~ %23.16e [%10.3e] No errors detected by derivative checker. Derivative checker detected %d error(s). TNLP's get_number_of_nonlinear_variables returns non-negative number, but get_list_of_nonlinear_variables returns false. get_list_of_nonlinear_variables has not been overwrittenOption nlp_scaling_method selected as user-scaling, but no user-scaling available, or it cannot be computed. User scaling chosen, but get_scaling_parameters returned false.warm_start_same_structure chosen, but TNLPAdapter is called for the first time.Reusing previous information for warm start in TNLPAdapter. !warm_start_same_structure_ || (n_full_x == n_full_x_ && n_full_g == n_full_g_ && nz_full_jac_g == nz_full_jac_g_ && nz_full_h == nz_full_h_)warm_start_same_structure chosen, but problem dimensions are different.There are inconsistent bounds on variable %d: lower = %25.16e and upper = %25.16e.There are inconsistent bounds on constraint %d: lower = %25.16e and upper = %25.16e.Too few degrees of freedom (n_x = %d, n_c = %d). Trying fixed_variable_treatment = RELAX_BOUNDS All variables are fixed, but constraints cannot be evaluated at fixed point.All variables are fixed, but objective cannot be evaluated at fixed point.All variables are fixed, and constraint violation is %eAll variables are fixed and constraint violation %e is below tolerance %e. Declaring success. NO_FREE_VARIABLES_BUT_FEASIBLEAll variables are fixed and constraint violation %e is above tolerance %e. Declaring that problem is infeasible. NO_FREE_VARIABLES_AND_INFEASIBLEDependent constraint detector had a problem, assume full rank. Detected %d linearly dependent equality constraints; taking those out. No dependent constraints detected. List of indices of dependent constraints: Option hessian_information is not chosen as limited_memory, but eval_h returns false. eval_h is called but has not been implementedNumber of nonzeros in equality constraint Jacobian...:%9d Number of nonzeros in inequality constraint Jacobian.:%9d Number of nonzeros in Lagrangian Hessian.............:%9d nlp_lower_bound_inf_ < nlp_upper_bound_inf_Option "nlp_lower_bound_inf" must be smaller than "nlp_upper_bound_inf".Ipopt has not been compiled with WSMP. You cannot choose "wsmp" for "dependency_detector".Ipopt has not been compiled with MA28. You cannot choose "ma28" for "dependency_detector".Something internally wrong for "dependency_detector".any bound less or equal this value will be considered -inf (i.e. not lower bounded).any bound greater or this value will be considered +inf (i.e. not upper bounded).The main difference between those options is that the starting point in the "make_constraint" case still has the fixed variables at their given values, whereas in the case "make_parameter" the functions are always evaluated with the fixed values for those variables. Also, for "relax_bounds", the fixing bound constraints are relaxed (according to" bound_relax_factor"). For both "make_constraints" and "relax_bounds", bound multipliers are computed for the fixed variables.Relax fixing bound constraintsAdd equality constraints fixing variablesRemove fixed variable from optimization variablesDetermines how fixed variables should be handled.The default and available choices depend on how Ipopt has been compiled.don't check; no extra work at beginningIndicates which linear solver should be used to detect linearly dependent equality constraints.Indicates if the right hand sides of the constraints should be considered during dependency detectionIf a random perturbation of a points is required, this number indicates the maximal perturbation. Currently, this is only used when we perturb the initial point in order to get a random Jacobian for the linear dependency detection of equality constraints.Maximal perturbation of an evaluation point.If this option is enabled, a (slow) derivative test will be performed before the optimization. The test is performed at the user provided starting point and marks derivative values that seem suspiciousperform test of first and second derivatives at starting pointperform test of first derivatives at starting pointdo not perform derivative testThis determines the relative perturbation of the variable entries.Size of the finite difference perturbation in derivative test.If the relative deviation of the estimated derivative from the given one is larger than this value, the corresponding derivative is marked as wrong.Threshold for indicating wrong derivative.Determines verbosity of derivative checker.Print only suspect derivativesIndicates whether information for all estimated derivatives should be printed.IsValid(tnlp_)IpTNLPAdapter.cppINVALID_TNLPIpNLP.hppUSER_SCALING_NOT_IMPLEMENTEDIsValid(dependency_detector_)OPTION_INVALIDIsValid(jnlst_)ERROR_IN_TNLP_DERIVATIVE_TESTfull_x_Eval_Errorc_dep[%d] = %d warm_start_same_structurepoint_perturbation_radiusdependency_detection_with_rhsdependency_detectornonemumpswsmpma28relax_boundsmake_constraintmake_parameteruse MA28use WSMPuse MUMPSalso consider right hand sideonly look at gradientssecond-orderfirst-orderEnable derivative checkerPrint all derivativesN5Ipopt11TNLPAdapterEN5Ipopt3NLPEN5Ipopt11TNLPAdapter29ERROR_IN_TNLP_DERIVATIVE_TESTEN5Ipopt11TNLPAdapter12INVALID_TNLPEN5Ipopt16DenseVectorSpaceEN5Ipopt11VectorSpaceEN5Ipopt8IpoptNLP10Eval_ErrorEN5Ipopt3NLP28USER_SCALING_NOT_IMPLEMENTEDE?:0yE>ư>=`X=`XC$@-C6?MUMPS/src/dmumps_bloc2.FError in DMUMPS_47: undef stratError in DMUMPS_BLOC2 undef strat Error in DMUMPS_440: size lastbloc NCOLim1, BLSIZE, NFRONT=Internal error 1 in DMUMPS_503Internal error 2 in DMUMPS_503Internal Error 1 in DMUMPS_50K=U?@@?MUMPS/src/dmumps_buffer.F** Warning: trying to cancel a request.** This might be problematic on SGI Try_send_cb: SIZE, POSITION = Try_send_master2slave: SIZE, POSITION = Internal error in DMUMPS_62 Buf size (bytes)= Error in DMUMPS_68 : wrong estimated sizeError in TRY_SEND_MAITRE2:Try_send_maitre2, SIZE,POSITION=Error in PARPIV/DMUMPS_67 contniv2: SIZE, POSITION = NBROW, LROW = Error in DMUMPS_71 :Problem in DMUMPS_4: IERR<0 ERROR 1 in TRY_SEND_MAPLIG:Wrong estimated size Error sending blocfacto : size < position Size,position= Error sending blfac slave : size < position Error sending contribution to root:Size0 and K47>2Internal error 1 in DMUMPS_185Unimplemented KEEP(80) StrategyInternal error 3 in DMUMPS_185Internal error 2 in DMUMPS_185PB allocation in DMUMPS_185Internal error in DMUMPS_384Internal error 2 in DMUMPS_499.Internal error 3 in DMUMPS_499.: Internal Error 1 in DMUMPS_504DMUMPS_COMPUTE_PARTI_ACTV_MEM_K10 should be called with KEEP(48) different from 4Internal Error 2 in DMUMPS_504NCB=,SOMME=: Internal Error 3 in DMUMPS_504: Internal error 4 in DMUMPS_504: Internal error 5 in DMUMPS_504:Internal error 6 in DMUMPS_504:Internal error 7 in DMUMPS_504:Internal error 12 in DMUMPS_504Internal error 13 in DMUMPS_504Internal error 14 in DMUMPS_504Internal error 15 in DMUMPS_504: Warning: negative load : Negative load : Internal error 1 in DMUMPS_518: Internal error 2 in DMUMPS_518: Internal error 3 in DMUMPS_518: Internal error 4 in DMUMPS_518: Internal error 5 in DMUMPS_518: Internal error 6 in DMUMPS_518: Internal error 7 in DMUMPS_518: Internal error 8 in DMUMPS_518: Internal error 9 in DMUMPS_518: Internal error 10 in DMUMPS_518: Internal error 11 in DMUMPS_518: Internal error 12 in DMUMPS_518: Internal error 13 in DMUMPS_518: Internal error 14 in DMUMPS_518: Internal error 15 in DMUMPS_518: Internal error 16 in DMUMPS_518: Internal error 17 in DMUMPS_518probleme de partition dans DMUMPS_545problem with partition in DMUMPS_518Strategy 6 not implementedDMUMPS_520 must be called with K47>=2Internal error 1 in DMUMPS_520: I did not find : negative pos_mem or pos_id  :->Internal error 2 in DMUMPS_187Internal error 3 in DMUMPS_187Internal error 7 in DMUMPS_187Internal error 8 in DMUMPS_187problem with NIV2_FLOPS message: Internal error 4 in DMUMPS_187: Internal error 5 in DMUMPS_187: Internal error 6 in DMUMPS_187Internal error 1 in DMUMPS_187Internal error 1 in DMUMPS_467Internal error 2 in DMUMPS_467: Bad value for CHECK_FLOPSInternal Error in DMUMPS_190 Internal Error in DMUMPS_471. NEW_LU must be zero if called from PROCESS_BANDE:Problem with increments in DMUMPS_471Internal Error in DMUMPS_471Internal error in DMUMPS_461Internal Error in DMUMPS_461Error 1 in DMUMPS_461Internal error: Unknown pool management strategyInternal Error in DMUMPS_500Internal Error 1 in DMUMPS_501Internal Error 3 in DMUMPS_501Internal error 1 in PROCESS_NIV2_MEM_MSG: Problem in DMUMPS_512Internal Error in DMUMPS_533PB allocation in COMPUTE_MAX_MEM: has not been found in COMPUTE_MAX_MEM: The first leaf is not ok: Not enough space for allocation?jHA@@.A?YP>?MUMPS/src/dmumps_ooc.F: DMUMPS I/O ERROR : INODE IS TOO BIG TO MATCH ONE HALF-BUFFER : Size of block= Size half-buffer=ENDINGENDED: Internal error (37) in OOC : Internal error (38) in OOC : Problem in DMUMPS_LOW_LEVEL_READ_OCCPB allocation in DMUMPS_INIT_OOC: PB in DMUMPS_LOW_LEVEL_INIT_OOC_CDIM_BUF_IO =: Inernal error (42) in OOC : Inernal error (43) in OOC : Internal error (39) in OOC Invalid Flag Value in DMUMPS_597: Internal error (40) in OOC : Internal error (41) in OOC LRLUS_SOLVE must be (1) > 0: Internal error (1) in OOC : Internal error (2) in OOC : Internal error (3) in OOC : Internal error (4) in OOC DMUMPS_597 : Internal error (10) in OOC DMUMPS_604: Internal error (11) in OOC : Internal error (12) in OOC : Internal error (14) in OOC DMUMPS_605: Internal error (15) in OOC : Internal error (16) in OOC : Internal error (18) in OOC : Internal error (19) in OOC DMUMPS_605: Internal error (20) in OOC Problem avec debut (2): Internal error (21) in OOC Problem with CURRENT_POS_T: Internal error (22) in OOC DMUMPS_607: Internal error (23) in OOC : Internal error (23b) in OOC : Internal error (25) in OOC : There are no free blocks in DMUMPS_608: Internal error (26) in OOC DMUMPS_608: Internal error (27) in OOC : Internal error (28) in OOC LRLUS_SOLVE must be (4) > 0: Internal error (29) in OOC : Internal error (30) in OOC Problem avec debut POSFAC_SOLVE: Internal error (31) in OOC : Internal error (8) in OOC Not enough space for Solve: Internal error (9) in OOC LRLUS_SOLVE must be (3) > 0: Internal error (32) in OOC DMUMPS_609: Internal error (33) in OOC LRLUS_SOLVE must be (5) ++ > 0: Internal error (34) in OOC LRLUS_SOLVE must be (5) > 0: Internal error (7) in OOC : Internal error (35) in OOC Unknown Flag value in DMUMPS_602: Internal error (5) in OOC Problem in DMUMPS_598: Internal error (6) in OOC : LRLUS_SOLVE must be (2) > 0PB allocation in DMUMPS_613: More space needed for solution step in DMUMPS_586 PB allocation in DMUMPS_586PB allocation in DMUMPS_INIT_OOC_SOLVEfff??? '(/'******INTERNAL VALUE OF PARAMETERS (ICNTL/KEEP)****'/)( 'ICNTL(1) Output stream for error messages =',I10/ 'ICNTL(2) Output stream for diagnostic messages =',I10/ 'ICNTL(3) Output stream for global information =',I10/ 'ICNTL(4) Level of printing =',I10)( 'ICNTL(5) Matrix format ( keep(55) ) =',I10/ 'ICNTL(6) Maximum transversal ( keep(23) ) =',I10/ 'ICNTL(7) Ordering =',I10/ 'ICNTL(12) LDLT ordering strat ( keep(95) ) =',I10/ 'ICNTL(13) Parallel root (0=on, 1=off) =',I10/ 'ICNTL(18) Distributed matrix ( keep(54) ) =',I10/ 'ICNTL(19) Schur option ( keep(60) 0=off,else=on ) =',I10/ 'ICNTL(22) Out-off-core option (0=Off, >0=ON) =',I10)( 'ICNTL(8) Scaling strategy ( keep(52) ) =',I10)( 'ICNTL(14) Percent of memory increase ( keep(12) ) =',I10)( 'ICNTL(9) Solve A x=b (1) or A''x = b (else) =',I10/ 'ICNTL(10) Max steps iterative refinement =',I10/ 'ICNTL(11) Error analysis ( 0= off, else=on) =',I10/ 'ICNTL(20) Dense (0) or sparse (1) RHS =',I10/ 'ICNTL(21) Gathered (0) or distributed(1) solution =',I10)d(/'***********CONTROL PARAMETERS (ICNTL)**************'/)( 'ICNTL(8) Scaling strategy =',I10)( ' Size of SCHUR matrix (SIZE_SHUR) =',I10)( 'ICNTL(14) Percent of memory increase =',I10)( 'ICNTL(9) Solve A x=b (1) or A''x = b (else) =',I10/ 'ICNTL(10) Max steps iterative refinement =',I10/ 'ICNTL(11) Error analysis ( 0= off, else=on) =',I10)fsУ Error return from MPI_TEST in DMUMPS_329 RECEPTION BUF TOO SMALL, Msgtag/len=Internal error 3 in DMUMPS_322: Internal error, routine DMUMPS_322.FAILURE, WORKSPACE TOO SMALL DURING FAILURE IN INTEGER ALLOCATION DURING FAILURE IN DYNAMIC ALLOCATION DURING PB compress ass..process_contribLRLU,LRLUS= FAILURE, WORKSPACE TOO SMALL DURING DMUMPS_264PB compress ass..blocfacto: LRLU,LRLUS= FAILURE IN INTEGER ALLOCATION DURING DMUMPS_264: Internal ERROR 1 in DMUMPS_264 INODE = MAITRE_DESC_BANDE not yet received from NL? ERROR 1 **** IN BLACFACTO  ERR: ERROR : NBROWS > NBROWF ERR: INODE = ERR: NBROW=NBROWF= ERR: ROW_LIST=PB compress ass..mpi51f_niv2 ERROR 2 during ass_niv2 FAILURE IN INTEGER DYNAMIC ALLOCATION DURING DMUMPS_253 FAILURE ALLOCATING TMP_SLAVES_LIST DURING DMUMPS_253 FAILURE IN INTEGER ALLOCATION DURING DMUMPS_253 FAILURE, WORKSPACE TOO SMALL DURING DMUMPS_253 FAILURE, SEND BUFFER TOO SMALL (1) DURING DMUMPS_253 FAILURE, RECV BUFFER TOO SMALL (1) DURING DMUMPS_253 FAILURE, SEND BUFFER TOO SMALL (2) DURING DMUMPS_253 FAILURE, RECV BUFFER TOO SMALL (2) DURING DMUMPS_253Error1 in mpi51f_niv1 PB compress ass..mpi51f_niv1.F ERROR 1 during ass_niv1 FAILURE IN INTEGER ALLOCATION DURING DMUMPS_252 FAILURE, WORKSPACE TOO SMALL DURING DMUMPS_252 FAILURE, SEND BUFFER TOO SMALL DURING DMUMPS_252 FAILURE IN INTEGER DYNAMIC ALLOCATION DURING DMUMPS_252error in process_bande: Could not allocate BUFI: goto 500: Could not allocate BUFR: goto 500: Could not allocate IW4: goto 500Error allocating IW4Error allocating BUFIError allocating BUFR:INTERNAL Error: root arrowhead :is not belonging to me. IARR,JARR=Error 1 in anal_arrowheadsError 2 in anal_arrowheads&(A,I4,I12,I15)Entering driver (DMUMPS) WITH JOB, N, NZ =Entering driver (DMUMPS) WITH JOB, N, NELT =(' Error in DMUMPS initialization: MPI is not running.')(' ** Allocation error: could not permute JCN.')Problem in allocating work array for analysis.Problem in allocating work array PROCNODEProblem in allocating work array ELTPROCError in analysis: IRN/JCN badly allocated.Error in analysis: ELTPTR/ELTVAR badly allocated.(A) SCHUR not associated SCHUR allocated but too small ** WARNING : SCALING ** scaling already computed during analysis ** column permutation applied: ** column scaling has to be permuted ** Warning: Scaling not applied. ** (incompatibility with null space) ** (incompatibility with Schur) ** (incompatibility with dist entry) ** Warning: Scaling option n.a. for symmetric matrix ** (only user scaling av. for elt. entry)Problems in allocations before facto SCHUR leading dimension SCHUR_LLD too small with respect to : Size Schur= SCHUR_LLD= SCHUR_MLOC= SCHUR_NLOC=(' ** ERROR RETURN ** FROM DMUMPS INFO(1)=', I3)(' ** INFO(2)=', I10)(A,I12) On return from DMUMPS, INFOG(1)= On return from DMUMPS, INFOG(2)=MUMPS/src/dmumps_part1.F NBROWF ERR: INODE = ERR: NBROW=NBROWF=PB compress ass..mpi51f_niv2LRLU,LRLUS= ERROR 2 during ass_niv2 FAILURE IN INTEGER DYNAMIC ALLOCATION during assembly FAILURE ALLOCATING TMP_SLAVES_LIST FAILURE IN INTEGER ALLOCATION DURING DMUMPS_37 FAILURE, WORKSPACE TOO SMALL DURING DMUMPS_37 FAILURE, SEND BUFFER TOO SMALL (1) DURING DMUMPS_37 FAILURE, RECV BUFFER TOO SMALL (1) DURING DMUMPS_37 FAILURE, SENDBUFFER TOO SMALL (2) DURING DMUMPS_37 FAILURE, RECVBUFFER TOO SMALL (2) DURING DMUMPS_37Error1 in mpi51f_niv1 PB compress ass..mpi51f_niv1.F ERROR 1 during ass_niv1_ELT FAILURE IN INTEGER ALLOCATION DURING DMUMPS_36 FAILURE, WORKSPACE TOO SMALL DURING DMUMPS_36 FAILURE, SEND BUFFER TOO SMALL DURING DMUMPS_36 FAILURE, RECV BUFFER TOO SMALL DURING DMUMPS_36 DYNAMIC ALLOCATION DURING DMUMPS_36(/3X,'Error message from DMUMPS_315: INFO(1) = ',I2)(3X,'LIW is insufficient. Upper bound on required work', 'space is ',I8)Error return from DMUMPS_315. INFO(1) = ERROR 1 in file DMUMPS_153 (/'Entering analysis phase with ...'/ ' N NELT LIW INFO(1)'/, 9X, I8, I11, I12, I14)('Element pointers: ELTPTR() '/(9X, 7I10))('Element variables: ELTVAR() '/(9X, 7I10))('IKEEP(.,1)=', 10I6/(12X, 10I6))Internal error in DMUMPS_128('IKEEP(.,2)=', 10I6/(12X, 10I6))('IKEEP(.,3)=', 10I6/(12X, 10I6))('NFSIZ(.) =', 10I6/(12X, 10I6))('FILS (.) =', 10I6/(12X, 10I6))('FRERE(.) =', 10I6/(12X, 10I6))(/'** Error return ** from Analysis * INFO(1)=', I3)('Error in permutation array KEEP INFO(2)=', I3): Extracting from a subtree for helping: Extracting from top inode=for helping: selecting from subtree: I must search for a task to save My friendError 2 in DMUMPS_509: unknown strategyError 1 in DMUMPS_509Error 3 in DMUMPS_509: ca a change pour moiError 5 in DMUMPS_509Internal error 1 in DMUMPS_270PB1 compress root2slave:LRLU,LRLUS=PB2 compress root2slave:LRLU,LRLUS=error 1 in PROCESS_ROOT2SLAVEbefore alloc_cb:IPTRLU = after alloc_cb:IPTRLU = (/' LEAVING FACTORIZATION PHASE WITH ...'/ ' INFO (1) =',I12/ ' --- (2) =',I12/ ' NUMBER OF NODES IN THE TREE =',I12/ ' INFO (9) REAL SPACE FOR FACTORS =',I12/ ' --- (10) INTEGER SPACE FOR FACTORS =',I12/ ' --- (11) MAXIMUM SIZE OF FRONTAL MATRICES =',I12/ ' --- (12) NUMBER OF OFF DIAGONAL PIVOTS =',I12/ ' --- (13) NUMBER OF DELAYED PIVOTS =',I12/ ' --- (14) NUMBER OF MEMORY COMPRESSES =',I12/ ' RINFO(2) OPERATIONS DURING NODE ASSEMBLY =',1PD10.3/ ' -----(3) OPERATIONS DURING NODE ELIMINATION =',1PD10.3)Internal error in DMUMPS_22Internal error 2 in DMUMPS_22PB compress... alloc_cbInternal error 3 in DMUMPS_22 Error in DMUMPS_243PB compress Stack_band:LRLU,LRLUS=: Internal error in DMUMPS_576 ERROR 1 compressLU:Should not point to a band. ERROR 2 compressLU:Stack not performed yet ERROR 3 compressLU: problem with level of inode error 2 in maplig_fils_niv1 : PB allocation NBROW in DMUMPS_211 : PB allocation SLAVES_PERE in DMUMPS_211 : PB allocation LMAP in DMUMPS_211: PB allocation PERM in DMUMPS_211: PB allocation SIZE_SLV_PERE in DMUMPS_211Error 1 in MAPLIG_FILS_NIV1: Error 2 in DMUMPS_211 : PB allocation MAX_ARRAY during DMUMPS_211: FAILURE, SEND BUFFER TOO SMALL DURING DMUMPS_211: FAILURE, RECV BUFFER TOO SMALL DURING DMUMPS_211: FAILURE, MAX_ARRAY ALLOC FAILED DURING DMUMPS_211error 3 in DMUMPS_211 : PB allocation SLAVES_PERE in DMUMPS_210 : PB allocation NBROW in DMUMPS_210 : PB allocation LMAP in DMUMPS_210: PB allocation PERM in DMUMPS_210: PB allocation SIZE_SLV_PERE in DMUMPS_210Error 1 in PARPIV/DMUMPS_210MAX_ARRAY allocation failed: Internal error in Maplig: PTRIST(STEP(ISON))=: I, NBROW(I)=: NSLAVES_PERE=: ISON, INODE_PERE=: Son header=FAILURE: SEND BUFFER TOO SMALL IN DMUMPS_210FAILURE: RECV BUFFER TOO SMALL IN DMUMPS_210FAILURE: MAX_ARRAY allocation failed IN DMUMPS_210)\(?MUMPS/src/dmumps_part4.F(A) END OF ROW SCALING END OF COLUMN SCALING**** STAT. OF MATRIX PRIOR ROW&COL SCALING MAXIMUM NORM-MAX OF COLUMNS: MINIMUM NORM-MAX OF COLUMNS: MINIMUM NORM-MAX OF ROWS : END OF SCALING BY MAX IN ROW AND COLMemory allocation error in DMUMPS_363Memory allocation error in DMUMPS_363Internal Error in reordertree: problem with perm parameter in reordertreeInternal Error in reordertree INODE < 0 !Probleme dans reorder!!!!INODE=RANKCOST=Internal Error in DMUMPS_363 Failure in int space allocation in CB area during assembly of root : DMUMPS_273 size required was : NELIM= NSLAVES= ERROR 1 in DMUMPS_294 NIV2:Detected 0 pivot, INODE,NPIV= Error in DMUMPS_272 . Internal error 1 IN DMUMPS_627Internal error 2 in DMUMPS_627Internal error 3 in DMUMPS_627Internal error 2 in DMUMPS_94Internal error 1 in DMUMPS_94UNR?LDetected a null pivot, INODE/NPIV=(/A/A,I3) **** Error return from DMUMPS_216 **** IFAIL = END OF DIAGONAL SCALING END OF SCALING USING MC29(/' ****** SCALING OF ORIGINAL MATRIX '/) DIAGONAL SCALING SCALING BASED ON (MC29) COLUMN SCALING ROW AND COLUMN SCALING (1 Pass) MC29 FOLLOWED BY ROW &COL SCALING MC29 FOLLOWED BY COLUMN SCALING*** ERROR: Not enough space to scale matrix ERROR 1 in DMUMPS_271 :Process root2son: INODE=Header=: ERROR 2 in DMUMPS_271 : INODE,LCONT, NROW, NPIV, NASS, NELIM=: IOLDPS=: internal error in DMUMPS_271 ERROR 1 in DMUMPS_246 : ERROR 2 in DMUMPS_246. ITOP = ERROR 3 in DMUMPS_246ERROR 4 in DMUMPS_246  MAX-NORM of computed solut. is zero(/' RESIDUAL IS ............ (MAX-NORM) =',1PD9.2/ ' .. (2-NORM) =',1PD9.2/ ' RINFOG(4):NORM OF input Matrix (MAX-NORM)=',1PD9.2/ ' RINFOG(5):NORM OF Computed SOLUT (MAX-NORM)=',1PD9.2/ ' RINFOG(6):SCALED RESIDUAL ...... (MAX-NORM)=',1PD9.2) MAX-NORM of exact solution is zero(/' ERROR IS ............ (MAX-NORM) =',1PD9.2/ ' ............ (2-NORM) =',1PD9.2/ ' RELATIVE ERROR........... (MAX-NORM) =',1PD9.2/ ' Comp. Wise ERROR......... (MAX-NORM) =',1PD9.2/ ' AND RESIDUAL IS ......... (MAX-NORM) =',1PD9.2/ ' .. (2-NORM) =',1PD9.2/ ' NORM OF input MATRIX ... (MAX-NORM) =',1PD9.2/ ' NORM of computed SOLUT... (MAX-NORM) =',1PD9.2/ ' SCALED RESIDUAL ......... (MAX-NORM) =',1PD9.2):INTERNAL Error: recvd root arrowhead :not belonging to me. IARR,JARR=:IROW_GRID,JCOL_GRID=:MYROW, MYCOL=:IPOSROOT,JPOSROOT=  ** Error allocating int buffer for matrix distribution** Error allocating real buffer for matrix distribution** Error allocating int recv buffer for matrix distribution** Error allocating IW4 for matrix distribution(A9,A42,I12) Maximum Average (/' ****** FACTORIZATION STEP ********'/)(/A,I3) Null space option :(A,I10) Max deficiency : Min deficiency : (A) ** Warning : SPD solver called, resetting CNTL(1) to 0.0D0(/' GLOBAL STATISTICS PRIOR NUMERICAL FACTORIZATION ...'/ ' NUMBER OF WORKING PROCESSES =',I12/ ' REAL SPACE FOR FACTORS =',I12/ ' INTEGER SPACE FOR FACTORS =',I12/ ' MAXIMUM FRONTAL SIZE (ESTIMATED) =',I12/ ' NUMBER OF NODES IN THE TREE =',I12) ABSOLUTE PIVOT THRESHOLD for rank revealing = ZERO PIVOT DETECTION ON, THRESHOLD = FIXATION FOR NULL PIVOTS =INFINITE FIXATION effective relaxed value of MAXS =: Warning: DIM_BUF_IO might be too big for Filesystem:Error allocating DBLARR : IERR = (' GLOBAL TIME FOR MATRIX DISTRIBUTION =',F12.4)(/' LOCAL REDISTRIB: DATA LOCAL/SENT =',I12,I12)(/' REDISTRIB: TOTAL DATA LOCAL/SENT =',I12,I12)( /,' Allocated buffers',/,' ------------------',/, ' Size of reception buffer in bytes ...... = ', I10, /, ' Size of async. emission buffer (bytes).. = ', I10,/, ' Small emission buffer (bytes) .......... = ', I10):Error allocating small Send buffer:IERR=:Error allocating Send buffer:IERR=:Error allocating BUFR:IERR=:Error allocating IS:IERR=:Error allocatingPTLUST:IERR = :Error allocatingPTRFAC:IERR = :Error allocating IWK : IERR = (/' STATISTICS PRIOR NUMERICAL FACTORIZATION ...'/ ' MAXS =',I12/ ' MAXIS =',I12/ ' MINIMUM (ICNTL(14)=0) VALUE OF MAXS =',I12/ ' MINIMUM (ICNTL(14)=0) VALUE OF MAXIS =',I12/ ' REAL SPACE FOR ORIGINAL MATRIX =',I12/ ' INTEGER SPACE FOR ORIGINAL MATRIX =',I12/ ' REAL SPACE FOR FACTORS =',I12/ ' INTEGER SPACE FOR FACTORS =',I12/ ' MAXIMUM FRONTAL SIZE (ESTIMATED) =',I12)(A,I10) ** Space in MBYTES used during factorization : ** Memory relaxation parameter ( ICNTL(14) ) : ** Rank of processor needing largest memory in facto : ** Space in MBYTES used by this processor for facto : ** Avg. Space in MBYTES per working proc during facto :(/' NUMBER OF ENTRIES FOR // ROOT =',I12)&(/' ELAPSED TIME FOR FACTORIZATION =',F12.4) ** Effective minimum Space in BYTES for facto :effective space used in S (KEEP(67) = ** EFF Min: Rank of processor needing largest memory : ** EFF Min: Space in MBYTES used by this processor : ** EFF Min: Avg. Space in MBYTES per working proc :((/' GLOBAL STATISTICS '/ ' RINFOG(2) OPERATIONS DURING NODE ASSEMBLY =',1PD10.3/ ' ------(3) OPERATIONS DURING NODE ELIMINATION =',1PD10.3/ ' INFOG (9) REAL SPACE FOR FACTORS =',I12/ ' INFOG(10) INTEGER SPACE FOR FACTORS =',I12/ ' INFOG(11) MAXIMUM FRONT SIZE =',I12)( ' INFOG(12) NEGATIVE PIVOTS =',I12)( ' INFOG(13) NUMBER OF OFF DIAGONAL PIVOTS =',I12)( ' INFOG(12) NUMBER OF DELAYED PIVOTS =',I12)( ' INFOG(25) NUMBER OF TINY PIVOTS =',I12)( ' NUMBER OF 2x2 PIVOTS in type 1 nodes =',I12)( ' NUMBER OF 2x2 PIVOTS in type 2 nodes =',I12)( ' NUMBER OF ZERO PIVOTS =',I12)( ' NUMBER OF ZERO PIVOTS WT ROOT =',I12)( ' DEFICIENCY ON LAST ROOT NODE =',I12)( ' INFOG(28) ESTIMATED DEFICIENCY =',I12)( ' INFOG(14) NUMBER OF MEMORY COMPRESS =',I12) ** Warning Static pivoting was necessary ** to factor interior variables with Schur ONError 1 in Gerror: more rows than columnsInternal error in DMUMPS_254 ERROR 1 in 51G : IOLDPS,PTLUST_S(STEP(INODE))= *** CHG_HEADER ERROR 1 : *** CHG_HEADER ERROR 2 : *** CHG_HEADER ERROR 3 : not rootDERROR 2 in DMUMPS_251Error .. in DMUMPS_251: INODE == KEEP(38) ERROR 1 in DMUMPS_251: NBFIN= NBROOT= Error allocating, real array of size before DMUMPS_146: Internal error in DMUMPS_576( /' DMUMPS Version ',A6,' -- Jan 2007' )L U Solver for unsymmetric matricesL D L^T Solver for symmetric positive definite matricesL D L^T Solver for general symmetric matricesType of parallelism: Working hostType of parallelism: Host not working(/' ****** ANALYSIS STEP ********'/) Resetting candidate strategy to 0 because NSLAVES=1 Resetting candidate strategy to 8 ** Ignoring user-ordering, because incompatible with Schur. ** ICNTL(7) treated as 0. Out-of-range value for ICNTL(18). Used 0 ie matrix not distributed Out-of-range value for ICNTL(5). Used 0 ie matrix is assembled ** Max-trans not compatible with LLT factorization ** Max-trans not allowed because of Schur ** Scaling not allowed because of Schur ** Max-trans not allowed because ordering is given ** Max-trans not allowed because matrix is distributed ** Scaling not allowed because matrix is distributed ** Max-trans not allowed for element matrix ** Scaling not allowed for element matrix ** Distributed entry not available for element matrixWARNING: ANAL_0 constrained ordering not available with selected ordering( /' ** FAILURE DURING DMUMPS_26, DYNAMIC ALLOCATION OF', A30)MEM_DISTREQPTRIRNJCN#$%NAME_NOT_INITIALIZED%%MatrixMarket matrix coordinate .rhs%%MatrixMarket matrix array general(I7)***********************************MAXIS1 and MAXIS1_CHECK are different !!MAXIS1, MAXIS1_CHECK=This might cause problems ...PB allocating an array of size 1 in Schur IWtempXNODELNODELSSARBR(A,F10.0) ** Peak of sequential stack size (number of real entries) : ** Peak of sequential total memory (number of real entries) : Internal error in DMUMPS_369FRTPTRPAR2_NODESInternal Error 2 in DMUMPS_26CANDIDATESnid%STEPid%PROCNODE_STEPSid%NE_STEPSid%ND_STEPSid%FRERE_STEPSid%DAD_STEPSid%SYM_PERMid%FILSid%PTRARid%FRTPTR/FRTELTid%UNS_PERMid%NAInternal error 2 in DMUMPS_26Internal error 3 in DMUMPS_26TEMP_MEMTEMP_LEAFTEMP_SIZETEMP_ROOTDEPTH_FIRSTCOST_TRAV_TMPid%DEPTH_FIRSTid%COST_TRAVid%COST_TRAV(1)id%MEM_SUBTREEid%MY_ROOT_SBTRid%MY_FIRST_LEAFid%MY_NB_LEAFMY_FIRST_LEAFMY_NB_LEAFid%MEM_SUBTREE(1)id%MY_ROOT_SBTR(1)id%MY_FIRST_LEAF(1)id%MY_NB_LEAF(1)PAR2_NODES/id%CANDIDATESid%ISTEP_TO_INIV2id%TAB_POS_IN_PEREFUTURE_NIV2Problem with your version of the BLACS.Try using a BLACS version from netlib. estimated INTEGER space for factors : estimated REAL space for factors : Minimum value of MAXIS for factorization : Minimum value of MAXS for IC factorization : Minimum value of MAXS for OOC factorization: Current value of space relaxation parameter : Relaxed Value of MAXIS for factorization : Relaxed Value of MAXS for IC factorization : Relaxed Value of MAXS for OOC factorization : Estimated maximum SENT message size (bytes) : Estimated maximum RECV message size (bytes) :id%MAPPINGIWtemp(N) Estimated space in MBYTES for IC factorization : ** Rank of proc needing largest memory in IC facto : ** Estimated corresponding MBYTES for IC facto : ** Estimated avg. MBYTES per work. proc at facto (IC) : ** TOTAL space in MBYTES for IC factorization : ** Rank of proc needing largest memory for OOC facto : ** Estimated corresponding MBYTES for OOC facto : ** Estimated avg. MBYTES per work. proc at facto (OOC) : ** TOTAL space in MBYTES for OOC factorization : Error 1 in mc51d NTOTPVTOT= Error 2 in mc51d NTOTPVTOT=MUMPS/src/dmumps_part5.F|=;dAj@ R : MEMORY ALLOCATION FAILURE in DMUMPS_80error: myrow,mycol= MYID,PDEST=: Error in b&scbroot: pb compress: LRLU, LRLUS=FAILURE, SEND BUFFER TOO SMALL DURING DMUMPS_80FAILURE, RECV BUFFER TOO SMALL DURING DMUMPS_80 :error, received negative NPIV in BLFACPB compress ass..blocfacto: LRLU,LRLUS= : PB allocation U in blfac_slave NT?! Internal error in PROCESS_BLFAC_SLAVE.UWARNING matrix may be singular : FACTO_NIV2 :failed to allocate integers Internal error related to null pivot row detection: FAILURE IN DMUMPS_274, REAL WORKSPACE TOO SMALL: FAILURE IN DMUMPS_274, INTEGER WORKSPACE TOO SMALL: Internal ERROR 1 in DMUMPS_274 INODE = MAITRE_DESC_BANDE not yet received from SymBLFC Error: NPIV1 + NCOL .NE. NASS1 :: ALLOCATION FAILURE FOR UIP21K IN DMUMPS_274: ALLOCATION FAILURE FOR LIST_SLAVES_FOLLOW IN DMUMPS_274L: FAILURE, SEND BUFFER TOO SMALL DURING DMUMPS_274: FAILURE, RECV BUFFER TOO SMALL DURING DMUMPS_274 Internal error in PROCESS_SYM_BLOCFACTO.: Error in calling transdiag:unsym:Error in CHECK_ORDER_LIST: not sorted:List = :Permuted list =:Error in MUMP_CHECK_ORDER_LIST: bad ITLOC:ITLOC( IW ) =: Bad First ITLOC ITLOC(IW(1)),NASS1+1=MUMPS/src/dmumps_part6.F _B90yE^#B ;MUMPS/src/dmumps_part7.F(/'*** Warning message from analysis routine ***')(I8,A,I8,A,I8,A)th entry (in row and column) ignoredst entry (in rownd entry (in rowrd entry (in row(A,I5) ... Structural symmetry (in percent)=(A,3I5) Density: NBdense, Average, Median =(A)** Warning: ICNTL(25) option disabled because** null space was not required during factorization** it is not available for the transposed systemERROR: WRONG VALUE FOR ICNTL(2) = ERROR: WRONG VALUE FOR ICNTL(1) = : problem allocating IPIV() in root Error: symmetrization only works for square block sizes, MBLOCK/NBLOCK=Not enough workspace for symmetrization.L Problem during solve of the root. Reduce number of right hand sides.NTL Problem during solve of the root Error1 in 41S : NROW L/RECU=?Error 2 in usedbympima41s.FU Internal Error type 2 node with no CB c Internal error in SOLVE_NODE for Root node Internal error 41r2 : Pool is too small.INTERNAL Error 41r: Pool is too small.Internal error in DMUMPS_63967 warning solve: lpool.NE.nsteps+1 :: ** ERROR RETURN FROM DMUMPS_248,INFO(1:2)=After DESCINIT, IERR = (//' LEAVING SOLVE (MPI41C) WITH')(' RHS (first column)'/(1X,1P,5D14.6))(' RHS (2 nd column)'/(1X,1P,5D14.6))(//' ****** SOLVE & CHECK STEP ********'/) Problem reallocation of MAXS (/' STATISTICS PRIOR SOLVE PHASE ...........'/ ' NUMBER OF RIGHT-HAND-SIDES =',I12/ ' BLOCKING FACTOR FOR MULTIPLE RHS =',I12/ ' ICNTL (9) =',I12/ ' --- (10) =',I12/ ' --- (11) =',I12/ ' --- (20) =',I12/ ' --- (21) =',I12)(' --- (25) =',I12)(' --- (26) =',I12)(A) WARNING: ICNTL(20) treated as if set to 0 (null space) WARNING: ICNTL(10) treated as if set to 0 (null space) WARNING: ICNTL(11) treated as if set to 0 (null space) WARNING: ICNTL(10) treated as if set to 0 (reduced RHS)) WARNING: ICNTL(11) treated as if set to 0 (reduced RHS) WARNING: ICNTL(11) treated as if set to zero WARNING: ICNTL(10) treated as if set to zero Problem in solve: error allocating SAVERHS Problem in solve: error allocating BUFR:Error allocating small Send buffer:IERR=:Error allocating Send buffer:IERR=ERREUR while allocating RHS on a slaveERROR in DMUMPS_301: LIWW<0 ERROR in DMUMPS_301: allocating POSINRHSCOMP_NError allocating temporary scaling arrayError allocating local scaling array:Error allocating RW1 in DMUMPS_SOLVE_DRIVE WARNING : -11 error code obtained in solve WARNING : -14 error code obtained in solveFIXME: to be implemented(//' ERROR ANALYSIS' )(//' BEGIN ITERATIVE REFINEMENT' )(1X, A42,I4)MAXIMUM NUMBER OF STEPS =(/' STATISTICS AFTER ITERATIVE REFINEMENT ')NUMBER OF STEPS OF ITERATIVE REFINEMENTS =(/' END ITERATIVE REFINEMENT ')(//' ERROR ANALYSIS AFTER ITERATIVE REFINEMENT')(1X, A44,1P,D9.2)RINFOG(7):COMPONENTWISE SCALED RESIDUAL(W1)=------(8):---------------------------- (W2)=------(9):Upper bound ERROR ...............=-----(10):CONDITION NUMBER (1) ............=-----(11):CONDITION NUMBER (2) ............=could not allocate integers.(//' LEAVING SOLVER WITH: INFO(1) ............ =',I12/ ' INFO(2) ............ =',I12)(//' VECTOR SOLUTION FOR COLUMN ',I12)(' RHS'/(1X,1P,5D14.6))MUMPS/src/dmumps_part8.FL=?Problem with low level writeProblem with low level readNAME_NOT_INITIALIZEDMUMPS_OOC_TMPDIR/tmpMUMPS_OOC_PREFIX_%s%d_XXXXXX/%s%s%s%s%s%s%d_XXXXXXUnable to open OOC fileProblem while opening OOC fileProblem while closing OOC fileInternal (1) error in low-level I/O operation %lfdmumps_ACUnable to remove OOC file%d: Volume of read i/o = %lf %d: Total i/o volume = %lf Error: unknown I/O strategy : %d %d: total time spent in i/o mode = %lf %d: Volume of write i/o = %lf Error: prefix not initialized Error: tmpdir not initialized dmumps_low_level_init_ooc_c should not be called with strategy %d dmumps_wait_all_requests should not be called with strategy %d dmumps_wait_request should not be called with strategy %d dmumps_test_request should not be called with strategy %d dmumps_clean_request should not be called with strategy %d dmumps_is_there_finished_request should not be called with strategy %d %s: %sInternal error: dmumps_low_level_init_ooc_c_th should not to be called with strat_IO=%d Error : Mumps_IO : Operation %d is neither READ nor WRITE Internal error in _dmumps_async_thread_functionUnable to create I/O threadMUMPS/libseq/mpi.fError. DESCSET should not be called.Error. PXERBLA should not be called.Error. PCHK2MAT should not be called.Error. CHK1MAT should not be called.Error. PDORMQR should not be called.Error. PSORMQR should not be called.Error. PZUNMQR should not be called.Error. PCUNMQR should not be called.Error. PDTRSM should not be called.Error. PSTRSM should not be called.Error. PZTRSM should not be called.Error. PCTRSM should not be called.Error. PDAXPY should not be called.Error. PSAXPY should not be called.Error. PZAXPY should not be called.Error. PCAXPY should not be called.Error. PDGEQPF should not be called.Error. PSGEQPF should not be called.Error. PZGEQPF should not be called.Error. PCGEQPF should not be called.Error. PDGECON should not be called.Error. PSGECON should not be called.Error. PZGECON should not be called.Error. PCGECON should not be called.Error. PDLANGE should not be called.Error. PSLANGE should not be called.Error. PZLANGE should not be called.Error. PCLANGE should not be called.Error. PDNRM2 should not be called.Error. PSNRM2 should not be called.Error. PZNRM2 should not be called.Error. PCNRM2 should not be called.Error. PDPOTRS should not be called.Error. PSPOTRS should not be called.Error. PZPOTRS should not be called.Error. PCPOTRS should not be called.Error. PDGETRS should not be called.Error. PSGETRS should not be called.Error. PZGETRS should not be called.Error. PCGETRS should not be called.Error. DGEBR2D should not be called.Error. SGEBR2D should not be called.Error. CGEBR2D should not be called.Error. ZGEBR2D should not be called.Error. DGEBS2D should not be called.Error. SGEBS2D should not be called.Error. CGEBS2D should not be called.Error. ZGEBS2D should not be called.Error. PSDOT should not be called.Error. PDDOT should not be called.Error. PZDOT should not be called.Error. PCDOT should not be called.Error. PSSCAL should not be called.Error. PDSCAL should not be called.Error. PZSCAL should not be called.Error. PCSCAL should not be called.Error. INFOG2L should not be called.Error. PDTRTRS should not be called.Error. PDGETRF should not be called.Error. PDPOTRF should not be called.Error. PSTRTRS should not be called.Error. PSGETRF should not be called.Error. PSPOTRF should not be called.Error. PZTRTRS should not be called.Error. PZGETRF should not be called.Error. PZPOTRF should not be called.Error. PCTRTRS should not be called.Error. PCGETRF should not be called.Error. PCPOTRF should not be called.Error. Last parameter from NUMROC should be 1Error. IPROC should be 0 in NUMROC.Error. DESCINIT should not be called.Error. BLACS_GRIDEXIT should not be called.Error. BLACS_GRIDINFO should not be called.Error. BLACS_GRIDINIT should not be called.** Abort calledError. MPI_WAITANY should not be called.Error. MPI_WAIT should not be called.Error. MPI_UNPACK should not be called.Error. MPI_SSEND should not be called.Error. MPI_SEND should not be called.Error. MPI_RECV should not be called.Error. MPI_PROBE should not be called.Error. MPI_PACK_SIZE should not be called.Error. MPI_PACKED should not be called.Error. MPI_ISEND should not be called.Error. MPI_GET_COUNT should not be called.ERROR in MPI_REDUCE. DATATYPE=ERROR in MPI_ALLREDUCE. DATATYPE=ERROR in MPI_GATHERV=ERROR in MPI_GATHER=vector::_M_insert_auxN5Ipopt11CGPenaltyCqEN5Ipopt15DependentResultIdEEeA@0B{Gz?N5Ipopt13CGPenaltyDataEChecking acceptability for trial step size alpha_primal_test=%13.6e: New values of penalty function = %23.16e (reference %23.16e): curr_barr = %23.16e curr_inf = %23.16e trial_barr = %23.16e trial_inf = %23.16e Starting tests for penalty parameter update: Penalty parameter becomes too large.Trying second order correction number %d Warning: SOC step rejected due to evaluation error Second order correction step accepted with %d corrections. trial_infeasi = %e is larger than theta_max = %e Value "acceptor" for option "alpha_for_y" not valid for this line search../../../Algorithm/IpBacktrackingLSAcceptor.hppnever_use_piecewise_penalty_lspenalty_update_infeasibility_tolpiecewisepenalty_gamma_infeasiOption "max_soc": This option is non-negative, but no linear solver for computing the SOC given to FilterLSAcceptor object.never use the piecewise penalty methodalways use the piecewise penalty methodToggle to switch off the piecewise penalty methodRelaxation factor in the Armijo condition for the penalty function.If the new constraint violation is smaller than this tolerance, the penalty parameter is not increased.Threshold for infeasibility in penalty parameter update test.The algorithmic parameter theta_max is determined as theta_max_fact times the maximum of 1 and the constraint violation at initial point. Any point with a constraint violation larger than theta_max is unacceptable to the filter (see Eqn. (21) in implementation paper).Determines upper bound for constraint violation in the filter.tolerance for deciding if the multipliers are diverginghelptrial infeasibility = %8.2 Max norm of step = %8.2 Minimal compl = %8.2 Maximal compl = %8.2 omega_test for c = %8.2 omega_test for d = %8.2 Updating eta to = %8.2 IpCGPenaltyLSAcceptor.cppjumpeta_penaltypenalty_update_compl_tolchi_hatchi_tildechi_cupgamma_hatgamma_tildeepsilon_cpiecewisepenalty_gamma_objpen_theta_max_factmin_alpha_primaltheta_minmult_diverg_feasibility_tolmult_diverg_y_tolpenalty_maxIsValid(pd_solver_)kappa_socLIFENG WRITES THIS.N5Ipopt19CGPenaltyLSAcceptorEN5Ipopt22BacktrackingLSAcceptorEN5Ipopt23AlgorithmStrategyObjectE<{Gz?@h㈵>& .>?vIh%<=Hz>חANhj Nh Nj Dj Dh Dhj dxperturb_always_cddelta_x perturbation is becoming too large: %e Degeneracy test for hess_degenerate_ = %d and jac_degenerate_ = %d test_status_ = %d Can't get_deltas_for_wrong_inertia for delta_x_curr_ = %e and delta_c_curr_ = %e jacobian_regularization_exponentN5Ipopt21CGPerturbationHandlerE=@O=penalty_init_maxpenalty_init_minkappa_x_diskappa_y_disvarthetadelta_y_maxfast_des_factpen_des_factpen_init_facInitializing penalty parameter for KKT matrix... Max(||y_c||_inf,||y_d||_inf = %8.2e Initial value of the penalty parameter for line search = %8.2e Initial value of the kkt penalty parameter for scaling the linear system = %8.2e Testing if fast direction can be used. diff_dx_nrm = %8.2e tilde_dx_norm = %8.2e Testing if fast direction can be used. tilde_dy_nrm = %8.2e bar_y_nrm = %8.2e dT_times_BarH_times_d = %23.15e fast_direct_deriv = %23.15e never_use_fact_cgpen_directionMaximal value for the intial penalty parameter (for Chen-Goldfarb line search).Minimal value for the intial penalty parameter for line search(for Chen-Goldfarb line search).Maximal value for the penalty parameter (for Chen-Goldfarb line search).a parameter used in penalty parameter computation (for Chen-Goldfarb line search).a parameter used to check if the fast direction can be used asthe line search direction (for Chen-Goldfarb line search).a parameter used to choose initial penalty parameterswhen the regularized Newton method is used.never compute the fast directionalways compute the fast directionToggle to switch off the fast Chen-Goldfarb directionN5Ipopt21CGSearchDirCalculatorEN5Ipopt25SearchDirectionCalculatorEꌠ9Y>)FmBI@The current piecewise penalty has %d entries. The min piecewise penalty is %d . pen_r barrier_obj infeasi We only allow %d entries. %5d %23.16e %23.16e %23.16e tau_minadaptive_mu_safeguard_factoradaptive_mu_kkterror_red_factadaptive_mu_globalizationfilter_max_marginfilter_margin_factquality_function_norm_typequality_function_centralityresto.Setting mu_max to %e. Remaining in fixed mu mode. IpAdaptiveMuUpdate.cppTINY_STEP_DETECTEDStaying in free mu mode. disables globalizationnever-monotone-modeobj-constr-filterkkt-errorrestore accepted iterateuse 2-normuse the infinity norm (max)max-norm2-norm-squareduse the 1-norm (abs sum)adaptive_mu_kkt_norm_typeadaptive_mu_kkterror_red_itersadaptive_mu_restore_previous_iterateadaptive_mu_monotone_init_factorquality_function_balancing_termKKT error in barrier update check: primal infeasibility: %15.6e dual infeasibility: %15.6e complementarity: %15.6e centrality: %15.6e kkt error: %15.6e pd system reference[%2d] = %.6e New fixed value for mu could not be computed from the mu_oracle. Switching back to free mu mode. Problem solved to best possible numerical accuracyReducing mu to %24.16e in fixed mu mode. Tau becomes %24.16e Restoring most recent accepted point. The mu oracle could not compute a new value of the barrier parameter. mu = %e smaller than safeguard = %e. Increasing mu. Barrier parameter mu computed by oracle is %e Barrier parameter mu after safeguards is %e Switching to fixed mu mode with mu = %24.16e and tau = %24.16e. This option determines the upper bound on the barrier parameter. This upper bound is computed as the average complementarity at the initial point times the value of this option. (Only used if option "mu_strategy" is chosen as "adaptive".)Factor for initialization of maximum value for barrier parameter.This option specifies an upper bound on the barrier parameter in the adaptive mu selection mode. If this option is set, it overwrites the effect of mu_max_fact. (Only used if option "mu_strategy" is chosen as "adaptive".)Maximum value for barrier parameter.This option specifies the lower bound on the barrier parameter in the adaptive mu selection mode. By default, it is set to the minimum of 1e-11 and min("tol","compl_inf_tol")/("barrier_tol_factor"+1), which should be a reasonable value. (Only used if option "mu_strategy" is chosen as "adaptive".)Minimum value for barrier parameter.To achieve global convergence of the adaptive version, the algorithm has to switch to the monotone mode (Fiacco-McCormick approach) when convergence does not seem to appear. This option sets the criterion used to decide when to do this switch. (Only used if option "mu_strategy" is chosen as "adaptive".)2-dim filter for objective and constraint violationnonmonotone decrease of kkt-errorGlobalization strategy for the adaptive mu selection mode.For the "kkt-error" based globalization strategy, sufficient progress must be made for "adaptive_mu_kkterror_red_iters" iterations. If this number of iterations is exceeded, the globalization strategy switches to the monotone mode.Maximum number of iterations requiring sufficient progress.For the "kkt-error" based globalization strategy, the error must decrease by this factor to be deemed sufficient decrease.Sufficient decrease factor for "kkt-error" globalization strategy.When using the adaptive globalization strategy, "obj-constr-filter", sufficient progress for a filter entry is defined as follows: (new obj) < (filter obj) - filter_margin_fact*(new constr-viol) OR (new constr-viol) < (filter constr-viol) - filter_margin_fact*(new constr-viol). For the description of the "kkt-error-filter" option see "filter_max_margin".Factor determining width of margin for obj-constr-filter adaptive globalization strategy.Maximum width of margin in obj-constr-filter adaptive globalization strategy.When the globalization strategy for the adaptive barrier algorithm switches to the monotone mode, it can either start from the most recent iterate (no), or from the last iterate that was accepted (yes).don't restore accepted iterateIndicates if the previous iterate should be restored if the monotone mode is entered.When the globalization strategy for the adaptive barrier algorithm switches to the monotone mode and fixed_mu_oracle is chosen as "average_compl", the barrier parameter is set to the current average complementarity times the value of "adaptive_mu_monotone_init_factor".Determines the initial value of the barrier parameter when switching to the monotone mode.When computing the KKT error for the globalization strategies, the norm to be used is specified with this option. Note, this options is also used in the QualityFunctionMuOracle.use the 2-norm squared (sum of squares)Norm used for the KKT error in the adaptive mu globalization strategies.N5Ipopt16AdaptiveMuUpdateEN5Ipopt8MuUpdateEN5Ipopt18TINY_STEP_DETECTEDEdy=H.?user-scalinggradient-basedequilibration-basedma27IpAlgBuilder.cppma57pardisotaucscustomIsValid(custom_solver_)mc19line_search_methodadaptivemonotoneloqoprobingquality-functionuse custom linear solveruse MUMPS packageuse WSMP packageuse the Pardiso packageuse the Harwell routine MA57use the Harwell routine MA27use the Harwell routine MC19no scaling will be performedminimize a quality functionLOQO's centrality ruleMehrotra's probing heuristicaverage_complFilter methodSelected linear solver MA27 not available.Selected linear solver MA57 not available.Selected linear solver Pardiso not available.Selected linear solver TAUCS not available.Selected linear solver WSMP not available.Selected linear solver CUSTOM not available.Selected linear system scaling method MC19 not available.!mehrotra_algorithm || smuupdate=="adaptive"If mehrotra_algorithm=yes, mu_strategy must be "adaptive".!mehrotra_algorithm || smuoracle=="probing"If mehrotra_algorithm=yes, mu_oracle must be "probing".Determines which linear algebra package is to be used for the solution of the augmented linear system (for obtaining the search directions). Note, the code must have been compiled with the linear solver you want to choose. Depending on your Ipopt installation, not all options are available.use TAUCS package (not yet working)Linear solver used for step computations.Determines the method used to compute symmetric scaling factors for the augmented system (see also the "linear_scaling_on_demand" option). This scaling is independentof the NLP problem scaling. By default, MC19 is only used if MA27 or MA57 are selected as linear solvers. This option is only available if Ipopt has been compiled with MC19.Method for scaling the linear system.Selects the technique used for scaling the problem internally before it is solved. For user-scaling, the parameters come from the NLP. If you are using AMPL, they can be specified through suffixes ("scaling_factor")scale the problem so that first derivatives are of order 1 at random points (only available with MC19)scale the problem so the maximum gradient at the starting point is scaling_max_gradientscaling parameters will come from the userno problem scaling will be performedSelect the technique used for scaling the NLP.Determines which barrier parameter update strategy is to be used.use the adaptive update strategyuse the monotone (Fiacco-McCormick) strategyUpdate strategy for barrier parameter.Determines how a new barrier parameter is computed in each "free-mode" iteration of the adaptive barrier parameter strategy. (Only considered if "adaptive" is selected for option "mu_strategy").Oracle for a new barrier parameter in the adaptive strategy.Determines how the first value of the barrier parameter should be computed when switching to the "monotone mode" in the adaptive strategy. (Only considered if "adaptive" is selected for option "mu_strategy".)base on current average complementarityOracle for the barrier parameter when switching to fixed mode.Chen-Goldfarb penalty functionGlobalization method used in backtracking line searchN5Ipopt17MultiVectorMatrixEN5Ipopt16AlgorithmBuilderEN5Ipopt14HessianUpdaterEMain AlgorithmN5Ipopt20AugRestoSystemSolverEN5Ipopt15AugSystemSolverEN5Ipopt15DependentResultINS_8SmartPtrINS_6VectorEEEEEN5Ipopt13CachedResultsINS_8SmartPtrINS_6VectorEEEEEStarting Watch Dog Trial step accepted. Trial step rejected. Mdelta_s_magicStopping Watch Dog CIpBacktrackingLineSearch.cppRESTORATION_FAILEDACCEPTABLE_POINT_REACHEDFailed restoration phase!!!alpha_red_factormagic_stepsaccept_every_trial_stepalpha_for_y_toltiny_step_toltiny_step_y_tolmax_soft_resto_itersDOESN'T REALLY WORK YET!don't take magic stepsEnables magic steps.always accept the full stepacceptordual-and-fullprimal-and-fullsafe_min_dual_infeastake a full step of size onebound_multuse primal step sizeFallback option activated in BacktrackingLineSearch! Relative step size for delta_x = %e Relative step size for delta_s = %e Tiny step of relative size %e detected. Trying soft restoration phase step with step length %13.6e Warning: Evaluation error during soft restoration phase step. Trial step acceptable with respect to original backtracking globalization. Primal-dual error at current point: %23.16e Primal-dual error at trial point : %23.16e Magic step with max-norm %.6e taken. minimal step size ALPHA_MIN = %E Starting checks for alpha (primal) = %8.2e Warning: Cutting back alpha due to evaluation error --> Starting filter line search in iteration %d <-- Mu has changed in line search - resetting watchdog counters. Storing current iterate as backup acceptable point. We are in an emergency mode, but not restoration phase or other fall back is available.Tiny step detected. Use step size alpha = %e unchecked Watch dog procedure successful! Skipping call of restoration phase... --> Starting soft restoration phase <-- Restoration phase called at acceptable point.No Restoration Phase given to this Filter Line Search Object!Restoration phase is called at almost feasible point, but acceptable point from iteration %d could be restored. Restoration phase called at almost feasible point, but acceptable point could be restored. Restoration phase is called at point that is almost feasible, with constraint violation %e. Abort. Restoration phase called, but point is almost feasible.Restoration phase failed, but acceptable point could be restore. Constraint violation is with %e less than expect_infeasible_problem_ctol. Disable expect_infeasible_problem_heuristic. At every step of the backtracking line search, the trial step size is reduced by this factor.Fractional reduction of the trial step size in the backtracking line search.Setting this option to "yes" essentially disables the line search and makes the algorithm take aggressive steps, without global convergence guarantees.don't arbitrarily accept the full stepAlways accept the first trial step.This option determines how the step size (alpha_y) will be calculated when updating the constraint multipliers.Call LSAcceptor to get step size for yuse the dual step size, and full step if delta_x <= alpga_for_y_toluse the primal step size, and full step if delta_x <= alpga_for_y_tollike "min_dual_infeas", but safeguarded by "min" and "max"choose step size minimizing new dual infeasibilityuse the max of primal and bound multipliersuse the min of primal and bound multipliersuse step size for the bound multipliers (good for LPs)Method to determine the step size for constraint multipliers.This is only relevant if "alpha_for_y" is chosen "primal-and-full" or "dual-and-full". The step size for the equality constraint multipliers is taken to be one if the max-norm of the primal step is less than this tolerance.Tolerance for switching to full equality multiplier steps.If the search direction in the primal variables (x and s) is, in relative terms for each component, less than this value, the algorithm accepts the full step without line search. If this happens repeatedly, the algorithm will terminate with a corresponding exit message. The default value is 10 times machine precision.Tolerance for detecting numerically insignificant steps.If the search direction in the primal variables (x and s) is, in relative terms for each component, repeatedly less than tiny_step_tol, and the step in the y variables is smaller than this threshold, the algorithm will terminate.Tolerance for quitting because of numerically insignificant steps.If the number of successive iterations in which the backtracking line search did not accept the first trial point exceeds this number, the watchdog procedure is activated. Choosing "0" here disables the watchdog procedure.Number of shortened iterations that trigger the watchdog.This option determines the number of trial iterations allowed before the watchdog procedure is aborted and the algorithm returns to the stored point.Maximum number of watchdog iterations.This options is meant to activate heuristics that may speed up the infeasibility determination if you expect that there is a good chance for the problem to be infeasible. In the filter line search procedure, the restoration phase is called more quickly than usually, and more reduction in the constraint violation is enforced before the restoration phase is left. If the problem is square, this option is enabled automatically.the problem has a good chance to be infeasiblethe problem probably be feasibleEnable heuristics to quickly detect an infeasible problem.If the constraint violation becomes smaller than this threshold, the "expect_infeasible_problem" heuristics in the filter line search are disabled. If the problem is square, this options is set to 0.Threshold for disabling "expect_infeasible_problem" option.Setting this option to "yes" forces the algorithm to switch to the feasibility restoration phase in the first iteration. If the initial point is feasible, the algorithm will abort with a failure.force start in restoration phasedon't force start in restoration phaseTells algorithm to switch to restoration phase in first iteration.The soft restoration phase attempts to reduce the primal-dual error with regular steps. If the damped primal-dual step (damped only to satisfy the fraction-to-the-boundary rule) is not decreasing the primal-dual error by at least this factor, then the regular restoration phase is called. Choosing "0" here disables the soft restoration phase.Required reduction in primal-dual error in the soft restoration phase.If the soft restoration phase is performed for more than so many iterations in a row, the regular restoration phase is called.Maximum number of iterations performed successively in soft restoration phase.3Mg~Yll3MN5Ipopt22BacktrackingLineSearchEN5Ipopt10LineSearchEN5Ipopt18RESTORATION_FAILEDEN5Ipopt24ACCEPTABLE_POINT_REACHEDEMbP?Determines how much the initial point might have to be modified in order to be sufficiently inside the bounds (together with "bound_frac"). (This is kappa_1 in Section 3.6 of implementation paper.)Desired minimum absolute distance from the initial point to bound.Determines how much the initial point might have to be modified in order to be sufficiently inside the bounds (together with "bound_push"). (This is kappa_2 in Section 3.6 of implementation paper.)Desired minimum relative distance from the initial point to bound.Determines how much the initial slack variables might have to be modified in order to be sufficiently inside the inequality bounds (together with "slack_bound_frac"). (This is kappa_1 in Section 3.6 of implementation paper.)Desired minimum absolute distance from the initial slack to bound.Determines how much the initial slack variables might have to be modified in order to be sufficiently inside the inequality bounds (together with "slack_bound_push"). (This is kappa_2 in Section 3.6 of implementation paper.)Desired minimum relative distance from the initial slack to bound.Determines how large the initial least-square guesses of the constraint multipliers are allowed to be (in max-norm). If the guess is larger than this value, it is discarded and all constraint multipliers are set to zero. This options is also used when initializing the restoration phase. By default, "resto.constr_mult_init_max" (the one used in RestoIterateInitializer) is set to zero.Maximum allowed least-square guess of constraint multipliers.All dual variables corresponding to bound constraints are initialized to this value.Initial value for the bound multipliers.If set to yes, Ipopt ignores the user provided point and solves a least square problem for the primal variables (x and s), to fit the linearize equality and inequality constraints. This might be useful if the user doesn't know anything about the starting point, or for solving an LP or QP.overwrite user-provided point with least-square estimatesLeast square initialization of the primal variablesIf set to yes, Ipopt tries to compute least-square multipliers (considering ALL dual variables). If successful, the bound multipliers are possibly corrected to be at least bound_mult_init_val. This might be useful if the user doesn't know anything about the starting point, or for solving an LP or QP.use bound_mult_init_val and least-square equality constraint multipliersLeast square initialization of all dual variablesIndicates whether this optimization should use a warm start initialization, where values of primal and dual variables are given (e.g., from a previous optimization of a related problem.)use the warm start initializationdo not use the warm start initializationLeast square estimates max(y_c) = %e, max(y_d) = %e !least_square_init_primal_ || IsValid(aug_system_solver_)The least_square_init_primal can only be chosen if the DefaultInitializer object has an AugSystemSolver. IpDefaultIterateInitializer.cpp!least_square_init_duals_ || IsValid(aug_system_solver_)The least_square_init_duals can only be chosen if the DefaultInitializer object has an AugSystemSolver. Moved initial values of %s sufficiently inside the bounds. Initial values of %s sufficiently inside the bounds. Least square intial values for x and s computed. Least square initialization of x and s failed! Least square intial values for z_L, z_U,v_L, v_U, y_c, y_d computed. Least square initialization of z_L, z_U,v_L, v_U, y_c, y_d failed! take user-provided pointleast_square_init_primalleast_square_init_dualsWarm-start for initial pointoriginal varsnew varsx_lss_lszL_newzU_newvL_newvU_newyc_newyd_newN5Ipopt25DefaultIterateInitializerEN5Ipopt18IterateInitializerEyIpEquilibrationScaling.cppFAILED_INITIALIZATIONError getting initial point from NLP in EquilibrationScaling. Too many evaluation failures during equilibiration-based scaling.Currently cannot do equilibration-based NLP scaling if MC19 is not available.Error evaluating first derivatives as at perturbed point for equilibration-based scaling. N5Ipopt20EquilibrationScalingEN5Ipopt14PointPerturberEN5Ipopt21FAILED_INITIALIZATIONEu<7u<7~?N5Ipopt19ExactHessianUpdaterEThe current filter has %d entries. phi theta iter %23.16e %5d reference_theta = %e reference_gradBarrTDelta = %e reference_theta is slightly positive at feasible point. Setting it to %e The algorithmic parameter theta_max is determined as theta_max_fact times the maximum of 1 and the constraint violation at initial point. Any point with a constraint violation larger than theta_max is unacceptable to the filter (see Eqn. (21) in the implementation paper).The algorithmic parameter theta_min is determined as theta_min_fact times the maximum of 1 and the constraint violation at initial point. The switching rules treats an iteration as an h-type iteration whenever the current constraint violation is larger than theta_min (see paragraph before Eqn. (19) in the implementation paper).Determines constraint violation threshold in the switching rule.(See Eqn. (20) in the implementation paper)Relaxation factor in the Armijo condition.(See Eqn. (19) in the implementation paper.)Multiplier for constraint violation in the switching rule.Exponent for linear barrier function model in the switching rule.Exponent for current constraint violation in the switching rule.(See Eqn. (18a) in the implementation paper.)Relaxation factor in the filter margin for the barrier function.(See Eqn. (18b) in the implementation paper.)Relaxation factor in the filter margin for the constraint violation.(This is gamma_alpha in Eqn. (20) in the implementation paper.)Safety factor for the minimal step size (before switching to restoration phase).Choosing 0 disables the second order corrections. (This is p^{max} of Step A-5.9 of Algorithm A in the implementation paper.)Maximum number of second order correction trial steps at each iteration.This option determines how much a second order correction step must reduce the constraint violation so that further correction steps are attempted. (See Step A-5.9 of Algorithm A in the implementation paper.)Factor in the sufficient reduction rule for second order correction.Trial points are rejected if they lead to an increase in the barrier objective function by more than obj_max_inc orders of magnitude.Determines the upper bound on the acceptable increase of barrier objective function.A positive number enables a heuristic that resets the filter, whenever in more than "filter_reset_trigger" successive iterations the last rejected trial steps size was rejected because of the filter. This option determine the maximal number of resets that are allowed to take place.Maximal allowed number of filter resetsIf the filter reset heuristic is active and the number of successive iterations in which the last rejected trial step size was rejected because of the filter, the filter is reset.Number of iterations that trigger the filter reset.If "mu_strategy" is "adaptive", this option determines what kind of corrector steps should be tried.corrector step towards current muThe type of corrector steps that should be taken (unsupported!).The corrector step is not tried if negative curvature has been encountered during the computation of the search direction in the current iteration. This option is only used if "mu_strategy" is "adaptive".Skip the corrector step in negative curvature iteration (unsupported!).The corrector step is not tried if the algorithm is currently in the monotone mode (see also option "barrier_strategy").This option is only used if "mu_strategy" is "adaptive".Skip the corrector step during monotone barrier parameter mode (unsupported!).This option determines the factor by which complementarity is allowed to increase for a corrector step to be accepted.Complementarity tolerance factor for accepting corrector step (unsupported!).Rejecting trial point because barrier objective function increasing too rapidly (from %27.15e to %27.15e) trial_max is initialized to %e trial_min is initialized to %e trial_theta = %e is larger than theta_max = %e New values of barrier function = %23.16e (reference %23.16e): New values of constraint violation = %23.16e (reference %23.16e): Checking filter acceptability... Resetting filter because in %d iterations last rejection was due to filterFilter should be reset, but maximal number of resets already exceeded. Checking sufficient reduction... theta_min_fact_ < theta_max_fact_Option "theta_min_fact": This value must be larger than 0 and less than theta_max_fact.The linear system could not be solved for the corrector step. Solving the Primal Dual System for the affine step avrg_compl(curr) = %e, avrg_compl(trial) = %e Rejecting corrector step, because trial complementarity is too large. Warning: Corrector step rejected due to evaluation error Corrector step accepted with alpha_primal = %e *** Accepted corrector for Iteration: %d theta_min_facteta_phideltas_phis_thetagamma_phigamma_thetaalpha_min_fracobj_max_incmax_filter_resetsfilter_reset_triggerprimal-dualcorrector step towards mu=0affineno correctordon't skipskip_corr_if_neg_curvskip_corr_in_monotone_modecorrector_compl_avrg_red_factTmaxChecking Armijo Condition... Failed... Succeeded... F+F-IpFilterLSAcceptor.cppIpBacktrackingLSAcceptor.hppdelta_corrN5Ipopt16FilterLSAcceptorEffffff@??Gz?IpGenAugSystemSolver.cppFATAL_ERROR_IN_LINEAR_SOLVERA fatal error occured in the linear solver.Factorization failed with retval = %d N5Ipopt18GenAugSystemSolverEN5Ipopt28FATAL_ERROR_IN_LINEAR_SOLVEREError getting initial point from NLP in GradientScaling. Gradient of objective function is zero at starting point. Cannot determine scaling factor based on scaling_obj_target_gradient option. Scaling parameter for objective function = %e Error evaluating objective gradient at user provided starting point. No scaling factor for objective function computed! Equality constraints are scaled with smallest scaling parameter is %e Equality constraints are not scaled. Equality constraints are scaled uniformly by %e Error evaluating Jacobian of equality constraints at user provided starting point. No scaling factors for equality constraints computed! Inequality constraints are scaled with smallest scaling parameter is %e Inequality constraints are not scaled. Inequality constraints are scaled uniformly by %e Error evaluating Jacobian of inequality constraints at user provided starting point. No scaling factors for inequality constraints computed! nlp_scaling_obj_target_gradientnlp_scaling_constr_target_gradientThis is the gradient scaling cut-off. If the maximum gradient is above this value, then gradient based scaling will be performed. Scaling parameters are calculated to scale the maximum gradient back to this value. (This is g_max in Section 3.8 of the implementation paper.) Note: This option is only used if "nlp_scaling_method" is chosen as "gradient-based".Maximum gradient after NLP scaling.If a positive number is chosen, the scaling factor the objective function is computed so that the gradient has the max norm of the given size at the starting point. This overrides nlp_scaling_max_gradient for the objective function.Target value for objective function gradient size.If a positive number is chosen, the scaling factor the constraint functions is computed so that the gradient has the max norm of the given size at the starting point. This overrides nlp_scaling_max_gradient for the constraint functions.Target value for constraint function gradient size.IpGradientScaling.cppN5Ipopt15GradientScalingE ************************************************** *** Update Barrier Parameter for Iteration %d: ************************************************** Barrier parameter could not be updated! *** Finding Acceptable Trial Point for Iteration %d:*** Update HessianMatrix for Iteration %d:Total number of variables............................: %8d variables with only lower bounds: %8d variables with lower and upper bounds: %8d variables with only upper bounds: %8d Total number of equality constraints.................: %8d Total number of inequality constraints...............: %8d inequality constraints with only lower bounds: %8d inequality constraints with lower and upper bounds: %8d inequality constraints with only upper bounds: %8d This is a square problem, but multipliers cannot be recomputed at solution, since no eq_mult_calculator object is available in IpoptAlgorithm Cannot recompute multipliers for feasibility problem. Error in eq_mult_calculator Error while obtaining initial iterates.*** Solving the Primal Dual System for Iteration %d:*** Step Calculated for Iteration: %d *** Step could not be computed in iteration %d! If the dual variables deviate from their primal estimates, a correction is performed. (See Eqn. (16) in the implementation paper.) Setting the value to less than 1 disables the correction.Factor limiting the deviation of dual variables from primal estimates.This asks the algorithm to recompute the multipliers, whenever the current infeasibility is less than recalc_y_feas_tol. Choosing yes might be helpful in the quasi-Newton option. However, each recalculation requires an extra factorization of the linear system. If a limited memory quasi-Newton option is chosen, this is used by default.use least-square multiplier estimatesuse the Newton step to update the multipliersTells the algorithm to recalculate the equality and inequality multipliers as least square estimates.If recalc_y is chosen and the current infeasibility is less than this value, then the multipliers are recomputed.Feasibility threshold for recomputation of multipliers.If set to yes, Ipopt runs as Mehrotra's predictor-corrector algorithm. This works usually very well for LPs and convex QPs. This automatically disables the line search, and chooses the (unglobalized) adaptive mu strategy with the "probing" oracle, and uses "corrector_type=affine" without any safeguards; you should not set any of those options explicitly in addition. Also, unlessotherwise specified, the values of "bound_push", "bound_frac", and "bound_mult_init_val" are set more aggressive, and sets "alpha_for_y=bound_mult".Do Mehrotra's predictor-corrector algorithm.Indicates if we want to do Mehrotra's algorithm.Line search didn't find acceptable trial point. In iteration %d, %d Slack too small, adjusting variable bound In iteration %d, %d Slacks too small, adjusting variable bounds Some value in z_L becomes too large - maximal correction = %8.2e Some value in z_U becomes too large - maximal correction = %8.2e Some value in v_L becomes too large - maximal correction = %8.2e Some value in v_U becomes too large - maximal correction = %8.2e dual infeasisibility before least square multiplier update = %e Recalculation of y multipliers skipped because eq_mult_calc returned false. ****************************************************************************** This program contains Ipopt, a library for large-scale nonlinear optimization. Ipopt is released as open source code under the Common Public License (CPL). For more information visit http://projects.coin-or.org/Ipopt ****************************************************************************** NOTE: You are using Ipopt only with the MUMPS linear solver. Other linear solvers might be more efficient (see Ipopt documentation). WARNING: Problem in step computation; switching to emergency mode. ERROR: Problem in step computation, but emergency mode cannot be activated. string_option=="never-monotone-mode"If mehrotra_algorithm=yes, adaptive_mu_globalization must be "never-monotone-mode".If mehrotra_algorithm=yes, corrector_type must be "afnone".If mehrotra_algorithm=yes, accept_every_trial_step must be "yes".the IpIpoptData object failed to initialize.the IpIpoptCalculatedQuantities object failed to initialize.the IpIpoptNLP object failed to initialize.the iterate_initializer strategy failed to initialize.the mu_update strategy failed to initialize.the search_direction_calculator strategy failed to initialize.the line_search strategy failed to initialize.the conv_check strategy failed to initialize.the iter_output strategy failed to initialize.the hessian_updater strategy failed to initialize.Barrier Parameter: %e IpIpoptAlg.cppkappa_sigmaDo the usual Ipopt algorithm.sbold_x_Lold_x_Uold_d_Lold_d_Unew_x_Lnew_x_Unew_d_Lnew_d_Uzy Step computation failed.STEP_COMPUTATION_FAILEDstring_option=="none"string_option=="yes"retvalueN5Ipopt14IpoptAlgorithmEN5Ipopt23STEP_COMPUTATION_FAILEDEN5Ipopt18LOCALLY_INFEASIBLEEN5Ipopt39RESTORATION_CONVERGED_TO_FEASIBLE_POINTEN5Ipopt28RESTORATION_MAXITER_EXCEEDEDEN5Ipopt21RESTORATION_USER_STOPEN5Ipopt26FEASIBILITY_PROBLEM_SOLVEDEN5Ipopt14INTERNAL_ABORTEs_maxkappa_dslack_moveLine searchuse the infinity normuse the 2-normuse the 1-normconstraint_violation_norm_type(See paragraph after Eqn. (6) in the implementation paper.)Scaling threshold for the NLP error.(see Section 3.7 in implementation paper.)Weight for linear damping term (to handle one-sided bounds).Due to numerical issues or the lack of an interior, the slack variables might become very small. If a slack becomes very small compared to machine precision, the corresponding bound is moved slightly. This parameter determines how large the move should be. Its default value is mach_eps^{3/4}. (See also end of Section 3.5 in implementation paper - but actual implementation might be somewhat different.)Correction size for very small slacks.Determines which norm should be used when the algorithm computes the constraint violation in the line search.Norm to be used for the constraint violation in the line search.N5Ipopt25IpoptCalculatedQuantitiesEN5Ipopt15DependentResultINS_8SmartPtrIKNS_6VectorEEEEEN5Ipopt15DependentResultINS_8SmartPtrIKNS_9SymMatrixEEEEEN5Ipopt15DependentResultINS_8SmartPtrIKNS_6MatrixEEEEEN5Ipopt13CachedResultsINS_8SmartPtrIKNS_9SymMatrixEEEEEN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6MatrixEEEEEN5Ipopt13CachedResultsINS_8SmartPtrIKNS_6VectorEEEEEresto.tolDetermines the convergence tolerance for the algorithm. The algorithm terminates successfully, if the (scaled) NLP error becomes smaller than this value, and if the (absolute) criteria according to "dual_inf_tol", "primal_inf_tol", and "cmpl_inf_tol" are met. (This is epsilon_tol in Eqn. (6) in implementation paper). See also "acceptable_tol" as a second termination criterion. Note, some other algorithmic features also use this quantity to determine thresholds etc.Desired convergence tolerance (relative).N5Ipopt9IpoptDataEN5Ipopt16TimingStatisticsEN5Ipopt19IteratesVectorSpaceEN5Ipopt14IteratesVectorEN5Ipopt22LeastSquareMultipliersEN5Ipopt22EqMultiplierCalculatorE Average complemantarity is %lf Xi (distance from uniformity) is %lf Barrier parameter proposed by LOQO rule is %lf sigma=%8.2e xi=%8.2e N5Ipopt12LoqoMuOracleEN5Ipopt8MuOracleELowRankAugSystemSolver: SolveMultiVector returned retval = %d for V. LowRankAugSystemSolver: Cholesky for M1 returned error! LowRankAugSystemSolver: SolveMultiVector returned retval = %d for U. LowRankAugSystemSolver: Cholesky for M2 returned error. LowRankAugSystemSolver: AugSystemSolver returned retval = %d for right hand side. N5Ipopt22LowRankAugSystemSolverEN5Ipopt15DiagMatrixSpaceEN5Ipopt14SymMatrixSpaceEN5Ipopt11MatrixSpaceEIpMonotoneMuUpdate.cppmu_allow_fast_monotone_decreaseThis option determines the initial value for the barrier parameter (mu). It is only relevant in the monotone, Fiacco-McCormick version of the algorithm. (i.e., if "mu_strategy" is chosen as "monotone")Initial value for the barrier parameter.The convergence tolerance for each barrier problem in the monotone mode is the value of the barrier parameter times "barrier_tol_factor". This option is also used in the adaptive mu strategy during the monotone mode. (This is kappa_epsilon in implementation paper).Factor for mu in barrier stop test.For the Fiacco-McCormick update procedure the new barrier parameter mu is obtained by taking the minimum of mu*"mu_linear_decrease_factor" and mu^"superlinear_decrease_power". (This is kappa_mu in implementation paper.) This option is also used in the adaptive mu strategy during the monotone mode.Determines linear decrease rate of barrier parameter.For the Fiacco-McCormick update procedure the new barrier parameter mu is obtained by taking the minimum of mu*"mu_linear_decrease_factor" and mu^"superlinear_decrease_power". (This is theta_mu in implementation paper.) This option is also used in the adaptive mu strategy during the monotone mode.Determines superlinear decrease rate of barrier parameter.If set to "no", the algorithm enforces at least one iteration per barrier problem, even if the barrier test is already met for the updated barrier parameter.Allow fast decrease of mu if barrier test it metTake at least one iteration per barrier problemAllow skipping of barrier problem if barrier test is already met.(This is tau_min in the implementation paper.) This option is also used in the adaptive mu strategy during the monotone mode.Lower bound on fraction-to-the-boundary parameter tau.Optimality Error for Barrier Sub-problem = %e sub_problem_error < kappa_eps * mu (%e) Updating mu=%25.16e and tau=%25.16e to new_mu=%25.16e and new_tau=%25.16e N5Ipopt16MonotoneMuUpdateEx scaling vectorNo x scaling provided c scaling vectorNo c scaling provided d scaling vectorNo d scaling provided This option sets a scaling factor for the objective function. The scaling is seen internally by Ipopt but the unscaled objective is reported in the console output. If additional scaling parameters are computed (e.g. user-scaling or gradient-based), both factors are multiplied. If this value is chosen to be negative, Ipopt will maximize the objective function instead of minimizing it.Scaling factor for the objective function.objective scaling factor = %g N5Ipopt18NoNLPScalingObjectEN5Ipopt19StandardScalingBaseEN5Ipopt16NLPScalingObjectEN5Ipopt20SymScaledMatrixSpaceEAacceptable_iterMaximum number of iterations.The algorithm terminates with an error message if the number of iterations exceeded this number.Absolute tolerance on the dual infeasibility. Successful termination requires that the max-norm of the (unscaled) dual infeasibility is less than this threshold.Desired threshold for the dual infeasibility.Absolute tolerance on the constraint violation. Successful termination requires that the max-norm of the (unscaled) constraint violation is less than this threshold.Desired threshold for the constraint violation.Absolute tolerance on the complementarity. Successful termination requires that the max-norm of the (unscaled) complementarity is less than this threshold.Desired threshold for the complementarity conditions.Determines which (scaled) overall optimality error is considered to be "acceptable." There are two levels of termination criteria. If the usual "desired" tolerances (see tol, dual_inf_tol etc) are satisfied at an iteration, the algorithm immediately terminates with a success message. On the other hand, if the algorithm encounters "acceptable_iter" many iterations in a row that are considered "acceptable", it will terminate before the desired convergence tolerance is met. This is useful in cases where the algorithm might not be able to achieve the "desired" level of accuracy."Acceptable" convergence tolerance (relative).If the algorithm encounters this many successive "acceptable" iterates (see "acceptable_tol"), it terminates, assuming that the problem has been solved to best possible accuracy given round-off. If it is set to zero, this heuristic is disabled.Number of "acceptable" iterates before triggering termination.Absolute tolerance on the dual infeasibility. "Acceptable" termination requires that the (max-norm of the unscaled) dual infeasibility is less than this threshold; see also acceptable_tol."Acceptance" threshold for the dual infeasibility.Absolute tolerance on the constraint violation. "Acceptable" termination requires that the max-norm of the (unscaled) constraint violation is less than this threshold; see also acceptable_tol."Acceptance" threshold for the constraint violation.Absolute tolerance on the complementarity. "Acceptable" termination requires that the max-norm of the (unscaled) complementarity is less than this threshold; see also acceptable_tol."Acceptance" threshold for the complementarity conditions.If any component of the primal iterates exceeded this value (in absolute terms), the optimization is aborted with the exit message that the iterates seem to be diverging.Threshold for maximal value of primal iterates.N5Ipopt31OptimalityErrorConvergenceCheckEN5Ipopt16ConvergenceCheckE@xDFunction Evaluations................: %10.3f Objective function.................: %10.3f Equality constraints...............: %10.3f Inequality constraints.............: %10.3f Equality constraint Jacobian.......: %10.3f Inequality constraint Jacobian.....: %10.3f Lagrangian Hessian.................: %10.3f Before start of the optimization, the bounds given by the user are relaxed. This option sets the factor for this relaxation. If it is set to zero, then then bounds relaxation is disabled. (See Eqn.(35) in implementation paper.)Factor for initial relaxation of the bounds.Ipopt might relax the bounds during the optimization (see, e.g., option "bound_relax_factor"). This option determines whether the final point should be projected back into the user-provide original bounds after the optimization.Project final point back into original boundsIndicates whether final points should be projected into original bounds.If "yes" is chosen, then the algorithm assumes that an NLP is now to be solved, whose strcture is identical to one that already was considered (with the same NLP object).Assume this is problem has known structureIndicates whether a problem with a structure identical to the previous one is to be solved.Activating this option will cause an error if an invalid number is detected in the constraint Jacobians or the Lagrangian Hessian. If this is not activated, the test is skipped, and the algorithm might proceed with invalid numbers and fail.Check Jacobians and Hessian for Nan and Inf.Indicates whether it is desired to check for Nan/Inf in derivative matricesActivating this option will cause Ipopt to ask for the Jacobian of the equality constraints only once from the NLP and reuse this information later.Assume that equality constraints Jacobian are constantDon't assume that all equality constraints are linearIndicates whether all equality constraints are linearActivating this option will cause Ipopt to ask for the Jacobian of the inequality constraints only once from the NLP and reuse this information later.Don't assume that all inequality constraints are linearIndicates whether all inequality constraints are linearActivating this option will cause Ipopt to ask for the Hessian of the Lagrangian function only once from the NLP and reuse this information later.Assume that Hessian is constantIndicates whether the problem is a quadratic problemThis determines which kind of information for the Hessian of the Lagrangian function is used by the algorithm.Perform a limited-memory quasi-Newton approximationUse second derivatives provided by the NLP.Indicates what Hessian information is to be used.in space of all variables (without slacks)only in space of nonlinear variables.Indicates in which subspace the Hessian information is to be approximated.ERROR: This method is only a for h(mu) and should not be calledError evaluating the hessian of the lagrangianError evaluating the jacobian of the inequality constraintsError evaluating the jacobian of the equality constraintsThe Jacobian for the equality constraints contains an invalid number The Jacobian for the equality constraints contains an invalid numbersuccess && IsFiniteNumber(unscaled_d->Nrm2())Error evaluating the inequality constraintssuccess && IsFiniteNumber(unscaled_c->Nrm2())Error evaluating the equality constraintsERROR: This method is only a placeholder for grad_f(mu) and should not be calledsuccess && IsFiniteNumber(unscaled_grad_f->Nrm2())Error evaluating the gradient of the objective functionsuccess && IsFiniteNumber(ret)Error evaluating the objective functionGetSpaces method for the NLP returns false. Hessian approximation will be done in smaller space of dimension %d (instead of %d) Hessian approximation will be done in the space of all %d x variables. Too few degrees of freedom: %d equality constriants but only %d variablesToo few degrees of freedom (no free variables)!OrigIpoptNLP called with warm_start_same_structure, but the problem is solved for the first time.hessian_approximation_spaceLeave final point unchangedAssume this is a new problem.Don't check (faster).Assume that Hessian changeslimited-memoryexactall-variablesnonlinear-variablesfinal x unscaledfinal y_c unscaledfinal y_d unscaledfinal z_L unscaledfinal z_U unscaledIpOrigIpoptNLP.cppINTERNAL_ABORTsuccessx_space_->Dim() > 0IsValid(x_space_)original x_L unscaledoriginal x_U unscaledoriginal d_L unscaledoriginal d_U unscaledmodified x_L scaledmodified x_U scaledmodified d_L scaledmodified d_U scaledinitial x unscaledinitial y_c unscaledinitial y_d unscaledinitial z_L unscaledinitial z_U unscaledN5Ipopt12OrigIpoptNLPEN5Ipopt8IpoptNLPEN5Ipopt27LowRankUpdateSymMatrixSpaceEiter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls ************************************************** %4d%c%14.7e %7.2e %7.2e %5.1f %7.2e %5s %7.2e %7.2e%c%3d*** Beginning Iteration %d from the following point:Current barrier parameter mu = %21.16e Current fraction-to-the-boundary parameter tau = %21.16e No search direction has been computed yet. ***Current NLP Values for Iteration %d: This string contains some insider information about the current iteration.print string at end of each iteration outputEnables printing of additional info string at end of iteration output.*** Summary of Iteration: %d:%5.1f||curr_x||_inf = %.16e ||curr_s||_inf = %.16e ||curr_y_c||_inf = %.16e ||curr_y_d||_inf = %.16e ||curr_z_L||_inf = %.16e ||curr_z_U||_inf = %.16e ||curr_v_L||_inf = %.16e ||curr_v_U||_inf = %.16e ||delta_x||_inf = %.16e ||delta_s||_inf = %.16e ||delta_y_c||_inf = %.16e ||delta_y_d||_inf = %.16e ||delta_z_L||_inf = %.16e ||delta_z_U||_inf = %.16e ||delta_v_L||_inf = %.16e ||delta_v_U||_inf = %.16e curr_xcurr_y_ccurr_y_dcurr_slack_x_Lcurr_slack_x_Ucurr_z_Lcurr_z_Ucurr_slack_s_Lcurr_slack_s_Ucurr_v_Lcurr_v_Ucurr_grad_lag_xcurr_grad_lag_sgrad_fcurr_dcurr_d - curr_sjac_cjac_d - print_info_stringdon't print stringN5Ipopt19OrigIterationOutputEN5Ipopt15IterationOutputE Skip the update. Perform the update. IsValid(h_space_)curr_eta (for B0) is %e WrWSWesigma (for B0) is %e WpWbWslimited_memory_update_typelimited_memory_initializationlimited_memory_init_valSR1 (not working well)sr1BFGS update (with skipping)bfgssigma = y^Ty/s^Tyscalar2sigma = s^Ty/s^Tsscalar1Limited-Memory test for skipping: s^Ty = %e snrm = %e ynrm = %e Eigenvalues in SR1 update: emin=%e emax=%e ratio=%e Limited-memory quasi-Newton option chosen, but NLP doesn't provide LowRankUpdateSymMatrixSpace.IpLimMemQuasiNewtonUpdater.cppLimited-Memory approximation started; store data at current iterate. Resetting Limited-Memory Update. In limited-memory update, s_new_max is %e Skipping BFGS update in restoration phase because Dmin is %e Projecting sigma into safeguards to be %e! Cholesky factorization failed for LBFGS update! Skipping update. Undoing most recent SR1 update. Eigenvalue decomposition failed for limited-memory SR1 update.Number of successive iterations with skipping: %d This option determines the number of most recent iterations that are taken into account for the limited-memory quasi-Newton approximation.Maximum size of the history for the limited quasi-Newton Hessian approximation.Determines which update formula is to be used for the limited-memory quasi-Newton approximation.Quasi-Newton update formula for the limited memory approximation.Determines how the diagonal Matrix B_0 as the first term in the limited memory approximation should be computed.sigma = limited_memory_init_valInitialization strategy for the limited memory quasi-Newton approximation.The starting matrix in the low rank update, B0, is chosen to be this multiple of the identity in the first iteration (when no updates have been performed yet), and is constantly chosen as this value, if "limited_memory_initialization" is "constant".Value for B0 in low-rank update.If the update is skipped more than this number of successive iterations, we quasi-Newton approximation is reset.Threshold for successive iterations where update is skipped.N5Ipopt24LimMemQuasiNewtonUpdaterE-q=Iterative refinement (on the full unsymmetric system) is performed for each right hand side. This option determines the minimum number of iterative refinements (i.e. at least "min_refinement_steps" iterative refinement steps are enforced per right hand side.)Minimum number of iterative refinement steps per linear system solve.Iterative refinement (on the full unsymmetric system) is performed for each right hand side. This option determines the maximum number of iterative refinement steps.Maximum number of iterative refinement steps per linear system solve.Iterative refinement is performed until the residual test ratio is less than this tolerance (or until "max_refinement_steps" refinement steps are performed).Iterative refinement toleranceIf the residual test ratio is larger than this value after failed iterative refinement, the algorithm pretends that the linear system is singular.Threshold for declaring linear system singular after failed iterative refinement.If the improvement of the residual test ratio made by one iterative refinement step is not better than this factor, iterative refinement is aborted.Minimal required reduction of residual test ratio in iterative refinement.If positive, incorrect inertia in the augmented system is ignored, and we test if the direction is a direction of positive curvature. This tolerance determines when the direction is considered to be sufficiently positive.Tolerance for heurstic to ignore wrong inertia.nrm_rhs = %8.2e nrm_sol = %8.2e nrm_resid = %8.2e max_refinement_steps_ >= min_refinement_steps_Option "max_refinement_steps": This value must be larger than or equal to min_refinement_steps (default 1)residual_ratio_singular_ >= residual_ratio_max_Option "residual_ratio_singular": This value must be not smaller than residual_ratio_max.Solving system with delta_x=%e delta_s=%e delta_c=%e delta_d=%e PerturbForSingularity can't be done Number of negative eigenvalues too small! Asking augmented system solver to improve quality of its solutions. Quality could not be improved PerturbForSingularity can't be done for assume singular. PerturbForWrongInertia can't be done for wrong interia or singular. In inertia heuristic: xWx = %e xx = %e -> Redo with modified matrix. PerturbForWrongInertia can't be done for inertia heuristic. Number of trial factorizations performed: %d Perturbation parameters: delta_x=%e delta_s=%e delta_c=%e delta_d=%e SolveOnce returns false during iterative refinement.Iterative refinement failed with residual_ratio = %e Just accept current solution. Pretend that the current system (including modifications) is singular. residual_ratio_maxresidual_ratio_singularresidual_improvement_factorneg_curv_test_tolIpPDFullSpaceSolver.cppqresidmax-norm resid_x %e max-norm resid_s %e max-norm resid_c %e max-norm resid_d %e max-norm resid_zL %e max-norm resid_zU %e max-norm resid_vL %e max-norm resid_vU %e residual_ratio = %e solve_retvalN5Ipopt17PDFullSpaceSolverEN5Ipopt14PDSystemSolverEN5Ipopt15DependentResultIPvEEN5Ipopt13CachedResultsIPvEEv?In order to guarantee that the search directions are indeed proper descent directions, Ipopt requires that the inertia of the (augmented) linear system for the step computation has the correct number of negative and positive eigenvalues. The idea is that this guides the algorithm away from maximizers and makes Ipopt more likely converge to first order optimal points that are minimizers. If the inertia is not correct, a multiple of the identity matrix is added to the Hessian of the Lagrangian in the augmented system. This parameter gives the maximum value of the regularization parameter. If a regularization of that size is not enough, the algorithm skips this iteration and goes to the restoration phase. (This is delta_w^max in the implementation paper.)Maximum value of regularization parameter for handling negative curvature.The size of the perturbation of the Hessian block is never selected smaller than this value, unless no perturbation is necessary. (This is delta_w^min in implementation paper.)Smallest perturbation of the Hessian block.The factor by which the perturbation is increased when a trial value was not sufficient - this value is used for the computation of the very first perturbation and allows a different value for for the first perturbation than that used for the remaining perturbations. (This is bar_kappa_w^+ in the implementation paper.)Increase factor for x-s perturbation for very first perturbation.The factor by which the perturbation is increased when a trial value was not sufficient - this value is used for the computation of all perturbations except for the first. (This is kappa_w^+ in the implementation paper.)Increase factor for x-s perturbation.The factor by which the perturbation is decreased when a trial value is deduced from the size of the most recent successful perturbation. (This is kappa_w^- in the implementation paper.)Decrease factor for x-s perturbation.The first value tried for the x-s perturbation in the inertia correction scheme.(This is delta_0 in the implementation paper.)Size of first x-s perturbation tried.(This is bar delta_c in the implementation paper.)Size of the regularization for rank-deficient constraint Jacobians.(This is kappa_c in the implementation paper.)Exponent for mu in the regularization for rank-deficient constraint Jacobians.This options makes the delta_c and delta_d perturbation be used for the computation of every search direction. Usually, it is only used when the iteration matrix is singular.perturbation only used when requiredActive permanent perturbation of constraint linearization.always use perturbationN5Ipopt21PDPerturbationHandlerE @UUUUUU?fast_step_computationIf set to yes, the algorithm assumes that the linear system that is solved to obtain the search direction, is solved sufficiently well. In that case, no residuals are computed, and the computation of the search direction is a little faster.Trust that linear systems are solved well.Verify solution of linear system by computing residuals.Indicates if the linear system should be solved quickly.N5Ipopt21PDSearchDirCalculatorEsigma_maxThe linear system could not be solved for the affine step! The affine maximal step sizes are alpha_primal_aff = %23.16e alpha_dual_aff = %23.16e The average complementariy at the affine step is %23.16e The average complementariy at the current point is %23.16e N5Ipopt15ProbingMuOracleEsigma = %8.2e d_inf = %18.12e p_inf = %18.12e cmpl = %18.12e q = %18.12e a_pri = %8.2e a_dual = %8.2e xi = %8.2e Solving the Primal Dual System for the centering step The linear system could not be solved for the centering step! quality_function_section_sigma_tolquality_function_section_qf_tolThis is the upper bound for the centering parameter chosen by the quality function based barrier parameter update. (Only used if option "mu_oracle" is set to "quality-function".)Maximum value of the centering parameter.This is the lower bound for the centering parameter chosen by the quality function based barrier parameter update. (Only used if option "mu_oracle" is set to "quality-function".)Minimum value of the centering parameter.(Only used if option "mu_oracle" is set to "quality-function".)Norm used for components of the quality function.This determines whether a term is added to the quality function to penalize deviation from centrality with respect to complementarity. The complementarity measure here is the xi in the Loqo update rule. (Only used if option "mu_oracle" is set to "quality-function".)complementarity * the reciprocal of the centrality measure cubedcomplementarity * the reciprocal of the centrality measurecomplementarity * the log of the centrality measureThe penalty term for centrality that is included in quality function.This determines whether a term is added to the quality function that penalizes situations where the complementarity is much smaller than dual and primal infeasibilities. (Only used if option "mu_oracle" is set to "quality-function".)Max(0,Max(dual_inf,primal_inf)-compl)^3The balancing term included in the quality function for centrality.The golden section search is performed for the quality function based mu oracle. (Only used if option "mu_oracle" is set to "quality-function".)Maximum number of search steps during direct search procedure determining the optimal centering parameter.Tolerance for the section search procedure determining the optimal centering parameter (in sigma space).Tolerance for the golden section search procedure determining the optimal centering parameter (in the function value space).qf_tol Sigma = %e qf=%dsigma_mincubed-reciprocallogno penalty term is addedcubicno balancing term is addedN5Ipopt23QualityFunctionMuOracleE`-!r?P/7?The restoration phase algorithm is performed, until a point is found that is acceptable to the filter and the infeasibility has been reduced by at least the fraction given by this option.Required reduction of infeasibility before leaving restoration phase.The algorithm terminates with an error message if the number of iterations successively taken in the restoration phase exceeds this number.Maximum number of successive iterations in restoration phase.More than %d successive iterations taken in restoration phase. orig_curr_theta = %8.2e, orig_trial_theta = %8.2e This is the first iteration - continue to take at least one step. Point does not provide sufficient reduction w.r.t the original theta (orig_theta_max=%e). Restoration phase found points satisfying feasibility tolerance in square problem. Point is not acceptable to the original filter. Point is not acceptable to the original current point. Restoration found a point that provides sufficient reduction in theta and is acceptable to the current filter. Tightening restoration phase tolerance to %e. Restoration phase converged to a feasible point that is unacceptable to the filter for the original problem. Restoration phase converged to a feasible point that is unacceptable to the filter for the original problem.RESTORATION_CONVERGED_TO_FEASIBLE_POINTRestoration phase converged to a point of local infeasibilitymax_resto_iterorig_trial_barr = %8.2e IpRestoFilterConvCheck.cppLOCALLY_INFEASIBLEN5Ipopt27RestoFilterConvergenceCheckE?ERROR: In RestoIpoptNLP f() is called without mu!Setting this option to "yes" makes the restoration phase algorithm evaluate the objective function of the original problem at every trial point encountered during the restoration phase, even if this value is not required. In this way, it is guaranteed that the original objective function can be evaluated without error at all accepted iterates; otherwise the algorithm might fail at a point where the restoration phase accepts an iterate that is good for the restoration phase problem, but not the original problem. On the other hand, if the evaluation of the original objective is expensive, this might be costly.Determines if the original objective function should be evaluated at restoration phase trial points.ERROR: In RestoIpoptNLP grad_f() is called without mu!IpRestoIpoptNLP.cppevaluate at every trial pointskip evaluationN5Ipopt13RestoIpoptNLPEN5Ipopt19IdentityMatrixSpaceEInitial barrier parameter resto_mu = %e N5Ipopt23RestoIterateInitializerE*** Summary of Iteration %d for original NLP:Primal infeasibility for restoration phase problem = %.16e Dual infeasibility for restoration phase problem = %.16e ***Current NLP Values for Iteration (Restoration phase problem) %d: N5Ipopt20RestoIterationOutputEAfter returning from the restoration phase, the bound multipliers are updated with a Newton step for complementarity. Here, the change in the primal variables during the entire restoration phase is taken to be the corresponding primal Newton step. However, if after the update the largest bound multiplier exceeds the threshold specified by this option, the multipliers are all reset to 1.Threshold for resetting bound multipliers after the restoration phase.After returning from the restoration phase, the constraint multipliers are recomputed by a least square estimate. This option triggers when those least-square estimates should be ignored.Threshold for resetting equality and inequality multipliers after restoration phase.Starting Restoration Phase for the %d. time resto.required_infeasibility_reductionOptimal Objective Value = %.16E Restoration phase converged to a point with small primal infeasibility. Restoration phase converged to a point with small primal infeasibilityMaximal number of iterations exceeded in restoration phase.Restoration phase in the restoration phase failed. Restoration phase in the restoration phase failed.User requested stop during restoration phaseRecursive restoration phase algorithm termined successfully for square problem. Restoration phase converged to sufficiently feasible point of original square problem.Step size for bound multipliers: %8.2e Bound multipliers after restoration phase too large (max=%8.2e). Set all to 1. resto.start_with_restoresto.theta_max_fact RESTORATION PHASE RESULTS Optimal solution found! Number of Iterations = %d currIpRestoMinC_1Nrm.cppRESTORATION_MAXITER_EXCEEDEDRESTORATION_USER_STOPSorry, things failed ?!?! FEASIBILITY_PROBLEM_SOLVEDN5Ipopt25MinC_1NrmRestorationPhaseEN5Ipopt16RestorationPhaseEPerforming second level restoration phase for current constriant violation %8.2e N5Ipopt21RestoRestorationPhaseEIsValid(augmented_system_)IpStdAugSystemSolver.cppRHS[%2d]KKT******* KKT SYSTEM ******* (%d) KKT[%d][%d] = %23.15e Factorization successful. SOL[%2d]StdAugSystemSolver called with warm_start_same_structure, but augmented system is not initialized.N5Ipopt18StdAugSystemSolverEOverallAlgorithm....................: %10.3f PrintProblemStatistics.............: %10.3f InitializeIterates.................: %10.3f UpdateHessian......................: %10.3f OutputIteration....................: %10.3f UpdateBarrierParameter.............: %10.3f ComputeSearchDirection.............: %10.3f ComputeAcceptableTrialPoint........: %10.3f AcceptTrialPoint...................: %10.3f CheckConvergence...................: %10.3f PDSystemSolverTotal.................: %10.3f PDSystemSolverSolveOnce............: %10.3f ComputeResiduals...................: %10.3f LinearSystemScaling................: %10.3f LinearSystemSymbolicFactorization..: %10.3f LinearSystemFactorization..........: %10.3f LinearSystemBackSolve..............: %10.3f LinearSystemStructureConverter.....: %10.3f LinearSystemStructureConverterInit: %10.3f QualityFunctionSearch...............: %10.3f TryCorrector........................: %10.3f Task1...............................: %10.3f Task2...............................: %10.3f Task3...............................: %10.3f Task4...............................: %10.3f Task5...............................: %10.3f N5Ipopt11UserScalingEwarm_start_target_muwarm_start_entire_iterateUnsupported!NWuser-provided y_cuser-provided y_duser-provided z_Luser-provided z_Ud at user-provided xinitial xinitial sinitial y_cinitial y_dinitial z_Linitial z_Uinitial v_Linitial v_Uinitial slack_x_Linitial slack_x_Uinitial slack_s_Linitial slack_s_Usame as bound_push for the regular initializer.same as bound_frac for the regular initializer.same as slack_bound_push for the regular initializer.same as slack_bound_frac for the regular initializer.same as mult_bound_push for the regular initializer.Maximum initial value for the equality multipliers.call GetWarmStartIterate in the NLPcall GetStartingPoint in the NLPTells algorithm whether to use the GetWarmStartIterate method in the NLP.Tried to obtain entire warm start iterate from NLP, but it returned false. N5Ipopt27WarmStartIterateInitializerEN5Ipopt21TripletToCSRConverterEtsym_linear_solver_->ProvidesDegeneracyDetection()Selected linear solver does not support dependency detectionIpTSymDependencyDetector.cppN5Ipopt22TSymDependencyDetectorEN5Ipopt19TDependencyDetectorEThis option is only important if a linear scaling method (e.g., mc19) is used. If you choose "no", then the scaling factors are computed for every linear system from the start. This can be quite expensive. Choosing "yes" means that the algorithm will start the scaling method only when the solutions to the linear system seem not good, and then use it until the end.Start using linear system scaling if solutions seem not good.Always scale the linear system.Flag indicating that linear scaling is only done if it seems required.Error during computation of scaling factors. scaling_method_->ComputeSymTScalingFactors returned false.ERROR_IN_LINEAR_SCALING_METHODscaling factor[%6d] = %22.17e TSymLinearSolver called with warm_start_same_structure, but the internal structures are not initialized.Switching on scaling of the linear system (on demand). TSymLinearSolver called with warm_start_same_structure, but the problem is solved for the first time.Right hand side %d in TSymLinearSolver: Solution %d in TSymLinearSolver: IpTSymLinearSolver.cpphave_structure_Mcdim_==sym_A.Dim()Trhs[%5d,%5d] = %23.16e Tsol[%5d,%5d] = %23.16e N5Ipopt16TSymLinearSolverEN5Ipopt15SymLinearSolverEN5Ipopt30ERROR_IN_LINEAR_SCALING_METHODEIncreasing pivot tolerance for MUMPS from %7.2e MUMPS returned INFO(1) = %d and requires more memory, reallocating. Attempt %d Increasing icntl[13] from %d to MUMPS was not able to obtain enough memory. MUMPS returned INFO(1) = %d matrix is singular. In MumpsSolverInterface::Factorization: negevals_ = %d, but numberOfNegEVals = %d MUMPS returned INFO(1) =%d - out or memory. MUMPS returned INFO(1) =%d MUMPS failure. MUMPS used permuting_scaling %d and pivot_order %d. scaling will be %d. Error=%d returned from MUMPS in Factorization. Error=%d returned from MUMPS in Solve. mumps_->n==dim && mumps_->nz==nonzerosMumpsSolverInterface called with warm_start_same_structure, but the problem size has changed.Option "mumps_pivtolmax": This value must be between mumps_pivtol and 1.MumpsSolverInterface called with warm_start_same_structure, but the problem is solved for the first time.A smaller number pivots for sparsity, a larger number pivots for stability. This option is only available if Ipopt has been compiled with MUMPS.Pivot tolerance for the linear solver MUMPS.Ipopt may increase pivtol as high as pivtolmax to get a more accurate solution to the linear system. This option is only available if Ipopt has been compiled with MUMPS.Maximum pivot tolerance for the linear solver MUMPS.In MUMPS when significant extra fill-in is caused by numerical pivoting, larger values of mumps_mem_percent may help use the workspace more efficiently.Percentage increase in the estimated working space for MUMPS.Controls permuting and scaling in MUMPSWhen MUMPS is used to determine linearly dependent constraints, this is determines the threshold for a pivot to be considered zero. This is CNTL(3) in MUMPS.Pivot threshold for detection of linearly dependent constraints in MUMPS.to %7.2e. %d. IpMumpsSolverInterface.cpppivtolmax_>=pivtol_mumps_dep_tolmumps_->n>0 && mumps_->nz>0This is ICTL(6) in MUMPS.This is ICTL(7) in MUMPS.Controls pivot order in MUMPSThis is ICTL(8) in MUMPS.Controls scaling in MUMPSN5Ipopt20MumpsSolverInterfaceEN5Ipopt30SparseSymLinearSolverInterfaceEstderrw+N5Ipopt13StreamJournalEN5Ipopt11FileJournalEN5Ipopt7JournalEN5Ipopt10JournalistE# times usedValueName%40s %-20s %s %40s = %-20s %6d %40s = %-20s %4s Tried to set Option: String UnknownSetting: " to a value of "%g IntegerIpOptionsList.cppOption "" found. falseofftrueRead Option: result. It is not a valid option. Please check the list of available options. . It is a valid option, but it is of type , not of type Integer. Please check the documentation for options. is not a valid setting for Option: . Check the option documentation. WARNING: Tried to set option "", but the previous value is set to disallow clobbering. The setting will remain as: ", not of type Number. Please check the documentation for options. , not of type String. Please check the documentation for options. IPOPT tried to get the value of Option: . It is not a valid registered option., not of type Integer. Please check the documentation for options.": Integer value expected, but non-integer value ", not of type Number. Please check the documentation for options.": Double value expected, but non-numeric value ", not of type String. Please check the documentation for options.Tried to get a boolean from an option and failed.Start reading options from stream. Finished reading options from file. Error reading value for tag %s from file. Adding option "%s" with value "%s" to OptionsList. . It is not a valid option. Check the list of available options.Error setting string value read from option file.": Double value expected, but non-numeric option value "Error setting numeric value read from file.": Integer value expected, but non-integer option value "Error setting integer value read from option file.Error setting value read from option file.%-30s%10g-inf%10s(%11g)%-10g +inf%10d <= %10s < (%11d) <= %-10d < %-10s ("%s") Possible values: - %-23s [] Real Number < (%g)%g +inf <= (%d) <= Valid Settings: %s (%s) Default: "%s" OPTION_ALREADY_REGISTERED\_\^ \cdot 10^{\paragraph{%s:} %s $\;$ \\ {\tt -inf}{\tt %s }{\tt +inf}%d \le %s < \le %d < %s\\ Possible values: \begin{itemize} \item %s: \end{itemize} basic_string::substr\subsection{%s} ### %s ### The option: IpRegOptions.cppi->value_ != "*"matched_setting != -1 in option: ### %s (%s) ### Category: %s Description: %s The valid range for this real option is $$ and its default value is $%s$. The valid range for this integer option is $$ and its default value is $%d$. The default value for this string option is "%s". registered_options_.find(name) == registered_options_.end() has already been registered by someone elseCannot map a wildcard setting to an enumerationCould not find a match for setting ERROR_CONVERTING_STRING_TO_ENUMN5Ipopt17RegisteredOptions25OPTION_ALREADY_REGISTEREDEN5Ipopt16RegisteredOptionEN5Ipopt16RegisteredOption31ERROR_CONVERTING_STRING_TO_ENUME%s[%2d][%2d]%sCompoundMatrix "%s" with %d row and %d columns components: %sComponent for row %d and column %d: %sComponent has not been set. N5Ipopt14CompoundMatrixEN5Ipopt19CompoundMatrixSpaceE%s[%d][%d]%sCompoundSymMatrix "%s" with %d rows and columns components: %sThis component has not been set. N5Ipopt17CompoundSymMatrixEN5Ipopt22CompoundSymMatrixSpaceE%sComponent %d: %s[%2d]%sCompoundVector "%s" with %d components: %sComponent %d is not yet set! N5Ipopt19CompoundVectorSpaceEN5Ipopt14CompoundVectorE%s%s[%5d,%5d]=%23.16e %sDenseGenMatrix "%s" with %d rows and %d columns: The matrix has not yet been initialized! N5Ipopt14DenseGenMatrixEN5Ipopt19DenseGenMatrixSpaceE%sDenseSymMatrix "%s" of dimension %d (only lower triangular part printed): N5Ipopt14DenseSymMatrixEN5Ipopt19DenseSymMatrixSpaceE%sDenseVector "%s" with %d elements: %sHomogeneous vector, all elements have value %23.16e %s%s[%5d]=%23.16e %sUninitialized! N5Ipopt11DenseVectorE%sDiagonal elements not set! %sDiagMatrix "%s" with %d rows and columns, and with diagonal elements: N5Ipopt10DiagMatrixE%s%s[%5d,%5d]=%23.16e (%d) %sExpansionMatrix "%s" with %d nonzero elements: N5Ipopt15ExpansionMatrixEN5Ipopt20ExpansionMatrixSpaceE%sIdentityMatrix "%s" with %d rows and columns and the factor %23.16e. N5Ipopt14IdentityMatrixE%sDiagonal matrix: -D%sDiagonal matrix not set! -V%sV matrix not set! -U%sU matrix not set! %sLowRankUpdateSymMatrix "%s" with %d rows and columns: %sThis matrix has reduced diagonal. %sThis matrix has full diagonal. %sMultiVectorMatrix V for positive update: %sMultiVectorMatrix U for positive update: N5Ipopt22LowRankUpdateSymMatrixEN5Ipopt6MatrixE%sMultiVectorMatrix "%s" with %d columns: %sVector in column %d is not yet set! N5Ipopt22MultiVectorMatrixSpaceE_row_scalingRowScaling is NULL _unscaled_matrix%sunscaled matrix is NULL _column_scaling%sColumnScaling is NULL %sScaledMatrix "%s" of dimension %d x %d: N5Ipopt12ScaledMatrixEN5Ipopt17ScaledMatrixSpaceETerm: %d%sSumMatrix "%s" of dimension %d x %d with %d terms: %sTerm %d with factor %23.16e and the following matrix: N5Ipopt14SumMatrixSpaceEN5Ipopt9SumMatrixE%sSumSymMatrix "%s" of dimension %d with %d terms: N5Ipopt17SumSymMatrixSpaceEN5Ipopt12SumSymMatrixEN5Ipopt9SymMatrixE_row_col_scaling%sSymScaledMatrix "%s" of dimension %d x %d: N5Ipopt15SymScaledMatrixE%sZeroMatrix "%s" with %d row and %d columns components: N5Ipopt10ZeroMatrixE%sGenTMatrix "%s" of dimension %d by %d with %d nonzero elements: N5Ipopt10GenTMatrixEN5Ipopt15GenTMatrixSpaceE%sSymTMatrix "%s" of dimension %d with %d nonzero elements: N5Ipopt15SymTMatrixSpaceEN5Ipopt10SymTMatrixEUnknown matrix type passed to TripletHelper::GetNumberEntriesUnknown matrix type passed to TripletHelper::FillRowColUnknown vector type passed to TripletHelper::PutValuesInVectorUnknown vector type passed to TripletHelper::FillValuesUnknown matrix type passed to TripletHelper::FillValuesIpTripletHelper.cppUNKNOWN_MATRIX_TYPEUNKNOWN_VECTOR_TYPEN5Ipopt13TripletHelper19UNKNOWN_VECTOR_TYPEEN5Ipopt13TripletHelper19UNKNOWN_MATRIX_TYPEE;N O@@OPXO`0PN0NPO`pOO`OhPP@P0QxQpQ (R`HRp`RxRRRS@ SPSSPShT TU8WpWpiWoWОPXX@XpXYPSгS@T(T HTTTT(UUV@V0pV@V0V0W8Y PY0hYPYpYYYYZ0ZHZZ0YPZ`Z[[0[`[[`[(\X\\\[]]@0]H]`]]p]]p^h^P^_0`H``x```Pa00aha aaabb@b(c pcp9X_9p_9_9_9_9_9_9`9`: bp:@b:Xb:pb ;b=d=8d>Pd>d0>d>e ?8e@?Xe`@xeBeCe DepDfD8fHf KxgLhMHhPYxhdhpfhh0ijitiP}8jЈhjjl`mhmm0m@2PnQn@cPc`cpdhdЅdPfІfgg 0g@HgP`g`g@j0jk 8kXkxkkkpkk`lЏ8l@Xlxl@lo o0oHopoooo@Hp@xpppq q@q`Xqppqq`q0qPrpr0rxrrrЮrs(spPs0ss@ttt@u`u0uPvPvPvp v@w@w wx`x!xp%x )0y/y/y00y0y1y@2z2`z@3xz3z 4zP4z 6zp9{P?@{pB{F{ H|M`|O|\|c(}0fx}f}k}r~0uP~P}~@~@`0Ѐ Pp@P `30LRЃ h`ФЄ @8 PЫ@ `P p`p@sht`xy( ~xPȉH 80hp`п0X H`p@0PHhp``p(@0x0Џ@ @P`h 0P @@p*P;?`PCp\bPexg`jؔn(nPoxyȕPHxȖ Hpؗ@(P p@Pp `8pxX29THy~@0PН Pp` `@$+, aXstv8wpxx`Ќ8pȢh`00B_ФPi `np`PP@! 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HHIH f.USHHH; HPHHGHH@H{8HtoGt"HC8HHH[]܍@HPHHfH{H{8HHtoWuHPHC8ǐH f.USHHH: HPHHGHH@&H{8HtoOtHC8HH[]HPHH HɎH{8HHtowuHPHC8ɐAVHw: LwAUHATUSLoHM;nHDMEM`IPML$HLH)HHHH~}H9H;ZHJH;ZHJH;ZHzu:fH9_H9_HOH9_HOH H9HHOuLH)HHtlHt]HtiL@IHھLPIT$HuH9uMIl$IM9n I>Ht訋H[]A\A]A^阋H9tHH9t HH9uHH)HHI>HHtZHH f.AWIAVLwAUATUSH_HH88 LoHHH8 HM;nHGMEM`IPML$HLH)HHHH~~H;H;ZHJH;ZHJH;ZHzu;H9_H9_HOH9_HOH H9HHOuLH)HHt|HtmHtyLIHھLPIT$HuH9uaIl$IM9nI>HtHp7 LHIH[]A\A]A^A_ӉH9tHH9t HH9uHH)HHkI>HHt葉H 7 HHI;H f.AWIAVLwAUATUSH_HHh6 LoHHH6 HM;nHGMEM`IPML$HLH)HHHH~~H;H;ZHJH;ZHJH;ZHzu;H9_H9_HOH9_HOH H9HHOuLH)HHtuHtfHtrLIHھLPIT$HuH9uZIl$IM9nI>Ht'H5 HIH[]A\A]A^A_H9tHH9t HH9uHH)H3HrI>HHtɇHB5 HHIsH, ÐUHSH, HH, HtHHHuH[ÐH诉H15MatlabExceptionMatlab array must be of type doubleThe Matlab array must be a scalarMatlab array must be a string (of type CHAR)Unable to obtain string from Matlab arrayString buffer it too short for all the characters to be printed to MATLAB consolematlabN5Ipopt13MatlabJournalEUnable to perform copy; arrays have different lengthsMatlab array must be a matrixThe Matlab array must either be a cell array or a double precision matrixMatlab array must be a sparse matrixMatlab array must be an N x N matrixUnable to copy sparse matrix; 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5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/examplehs071.m0000644000335100017570000000511311133661701027037 0ustar pgehlerwebadm% Test the "ipopt" Matlab interface on the Hock & Schittkowski test problem % #71. See: Willi Hock and Klaus Schittkowski. (1981) Test Examples for % Nonlinear Programming Codes. Lecture Notes in Economics and Mathematical % Systems Vol. 187, Springer-Verlag. % % Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function [x, multipliers] = examplehs071 % The starting point. x0 = [1 5 5 1]; % The starting point. lb = [1 1 1 1]; % Lower bound on the variables. ub = [5 5 5 5]; % Upper bound on the variables. lbc = [25 40]; % Lower bounds on the constraint functions. ubc = [inf 40]; % Upper bounds on the constraint functions. % Initial dual point. multipliers.zl = [1 1 1 1]; multipliers.zu = [1 1 1 1]; multipliers.lambda = [1 1]; [x multipliers] = ipopt(x0,lb,ub,lbc,ubc,@computeObjective,... @computeGradient,@computeConstraints,... @computeJacobian,@computeHessian,[],'',... multipliers,'mu_strategy','adaptive','tol',1e-7); % ---------------------------------------------------------------------- function f = computeObjective (x) f = x(1)*x(4)*sum(x(1:3)) + x(3); % ---------------------------------------------------------------------- function c = computeConstraints (x) c(1) = prod(x); c(2) = sum(x.^2); % ---------------------------------------------------------------------- function g = computeGradient (x) g(1) = x(1)*x(4) + x(4)*sum(x(1:3)); g(2) = x(1)*x(4); g(3) = x(1)*x(4) + 1; g(4) = x(1)*sum(x(1:3)); % ---------------------------------------------------------------------- function J = computeJacobian (x, returnStructureOnly) if returnStructureOnly J = sparse(ones(2,4)); else J = sparse([prod(x) ./ x; 2*x]); end % ---------------------------------------------------------------------- function H = computeHessian (x, sigma, lambda, returnStructureOnly) if returnStructureOnly H = sparse(tril(ones(4))); else H = sigma*[ 2*x(4) 0 0 0; x(4) 0 0 0; x(4) 0 0 0; 2*x(1)+x(2)+x(3) x(1) x(1) 0 ]; H = H + lambda(1)*[ 0 0 0 0; x(3)*x(4) 0 0 0; x(2)*x(4) x(1)*x(4) 0 0; x(2)*x(3) x(1)*x(3) x(1)*x(2) 0 ]; H = H + lambda(2)*diag([2 2 2 2]); H = sparse(H); end mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/examplehs038.m0000644000335100017570000000444311133661701027047 0ustar pgehlerwebadm% Test the "ipopt" Matlab interface on the Hock & Schittkowski test problem % #38. See: Willi Hock and Klaus Schittkowski. (1981) Test Examples for % Nonlinear Programming Codes. Lecture Notes in Economics and Mathematical % Systems Vol. 187, Springer-Verlag. % % Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function x = examplehs038 % The starting point. x0 = [-3 -1 -3 -1]; % The starting point. lb = [-10 -10 -10 -10]; % Lower bound on the variables. ub = [+10 +10 +10 +10]; % Upper bound on the variables. x = ipopt(x0,lb,ub,[],[],@computeObjective,@computeGradient,'','',... @computeHessian,[],@callback,[],'mu_strategy','adaptive',... 'tol',1e-7,'max_iter',100); % ---------------------------------------------------------------------- function f = computeObjective (x) x1 = x(1); x2 = x(2); x3 = x(3); x4 = x(4); f = 100*(x2-x1^2)^2 + (1-x1)^2 + 90*(x4-x3^2)^2 + (1-x3)^2 + ... 10.1*(x2-1)^2 + 10.1*(x4-1)^2 + 19.8*(x2-1)*(x4-1); % ---------------------------------------------------------------------- function g = computeGradient (x) x1 = x(1); x2 = x(2); x3 = x(3); x4 = x(4); g(1) = -400*x1*(x2-x1^2) - 2*(1-x1); g(2) = 200*(x2-x1^2) + 20.2*(x2-1) + 19.8*(x4-1); g(3) = -360*x3*(x4-x3^2) -2*(1-x3); g(4) = 180*(x4-x3^2) + 20.2*(x4-1) + 19.8*(x2-1); % ---------------------------------------------------------------------- function H = computeHessian (x, sigma, lambda, returnStructureOnly) if returnStructureOnly H = sparse([1 0 0 0; 1 1 0 0; 0 0 1 0; 0 1 1 1]); else x1 = x(1); x2 = x(2); x3 = x(3); x4 = x(4); H = sigma*[ 1200*x1^2 - 400*x2 + 2 0 0 0; -400*x1 220.2 0 0; 0 0 1080*x3^2 - 360*x4 + 2 0; 0 19.8 -360*x3 200.2]; H = sparse(H); end % ---------------------------------------------------------------------- function callback (t, f, x) fprintf('%3d %0.3g \n', t, f); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/examplehs051.m0000644000335100017570000000327611133661701027045 0ustar pgehlerwebadm% Test the "ipopt" Matlab interface on the Hock & Schittkowski test problem % #51. See: Willi Hock and Klaus Schittkowski. (1981) Test Examples for % Nonlinear Programming Codes. Lecture Notes in Economics and Mathematical % Systems Vol. 187, Springer-Verlag. % % Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function x = examplehs051 x0 = [ +2.5 +0.5 +2.0 -1.0 +0.5 ]; lb = [-inf -inf -inf -inf -inf]; ub = [+inf +inf +inf +inf +inf]; lbc = [4 0 0]; ubc = [4 0 0]; x = ipopt(x0,lb,ub,lbc,ubc,@computeObjective,@computeGradient,... @computeConstraints,@computeJacobian,'',[],'',[],... 'jac_c_constant','yes','hessian_approximation','limited-memory',... 'mu_strategy','adaptive','tol',1e-7); % ---------------------------------------------------------------------- function f = computeObjective (x) f = (x(1) - x(2))^2 + ... (x(2) + x(3) - 2)^2 + ... (x(4) - 1)^2 + (x(5) - 1)^2; % ---------------------------------------------------------------------- function g = computeGradient (x) g = 2*[ x(1) - x(2); x(2) + x(3) - 2 - x(1) + x(2); x(2) + x(3) - 2; x(4) - 1; x(5) - 1 ]; % ---------------------------------------------------------------------- function c = computeConstraints (x) c = [ x(1) + 3*x(2); x(3) + x(4) - 2*x(5); x(2) - x(5) ]; % ---------------------------------------------------------------------- function J = computeJacobian (x, returnStructureOnly) J = sparse([ 1 3 0 0 0; 0 0 1 1 -2; 0 1 0 0 -1 ]); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/bayesnet/0000755000335100017570000000000011133662012026251 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/bayesnet/examplelauritzen.m0000644000335100017570000000657411133661701032040 0ustar pgehlerwebadm% This is a small script to compute the Bethe approximation of a small % factor graph. The factor graph is generated by taking the Bayes net of % Lauritzen and Spiegelhalter (1988) and setting the following random % variables as evidence % % visit to Asia = false % positive X-ray = true % smoking = true % % For this particular set of evidence, the Bethe approximation is exact % because the corresponding factor graph does not have any cycles. % % For more information, see: Lauritzen and Speigelhalter (1988). "Local % computations with probabilities on graphical structures and their % application to expert systems." Journal of the Royal Statistical % Society, Series B, Vol. 50, No. 2, pp. 157-194. % % Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 verbose = true; % The remaining random variables can either be true or false. They are: % 1. tuberculosis % 2. lung cancer % 3. bronchitis % 4. tuberculosis or lung cancer % 5. dysponoea nv = 5; % The number of random variables. nf = 6; % The number of factors. K = 2*ones(nv,1); % The number of possible discrete assignments to the % random variable at each site. C = cell(nf,1); % The factor neighbourhoods. f = cell(nf,1); % The factors. % Set up the factors. % ------------------ % This is p(tuberculosis | visit to Asia = false). C{1} = 1; f{1} = [ 0.01 0.99 ]'; % This is p(lung cancer | smoking = true). C{2} = 2; f{2} = [ 0.1 0.9 ]'; % This is p(bronchitis | smoking = true). C{3} = 3; f{3} = [ 0.6 0.4 ]'; % This is p(tuberculosis or lung cancer | tuberculosis, lung cancer). C{4} = [4 1 2]; p = zeros(2,2,2); p(:,1,1) = [ 0.99 0.01 ]'; p(:,1,2) = [ 0.99 0.01 ]'; p(:,2,1) = [ 0.99 0.01 ]'; p(:,2,2) = [ 0.99 0.01 ]'; f{4} = p; % This is p(positive X-ray = true | tuberculosis or lung cancer). C{5} = 4; f{5} = [ 0.98 0.5 ]'; % This is p(dyspnoea | tuberculosis or lung cancer, bronchitis). C{6} = [5 4 3]; p = zeros(2,2,2); p(:,1,1) = [ 0.9 0.1 ]'; p(:,1,2) = [ 0.7 0.3 ]'; p(:,2,1) = [ 0.8 0.2 ]'; p(:,2,2) = [ 0.1 0.9 ]'; f{6} = p; clear p % Set up the junction graph. % ------------------------- % These are the large regions of the junction graph. Rv{1} = [1 2 4]; Rf{1} = [1 2 4]; NR{1} = [1]; Rv{2} = [3 4 5]; Rf{2} = [3 5 6]; NR{2} = [1]; % These are the small regions of the junction graph. Sv{1} = [4]; Sf{1} = []; NS{1} = [1 2]; nr = length(Rv); % The number of large regions. ns = length(Sv); % The number of small regions. % Infer the region marginals. % -------------------------- % Compute the junction graph approximation to the variational free % energy. Since the junction graph is a tree in this case (trivially, % because there are only two large regions), the junction graph % approximation is exact. qR = bopt(K,C,f,Rv,Rf,Sv,Sf,NS,verbose); % Output the marginal probabilities. % --------------------------------- p = ndsum(qR{1},[2 3]); fprintf('Pr(tuberculosis | evidence) = %0.2f \n', p(1)); p = ndsum(qR{1},[1 3]); fprintf('Pr(lung cancer | evidence) = %0.2f \n', p(1)); p = ndsum(qR{2},[2 3]); fprintf('Pr(bronchitis | evidence) = %0.2f \n', p(1)); p = ndsum(qR{2},[1 2]); fprintf('Pr(dysponoea | evidence) = %0.2f \n', p(1)); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/bayesnet/margfactor.m0000644000335100017570000000230311133661701030556 0ustar pgehlerwebadm% MARGFACTOR(F,R,S) takes as input a factor F defined on set R, and % another set S which must be a (non-strict and non-empty) subset of % R. The return value is a factor defined on the set S. % % Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function f = margfactor (f, R, S) nr = length(R); % The size of set R. ns = length(S); % The size of set S. % Get the intersection of sets S and R. [ans RI SI] = intersect(R,S); % Get the set of elements that belong to R but don't belong to S. [ans RD] = setdiff(R,S); % Reorder the factor table so that the elements belonging to the % intersection of R and S supersede the elements that are not in the % intersection. if nr > 1 f = permute(f,[RI RD]); end % Sum over the dimensions in R - S. if nr > ns f = ndsum(f,ns+1:nr); end if ns > 1 % This is the reverse mapping to recover a factor table on S. SR = zeros(1,ns); SR(SI) = 1:ns; % Reorder the final result. f = permute(f,SR); endmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/bayesnet/computeJGConstraints.m0000644000335100017570000000305011133661701032556 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function g = computeJGConstraints (qR, qS, auxdata) [K C f Rv Rf Sv Sf NS d] = deal(auxdata{:}); nr = length(Rv); % The number of large regions. ns = length(Sv); % The number of small regions. % Reshape the input vectors. qR = reshapemarginals(qR,Rv,K); qS = reshapemarginals(qS,Sv,K); % Compute the normalization constraints for the large regions. Repeat % for each large region. gR = zeros(nr,1); for r = 1:nr gR(r) = sum(qR{r}(:)); end % Compute the normalization constraints for the small regions. Repeat % for each small region. gS = zeros(ns,1); for s = 1:ns gS(s) = sum(qS{s}(:)); end % These are the consistency constraints. There is a constraint for % every small region S, then again for every neighbouring large % region R, and then for every table entry of the marginal qS(xS). % These constraints are stored in the cell array "cRS". It has the % same structure as the collection of sum-product messages "mRS" in % the MATLAB function "bp". cRS = cell(1,ns); for s = 1:ns is = Sv{s}; % The variable nodes in the small region. rs = NS{s}; % The set of neighbouring large regions. for i = 1:length(rs) r = rs(i); cRS{s}{i} = margfactor(qR{r},Rv{r},is) - qS{s}; end end g = [gR; gS; vectorize(cRS)]; mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/bayesnet/vectorize.m0000644000335100017570000000101111133661701030436 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function v = vectorize (cells) n = length(cells); % The number of cells in the cell array. v = []; % The return value. % Repeat for each cell. for i = 1:n c = cells{i}; if iscell(c) c = vectorize(c); end v = [v; c(:)]; end mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/bayesnet/ndsum.m0000644000335100017570000000033011133661701027555 0ustar pgehlerwebadmfunction X = ndsum (X, dims) % Sum over requested dimensions. s = size(X); for i = dims X = sum(X,i); end % Remove singleton dimensions. s(dims) = []; X = reshape(X,[s 1 1]); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/bayesnet/computeJGJacobian.m0000644000335100017570000001217711133661701031767 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function J = computeJGJacobian (qR, qS, returnStructureOnly, auxdata) [K C f Rv Rf Sv Sf NS d] = deal(auxdata{:}); nr = length(Rv); % The number of large regions. ns = length(Sv); % The number of small regions. nqr = length(qR); % The number of variables associated with the % marginals defined on the large regions. nqs = length(qS); % The number of variables associated with the % marginals defined on the small regions. % Reshape the input vectors. qR = reshapemarginals(qR,Rv,K); qS = reshapemarginals(qS,Sv,K); % This block is the part of the Jacobian that represents the % first-order partial derivatives of the normalization constraints on % the large regions. We have one constraint function for every small % region. rows = zeros(1,nqr); is = 0; for r = 1:nr tablesize = numel(qR{r}); is = is(end) + (1:tablesize); rows(is) = r; end JR = sparse(rows,1:nqr,ones(1,nqr),nr,nqr); % This block is the part of the Jacobian that represents the % first-order partial derivatives of the normalization constraints on % the small regions. We have one constraint function for every small % region. rows = zeros(1,nqs); is = 0; for s = 1:ns tablesize = numel(qS{s}); is = is(end) + (1:tablesize); rows(is) = s; end JS = sparse(rows,1:nqs,ones(1,nqs),ns,nqs); % This block is the part of the Jacobian that represents the % first-order partial derivatives of the consistency constraints with % respect to the variables qS(xS). What we will do is form subblocks of % this block. We have one constraint function for each small region, % then again for each neighbouring large region, and then for every % possible configuration on the small region. JCS = cell(1,ns); for s = 1:ns w = numel(qS{s}); % The width of the Jacobian subblock. h = d(s) * w; % The height of the Jacobian subblock. cols = repmat(1:w,1,d(s)); JCS{s} = sparse(1:h,cols,-ones(1,h),h,w); end % This block is the part of the Jacobian that represents the % first-order partial derivatives of the consistency constraints with % respect to the variables qR(xR). What we will do is form subblocks of % this block. We have one constraint function for each small region, % then again for each neighbouring large region, and then for every % possible configuration on the small region. JCR = cell(sum(d),1); t = 1; % The row of the current subblock. for s = 1:ns for r = NS{s} h = numel(qS{s}); % The height of the Jacobian subblock. w = numel(qR{r}); % The width of the Jacobian subblock. rows = multiplyfactors(ones([K(Rv{r}) 1]),Rv{r},... reshape(1:h,[K(Sv{s}) 1]),Sv{s}); JCR{t} = sparse(rows(:)',1:w,ones(1,w),h,w); t = t + 1; % Move to the next row. end end % Construct the full Jacobian from its blocks. J = [ JR spzeros(nr,nqs); spzeros(ns,nqr) JS; spblockrows(JCR,vectorize(NS)) spblockdiag(JCS) ]; % ------------------------------------------------------------------ function A = spblockdiag (blocks) n = length(blocks); % The number of blocks. h = 0; % The height of the full matrix. w = 0; % The width of the full matrix. rows = []; cols = []; entries = []; % Repeat for each block. for i = 1:n [hb wb] = size(blocks{i}); % The height and width of the block. [is js xs] = find(blocks{i}); rows = [rows h + is']; cols = [cols w + js']; entries = [entries xs']; h = h + hb; w = w + wb; end % Construct the full matrix from the diagonal subblocks. A = sparse(rows,cols,entries,h,w); % ------------------------------------------------------------------ function A = spblockrows (blocks, c) n = length(blocks); % The number of blocks. nc = max(c); % The number of block columns. h = 0; % The height of the full matrix. rows = []; % The rows of the individual entries. cols = []; % The columns of the individual entries. entries = []; % Compute the width of every subblock. bw = zeros(1,nc); for i = 1:n bw(c(i)) = size(blocks{i},2); end % Compute the starting column index (minus 1) for every subblock. Also, % compute the width of the full matrix, w. bw = cumsum(bw); w = bw(nc); bw = [0 bw(1:nc-1)]; % Repeat for each block. for i = 1:n hb = size(blocks{i},1); % The height of the block. [is js xs] = find(blocks{i}); rows = [rows h + is']; cols = [cols bw(c(i)) + js']; entries = [entries xs']; h = h + hb; end % Construct the full matrix from the subblocks. A = sparse(rows,cols,entries,h,w); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/bayesnet/multiplyfactors.m0000644000335100017570000000345711133661701031705 0ustar pgehlerwebadm% MULTIPLYFACTORS(FR,R,FS,S) returns the pointwise product of factors FR % and FS defined on sets R and S, respectively. We require that S be a % (non-strict and non-empty) subset of R. The resulting factor is defined % on set R. % % Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function fR = multiplyfactors (fR, R, fS, S) nr = length(R); % The size of set R. ns = length(S); % The size of set S. K = size(fR); % The number of possible discrete assignments to each % random variable in R. if nr == 1 fR = fR .* fS; else % Get the intersection of sets S and R. [ans RI SI] = intersect(R,S); % Get the set of elements that belong to R but don't belong to C. [ans RD] = setdiff(R,S); % Check whether or not the intersection is the same size as S. If not, % it means that R is not a superset of S. if length(SI) ~= length(S) error('R is not a superset of S'); end % Reorder the factor tables so that the elements belonging to the % intersection of R and S supersede the elements that are not in the % intersection. RT = [RI RD]; fR = permute(fR,RT); if ns > 1 fS = permute(fS,SI); end % This is the reverse mapping to recover the original factor table. RR = zeros(1,nr); RR(RT) = 1:nr; % Expand the table fS so that it is of the same size as fR. fS = repmat(fS,[ones(1,ns) K(RD)]); % Multiply fR with the expanded version of fS. fR = fR .* fS; % Finally, we need to return the factor table fR back to its original % ordering. fR = permute(fR,RR); end mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/bayesnet/computeJGGradient.m0000644000335100017570000000266311133661701032015 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function [dR, dS] = computeJGGradient (qR, qS, auxdata) [K C f Rv Rf Sv Sf NS d] = deal(auxdata{:}); nr = length(Rv); % The number of large regions. ns = length(Sv); % The number of small regions. % Reshape the input vectors. qR = reshapemarginals(qR,Rv,K); qS = reshapemarginals(qS,Sv,K); % Compute the gradient terms for the large regions. Repeat for each % large region. dR = cell(1,nr); for r = 1:nr is = Rv{r}; % The variable nodes in the large region. table = 1 ./ qR{r}; % Multiply the table by all the factors in the large region. for j = Rf{r} table = multiplyfactors(table,is,f{j},C{j}); end dR{r} = -log(table) + 1; end % Compute the gradient terms for the small regions. Repeat for each % small region. dS = cell(1,ns); for s = 1:ns is = Sv{s}; % The variable nodes in the small region. table = 1 ./ qS{s}; % Multiply the table by all the factors in the small region. for j = Sf{s} table = multiplyfactors(table,is,f{j},C{j}); end dS{s} = (1 - d(s)) * (-log(table) + 1); end % Convert the computed gradients into vectors. dR = vectorize(dR); dS = vectorize(dS); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/bayesnet/computeJGHessian.m0000644000335100017570000000327311133661701031650 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function H = computeJGHessian (qR, qS, sigma, lambda, ... returnStructureOnly, auxdata) [K C f Rv Rf Sv Sf NS d] = deal(auxdata{:}); ns = length(Sv); % The number of small regions. nqr = length(qR); % The number of variables associated with the % marginals defined on the large regions. nqs = length(qS); % The number of variables associated with the % marginals defined on the small regions. if returnStructureOnly H = speye(nqr + nqs); else % Compute the second-order partial derivatives on the subblock HR. HR = spdiag(1 ./ qR); % Compute the second-order partial derivatives on the subblock % HS. Repeat for each small region. entries = zeros(1,nqs); is = 0; for s = 1:ns tablesize = prod(K(Sv{s})); % The number of entries necessary to % store the entire marginal. is = is(end) + (1:tablesize); entries(is) = (1-d(s)) ./ qS(is); end HS = spdiag(entries); % Construct the full Hessian from its blocks. H = sigma * [ HR spzeros(nqr,nqs); spzeros(nqs,nqr) HS ]; end % ------------------------------------------------------------------ function A = spdiag (xs) n = length(xs); % The height and width of the sparse matrix. % Produce the sparse, diagonal matrix. A = sparse(1:n,1:n,xs,n,n); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/bayesnet/reshapemarginals.m0000644000335100017570000000140311133661701031756 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function qR = reshapemarginals (q, R, K) nr = length(R); % The number of regions. qR = cell(1,nr); % The cell array to return. pos = 0; % Current entry of the vector q. % Repeat for each region. for r = 1:nr is = R{r}; % The variable nodes in region r. tablesize = prod(K(is)); % The size of the table. table = q(pos + (1:tablesize)); qR{r} = reshape(table,[K(is) 1]); % Move to the next location in the table. pos = pos + tablesize; end mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/bayesnet/spzeros.m0000644000335100017570000000046411133661701030144 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function A = spzeros (m, n) A = sparse([],[],[],m,n,0); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/bayesnet/computeJGObjective.m0000644000335100017570000000327711133661701032174 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function F = computeJGObjective (qR, qS, auxdata) [K C f Rv Rf Sv Sf NS d] = deal(auxdata{:}); % Reshape the input vectors. qR = reshapemarginals(qR,Rv,K); qS = reshapemarginals(qS,Sv,K); % The objective function is the junction graph approximation to the % variational free energy. F = computefreeenergy(C,f,Rv,Rf,Sv,Sf,qR,qS,d); % ------------------------------------------------------------------ function F = computefreeenergy (C, f, Rv, Rf, Sv, Sf, qR, qS, d) nr = length(Rv); % The number of large regions. ns = length(Sv); % The number of small regions. F = 0; % The return value. % Compute the average energy of each large region. Repeat for each % large region, then for each factor within the large region. for r = 1:nr for j = Rf{r} table = multiplyfactors(qR{r},Rv{r},log(f{j}),C{j}); F = F - sum(table(:)); end end % Compute the average energy of each small region. Repeat for each % small region, then for each factor within the small region. for s = 1:ns for j = Sf{s} table = multiplyfactors(qS{s},Sv{s},log(f{j}),C{j}); F = F - (1 - d(s)) * sum(table(:)); end end % Compute the entropy of each large region. for r = 1:nr table = qR{r} .* log(qR{r}); F = F + sum(table(:)); end % Compute the entropy of each small region. for s = 1:ns table = qS{s} .* log(qS{s}); F = F + (1 - d(s)) * sum(table(:)); end mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/bayesnet/genericcallback.m0000644000335100017570000000047511133661701031532 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function callbackHS038 (t, f, x) fprintf('%3d %0.3g \n', t, f); mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/examples/bayesnet/bopt.m0000644000335100017570000000537011133661701027404 0ustar pgehlerwebadm% Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % May 19, 2007 function qR = bopt (K, C, f, Rv, Rf, Sv, Sf, NS, verbose) maxiter = 100; % The maximum number of iterations. tolerance = 1e-6; % The convergence criterion. nr = length(Rv); % The number of large regions. ns = length(Sv); % The number of small regions. % Convert the input K to a row vector. K = K(:)'; % Get the degrees of the separators. d = degrees(NS); % Initialize the marginals on the large and small regions to uniform % probability tables. qR = initmarginals(K,Rv); qS = initmarginals(K,Sv); % Compute the total number consistency constaints. We have one % constraint for every pair (R,S), then again for every possible % configuration xS. nc = numconsistencyconstraints(qS,d); % Run the IPOPT solver. qR = vectorize(qR); qS = vectorize(qS); nqr = length(qR); nqs = length(qS); [qR qS] = ipopt({qR qS},{ repmat(eps,nqr,1) repmat(eps,nqs,1) },... { repmat(inf,nqr,1) repmat(inf,nqs,1) },... [ ones(1,nr) ones(1,ns) zeros(1,nc) ],... [ ones(1,nr) ones(1,ns) zeros(1,nc) ],... @computeJGObjective,@computeJGGradient,... @computeJGConstraints,@computeJGJacobian,... @computeJGHessian,{ K C f Rv Rf Sv Sf NS d },'',... [],'mu_strategy','adaptive','max_iter',maxiter,... 'tol',tolerance,'jac_c_constant','yes',... 'jac_d_constant','yes','print_level',verbose*5); % Reshape the solution. qR = reshapemarginals(qR,Rv,K); qS = reshapemarginals(qS,Sv,K); % ------------------------------------------------------------------ function d = degrees (NS) ns = length(NS); % The number of separators. d = zeros(1,ns); % Repeat for each separator. for s = 1:ns d(s) = length(NS{s}); end % ---------------------------------------------------------------- function qR = initmarginals (K, Rv) nr = length(Rv); % The number of regions. qR = cell(1,nr); % The return value. % Repeat for each region. for r = 1:nr is = Rv{r}; % The variables nodes in the large region. table = ones([K(is) 1]); qR{r} = table / sum(table(:)); end % ---------------------------------------------------------------- function nc = numconsistencyconstraints (qS, d) ns = length(qS); % The number of small regions. nc = 0; % The return value. % Repeat for each small region. for s = 1:ns tablesize = numel(qS{s}); nc = nc + d(s) * tablesize; end mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/matlab/ipopt.m0000644000335100017570000002356711133661701024153 0ustar pgehlerwebadm% IPOPT Call the IPOPT constrained, nonlinear solver. % The basic function call is % % x = IPOPT(x0,lb,ub,constraintlb,constraintub,objfunc,gradfunc,... % constraintfunc,jacobianfunc,hessianfunc) % % The first input argument x0 is either a matrix or a cell array of % matrices. It declares the starting point for the solver. % % The second and third input arguments lb and ub must be of the same % structure as x0. They declare the lower and upper bounds on the % variables, respectively. Set an entry of lb to Inf to declare no lower % bound, and likewise for the upper bounds. % % The fourth and fifth input arguments constraintlb and constraintub set % the lower and upper bounds on the constraints, respectively. Each should % be a vector of length equal to the number of constraints. As before, a % bound is removed by setting the entry to Inf. An equality constraint is % achieved by setting constraintlb(i) = constraintub(i). % % If the solver successfully converges to a stationary point or terminated % without an unrecoverable error, the function IPOPT outputs the candidate % solution. In all other cases, an error is thrown. The number of outputs % is equal to the number of cell entries in x0. If x0 is a matrix, IPOPT % produces a single output. % % EXAMPLE. Here is an example of a basic call to IPOPT, in which the % optimization variables are specified by an array of length n and another % array of length m: % % [r s] = ipopt({r s},{ zeros(n,1) zeros(m,1) },... % { repmat(inf,n,1) repmat(inf,m,1) },lb,ub,... % @computeobjective,@computegradient,@computeconstraints',... % @computejacobian,@computeJGHessian,auxdata); % % The optimization variables are bounded below by 0. The bounds on the % equality and inequality constraints are specified by the MATLAB % variables lb and ub. Upon convergence to a feasible, stationary point, % the routine will return the values of the primal variables r and % s. Also, auxiliary data ('auxdata') is passed to all the callback % routines. For more information, continue reading below. % % The remaining input arguments must be function handles for various % MATLAB routines (M-files or subfunctions): % % objfunc Calculates the objective function at the current % point. The routine must accept as many inputs as cell % entries in x0 (or one input if x0 is a matrix). The % output must be a scalar representing the objective % evaluated at the current point. % % gradfunc Computes the gradient of the objective at the current % point. The input is the same as objfunc, but it must % return as many outputs as there are inputs, and each % of the outputs must have the same matrix structure % as its corresponding input. % % constraintfunc Evaluates the constraint functions at the current % point. The MATLAB routine must accept the same inputs % as objfunc. The return value is a vector of length % equal to the number of constraints (it must be of the % same length as constraintlb and constraintub). If % there are no constraints, constraintfunc may be set % to the empty string because the MATLAB callback % routine will never be called. % % jacobianfunc Evaluates the Jacobian of the constraints at the % current point. The inputs are the same as the % previously described callback functions, except that % it has an additional input "returnStructureOnly" which % will either true or false. If returnStructureOnly is % false, return the Jacobian matrix evaluated at the % specified point. If returnStructureOnly is true, % return a sparse matrix whereby an entry is zero if and % only if the Jacobian of the constraints is zero at % EVERY possible (feasible or non-feasible) point. The % output must always be an M x N sparse matrix, where M % is the number of constraints and N is the number of % variables. Type HELP SPARSE for more information on % constructing sparse matrices in MATLAB. If there % are no constraints, jacobianfunc may be set to the % empty string because the MATLAB callback routine % will never be called. % % hessianfunc Evaluates the Hessian of the Lagrangian at the current % point. Its inputs are the same as the objective and % gradient callback functions, but it also has three % additional inputs. The first additional input is % "sigma". It is a scalar factor on the % objective. The second additional input is "lambda", % which is a vector of length equal to the number of % constraints. The remaining additional input is % returnStructureOnly (see the description of % jacobianfunc above). The function should compute % % sigma*H + lambda(1)*G1 + ... + lambda(M)*GM % % where M is the number of constraints, H is the Hessian % of the objective and the G's are the Hessians of the % constraint functions. The output must always be an N x % N sparse, lower triangular matrix, where N is the % number of variables. In other words, if X is the % output value, then X must be the same as TRIL(X). % % For more information on using functions and function handles in MATLAB, % type HELP FUNCTION and HELP FUNCTION_HANDLE in the MATLAB prompt. % % Optionally, one may choose to pass additional auxiliary data to the % MATLAB callback routines listed above through the function call % IPOPT(...,auxdata). If auxdata is the empty matrix, no extra information % is passed. It is important to observe that the auxiliary data MAY NOT % CHANGE through the course of the IPOPT optimization! The auxiliary data % keep the same values as they possessed in the initial call. If you need % variables that change over time, you may want to consider global % variables (type HELP GLOBAL). % % EXAMPLE (continued). Returning to the above example, the 5 callback % routines would be of the following form: % % function F = computeobjective (r,s,auxdata) % function [dr, ds] = computegradient (r,s,auxdata) % function g = computeconstraints (r,s,auxdata) % function J = computejacobian (r,s,returnStructOnly,auxdata) % function H = computehessian (r,s,sigma,lambda,... % returnStructOnly,auxdata) % % IPOPT(...,auxdata,iterfunc) specifies a a function handle to an % additional callback routine which is called once per algorithm % iteration. The callback routine must take the form ITERFUNC(T,F,AUXDATA). % T is the current iteration of the algorithm. F is the current value of % the objective. Finally, extra information may be passed through the % input AUXDATA. No outputs are expected from iterfunc. Also note that the % value of the optimization variables is not passed to this callback % routine. If iterfunc is the empty string, no routine is called. % % By default, the print level is set so that IPOPT displays the % progress of the algorithm. However, if the user implements an % iterative callback routine, the print level is automatically set so % that IPOPT does not generate output to the MATLAB console. % % IPOPT(...,auxdata,iterfunc,multipliers) specifies the initial values for % the Lagrange multipliers. The input "multipliers" must have three % fields. The field "zl" must be an array of length equal to the total % number of optimization variables. It contains the initial values for the % Lagrange multipliers associated with the lower bounds on the % optimization variables. The field "zu" is an array of the same % length. It contains the initial values for the upper bounds Lagrange % multipliers. The field "lambda" must be an array of length equal to the % number of constraints. It specifies the initial values for the Lagrange % multipliers associated with the equality and inequality constraints. % % These inputs may be followed by parameter/value pairs to modify the % default IPOPT algorithm options. See the IPOPT documentation for more % information. If the IPOPT option 'hessian_approximation' is set to % 'limited-memory', the Hessian callback routine will never be called % so it may be set to the empty string. % % [..., multipliers, numiter] = IPOPT(...) returns some additional % information to the user: the values of the Lagrange multipliers at the % solution (the multipliers corresponding to the lower bounds on the % variables, the upper bounds on the variables, and the equality and % inequality constraints), and the number of iterations needed to converge % to the stationary point (provided it has converged at all). % % For more information, please consult: A. Wachter and L. T. Biegler. "On % the Implementation of a Primal-Dual Interior Point Filter Line Search % Algorithm for Large-Scale Nonlinear Programming." Mathematical % Programming 106(1), pp. 25-57, 2006. % % Copyright (C) 2007 Peter Carbonetto. All Rights Reserved. % This code is published under the Common Public License. % % Author: Peter Carbonetto % Dept. of Computer Science % University of British Columbia % October 13, 2007 mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/0000755000335100017570000000000011133662012023004 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/0000755000335100017570000000000011133662012023734 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/config_ipopt.h0000644000335100017570000001226611133661702026601 0ustar pgehlerwebadm/* inc/config_ipopt.h. Generated by configure. */ /* inc/config_ipopt.h.in. Generated from configure.ac by autoheader. */ /* If defined, the Ampl Solver Library is available. */ /* #undef COIN_HAS_ASL */ /* If defined, the BLAS Library is available. */ #define COIN_HAS_BLAS 1 /* Define to 1 if the Ipopt package is used */ #define COIN_HAS_IPOPT 1 /* If defined, the LAPACK Library is available. */ #define COIN_HAS_LAPACK 1 /* If defined, the MUMPS Library is available. */ #define COIN_HAS_MUMPS 1 /* Define to the debug sanity check level (0 is no test) */ #define COIN_IPOPT_CHECKLEVEL 0 /* Define to the debug verbosity level (0 is no output) */ #define COIN_IPOPT_VERBOSITY 0 /* Define to dummy `main' function (if any) required to link to the Fortran libraries. */ /* #undef F77_DUMMY_MAIN */ /* Define to a macro mangling the given C identifier (in lower and upper case), which must not contain underscores, for linking with Fortran. */ #define F77_FUNC(name,NAME) name ## _ /* As F77_FUNC, but for C identifiers containing underscores. */ #define F77_FUNC_(name,NAME) name ## _ /* Define if F77 and FC dummy `main' functions are identical. */ /* #undef FC_DUMMY_MAIN_EQ_F77 */ /* Define to the C type corresponding to Fortran INTEGER */ #define FORTRAN_INTEGER_TYPE int /* Define to 1 if you have the header file. */ /* #undef HAVE_ASSERT_H */ /* Define to 1 if you have the header file. */ #define HAVE_CASSERT 1 /* Define to 1 if you have the header file. */ #define HAVE_CCTYPE 1 /* Define to 1 if you have the header file. */ #define HAVE_CFLOAT 1 /* Define to 1 if you have the header file. */ /* #undef HAVE_CIEEEFP */ /* Define to 1 if you have the header file. */ #define HAVE_CMATH 1 /* Define to 1 if you have the header file. */ #define HAVE_CSTDARG 1 /* Define to 1 if you have the header file. */ #define HAVE_CSTDIO 1 /* Define to 1 if you have the header file. */ #define HAVE_CSTDLIB 1 /* Define to 1 if you have the header file. */ #define HAVE_CSTRING 1 /* Define to 1 if you have the header file. */ #define HAVE_CTIME 1 /* Define to 1 if you have the header file. */ /* #undef HAVE_CTYPE_H */ /* Define to 1 if you have the header file. */ #define HAVE_DLFCN_H 1 /* Define to 1 if function drand48 is available */ #define HAVE_DRAND48 1 /* Define to 1 if you have the header file. */ /* #undef HAVE_FLOAT_H */ /* Define to 1 if you have the header file. */ /* #undef HAVE_IEEEFP_H */ /* Define to 1 if you have the header file. */ #define HAVE_INTTYPES_H 1 /* Define to 1 if MA27 is available */ /* #undef HAVE_MA27 */ /* Define to 1 if MA28 is available */ /* #undef HAVE_MA28 */ /* Define to 1 if MA57 is available */ /* #undef HAVE_MA57 */ /* Define to 1 if you have the header file. */ /* #undef HAVE_MATH_H */ /* Define to 1 if MC19 is available */ /* #undef HAVE_MC19 */ /* Define to 1 if you have the header file. */ #define HAVE_MEMORY_H 1 /* Define to 1 if Pardiso is available */ /* #undef HAVE_PARDISO */ /* Define to 1 if you are using the parallel version of Pardiso */ /* #undef HAVE_PARDISO_PARALLEL */ /* Define to 1 if function rand is available */ #define HAVE_RAND 1 /* Define to 1 if you have the header file. */ /* #undef HAVE_STDARG_H */ /* Define to 1 if you have the header file. */ #define HAVE_STDINT_H 1 /* Define to 1 if you have the header file. */ /* #undef HAVE_STDIO_H */ /* Define to 1 if you have the header file. */ #define HAVE_STDLIB_H 1 /* Define to 1 if function std::rand is available */ #define HAVE_STD__RAND 1 /* Define to 1 if you have the header file. */ #define HAVE_STRINGS_H 1 /* Define to 1 if you have the header file. */ #define HAVE_STRING_H 1 /* Define to 1 if you have the header file. */ #define HAVE_SYS_STAT_H 1 /* Define to 1 if you have the header file. */ #define HAVE_SYS_TYPES_H 1 /* Define to 1 if TAUCS is available */ /* #undef HAVE_TAUCS */ /* Define to 1 if you have the header file. */ /* #undef HAVE_TIME_H */ /* Define to 1 if you have the header file. */ #define HAVE_UNISTD_H 1 /* Define to 1 if va_copy is avaliable */ #define HAVE_VA_COPY 1 /* Define to 1 if WSMP is available */ /* #undef HAVE_WSMP */ /* Define to be the name of C-function for NaNInf check */ #define MY_C_FINITE finite /* Name of package */ #define PACKAGE "ipopt" /* Define to the address where bug reports for this package should be sent. */ #define PACKAGE_BUGREPORT "http://projects.coin-or.org/Ipopt/newticket" /* Define to the full name of this package. */ #define PACKAGE_NAME "Ipopt" /* Define to the full name and version of this package. */ #define PACKAGE_STRING "Ipopt 3.3.5" /* Define to the one symbol short name of this package. */ #define PACKAGE_TARNAME "ipopt" /* Define to the version of this package. */ #define PACKAGE_VERSION "3.3.5" /* The size of a `int *', as computed by sizeof. */ #define SIZEOF_INT_P 8 /* Define to 1 if you have the ANSI C header files. */ #define STDC_HEADERS 1 /* Version number of package */ #define VERSION "3.3.5" mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpoptConfig.h0000644000335100017570000000531411133661702026336 0ustar pgehlerwebadm#ifndef HAVE_CONFIG_H #define HAVE_CONFIG_H #endif /* * Include file for the configuration of Ipopt. * * On systems where the code is configured with the configure script * (i.e., compilation is always done with HAVE_CONFIG_H defined), this * header file includes the automatically generated header file, and * undefines macros that might configure with other Config.h files. * * On systems that are compiled in other ways (e.g., with the * Developer Studio), a header files is included to define those * macros that depend on the operating system and the compiler. The * macros that define the configuration of the particular user setting * (e.g., presence of other COIN packages or third party code) are set * here. The project maintainer needs to remember to update this file * and choose reasonable defines. A user can modify the default * setting by editing this file here. * */ #ifndef __IPOPTCONFIG_H__ #define __IPOPTCONFIG_H__ #ifdef HAVE_CONFIG_H #include "config_ipopt.h" /* undefine macros that could conflict with those in other config.h files */ #undef PACKAGE #undef PACKAGE_BUGREPORT #undef PACKAGE_NAME #undef PACKAGE_STRING #undef PACKAGE_TARNAME #undef PACKAGE_VERSION #undef VERSION #else /* HAVE_CONFIG_H */ /* include the COIN-wide system specific configure header */ #include "configall_system.h" /***************************************************************************/ /* HERE DEFINE THE CONFIGURATION SPECIFIC MACROS */ /***************************************************************************/ /* Define to the debug sanity check level (0 is no test) */ #define COIN_IPOPT_CHECKLEVEL 0 /* Define to the debug verbosity level (0 is no output) */ #define COIN_IPOPT_VERBOSITY 0 /* If defined, the Ampl Solver Library is available. */ #define COIN_HAS_ASL 1 /* If defined, the BLAS Library is available. */ #define COIN_HAS_BLAS 1 /* Define to 1 if the Ipopt package is used */ #define COIN_HAS_IPOPT 1 /* If defined, the LAPACK Library is available. */ #define COIN_HAS_LAPACK 1 /* Define to 1 if MA27 is available */ #define HAVE_MA27 1 /* Define to 1 if MA57 is available */ /* #undef HAVE_MA57 */ /* Define to 1 if MC19 is available */ #define HAVE_MC19 1 /* Define to 1 if MUMPS is available */ /* #undef HAVE_MUMPS */ /* Define to 1 if Pardiso is available */ /* #undef HAVE_PARDISO */ /* Define to 1 if you are using the parallel version of Pardiso */ /* #undef HAVE_PARDISO_PARALLEL */ /* Define to 1 if TAUCS is available */ /* #undef HAVE_TAUCS */ /* Define to 1 if WSMP is available */ /* #undef HAVE_WSMP */ #endif /* HAVE_CONFIG_H */ /* Type of Fortran integer translated into C */ typedef FORTRAN_INTEGER_TYPE ipfint; #endif /*__IPOPTCONFIG_H__*/ mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpTNLP.hpp0000644000335100017570000002534211133661702025526 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpTNLP.hpp 1010 2007-06-21 15:54:36Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPTNLP_HPP__ #define __IPTNLP_HPP__ #include "IpUtils.hpp" #include "IpReferenced.hpp" #include "IpException.hpp" #include "IpAlgTypes.hpp" #include "IpReturnCodes.hpp" namespace Ipopt { // forward declarations class IpoptData; class IpoptCalculatedQuantities; class IteratesVector; /** Base class for all NLP's that use standard triplet matrix form * and dense vectors. This is the standard base class for all * NLP's that use the standard triplet matrix form (as for Harwell * routines) and dense vectors. The class TNLPAdapter then converts * this interface to an interface that can be used directly by * ipopt. * * This interface presents the problem form: * * min f(x) * * s.t. gL <= g(x) <= gU * * xL <= x <= xU * * In order to specify an equality constraint, set gL_i = gU_i = * rhs. The value that indicates "infinity" for the bounds * (i.e. the variable or constraint has no lower bound (-infinity) * or upper bound (+infinity)) is set through the option * nlp_lower_bound_inf and nlp_upper_bound_inf. To indicate that a * variable has no upper or lower bound, set the bound to * -ipopt_inf or +ipopt_inf respectively */ class TNLP : public ReferencedObject { public: /** Type of the constraints*/ enum LinearityType { LINEAR/** Constraint/Variable is linear.*/, NON_LINEAR/**Constraint/Varaible is non-linear.*/ }; /**@name Constructors/Destructors */ //@{ TNLP() {} /** Default destructor */ virtual ~TNLP() {} //@} DECLARE_STD_EXCEPTION(INVALID_TNLP); /**@name methods to gather information about the NLP */ //@{ /** overload this method to return the number of variables * and constraints, and the number of non-zeros in the jacobian and * the hessian. The index_style parameter lets you specify C or Fortran * style indexing for the sparse matrix iRow and jCol parameters. * C_STYLE is 0-based, and FORTRAN_STYLE is 1-based. */ enum IndexStyleEnum { C_STYLE=0, FORTRAN_STYLE=1 }; virtual bool get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, Index& nnz_h_lag, IndexStyleEnum& index_style)=0; /** overload this method to return the information about the bound * on the variables and constraints. The value that indicates * that a bound does not exist is specified in the parameters * nlp_lower_bound_inf and nlp_upper_bound_inf. By default, * nlp_lower_bound_inf is -1e19 and nlp_upper_bound_inf is * 1e19. (see TNLPAdapter) */ virtual bool get_bounds_info(Index n, Number* x_l, Number* x_u, Index m, Number* g_l, Number* g_u)=0; /** overload this method to return scaling parameters. This is * only called if the options are set to retrieve user scaling. * There, use_x_scaling (or use_g_scaling) should get set to true * only if the variables (or constraints) are to be scaled. This * method should return true only if the scaling parameters could * be provided. */ virtual bool get_scaling_parameters(Number& obj_scaling, bool& use_x_scaling, Index n, Number* x_scaling, bool& use_g_scaling, Index m, Number* g_scaling) { return false; } /** overload this method to return the variables linearity * (TNLP::Linear or TNLP::NonLinear). The var_types * array should be allocated with length at least n. (default implementation * just return false and does not fill the array).*/ virtual bool get_variables_linearity(Index n, LinearityType* var_types) { return false; } /** overload this method to return the constraint linearity. * array should be alocated with length at least n. (default implementation * just return false and does not fill the array).*/ virtual bool get_constraints_linearity(Index m, LinearityType* const_types) { return false; } /** overload this method to return the starting point. The bools * init_x and init_lambda are both inputs and outputs. As inputs, * they indicate whether or not the algorithm wants you to * initialize x and lambda respectively. If, for some reason, the * algorithm wants you to initialize these and you cannot, set * the respective bool to false. */ virtual bool get_starting_point(Index n, bool init_x, Number* x, bool init_z, Number* z_L, Number* z_U, Index m, bool init_lambda, Number* lambda)=0; /** overload this method to provide an Ipopt iterate (already in * the form Ipopt requires it internally) for a warm start. * Since this is only for expert users, a default dummy * implementation is provided and returns false. */ virtual bool get_warm_start_iterate(IteratesVector& warm_start_iterate) { return false; } /** overload this method to return the value of the objective function */ virtual bool eval_f(Index n, const Number* x, bool new_x, Number& obj_value)=0; /** overload this method to return the vector of the gradient of * the objective w.r.t. x */ virtual bool eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f)=0; /** overload this method to return the vector of constraint values */ virtual bool eval_g(Index n, const Number* x, bool new_x, Index m, Number* g)=0; /** overload this method to return the jacobian of the * constraints. The vectors iRow and jCol only need to be set * once. The first call is used to set the structure only (iRow * and jCol will be non-NULL, and values will be NULL) For * subsequent calls, iRow and jCol will be NULL. */ virtual bool eval_jac_g(Index n, const Number* x, bool new_x, Index m, Index nele_jac, Index* iRow, Index *jCol, Number* values)=0; /** overload this method to return the hessian of the * lagrangian. The vectors iRow and jCol only need to be set once * (during the first call). The first call is used to set the * structure only (iRow and jCol will be non-NULL, and values * will be NULL) For subsequent calls, iRow and jCol will be * NULL. This matrix is symmetric - specify the lower diagonal * only. A default implementation is provided, in case the user * wants to se quasi-Newton approximations to estimate the second * derivatives and doesn't not neet to implement this method. */ virtual bool eval_h(Index n, const Number* x, bool new_x, Number obj_factor, Index m, const Number* lambda, bool new_lambda, Index nele_hess, Index* iRow, Index* jCol, Number* values) { return false; } /** this method is called if option ??? is set. In that case, the algorithm calls this method with the values of variables and multipliers that will be used for the next calls of any evaluation method. */ virtual bool set_variable_values(Index n, const Number* x, bool new_x, Index m, const Number* lambda, bool new_lambda) { return false; } //@} /** @name Solution Methods */ //@{ /** This method is called when the algorithm is complete so the TNLP can store/write the solution */ virtual void finalize_solution(SolverReturn status, Index n, const Number* x, const Number* z_L, const Number* z_U, Index m, const Number* g, const Number* lambda, Number obj_value, const IpoptData* ip_data, IpoptCalculatedQuantities* ip_cq)=0; /** Intermediate Callback method for the user. Providing dummy * default implementation. For details see IntermediateCallBack * in IpNLP.hpp. */ virtual bool intermediate_callback(AlgorithmMode mode, Index iter, Number obj_value, Number inf_pr, Number inf_du, Number mu, Number d_norm, Number regularization_size, Number alpha_du, Number alpha_pr, Index ls_trials, const IpoptData* ip_data, IpoptCalculatedQuantities* ip_cq) { return true; } //@} /** @name Methods for quasi-Newton approximation. If the second * derivatives are approximated by Ipopt, it is better to do this * only in the space of nonlinear variables. The following * methods are call by Ipopt if the quasi-Newton approximation is * selected. If -1 is returned as number of nonlinear variables, * Ipopt assumes that all variables are nonlinear. Otherwise, it * calls get_list_of_nonlinear_variables with an array into which * the indices of the nonlinear variables should be written - the * array has the lengths num_nonlin_vars, which is identical with * the return value of get_number_of_nonlinear_variables(). It * is assumed that the indices are counted starting with 1 in the * FORTRAN_STYLE, and 0 for the C_STYLE. */ //@{ virtual Index get_number_of_nonlinear_variables() { return -1; } virtual bool get_list_of_nonlinear_variables(Index num_nonlin_vars, Index* pos_nonlin_vars) { return false; } //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ //TNLP(); /** Copy Constructor */ TNLP(const TNLP&); /** Overloaded Equals Operator */ void operator=(const TNLP&); //@} }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpRegOptions.hpp0000644000335100017570000006163211133661702027044 0ustar pgehlerwebadm// Copyright (C) 2004, 2007 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpRegOptions.hpp 1091 2007-09-25 12:16:28Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2005-06-18 #ifndef __IPREGOPTIONS_HPP__ #define __IPREGOPTIONS_HPP__ #include "IpUtils.hpp" #include "IpReferenced.hpp" #include "IpException.hpp" #include "IpSmartPtr.hpp" #include namespace Ipopt { enum RegisteredOptionType { OT_Number, OT_Integer, OT_String, OT_Unknown }; /** Base class for registered options. The derived types are more * specific to a string option or a Number (real) option, etc. */ class RegisteredOption : public ReferencedObject { public: /** Constructors / Destructors */ //@{ RegisteredOption() : type_(OT_Unknown), has_lower_(false), has_upper_(false), counter_(0) {} RegisteredOption(const std::string& name, const std::string& short_description, const std::string& long_description, const std::string& registering_category) : name_(name), short_description_(short_description), long_description_(long_description), registering_category_(registering_category), type_(OT_Unknown), has_lower_(false), has_upper_(false), counter_(next_counter_++) {} RegisteredOption(const RegisteredOption& copy) : name_(copy.name_), short_description_(copy.short_description_), long_description_(copy.long_description_), registering_category_(copy.registering_category_), type_(copy.type_), has_lower_(copy.has_lower_), lower_(copy.lower_), has_upper_(copy.has_upper_), upper_(copy.upper_), valid_strings_(copy.valid_strings_), counter_(copy.counter_) {} virtual ~RegisteredOption() {} //@} DECLARE_STD_EXCEPTION(ERROR_CONVERTING_STRING_TO_ENUM); /** Standard Get / Set Methods */ //@{ /** Get the option's name (tag in the input file) */ const std::string& Name() const { return name_; } /** Set the option's name (tag in the input file) */ void SetName(const std::string& name) { name_ = name; } /** Get the short description */ const std::string& ShortDescription() const { return short_description_; } /** Get the long description */ const std::string& LongDescription() const { return long_description_; } /** Set the short description */ void SetShortDescription(const std::string& short_description) { short_description_ = short_description; } /** Set the long description */ void SetLongDescription(const std::string& long_description) { long_description_ = long_description; } /** Get the registering class */ const std::string& RegisteringCategory() const { return registering_category_; } /** Set the registering class */ void SetRegisteringCategory(const std::string& registering_category) { registering_category_ = registering_category; } /** Get the Option's type */ const RegisteredOptionType& Type() const { return type_; } /** Get the Option's type */ void SetType(const RegisteredOptionType& type) { type_ = type; } /** Counter */ Index Counter() const { return counter_; } //@} /** @name Get / Set methods valid for specific types - NOTE: the Type * must be set before calling these methods. */ //@{ /** check if the option has a lower bound - can be called for * OT_Number & OT_Integer*/ const bool& HasLower() const { DBG_ASSERT(type_ == OT_Number || type_ == OT_Integer); return has_lower_; } /** check if the lower bound is strict - can be called for OT_Number */ const bool& LowerStrict() const { DBG_ASSERT(type_ == OT_Number && has_lower_ == true); return lower_strict_; } /** get the Number version of the lower bound - can be called for * OT_Number */ Number LowerNumber() const { DBG_ASSERT(has_lower_ == true && type_ == OT_Number); return lower_; } /** set the Number version of the lower bound - can be called for * OT_Number */ void SetLowerNumber(const Number& lower, const bool& strict) { DBG_ASSERT(type_ == OT_Number); lower_ = lower; lower_strict_ = strict, has_lower_ = true; } /** get the Integer version of the lower bound can be called for * OT_Integer*/ Index LowerInteger() const { DBG_ASSERT(has_lower_ == true && type_ == OT_Integer); return (Index)lower_; } /** set the Integer version of the lower bound - can be called for * OT_Integer */ void SetLowerInteger(const Index& lower) { DBG_ASSERT(type_ == OT_Integer); lower_ = (Number)lower; has_lower_ = true; } /** check if the option has an upper bound - can be called for * OT_Number & OT_Integer*/ const bool& HasUpper() const { DBG_ASSERT(type_ == OT_Number || type_ == OT_Integer); return has_upper_; } /** check if the upper bound is strict - can be called for * OT_Number */ const bool& UpperStrict() const { DBG_ASSERT(type_ == OT_Number && has_upper_ == true); return upper_strict_; } /** get the Number version of the upper bound - can be called for * OT_Number */ Number UpperNumber() const { DBG_ASSERT(has_upper_ == true && type_ == OT_Number); return upper_; } /** set the Number version of the upper bound - can be called for * OT_Number */ void SetUpperNumber(const Number& upper, const bool& strict) { DBG_ASSERT(type_ == OT_Number); upper_ = upper; upper_strict_ = strict; has_upper_ = true; } /** get the Integer version of the upper bound - can be called for * OT_Integer*/ Index UpperInteger() const { DBG_ASSERT(has_upper_ == true && type_ == OT_Integer); return (Index)upper_; } /** set the Integer version of the upper bound - can be called for * OT_Integer */ void SetUpperInteger(const Index& upper) { DBG_ASSERT(type_ == OT_Integer); upper_ = (Number)upper; has_upper_ = true; } /** method to add valid string entries - can be called for * OT_String */ void AddValidStringSetting(const std::string value, const std::string description) { DBG_ASSERT(type_ == OT_String); valid_strings_.push_back(string_entry(value, description)); } /** get the default as a Number - can be called for OT_Number */ Number DefaultNumber() const { DBG_ASSERT(type_ == OT_Number); return default_number_; } /** Set the default as a Number - can be called for OT_Number */ void SetDefaultNumber(const Number& default_value) { DBG_ASSERT(type_ == OT_Number); default_number_ = default_value; } /** get the default as an Integer - can be called for OT_Integer*/ Index DefaultInteger() const { DBG_ASSERT(type_ == OT_Integer); return (Index)default_number_; } /** Set the default as an Integer - can be called for OT_Integer */ void SetDefaultInteger(const Index& default_value) { DBG_ASSERT(type_ == OT_Integer); default_number_ = (Number)default_value; } /** get the default as a string - can be called for OT_String */ std::string DefaultString() const { DBG_ASSERT(type_ == OT_String); return default_string_; } /** get the default as a string, but as the index of the string in * the list - helps map from a string to an enum- can be called * for OT_String */ Index DefaultStringAsEnum() const { DBG_ASSERT(type_ == OT_String); return MapStringSettingToEnum(default_string_); } /** Set the default as a string - can be called for OT_String */ void SetDefaultString(const std::string& default_value) { DBG_ASSERT(type_ == OT_String); default_string_ = default_value; } /** Check if the Number value is a valid setting - can be called * for OT_Number */ bool IsValidNumberSetting(const Number& value) const { DBG_ASSERT(type_ == OT_Number); if (has_lower_ && ((lower_strict_ == true && value <= lower_) || (lower_strict_ == false && value < lower_))) { return false; } if (has_upper_ && ((upper_strict_ == true && value >= upper_) || (upper_strict_ == false && value > upper_))) { return false; } return true; } /** Check if the Integer value is a valid setting - can be called * for OT_Integer */ bool IsValidIntegerSetting(const Index& value) const { DBG_ASSERT(type_ == OT_Integer); if (has_lower_ && value < lower_) { return false; } if (has_upper_ && value > upper_) { return false; } return true; } /** Check if the String value is a valid setting - can be called * for OT_String */ bool IsValidStringSetting(const std::string& value) const; /** Map a user setting (allowing any case) to the case used when * the setting was registered. */ std::string MapStringSetting(const std::string& value) const; /** Map a user setting (allowing any case) to the index of the * matched setting in the list of string settings. Helps map a * string setting to an enumeration. */ Index MapStringSettingToEnum(const std::string& value) const; //@} /** output a description of the option */ void OutputDescription(const Journalist& jnlst) const; /** output a more concise version */ void OutputShortDescription(const Journalist& jnlst) const; /** output a latex version */ void OutputLatexDescription(const Journalist& jnlst) const; private: std::string name_; std::string short_description_; std::string long_description_; std::string registering_category_; RegisteredOptionType type_; bool has_lower_; bool lower_strict_; Number lower_; bool has_upper_; bool upper_strict_; Number upper_; Number default_number_; void MakeValidLatexString(std::string source, std::string& dest) const; std::string MakeValidLatexNumber(Number value) const; /** Compare two strings and return true if they are equal (case insensitive comparison) */ bool string_equal_insensitive(const std::string& s1, const std::string& s2) const; /** class to hold the valid string settings for a string option */ class string_entry { public: string_entry(const std::string& value, const std::string& description) : value_(value), description_(description) {} std::string value_; std::string description_; }; std::vector valid_strings_; std::string default_string_; /** Has the information as how many-th option this one was * registered. */ const Index counter_; static Index next_counter_; }; /** Class for storing registered options. Used for validation and * documentation. */ class RegisteredOptions : public ReferencedObject { public: /** Constructors / Destructors */ //@{ /** Standard Constructor */ RegisteredOptions() : current_registering_category_("Uncategorized") {} /** Standard Destructor */ ~RegisteredOptions() {} //@} DECLARE_STD_EXCEPTION(OPTION_ALREADY_REGISTERED); /** Methods to interact with registered options */ //@{ /** set the registering class. All subsequent options will be * added with the registered class */ void SetRegisteringCategory(const std::string& registering_category) { current_registering_category_ = registering_category; } /** retrieve the value of the current registering category */ std::string RegisteringCategory() { return current_registering_category_; } /** Add a Number option (with no restrictions) */ void AddNumberOption(const std::string& name, const std::string& short_description, Number default_value, const std::string& long_description=""); /** Add a Number option (with a lower bound) */ void AddLowerBoundedNumberOption(const std::string& name, const std::string& short_description, Number lower, bool strict, Number default_value, const std::string& long_description=""); /** Add a Number option (with a upper bound) */ void AddUpperBoundedNumberOption(const std::string& name, const std::string& short_description, Number upper, bool strict, Number default_value, const std::string& long_description=""); /** Add a Number option (with a both bounds) */ void AddBoundedNumberOption(const std::string& name, const std::string& short_description, Number lower, bool lower_strict, Number upper, bool upper_strict, Number default_value, const std::string& long_description=""); /** Add a Integer option (with no restrictions) */ void AddIntegerOption(const std::string& name, const std::string& short_description, Index default_value, const std::string& long_description=""); /** Add a Integer option (with a lower bound) */ void AddLowerBoundedIntegerOption(const std::string& name, const std::string& short_description, Index lower, Index default_value, const std::string& long_description=""); /** Add a Integer option (with a upper bound) */ void AddUpperBoundedIntegerOption(const std::string& name, const std::string& short_description, Index upper, Index default_value, const std::string& long_description=""); /** Add a Integer option (with a both bounds) */ void AddBoundedIntegerOption(const std::string& name, const std::string& short_description, Index lower, Index upper, Index default_value, const std::string& long_description=""); /** Add a String option (with no restrictions) */ void AddStringOption(const std::string& name, const std::string& short_description, const std::string& default_value, const std::vector& settings, const std::vector& descriptions, const std::string& long_description=""); /** Methods that make adding string options with only a few * entries easier */ void AddStringOption1(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& long_description=""); void AddStringOption2(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& long_description=""); void AddStringOption3(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& long_description=""); void AddStringOption4(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& setting4, const std::string& description4, const std::string& long_description=""); void AddStringOption5(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& setting4, const std::string& description4, const std::string& setting5, const std::string& description5, const std::string& long_description=""); void AddStringOption6(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& setting4, const std::string& description4, const std::string& setting5, const std::string& description5, const std::string& setting6, const std::string& description6, const std::string& long_description=""); void AddStringOption7(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& setting4, const std::string& description4, const std::string& setting5, const std::string& description5, const std::string& setting6, const std::string& description6, const std::string& setting7, const std::string& description7, const std::string& long_description=""); void AddStringOption8(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& setting4, const std::string& description4, const std::string& setting5, const std::string& description5, const std::string& setting6, const std::string& description6, const std::string& setting7, const std::string& description7, const std::string& setting8, const std::string& description8, const std::string& long_description=""); void AddStringOption9(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& setting4, const std::string& description4, const std::string& setting5, const std::string& description5, const std::string& setting6, const std::string& description6, const std::string& setting7, const std::string& description7, const std::string& setting8, const std::string& description8, const std::string& setting9, const std::string& description9, const std::string& long_description=""); void AddStringOption10(const std::string& name, const std::string& short_description, const std::string& default_value, const std::string& setting1, const std::string& description1, const std::string& setting2, const std::string& description2, const std::string& setting3, const std::string& description3, const std::string& setting4, const std::string& description4, const std::string& setting5, const std::string& description5, const std::string& setting6, const std::string& description6, const std::string& setting7, const std::string& description7, const std::string& setting8, const std::string& description8, const std::string& setting9, const std::string& description9, const std::string& setting10, const std::string& description10, const std::string& long_description=""); /** Get a registered option - this will return NULL if the option * does not exist */ SmartPtr GetOption(const std::string& name); /** Output documentation for the options - gives a description, * etc. */ void OutputOptionDocumentation(const Journalist& jnlst, std::list& categories); /** Output documentation in Latex format to include in a latex file */ void OutputLatexOptionDocumentation(const Journalist& jnlst, std::list& categories); //@} typedef std::map > RegOptionsList; /** Giving access to iteratable representation of the registered * options */ const RegOptionsList& RegisteredOptionsList () const { return registered_options_; } private: std::string current_registering_category_; std::map > registered_options_; }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpReturnCodes.inc0000644000335100017570000000356611133661702027174 0ustar pgehlerwebadmC Copyright (C) 2005, 2006 International Business Machines and others. C All Rights Reserved. C This code is published under the Common Public License. C C $Id: IpReturnCodes.inc 1010 2007-06-21 15:54:36Z andreasw $ C C Author: Andreas Waechter IBM 2005-08-11 C INTEGER IP_SOLVE_SUCCEEDED PARAMETER( IP_SOLVE_SUCCEEDED = 0 ) INTEGER IP_ACCEPTABLE_LEVEL PARAMETER( IP_ACCEPTABLE_LEVEL = 1 ) INTEGER IP_INFEASIBLE_PROBLEM PARAMETER( IP_INFEASIBLE_PROBLEM = 2 ) INTEGER IP_SEARCH_DIRECTION_TOO_SMALL PARAMETER( IP_SEARCH_DIRECTION_TOO_SMALL = 3 ) INTEGER IP_DIVERGING_ITERATES PARAMETER( IP_DIVERGING_ITERATES = 4 ) INTEGER IP_USER_REQUESTED_STOP PARAMETER( IP_USER_REQUESTED_STOP = 5 ) INTEGER IP_ITERATION_EXCEEDED PARAMETER( IP_ITERATION_EXCEEDED = -1 ) INTEGER IP_RESTORATION_FAILED PARAMETER( IP_RESTORATION_FAILED = -2 ) INTEGER IP_ERROR_IN_STEP_COMPUTATION PARAMETER( IP_ERROR_IN_STEP_COMPUTATION = -3 ) INTEGER IP_NOT_ENOUGH_DEGREES_OF_FRE PARAMETER( IP_NOT_ENOUGH_DEGREES_OF_FRE = -10 ) INTEGER IP_INVALID_PROBLEM_DEFINITION PARAMETER( IP_INVALID_PROBLEM_DEFINITION = -11) INTEGER IP_INVALID_OPTION PARAMETER( IP_INVALID_OPTION = -12 ) INTEGER IP_INVALID_NUMBER_DETECTED PARAMETER( IP_INVALID_NUMBER_DETECTED = -13 ) INTEGER IP_UNRECOVERABLE_EXCEPTION PARAMETER( IP_UNRECOVERABLE_EXCEPTION = -100 ) INTEGER IP_NON_IPOPT_EXCEPTION PARAMETER( IP_NON_IPOPT_EXCEPTION = -101 ) INTEGER IP_INSUFFICIENT_MEMORY PARAMETER( IP_INSUFFICIENT_MEMORY = -102 ) INTEGER IP_INTERNAL_ERROR PARAMETER( IP_INTERNAL_ERROR = -199 ) INTEGER IP_REGULAR_MODE PARAMETER( IP_REGULAR_MODE = 0 ) INTEGER IP_RESTORATION_PHASE_MODE PARAMETER( IP_RESTORATION_PHASE_MODE = 1 ) mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpUtils.hpp0000644000335100017570000000417411133661702026051 0ustar pgehlerwebadm// Copyright (C) 2004, 2007 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpUtils.hpp 1010 2007-06-21 15:54:36Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPUTILS_HPP__ #define __IPUTILS_HPP__ // Standard Ip Include Files #include "IpTypes.hpp" #include "IpDebug.hpp" namespace Ipopt { inline Index Max(Index a, Index b) { return ((a) > (b) ? (a) : (b)); } inline Index Max(Index a, Index b, Index c) { Index max = Max(a,b); max = Max(max, c); return max; } inline Index Max(Index a, Index b, Index c, Index d) { Index max = Max(a, b, c); max = Max(max, d); return max; } inline Index Min(Index a, Index b) { return ((a) < (b) ? (a) : (b)); } inline Index Min(Index a, Index b, Index c) { Index min = Min(a,b); min = Min(min, c); return min; } inline Index Min(Index a, Index b, Index c, Index d) { Index min = Min(a, b, c); min = Min(min, d); return min; } /////////////////////////////////////////// inline Number Max(Number a, Number b) { return ((a) > (b) ? (a) : (b)); } inline Number Max(Number a, Number b, Number c) { Number max = Max(a,b); max = Max(max, c); return max; } inline Number Max(Number a, Number b, Number c, Number d) { Number max = Max(a, b, c); max = Max(max, d); return max; } inline Number Min(Number a, Number b) { return ((a) < (b) ? (a) : (b)); } inline Number Min(Number a, Number b, Number c) { Number min = Min(a,b); min = Min(min, c); return min; } inline Number Min(Number a, Number b, Number c, Number d) { Number min = Min(a, b, c); min = Min(min, d); return min; } /** Function returning true iff the argument is a valid double number * (not NaN or Inf). */ bool IsFiniteNumber(Number val); /** Function returning a random number between 0 and 1 */ Number IpRandom01(); /** Function resetting the random number generator */ void IpResetRandom01(); } //namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpTypes.hpp0000644000335100017570000000106011133661702026044 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpTypes.hpp 759 2006-07-07 03:07:08Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPTYPES_HPP__ #define __IPTYPES_HPP__ namespace Ipopt { /** Type of all numbers */ typedef double Number; /** Type of all indices of vectors, matrices etc */ typedef int Index; /** Type of default integer */ typedef int Int; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpReturnCodes.hpp0000644000335100017570000000111511133661702027176 0ustar pgehlerwebadm/*********************************************************************** // Copyright (C) 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpReturnCodes.hpp 759 2006-07-07 03:07:08Z andreasw $ // // Authors: Andreas Waechter IBM 2006-03-01 ************************************************************************/ #ifndef __IPRETURNCODES_HPP__ #define __IPRETURNCODES_HPP__ /* include from a common include file */ namespace Ipopt { #include "IpReturnCodes_inc.h" } #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpReturnCodes_inc.h0000644000335100017570000000264411133661702027477 0ustar pgehlerwebadm/*********************************************************************** // Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpReturnCodes_inc.h 759 2006-07-07 03:07:08Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 ************************************************************************/ /* !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! */ /* !!!!!!!!!!!!!!!! REMEMBER TO UPDATE IpReturnCodes.inc !!!!!!!!!!!!!!!! */ /* !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! */ /** Return codes for the Optimize call for an application */ enum ApplicationReturnStatus { Solve_Succeeded=0, Solved_To_Acceptable_Level=1, Infeasible_Problem_Detected=2, Search_Direction_Becomes_Too_Small=3, Diverging_Iterates=4, User_Requested_Stop=5, Maximum_Iterations_Exceeded=-1, Restoration_Failed=-2, Error_In_Step_Computation=-3, Not_Enough_Degrees_Of_Freedom=-10, Invalid_Problem_Definition=-11, Invalid_Option=-12, Invalid_Number_Detected=-13, Unrecoverable_Exception=-100, NonIpopt_Exception_Thrown=-101, Insufficient_Memory=-102, Internal_Error=-199 }; /** enum to indicate the mode in which the algorithm is */ enum AlgorithmMode { RegularMode=0, RestorationPhaseMode=1 }; mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpOptionsList.hpp0000644000335100017570000002176011133661702027240 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpOptionsList.hpp 1010 2007-06-21 15:54:36Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPOPTLIST_HPP__ #define __IPOPTLIST_HPP__ #include "IpUtils.hpp" #include "IpReferenced.hpp" #include "IpException.hpp" #include "IpRegOptions.hpp" #include #include namespace Ipopt { /** Exception that can be used to indicate errors with options */ DECLARE_STD_EXCEPTION(OPTION_INVALID); /** This class stores a list of user set options. Each options is * identified by a case-insensitive keyword (tag). Its value is * stored internally as a string (always lower case), but for * convenience set and get methods are provided to obtain Index and * Number type values. For each keyword we also keep track of how * often the value of an option has been requested by a get method. */ class OptionsList : public ReferencedObject { /** Class for storing the value and counter for each option in * OptionsList. */ class OptionValue { public: /**@name Constructors/Destructors */ //@{ /** Default constructor (needed for the map) */ OptionValue() : initialized_(false) {} /** Constructor given the value */ OptionValue(std::string value, bool allow_clobber, bool dont_print) : value_(value), counter_(0), initialized_(true), allow_clobber_(allow_clobber), dont_print_(dont_print) {} /** Copy Constructor */ OptionValue(const OptionValue& copy) : value_(copy.value_), counter_(copy.counter_), initialized_(copy.initialized_), allow_clobber_(copy.allow_clobber_), dont_print_(copy.dont_print_) {} /** Equals operator */ void operator=(const OptionValue& copy) { value_=copy.value_; counter_=copy.counter_; initialized_=copy.initialized_; allow_clobber_=copy.allow_clobber_; dont_print_=copy.dont_print_; } /** Default Destructor */ ~OptionValue() {} //@} /** Method for retrieving the value of an option. Calling this * method will increase the counter by one. */ std::string GetValue() const { DBG_ASSERT(initialized_); counter_++; return value_; } /** Method for retrieving the value without increasing the * counter */ std::string Value() const { DBG_ASSERT(initialized_); return value_; } /** Method for accessing current value of the request counter */ Index Counter() const { DBG_ASSERT(initialized_); return counter_; } /** True if the option can be overwritten */ bool AllowClobber() const { DBG_ASSERT(initialized_); return allow_clobber_; } /** True if this option is not to show up in the * print_user_options output */ bool DontPrint() const { DBG_ASSERT(initialized_); return dont_print_; } private: /** Value for this option */ std::string value_; /** Counter for requests */ mutable Index counter_; /** for debugging */ bool initialized_; /** True if the option can be overwritten */ bool allow_clobber_; /** True if this option is not to show up in the * print_user_options output */ bool dont_print_; }; public: /**@name Constructors/Destructors */ //@{ OptionsList(SmartPtr reg_options, SmartPtr jnlst) : reg_options_(reg_options), jnlst_(jnlst) {} OptionsList() {} /** Copy Constructor */ OptionsList(const OptionsList& copy) { // copy all the option strings and values options_ = copy.options_; // copy the registered options pointer reg_options_ = copy.reg_options_; } /** Default destructor */ virtual ~OptionsList() {} /** Overloaded Equals Operator */ void operator=(const OptionsList& source) { options_ = source.options_; reg_options_ = source.reg_options_; jnlst_ = source.jnlst_; } //@} /** Method for clearing all previously set options */ void clear() { options_.clear(); } /** @name Get / Set Methods */ //@{ void SetRegisteredOptions(const SmartPtr reg_options) { reg_options_ = reg_options; } void SetJournalist(const SmartPtr jnlst) { jnlst_ = jnlst; } //@} /** @name Methods for setting options */ //@{ bool SetStringValue(const std::string& tag, const std::string& value, bool allow_clobber = true, bool dont_print = false); bool SetNumericValue(const std::string& tag, Number value, bool allow_clobber = true, bool dont_print = false); bool SetIntegerValue(const std::string& tag, Index value, bool allow_clobber = true, bool dont_print = false); //@} /** @name Methods for setting options only if they have not been * set before*/ //@{ bool SetStringValueIfUnset(const std::string& tag, const std::string& value, bool allow_clobber = true, bool dont_print = false); bool SetNumericValueIfUnset(const std::string& tag, Number value, bool allow_clobber = true, bool dont_print = false); bool SetIntegerValueIfUnset(const std::string& tag, Index value, bool allow_clobber = true, bool dont_print = false); //@} /** @name Methods for retrieving values from the options list. If * a tag is not found, the methods return false, and value is set * to the default value defined in the registered options. */ //@{ bool GetStringValue(const std::string& tag, std::string& value, const std::string& prefix) const; bool GetEnumValue(const std::string& tag, Index& value, const std::string& prefix) const; bool GetBoolValue(const std::string& tag, bool& value, const std::string& prefix) const; bool GetNumericValue(const std::string& tag, Number& value, const std::string& prefix) const; bool GetIntegerValue(const std::string& tag, Index& value, const std::string& prefix) const; //@} /** Get a string with the list of all options (tag, value, counter) */ void PrintList(std::string& list) const; /** Get a string with the list of all options set by the user * (tag, value, use/notused). Here, options with dont_print flag * set to true are not printed. */ void PrintUserOptions(std::string& list) const; /** Read options from the stream is. Returns false if * an error was encountered. */ bool ReadFromStream(const Journalist& jnlst, std::istream& is); private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ // OptionsList(); //@} /** map for storing the options */ std::map< std::string, OptionValue > options_; /** list of all the registered options to validate against */ SmartPtr reg_options_; /** Journalist for writing error messages, etc. */ SmartPtr jnlst_; /** auxilliary method for converting sting to all lower-case * letters */ const std::string& lowercase(const std::string tag) const; /** auxilliary method for finding the value for a tag in the * options list. This method first looks for the concatenated * string prefix+tag (if prefix is not ""), and if this is not * found, it looks for tag. The return value is true iff * prefix+tag or tag is found. In that case, the corresponding * string value is copied into value. */ bool find_tag(const std::string& tag, const std::string& prefix, std::string& value) const; /** tells whether or not we can clobber a particular option. * returns true if the option does not already exist, or if * the option exists but is set to allow_clobber */ bool will_allow_clobber(const std::string& tag) const; /** read the next token from stream is. Returns false, if EOF was * reached before a tokens was ecountered. */ bool readnexttoken(std::istream& is, std::string& token); /** auxilliary string set by lowercase method */ mutable std::string lowercase_buffer_; }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpAlgTypes.hpp0000644000335100017570000000323711133661702026500 0ustar pgehlerwebadm// Copyright (C) 2005, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpAlgTypes.hpp 1054 2007-07-17 23:09:07Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2005-07-19 #ifndef __IPALGTYPES_HPP__ #define __IPALGTYPES_HPP__ #include "IpTypes.hpp" #include "IpException.hpp" namespace Ipopt { /**@name Enumerations */ //@{ /** enum for the return from the optimize algorithm * (obviously we need to add more) */ enum SolverReturn { SUCCESS, MAXITER_EXCEEDED, STOP_AT_TINY_STEP, STOP_AT_ACCEPTABLE_POINT, LOCAL_INFEASIBILITY, USER_REQUESTED_STOP, DIVERGING_ITERATES, RESTORATION_FAILURE, ERROR_IN_STEP_COMPUTATION, INVALID_NUMBER_DETECTED, TOO_FEW_DEGREES_OF_FREEDOM, INTERNAL_ERROR }; //@} /** @name Some exceptions used in multiple places */ //@{ DECLARE_STD_EXCEPTION(LOCALLY_INFEASIBLE); DECLARE_STD_EXCEPTION(TOO_FEW_DOF); DECLARE_STD_EXCEPTION(TINY_STEP_DETECTED); DECLARE_STD_EXCEPTION(ACCEPTABLE_POINT_REACHED); DECLARE_STD_EXCEPTION(FEASIBILITY_PROBLEM_SOLVED); DECLARE_STD_EXCEPTION(INVALID_WARMSTART); DECLARE_STD_EXCEPTION(INTERNAL_ABORT); DECLARE_STD_EXCEPTION(NO_FREE_VARIABLES_BUT_FEASIBLE); DECLARE_STD_EXCEPTION(NO_FREE_VARIABLES_AND_INFEASIBLE); /** Exception FAILED_INITIALIZATION for problem during * initialization of a strategy object (or other problems). This * is thrown by a strategy object, if a problem arises during * initialization, such as a value out of a feasible range. */ DECLARE_STD_EXCEPTION(FAILED_INITIALIZATION); //@} } #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpReferenced.hpp0000644000335100017570000002124011133661702027004 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpReferenced.hpp 1020 2007-06-24 04:06:59Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPREFERENCED_HPP__ #define __IPREFERENCED_HPP__ #include "IpTypes.hpp" #include "IpDebug.hpp" #include namespace Ipopt { /** Psydo-class, from which everything has to inherit that wants to * use be registered as a Referencer for a ReferencedObject. */ class Referencer {} ; /** ReferencedObject class. * This is part of the implementation of an intrusive smart pointer * design. This class stores the reference count of all the smart * pointers that currently reference it. See the documentation for * the SmartPtr class for more details. * * A SmartPtr behaves much like a raw pointer, but manages the lifetime * of an object, deleting the object automatically. This class implements * a reference-counting, intrusive smart pointer design, where all * objects pointed to must inherit off of ReferencedObject, which * stores the reference count. Although this is intrusive (native types * and externally authored classes require wrappers to be referenced * by smart pointers), it is a safer design. A more detailed discussion of * these issues follows after the usage information. * * Usage Example: * Note: to use the SmartPtr, all objects to which you point MUST * inherit off of ReferencedObject. * * \verbatim * * In MyClass.hpp... * * #include "IpReferenced.hpp" * namespace Ipopt { * * class MyClass : public ReferencedObject // must derive from ReferencedObject * { * ... * } * } // namespace Ipopt * * * In my_usage.cpp... * * #include "IpSmartPtr.hpp" * #include "MyClass.hpp" * * void func(AnyObject& obj) * { * SmartPtr ptr_to_myclass = new MyClass(...); * // ptr_to_myclass now points to a new MyClass, * // and the reference count is 1 * * ... * * obj.SetMyClass(ptr_to_myclass); * // Here, let's assume that AnyObject uses a * // SmartPtr internally here. * // Now, both ptr_to_myclass and the internal * // SmartPtr in obj point to the same MyClass object * // and its reference count is 2. * * ... * * // No need to delete ptr_to_myclass, this * // will be done automatically when the * // reference count drops to zero. * * } * * \endverbatim * * Other Notes: * The SmartPtr implements both dereference operators -> & *. * The SmartPtr does NOT implement a conversion operator to * the raw pointer. Use the GetRawPtr() method when this * is necessary. Make sure that the raw pointer is NOT * deleted. * The SmartPtr implements the comparison operators == & != * for a variety of types. Use these instead of * \verbatim * if (GetRawPtr(smrt_ptr) == ptr) // Don't use this * \endverbatim * SmartPtr's, as currently implemented, do NOT handle circular references. * For example: consider a higher level object using SmartPtrs to point to * A and B, but A and B also point to each other (i.e. A has a SmartPtr * to B and B has a SmartPtr to A). In this scenario, when the higher * level object is finished with A and B, their reference counts will * never drop to zero (since they reference each other) and they * will not be deleted. This can be detected by memory leak tools like * valgrind. If the circular reference is necessary, the problem can be * overcome by a number of techniques: * * 1) A and B can have a method that "releases" each other, that is * they set their internal SmartPtrs to NULL. * \verbatim * void AClass::ReleaseCircularReferences() * { * smart_ptr_to_B = NULL; * } * \endverbatim * Then, the higher level class can call these methods before * it is done using A & B. * * 2) Raw pointers can be used in A and B to reference each other. * Here, an implicit assumption is made that the lifetime is * controlled by the higher level object and that A and B will * both exist in a controlled manner. Although this seems * dangerous, in many situations, this type of referencing * is very controlled and this is reasonably safe. * * 3) This SmartPtr class could be redesigned with the Weak/Strong * design concept. Here, the SmartPtr is identified as being * Strong (controls lifetime of the object) or Weak (merely * referencing the object). The Strong SmartPtr increments * (and decrements) the reference count in ReferencedObject * but the Weak SmartPtr does not. In the example above, * the higher level object would have Strong SmartPtrs to * A and B, but A and B would have Weak SmartPtrs to each * other. Then, when the higher level object was done with * A and B, they would be deleted. The Weak SmartPtrs in A * and B would not decrement the reference count and would, * of course, not delete the object. This idea is very similar * to item (2), where it is implied that the sequence of events * is controlled such that A and B will not call anything using * their pointers following the higher level delete (i.e. in * their destructors!). This is somehow safer, however, because * code can be written (however expensive) to perform run-time * detection of this situation. For example, the ReferencedObject * could store pointers to all Weak SmartPtrs that are referencing * it and, in its destructor, tell these pointers that it is * dying. They could then set themselves to NULL, or set an * internal flag to detect usage past this point. * * Comments on Non-Intrusive Design: * In a non-intrusive design, the reference count is stored somewhere other * than the object being referenced. This means, unless the reference * counting pointer is the first referencer, it must get a pointer to the * referenced object from another smart pointer (so it has access to the * reference count location). In this non-intrusive design, if we are * pointing to an object with a smart pointer (or a number of smart * pointers), and we then give another smart pointer the address through * a RAW pointer, we will have two independent, AND INCORRECT, reference * counts. To avoid this pitfall, we use an intrusive reference counting * technique where the reference count is stored in the object being * referenced. */ class ReferencedObject { public: ReferencedObject() : reference_count_(0) {} virtual ~ReferencedObject() { DBG_ASSERT(reference_count_ == 0); } Index ReferenceCount() const; void AddRef(const Referencer* referencer) const; void ReleaseRef(const Referencer* referencer) const; private: mutable Index reference_count_; # ifdef REF_DEBUG mutable std::list referencers_; # endif }; /* inline methods */ inline Index ReferencedObject::ReferenceCount() const { // DBG_START_METH("ReferencedObject::ReferenceCount()", 0); // DBG_PRINT((1,"Returning reference_count_ = %d\n", reference_count_)); return reference_count_; } inline void ReferencedObject::AddRef(const Referencer* referencer) const { // DBG_START_METH("ReferencedObject::AddRef(const Referencer* referencer)", 0); reference_count_++; // DBG_PRINT((1, "New reference_count_ = %d\n", reference_count_)); # ifdef REF_DEBUG referencers_.push_back(referencer); # endif } inline void ReferencedObject::ReleaseRef(const Referencer* referencer) const { // DBG_START_METH("ReferencedObject::ReleaseRef(const Referencer* referencer)", // 0); reference_count_--; // DBG_PRINT((1, "New reference_count_ = %d\n", reference_count_)); # ifdef REF_DEBUG bool found = false; std::list::iterator iter; for (iter = referencers_.begin(); iter != referencers_.end(); iter++) { if ((*iter) == referencer) { found = true; break; } } // cannot call release on a reference that was never added... DBG_ASSERT(found); if (found) { referencers_.erase(iter); } # endif } } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpTaggedObject.hpp0000644000335100017570000001126411133661702027271 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpTaggedObject.hpp 1020 2007-06-24 04:06:59Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPTAGGEDOBJECT_HPP__ #define __IPTAGGEDOBJECT_HPP__ #include "IpUtils.hpp" #include "IpDebug.hpp" #include "IpReferenced.hpp" #include "IpObserver.hpp" #include namespace Ipopt { /** TaggedObject class. * Often, certain calculations or operations are expensive, * and it can be very inefficient to perform these calculations * again if the input to the calculation has not changed * since the result was last stored. * This base class provides an efficient mechanism to update * a tag, indicating that the object has changed. * Users of a TaggedObject class, need their own Tag data * member to keep track of the state of the TaggedObject, the * last time they performed a calculation. A basic use case for * users of a class inheriting off of TaggedObject follows like * this: * * Initialize your own Tag to zero in constructor. * * * * Before an expensive calculation, * check if the TaggedObject has changed, passing in * your own Tag, indicating the last time you used * the object for the calculation. If it has changed, * perform the calculation again, and store the result. * If it has not changed, simply return the stored result. * * Here is a simple example: \verbatim if (vector.HasChanged(my_vector_tag_)) { my_vector_tag_ = vector.GetTag(); result = PerformExpensiveCalculation(vector); return result; } else { return result; } \endverbatim * * Objects derived from TaggedObject: * Objects derived from TaggedObject must indicate that they have changed to * the base class using the protected member function ObjectChanged(). For * example, a Vector class, inside its own set method, MUST call * ObjectChanged() to update the internally stored tag for comparison. */ class TaggedObject : public ReferencedObject, public Subject { public: /** Type for the Tag values */ typedef unsigned int Tag; /** Constructor. */ TaggedObject() : Subject() { ObjectChanged(); } /** Destructor. */ virtual ~TaggedObject() {} /** Users of TaggedObjects call this to * update their own internal tags every time * they perform the expensive operation. */ Tag GetTag() const { return tag_; } /** Users of TaggedObjects call this to * check if the object HasChanged since * they last updated their own internal * tag. */ bool HasChanged(const Tag comparison_tag) const { return (comparison_tag == tag_) ? false : true; } protected: /** Objects derived from TaggedObject MUST call this * method every time their internal state changes to * update the internal tag for comparison */ void ObjectChanged() { DBG_START_METH("TaggedObject::ObjectChanged()", 0); tag_ = unique_tag_; unique_tag_++; DBG_ASSERT(unique_tag_ < std::numeric_limits::max()); // The Notify method from the Subject base class notifies all // registed Observers that this subject has changed. Notify(Observer::NT_Changed); } private: /**@name Default Compiler Generated Methods (Hidden to avoid * implicit creation/calling). These methods are not implemented * and we do not want the compiler to implement them for us, so we * declare them private and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Copy Constructor */ TaggedObject(const TaggedObject&); /** Overloaded Equals Operator */ void operator=(const TaggedObject&); //@} /** static data member that is incremented every * time ANY TaggedObject changes. This allows us * to obtain a unique Tag when the object changes */ static Tag unique_tag_; /** The tag indicating the current state of the object. * We use this to compare against the comparison_tag * in the HasChanged method. This member is updated * from the unique_tag_ every time the object changes. */ Tag tag_; /** The index indicating the cache priority for this * TaggedObject. If a result that depended on this * TaggedObject is cached, it will be cached with this * priority */ Index cache_priority_; }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpVector.hpp0000644000335100017570000004751411133661702026220 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpVector.hpp 1054 2007-07-17 23:09:07Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPVECTOR_HPP__ #define __IPVECTOR_HPP__ #include "IpTypes.hpp" #include "IpTaggedObject.hpp" #include "IpCachedResults.hpp" #include "IpSmartPtr.hpp" #include "IpJournalist.hpp" #include namespace Ipopt { /* forward declarations */ class VectorSpace; /** Vector Base Class. * This is the base class for all derived vector types. Those vectors * are meant to store entities like iterates, Lagrangian multipliers, * constraint values etc. The implementation of a vector type depends * on the computational environment (e.g. just a double array on a shared * memory machine, or distributed double arrays for a distributed * memory machine.) * * Deriving from Vector: This class inherits from tagged object to * implement an advanced caching scheme. Because of this, the * TaggedObject method ObjectChanged() must be called each time the * Vector changes. If you overload the XXXX_Impl protected methods, * this taken care of (along with caching if possible) for you. If * you have additional methods in your derived class that change the * underlying data (vector values), you MUST remember to call * ObjectChanged() AFTER making the change! */ class Vector : public TaggedObject { public: /** @name Constructor/Destructor */ //@{ /** Constructor. It has to be given a pointer to the * corresponding VectorSpace. */ Vector(const VectorSpace* owner_space); /** Destructor */ virtual ~Vector(); //@} /** Create new Vector of the same type with uninitialized data */ Vector* MakeNew() const; /** Create new Vector of the same type and copy the data over */ Vector* MakeNewCopy() const; /**@name Standard BLAS-1 Operations * (derived classes do NOT overload these * methods, instead, overload the * protected versions of these methods). */ //@{ /** Copy the data of the vector x into this vector (DCOPY). */ void Copy(const Vector& x); /** Scales the vector by scalar alpha (DSCAL) */ void Scal(Number alpha); /** Add the multiple alpha of vector x to this vector (DAXPY) */ void Axpy(Number alpha, const Vector &x); /** Computes inner product of vector x with this (DDOT) */ Number Dot(const Vector &x) const; /** Computes the 2-norm of this vector (DNRM2) */ Number Nrm2() const; /** Computes the 1-norm of this vector (DASUM) */ Number Asum() const; /** Computes the max-norm of this vector (based on IDAMAX) */ Number Amax() const; //@} /** @name Additional (Non-BLAS) Vector Methods * (derived classes do NOT overload these * methods, instead, overload the * protected versions of these methods). */ //@{ /** Set each element in the vector to the scalar alpha. */ void Set(Number alpha); /** Element-wise division \f$y_i \gets y_i/x_i\f$*/ void ElementWiseDivide(const Vector& x); /** Element-wise multiplication \f$y_i \gets y_i*x_i\f$ */ void ElementWiseMultiply(const Vector& x); /** Element-wise max against entries in x */ void ElementWiseMax(const Vector& x); /** Element-wise min against entries in x */ void ElementWiseMin(const Vector& x); /** Reciprocates the entries in the vector */ void ElementWiseReciprocal(); /** Absolute values of the entries in the vector */ void ElementWiseAbs(); /** Element-wise square root of the entries in the vector */ void ElementWiseSqrt(); /** Replaces the vector values with their sgn values ( -1 if x_i < 0, 0 if x_i == 0, and 1 if x_i > 0) */ void ElementWiseSgn(); /** Add scalar to every vector component */ void AddScalar(Number scalar); /** Returns the maximum value in the vector */ Number Max() const; /** Returns the minimum value in the vector */ Number Min() const; /** Returns the sum of the vector entries */ Number Sum() const; /** Returns the sum of the logs of each vector entry */ Number SumLogs() const; //@} /** @name Methods for specialized operations. A prototype * implementation is provided, but for efficient implementation * those should be specially implemented. */ //@{ /** Add one vector, y = a * v1 + c * y. This is automatically * reduced to call AddTwoVectors. */ void AddOneVector(Number a, const Vector& v1, Number c); /** Add two vectors, y = a * v1 + b * v2 + c * y. Here, this * vector is y */ void AddTwoVectors(Number a, const Vector& v1, Number b, const Vector& v2, Number c); /** Fraction to the boundary parameter. Computes \f$\alpha = * \max\{\bar\alpha\in(0,1] : x + \bar\alpha \Delta \geq (1-\tau)x\}\f$ */ Number FracToBound(const Vector& delta, Number tau) const; /** Add the quotient of two vectors, y = a * z/s + c * y. */ void AddVectorQuotient(Number a, const Vector& z, const Vector& s, Number c); //@} /** Method for determining if all stored numbers are valid (i.e., * no Inf or Nan). */ bool HasValidNumbers() const; /** @name Accessor methods */ //@{ /** Dimension of the Vector */ Index Dim() const; /** Return the owner VectorSpace*/ SmartPtr OwnerSpace() const; //@} /** @name Output methods * (derived classes do NOT overload these * methods, instead, overload the * protected versions of these methods). */ //@{ /** Print the entire vector */ void Print(SmartPtr jnlst, EJournalLevel level, EJournalCategory category, const std::string& name, Index indent=0, const std::string& prefix="") const; void Print(const Journalist& jnlst, EJournalLevel level, EJournalCategory category, const std::string& name, Index indent=0, const std::string& prefix="") const; //@} protected: /** @name implementation methods (derived classes MUST * overload these pure virtual protected methods.) */ //@{ /** Copy the data of the vector x into this vector (DCOPY). */ virtual void CopyImpl(const Vector& x)=0; /** Scales the vector by scalar alpha (DSCAL) */ virtual void ScalImpl(Number alpha)=0; /** Add the multiple alpha of vector x to this vector (DAXPY) */ virtual void AxpyImpl(Number alpha, const Vector &x)=0; /** Computes inner product of vector x with this (DDOT) */ virtual Number DotImpl(const Vector &x) const =0; /** Computes the 2-norm of this vector (DNRM2) */ virtual Number Nrm2Impl() const =0; /** Computes the 1-norm of this vector (DASUM) */ virtual Number AsumImpl() const =0; /** Computes the max-norm of this vector (based on IDAMAX) */ virtual Number AmaxImpl() const =0; /** Set each element in the vector to the scalar alpha. */ virtual void SetImpl(Number alpha)=0; /** Element-wise division \f$y_i \gets y_i/x_i\f$*/ virtual void ElementWiseDivideImpl(const Vector& x)=0; /** Element-wise multiplication \f$y_i \gets y_i*x_i\f$ */ virtual void ElementWiseMultiplyImpl(const Vector& x)=0; /** Element-wise max against entries in x */ virtual void ElementWiseMaxImpl(const Vector& x)=0; /** Element-wise min against entries in x */ virtual void ElementWiseMinImpl(const Vector& x)=0; /** Reciprocates the elements of the vector */ virtual void ElementWiseReciprocalImpl()=0; /** Take elementwise absolute values of the elements of the vector */ virtual void ElementWiseAbsImpl()=0; /** Take elementwise square-root of the elements of the vector */ virtual void ElementWiseSqrtImpl()=0; /** Replaces entries with sgn of the entry */ virtual void ElementWiseSgnImpl()=0; /** Add scalar to every component of vector */ virtual void AddScalarImpl(Number scalar)=0; /** Max value in the vector */ virtual Number MaxImpl() const=0; /** Min number in the vector */ virtual Number MinImpl() const=0; /** Sum of entries in the vector */ virtual Number SumImpl() const=0; /** Sum of logs of entries in the vector */ virtual Number SumLogsImpl() const=0; /** Add two vectors (a * v1 + b * v2). Result is stored in this vector. */ virtual void AddTwoVectorsImpl(Number a, const Vector& v1, Number b, const Vector& v2, Number c); /** Fraction to boundary parameter. */ virtual Number FracToBoundImpl(const Vector& delta, Number tau) const; /** Add the quotient of two vectors */ virtual void AddVectorQuotientImpl(Number a, const Vector& z, const Vector& s, Number c); /** Method for determining if all stored numbers are valid (i.e., * no Inf or Nan). A default implementation using Asum is * provided. */ virtual bool HasValidNumbersImpl() const; /** Print the entire vector */ virtual void PrintImpl(const Journalist& jnlst, EJournalLevel level, EJournalCategory category, const std::string& name, Index indent, const std::string& prefix) const =0; //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default constructor */ Vector(); /** Copy constructor */ Vector(const Vector&); /** Overloaded Equals Operator */ Vector& operator=(const Vector&); //@} /** Vector Space */ const SmartPtr owner_space_; /**@name CachedResults data members */ //@{ /** Cache for dot products */ mutable CachedResults dot_cache_; mutable TaggedObject::Tag nrm2_cache_tag_; mutable Number cached_nrm2_; mutable TaggedObject::Tag asum_cache_tag_; mutable Number cached_asum_; mutable TaggedObject::Tag amax_cache_tag_; mutable Number cached_amax_; mutable TaggedObject::Tag max_cache_tag_; mutable Number cached_max_; mutable TaggedObject::Tag min_cache_tag_; mutable Number cached_min_; mutable TaggedObject::Tag sum_cache_tag_; mutable Number cached_sum_; mutable TaggedObject::Tag sumlogs_cache_tag_; mutable Number cached_sumlogs_; mutable TaggedObject::Tag valid_cache_tag_; mutable bool cached_valid_; // AW: I removed this cache since it gets in the way for the // quality function search // /** Cache for FracToBound */ // mutable CachedResults frac_to_bound_cache_; //@} }; /** VectorSpace base class, corresponding to the Vector base class. * For each Vector implementation, a corresponding VectorSpace has * to be implemented. A VectorSpace is able to create new Vectors * of a specific type. The VectorSpace should also store * information that is common to all Vectors of that type. For * example, the dimension of a Vector is stored in the VectorSpace * base class. */ class VectorSpace : public ReferencedObject { public: /** @name Constructors/Destructors */ //@{ /** Constructor, given the dimension of all vectors generated by * this VectorSpace. */ VectorSpace(Index dim); /** Destructor */ virtual ~VectorSpace() {} //@} /** Pure virtual method for creating a new Vector of the * corresponding type. */ virtual Vector* MakeNew() const=0; /** Accessor function for the dimension of the vectors of this type.*/ Index Dim() const { return dim_; } private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** default constructor */ VectorSpace(); /** Copy constructor */ VectorSpace(const VectorSpace&); /** Overloaded Equals Operator */ VectorSpace& operator=(const VectorSpace&); //@} /** Dimension of the vectors in this vector space. */ const Index dim_; }; /* inline methods */ inline Vector::~Vector() {} inline Vector::Vector(const VectorSpace* owner_space) : TaggedObject(), owner_space_(owner_space), dot_cache_(10), nrm2_cache_tag_(0), asum_cache_tag_(0), amax_cache_tag_(0), max_cache_tag_(0), min_cache_tag_(0), sum_cache_tag_(0), sumlogs_cache_tag_(0), cached_valid_(0) { DBG_ASSERT(IsValid(owner_space_)); } inline Vector* Vector::MakeNew() const { return owner_space_->MakeNew(); } inline Vector* Vector::MakeNewCopy() const { // ToDo: We can probably copy also the cached values for Norms etc here Vector* copy = MakeNew(); copy->Copy(*this); return copy; } inline void Vector::Copy(const Vector& x) { CopyImpl(x); ObjectChanged(); // Also copy any cached scalar values from the original vector // ToDo: Check if that is too much overhead TaggedObject::Tag x_tag = x.GetTag(); if (x_tag == x.nrm2_cache_tag_) { nrm2_cache_tag_ = GetTag(); cached_nrm2_ = x.cached_nrm2_; } if (x_tag == x.asum_cache_tag_) { asum_cache_tag_ = GetTag(); cached_asum_ = x.cached_asum_; } if (x_tag == x.amax_cache_tag_) { amax_cache_tag_ = GetTag(); cached_amax_ = x.cached_amax_; } if (x_tag == x.max_cache_tag_) { max_cache_tag_ = GetTag(); cached_max_ = x.cached_max_; } if (x_tag == x.min_cache_tag_) { min_cache_tag_ = GetTag(); cached_min_ = x.cached_min_; } if (x_tag == x.sum_cache_tag_) { sum_cache_tag_ = GetTag(); cached_sum_ = x.cached_sum_; } if (x_tag == x.sumlogs_cache_tag_) { sumlogs_cache_tag_ = GetTag(); cached_sumlogs_ = x.cached_sumlogs_; } } inline void Vector::Axpy(Number alpha, const Vector &x) { AxpyImpl(alpha, x); ObjectChanged(); } inline Number Vector::Dot(const Vector &x) const { // The current implementation of the caching doesn't allow to have // a dependency of something with itself. Therefore, we use the // Nrm2 method if the dot product is to be taken with the vector // itself. Might be more efficient anyway. if (this==&x) { Number nrm2 = Nrm2(); return nrm2*nrm2; } Number retValue; if (!dot_cache_.GetCachedResult2Dep(retValue, this, &x)) { retValue = DotImpl(x); dot_cache_.AddCachedResult2Dep(retValue, this, &x); } return retValue; } inline Number Vector::Nrm2() const { if (nrm2_cache_tag_ != GetTag()) { cached_nrm2_ = Nrm2Impl(); nrm2_cache_tag_ = GetTag(); } return cached_nrm2_; } inline Number Vector::Asum() const { if (asum_cache_tag_ != GetTag()) { cached_asum_ = AsumImpl(); asum_cache_tag_ = GetTag(); } return cached_asum_; } inline Number Vector::Amax() const { if (amax_cache_tag_ != GetTag()) { cached_amax_ = AmaxImpl(); amax_cache_tag_ = GetTag(); } return cached_amax_; } inline Number Vector::Sum() const { if (sum_cache_tag_ != GetTag()) { cached_sum_ = SumImpl(); sum_cache_tag_ = GetTag(); } return cached_sum_; } inline Number Vector::SumLogs() const { if (sumlogs_cache_tag_ != GetTag()) { cached_sumlogs_ = SumLogsImpl(); sumlogs_cache_tag_ = GetTag(); } return cached_sumlogs_; } inline void Vector::ElementWiseSgn() { ElementWiseSgnImpl(); ObjectChanged(); } inline void Vector::Set(Number alpha) { // Could initialize caches here SetImpl(alpha); ObjectChanged(); } inline void Vector::ElementWiseDivide(const Vector& x) { ElementWiseDivideImpl(x); ObjectChanged(); } inline void Vector::ElementWiseMultiply(const Vector& x) { ElementWiseMultiplyImpl(x); ObjectChanged(); } inline void Vector::ElementWiseReciprocal() { ElementWiseReciprocalImpl(); ObjectChanged(); } inline void Vector::ElementWiseMax(const Vector& x) { // Could initialize some caches here ElementWiseMaxImpl(x); ObjectChanged(); } inline void Vector::ElementWiseMin(const Vector& x) { // Could initialize some caches here ElementWiseMinImpl(x); ObjectChanged(); } inline void Vector::ElementWiseAbs() { // Could initialize some caches here ElementWiseAbsImpl(); ObjectChanged(); } inline void Vector::ElementWiseSqrt() { ElementWiseSqrtImpl(); ObjectChanged(); } inline void Vector::AddScalar(Number scalar) { // Could initialize some caches here AddScalarImpl(scalar); ObjectChanged(); } inline Number Vector::Max() const { if (max_cache_tag_ != GetTag()) { cached_max_ = MaxImpl(); max_cache_tag_ = GetTag(); } return cached_max_; } inline Number Vector::Min() const { if (min_cache_tag_ != GetTag()) { cached_min_ = MinImpl(); min_cache_tag_ = GetTag(); } return cached_min_; } inline void Vector::AddOneVector(Number a, const Vector& v1, Number c) { AddTwoVectorsImpl(a, v1, 0., v1, c); } inline void Vector::AddTwoVectors(Number a, const Vector& v1, Number b, const Vector& v2, Number c) { AddTwoVectorsImpl(a, v1, b, v2, c); ObjectChanged(); } inline Number Vector::FracToBound(const Vector& delta, Number tau) const { /* AW: I avoid the caching here, since it leads to overhead in the quality function search. Caches for this are in CalculatedQuantities. Number retValue; std::vector tdeps(1); tdeps[0] = δ std::vector sdeps(1); sdeps[0] = tau; if (!frac_to_bound_cache_.GetCachedResult(retValue, tdeps, sdeps)) { retValue = FracToBoundImpl(delta, tau); frac_to_bound_cache_.AddCachedResult(retValue, tdeps, sdeps); } return retValue; */ return FracToBoundImpl(delta, tau); } inline void Vector::AddVectorQuotient(Number a, const Vector& z, const Vector& s, Number c) { AddVectorQuotientImpl(a, z, s, c); ObjectChanged(); } inline bool Vector::HasValidNumbers() const { if (valid_cache_tag_ != GetTag()) { cached_valid_ = HasValidNumbersImpl(); valid_cache_tag_ = GetTag(); } return cached_valid_; } inline Index Vector::Dim() const { return owner_space_->Dim(); } inline SmartPtr Vector::OwnerSpace() const { return owner_space_; } inline VectorSpace::VectorSpace(Index dim) : dim_(dim) {} } // namespace Ipopt // Macro definitions for debugging vectors #if COIN_IPOPT_VERBOSITY == 0 # define DBG_PRINT_VECTOR(__verbose_level, __vec_name, __vec) #else # define DBG_PRINT_VECTOR(__verbose_level, __vec_name, __vec) \ if (dbg_jrnl.Verbosity() >= (__verbose_level)) { \ if (dbg_jrnl.Jnlst()!=NULL) { \ (__vec).Print(dbg_jrnl.Jnlst(), \ J_ERROR, J_DBG, \ __vec_name, \ dbg_jrnl.IndentationLevel()*2, \ "# "); \ } \ } #endif //if COIN_IPOPT_VERBOSITY == 0 #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpMatrix.hpp0000644000335100017570000002212211133661702026206 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpMatrix.hpp 1054 2007-07-17 23:09:07Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPMATRIX_HPP__ #define __IPMATRIX_HPP__ #include "IpVector.hpp" namespace Ipopt { /* forward declarations */ class MatrixSpace; /** Matrix Base Class. This is the base class for all derived matrix * types. All Matrices, such as Jacobian and Hessian matrices, as * well as possibly the iteration matrices needed for the step * computation, are of this type. * * Deriving from Matrix: Overload the protected XXX_Impl method. */ class Matrix : public TaggedObject { public: /** @name Constructor/Destructor */ //@{ /** Constructor. It has to be given a pointer to the * corresponding MatrixSpace. */ Matrix(const MatrixSpace* owner_space) : TaggedObject(), owner_space_(owner_space), valid_cache_tag_(0) {} /** Destructor */ virtual ~Matrix() {} //@} /**@name Operations of the Matrix on a Vector */ //@{ /** Matrix-vector multiply. Computes y = alpha * Matrix * x + * beta * y. Do not overload. Overload MultVectorImpl instead. */ void MultVector(Number alpha, const Vector& x, Number beta, Vector& y) const { MultVectorImpl(alpha, x, beta, y); } /** Matrix(transpose) vector multiply. Computes y = alpha * * Matrix^T * x + beta * y. Do not overload. Overload * TransMultVectorImpl instead. */ void TransMultVector(Number alpha, const Vector& x, Number beta, Vector& y) const { TransMultVectorImpl(alpha, x, beta, y); } //@} /** @name Methods for specialized operations. A prototype * implementation is provided, but for efficient implementation * those should be specially implemented. */ //@{ /** X = X + alpha*(Matrix S^{-1} Z). Should be implemented * efficiently for the ExansionMatrix */ void AddMSinvZ(Number alpha, const Vector& S, const Vector& Z, Vector& X) const; /** X = S^{-1} (r + alpha*Z*M^Td). Should be implemented * efficiently for the ExansionMatrix */ void SinvBlrmZMTdBr(Number alpha, const Vector& S, const Vector& R, const Vector& Z, const Vector& D, Vector& X) const; //@} /** Method for determining if all stored numbers are valid (i.e., * no Inf or Nan). */ bool HasValidNumbers() const; //* @name Information about the size of the matrix */ //@{ /** Number of rows */ Index NRows() const; /** Number of columns */ Index NCols() const; //@} /** Print detailed information about the matrix. Do not overload. * Overload PrintImpl instead. */ //@{ virtual void Print(SmartPtr jnlst, EJournalLevel level, EJournalCategory category, const std::string& name, Index indent=0, const std::string& prefix="") const; virtual void Print(const Journalist& jnlst, EJournalLevel level, EJournalCategory category, const std::string& name, Index indent=0, const std::string& prefix="") const; //@} /** Return the owner MatrixSpace*/ SmartPtr OwnerSpace() const; protected: /** @name implementation methods (derived classes MUST * overload these pure virtual protected methods. */ //@{ /** Matrix-vector multiply. Computes y = alpha * Matrix * x + * beta * y */ virtual void MultVectorImpl(Number alpha, const Vector& x, Number beta, Vector& y) const =0; /** Matrix(transpose) vector multiply. * Computes y = alpha * Matrix^T * x + beta * y */ virtual void TransMultVectorImpl(Number alpha, const Vector& x, Number beta, Vector& y) const =0; /** X = X + alpha*(Matrix S^{-1} Z). Prototype for this * specialize method is provided, but for efficient * implementation it should be overloaded for the expansion matrix. */ virtual void AddMSinvZImpl(Number alpha, const Vector& S, const Vector& Z, Vector& X) const; /** X = S^{-1} (r + alpha*Z*M^Td). Should be implemented * efficiently for the ExpansionMatrix. */ virtual void SinvBlrmZMTdBrImpl(Number alpha, const Vector& S, const Vector& R, const Vector& Z, const Vector& D, Vector& X) const; /** Method for determining if all stored numbers are valid (i.e., * no Inf or Nan). A default implementation always returning true * is provided, but if possible it should be implemented. */ virtual bool HasValidNumbersImpl() const { return true; } /** Print detailed information about the matrix. */ virtual void PrintImpl(const Journalist& jnlst, EJournalLevel level, EJournalCategory category, const std::string& name, Index indent, const std::string& prefix) const =0; //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** default constructor */ Matrix(); /** Copy constructor */ Matrix(const Matrix&); /** Overloaded Equals Operator */ Matrix& operator=(const Matrix&); //@} const SmartPtr owner_space_; /**@name CachedResults data members */ //@{ mutable TaggedObject::Tag valid_cache_tag_; mutable bool cached_valid_; //@} }; /** MatrixSpace base class, corresponding to the Matrix base class. * For each Matrix implementation, a corresponding MatrixSpace has * to be implemented. A MatrixSpace is able to create new Matrices * of a specific type. The MatrixSpace should also store * information that is common to all Matrices of that type. For * example, the dimensions of a Matrix is stored in the MatrixSpace * base class. */ class MatrixSpace : public ReferencedObject { public: /** @name Constructors/Destructors */ //@{ /** Constructor, given the number rows and columns of all matrices * generated by this MatrixSpace. */ MatrixSpace(Index nRows, Index nCols) : nRows_(nRows), nCols_(nCols) {} /** Destructor */ virtual ~MatrixSpace() {} //@} /** Pure virtual method for creating a new Matrix of the * corresponding type. */ virtual Matrix* MakeNew() const=0; /** Accessor function for the number of rows. */ Index NRows() const { return nRows_; } /** Accessor function for the number of columns. */ Index NCols() const { return nCols_; } /** Method to test if a given matrix belongs to a particular * matrix space. */ bool IsMatrixFromSpace(const Matrix& matrix) const { return (matrix.OwnerSpace() == this); } private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** default constructor */ MatrixSpace(); /** Copy constructor */ MatrixSpace(const MatrixSpace&); /** Overloaded Equals Operator */ MatrixSpace& operator=(const MatrixSpace&); //@} /** Number of rows for all matrices of this type. */ const Index nRows_; /** Number of columns for all matrices of this type. */ const Index nCols_; }; /* Inline Methods */ inline Index Matrix::NRows() const { return owner_space_->NRows(); } inline Index Matrix::NCols() const { return owner_space_->NCols(); } inline SmartPtr Matrix::OwnerSpace() const { return owner_space_; } } // namespace Ipopt // Macro definitions for debugging matrices #if COIN_IPOPT_VERBOSITY == 0 # define DBG_PRINT_MATRIX(__verbose_level, __mat_name, __mat) #else # define DBG_PRINT_MATRIX(__verbose_level, __mat_name, __mat) \ if (dbg_jrnl.Verbosity() >= (__verbose_level)) { \ if (dbg_jrnl.Jnlst()!=NULL) { \ (__mat).Print(dbg_jrnl.Jnlst(), \ J_ERROR, J_DBG, \ __mat_name, \ dbg_jrnl.IndentationLevel()*2, \ "# "); \ } \ } #endif // #if COIN_IPOPT_VERBOSITY == 0 #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpSolveStatistics.hpp0000644000335100017570000001123011133661702030103 0ustar pgehlerwebadm// Copyright (C) 2005, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpSolveStatistics.hpp 759 2006-07-07 03:07:08Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2005-08-15 #ifndef __IPSOLVESTATISTICS_HPP__ #define __IPSOLVESTATISTICS_HPP__ #include "IpReferenced.hpp" #include "IpSmartPtr.hpp" namespace Ipopt { // forward declaration (to avoid inclusion of too many header files) class IpoptNLP; class IpoptData; class IpoptCalculatedQuantities; /** This class collects statistics about an optimziation run, such * as iteration count, final infeasibilities etc. It is meant to * provide such information to a user of Ipopt during the * finalize_solution call. */ class SolveStatistics : public ReferencedObject { public: /**@name Constructors/Destructors */ //@{ /** Default constructor. It takes in those collecting Ipopt * objects that can provide the statistics information. Those * statistics are retrieved at the time of the constructor * call. */ SolveStatistics(const SmartPtr& ip_nlp, const SmartPtr& ip_data, const SmartPtr& ip_cq); /** Default destructor */ virtual ~SolveStatistics() {} //@} /** @name Accessor methods for retrieving different kind of solver * statistics information */ //@{ /** Iteration counts. */ Index IterationCount() const; /** Total CPU time, including function evaluations. */ Number TotalCPUTime() const; /** Number of NLP function evaluations. */ void NumberOfEvaluations(Index& num_obj_evals, Index& num_constr_evals, Index& num_obj_grad_evals, Index& num_constr_jac_evals, Index& num_hess_evals) const; /** Unscaled solution infeasibilities */ void Infeasibilities(Number& dual_inf, Number& constr_viol, Number& complementarity, Number& kkt_error) const; /** Scaled solution infeasibilities */ void ScaledInfeasibilities(Number& scaled_dual_inf, Number& scaled_constr_viol, Number& scaled_complementarity, Number& scaled_kkt_error) const; /** Final value of objective function */ Number FinalObjective() const; /** Final scaled value of objective function */ Number FinalScaledObjective() const; //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ SolveStatistics(); /** Copy Constructor */ SolveStatistics(const SolveStatistics&); /** Overloaded Equals Operator */ void operator=(const SolveStatistics&); //@} /** @name Fields for storing the statistics data */ //@{ /** Number of iterations. */ Index num_iters_; /* Total CPU time */ Number total_cpu_time_; /** Number of objective function evaluations. */ Index num_obj_evals_; /** Number of constraints evaluations (max of equality and * inequality) */ Index num_constr_evals_; /** Number of objective gradient evaluations. */ Index num_obj_grad_evals_; /** Number of constraint Jacobian evaluations. */ Index num_constr_jac_evals_; /** Number of Lagrangian Hessian evaluations. */ Index num_hess_evals_; /** Final scaled value of objective function */ Number scaled_obj_val_; /** Final unscaled value of objective function */ Number obj_val_; /** Final scaled dual infeasibility (max-norm) */ Number scaled_dual_inf_; /** Final unscaled dual infeasibility (max-norm) */ Number dual_inf_; /** Final scaled constraint violation (max-norm) */ Number scaled_constr_viol_; /** Final unscaled constraint violation (max-norm) */ Number constr_viol_; /** Final scaled complementarity error (max-norm) */ Number scaled_compl_; /** Final unscaled complementarity error (max-norm) */ Number compl_; /** Final overall scaled KKT error (max-norm) */ Number scaled_kkt_error_; /** Final overall unscaled KKT error (max-norm) */ Number kkt_error_; //@} }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpDebug.hpp0000644000335100017570000000740111133661702025773 0ustar pgehlerwebadm// Copyright (C) 2004, 2007 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpDebug.hpp 1010 2007-06-21 15:54:36Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPDEBUG_HPP__ #define __IPDEBUG_HPP__ #include "IpoptConfig.h" #include "IpTypes.hpp" #ifdef HAVE_CASSERT # include #else # ifdef HAVE_ASSERT_H # include # else # error "don't have header file for assert" # endif #endif #if COIN_IPOPT_CHECKLEVEL > 0 # ifdef NDEBUG # undef NDEBUG # endif # define DBG_ASSERT(test) assert(test) # define DBG_ASSERT_EXCEPTION(__condition, __except_type, __msg) \ ASSERT_EXCEPTION( (__condition), __except_type, __msg); # define DBG_DO(__cmd) __cmd #else # define DBG_ASSERT(test) # define DBG_ASSERT_EXCEPTION(__condition, __except_type, __msg) # define DBG_DO(__cmd) #endif #if COIN_IPOPT_VERBOSITY < 1 # define DBG_START_FUN(__func_name, __verbose_level) # define DBG_START_METH(__func_name, __verbose_level) # define DBG_PRINT(__printf_args) # define DBG_PRINT_VECTOR(__verbose_level, __vec_name, __vec) # define DBG_PRINT_MATRIX(__verbose_level, __mat_name, __mat) # define DBG_EXEC(__verbosity, __cmd) # define DBG_VERBOSITY() 0 #else #include namespace Ipopt { // forward definition class Journalist; /** Class that lives throughout the execution of a method or * function for which debug output is to be generated. The output * is sent to the unique debug journalist that is set with * SetJournalist at the beginning of program execution. */ class DebugJournalistWrapper { public: /** @name Constructors/Destructors. */ //@{ DebugJournalistWrapper(std::string func_name, Index verbose_level); DebugJournalistWrapper(std::string func_name, Index verbose_level, const void* const method_owner); ~DebugJournalistWrapper(); //@} /** @name accessor methods */ //@{ Index Verbosity() { return verbose_level_; } const Journalist* Jnlst() { return jrnl_; } Index IndentationLevel() { return indentation_level_; } //@} /** Printing */ void DebugPrintf(Index verbosity, const char* pformat, ...); /* Method for initialization of the static GLOBAL journalist, * through with all debug printout is to be written. This needs * to be set before any debug printout can be done. */ static void SetJournalist(Journalist* jrnl); private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** default constructor */ DebugJournalistWrapper(); /** copy contructor */ DebugJournalistWrapper(const DebugJournalistWrapper&); /** Overloaded Equals Operator */ DebugJournalistWrapper& operator=(const DebugJournalistWrapper&); //@} static Index indentation_level_; std::string func_name_; Index verbose_level_; const void* method_owner_; static Journalist* jrnl_; }; } # define DBG_START_FUN(__func_name, __verbose_level) \ DebugJournalistWrapper dbg_jrnl((__func_name), (__verbose_level)); \ # define DBG_START_METH(__func_name, __verbose_level) \ DebugJournalistWrapper dbg_jrnl((__func_name), (__verbose_level), this); # define DBG_PRINT(__args) \ dbg_jrnl.DebugPrintf __args; # define DBG_EXEC(__verbose_level, __cmd) \ if (dbg_jrnl.Verbosity() >= (__verbose_level)) { \ (__cmd); \ } # define DBG_VERBOSITY() \ dbg_jrnl.Verbosity() #endif #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpNLP.hpp0000644000335100017570000002214711133661702025402 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpNLP.hpp 1010 2007-06-21 15:54:36Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPNLP_HPP__ #define __IPNLP_HPP__ #include "IpUtils.hpp" #include "IpVector.hpp" #include "IpSmartPtr.hpp" #include "IpMatrix.hpp" #include "IpSymMatrix.hpp" #include "IpOptionsList.hpp" #include "IpAlgTypes.hpp" #include "IpReturnCodes.hpp" namespace Ipopt { // forward declarations class IpoptData; class IpoptCalculatedQuantities; class IteratesVector; /** Brief Class Description. * Detailed Class Description. */ class NLP : public ReferencedObject { public: /**@name Constructors/Destructors */ //@{ /** Default constructor */ NLP() {} /** Default destructor */ virtual ~NLP() {} //@} /** Exceptions */ //@{ DECLARE_STD_EXCEPTION(USER_SCALING_NOT_IMPLEMENTED); //@} /** @name NLP Initialization (overload in * derived classes).*/ //@{ /** Overload if you want the chance to process options or parameters that * may be specific to the NLP */ virtual bool ProcessOptions(const OptionsList& options, const std::string& prefix) { return true; } /** Method for creating the derived vector / matrix types. The * Hess_lagrangian_space pointer can be NULL if a quasi-Newton * options is chosen. */ virtual bool GetSpaces(SmartPtr& x_space, SmartPtr& c_space, SmartPtr& d_space, SmartPtr& x_l_space, SmartPtr& px_l_space, SmartPtr& x_u_space, SmartPtr& px_u_space, SmartPtr& d_l_space, SmartPtr& pd_l_space, SmartPtr& d_u_space, SmartPtr& pd_u_space, SmartPtr& Jac_c_space, SmartPtr& Jac_d_space, SmartPtr& Hess_lagrangian_space)=0; /** Method for obtaining the bounds information */ virtual bool GetBoundsInformation(const Matrix& Px_L, Vector& x_L, const Matrix& Px_U, Vector& x_U, const Matrix& Pd_L, Vector& d_L, const Matrix& Pd_U, Vector& d_U)=0; /** Method for obtaining the starting point for all the * iterates. ToDo it might not make sense to ask for initial * values for v_L and v_U? */ virtual bool GetStartingPoint( SmartPtr x, bool need_x, SmartPtr y_c, bool need_y_c, SmartPtr y_d, bool need_y_d, SmartPtr z_L, bool need_z_L, SmartPtr z_U, bool need_z_U )=0; /** Method for obtaining an entire iterate as a warmstart point. * The incoming IteratesVector has to be filled. The default * dummy implementation returns false. */ virtual bool GetWarmStartIterate(IteratesVector& warm_start_iterate) { return false; } //@} /** @name NLP evaluation routines (overload * in derived classes. */ //@{ virtual bool Eval_f(const Vector& x, Number& f) = 0; virtual bool Eval_grad_f(const Vector& x, Vector& g_f) = 0; virtual bool Eval_c(const Vector& x, Vector& c) = 0; virtual bool Eval_jac_c(const Vector& x, Matrix& jac_c) = 0; virtual bool Eval_d(const Vector& x, Vector& d) = 0; virtual bool Eval_jac_d(const Vector& x, Matrix& jac_d) = 0; virtual bool Eval_h(const Vector& x, Number obj_factor, const Vector& yc, const Vector& yd, SymMatrix& h) = 0; //@} /** @name NLP solution routines. Have default dummy * implementations that can be overloaded. */ //@{ /** This method is called at the very end of the optimization. It * provides the final iterate to the user, so that it can be * stored as the solution. The status flag indicates the outcome * of the optimization, where SolverReturn is defined in * IpAlgTypes.hpp. */ virtual void FinalizeSolution(SolverReturn status, const Vector& x, const Vector& z_L, const Vector& z_U, const Vector& c, const Vector& d, const Vector& y_c, const Vector& y_d, Number obj_value, const IpoptData* ip_data, IpoptCalculatedQuantities* ip_cq) {} /** This method is called once per iteration, after the iteration * summary output has been printed. It provides the current * information to the user to do with it anything she wants. It * also allows the user to ask for a premature termination of the * optimization by returning false, in which case Ipopt will * terminate with a corresponding return status. The basic * information provided in the argument list has the quantities * values printed in the iteration summary line. If more * information is required, a user can obtain it from the IpData * and IpCalculatedQuantities objects. However, note that the * provided quantities are all for the problem that Ipopt sees, * i.e., the quantities might be scaled, fixed variables might be * sorted out, etc. The status indicates things like whether the * algorithm is in the restoration phase... In the restoration * phase, the dual variables are probably not not changing. */ virtual bool IntermediateCallBack(AlgorithmMode mode, Index iter, Number obj_value, Number inf_pr, Number inf_du, Number mu, Number d_norm, Number regularization_size, Number alpha_du, Number alpha_pr, Index ls_trials, const IpoptData* ip_data, IpoptCalculatedQuantities* ip_cq) { return true; } //@} /** Routines to get the scaling parameters. These do not need to * be overloaded unless the options are set for User scaling */ //@{ virtual void GetScalingParameters( const SmartPtr x_space, const SmartPtr c_space, const SmartPtr d_space, Number& obj_scaling, SmartPtr& x_scaling, SmartPtr& c_scaling, SmartPtr& d_scaling) const { THROW_EXCEPTION(USER_SCALING_NOT_IMPLEMENTED, "You have set options for user provided scaling, but have" " not implemented GetScalingParameters in the NLP interface"); } //@} /** Method for obtaining the subspace in which the limited-memory * Hessian approximation should be done. This is only called if * the limited-memory Hessian approximation is chosen. Since the * Hessian is zero in the space of all variables that appear in * the problem functions only linearly, this allows the user to * provide a VectorSpace for all nonlinear variables, and an * ExpansionMatrix to lift from this VectorSpace to the * VectorSpace of the primal variables x. If the returned values * are NULL, it is assumed that the Hessian is to be approximated * in the space of all x variables. The default instantiation of * this method returns NULL, and a user only has to overwrite * this method if the approximation is to be done only in a * subspace. */ virtual void GetQuasiNewtonApproximationSpaces(SmartPtr& approx_space, SmartPtr& P_approx) { approx_space = NULL; P_approx = NULL; } private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Copy Constructor */ NLP(const NLP&); /** Overloaded Equals Operator */ void operator=(const NLP&); //@} }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpSmartPtr.hpp0000644000335100017570000004630311133661702026525 0ustar pgehlerwebadm// Copyright (C) 2004, 2007 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpSmartPtr.hpp 1020 2007-06-24 04:06:59Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPSMARTPTR_HPP__ #define __IPSMARTPTR_HPP__ #include "IpReferenced.hpp" #include "IpDebug.hpp" #if COIN_IPOPT_CHECKLEVEL > 2 # define IP_DEBUG_SMARTPTR #endif namespace Ipopt { /** Template class for Smart Pointers. * A SmartPtr behaves much like a raw pointer, but manages the lifetime * of an object, deleting the object automatically. This class implements * a reference-counting, intrusive smart pointer design, where all * objects pointed to must inherit off of ReferencedObject, which * stores the reference count. Although this is intrusive (native types * and externally authored classes require wrappers to be referenced * by smart pointers), it is a safer design. A more detailed discussion of * these issues follows after the usage information. * * Usage Example: * Note: to use the SmartPtr, all objects to which you point MUST * inherit off of ReferencedObject. * * \verbatim * * In MyClass.hpp... * * #include "IpReferenced.hpp" * namespace Ipopt { * * class MyClass : public ReferencedObject // must derive from ReferencedObject * { * ... * } * } // namespace Ipopt * * * In my_usage.cpp... * * #include "IpSmartPtr.hpp" * #include "MyClass.hpp" * * void func(AnyObject& obj) * { * SmartPtr ptr_to_myclass = new MyClass(...); * // ptr_to_myclass now points to a new MyClass, * // and the reference count is 1 * * ... * * obj.SetMyClass(ptr_to_myclass); * // Here, let's assume that AnyObject uses a * // SmartPtr internally here. * // Now, both ptr_to_myclass and the internal * // SmartPtr in obj point to the same MyClass object * // and its reference count is 2. * * ... * * // No need to delete ptr_to_myclass, this * // will be done automatically when the * // reference count drops to zero. * * } * * \endverbatim * * It is not necessary to use SmartPtr's in all cases where an * object is used that has been allocated "into" a SmartPtr. It is * possible to just pass objects by reference or regular pointers, * even if lower down in the stack a SmartPtr is to be held on to. * Everything should work fine as long as a pointer created by "new" * is immediately passed into a SmartPtr, and if SmartPtr's are used * to hold on to objects. * * Other Notes: * The SmartPtr implements both dereference operators -> & *. * The SmartPtr does NOT implement a conversion operator to * the raw pointer. Use the GetRawPtr() method when this * is necessary. Make sure that the raw pointer is NOT * deleted. * The SmartPtr implements the comparison operators == & != * for a variety of types. Use these instead of * \verbatim * if (GetRawPtr(smrt_ptr) == ptr) // Don't use this * \endverbatim * SmartPtr's, as currently implemented, do NOT handle circular references. * For example: consider a higher level object using SmartPtrs to point to * A and B, but A and B also point to each other (i.e. A has a SmartPtr * to B and B has a SmartPtr to A). In this scenario, when the higher * level object is finished with A and B, their reference counts will * never drop to zero (since they reference each other) and they * will not be deleted. This can be detected by memory leak tools like * valgrind. If the circular reference is necessary, the problem can be * overcome by a number of techniques: * * 1) A and B can have a method that "releases" each other, that is * they set their internal SmartPtrs to NULL. * \verbatim * void AClass::ReleaseCircularReferences() * { * smart_ptr_to_B = NULL; * } * \endverbatim * Then, the higher level class can call these methods before * it is done using A & B. * * 2) Raw pointers can be used in A and B to reference each other. * Here, an implicit assumption is made that the lifetime is * controlled by the higher level object and that A and B will * both exist in a controlled manner. Although this seems * dangerous, in many situations, this type of referencing * is very controlled and this is reasonably safe. * * 3) This SmartPtr class could be redesigned with the Weak/Strong * design concept. Here, the SmartPtr is identified as being * Strong (controls lifetime of the object) or Weak (merely * referencing the object). The Strong SmartPtr increments * (and decrements) the reference count in ReferencedObject * but the Weak SmartPtr does not. In the example above, * the higher level object would have Strong SmartPtrs to * A and B, but A and B would have Weak SmartPtrs to each * other. Then, when the higher level object was done with * A and B, they would be deleted. The Weak SmartPtrs in A * and B would not decrement the reference count and would, * of course, not delete the object. This idea is very similar * to item (2), where it is implied that the sequence of events * is controlled such that A and B will not call anything using * their pointers following the higher level delete (i.e. in * their destructors!). This is somehow safer, however, because * code can be written (however expensive) to perform run-time * detection of this situation. For example, the ReferencedObject * could store pointers to all Weak SmartPtrs that are referencing * it and, in its destructor, tell these pointers that it is * dying. They could then set themselves to NULL, or set an * internal flag to detect usage past this point. * * Comments on Non-Intrusive Design: * In a non-intrusive design, the reference count is stored somewhere other * than the object being referenced. This means, unless the reference * counting pointer is the first referencer, it must get a pointer to the * referenced object from another smart pointer (so it has access to the * reference count location). In this non-intrusive design, if we are * pointing to an object with a smart pointer (or a number of smart * pointers), and we then give another smart pointer the address through * a RAW pointer, we will have two independent, AND INCORRECT, reference * counts. To avoid this pitfall, we use an intrusive reference counting * technique where the reference count is stored in the object being * referenced. */ template class SmartPtr : public Referencer { public: #define dbg_smartptr_verbosity 0 /**@name Constructors/Destructors */ //@{ /** Default constructor, initialized to NULL */ SmartPtr(); /** Copy constructor, initialized from copy */ SmartPtr(const SmartPtr& copy); /** Constructor, initialized from T* ptr */ SmartPtr(T* ptr); /** Destructor, automatically decrements the * reference count, deletes the object if * necessary.*/ ~SmartPtr(); //@} /**@name Overloaded operators. */ //@{ /** Overloaded arrow operator, allows the user to call * methods using the contained pointer. */ T* operator->() const; /** Overloaded dereference operator, allows the user * to dereference the contained pointer. */ T& operator*() const; /** Overloaded equals operator, allows the user to * set the value of the SmartPtr from a raw pointer */ SmartPtr& operator=(T* rhs); /** Overloaded equals operator, allows the user to * set the value of the SmartPtr from another * SmartPtr */ SmartPtr& operator=(const SmartPtr& rhs); /** Overloaded equality comparison operator, allows the * user to compare the value of two SmartPtrs */ template friend bool operator==(const SmartPtr& lhs, const SmartPtr& rhs); /** Overloaded equality comparison operator, allows the * user to compare the value of a SmartPtr with a raw pointer. */ template friend bool operator==(const SmartPtr& lhs, U2* raw_rhs); /** Overloaded equality comparison operator, allows the * user to compare the value of a raw pointer with a SmartPtr. */ template friend bool operator==(U1* lhs, const SmartPtr& raw_rhs); /** Overloaded in-equality comparison operator, allows the * user to compare the value of two SmartPtrs */ template friend bool operator!=(const SmartPtr& lhs, const SmartPtr& rhs); /** Overloaded in-equality comparison operator, allows the * user to compare the value of a SmartPtr with a raw pointer. */ template friend bool operator!=(const SmartPtr& lhs, U2* raw_rhs); /** Overloaded in-equality comparison operator, allows the * user to compare the value of a SmartPtr with a raw pointer. */ template friend bool operator!=(U1* lhs, const SmartPtr& raw_rhs); //@} /**@name friend method declarations. */ //@{ /** Returns the raw pointer contained. * Use to get the value of * the raw ptr (i.e. to pass to other * methods/functions, etc.) * Note: This method does NOT copy, * therefore, modifications using this * value modify the underlying object * contained by the SmartPtr, * NEVER delete this returned value. */ template friend U* GetRawPtr(const SmartPtr& smart_ptr); /** Returns a const pointer */ template friend SmartPtr ConstPtr(const SmartPtr& smart_ptr); /** Returns true if the SmartPtr is NOT NULL. * Use this to check if the SmartPtr is not null * This is preferred to if(GetRawPtr(sp) != NULL) */ template friend bool IsValid(const SmartPtr& smart_ptr); /** Returns true if the SmartPtr is NULL. * Use this to check if the SmartPtr IsNull. * This is preferred to if(GetRawPtr(sp) == NULL) */ template friend bool IsNull(const SmartPtr& smart_ptr); //@} private: /**@name Private Data/Methods */ //@{ /** Actual raw pointer to the object. */ T* ptr_; /** Set the value of the internal raw pointer * from another raw pointer, releasing the * previously referenced object if necessary. */ SmartPtr& SetFromRawPtr_(T* rhs); /** Set the value of the internal raw pointer * from a SmartPtr, releasing the previously referenced * object if necessary. */ SmartPtr& SetFromSmartPtr_(const SmartPtr& rhs); /** Release the currently referenced object. */ void ReleasePointer_(); //@} }; /**@name SmartPtr friend function declarations.*/ //@{ template U* GetRawPtr(const SmartPtr& smart_ptr); template SmartPtr ConstPtr(const SmartPtr& smart_ptr); template bool IsNull(const SmartPtr& smart_ptr); template bool IsValid(const SmartPtr& smart_ptr); template bool operator==(const SmartPtr& lhs, const SmartPtr& rhs); template bool operator==(const SmartPtr& lhs, U2* raw_rhs); template bool operator==(U1* lhs, const SmartPtr& raw_rhs); template bool operator!=(const SmartPtr& lhs, const SmartPtr& rhs); template bool operator!=(const SmartPtr& lhs, U2* raw_rhs); template bool operator!=(U1* lhs, const SmartPtr& raw_rhs); //@} template SmartPtr::SmartPtr() : ptr_(NULL) { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH("SmartPtr::SmartPtr()", dbg_smartptr_verbosity); #endif #ifdef CHECK_SMARTPTR const ReferencedObject* trying_to_use_SmartPtr_with_an_object_that_does_not_inherit_from_ReferencedObject_ = ptr_; trying_to_use_SmartPtr_with_an_object_that_does_not_inherit_from_ReferencedObject_ = NULL; #endif } template SmartPtr::SmartPtr(const SmartPtr& copy) : ptr_(NULL) { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH("SmartPtr::SmartPtr(const SmartPtr& copy)", dbg_smartptr_verbosity); #endif #ifdef CHECK_SMARTPTR const ReferencedObject* trying_to_use_SmartPtr_with_an_object_that_does_not_inherit_from_ReferencedObject_ = ptr_; trying_to_use_SmartPtr_with_an_object_that_does_not_inherit_from_ReferencedObject_ = NULL; #endif (void) SetFromSmartPtr_(copy); } template SmartPtr::SmartPtr(T* ptr) : ptr_(NULL) { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH("SmartPtr::SmartPtr(T* ptr)", dbg_smartptr_verbosity); #endif #ifdef CHECK_SMARTPTR const ReferencedObject* trying_to_use_SmartPtr_with_an_object_that_does_not_inherit_from_ReferencedObject_ = ptr_; trying_to_use_SmartPtr_with_an_object_that_does_not_inherit_from_ReferencedObject_ = NULL; #endif (void) SetFromRawPtr_(ptr); } template SmartPtr::~SmartPtr() { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH("SmartPtr::~SmartPtr(T* ptr)", dbg_smartptr_verbosity); #endif ReleasePointer_(); } template T* SmartPtr::operator->() const { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH("T* SmartPtr::operator->()", dbg_smartptr_verbosity); #endif // cannot deref a null pointer #if COIN_IPOPT_CHECKLEVEL > 0 assert(ptr_); #endif return ptr_; } template T& SmartPtr::operator*() const { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH("T& SmartPtr::operator*()", dbg_smartptr_verbosity); #endif // cannot dereference a null pointer #if COIN_IPOPT_CHECKLEVEL > 0 assert(ptr_); #endif return *ptr_; } template SmartPtr& SmartPtr::operator=(T* rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH("SmartPtr& SmartPtr::operator=(T* rhs)", dbg_smartptr_verbosity); #endif return SetFromRawPtr_(rhs); } template SmartPtr& SmartPtr::operator=(const SmartPtr& rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH( "SmartPtr& SmartPtr::operator=(const SmartPtr& rhs)", dbg_smartptr_verbosity); #endif return SetFromSmartPtr_(rhs); } template SmartPtr& SmartPtr::SetFromRawPtr_(T* rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH( "SmartPtr& SmartPtr::SetFromRawPtr_(T* rhs)", dbg_smartptr_verbosity); #endif // Release any old pointer ReleasePointer_(); if (rhs != NULL) { rhs->AddRef(this); ptr_ = rhs; } return *this; } template SmartPtr& SmartPtr::SetFromSmartPtr_(const SmartPtr& rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH( "SmartPtr& SmartPtr::SetFromSmartPtr_(const SmartPtr& rhs)", dbg_smartptr_verbosity); #endif T* ptr = GetRawPtr(rhs); /* AW: I changed this so that NULL is correctly copied from the right hand side */ // if (ptr != NULL) { // SetFromRawPtr_(ptr); // } SetFromRawPtr_(ptr); return (*this); } template void SmartPtr::ReleasePointer_() { #ifdef IP_DEBUG_SMARTPTR DBG_START_METH( "void SmartPtr::ReleasePointer()", dbg_smartptr_verbosity); #endif if (ptr_) { ptr_->ReleaseRef(this); if (ptr_->ReferenceCount() == 0) { delete ptr_; } ptr_ = NULL; } } template U* GetRawPtr(const SmartPtr& smart_ptr) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "T* GetRawPtr(const SmartPtr& smart_ptr)", 0); #endif return smart_ptr.ptr_; } template SmartPtr ConstPtr(const SmartPtr& smart_ptr) { // compiler should implicitly cast return GetRawPtr(smart_ptr); } template bool IsValid(const SmartPtr& smart_ptr) { return !IsNull(smart_ptr); } template bool IsNull(const SmartPtr& smart_ptr) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool IsNull(const SmartPtr& smart_ptr)", 0); #endif return (smart_ptr.ptr_ == NULL); } template bool ComparePointers(const U1* lhs, const U2* rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool ComparePtrs(const U1* lhs, const U2* rhs)", dbg_smartptr_verbosity); #endif if (lhs == rhs) { return true; } // Even if lhs and rhs point to the same object // with different interfaces U1 and U2, we cannot guarantee that // the value of the pointers will be equivalent. We can // guarantee this if we convert to void* const void* v_lhs = static_cast(lhs); const void* v_rhs = static_cast(rhs); if (v_lhs == v_rhs) { return true; } // They must not be the same return false; } template bool operator==(const SmartPtr& lhs, const SmartPtr& rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool operator==(const SmartPtr& lhs, const SmartPtr& rhs)", dbg_smartptr_verbosity); #endif U1* raw_lhs = GetRawPtr(lhs); U2* raw_rhs = GetRawPtr(rhs); return ComparePointers(raw_lhs, raw_rhs); } template bool operator==(const SmartPtr& lhs, U2* raw_rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool operator==(SmartPtr& lhs, U2* rhs)", dbg_smartptr_verbosity); #endif U1* raw_lhs = GetRawPtr(lhs); return ComparePointers(raw_lhs, raw_rhs); } template bool operator==(U1* raw_lhs, const SmartPtr& rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool operator==(U1* raw_lhs, SmartPtr& rhs)", dbg_smartptr_verbosity); #endif const U2* raw_rhs = GetRawPtr(rhs); return ComparePointers(raw_lhs, raw_rhs); } template bool operator!=(const SmartPtr& lhs, const SmartPtr& rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool operator!=(const SmartPtr& lhs, const SmartPtr& rhs)", dbg_smartptr_verbosity); #endif bool retValue = operator==(lhs, rhs); return !retValue; } template bool operator!=(const SmartPtr& lhs, U2* raw_rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool operator!=(SmartPtr& lhs, U2* rhs)", dbg_smartptr_verbosity); #endif bool retValue = operator==(lhs, raw_rhs); return !retValue; } template bool operator!=(U1* raw_lhs, const SmartPtr& rhs) { #ifdef IP_DEBUG_SMARTPTR DBG_START_FUN( "bool operator!=(U1* raw_lhs, SmartPtr& rhs)", dbg_smartptr_verbosity); #endif bool retValue = operator==(raw_lhs, rhs); return !retValue; } } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpTimedTask.hpp0000644000335100017570000000756411133661702026644 0ustar pgehlerwebadm// Copyright (C) 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpTimedTask.hpp 759 2006-07-07 03:07:08Z andreasw $ // // Authors: Andreas Waechter IBM 2005-09-19 #ifndef __IPTIMEDTASK_HPP__ #define __IPTIMEDTASK_HPP__ #ifdef HAVE_CTIME # include #else # ifdef HAVE_TIME_H # include # else # error "don't have header file for time" # endif #endif // The following lines are copied from CoinTime.hpp // We should probably make some more tests here #if defined(_MSC_VER) // Turn off compiler warning about long names # pragma warning(disable:4786) #else // MacOS-X and FreeBSD needs sys/time.h # if defined(__MACH__) || defined (__FreeBSD__) # include # endif # if !defined(__MSVCRT__) # include # endif #endif namespace Ipopt { /** This class is used to collect timing information for a * particular task. */ class TimedTask { public: /**@name Constructors/Destructors */ //@{ /** Default constructor. */ TimedTask() : total_time_(0.), start_called_(false), end_called_(true) {} /** Default destructor */ ~TimedTask() {} //@} /** Method for resetting time to zero. */ void Reset() { total_time_ = 0.; start_called_ = false; end_called_ = true; } /** Method that is called before execution of the task. */ void Start() { DBG_ASSERT(end_called_); DBG_ASSERT(!start_called_); end_called_ = false; start_called_ = true; start_time_ = CpuTime(); } /** Method that is called after execution of the task. */ void End() { DBG_ASSERT(!end_called_); DBG_ASSERT(start_called_); end_called_ = true; start_called_ = false; total_time_ += CpuTime() - start_time_; } /** Method that is called after execution of the task for which * timing might have been started. This only updates the timing * if the timing has indeed been conducted. This is useful to * stop timing after catching exceptions. */ void EndIfStarted() { if (start_called_) { end_called_ = true; start_called_ = false; total_time_ += CpuTime() - start_time_; } DBG_ASSERT(end_called_); } /** Method returning total time spend for task so far. */ Number TotalTime() const { DBG_ASSERT(end_called_); return total_time_; } // The following lines were taken from CoinTime.hpp in COIN/Coin /** method determining CPU executed since start of program */ static inline Number CpuTime() { double cpu_temp; #if defined(_MSC_VER) || defined(__MSVCRT__) unsigned int ticksnow; /* clock_t is same as int */ ticksnow = (unsigned int)clock(); cpu_temp = (double)((double)ticksnow/CLOCKS_PER_SEC); #else struct rusage usage; getrusage(RUSAGE_SELF,&usage); cpu_temp = usage.ru_utime.tv_sec; cpu_temp += 1.0e-6*((double) usage.ru_utime.tv_usec); #endif return cpu_temp; } private: /**@name Default Compiler Generated Methods (Hidden to avoid * implicit creation/calling). These methods are not * implemented and we do not want the compiler to implement them * for us, so we declare them private and do not define * them. This ensures that they will not be implicitly * created/called. */ //@{ /** Copy Constructor */ TimedTask(const TimedTask&); /** Overloaded Equals Operator */ void operator=(const TimedTask&); //@} /** Time at beginning of task. */ Number start_time_; /** Total time for task measured so far. */ Number total_time_; /** @name fields for debugging */ //@{ bool start_called_; bool end_called_; //@} }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpJournalist.hpp0000644000335100017570000003641711133661702027110 0ustar pgehlerwebadm// Copyright (C) 2004, 2007 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpJournalist.hpp 1136 2007-12-27 19:36:21Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPJOURNALIST_HPP__ #define __IPJOURNALIST_HPP__ #include "IpoptConfig.h" #include "IpTypes.hpp" #include "IpReferenced.hpp" #include "IpSmartPtr.hpp" #ifdef HAVE_CSTDARG # include #else # ifdef HAVE_STDARG_H # include # else # error "don't have header file for stdarg" # endif #endif #include #include #include namespace Ipopt { // forward declarations class Journal; class FileJournal; /**@name Journalist Enumerations. */ //@{ /** Print Level Enum. */ enum EJournalLevel { J_INSUPPRESSIBLE=-1, J_NONE=0, J_ERROR, J_STRONGWARNING, J_SUMMARY, J_WARNING, J_ITERSUMMARY, J_DETAILED, J_MOREDETAILED, J_VECTOR, J_MOREVECTOR, J_MATRIX, J_MOREMATRIX, J_ALL, J_LAST_LEVEL }; /** Category Selection Enum. */ enum EJournalCategory { J_DBG=0, J_STATISTICS, J_MAIN, J_INITIALIZATION, J_BARRIER_UPDATE, J_SOLVE_PD_SYSTEM, J_FRAC_TO_BOUND, J_LINEAR_ALGEBRA, J_LINE_SEARCH, J_HESSIAN_APPROXIMATION, J_SOLUTION, J_DOCUMENTATION, J_NLP, J_TIMING_STATISTICS, J_USER_APPLICATION /** This can be used by the user's application*/ , J_USER1 /** This can be used by the user's application*/ , J_USER2 /** This can be used by the user's application*/ , J_USER3 /** This can be used by the user's application*/ , J_USER4 /** This can be used by the user's application*/ , J_USER5 /** This can be used by the user's application*/ , J_USER6 /** This can be used by the user's application*/ , J_USER7 /** This can be used by the user's application*/ , J_USER8 /** This can be used by the user's application*/ , J_USER9 /** This can be used by the user's application*/ , J_USER10 /** This can be used by the user's application*/ , J_USER11 /** This can be used by the user's application*/ , J_USER12 /** This can be used by the user's application*/ , J_USER13 /** This can be used by the user's application*/ , J_USER14 /** This can be used by the user's application*/ , J_USER15 /** This can be used by the user's application*/ , J_USER16 /** This can be used by the user's application*/ , J_USER17 /** This can be used by the user's application*/ , J_LAST_CATEGORY }; //@} /** Class responsible for all message output. * This class is responsible for all messaging and output. * The "printing" code or "author" should send ALL messages to the * Journalist, indicating an appropriate category and print level. * The journalist then decides, based on reader specified * acceptance criteria, which message is actually printed in which * journals. * This allows the printing code to send everything, while the * "reader" can decide what they really want to see. * * Authors: * Authors use the * Journals: You can add as many Journals as you like to the * Journalist with the AddJournal or the AddFileJournal methods. * Each one represents a different printing location (or file). * Then, you can call the "print" methods of the Journalist to output * information to each of the journals. * * Acceptance Criteria: Each print message should be flagged * appropriately with an EJournalCategory and EJournalLevel. * * The AddFileJournal * method returns a pointer to the newly created Journal object * (if successful) so you can set Acceptance criteria for that * particular location. * */ class Journalist : public ReferencedObject { public: /**@name Constructor / Desructor. */ //@{ /** Constructor. */ Journalist(); /** Destructor... */ virtual ~Journalist(); //@} /**@name Author Methods. * These methods are used by authoring code, or code that wants * to report some information. */ //@{ /** Method to print a formatted string */ void Printf(EJournalLevel level, EJournalCategory category, const char* format, ...) const; /** Method to print a long string including indentation. The * string is printed starting at the current position. If the * position (counting started at the current position) exceeds * max_length, a new line is inserted, and indent_spaces many * spaces are printed before the string is continued. This is * for example used during the printing of the option * documentation. */ void PrintStringOverLines(EJournalLevel level, EJournalCategory category, Index indent_spaces, Index max_length, const std::string& line) const; /** Method to print a formatted string with indentation */ void PrintfIndented(EJournalLevel level, EJournalCategory category, Index indent_level, const char* format, ...) const; /** Method to print a formatted string * using the va_list argument. */ void VPrintf(EJournalLevel level, EJournalCategory category, const char* pformat, va_list ap) const; /** Method to print a formatted string with indentation, * using the va_list argument. */ void VPrintfIndented(EJournalLevel level, EJournalCategory category, Index indent_level, const char* pformat, va_list ap) const; /** Method that returns true if there is a Journal that would * write output for the given JournalLevel and JournalCategory. * This is useful if expensive computation would be required for * a particular output. The author code can check with this * method if the computations are indeed required. */ bool ProduceOutput(EJournalLevel level, EJournalCategory category) const; /** Method that flushes the current buffer for all Journalists. Calling this method after one optimization run helps to avoid cluttering output with that produced by other parts of the program (e.g. written in Fortran) */ void FlushBuffer() const; //@} /**@name Reader Methods. * These methods are used by the reader. The reader will setup the * journalist with each output file and the acceptance * criteria for that file. * * Use these methods to setup the journals (files or other output). * These are the internal objects that keep track of the print levels * for each category. Then use the internal Journal objects to * set specific print levels for each category (or keep defaults). * */ //@{ /** Add a new journal. The location_name is a string identifier, * which can be used to obtain the pointer to the new Journal at * a later point using the GetJournal method. * The default_level is * used to initialize the * printing level for all categories. */ bool AddJournal(const SmartPtr jrnl); /** Add a new FileJournal. fname is the name * of the * file to which this Journal corresponds. Use * fname="stdout" * for stdout, and use fname="stderr" for * stderr. This method * returns the Journal pointer so you can * set specific acceptance criteria. It returns NULL if there * was a problem creating a new Journal. */ SmartPtr AddFileJournal( const std::string& location_name, /** identifier */ const std::string& fname, EJournalLevel default_level = J_WARNING ); /** Get an existing journal. You can use this method to change * the acceptance criteria at runtime. */ SmartPtr GetJournal(const std::string& location_name); //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Copy Constructor */ Journalist(const Journalist&); /** Overloaded Equals Operator */ void operator=(const Journalist&); //@} //** Private Data Members. */ //@{ std::vector< SmartPtr > journals_; //@} }; /** Journal class (part of the Journalist implementation.). This * class is the base class for all Journals. It controls the * acceptance criteria for print statements etc. Derived classes * like the FileJournal - output those messages to specific locations */ class Journal : public ReferencedObject { public: /** Constructor. */ Journal(const std::string& name, EJournalLevel default_level); /** Destructor. */ virtual ~Journal(); /** Get the name of the Journal */ std::string Name(); /** Set the print level for a particular category. */ void SetPrintLevel( EJournalCategory category, EJournalLevel level ); /** Set the print level for all category. */ void SetAllPrintLevels( EJournalLevel level ); /**@name Journal Output Methods. These methods are called by the * Journalist who first checks if the output print level and category * are acceptable. * Calling the Print methods explicitly (instead of through the * Journalist will output the message regardless of print level * and category. You should use the Journalist to print & flush instead */ //@{ /** Ask if a particular print level/category is accepted by the * journal. */ bool IsAccepted( EJournalCategory category, EJournalLevel level ) const; /** Print to the designated output location */ void Print(EJournalCategory category, EJournalLevel level, const char* str) { PrintImpl(category, level, str); } /** Printf to the designated output location */ void Printf(EJournalCategory category, EJournalLevel level, const char* pformat, va_list ap) { PrintfImpl(category, level, pformat, ap); } /** Flush output buffer.*/ void FlushBuffer() { FlushBufferImpl(); } //@} protected: /**@name Implementation version of Print methods. Derived classes * should overload the Impl methods. */ //@{ /** Print to the designated output location */ virtual void PrintImpl(EJournalCategory category, EJournalLevel level, const char* str)=0; /** Printf to the designated output location */ virtual void PrintfImpl(EJournalCategory category, EJournalLevel level, const char* pformat, va_list ap)=0; /** Flush output buffer.*/ virtual void FlushBufferImpl()=0; //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ Journal(); /** Copy Constructor */ Journal(const Journal&); /** Overloaded Equals Operator */ void operator=(const Journal&); //@} /** Name of the output location */ std::string name_; /** vector of integers indicating the level for each category */ Index print_levels_[J_LAST_CATEGORY]; }; /** FileJournal class. This is a particular Journal implementation that * writes to a file for output. It can write to (stdout, stderr, or disk) * by using "stdout" and "stderr" as filenames. */ class FileJournal : public Journal { public: /** Constructor. */ FileJournal(const std::string& name, EJournalLevel default_level); /** Destructor. */ virtual ~FileJournal(); /** Open a new file for the output location. * Special Names: stdout means stdout, * : stderr means stderr. * * Return code is false only if the file with the given name * could not be opened. */ bool Open(const char* fname); protected: /**@name Implementation version of Print methods - Overloaded from * Journal base class. */ //@{ /** Print to the designated output location */ virtual void PrintImpl(EJournalCategory category, EJournalLevel level, const char* str); /** Printf to the designated output location */ virtual void PrintfImpl(EJournalCategory category, EJournalLevel level, const char* pformat, va_list ap); /** Flush output buffer.*/ virtual void FlushBufferImpl(); //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ FileJournal(); /** Copy Constructor */ FileJournal(const FileJournal&); /** Overloaded Equals Operator */ void operator=(const FileJournal&); //@} /** FILE pointer for the output destination */ FILE* file_; }; /** StreamJournal class. This is a particular Journal implementation that * writes to a stream for output. */ class StreamJournal : public Journal { public: /** Constructor. */ StreamJournal(const std::string& name, EJournalLevel default_level); /** Destructor. */ virtual ~StreamJournal() {} /** Setting the output stream pointer */ void SetOutputStream(std::ostream* os); protected: /**@name Implementation version of Print methods - Overloaded from * Journal base class. */ //@{ /** Print to the designated output location */ virtual void PrintImpl(EJournalCategory category, EJournalLevel level, const char* str); /** Printf to the designated output location */ virtual void PrintfImpl(EJournalCategory category, EJournalLevel level, const char* pformat, va_list ap); /** Flush output buffer.*/ virtual void FlushBufferImpl(); //@} private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ StreamJournal(); /** Copy Constructor */ StreamJournal(const StreamJournal&); /** Overloaded Equals Operator */ void operator=(const StreamJournal&); //@} /** pointer to output stream for the output destination */ std::ostream* os_; /** buffer for sprintf. Being generous in size here... */ char buffer_[32768]; }; } #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpException.hpp0000644000335100017570000001011711133661702026701 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpException.hpp 1020 2007-06-24 04:06:59Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPEXCEPTION_HPP__ #define __IPEXCEPTION_HPP__ #include "IpUtils.hpp" #include "IpJournalist.hpp" /* This file contains a base class for all exceptions * and a set of macros to help with exceptions */ namespace Ipopt { /** This is the base class for all exceptions. The easiest way to * use this class is by means of the following macros: * * \verbatim DECLARE_STD_EXCEPTION(ExceptionType); \endverbatim * * This macro defines a new class with the name ExceptionType, * inherited from the base class IpoptException. After this, * exceptions of this type can be thrown using * * \verbatim THROW_EXCEPTION(ExceptionType, Message); \endverbatim * * where Message is a std::string with a message that gives an * indication of what caused the exception. Exceptions can also be * thrown using the macro * * \verbatim ASSERT_EXCEPTION(Condition, ExceptionType, Message); \endverbatim * * where Conditions is an expression. If Condition evaluates to * false, then the exception of the type ExceptionType is thrown * with Message. * * When an exception is caught, the method ReportException can be * used to write the information about the exception to the * Journalist, using the level J_ERROR and the category J_MAIN. * */ class IpoptException { public: /**@name Constructors/Destructors */ //@{ /** Constructor */ IpoptException(std::string msg, std::string file_name, Index line_number, std::string type="IpoptException") : msg_(msg), file_name_(file_name), line_number_(line_number), type_(type) {} /** Copy Constructor */ IpoptException(const IpoptException& copy) : msg_(copy.msg_), file_name_(copy.file_name_), line_number_(copy.line_number_), type_(copy.type_) {} /** Default destructor */ virtual ~IpoptException() {} //@} /** Method to report the exception to a journalist */ void ReportException(const Journalist& jnlst, EJournalLevel level = J_ERROR) const { jnlst.Printf(level, J_MAIN, "Exception of type: %s in file \"%s\" at line %d:\n Exception message: %s\n", type_.c_str(), file_name_.c_str(), line_number_, msg_.c_str()); } const std::string& Message() const { return msg_; } private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ IpoptException(); /** Overloaded Equals Operator */ void operator=(const IpoptException&); //@} std::string msg_; std::string file_name_; Index line_number_; std::string type_; }; #define THROW_EXCEPTION(__except_type, __msg) \ throw __except_type( (__msg), (__FILE__), (__LINE__) ); #define ASSERT_EXCEPTION(__condition, __except_type, __msg) \ if (! (__condition) ) { \ std::string newmsg = #__condition; \ newmsg += " evaluated false: "; \ newmsg += __msg; \ throw __except_type( (newmsg), (__FILE__), (__LINE__) ); \ } #define DECLARE_STD_EXCEPTION(__except_type) \ class __except_type : public IpoptException \ { \ public: \ __except_type(std::string msg, std::string fname, Index line) \ : IpoptException(msg,fname,line, #__except_type) {} \ __except_type(const __except_type& copy) \ : IpoptException(copy) {} \ private: \ __except_type(); \ void operator=(const __except_type&); \ } } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpCachedResults.hpp0000644000335100017570000005727311133661702027512 0ustar pgehlerwebadm// Copyright (C) 2004, 2007 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpCachedResults.hpp 1010 2007-06-21 15:54:36Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPCACHEDRESULTS_HPP__ #define __IPCACHEDRESULTS_HPP__ #include "IpTaggedObject.hpp" #include "IpObserver.hpp" #include #include #include namespace Ipopt //#define IP_DEBUG_CACHE #if COIN_IPOPT_CHECKLEVEL > 2 # define IP_DEBUG_CACHE #endif #ifdef IP_DEBUG_CACHE # include "IpDebug.hpp" #endif { // Forward Declarations template class DependentResult; // AW: I'm taking this out, since this is by far the most used // class. We should keep it as simple as possible. // /** Cache Priority Enum */ // enum CachePriority // { // CP_Lowest, // CP_Standard, // CP_Trial, // CP_Iterate // }; /** Templated class for Chached Results. This class stores up to a * given number of "results", entities that are stored here * together with identifies, that can be used to later retrieve the * information again. * * Typically, T is a SmartPtr for some calculated quantity that * should be stored (such as a Vector). The identifiers (or * dependencies) are a (possibly varying) number of Tags from * TaggedObjects, and a number of Numbers. Results are added to * the cache using the AddCachedResults methods, and the can be * retrieved with the GetCachedResults methods. The second set of * methods checks whether a result has been cached for the given * identifiers. If a corresponding results is found, a copy of it * is returned and the method evaluates to true, otherwise it * evaluates to false. * * Note that cached results can become "stale", namely when a * TaggedObject that is used to identify this CachedResult is * changed. When this happens, the cached result can never be * asked for again, so that there is no point in storing it any * longer. For this purpose, a cached result, which is stored as a * DependentResult, inherits off an Observer. This Observer * retrieves notification whenever a TaggedObject dependency has * changed. Stale results are later removed from the cache. */ template class CachedResults { public: #ifdef IP_DEBUG_CACHE /** (Only if compiled in DEBUG mode): debug verbosity level */ static const Index dbg_verbosity; #endif /** @name Constructors and Destructors. */ //@{ /** Constructor, where max_cache_size is the maximal number of * results that should be cached. If max_cache_size is negative, * we allow an infinite abount of cache. */ CachedResults(Int max_cache_size); /** Destructor */ virtual ~CachedResults(); //@} /** @name Generic methods for adding and retrieving cached results. */ //@{ /** Generic method for adding a result to the cache, given a * std::vector of TaggesObjects and a std::vector of Numbers. */ void AddCachedResult(const T& result, const std::vector& dependents, const std::vector& scalar_dependents); /** Generic method for retrieving a cached results, given the * dependencies as a std::vector of TaggesObjects and a * std::vector of Numbers. */ bool GetCachedResult(T& retResult, const std::vector& dependents, const std::vector& scalar_dependents) const; /** Method for adding a result, providing only a std::vector of * TaggedObjects. */ void AddCachedResult(const T& result, const std::vector& dependents); /** Method for retrieving a cached result, providing only a * std::vector of TaggedObjects. */ bool GetCachedResult(T& retResult, const std::vector& dependents) const; //@} /** @name Pointer-based methods for adding and retrieving cached * results, providing dependencies explicitly. */ //@{ /** Method for adding a result to the cache, proving one * dependency as a TaggedObject explicitly. */ void AddCachedResult1Dep(const T& result, const TaggedObject* dependent1); /** Method for retrieving a cached result, proving one dependency * as a TaggedObject explicitly. */ bool GetCachedResult1Dep(T& retResult, const TaggedObject* dependent1); /** Method for adding a result to the cache, proving two * dependencies as a TaggedObject explicitly. */ void AddCachedResult2Dep(const T& result, const TaggedObject* dependent1, const TaggedObject* dependent2); /** Method for retrieving a cached result, proving two * dependencies as a TaggedObject explicitly. */ bool GetCachedResult2Dep(T& retResult, const TaggedObject* dependent1, const TaggedObject* dependent2); /** Method for adding a result to the cache, proving three * dependencies as a TaggedObject explicitly. */ void AddCachedResult3Dep(const T& result, const TaggedObject* dependent1, const TaggedObject* dependent2, const TaggedObject* dependent3); /** Method for retrieving a cached result, proving three * dependencies as a TaggedObject explicitly. */ bool GetCachedResult3Dep(T& retResult, const TaggedObject* dependent1, const TaggedObject* dependent2, const TaggedObject* dependent3); /** @name Pointer-free version of the Add and Get methods */ //@{ bool GetCachedResult1Dep(T& retResult, const TaggedObject& dependent1) { return GetCachedResult1Dep(retResult, &dependent1); } bool GetCachedResult2Dep(T& retResult, const TaggedObject& dependent1, const TaggedObject& dependent2) { return GetCachedResult2Dep(retResult, &dependent1, &dependent2); } bool GetCachedResult3Dep(T& retResult, const TaggedObject& dependent1, const TaggedObject& dependent2, const TaggedObject& dependent3) { return GetCachedResult3Dep(retResult, &dependent1, &dependent2, &dependent3); } void AddCachedResult1Dep(const T& result, const TaggedObject& dependent1) { AddCachedResult1Dep(result, &dependent1); } void AddCachedResult2Dep(const T& result, const TaggedObject& dependent1, const TaggedObject& dependent2) { AddCachedResult2Dep(result, &dependent1, &dependent2); } void AddCachedResult3Dep(const T& result, const TaggedObject& dependent1, const TaggedObject& dependent2, const TaggedObject& dependent3) { AddCachedResult3Dep(result, &dependent1, &dependent2, &dependent3); } //@} /** Invalidates the result for given dependecies. Sets the stale * flag for the corresponding cached result to true if it is * found. Returns true, if the result was found. */ bool InvalidateResult(const std::vector& dependents, const std::vector& scalar_dependents); /** Invalidates all cached results */ void Clear(); /** Invalidate all cached results and changes max_cache_size */ void Clear(Int max_cache_size); private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ CachedResults(); /** Copy Constructor */ CachedResults(const CachedResults&); /** Overloaded Equals Operator */ void operator=(const CachedResults&); //@} /** maximum number of cached results */ Int max_cache_size_; /** list of currently cached results. */ mutable std::list*>* cached_results_; /** internal method for removing stale DependentResults from the * list. It is called at the beginning of every * GetDependentResult method. */ void CleanupInvalidatedResults() const; /** Print list of currently cached results */ void DebugPrintCachedResults() const; }; /** Templated class which stores one entry for the CachedResult * class. It stores the result (of type T), together with its * dependencies (vector of TaggedObjects and vector of Numbers). * It also stores a priority. */ template class DependentResult : public Observer { public: #ifdef IP_DEBUG_CACHE static const Index dbg_verbosity; #endif /** @name Constructor, Destructors */ //@{ /** Constructor, given all information about the result. */ DependentResult(const T& result, const std::vector& dependents, const std::vector& scalar_dependents); /** Destructor. */ ~DependentResult(); //@} /** @name Accessor method. */ //@{ /** This returns true, if the DependentResult is no longer valid. */ bool IsStale() const; /** Invalidates the cached result. */ void Invalidate(); /** Returns the cached result. */ const T& GetResult() const; //@} /** This method returns true if the dependencies provided to this * function are identical to the ones stored with the * DependentResult. */ bool DependentsIdentical(const std::vector& dependents, const std::vector& scalar_dependents) const; /** Print information about this DependentResults. */ void DebugPrint() const; protected: /** This method is overloading the pure virtual method from the * Observer base class. This method is called when a Subject * registered for this Observer sends a notification. In this * particular case, if this method is called with * notify_type==NT_Changed or NT_BeingDeleted, then this results * is marked as stale. */ virtual void RecieveNotification(NotifyType notify_type, const Subject* subject); private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ DependentResult(); /** Copy Constructor */ DependentResult(const DependentResult&); /** Overloaded Equals Operator */ void operator=(const DependentResult&); //@} /** Flag indicating, if the cached result is still valid. A result becomes invalid, if the RecieveNotification method is called with NT_Changed */ bool stale_; /** The value of the dependent results */ const T result_; /** Dependencies in form of TaggedObjects */ std::vector dependent_tags_; /** Dependencies in form a Numbers */ std::vector scalar_dependents_; }; #ifdef IP_DEBUG_CACHE template const Index CachedResults::dbg_verbosity = 0; template const Index DependentResult::dbg_verbosity = 0; #endif template DependentResult::DependentResult( const T& result, const std::vector& dependents, const std::vector& scalar_dependents) : stale_(false), result_(result), dependent_tags_(dependents.size()), scalar_dependents_(scalar_dependents) { #ifdef IP_DEBUG_CACHE DBG_START_METH("DependentResult::DependentResult()", dbg_verbosity); #endif for (Index i=0; i<(Index)dependents.size(); i++) { if (dependents[i]) { // Call the RequestAttach method of the Observer base class. // This will add this dependent result in the Observer list // for the Subject dependents[i]. As a consequence, the // RecieveNotification method of this DependentResult will be // called with notify_type=NT_Changed, whenever the // TaggedResult dependents[i] is changed (i.e. its HasChanged // method is called). RequestAttach(NT_Changed, dependents[i]); dependent_tags_[i] = dependents[i]->GetTag(); } else { dependent_tags_[i] = 0; } } } template DependentResult::~DependentResult() { #ifdef IP_DEBUG_CACHE DBG_START_METH("DependentResult::~DependentResult()", dbg_verbosity); //DBG_ASSERT(stale_ == true); #endif // Nothing to be done here, destructor // of T should sufficiently remove // any memory, etc. } template bool DependentResult::IsStale() const { return stale_; } template void DependentResult::Invalidate() { stale_ = true; } template void DependentResult::RecieveNotification(NotifyType notify_type, const Subject* subject) { #ifdef IP_DEBUG_CACHE DBG_START_METH("DependentResult::RecieveNotification", dbg_verbosity); #endif if (notify_type == NT_Changed || notify_type==NT_BeingDestroyed) { stale_ = true; // technically, I could unregister the notifications here, but they // aren't really hurting anything } } template bool DependentResult::DependentsIdentical(const std::vector& dependents, const std::vector& scalar_dependents) const { #ifdef IP_DEBUG_CACHE DBG_START_METH("DependentResult::DependentsIdentical", dbg_verbosity); DBG_ASSERT(stale_ == false); DBG_ASSERT(dependents.size() == dependent_tags_.size()); #endif bool retVal = true; if (dependents.size() != dependent_tags_.size() || scalar_dependents.size() != scalar_dependents_.size()) { retVal = false; } else { for (Index i=0; i<(Index)dependents.size(); i++) { if ( (dependents[i] && dependents[i]->GetTag() != dependent_tags_[i]) || (!dependents[i] && dependent_tags_[i] != 0) ) { retVal = false; break; } } if (retVal) { for (Index i=0; i<(Index)scalar_dependents.size(); i++) { if (scalar_dependents[i] != scalar_dependents_[i]) { retVal = false; break; } } } } return retVal; } template const T& DependentResult::GetResult() const { #ifdef IP_DEBUG_CACHE DBG_START_METH("DependentResult::GetResult()", dbg_verbosity); DBG_ASSERT(stale_ == false); #endif return result_; } template void DependentResult::DebugPrint() const { #ifdef IP_DEBUG_CACHE DBG_START_METH("DependentResult::DebugPrint", dbg_verbosity); #endif } template CachedResults::CachedResults(Int max_cache_size) : max_cache_size_(max_cache_size), cached_results_(NULL) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::CachedResults", dbg_verbosity); #endif } template CachedResults::~CachedResults() { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::!CachedResults()", dbg_verbosity); #endif if (cached_results_) { for (typename std::list< DependentResult* >::iterator iter = cached_results_-> begin(); iter != cached_results_->end(); iter++) { delete *iter; } delete cached_results_; } /* while (!cached_results_.empty()) { DependentResult* result = cached_results_.back(); cached_results_.pop_back(); delete result; } */ } template void CachedResults::AddCachedResult(const T& result, const std::vector& dependents, const std::vector& scalar_dependents) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::AddCachedResult", dbg_verbosity); #endif CleanupInvalidatedResults(); // insert the new one here DependentResult* newResult = new DependentResult(result, dependents, scalar_dependents); if (!cached_results_) { cached_results_ = new std::list*>; } cached_results_->push_front(newResult); // keep the list small enough if (max_cache_size_ >= 0) { // if negative, allow infinite cache // non-negative - limit size of list to max_cache_size DBG_ASSERT((Int)cached_results_->size()<=max_cache_size_+1); if ((Int)cached_results_->size() > max_cache_size_) { delete cached_results_->back(); cached_results_->pop_back(); } } #ifdef IP_DEBUG_CACHE DBG_EXEC(2, DebugPrintCachedResults()); #endif } template void CachedResults::AddCachedResult(const T& result, const std::vector& dependents) { std::vector scalar_dependents; AddCachedResult(result, dependents, scalar_dependents); } template bool CachedResults::GetCachedResult(T& retResult, const std::vector& dependents, const std::vector& scalar_dependents) const { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::GetCachedResult", dbg_verbosity); #endif if (!cached_results_) return false; CleanupInvalidatedResults(); bool retValue = false; typename std::list< DependentResult* >::const_iterator iter; for (iter = cached_results_->begin(); iter != cached_results_->end(); iter++) { if ((*iter)->DependentsIdentical(dependents, scalar_dependents)) { retResult = (*iter)->GetResult(); retValue = true; break; } } #ifdef IP_DEBUG_CACHE DBG_EXEC(2, DebugPrintCachedResults()); #endif return retValue; } template bool CachedResults::GetCachedResult( T& retResult, const std::vector& dependents) const { std::vector scalar_dependents; return GetCachedResult(retResult, dependents, scalar_dependents); } template void CachedResults::AddCachedResult1Dep(const T& result, const TaggedObject* dependent1) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::AddCachedResult1Dep", dbg_verbosity); #endif std::vector dependents(1); dependents[0] = dependent1; AddCachedResult(result, dependents); } template bool CachedResults::GetCachedResult1Dep(T& retResult, const TaggedObject* dependent1) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::GetCachedResult1Dep", dbg_verbosity); #endif std::vector dependents(1); dependents[0] = dependent1; return GetCachedResult(retResult, dependents); } template void CachedResults::AddCachedResult2Dep(const T& result, const TaggedObject* dependent1, const TaggedObject* dependent2) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::AddCachedResult2dDep", dbg_verbosity); #endif std::vector dependents(2); dependents[0] = dependent1; dependents[1] = dependent2; AddCachedResult(result, dependents); } template bool CachedResults::GetCachedResult2Dep(T& retResult, const TaggedObject* dependent1, const TaggedObject* dependent2) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::GetCachedResult2Dep", dbg_verbosity); #endif std::vector dependents(2); dependents[0] = dependent1; dependents[1] = dependent2; return GetCachedResult(retResult, dependents); } template void CachedResults::AddCachedResult3Dep(const T& result, const TaggedObject* dependent1, const TaggedObject* dependent2, const TaggedObject* dependent3) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::AddCachedResult2dDep", dbg_verbosity); #endif std::vector dependents(3); dependents[0] = dependent1; dependents[1] = dependent2; dependents[2] = dependent3; AddCachedResult(result, dependents); } template bool CachedResults::GetCachedResult3Dep(T& retResult, const TaggedObject* dependent1, const TaggedObject* dependent2, const TaggedObject* dependent3) { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::GetCachedResult2Dep", dbg_verbosity); #endif std::vector dependents(3); dependents[0] = dependent1; dependents[1] = dependent2; dependents[2] = dependent3; return GetCachedResult(retResult, dependents); } template bool CachedResults::InvalidateResult(const std::vector& dependents, const std::vector& scalar_dependents) { if (!cached_results_) return false; CleanupInvalidatedResults(); bool retValue = false; typename std::list< DependentResult* >::const_iterator iter; for (iter = cached_results_->begin(); iter != cached_results_->end(); iter++) { if ((*iter)->DependentsIdentical(dependents, scalar_dependents)) { (*iter)->Invalidate(); retValue = true; break; } } return retValue; } template void CachedResults::Clear() { if (!cached_results_) return; typename std::list< DependentResult* >::const_iterator iter; for (iter = cached_results_->begin(); iter != cached_results_->end(); iter++) { (*iter)->Invalidate(); } CleanupInvalidatedResults(); } template void CachedResults::Clear(Int max_cache_size) { Clear(); max_cache_size_ = max_cache_size; } template void CachedResults::CleanupInvalidatedResults() const { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::CleanupInvalidatedResults", dbg_verbosity); #endif if (!cached_results_) return; typename std::list< DependentResult* >::iterator iter; iter = cached_results_->begin(); while (iter != cached_results_->end()) { if ((*iter)->IsStale()) { typename std::list< DependentResult* >::iterator iter_to_remove = iter; iter++; DependentResult* result_to_delete = (*iter_to_remove); cached_results_->erase(iter_to_remove); delete result_to_delete; } else { iter++; } } } template void CachedResults::DebugPrintCachedResults() const { #ifdef IP_DEBUG_CACHE DBG_START_METH("CachedResults::DebugPrintCachedResults", dbg_verbosity); if (DBG_VERBOSITY()>=2 ) { if (!chached_results_) { DBG_PRINT((2," DependentResult:0x%x\n", (*iter))); } else { typename std::list< DependentResult* >::const_iterator iter; DBG_PRINT((2,"Current set of cached results:\n")); for (iter = cached_results_->begin(); iter != cached_results_->end(); iter++) { DBG_PRINT((2," DependentResult:0x%x\n", (*iter))); } } } #endif } } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpObserver.hpp0000644000335100017570000002405411133661702026537 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpObserver.hpp 1020 2007-06-24 04:06:59Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPOBSERVER_HPP__ #define __IPOBSERVER_HPP__ #include "IpUtils.hpp" #include #include //#define IP_DEBUG_OBSERVER #if COIN_IPOPT_CHECKLEVEL > 2 # define IP_DEBUG_OBSERVER #endif #ifdef IP_DEBUG_OBSERVER # include "IpDebug.hpp" #endif namespace Ipopt { /** Forward declarations */ class Subject; /** Slight Variation of the Observer Design Pattern. * This class implements the Observer class of the * Observer Design Pattern. An Observer "Attach"es * to a Subject, indicating that it would like to * be notified of changes in the Subject. * Any derived class wishing to recieve notifications * from a Subject should inherit off of * Observer and overload the protected method, * RecieveNotification_(...). */ class Observer { public: #ifdef IP_DEBUG_OBSERVER static const Index dbg_verbosity; #endif /**@name Constructors/Destructors */ //@{ /** Default Constructor */ Observer() {} /** Default destructor */ virtual ~Observer(); //@} /** Enumeration specifying the type of notification */ enum NotifyType { NT_All, NT_BeingDestroyed, NT_Changed }; protected: /** Derived classes should call this method * to request an "Attach" to a Subject. Do * not call "Attach" explicitly on the Subject * since further processing is done here */ void RequestAttach(NotifyType notify_type, const Subject* subject); /** Derived classes should call this method * to request a "Detach" to a Subject. Do * not call "Detach" explicitly on the Subject * since further processing is done here */ void RequestDetach(NotifyType notify_type, const Subject* subject); /** Derived classes should overload this method to * recieve the requested notification from * attached Subjects */ virtual void RecieveNotification(NotifyType notify_type, const Subject* subject)=0; private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Copy Constructor */ Observer(const Observer&); /** Overloaded Equals Operator */ void operator=(const Observer&); //@} /** A list of the subjects currently being * observed. */ std::vector subjects_; /** Private Method for Recieving Notification * should only be called by the friend class * Subject. This method will, in turn, call * the overloaded RecieveNotification method * for the derived class to process. */ void ProcessNotification(NotifyType notify_type, const Subject* subject); friend class Subject; }; /** Slight Variation of the Observer Design Pattern (Subject part). * This class implements the Subject class of the Observer Design * Pattern. An Observer "Attach"es to a Subject, indicating that it * would like to be notified of changes in the Subject. Any * derived class that is to be observed has to inherit off the * Subject base class. If the subject needs to notify the * Observer, it calls the Notify method. */ class Subject { public: #ifdef IP_DEBUG_OBSERVER static const Index dbg_verbosity; #endif /**@name Constructors/Destructors */ //@{ /** Default Constructor */ Subject() {} /** Default destructor */ virtual ~Subject(); //@} /**@name Methods to Add and Remove Observers. * Currently, the notify_type flags are not used, * and Observers are attached in general and will * recieve all notifications (of the type requested * and possibly of types not requested). It is * up to the observer to ignore the types they * are not interested in. The NotifyType in the * parameter list is so a more efficient mechanism * depending on type could be implemented later if * necessary.*/ //@{ /** Attach the specified observer * (i.e., begin recieving notifications). */ void AttachObserver(Observer::NotifyType notify_type, Observer* observer) const; /** Detach the specified observer * (i.e., no longer recieve notifications). */ void DetachObserver(Observer::NotifyType notify_type, Observer* observer) const; //@} protected: void Notify(Observer::NotifyType notify_type) const; private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Copy Constructor */ Subject(const Subject&); /** Overloaded Equals Operator */ void operator=(const Subject&); //@} mutable std::vector observers_; }; /* inline methods */ inline Observer::~Observer() { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Observer::~Observer", dbg_verbosity); if (DBG_VERBOSITY()>=1) { for (Index i=0; i<(Index)subjects_.size(); i++) { DBG_PRINT((1,"subjects_[%d] = 0x%x\n", i, subjects_[i])); } } #endif // Detach all subjects for (Int i=(Int)(subjects_.size()-1); i>=0; i--) { #ifdef IP_DEBUG_OBSERVER DBG_PRINT((1,"About to detach subjects_[%d] = 0x%x\n", i, subjects_[i])); #endif RequestDetach(NT_All, subjects_[i]); } } inline void Observer::RequestAttach(NotifyType notify_type, const Subject* subject) { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Observer::RequestAttach", dbg_verbosity); // Add the subject to the list if it does not already exist std::vector::iterator attached_subject; attached_subject = std::find(subjects_.begin(), subjects_.end(), subject); DBG_ASSERT(attached_subject == subjects_.end()); DBG_ASSERT(subject); #endif // add the subject to the list subjects_.push_back(subject); // Attach the observer to the subject subject->AttachObserver(notify_type, this); } inline void Observer::RequestDetach(NotifyType notify_type, const Subject* subject) { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Observer::RequestDetach", dbg_verbosity); DBG_PRINT((1, "Requesting detach of subject: 0x%x\n", subject)); DBG_ASSERT(subject); #endif if (subject) { std::vector::iterator attached_subject; attached_subject = std::find(subjects_.begin(), subjects_.end(), subject); #ifdef IP_DEBUG_OBSERVER DBG_ASSERT(attached_subject != subjects_.end()); #endif if (attached_subject != subjects_.end()) { #ifdef IP_DEBUG_OBSERVER DBG_PRINT((1, "Removing subject: 0x%x from the list\n", subject)); #endif subjects_.erase(attached_subject); } // Detach the observer from the subject subject->DetachObserver(notify_type, this); } } inline void Observer::ProcessNotification(NotifyType notify_type, const Subject* subject) { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Observer::ProcessNotification", dbg_verbosity); DBG_ASSERT(subject); #endif if (subject) { std::vector::iterator attached_subject; attached_subject = std::find(subjects_.begin(), subjects_.end(), subject); // We must be processing a notification for a // subject that was previously attached. #ifdef IP_DEBUG_OBSERVER DBG_ASSERT(attached_subject != subjects_.end()); #endif this->RecieveNotification(notify_type, subject); if (notify_type == NT_BeingDestroyed) { // the subject is going away, remove it from our list subjects_.erase(attached_subject); } } } inline Subject::~Subject() { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Subject::~Subject", dbg_verbosity); #endif std::vector::iterator iter; for (iter = observers_.begin(); iter != observers_.end(); iter++) { (*iter)->ProcessNotification(Observer::NT_BeingDestroyed, this); } } inline void Subject::AttachObserver(Observer::NotifyType notify_type, Observer* observer) const { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Subject::AttachObserver", dbg_verbosity); // current implementation notifies all observers of everything // they must filter the notifications that they are not interested // in (i.e. a hub, not a router) DBG_ASSERT(observer); std::vector::iterator attached_observer; attached_observer = std::find(observers_.begin(), observers_.end(), observer); DBG_ASSERT(attached_observer == observers_.end()); DBG_ASSERT(observer); #endif observers_.push_back(observer); } inline void Subject::DetachObserver(Observer::NotifyType notify_type, Observer* observer) const { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Subject::DetachObserver", dbg_verbosity); DBG_ASSERT(observer); #endif if (observer) { std::vector::iterator attached_observer; attached_observer = std::find(observers_.begin(), observers_.end(), observer); #ifdef IP_DEBUG_OBSERVER DBG_ASSERT(attached_observer != observers_.end()); #endif if (attached_observer != observers_.end()) { observers_.erase(attached_observer); } } } inline void Subject::Notify(Observer::NotifyType notify_type) const { #ifdef IP_DEBUG_OBSERVER DBG_START_METH("Subject::Notify", dbg_verbosity); #endif std::vector::iterator iter; for (iter = observers_.begin(); iter != observers_.end(); iter++) { (*iter)->ProcessNotification(notify_type, this); } } } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpIpoptApplication.hpp0000644000335100017570000001475511133661702030236 0ustar pgehlerwebadm// Copyright (C) 2004, 2007 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpIpoptApplication.hpp 1091 2007-09-25 12:16:28Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPIPOPTAPPLICATION_HPP__ #define __IPIPOPTAPPLICATION_HPP__ #include #include "IpJournalist.hpp" #include "IpTNLP.hpp" #include "IpNLP.hpp" /* Return codes for the Optimize call for an application */ #include "IpReturnCodes.hpp" namespace Ipopt { DECLARE_STD_EXCEPTION(IPOPT_APPLICATION_ERROR); /* forward declarations */ class IpoptAlgorithm; class IpoptNLP; class IpoptData; class IpoptCalculatedQuantities; class AlgorithmBuilder; class RegisteredOptions; class OptionsList; class SolveStatistics; /** This is the main application class for making calls to Ipopt. */ class IpoptApplication : public ReferencedObject { public: IpoptApplication(bool create_console_out = true, bool create_empty = false); /** Another constructor that assumes that the code in the * (default) constructor has already been executed */ IpoptApplication(SmartPtr reg_options, SmartPtr options, SmartPtr jnlst); virtual ~IpoptApplication(); /** Method for creating a new IpoptApplication that uses the same * journalist and registered options, and a copy of the options list. */ SmartPtr clone(); /** Initialize method. This method reads the params file and * initializes the journalists. You should call this method at * some point before the first optimize call. Note: you can skip * the processing of a params file by setting params_file to "". * It returns something other than Solve_Succeeded if there was a * problem in the initialization (such as an invalid option). */ ApplicationReturnStatus Initialize(std::string params_file = "ipopt.opt"); ApplicationReturnStatus Initialize(std::istream& is); /**@name Solve methods */ //@{ /** Solve a problem that inherits from TNLP */ ApplicationReturnStatus OptimizeTNLP(const SmartPtr& tnlp); /** Solve a problem that inherits from NLP */ ApplicationReturnStatus OptimizeNLP(const SmartPtr& nlp); /** Solve a problem that inherits from NLP */ ApplicationReturnStatus OptimizeNLP(const SmartPtr& nlp, SmartPtr& alg_builder); /** Solve a problem (that inherits from TNLP) for a repeated time. * The OptimizeTNLP method must have been called before. The * TNLP must be the same object, and the structure (number of * variables and constraints and position of nonzeros in Jacobian * and Hessian must be the same). */ ApplicationReturnStatus ReOptimizeTNLP(const SmartPtr& tnlp); /** Solve a problem (that inherits from NLP) for a repeated time. * The OptimizeNLP method must have been called before. The * NLP must be the same object, and the structure (number of * variables and constraints and position of nonzeros in Jacobian * and Hessian must be the same). */ ApplicationReturnStatus ReOptimizeNLP(const SmartPtr& nlp); //@} /** Method for opening an output file with given print_level. * Returns false if there was a problem. */ bool OpenOutputFile(std::string file_name, EJournalLevel print_level); /**@name Accessor methods */ //@{ /** Get the Journalist for printing output */ SmartPtr Jnlst() { return jnlst_; } /** Get a pointer to RegisteredOptions object to * add new options */ SmartPtr RegOptions() { return reg_options_; } /** Get the options list for setting options */ SmartPtr Options() { return options_; } /** Get the options list for setting options (const version) */ SmartPtr Options() const { return ConstPtr(options_); } /** Get the object with the statistics about the most recent * optimization run. */ SmartPtr Statistics(); //@} /** @name Methods for IpoptTypeInfo */ //@{ static void RegisterOptions(SmartPtr roptions); //@} /** Method to registering all Ipopt options. */ static void RegisterAllIpoptOptions(const SmartPtr& roptions); private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** Default Constructor */ // IpoptApplication(); /** Copy Constructor */ IpoptApplication(const IpoptApplication&); /** Overloaded Equals Operator */ void operator=(const IpoptApplication&); //@} /** Method for the actual optimize call of the Ipopt algorithm. * This is used both for Optimize and ReOptimize */ ApplicationReturnStatus call_optimize(); /**@name Variables that customize the application behavior */ //@{ /** Decide whether or not the ipopt.opt file should be read */ bool read_params_dat_; //@} /** Journalist for reporting output */ SmartPtr jnlst_; /** RegisteredOptions */ SmartPtr reg_options_; /** OptionsList used for the application */ SmartPtr options_; /** Object for storing statistics about the most recent * optimization run. */ SmartPtr statistics_; /** Object with the algorithm sceleton. */ SmartPtr alg_; /** IpoptNLP Object for the NLP. We keep this around for a * ReOptimize warm start. */ SmartPtr ip_nlp_; /** IpoptData Object for the NLP. We keep this around for a * ReOptimize warm start. */ SmartPtr ip_data_; /** IpoptCalculatedQuantities Object for the NLP. We keep this * around for a ReOptimize warm start. */ SmartPtr ip_cq_; /** Pointer to the TNLPAdapter used to convert the TNLP to an NLP. * We keep this around for the ReOptimizerTNLP call. */ SmartPtr nlp_adapter_; }; } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpSymMatrix.hpp0000644000335100017570000000701611133661702026704 0ustar pgehlerwebadm// Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpSymMatrix.hpp 1010 2007-06-21 15:54:36Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 #ifndef __IPSYMMATRIX_HPP__ #define __IPSYMMATRIX_HPP__ #include "IpUtils.hpp" #include "IpMatrix.hpp" namespace Ipopt { /* forward declarations */ class SymMatrixSpace; /** This is the base class for all derived symmetric matrix types. */ class SymMatrix : public Matrix { public: /** @name Constructor/Destructor */ //@{ /** Constructor, taking the owner_space. */ SymMatrix(const SymMatrixSpace* owner_space); /** Destructor */ virtual ~SymMatrix() {} //@} /** @name Information about the size of the matrix */ //@{ /** Dimension of the matrix (number of rows and columns) */ Index Dim() const; //@} SmartPtr OwnerSymMatrixSpace() const; protected: /** @name Overloaded methods from Matrix. Since the matrix is * symmetric, it is only necessary to implement the * MultVectorImpl method in a class that inherits from this base * class. If the TransMultVectorImpl is called, this base class * automatically calls MultVectorImpl instead. */ //@{ virtual void TransMultVectorImpl(Number alpha, const Vector& x, Number beta, Vector& y) const; //@} private: /** Copy of the owner space ptr as a SymMatrixSpace instead * of a MatrixSpace */ const SymMatrixSpace* owner_space_; }; /** SymMatrixSpace base class, corresponding to the SymMatrix base * class. */ class SymMatrixSpace : public MatrixSpace { public: /** @name Constructors/Destructors */ //@{ /** Constructor, given the dimension (identical to the number of * rows and columns). */ SymMatrixSpace(Index dim) : MatrixSpace(dim,dim) {} /** Destructor */ virtual ~SymMatrixSpace() {} //@} /** Pure virtual method for creating a new matrix of this specific * type. */ virtual SymMatrix* MakeNewSymMatrix() const=0; /** Overloaded MakeNew method for the MatrixSpace base class. */ virtual Matrix* MakeNew() const { return MakeNewSymMatrix(); } /** Accessor method for the dimension of the matrices in this * matrix space. */ Index Dim() const { DBG_ASSERT(NRows() == NCols()); return NRows(); } private: /**@name Default Compiler Generated Methods * (Hidden to avoid implicit creation/calling). * These methods are not implemented and * we do not want the compiler to implement * them for us, so we declare them private * and do not define them. This ensures that * they will not be implicitly created/called. */ //@{ /** default constructor */ SymMatrixSpace(); /* Copy constructor */ SymMatrixSpace(const SymMatrixSpace&); /** Overloaded Equals Operator */ SymMatrixSpace& operator=(const SymMatrixSpace&); //@} }; /* inline methods */ inline SymMatrix::SymMatrix(const SymMatrixSpace* owner_space) : Matrix(owner_space), owner_space_(owner_space) {} inline Index SymMatrix::Dim() const { return owner_space_->Dim(); } inline SmartPtr SymMatrix::OwnerSymMatrixSpace() const { return owner_space_; } } // namespace Ipopt #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpReturnCodes.h0000644000335100017570000000107311133661702026641 0ustar pgehlerwebadm/*********************************************************************** // Copyright (C) 2004, 2006 International Business Machines and others. // All Rights Reserved. // This code is published under the Common Public License. // // $Id: IpReturnCodes.h 759 2006-07-07 03:07:08Z andreasw $ // // Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 ************************************************************************/ #ifndef __IPRETURNCODES_H__ #define __IPRETURNCODES_H__ /* include from a common include file */ #include "IpReturnCodes_inc.h" #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/include/coin/IpStdCInterface.h0000644000335100017570000002441411133661702027066 0ustar pgehlerwebadm/************************************************************************* Copyright (C) 2004, 2006 International Business Machines and others. All Rights Reserved. This code is published under the Common Public License. $Id: IpStdCInterface.h 820 2006-12-14 04:54:14Z andreasw $ Authors: Carl Laird, Andreas Waechter IBM 2004-09-02 *************************************************************************/ #ifndef __IPSTDCINTERFACE_H__ #define __IPSTDCINTERFACE_H__ #ifdef __cplusplus extern "C" { #endif /** Type for all number. We need to make sure that this is identical with what is defined in Common/IpTypes.hpp */ typedef double Number; /** Type for all incides. We need to make sure that this is identical with what is defined in Common/IpTypes.hpp */ typedef int Index; /** Type for all integers. We need to make sure that this is identical with what is defined in Common/IpTypes.hpp */ typedef int Int; /* This includes the SolverReturn enum type */ #include "IpReturnCodes.h" /** Structure collecting all information about the problem * definition and solve statistics etc. This is defined in the * source file. */ struct IpoptProblemInfo; /** Pointer to a Ipopt Problem. */ typedef struct IpoptProblemInfo* IpoptProblem; /** define a boolean type for C */ typedef int Bool; #ifndef TRUE # define TRUE (1) #endif #ifndef FALSE # define FALSE (0) #endif /** A pointer for anything that is to be passed between the called * and individual callback function */ typedef void * UserDataPtr; /** Type defining the callback function for evaluating the value of * the objective function. Return value should be set to false if * there was a problem doing the evaluation. */ typedef Bool (*Eval_F_CB)(Index n, Number* x, Bool new_x, Number* obj_value, UserDataPtr user_data); /** Type defining the callback function for evaluating the gradient of * the objective function. Return value should be set to false if * there was a problem doing the evaluation. */ typedef Bool (*Eval_Grad_F_CB)(Index n, Number* x, Bool new_x, Number* grad_f, UserDataPtr user_data); /** Type defining the callback function for evaluating the value of * the constraint functions. Return value should be set to false if * there was a problem doing the evaluation. */ typedef Bool (*Eval_G_CB)(Index n, Number* x, Bool new_x, Index m, Number* g, UserDataPtr user_data); /** Type defining the callback function for evaluating the Jacobian of * the constrant functions. Return value should be set to false if * there was a problem doing the evaluation. */ typedef Bool (*Eval_Jac_G_CB)(Index n, Number *x, Bool new_x, Index m, Index nele_jac, Index *iRow, Index *jCol, Number *values, UserDataPtr user_data); /** Type defining the callback function for evaluating the Hessian of * the Lagrangian function. Return value should be set to false if * there was a problem doing the evaluation. */ typedef Bool (*Eval_H_CB)(Index n, Number *x, Bool new_x, Number obj_factor, Index m, Number *lambda, Bool new_lambda, Index nele_hess, Index *iRow, Index *jCol, Number *values, UserDataPtr user_data); /** Function for creating a new Ipopt Problem object. This function * returns an object that can be passed to the IpoptSolve call. It * contains the basic definition of the optimization problem, such * as number of variables and constraints, bounds on variables and * constraints, information about the derivatives, and the callback * function for the computation of the optimization problem * functions and derivatives. During this call, the options file * PARAMS.DAT is read as well. * * If NULL is returned, there was a problem with one of the inputs * or reading the options file. */ IpoptProblem CreateIpoptProblem( Index n /** Number of optimization variables */ , Number* x_L /** Lower bounds on variables. This array of size n is copied internally, so that the caller can change the incoming data after return without that IpoptProblem is modified. Any value less or equal than the number specified by option 'nlp_lower_bound_inf' is interpreted to be minus infinity. */ , Number* x_U /** Upper bounds on variables. This array of size n is copied internally, so that the caller can change the incoming data after return without that IpoptProblem is modified. Any value greater or equal than the number specified by option 'nlp_upper_bound_inf' is interpreted to be plus infinity. */ , Index m /** Number of constraints. */ , Number* g_L /** Lower bounds on constraints. This array of size m is copied internally, so that the caller can change the incoming data after return without that IpoptProblem is modified. Any value less or equal than the number specified by option 'nlp_lower_bound_inf' is interpreted to be minus infinity. */ , Number* g_U /** Upper bounds on constraints. This array of size m is copied internally, so that the caller can change the incoming data after return without that IpoptProblem is modified. Any value greater or equal than the number specified by option 'nlp_upper_bound_inf' is interpreted to be plus infinity. */ , Index nele_jac /** Number of non-zero elements in constraint Jacobian. */ , Index nele_hess /** Number of non-zero elements in Hessian of Lagrangian. */ , Index index_style /** indexing style for iRow & jCol, 0 for C style, 1 for Fortran style */ , Eval_F_CB eval_f /** Callback function for evaluating objective function */ , Eval_G_CB eval_g /** Callback function for evaluating constraint functions */ , Eval_Grad_F_CB eval_grad_f /** Callback function for evaluating gradient of objective function */ , Eval_Jac_G_CB eval_jac_g /** Callback function for evaluating Jacobian of constraint functions */ , Eval_H_CB eval_h /** Callback function for evaluating Hessian of Lagrangian function */ ); /** Method for freeing a previously created IpoptProblem. After freeing an IpoptProblem, it cannot be used anymore. */ void FreeIpoptProblem(IpoptProblem ipopt_problem); /** Function for adding a string option. Returns FALSE the option * could not be set (e.g., if keyword is unknown) */ Bool AddIpoptStrOption(IpoptProblem ipopt_problem, char* keyword, char* val); /** Function for adding a Number option. Returns FALSE the option * could not be set (e.g., if keyword is unknown) */ Bool AddIpoptNumOption(IpoptProblem ipopt_problem, char* keyword, Number val); /** Function for adding an Int option. Returns FALSE the option * could not be set (e.g., if keyword is unknown) */ Bool AddIpoptIntOption(IpoptProblem ipopt_problem, char* keyword, Int val); /** Function for opening an output file for a given name with given * printlevel. Returns false, if there was a problem opening the * file. */ Bool OpenIpoptOutputFile(IpoptProblem ipopt_problem, char* file_name, Int print_level); /** Optional function for setting scaling parameter for the NLP. * This corresponds to the get_scaling_parameters method in TNLP. * If the pointers x_scaling or g_scaling are NULL, then no scaling * for x resp. g is done. */ Bool SetIpoptProblemScaling(IpoptProblem ipopt_problem, Number obj_scaling, Number* x_scaling, Number* g_scaling); /** Function calling the Ipopt optimization algorithm for a problem previously defined with CreateIpoptProblem. The return specified outcome of the optimization procedure (e.g., success, failure etc). */ enum ApplicationReturnStatus IpoptSolve( IpoptProblem ipopt_problem /** Problem that is to be optimized. Ipopt will use the options previously specified with AddIpoptOption (etc) for this problem. */ , Number* x /** Input: Starting point Output: Optimal solution */ , Number* g /** Values of constraint at final point (output only - ignored if set to NULL) */ , Number* obj_val /** Final value of objective function (output only - ignored if set to NULL) */ , Number* mult_g /** Final multipliers for constraints (output only - ignored if set to NULL) */ , Number* mult_x_L /** Final multipliers for lower variable bounds (output only - ignored if set to NULL) */ , Number* mult_x_U /** Final multipliers for upper variable bounds (output only - ignored if set to NULL) */ , UserDataPtr user_data /** Pointer to user data. This will be passed unmodified to the callback functions. */ ); /** void IpoptStatisticsCounts; void IpoptStatisticsInfeasibilities; */ #ifdef __cplusplus } /* extern "C" { */ #endif #endif mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/share/0000755000335100017570000000000011133662012022463 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/share/doc/0000755000335100017570000000000011133662012023230 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/share/doc/coin/0000755000335100017570000000000011133662012024160 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/share/doc/coin/Ipopt/0000755000335100017570000000000011133662012025253 5ustar pgehlerwebadmmpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/share/doc/coin/Ipopt/LICENSE0000644000335100017570000002653511133661701026277 0ustar pgehlerwebadmCommon Public License Version 1.0 THE ACCOMPANYING PROGRAM IS PROVIDED UNDER THE TERMS OF THIS COMMON PUBLIC LICENSE ("AGREEMENT"). 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Each party waives its rights to a jury trial in any resulting litigation. mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/share/doc/coin/Ipopt/ipopt_addlibs_f.txt0000644000335100017570000000004011133661701031134 0ustar pgehlerwebadm-lpthread -llapack -lblas -lm mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/share/doc/coin/Ipopt/README0000644000335100017570000000044011133661701026135 0ustar pgehlerwebadmThese directories contain the C++ reimplementation of the interior point NLP solver Ipopt. For more inforamation, see Docs/documentation.pdf or see the online version of it at http://www.coin-or.org/Ipopt/documentation/ The project's website is at http://projects.coin-or.org/Ipopt mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/share/doc/coin/Ipopt/ipopt_addlibs_cpp.txt0000644000335100017570000000036311133661701031501 0ustar pgehlerwebadm-lpthread -llapack -lblas -lm -L/usr/lib/gcc/x86_64-linux-gnu/4.2.4 -L/usr/lib/gcc/x86_64-linux-gnu/4.2.4/../../../../lib -L/lib/../lib -L/usr/lib/../lib -L/usr/lib/gcc/x86_64-linux-gnu/4.2.4/../../.. -lgfortranbegin -lgfortran -lm -lgcc_s mpi-ikl-simplemkl-1.0/ipopt/ipopt-3.3.5-mumps-amd64/share/doc/coin/Ipopt/AUTHORS0000644000335100017570000000371511133661701026335 0ustar pgehlerwebadmMain authors: Andreas Waechter, project leader (IBM) Carl Laird (IBM, Carnegie Mellon University) Contributors: - Yifan Hu (Wolfram): Contributed TAUCS interface, implemented in the files PDSystemImpl/PDFullSpace/IpTAUCSSolverInterface.hpp PDSystemImpl/PDFullSpace/IpTAUCSSolverInterface.cpp - Yoshiaki Kawajiri (Carnegie Mellon Univeristy): First version of Latex documentation file Docs/documentation.tex - Olaf Schenk (Universirty of Basel): Modifications to the PARDISO interface in the files Algorithm/LinearSolvers/IpPardisoSolverInterface.cpp Algorithm/LinearSolvers/IpPardisoSolverInterface.hpp - Michael Hagemann (Universirty of Basel): MA57 interface Algorithm/LinearSolvers/IpMa57SolverInterface.cpp Algorithm/LinearSolvers/IpMa57SolverInterface.hpp - Damien Hocking (KBC Advanced Technologies) MUMPS interface Algorithm/LinearSolvers/IpMumpsSolverInterface.cpp Algorithm/LinearSolvers/IpMumpsSolverInterface.hpp - Jon Lee (IBM Research) example nl file (writting by hand) for unitTest Test/mytoy.nl - Peter Carbonetto (University of British Columbia) Matlab interface files in directory (and subdirctories of) contrib/MatlabInterface - Lifeng Chen/Zaiwen Wen (Columbia University) Changes and additions to the implementation of the Chen-Goldfarb penalty function algorithm src/contrib/CGPenalty/IpCGPenaltyCq.cpp src/contrib/CGPenalty/IpCGPenaltyCq.hpp src/contrib/CGPenalty/IpCGPenaltyData.cpp src/contrib/CGPenalty/IpCGPenaltyData.hpp src/contrib/CGPenalty/IpCGPenaltyLSAcceptor.cpp src/contrib/CGPenalty/IpCGPenaltyLSAcceptor.hpp src/contrib/CGPenalty/IpCGPenaltyRegOp.cpp src/contrib/CGPenalty/IpCGPenaltyRegOp.hpp src/contrib/CGPenalty/IpCGPerturbationHandler.cpp src/contrib/CGPenalty/IpCGPerturbationHandler.hpp src/contrib/CGPenalty/IpCGSearchDirCalc.cpp src/contrib/CGPenalty/IpCGSearchDirCalc.hpp src/contrib/CGPenalty/IpPiecewisePenalty.cpp src/contrib/CGPenalty/IpPiecewisePenalty.hpp mpi-ikl-simplemkl-1.0/IKLsubproblem_nonnegisotropic.m0000644000335100017570000002342211133661710022556 0ustar pgehlerwebadm% Subproblem solver for Gaussian kernels with non-negative % isotropic covariance matrix % % This functions aims to find local maxima of the function % T(\theta) = \sum_i\sum_j \alpha_i\alpha_j k(x_i,x_j;\theta) % where \alpha can be positive or negative. For this function the % kernel function is of the form % % k(x,x';\theta) = \exp(- \sum_{d=1}^D \theta_d (x_d-x'_d)^2 ) % % % Usage: % [K,new_facs,parm] = IKLsubproblem_nonnegisotropic(operation,alpha,lambda,Ds,parm,w,parm_range,opts) % % Input: (needed for IKLmain!) % operation - either 'init' : generate the starting matrices % 'search' : search for violating constraint % 'build' : build a Gram matrix given the data % alpha - current SVM multipliers alpha % lambda - Lagrange multiplier of the constraint sum(w)=1, % this is the threshold for violating constraints (T(theta)>lambda) % w - mixing coefficients (only using 'build') % parm - subproblem parameters, startpoints, points included, % etc. (can differ for any subproblem) % % Input: (user defined, specific to the subproblem) % Ds - distances between training data % parm_range - admissible range for kernel parameters % opts - opts.eps (1e-3) = Tolerance for Ipopt solver (for subproblem) % % Output: % K - generated kernel matrices [n x n x M] or [n x n_test] % for 'build' % new_facs - new kernel parameters % parm - additional data untouched from IKL % % % Peter Gehler 07/2008 pgehler@tuebingen.mpg.de function [K,new_facs,parm] = IKLsubproblem_nonnegisotropic(operation,al,lam,Ds,parm,w,parm_range,opts) if ~exist('opts','var'), opts = []; end %Multiple Pricing: how many constraints to return in one search if ~isfield(opts,'constraints_per_iteration') opts.constraints_per_iteration = 1; end % Tolerance for Ipopt solver if ~isfield(opts,'eps') opts.eps = 1e-3; end % The violation must be exceed this threshold in order to include % the corresponding constraint. if ~isfield(opts,'constraint_eps') opts.constraint_eps = 1e-2; end warning('off','MATLAB:dispatcher:pathWarning') addpath('~/software/libsvm-matlab'); switch computer case 'GLNX86' addpath('./ipopt/ipopt-3.3.5-mumps-i686/matlab'); case 'GLNXA64' % cluster addpath('./ipopt/ipopt-3.3.5-mumps-amd64/matlab'); end warning('on','MATLAB:dispatcher:pathWarning') DO_TEST = 1; if ~exist('parm_range','var') & ~(strcmp(operation,'build')>0) warning('no range for valid exponents given: setting to [0.1,4]'); parm_range = [0.1;4]; end nFactors = size(Ds,3); new_facs = []; switch operation case 'init' % % initialization : % all single kernel matrices % + product of them % % 3Dimensional example with parm_range [1,4], the start points will be % % [1,0,0] % [2,0,0] % [3,0,0] % [4,0,0] % [0,1,0] % ... % [0,0,4] % [1,1,1] % [2,2,2] % [3,3,3] % [4,4,4] cntr = 1; %parm.facs = zeros(size(Ds,3),size(Ds,3)); sig0 = mean(parm_range); if nFactors ==1 scales = linspace(parm_range(1),parm_range(2),50); else scales = linspace(parm_range(1),parm_range(2),4); end start_points = zeros(numel(scales)*nFactors,nFactors); K = []; for k=1:nFactors %tD = Ds(:,:,k); %K(:,:,k) = exp(-sig0 * Ds(:,:,k)); %parm.facs(k,k) = sig0; for kk=1:numel(scales) start_points(cntr,k) = scales(kk); cntr = cntr + 1; end end tD = sum(Ds,3)/nFactors; sig0 = mean(parm_range); %K(:,:,end+1) = exp(-sig0*tD); %parm.facs(end+1,1:nFactors) = sig0; K(:,:,1) = exp(-sig0*tD); parm.facs(1,1:nFactors) = sig0; if nFactors > 1 for kk=1:numel(scales) start_points(cntr,:) = scales(kk); cntr = cntr + 1; end end start_points = removeDuplicates(start_points')'; new_facs = parm.facs; parm.startpoints = start_points; fprintf('SP: generated %d Gram matrices\n',size(K,3)); fprintf('SP: generated %d start_points\n',size(start_points,1)); assert(size(start_points,2)==nFactors); case 'build' % PREDICTION % build the Gram matrix and return assert(ndims(Ds)==2 | (size(Ds,3)==size(parm,2)),'dim mismatch'); K = 0; for k=1:size(parm,1) d = 0; for j=1:size(parm,2) d = d + parm(k,j) * Ds(:,:,j); end K = K + w(k) * exp(-d); end case 'search' K = []; % matrix to return svset = find(al~=0); al = al(svset); % lower and upper bounds for exponents lb = parm_range(1) * ones(nFactors,1); ub = parm_range(2) * ones(nFactors,1); % specify the starting points ... if isfield(parm,'startpoints') startpoints = [parm.startpoints;parm.facs]'; else startpoints = parm.facs'; end startpoints = removeDuplicates(startpoints); nof_startpoints = size(startpoints,2); shuff = randperm(nof_startpoints); startpoints = startpoints(:,shuff); % some global variabels for ipopt optimization global subprob_obj_counter subprob_deriv_counter subprob_hessian_counter global subprob_recompute_counter global subprob_f subprob_df subprob_fac subprob_H subprob_sig nof_violating_constraints = 0; for n=1:nof_startpoints fac0 = startpoints(:,n); subprob_obj_counter=0; subprob_deriv_counter=0; subprob_hessian_counter=0; subprob_recompute_counter = 0; subprob_fac = Inf; subprob_f = -Inf; subprob_df = -Inf; subprob_H = -Inf; subprob_sig = Inf; [fac0,multiplier,numiter] = ipopt(fac0,lb,ub,[],[],... @(fac) compute_objective(fac,al,Ds,svset),... @(fac) compute_gradient(fac,al,Ds,svset),... @(fac) compute_constraints(fac,al,Ds,svset),... @(fac, rso) compute_jacobian(fac, rso),... @(fac, sigma, lambda,rso) compute_hessian(fac, sigma, lambda, rso, al,Ds,svset),... [],'',[],... 'hessian_approximation','exact',... 'jac_c_constant','yes',... 'mu_strategy','adaptive',... 'tol',opts.eps,... 'print_level',0,... 'max_iter',20); numiter = numiter + 1; %C->Matlab assert(numiter == subprob_deriv_counter,'#derivative calls != #ipopt iterations'); fprintf('SP: IpOpt iter %d, ',numiter); fprintf('Evaluations: F:%d, dF:%d, H:%d, ',subprob_obj_counter,subprob_deriv_counter,subprob_hessian_counter); fprintf('Computations needed: %d\n',subprob_recompute_counter); if (DO_TEST) tmp_f = subprob_f; subprob_fac = -Inf(nFactors,1); f = compute_objective(fac0,al,Ds,svset); assert(tmp_f==f|abs(tmp_f-f)./f<1e-5,'objective differs'); end % check if the found local maxima violates the contraint % pg : to do: criterion relative to absolute value if (-subprob_f - lam) >= opts.constraint_eps % yes it did ... fprintf('found violating constraint...'); % ... already in the list? already_included = any(dist_euclid(parm.facs,fac0')= opts.constraints_per_iteration) fprintf('SP: found maximum of %d constraints, returning\n',nof_violating_constraints); return; end end end % ... no violating constraint has been found, return nothing ... if nof_violating_constraints == 0 K = []; new_facs = []; else fprintf('SP: found only %d constraints (of %d requested) returning\n',nof_violating_constraints,opts.constraints_per_iteration); end return; end % Objective function f = compute_objective(fac,al,Ks,svset) global subprob_obj_counter subprob_obj_counter = subprob_obj_counter + 1; f = update_objective(fac,al,Ks,svset); %Gradient function df = compute_gradient(fac,al,Ks,svset) global subprob_deriv_counter subprob_deriv_counter = subprob_deriv_counter +1; [foo,df] = update_objective(fac,al,Ks,svset); % Hessian function H = compute_hessian(fac,ipopt_sigma,ipopt_lambda,return_structure_only,al,D,svset) global subprob_hessian_counter subprob_hessian_counter = subprob_hessian_counter +1; if return_structure_only H = sparse(tril(ones(numel(fac),numel(fac)))); else [foo,foo2,H] = update_objective(fac,al,D,svset); H = ipopt_sigma * sparse(H); end % Constraints function g = compute_constraints(fac) g=fac; % Jacobian function J = compute_jacobian(fac,return_structure_only); J = sparse(numel(fac),1); % % compute the objective, the gradient % function [f,df,H] = update_objective(fac,al,Ds,svset) assert(numel(fac)==size(Ds,3),'number of arguments != number of distances'); %assert(all(fac>=-1e-5),'factor turned negative'); %fac = max(fac,0); global subprob_f subprob_df subprob_fac subprob_H % % All what is following should be computed in a mex file for % more speed % if any(size(fac)~=size(subprob_fac)) | any(fac~=subprob_fac) global subprob_recompute_counter subprob_recompute_counter = subprob_recompute_counter + 1; [subprob_f,subprob_df,subprob_H] = subproblem_product_objective(double(Ds(svset,svset,:)),fac,al); subprob_fac = fac; end f = subprob_f; if nargout>1 df = subprob_df; if nargout >2 H = subprob_H; end end % remove the duplicate rows of the matrix points e.g. % % [1 0 0 => [1 0 % 0 0 0] 0 0] function points = removeDuplicates(points); D = dist_euclid(points'); [i1,i2] = ind2sub(size(D),find(D<1e-6)); indx = find(i1<=i2); i1(indx) = []; i2(indx) = []; indx = []; for i=1:max(i2) indx = [indx;i1(find(i2==i))]; end indx = unique(indx); points(:,indx) = []; % sanity check D = dist_euclid(points'); assert(sum(D(:)<1e-6)==size(D,1)); mpi-ikl-simplemkl-1.0/Kbeta.mexglx0000755000335100017570000002153411133661710016647 0ustar pgehlerwebadmELF4T4 (!x|XXXPtdpppQtd   s +  K x|{? ?cx"__gmon_start___init_fini__cxa_finalize_Jv_RegisterClassesmexFunctionmxGetNumberOfDimensionsmxGetDimensionsmxGetPrmxGetMmxCreateDoubleMatrixmexErrMsgTxt__gxx_personality_v0libimf.solibm.so.6libmex.solibgcc_s.so.1libirc.solibc.so.6libdl.so.2_edata__bss_start_endGLIBC_2.1.3si  /ASpt ]6  " 4 F X PTXhlUS[ t X[ hhUSc u8t$ ҋuƃ[]Í&US. tt $҃[]Ë$ÐUWVSDEuEvy66D$ G>6D$8vD$T$ JL$,u‹xD$,$v;D$,;u2E3(vff/G3jD$0PSU PZv? `L$D$0$3ҍ |$L$f/gL$;rD$0D$0\$@3T$@+ѻ\$(}ǃÃ\$|$,T$L$ t$4D$ ЍAD$A$\$4t$@D$$L$t$T$ D$0ȍ; ЋT$ L$, D$$+ʋL$}|$,T$L$ \$(|?+ǃ|HT$L$ A\9D$,T$@L$0ӃT$,D$+NjD$(}ϋL$ D$@;YT$<\$@|$33D$D$@+ƉD$ lD$0\$@؋+׉|$8D$(\$Ӌ\$T$ ;v.T$0ʍ;s"|$\$4|$@D$|$<<\$+\$8;NjD$s|$(|$,3\$|$(\$ \$,ЉT$T$<|$8;ҋT$\$ tG\$$\$,|;|$ |$|$ |$(Y|$X |$(|$,3Nj|$ |$|$ |$ ub|$ f(fYfXf)f(\fYfX\f)\f(d fYfXd f)d f(l0fYfXl0f)l0;rx|$ fTfYfXf)\f\fYfX\f)\d fd(fYfXd f)d l0fl8fYfXl0f)l0;r|$;|$|$t$$t$YXڃ;r `t$4D$0\$@3ҍ ؉L$<4މt$s\$t$ 4t$f/8D$,,D$|$4T$\$3ɉL$83KL$ЉD$ <׉|$(D$@ mT$;zRPL| $d  8  "8  "8  "!.. . . .7.'.-.  .   8  "8  "8  ".... *  <, ( \ooodoXpPGCC: (GNU) 4.2.1 (Ubuntu 4.2.1-5ubuntu4)GCC: (GNU) 4.1.3 20070929 (prerelease) (Ubuntu 4.1.2-16ubuntu2)GCC: (GNU) 4.1.3 20070929 (prerelease) (Ubuntu 4.1.2-16ubuntu2)GCC: (GNU) 4.2.1 (Ubuntu 4.2.1-5ubuntu4)$"$/build/buildd/glibc-2.6.1/build-tree/i386-libc/csu/crti.S/build/buildd/glibc-2.6.1/build-tree/glibc-2.6.1/csuGNU AS 2.18 /build/buildd/glibc-2.6.1/build-tree/i386-libc/csu/crtn.S/build/buildd/glibc-2.6.1/build-tree/glibc-2.6.1/csuGNU AS 2.18U%U%P /build/buildd/glibc-2.6.1/build-tree/i386-libc/csucrti.S!/!=Z!gg//(!/!=Z!zP /build/buildd/glibc-2.6.1/build-tree/i386-libc/csucrtn.S !!!!!!.symtab.strtab.shstrtab.hash.dynsym.dynstr.gnu.version.gnu.version_r.rel.dyn.rel.plt.init.text.fini.rodata.eh_frame_hdr.eh_frame.gcc_except_table.ctors.dtors.jcr.dynamic.got.got.plt.data.bss.comment.debug_aranges.debug_info.debug_abbrev.debug_line.debug_rangesX! ,,)<<(1odd">o M V  _0Z0e kqypp000DDLLTTXXPP \\ppxxxPP"$ @0$| 9  !P,<d    p 0DLTXP\px D%L3T@xOtV lP xHP,Tp ` @ ! !   00\FpS jXs{   x|  ?'x."Jinitfini.ccrtstuff.c__CTOR_LIST____DTOR_LIST____JCR_LIST__completed.5982p.5980__do_global_dtors_auxframe_dummy__CTOR_END____DTOR_END____FRAME_END____JCR_END____do_global_ctors_aux/tmp/ipo_iccmRusM5.c_2il0floatpacket.1__STRING.3.3__STRING.2.3__STRING.1.3__STRING.0.3mexFunction$$LSDA_GLOBAL_OFFSET_TABLE___dso_handle__i686.get_pc_thunk.bx_DYNAMICmxGetPr__gmon_start___Jv_RegisterClasses_finimxGetNumberOfDimensions__gxx_personality_v0__bss_start_endmxGetMmexFunctionmxCreateDoubleMatrixmexErrMsgTxtmxGetDimensions_edata__cxa_finalize@@GLIBC_2.1.3_initmpi-ikl-simplemkl-1.0/subproblem_product_objective.mexa640000755000335100017570000002571411133661710023371 0ustar pgehlerwebadmELF>@@8@!   px   PtdXXXQtd   ` |CEqX4 0 + !X"&K"pH @ @ ?  0 X__gmon_start___init_fini__cxa_finalize_Jv_RegisterClassesmexFunctionmexErrMsgTxtmxGetNumberOfDimensionsmxGetDimensionsmxGetPrmxGetMmxGetNmxCreateDoubleMatrixexp__assert_fail__gxx_personality_v0libm.so.6libmex.solibc.so.6_edata__bss_start_endGLIBC_2.2.5 ui ui ( ( 0      8                H2H5z %| @%z h%r h%j h%b h%Z h%R h%J h%B hp%: h`%2 h PHH HtHÐ= UHu;H= tH= e HH H HHu fDUH=w HtH+ HtH=_ IAÐAWЃAVAUATUHSH|$0?void mexFunction(int, mxArray**, int, const mxArray**);H8zPRx  4$BGB B(A0D8G 0 XXo08   @ opooDo ^n~(  GCC: (GNU) 4.1.2 (Ubuntu 4.1.2-0ubuntu4)GCC: (GNU) 4.1.2 (Ubuntu 4.1.2-0ubuntu4)GCC: (GNU) 4.1.2 (Ubuntu 4.1.2-0ubuntu4)GCC: (GNU) 4.1.2 (Ubuntu 4.1.2-0ubuntu4)GCC: (GNU) 4.1.2 (Ubuntu 4.1.2-0ubuntu4)L0 X<Ca/build/buildd/glibc-2.5/build-tree/amd64-libc/csu/crti.S/build/buildd/glibc-2.5/build-tree/glibc-2.5/csuGNU AS 2.17.50P/build/buildd/glibc-2.5/build-tree/amd64-libc/csu/crtn.S/build/buildd/glibc-2.5/build-tree/glibc-2.5/csuGNU AS 2.17.50U%U%O /build/buildd/glibc-2.5/build-tree/amd64-libc/csucrti.S  Ku=/0K 0K X${O /build/buildd/glibc-2.5/build-tree/amd64-libc/csucrtn.S CK aK09X\CHaf.symtab.strtab.shstrtab.gnu.hash.dynsym.dynstr.gnu.version.gnu.version_r.rela.dyn.rela.plt.init.text.fini.rodata.eh_frame_hdr.eh_frame.ctors.dtors.jcr.dynamic.got.got.plt.data.bss.comment.debug_aranges.debug_info.debug_abbrev.debug_line.debug_rangesXXo<% 88-005oDD*Bopp@Q[@@ e00`HHkXqXXw XXpp\      h( (@ @@ $ ! 7 (X80Dp@ 0 H  X Xp      ( @  E U` n | @ 0   pU     De 78  (    !2 X8Pe"&@ H  "@  0initfini.c/build/buildd/glibc-2.5/build-tree/amd64-libc/csu/crti.Scall_gmon_startcrtstuff.c__CTOR_LIST____DTOR_LIST____JCR_LIST__completed.6100p.6098__do_global_dtors_auxframe_dummy__CTOR_END____DTOR_END____FRAME_END____JCR_END____do_global_ctors_aux/build/buildd/glibc-2.5/build-tree/amd64-libc/csu/crtn.Ssubproblem_product_objective.cpp_ZZ11mexFunctionE19__PRETTY_FUNCTION__DW.ref.__gxx_personality_v0_GLOBAL_OFFSET_TABLE___dso_handle_DYNAMICexp@@GLIBC_2.2.5mxGetPrmxGetN__gmon_start___Jv_RegisterClasses__assert_fail@@GLIBC_2.2.5_finimxGetNumberOfDimensions__gxx_personality_v0__cxa_finalize@@GLIBC_2.2.5__bss_start_endmxGetMmexFunctionmxCreateDoubleMatrixmexErrMsgTxtmxGetDimensions_edata_initmpi-ikl-simplemkl-1.0/BSD_LICENSE.txt0000644000335100017570000000257011133661707016710 0ustar pgehlerwebadmCopyright (c) 2009, Peter Vincent Gehler All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. 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. 3. The name of the author may not be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 AUTHOR 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. mpi-ikl-simplemkl-1.0/checkerboard.m0000644000335100017570000000140311133661710017153 0ustar pgehlerwebadmfunction [X,Y] = checkerboard(nTiles,nPoints,width) %nTiles = 10; %nPoints = 1000; DO_PLOTS = 0; dim = 2; if ~exist('width','var') width = sqrt(0.5) * ones(dim,1); widht = [0.03;0.1]; end if size(nTiles,1) == 1 nTiles(1:2,1) = nTiles; end center_x = ceil(nTiles(1)*rand(nPoints,1)); center_y = ceil(nTiles(2)*rand(nPoints,1)); Y = mod(center_x+center_y,2); Y(Y==0) = -1; dev = repmat(width,1,nPoints)' .* rand(nPoints,2); X = [center_x,center_y]; X = X + dev; if (DO_PLOTS) figure(1);clf; hold on ind = find(Y==1); scatter(center_x(ind),center_y(ind),'ro'); ind = find(Y==-1); scatter(center_x(ind),center_y(ind),'bo'); figure(2);clf; hold on ind = find(Y==1); scatter(X(ind,1),X(ind,2),'ro'); ind = find(Y==-1); scatter(X(ind,1),X(ind,2),'bo'); end