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| author | Harpreet | 2015-11-19 15:06:02 +0530 |
|---|---|---|
| committer | Harpreet | 2015-11-19 15:06:02 +0530 |
| commit | b949656e486ba8e8ac37a06cd6241bc48c945ea2 (patch) | |
| tree | 433c5dd7db16943d79eee9128957ccb7b1b5d65d | |
| parent | a8277e2be90bf00bd70ab5e7d6b92b91c49b7320 (diff) | |
| download | FOSSEE-Optimization-toolbox-b949656e486ba8e8ac37a06cd6241bc48c945ea2.tar.gz FOSSEE-Optimization-toolbox-b949656e486ba8e8ac37a06cd6241bc48c945ea2.tar.bz2 FOSSEE-Optimization-toolbox-b949656e486ba8e8ac37a06cd6241bc48c945ea2.zip | |
Bugs by Prof fixed 1
54 files changed, 403 insertions, 2747 deletions
diff --git a/demos/qpipoptmat.dem.sce~ b/demos/qpipoptmat.dem.sce~ deleted file mode 100644 index 79628a7..0000000 --- a/demos/qpipoptmat.dem.sce~ +++ /dev/null @@ -1,42 +0,0 @@ -mode(1) -// -// Demo of qpipoptmat.sci -// - -//Find x in R^6 such that: -halt() // Press return to continue - -Aeq= [1,-1,1,0,3,1; --1,0,-3,-4,5,6; -2,5,3,0,1,0]; -beq=[1; 2; 3]; -A= [0,1,0,1,2,-1; --1,0,2,1,1,0]; -b = [-1; 2.5]; -lb=[-1000; -10000; 0; -1000; -1000; -1000]; -ub=[10000; 100; 1.5; 100; 100; 1000]; -x0 = repmat(0,6,1); -param = list("MaxIter", 300, "CpuTime", 100); -//and minimize 0.5*x'*Q*x + p'*x with -f=[1; 2; 3; 4; 5; 6]; H=eye(6,6); -[xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,[],param); -clear H f A b Aeq beq lb ub; -halt() // Press return to continue - -//Find the value of x that minimize following function -// f(x) = 0.5*x1^2 + x2^2 - x1*x2 - 2*x1 - 6*x2 -// Subject to: -// x1 + x2 ≤ 2 -// –x1 + 2x2 ≤ 2 -// 2x1 + x2 ≤ 3 -// 0 ≤ x1, 0 ≤ x2. -H = [1 -1; -1 2]; -f = [-2; -6]; -A = [1 1; -1 2; 2 1]; -b = [2; 2; 3]; -lb = [0; 0]; -ub = [%inf; %inf]; -[xopt,fopt,exitflag,output,lambda] = qpipoptmat(H,f,A,b,[],[],lb,ub) -halt() // Press return to continue - -//========= E N D === O F === D E M O =========// diff --git a/demos/sci_symphony.dem.gateway.sce~ b/demos/sci_symphony.dem.gateway.sce~ deleted file mode 100644 index 9256ca2..0000000 --- a/demos/sci_symphony.dem.gateway.sce~ +++ /dev/null @@ -1,16 +0,0 @@ -// Copyright (C) 2015 - IIT Bombay - FOSSEE -// -// Author: Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: harpreet.mertia@gmail.com -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt - -demopath = get_absolute_file_path("sci_symphony.dem.gateway.sce"); - -subdemolist = ["Symphony for knapsack", "symphony_knapsack.sce"]; - -subdemolist(:,2) = demopath + subdemolist(:,2); diff --git a/demos/symphony_knapsack.sce b/demos/symphony_knapsack.sce deleted file mode 100644 index 42c192c..0000000 --- a/demos/symphony_knapsack.sce +++ /dev/null @@ -1,116 +0,0 @@ -mode (-1) - -// Reference -// -// This problem is taken from -// P.C.Chu and J.E.Beasley -// "A genetic algorithm for the multidimensional knapsack problem", -// Journal of Heuristics, vol. 4, 1998, pp63-86. - - -// -// The problem to be solved is: -// Max sum{j=1,...,n} p(j)x(j) -// st sum{j=1,...,n} r(i,j)x(j) <= b(i) i=1,...,m -// x(j)=0 or 1 - - -// The function to be maximize i.e. P(j) - -p = [ 504 803 667 1103 834 585 811 856 690 832 846 813 868 793 .. - 825 1002 860 615 540 797 616 660 707 866 647 746 1006 608 .. - 877 900 573 788 484 853 942 630 591 630 640 1169 932 1034 .. - 957 798 669 625 467 1051 552 717 654 388 559 555 1104 783 .. - 959 668 507 855 986 831 821 825 868 852 832 828 799 686 .. - 510 671 575 740 510 675 996 636 826 1022 1140 654 909 799 .. - 1162 653 814 625 599 476 767 954 906 904 649 873 565 853 1008 632] - - -//Constraint Matrix -conMatrix = [ - //Constraint 1 - 42 41 523 215 819 551 69 193 582 375 367 478 162 898 .. - 550 553 298 577 493 183 260 224 852 394 958 282 402 604 .. - 164 308 218 61 273 772 191 117 276 877 415 873 902 465 .. - 320 870 244 781 86 622 665 155 680 101 665 227 597 354 .. - 597 79 162 998 849 136 112 751 735 884 71 449 266 420 .. - 797 945 746 46 44 545 882 72 383 714 987 183 731 301 .. - 718 91 109 567 708 507 983 808 766 615 554 282 995 946 651 298; - - //Constraint 2 - 509 883 229 569 706 639 114 727 491 481 681 948 687 941 .. - 350 253 573 40 124 384 660 951 739 329 146 593 658 816 .. - 638 717 779 289 430 851 937 289 159 260 930 248 656 833 .. - 892 60 278 741 297 967 86 249 354 614 836 290 893 857 .. - 158 869 206 504 799 758 431 580 780 788 583 641 32 653 .. - 252 709 129 368 440 314 287 854 460 594 512 239 719 751 .. - 708 670 269 832 137 356 960 651 398 893 407 477 552 805 881 850; - - //Constraint 3 - 806 361 199 781 596 669 957 358 259 888 319 751 275 177 .. - 883 749 229 265 282 694 819 77 190 551 140 442 867 283 .. - 137 359 445 58 440 192 485 744 844 969 50 833 57 877 .. - 482 732 968 113 486 710 439 747 174 260 877 474 841 422 .. - 280 684 330 910 791 322 404 403 519 148 948 414 894 147 .. - 73 297 97 651 380 67 582 973 143 732 624 518 847 113 .. - 382 97 905 398 859 4 142 110 11 213 398 173 106 331 254 447 ; - - //Constraint 4 - 404 197 817 1000 44 307 39 659 46 334 448 599 931 776 .. - 263 980 807 378 278 841 700 210 542 636 388 129 203 110 .. - 817 502 657 804 662 989 585 645 113 436 610 948 919 115 .. - 967 13 445 449 740 592 327 167 368 335 179 909 825 614 .. - 987 350 179 415 821 525 774 283 427 275 659 392 73 896 .. - 68 982 697 421 246 672 649 731 191 514 983 886 95 846 .. - 689 206 417 14 735 267 822 977 302 687 118 990 323 993 525 322; - - //Constrain 5 - 475 36 287 577 45 700 803 654 196 844 657 387 518 143 .. - 515 335 942 701 332 803 265 922 908 139 995 845 487 100 .. - 447 653 649 738 424 475 425 926 795 47 136 801 904 740 .. - 768 460 76 660 500 915 897 25 716 557 72 696 653 933 .. - 420 582 810 861 758 647 237 631 271 91 75 756 409 440 .. - 483 336 765 637 981 980 202 35 594 689 602 76 767 693 .. - 893 160 785 311 417 748 375 362 617 553 474 915 457 261 350 635 ; - ]; - -nbCon = size(conMatrix,1) - -nbVar = size(conMatrix,2) - -// Lower Bound of variables -lb = repmat(0,1,nbVar) - -// Upper Bound of variables -ub = repmat(1,1,nbVar) - -// Row Matrix for telling symphony that the is integer or not -isInt = repmat(%t,1,nbVar) - -// Lower Bound of constrains -conLB=repmat(0,nbCon,1); - -// Upper Bound of constraints -conUB=[11927 13727 11551 13056 13460 ]'; - -options = ["tie_limit" "40"]; - -// The expected solution : - -// Output variables -xopt = [0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0] - -// Optimal value -fopt = [ 24381 ] - -// Calling Symphony -[x,f,status,output]= symphony(nbVar,nbCon,p,isInt,lb,ub,conMatrix,conLB,conUB,-1,options) - - -//========= E N D === O F === D E M O =========// -// -// Load this script into the editor -// -filename = "symphony_knapsack.sce"; -dname = get_absolute_file_path(filename); -editor ( dname + filename ); diff --git a/demos/symphony_mat_knapsack.sce b/demos/symphony_mat_knapsack.sce deleted file mode 100644 index 47c85e2..0000000 --- a/demos/symphony_mat_knapsack.sce +++ /dev/null @@ -1,90 +0,0 @@ -mode (-1) - -// Reference -// -// This problem is taken from -// P.C.Chu and J.E.Beasley -// "A genetic algorithm for the multidimensional knapsack problem", -// Journal of Heuristics, vol. 4, 1998, pp63-86. - - -// -// The problem to be solved is: -// Max sum{j=1,...,n} p(j)x(j) -// st sum{j=1,...,n} r(i,j)x(j) <= b(i) i=1,...,m -// x(j)=0 or 1 - - -// The function to be maximize i.e. P(j) -objCoef = -1*[ 504 803 667 1103 834 585 811 856 690 832 846 813 868 793 .. - 825 1002 860 615 540 797 616 660 707 866 647 746 1006 608 .. - 877 900 573 788 484 853 942 630 591 630 640 1169 932 1034 .. - 957 798 669 625 467 1051 552 717 654 388 559 555 1104 783 .. - 959 668 507 855 986 831 821 825 868 852 832 828 799 686 .. - 510 671 575 740 510 675 996 636 826 1022 1140 654 909 799 .. - 1162 653 814 625 599 476 767 954 906 904 649 873 565 853 1008 632] - -//Constraint Matrix -conMatrix = [ //Constraint 1 - 42 41 523 215 819 551 69 193 582 375 367 478 162 898 .. - 550 553 298 577 493 183 260 224 852 394 958 282 402 604 .. - 164 308 218 61 273 772 191 117 276 877 415 873 902 465 .. - 320 870 244 781 86 622 665 155 680 101 665 227 597 354 .. - 597 79 162 998 849 136 112 751 735 884 71 449 266 420 .. - 797 945 746 46 44 545 882 72 383 714 987 183 731 301 .. - 718 91 109 567 708 507 983 808 766 615 554 282 995 946 651 298; - //Constraint 2 - 509 883 229 569 706 639 114 727 491 481 681 948 687 941 .. - 350 253 573 40 124 384 660 951 739 329 146 593 658 816 .. - 638 717 779 289 430 851 937 289 159 260 930 248 656 833 .. - 892 60 278 741 297 967 86 249 354 614 836 290 893 857 .. - 158 869 206 504 799 758 431 580 780 788 583 641 32 653 .. - 252 709 129 368 440 314 287 854 460 594 512 239 719 751 .. - 708 670 269 832 137 356 960 651 398 893 407 477 552 805 881 850; - //Constraint 3 - 806 361 199 781 596 669 957 358 259 888 319 751 275 177 .. - 883 749 229 265 282 694 819 77 190 551 140 442 867 283 .. - 137 359 445 58 440 192 485 744 844 969 50 833 57 877 .. - 482 732 968 113 486 710 439 747 174 260 877 474 841 422 .. - 280 684 330 910 791 322 404 403 519 148 948 414 894 147 .. - 73 297 97 651 380 67 582 973 143 732 624 518 847 113 .. - 382 97 905 398 859 4 142 110 11 213 398 173 106 331 254 447 ; - //Constraint 4 - 404 197 817 1000 44 307 39 659 46 334 448 599 931 776 .. - 263 980 807 378 278 841 700 210 542 636 388 129 203 110 .. - 817 502 657 804 662 989 585 645 113 436 610 948 919 115 .. - 967 13 445 449 740 592 327 167 368 335 179 909 825 614 .. - 987 350 179 415 821 525 774 283 427 275 659 392 73 896 .. - 68 982 697 421 246 672 649 731 191 514 983 886 95 846 .. - 689 206 417 14 735 267 822 977 302 687 118 990 323 993 525 322; - //Constrain 5 - 475 36 287 577 45 700 803 654 196 844 657 387 518 143 .. - 515 335 942 701 332 803 265 922 908 139 995 845 487 100 .. - 447 653 649 738 424 475 425 926 795 47 136 801 904 740 .. - 768 460 76 660 500 915 897 25 716 557 72 696 653 933 .. - 420 582 810 861 758 647 237 631 271 91 75 756 409 440 .. - 483 336 765 637 981 980 202 35 594 689 602 76 767 693 .. - 893 160 785 311 417 748 375 362 617 553 474 915 457 261 350 635 ; - ]; -nbVar = size(objCoef,2) -conUB=[11927 13727 11551 13056 13460 ]; -// Lower Bound of variables -lb = repmat(0,1,nbVar) -// Upper Bound of variables -ub = repmat(1,1,nbVar) -// Lower Bound of constrains -intcon = [] -for i = 1:nbVar - intcon = [intcon i]; -end - -options = ["time_limit" "40"]; - -// The expected solution : -// Output variables -xopt = [0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0] -// Optimal value -fopt = [ 24381 ] -// Calling Symphony -[x,f,iter] = symphony_mat(objCoef,intcon,conMatrix,conUB,[],[],lb,ub,options); - diff --git a/demos/symphonymat.dem.sce~ b/demos/symphonymat.dem.sce~ deleted file mode 100644 index ef4d7cc..0000000 --- a/demos/symphonymat.dem.sce~ +++ /dev/null @@ -1,104 +0,0 @@ -mode(1) -// -// Demo of symphonymat.sci -// - -// Objective function -c = [350*5,330*3,310*4,280*6,500,450,400,100] -// Lower Bound of variable -lb = repmat(0,1,8); -// Upper Bound of variables -ub = [repmat(1,1,4) repmat(%inf,1,4)]; -// Constraint Matrix -Aeq = [5,3,4,6,1,1,1,1; -5*0.05,3*0.04,4*0.05,6*0.03,0.08,0.07,0.06,0.03; -5*0.03,3*0.03,4*0.04,6*0.04,0.06,0.07,0.08,0.09;] -beq = [ 25, 1.25, 1.25] -intcon = [1 2 3 4]; -// Calling Symphony -[x,f,status,output] = symphonymat(c,intcon,[],[],Aeq,beq,lb,ub) -halt() // Press return to continue - -// An advanced case where we set some options in symphony -// This problem is taken from -// P.C.Chu and J.E.Beasley -// "A genetic algorithm for the multidimensional knapsack problem", -// Journal of Heuristics, vol. 4, 1998, pp63-86. -// The problem to be solved is: -// Max sum{j=1,...,n} p(j)x(j) -// st sum{j=1,...,n} r(i,j)x(j) <= b(i) i=1,...,m -// x(j)=0 or 1 -// The function to be maximize i.e. P(j) -objCoef = -1*[ 504 803 667 1103 834 585 811 856 690 832 846 813 868 793 .. -825 1002 860 615 540 797 616 660 707 866 647 746 1006 608 .. -877 900 573 788 484 853 942 630 591 630 640 1169 932 1034 .. -957 798 669 625 467 1051 552 717 654 388 559 555 1104 783 .. -959 668 507 855 986 831 821 825 868 852 832 828 799 686 .. -510 671 575 740 510 675 996 636 826 1022 1140 654 909 799 .. -1162 653 814 625 599 476 767 954 906 904 649 873 565 853 1008 632] -//Constraint Matrix -conMatrix = [ //Constraint 1 -42 41 523 215 819 551 69 193 582 375 367 478 162 898 .. -550 553 298 577 493 183 260 224 852 394 958 282 402 604 .. -164 308 218 61 273 772 191 117 276 877 415 873 902 465 .. -320 870 244 781 86 622 665 155 680 101 665 227 597 354 .. -597 79 162 998 849 136 112 751 735 884 71 449 266 420 .. -797 945 746 46 44 545 882 72 383 714 987 183 731 301 .. -718 91 109 567 708 507 983 808 766 615 554 282 995 946 651 298; -//Constraint 2 -509 883 229 569 706 639 114 727 491 481 681 948 687 941 .. -350 253 573 40 124 384 660 951 739 329 146 593 658 816 .. -638 717 779 289 430 851 937 289 159 260 930 248 656 833 .. -892 60 278 741 297 967 86 249 354 614 836 290 893 857 .. -158 869 206 504 799 758 431 580 780 788 583 641 32 653 .. -252 709 129 368 440 314 287 854 460 594 512 239 719 751 .. -708 670 269 832 137 356 960 651 398 893 407 477 552 805 881 850; -//Constraint 3 -806 361 199 781 596 669 957 358 259 888 319 751 275 177 .. -883 749 229 265 282 694 819 77 190 551 140 442 867 283 .. -137 359 445 58 440 192 485 744 844 969 50 833 57 877 .. -482 732 968 113 486 710 439 747 174 260 877 474 841 422 .. -280 684 330 910 791 322 404 403 519 148 948 414 894 147 .. -73 297 97 651 380 67 582 973 143 732 624 518 847 113 .. -382 97 905 398 859 4 142 110 11 213 398 173 106 331 254 447 ; -//Constraint 4 -404 197 817 1000 44 307 39 659 46 334 448 599 931 776 .. -263 980 807 378 278 841 700 210 542 636 388 129 203 110 .. -817 502 657 804 662 989 585 645 113 436 610 948 919 115 .. -967 13 445 449 740 592 327 167 368 335 179 909 825 614 .. -987 350 179 415 821 525 774 283 427 275 659 392 73 896 .. -68 982 697 421 246 672 649 731 191 514 983 886 95 846 .. -689 206 417 14 735 267 822 977 302 687 118 990 323 993 525 322; -//Constrain 5 -475 36 287 577 45 700 803 654 196 844 657 387 518 143 .. -515 335 942 701 332 803 265 922 908 139 995 845 487 100 .. -447 653 649 738 424 475 425 926 795 47 136 801 904 740 .. -768 460 76 660 500 915 897 25 716 557 72 696 653 933 .. -420 582 810 861 758 647 237 631 271 91 75 756 409 440 .. -483 336 765 637 981 980 202 35 594 689 602 76 767 693 .. -893 160 785 311 417 748 375 362 617 553 474 915 457 261 350 635 ; -]; -nbVar = size(objCoef,2) -conUB=[11927 13727 11551 13056 13460 ]; -// Lower Bound of variables -lb = repmat(0,1,nbVar) -// Upper Bound of variables -ub = repmat(1,1,nbVar) -// Lower Bound of constrains -intcon = [] -for i = 1:nbVar -intcon = [intcon i]; -end -options = list("time_limit", 25); -// The expected solution : -// Output variables -xopt = [0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 1 0 1 .. -0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 .. -0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0] -// Optimal value -fopt = [ 24381 ] -// Calling Symphony -[x,f,status,output] = symphonymat(objCoef,intcon,conMatrix,conUB,[],[],lb,ub); -halt() // Press return to continue - -//========= E N D === O F === D E M O =========// diff --git a/etc/README.rst~ b/etc/README.rst~ deleted file mode 100644 index e69de29..0000000 --- a/etc/README.rst~ +++ /dev/null diff --git a/help/en_US/master_help.xml b/help/en_US/master_help.xml index 67338f2..66b8e0d 100644 --- a/help/en_US/master_help.xml +++ b/help/en_US/master_help.xml @@ -2,8 +2,10 @@ <!DOCTYPE book [ <!--Begin Entities--> <!ENTITY a6b85f6e0c98751f20b68663a23cb4cd2 SYSTEM "/home/harpreet/symphony_work/symphony/help/en_US/qpipopt.xml"> +<!ENTITY a44928acec52adf395379e18fcff06730 SYSTEM "/home/harpreet/symphony_work/symphony/help/en_US/qpipopt_mat.xml"> <!ENTITY a8549a3935858ed104f4749ca2243456a SYSTEM "/home/harpreet/symphony_work/symphony/help/en_US/qpipoptmat.xml"> <!ENTITY aca972f273143ecb39f56b42e4723ac67 SYSTEM "/home/harpreet/symphony_work/symphony/help/en_US/symphony.xml"> +<!ENTITY a9953e61e8dd264a86df73772d3055e7f SYSTEM "/home/harpreet/symphony_work/symphony/help/en_US/symphony_mat.xml"> <!ENTITY a9910ada35b57b0581e8a77d145abac4a SYSTEM "/home/harpreet/symphony_work/symphony/help/en_US/symphonymat.xml"> <!ENTITY acc223314e8a8bc290a13618df33a6237 SYSTEM "/home/harpreet/symphony_work/symphony/help/en_US/Symphony Native Function/sym_addConstr.xml"> <!ENTITY a5e032b3334f53385f0ce250f0d5c18f2 SYSTEM "/home/harpreet/symphony_work/symphony/help/en_US/Symphony Native Function/sym_addVar.xml"> @@ -80,8 +82,10 @@ <part xml:id='section_19f4f1e5726c01d683e8b82be0a7e910'> <title>Symphony Toolbox</title> &a6b85f6e0c98751f20b68663a23cb4cd2; +&a44928acec52adf395379e18fcff06730; &a8549a3935858ed104f4749ca2243456a; &aca972f273143ecb39f56b42e4723ac67; +&a9953e61e8dd264a86df73772d3055e7f; &a9910ada35b57b0581e8a77d145abac4a; <chapter xml:id='section_508f0b211d17ea6769714cc144e6b731'> <title>Symphony Native Functions</title> diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS Binary files differindex 02038f6..d5a7298 100644 --- a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS +++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB Binary files differindex 23181b6..0095d6c 100644 --- a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB +++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS b/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS Binary files differindex d20c98d..3fdc4c1 100644 --- a/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS +++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS b/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS Binary files differindex e2773de..d69ed8d 100644 --- a/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS +++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP b/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP Binary files differindex 7e8baef..39ca223 100644 --- a/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP +++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP diff --git a/help/en_US/scilab_en_US_help/index.html b/help/en_US/scilab_en_US_help/index.html index 12fb83c..b1cfd9c 100644 --- a/help/en_US/scilab_en_US_help/index.html +++ b/help/en_US/scilab_en_US_help/index.html @@ -38,6 +38,12 @@ +<li><a href="qpipopt_mat.html" class="refentry">qpipopt_mat</a> — <span class="refentry-description">Solves a linear quadratic problem.</span></li> + + + + + <li><a href="qpipoptmat.html" class="refentry">qpipoptmat</a> — <span class="refentry-description">Solves a linear quadratic problem.</span></li> @@ -50,6 +56,12 @@ +<li><a href="symphony_mat.html" class="refentry">symphony_mat</a> — <span class="refentry-description">Solves a mixed integer linear programming constrained optimization problem in intlinprog format.</span></li> + + + + + <li><a href="symphonymat.html" class="refentry">symphonymat</a> — <span class="refentry-description">Solves a mixed integer linear programming constrained optimization problem in intlinprog format.</span></li> <li><a href="section_508f0b211d17ea6769714cc144e6b731.html" class="chapter">Symphony Native Functions</a> diff --git a/help/en_US/scilab_en_US_help/jhelpmap.jhm b/help/en_US/scilab_en_US_help/jhelpmap.jhm index 9dfdea5..47f4429 100644 --- a/help/en_US/scilab_en_US_help/jhelpmap.jhm +++ b/help/en_US/scilab_en_US_help/jhelpmap.jhm @@ -4,8 +4,10 @@ <mapID target="index" url="index.html"/> <mapID target="section_19f4f1e5726c01d683e8b82be0a7e910" url="section_19f4f1e5726c01d683e8b82be0a7e910.html"/> <mapID target="qpipopt" url="qpipopt.html"/> +<mapID target="qpipopt_mat" url="qpipopt_mat.html"/> <mapID target="qpipoptmat" url="qpipoptmat.html"/> <mapID target="symphony" url="symphony.html"/> +<mapID target="symphony_mat" url="symphony_mat.html"/> <mapID target="symphonymat" url="symphonymat.html"/> <mapID target="section_508f0b211d17ea6769714cc144e6b731" url="section_508f0b211d17ea6769714cc144e6b731.html"/> <mapID target="sym_addConstr" url="sym_addConstr.html"/> diff --git a/help/en_US/scilab_en_US_help/jhelptoc.xml b/help/en_US/scilab_en_US_help/jhelptoc.xml index 84c6d37..95d4995 100644 --- a/help/en_US/scilab_en_US_help/jhelptoc.xml +++ b/help/en_US/scilab_en_US_help/jhelptoc.xml @@ -4,8 +4,10 @@ <tocitem target="index" text="Symphony Toolbox"> <tocitem target="section_19f4f1e5726c01d683e8b82be0a7e910" text="Symphony Toolbox"> <tocitem target="qpipopt" text="qpipopt"/> +<tocitem target="qpipopt_mat" text="qpipopt_mat"/> <tocitem target="qpipoptmat" text="qpipoptmat"/> <tocitem target="symphony" text="symphony"/> +<tocitem target="symphony_mat" text="symphony_mat"/> <tocitem target="symphonymat" text="symphonymat"/> <tocitem target="section_508f0b211d17ea6769714cc144e6b731" text="Symphony Native Functions"> <tocitem target="sym_addConstr" text="sym_addConstr"/> diff --git a/help/en_US/scilab_en_US_help/qpipopt.html b/help/en_US/scilab_en_US_help/qpipopt.html index b1c18ac..63da9d2 100644 --- a/help/en_US/scilab_en_US_help/qpipopt.html +++ b/help/en_US/scilab_en_US_help/qpipopt.html @@ -20,7 +20,7 @@ </td> <td width="30%" class="next"> - <span class="next"><a href="qpipoptmat.html">qpipoptmat >></a></span> + <span class="next"><a href="qpipopt_mat.html">qpipopt_mat >></a></span> </td> </tr></table> @@ -138,7 +138,7 @@ find the minimum of f(x) such that</p> </td> <td width="30%" class="next"> - <span class="next"><a href="qpipoptmat.html">qpipoptmat >></a></span> + <span class="next"><a href="qpipopt_mat.html">qpipopt_mat >></a></span> </td> </tr></table> diff --git a/help/en_US/scilab_en_US_help/qpipopt_mat.html b/help/en_US/scilab_en_US_help/qpipopt_mat.html index 5e30769..2089d8b 100644 --- a/help/en_US/scilab_en_US_help/qpipopt_mat.html +++ b/help/en_US/scilab_en_US_help/qpipopt_mat.html @@ -20,7 +20,7 @@ </td> <td width="30%" class="next"> - <span class="next"><a href="symphony.html">symphony >></a></span> + <span class="next"><a href="qpipoptmat.html">qpipoptmat >></a></span> </td> </tr></table> @@ -129,7 +129,7 @@ find the minimum of f(x) such that</p> </td> <td width="30%" class="next"> - <span class="next"><a href="symphony.html">symphony >></a></span> + <span class="next"><a href="qpipoptmat.html">qpipoptmat >></a></span> </td> </tr></table> diff --git a/help/en_US/scilab_en_US_help/qpipoptmat.html b/help/en_US/scilab_en_US_help/qpipoptmat.html index a9f97aa..10cb590 100644 --- a/help/en_US/scilab_en_US_help/qpipoptmat.html +++ b/help/en_US/scilab_en_US_help/qpipoptmat.html @@ -12,7 +12,7 @@ <div class="manualnavbar"> <table width="100%"><tr> <td width="30%"> - <span class="previous"><a href="qpipopt.html"><< qpipopt</a></span> + <span class="previous"><a href="qpipopt_mat.html"><< qpipopt_mat</a></span> </td> <td width="40%" class="center"> @@ -128,7 +128,7 @@ find the minimum of f(x) such that</p> <tr><td colspan="3" class="next"><a href="http://bugzilla.scilab.org/enter_bug.cgi?product=Scilab%20software&component=Documentation%20pages" class="ulink">Report an issue</a></td></tr> <tr> <td width="30%"> - <span class="previous"><a href="qpipopt.html"><< qpipopt</a></span> + <span class="previous"><a href="qpipopt_mat.html"><< qpipopt_mat</a></span> </td> <td width="40%" class="center"> diff --git a/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html b/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html index 1e5e538..52d6d9e 100644 --- a/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html +++ b/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html @@ -37,6 +37,12 @@ +<li><a href="qpipopt_mat.html" class="refentry">qpipopt_mat</a> — <span class="refentry-description">Solves a linear quadratic problem.</span></li> + + + + + <li><a href="qpipoptmat.html" class="refentry">qpipoptmat</a> — <span class="refentry-description">Solves a linear quadratic problem.</span></li> @@ -49,6 +55,12 @@ +<li><a href="symphony_mat.html" class="refentry">symphony_mat</a> — <span class="refentry-description">Solves a mixed integer linear programming constrained optimization problem in intlinprog format.</span></li> + + + + + <li><a href="symphonymat.html" class="refentry">symphonymat</a> — <span class="refentry-description">Solves a mixed integer linear programming constrained optimization problem in intlinprog format.</span></li> <li><a href="section_508f0b211d17ea6769714cc144e6b731.html" class="chapter">Symphony Native Functions</a> diff --git a/help/en_US/scilab_en_US_help/symphony.html b/help/en_US/scilab_en_US_help/symphony.html index c5b8336..48ea705 100644 --- a/help/en_US/scilab_en_US_help/symphony.html +++ b/help/en_US/scilab_en_US_help/symphony.html @@ -20,7 +20,7 @@ </td> <td width="30%" class="next"> - <span class="next"><a href="symphonymat.html">symphonymat >></a></span> + <span class="next"><a href="symphony_mat.html">symphony_mat >></a></span> </td> </tr></table> @@ -205,7 +205,7 @@ find the minimum or maximum of f(x) such that</p> </td> <td width="30%" class="next"> - <span class="next"><a href="symphonymat.html">symphonymat >></a></span> + <span class="next"><a href="symphony_mat.html">symphony_mat >></a></span> </td> </tr></table> diff --git a/help/en_US/scilab_en_US_help/symphony_mat.html b/help/en_US/scilab_en_US_help/symphony_mat.html index cc337f4..4f07aca 100644 --- a/help/en_US/scilab_en_US_help/symphony_mat.html +++ b/help/en_US/scilab_en_US_help/symphony_mat.html @@ -20,7 +20,7 @@ </td> <td width="30%" class="next"> - <span class="next"><a href="section_508f0b211d17ea6769714cc144e6b731.html">Symphony Native Functions >></a></span> + <span class="next"><a href="symphonymat.html">symphonymat >></a></span> </td> </tr></table> @@ -190,7 +190,7 @@ find the minimum or maximum of f(x) such that</p> </td> <td width="30%" class="next"> - <span class="next"><a href="section_508f0b211d17ea6769714cc144e6b731.html">Symphony Native Functions >></a></span> + <span class="next"><a href="symphonymat.html">symphonymat >></a></span> </td> </tr></table> diff --git a/help/en_US/scilab_en_US_help/symphonymat.html b/help/en_US/scilab_en_US_help/symphonymat.html index 8001136..b559c8a 100644 --- a/help/en_US/scilab_en_US_help/symphonymat.html +++ b/help/en_US/scilab_en_US_help/symphonymat.html @@ -12,7 +12,7 @@ <div class="manualnavbar"> <table width="100%"><tr> <td width="30%"> - <span class="previous"><a href="symphony.html"><< symphony</a></span> + <span class="previous"><a href="symphony_mat.html"><< symphony_mat</a></span> </td> <td width="40%" class="center"> @@ -183,7 +183,7 @@ find the minimum or maximum of f(x) such that</p> <tr><td colspan="3" class="next"><a href="http://bugzilla.scilab.org/enter_bug.cgi?product=Scilab%20software&component=Documentation%20pages" class="ulink">Report an issue</a></td></tr> <tr> <td width="30%"> - <span class="previous"><a href="symphony.html"><< symphony</a></span> + <span class="previous"><a href="symphony_mat.html"><< symphony_mat</a></span> </td> <td width="40%" class="center"> diff --git a/jar/scilab_en_US_help.jar b/jar/scilab_en_US_help.jar Binary files differindex 749cb54..79b480b 100644 --- a/jar/scilab_en_US_help.jar +++ b/jar/scilab_en_US_help.jar diff --git a/macros/README.rst~ b/macros/README.rst~ deleted file mode 100644 index 5a07f63..0000000 --- a/macros/README.rst~ +++ /dev/null @@ -1,36 +0,0 @@ -MACROS -====== - -These files mainly consist of functions for checking the input and calling the gateway functions - -symphony --------- - -It takes the input in symphony style and checks the input. After all the checks call the symphony_call function. - -symphonymat ------------ - -It takes the input in symphony style and checks the input. After all the checks call the symphony_call function. - -symphony_call -------------- - -It calls the gateway functions to initialize, set options and to solve it. After that it will call the functions to get the solution for the problem. - -setOptions ----------- - -It will set the options in the symphony. - -qpipopt -------- - -It synatize the input and call solveqp in the ipopt style. - -qpipopt -------- - -It synatize the input and call solveqp in the quadprog style. - - Binary files differdiff --git a/macros/names b/macros/names index e068c5a..4f0ba56 100644 --- a/macros/names +++ b/macros/names @@ -3,4 +3,5 @@ qpipoptmat setOptions symphony symphony_call +symphony_mat symphonymat diff --git a/macros/qpipopt.bin b/macros/qpipopt.bin Binary files differindex 6eea1fa..0c65c0b 100644 --- a/macros/qpipopt.bin +++ b/macros/qpipopt.bin diff --git a/macros/qpipopt.sci b/macros/qpipopt.sci index 5f08067..c17371e 100644 --- a/macros/qpipopt.sci +++ b/macros/qpipopt.sci @@ -226,40 +226,69 @@ function [xopt,fopt,exitflag,output,lambda] = qpipopt (varargin) //Check the number of constraint if ( size(conMatrix,1) ~= nbCon) then - errmsg = msprintf(gettext("%s: The number of constraints is not equal to the number of constraint given i.e. %d"), "qpipopt", nbCon); + errmsg = msprintf(gettext("%s: The size of constraint matrix is not equal to the number of constraint given i.e. %d"), "qpipopt", nbCon); error(errmsg); end //Check the size of Lower Bound which should equal to the number of variables if ( size(LB,2) ~= nbVar) then - errmsg = msprintf(gettext("%s: The Lower Bound is not equal to the number of variables"), "qpipopt"); + errmsg = msprintf(gettext("%s: The size of Lower Bound is not equal to the number of variables"), "qpipopt"); error(errmsg); end //Check the size of Upper Bound which should equal to the number of variables if ( size(UB,2) ~= nbVar) then - errmsg = msprintf(gettext("%s: The Upper Bound is not equal to the number of variables"), "qpipopt"); + errmsg = msprintf(gettext("%s: The size of Upper Bound is not equal to the number of variables"), "qpipopt"); error(errmsg); end //Check the size of constraints of Lower Bound which should equal to the number of constraints if ( size(conLB,2) ~= nbCon) then - errmsg = msprintf(gettext("%s: The Lower Bound of constraints is not equal to the number of constraints"), "qpipopt"); + errmsg = msprintf(gettext("%s: The size of Lower Bound of constraints is not equal to the number of constraints"), "qpipopt"); error(errmsg); end //Check the size of constraints of Upper Bound which should equal to the number of constraints if ( size(conUB,2) ~= nbCon) then - errmsg = msprintf(gettext("%s: The Upper Bound of constraints is not equal to the number of constraints"), "qpipopt"); + errmsg = msprintf(gettext("%s: The size of Upper Bound of constraints is not equal to the number of constraints"), "qpipopt"); error(errmsg); end //Check the size of initial of variables which should equal to the number of variables - if ( size(x0,2) ~= nbVar) then + if ( size(x0,2) ~= nbVar | size(x0,"*")>nbVar) then warnmsg = msprintf(gettext("%s: Ignoring initial guess of variables as it is not equal to the number of variables"), "qpipopt"); warning(warnmsg); end + + //Check if the user gives a matrix instead of a vector + + if ((size(p,1)~=1)& (size(p,2)~=1)) then + errmsg = msprintf(gettext("%s: p should be a vector"), "qpipopt"); + error(errmsg); + end + + if (size(LB,1)~=1)& (size(LB,2)~=1) then + errmsg = msprintf(gettext("%s: Lower Bound should be a vector"), "qpipopt"); + error(errmsg); + end + + if (size(UB,1)~=1)& (size(UB,2)~=1) then + errmsg = msprintf(gettext("%s: Upper Bound should be a vector"), "qpipopt"); + error(errmsg); + end + + if (nbCon) then + if ((size(conLB,1)~=1)& (size(b,2)~=1)) then + errmsg = msprintf(gettext("%s: Constraint Lower Bound should be a vector"), "qpipopt"); + error(errmsg); + end + if (size(conUB,1)~=1)& (size(beq,2)~=1) then + errmsg = msprintf(gettext("%s: Constraint should be a vector"), "qpipopt"); + error(errmsg); + end + end + [xopt,fopt,status,iter,Zl,Zu,lmbda] = solveqp(nbVar,nbCon,Q,p,conMatrix,conLB,conUB,LB,UB,x0,options); @@ -275,6 +304,43 @@ function [xopt,fopt,exitflag,output,lambda] = qpipopt (varargin) lambda.lower = Zl; lambda.upper = Zu; lambda.constraint = lmbda; + + select status + + case 0 then + printf("\nOptimal Solution Found.\n"); + case 1 then + printf("\nMaximum Number of Iterations Exceeded. Output may not be optimal.\n"); + case 2 then + printf("\nMaximum CPU Time exceeded. Output may not be optimal.\n"); + case 3 then + printf("\nStop at Tiny Step\n"); + case 4 then + printf("\nSolved To Acceptable Level\n"); + case 5 then + printf("\nConverged to a point of local infeasibility.\n"); + case 6 then + printf("\nStopping optimization at current point as requested by user.\n"); + case 7 then + printf("\nFeasible point for square problem found.\n"); + case 8 then + printf("\nIterates divering; problem might be unbounded.\n"); + case 9 then + printf("\nRestoration Failed!\n"); + case 10 then + printf("\nError in step computation (regularization becomes too large?)!\n"); + case 12 then + printf("\nProblem has too few degrees of freedom.\n"); + case 13 then + printf("\nInvalid option thrown back by IPOpt\n"); + case 14 then + printf("\nNot enough memory.\n"); + case 15 then + printf("\nINTERNAL ERROR: Unknown SolverReturn value - Notify IPOPT Authors.\n"); + else + printf("\nInvalid status returned. Notify the Toolbox authors\n"); + break; + end endfunction diff --git a/macros/qpipopt.sci~ b/macros/qpipopt.sci~ deleted file mode 100644 index 35e604b..0000000 --- a/macros/qpipopt.sci~ +++ /dev/null @@ -1,233 +0,0 @@ -// Copyright (C) 2015 - IIT Bombay - FOSSEE -// -// Author: Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: harpreet.mertia@gmail.com -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt - - -function [xopt,fopt,exitflag,output,lambda] = qpipopt (varargin) - // Solves a linear quadratic problem. - // - // Calling Sequence - // xopt = qpipopt(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB) - // xopt = qpipopt(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB,x0) - // xopt = qpipopt(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB,x0,param) - // [xopt,fopt,exitflag,output,lamda] = qpipopt( ... ) - // - // Parameters - // nbVar : a 1 x 1 matrix of doubles, number of variables - // nbCon : a 1 x 1 matrix of doubles, number of constraints - // Q : a n x n symmetric matrix of doubles, where n is number of variables, represents coefficients of quadratic in the quadratic problem. - // p : a n x 1 matrix of doubles, where n is number of variables, represents coefficients of linear in the quadratic problem - // LB : a n x 1 matrix of doubles, where n is number of variables, contains lower bounds of the variables. - // UB : a n x 1 matrix of doubles, where n is number of variables, contains upper bounds of the variables. - // conMatrix : a m x n matrix of doubles, where n is number of variables and m is number of constraints, contains matrix representing the constraint matrix - // conLB : a m x 1 matrix of doubles, where m is number of constraints, contains lower bounds of the constraints. - // conUB : a m x 1 matrix of doubles, where m is number of constraints, contains upper bounds of the constraints. - // x0 : a m x 1 matrix of doubles, where m is number of constraints, contains initial guess of variables. - // param : a list containing the the parameters to be set. - // xopt : a 1xn matrix of doubles, the computed solution of the optimization problem. - // fopt : a 1x1 matrix of doubles, the function value at x. - // exitflag : Integer identifying the reason the algorithm terminated. - // output : Structure containing information about the optimization. - // lambda : Structure containing the Lagrange multipliers at the solution x (separated by constraint type). - // - // Description - // Search the minimum of a constrained linear quadratic optimization problem specified by : - // find the minimum of f(x) such that - // - // <latex> - // \begin{eqnarray} - // &\mbox{min}_{x} - // & 1/2*x'*Q*x + p'*x \\ - // & \text{subject to} & conLB \leq C(x) \leq conUB \\ - // & & lb \leq x \leq ub \\ - // \end{eqnarray} - // </latex> - // - // We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. The code has been written by Andreas Wächter and Carl Laird. - // - // Examples - // //Find x in R^6 such that: - // conMatrix= [1,-1,1,0,3,1; - // -1,0,-3,-4,5,6; - // 2,5,3,0,1,0 - // 0,1,0,1,2,-1; - // -1,0,2,1,1,0]; - // conLB=[1;2;3;-%inf;-%inf]; - // conUB = [1;2;3;-1;2.5]; - // lb=[-1000;-10000; 0; -1000; -1000; -1000]; - // ub=[10000; 100; 1.5; 100; 100; 1000]; - // //and minimize 0.5*x'*Q*x + p'*x with - // p=[1; 2; 3; 4; 5; 6]; Q=eye(6,6); - // nbVar = 6; - // nbCon = 5; - // x0 = repmat(0,nbVar,1); - // param = list("MaxIter", 300, "CpuTime", 100); - // [xopt,fopt,exitflag,output,lambda]=qpipopt(nbVar,nbCon,Q,p,lb,ub,conMatrix,conLB,conUB,x0,param) - // - // Examples - // //Find the value of x that minimize following function - // // f(x) = 0.5*x1^2 + x2^2 - x1*x2 - 2*x1 - 6*x2 - // // Subject to: - // // x1 + x2 ≤ 2 - // // –x1 + 2x2 ≤ 2 - // // 2x1 + x2 ≤ 3 - // // 0 ≤ x1, 0 ≤ x2. - // Q = [1 -1; -1 2]; - // p = [-2; -6]; - // conMatrix = [1 1; -1 2; 2 1]; - // conUB = [2; 2; 3]; - // conLB = [-%inf; -%inf; -%inf]; - // lb = [0; 0]; - // ub = [%inf; %inf]; - // nbVar = 2; - // nbCon = 3; - // [xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,Q,p,lb,ub,conMatrix,conLB,conUB) - // - // Authors - // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh - - -//To check the number of input and output argument - [lhs , rhs] = argn(); - -//To check the number of argument given by user - if ( rhs < 9 | rhs > 11 ) then - errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be 9, 10 or 11"), "qpipopt", rhs); - error(errmsg) - end - - - nbVar = varargin(1); - nbCon = varargin(2); - Q = varargin(3); - p = varargin(4); - LB = varargin(5); - UB = varargin(6); - conMatrix = varargin(7); - conLB = varargin(8); - conUB = varargin(9); - - - if ( rhs<10 | size(varargin(10)) ==0 ) then - x0 = repmat(0,nbVar,1); - else - x0 = varargin(10); - end - - if ( rhs<11 ) then - param = []; - else - param =varargin(11); - end - - if (modulo(size(param),2)) then - errmsg = msprintf(gettext("%s: Size of parameters should be even"), "qpipopt"); - error(errmsg); - end - - - options = list(.. - "MaxIter" , [3000], ... - "CpuTime" , [600] ... - ); - - for i = 1:(size(param))/2 - - select param(2*i-1) - case "MaxIter" then - options(1) = param(2*i); - case "CpuTime" then - options(3) = param(2*i); - else - errmsg = msprintf(gettext("%s: Unrecognized parameter name ''%s''."), "qpipopt", param(2*i-1)); - error(errmsg) - end - end - - //IPOpt wants it in row matrix form - p = p'; - LB = LB'; - UB = UB'; - conLB = conLB'; - conUB = conUB'; - x0 = x0'; - - //Checking the Q matrix which needs to be a symmetric matrix - if ( ~isequal(Q,Q') ) then - errmsg = msprintf(gettext("%s: Q is not a symmetric matrix"), "qpipopt"); - error(errmsg); - end - - //Check the size of Q which should equal to the number of variable - if ( size(Q) ~= [nbVar nbVar]) then - errmsg = msprintf(gettext("%s: The Size of Q is not equal to the number of variables"), "qpipopt"); - error(errmsg); - end - - //Check the size of p which should equal to the number of variable - if ( size(p,2) ~= [nbVar]) then - errmsg = msprintf(gettext("%s: The Size of p is not equal to the number of variables"), "qpipopt"); - error(errmsg); - end - - - //Check the size of constraint which should equal to the number of variables - if ( size(conMatrix,2) ~= nbVar) then - errmsg = msprintf(gettext("%s: The size of constraints is not equal to the number of variables"), "qpipopt"); - error(errmsg); - end - - //Check the size of Lower Bound which should equal to the number of variables - if ( size(LB,2) ~= nbVar) then - errmsg = msprintf(gettext("%s: The Lower Bound is not equal to the number of variables"), "qpipopt"); - error(errmsg); - end - - //Check the size of Upper Bound which should equal to the number of variables - if ( size(UB,2) ~= nbVar) then - errmsg = msprintf(gettext("%s: The Upper Bound is not equal to the number of variables"), "qpipopt"); - error(errmsg); - end - - //Check the size of constraints of Lower Bound which should equal to the number of constraints - if ( size(conLB,2) ~= nbCon) then - errmsg = msprintf(gettext("%s: The Lower Bound of constraints is not equal to the number of constraints"), "qpipopt"); - error(errmsg); - end - - //Check the size of constraints of Upper Bound which should equal to the number of constraints - if ( size(conUB,2) ~= nbCon) then - errmsg = msprintf(gettext("%s: The Upper Bound of constraints is not equal to the number of constraints"), "qpipopt"); - error(errmsg); - end - - //Check the size of initial of variables which should equal to the number of variables - if ( size(x0,2) ~= nbVar) then - errmsg = msprintf(gettext("%s: The initial guess of variables is not equal to the number of variables"), "qpipopt"); - error(errmsg); - end - - - [xopt,fopt,status,iter,Zl,Zu,lmbda] = solveqp(nbVar,nbCon,Q,p,conMatrix,conLB,conUB,LB,UB,x0,options); - - xopt = xopt'; - exitflag = status; - output = struct("Iterations" , []); - output.Iterations = iter; - lambda = struct("lower" , [], .. - "upper" , [], .. - "constraint" , []); - - lambda.lower = Zl; - lambda.upper = Zu; - lambda.constraint = lmbda; - - -endfunction diff --git a/macros/qpipoptmat.bin b/macros/qpipoptmat.bin Binary files differindex 2cb90c9..6ca5589 100644 --- a/macros/qpipoptmat.bin +++ b/macros/qpipoptmat.bin diff --git a/macros/qpipoptmat.sci b/macros/qpipoptmat.sci index 7924ba6..01b0eef 100644 --- a/macros/qpipoptmat.sci +++ b/macros/qpipoptmat.sci @@ -174,9 +174,9 @@ function [xopt,fopt,exitflag,output,lambda] = qpipoptmat (varargin) select param(2*i-1) case "MaxIter" then - options(2*i-1) = param(2*i); + options(2*i) = param(2*i); case "CpuTime" then - options(2*i-1) = param(2*i); + options(2*i) = param(2*i); else errmsg = msprintf(gettext("%s: Unrecognized parameter name ''%s''."), "qpipoptmat", param(2*i-1)); error(errmsg) @@ -270,7 +270,39 @@ function [xopt,fopt,exitflag,output,lambda] = qpipoptmat (varargin) warnmsg = msprintf(gettext("%s: Ignoring initial guess of variables as it is not equal to the number of variables"), "qpipopt"); warning(warnmsg); end - + + //Check if the user gives a matrix instead of a vector + + if ((size(f,1)~=1)& (size(f,2)~=1)) then + errmsg = msprintf(gettext("%s: f should be a vector"), "qpipopt"); + error(errmsg); + end + + if (size(LB,1)~=1)& (size(LB,2)~=1) then + errmsg = msprintf(gettext("%s: Lower Bound should be a vector"), "qpipopt"); + error(errmsg); + end + + if (size(UB,1)~=1)& (size(UB,2)~=1) then + errmsg = msprintf(gettext("%s: Upper Bound should be a vector"), "qpipopt"); + error(errmsg); + end + + if (nbConInEq) then + if ((size(b,1)~=1)& (size(b,2)~=1)) then + errmsg = msprintf(gettext("%s: Constraint Lower Bound should be a vector"), "qpipopt"); + error(errmsg); + end + end + + if (nbConEq) then + if (size(beq,1)~=1)& (size(beq,2)~=1) then + errmsg = msprintf(gettext("%s: Constraint should be a vector"), "qpipopt"); + error(errmsg); + end + end + + //Converting it into ipopt format f = f'; @@ -296,6 +328,43 @@ function [xopt,fopt,exitflag,output,lambda] = qpipoptmat (varargin) lambda.upper = Zu; lambda.eqlin = lmbda(1:nbConEq); lambda.ineqlin = lmbda(nbConEq+1:nbCon); + + select status + + case 0 then + printf("\nOptimal Solution Found.\n"); + case 1 then + printf("\nMaximum Number of Iterations Exceeded. Output may not be optimal.\n"); + case 2 then + printf("\nMaximum CPU Time exceeded. Output may not be optimal.\n"); + case 3 then + printf("\nStop at Tiny Step\n"); + case 4 then + printf("\nSolved To Acceptable Level\n"); + case 5 then + printf("\nConverged to a point of local infeasibility.\n"); + case 6 then + printf("\nStopping optimization at current point as requested by user.\n"); + case 7 then + printf("\nFeasible point for square problem found.\n"); + case 8 then + printf("\nIterates divering; problem might be unbounded.\n"); + case 9 then + printf("\nRestoration Failed!\n"); + case 10 then + printf("\nError in step computation (regularization becomes too large?)!\n"); + case 12 then + printf("\nProblem has too few degrees of freedom.\n"); + case 13 then + printf("\nInvalid option thrown back by IPOpt\n"); + case 14 then + printf("\nNot enough memory.\n"); + case 15 then + printf("\nINTERNAL ERROR: Unknown SolverReturn value - Notify IPOPT Authors.\n"); + else + printf("\nInvalid status returned. Notify the Toolbox authors\n"); + break; + end endfunction diff --git a/macros/qpipoptmat.sci~ b/macros/qpipoptmat.sci~ deleted file mode 100644 index e29da8f..0000000 --- a/macros/qpipoptmat.sci~ +++ /dev/null @@ -1,265 +0,0 @@ -// Copyright (C) 2015 - IIT Bombay - FOSSEE -// -// Author: Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: harpreet.mertia@gmail.com -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt - - -function [xopt,fopt,exitflag,output,lambda] = qpipoptmat (varargin) - // Solves a linear quadratic problem. - // - // Calling Sequence - // x = qpipoptmat(H,f) - // x = qpipoptmat(H,f,A,b) - // x = qpipoptmat(H,f,A,b,Aeq,beq) - // x = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub) - // x = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0) - // x = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0,param) - // [xopt,fopt,exitflag,output,lamda] = qpipoptmat( ... ) - // - // Parameters - // H : a n x n matrix of doubles, where n is number of variables, represents coefficients of quadratic in the quadratic problem. - // f : a n x 1 matrix of doubles, where n is number of variables, represents coefficients of linear in the quadratic problem - // A : a m x n matrix of doubles, represents the linear coefficients in the inequality constraints - // b : a column vector of doubles, represents the linear coefficients in the inequality constraints - // Aeq : a meq x n matrix of doubles, represents the linear coefficients in the equality constraints - // beq : a vector of doubles, represents the linear coefficients in the equality constraints - // LB : a n x 1 matrix of doubles, where n is number of variables, contains lower bounds of the variables. - // UB : a n x 1 matrix of doubles, where n is number of variables, contains upper bounds of the variables. - // x0 : a m x 1 matrix of doubles, where m is number of constraints, contains initial guess of variables. - // param : a list containing the the parameters to be set. - // xopt : a nx1 matrix of doubles, the computed solution of the optimization problem. - // fopt : a 1x1 matrix of doubles, the function value at x. - // exitflag : Integer identifying the reason the algorithm terminated. - // output : Structure containing information about the optimization. - // lambda : Structure containing the Lagrange multipliers at the solution x (separated by constraint type). - // - // Description - // Search the minimum of a constrained linear quadratic optimization problem specified by : - // find the minimum of f(x) such that - // - // <latex> - // \begin{eqnarray} - // &\mbox{min}_{x} - // & 1/2*x'*H*x + f'*x \\ - // & \text{subject to} & A.x \leq b \\ - // & & Aeq.x \leq beq \\ - // & & lb \leq x \leq ub \\ - // \end{eqnarray} - // </latex> - // - // We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. The code has been written by Andreas Wächter and Carl Laird. - // - // Examples - // //Find x in R^6 such that: - // - // Aeq= [1,-1,1,0,3,1; - // -1,0,-3,-4,5,6; - // 2,5,3,0,1,0]; - // beq=[1; 2; 3]; - // A= [0,1,0,1,2,-1; - // -1,0,2,1,1,0]; - // b = [-1; 2.5]; - // lb=[-1000; -10000; 0; -1000; -1000; -1000]; - // ub=[10000; 100; 1.5; 100; 100; 1000]; - // x0 = repmat(0,6,1); - // param = list("MaxIter", 300, "CpuTime", 100); - // //and minimize 0.5*x'*Q*x + p'*x with - // f=[1; 2; 3; 4; 5; 6]; H=eye(6,6); - // [xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,[],param) - // clear H f A b Aeq beq lb ub; - // - // Examples - // //Find the value of x that minimize following function - // // f(x) = 0.5*x1^2 + x2^2 - x1*x2 - 2*x1 - 6*x2 - // // Subject to: - // // x1 + x2 ≤ 2 - // // –x1 + 2x2 ≤ 2 - // // 2x1 + x2 ≤ 3 - // // 0 ≤ x1, 0 ≤ x2. - // H = [1 -1; -1 2]; - // f = [-2; -6]; - // A = [1 1; -1 2; 2 1]; - // b = [2; 2; 3]; - // lb = [0; 0]; - // ub = [%inf; %inf]; - // [xopt,fopt,exitflag,output,lambda] = qpipoptmat(H,f,A,b,[],[],lb,ub) - // - // Authors - // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh - - -//To check the number of input and output argument - [lhs , rhs] = argn(); - -//To check the number of argument given by user - if ( rhs < 2 | rhs == 3 | rhs == 5 | rhs == 7 | rhs > 10 ) then - errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be in the set of [2 4 6 8 9 10]"), "qpipoptmat", rhs); - error(errmsg) - end - - H = varargin(1); - f = varargin(2); - nbVar = size(H,1); - - - if ( rhs<2 ) then - A = [] - b = [] - else - A = varargin(3); - b = varargin(4); - end - - if ( rhs<4 ) then - Aeq = [] - beq = [] - else - Aeq = varargin(5); - beq = varargin(6); - end - - if ( rhs<6 ) then - LB = repmat(-%inf,nbVar,1); - UB = repmat(%inf,nbVar,1); - else - LB = varargin(7); - UB = varargin(8); - end - - - if ( rhs<10 | size(varargin(9)) ==0 ) then - x0 = repmat(0,nbVar,1) - else - x0 = varargin(9); - end - - if ( rhs<11 ) then - param = list(); - else - param =varargin(10); - end - - - if (modulo(size(param),2)) then - errmsg = msprintf(gettext("%s: Size of parameters should be even"), "qpipoptmat"); - error(errmsg); - end - - - options = list(.. - "MaxIter" , [3000], ... - "CpuTime" , [600] ... - ); - - for i = 1:(size(param))/2 - - select param(2*i-1) - case "MaxIter" then - options(2*i-1) = param(2*i); - case "CpuTime" then - options(2*i-1) = param(2*i); - else - errmsg = msprintf(gettext("%s: Unrecognized parameter name ''%s''."), "qpipoptmat", param(2*i-1)); - error(errmsg) - end - end - - nbConInEq = size(A,1); - nbConEq = size(Aeq,1); - - //Checking the H matrix which needs to be a symmetric matrix - if ( H~=H') then - errmsg = msprintf(gettext("%s: H is not a symmetric matrix"), "qpipoptmat"); - error(errmsg); - end - - //Check the size of H which should equal to the number of variable - if ( size(H) ~= [nbVar nbVar]) then - errmsg = msprintf(gettext("%s: The Size of H is not equal to the number of variables"), "qpipoptmat"); - error(errmsg); - end - - //Check the size of f which should equal to the number of variable - if ( size(f,1) ~= [nbVar]) then - errmsg = msprintf(gettext("%s: The Size of f is not equal to the number of variables"), "qpipoptmat"); - error(errmsg); - end - - - //Check the size of inequality constraint which should be equal to the number of variables - if ( size(A,2) ~= nbVar & size(A,2) ~= 0) then - errmsg = msprintf(gettext("%s: The size of inequality constraints is not equal to the number of variables"), "qpipoptmat"); - error(errmsg); - end - - //Check the size of equality constraint which should be equal to the number of variables - if ( size(Aeq,2) ~= nbVar & size(Aeq,2) ~= 0 ) then - errmsg = msprintf(gettext("%s: The size of equality constraints is not equal to the number of variables"), "qpipoptmat"); - error(errmsg); - end - - - //Check the size of Lower Bound which should be equal to the number of variables - if ( size(LB,1) ~= nbVar) then - errmsg = msprintf(gettext("%s: The Lower Bound is not equal to the number of variables"), "qpipoptmat"); - error(errmsg); - end - - //Check the size of Upper Bound which should equal to the number of variables - if ( size(UB,1) ~= nbVar) then - errmsg = msprintf(gettext("%s: The Upper Bound is not equal to the number of variables"), "qpipoptmat"); - error(errmsg); - end - - //Check the size of constraints of Lower Bound which should equal to the number of constraints - if ( size(b,1) ~= nbConInEq & size(b,1) ~= 0) then - errmsg = msprintf(gettext("%s: The Lower Bound of inequality constraints is not equal to the number of constraints"), "qpipoptmat"); - error(errmsg); - end - - //Check the size of constraints of Upper Bound which should equal to the number of constraints - if ( size(beq,1) ~= nbConEq & size(beq,1) ~= 0) then - errmsg = msprintf(gettext("%s: The Upper Bound of equality constraints is not equal to the number of constraints"), "qpipoptmat"); - error(errmsg); - end - - //Check the size of initial of variables which should equal to the number of variables - if ( size(x0,1) ~= nbVar) then - errmsg = msprintf(gettext("%s: The initial guess of variables is not equal to the number of variables"), "qpipoptmat"); - error(errmsg); - end - - - //Converting it into ipopt format - f = f'; - LB = LB'; - UB = UB'; - x0 = x0'; - conMatrix = [Aeq;A]; - nbCon = size(conMatrix,1); - conLB = [beq; repmat(-%inf,nbConInEq,1)]'; - conUB = [beq;b]' ; - [xopt,fopt,status,iter,Zl,Zu,lmbda] = solveqp(nbVar,nbCon,H,f,conMatrix,conLB,conUB,LB,UB,x0,options); - - xopt = xopt'; - exitflag = status; - output = struct("Iterations" , []); - output.Iterations = iter; - lambda = struct("lower" , [], .. - "upper" , [], .. - "ineqlin" , [], .. - "eqlin" , []); - - lambda.lower = Zl; - lambda.upper = Zu; - lambda.eqlin = lmbda(1:nbConEq); - lambda.ineqlin = lmbda(nbConEq+1:nbCon); - - -endfunction diff --git a/macros/setOptions.sci~ b/macros/setOptions.sci~ deleted file mode 100644 index ef5c36c..0000000 --- a/macros/setOptions.sci~ +++ /dev/null @@ -1,40 +0,0 @@ -// Copyright (C) 2015 - IIT Bombay - FOSSEE -// -// Author: Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: harpreet.mertia@gmail.com -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt - -function setOptions(varargin) - - options = varargin(1); - nbOpt = size(options); - - - if (nbOpt~=0) then - for i = 1:(nbOpt/2) - - //Setting the parameters - - //Check if the given parameter is String - if (type(options(2*i)) == 10 ) then - sym_setStrParam(options(2*i - 1),options(2*i)); - - //Check if the given parameter is Double - elseif(type(options(2*i))==1) then - sym_setDblParam(options(2*i - 1),options(2*i)); - - //Check if the given parameter is Integer - elseif(type(options(2*i))==8) - sym_setIntParam(options(2*i - 1),options(2*i)); - end - - end - end - -endfunction - diff --git a/macros/symphony.bin b/macros/symphony.bin Binary files differindex 2ef2f57..d126d9f 100644 --- a/macros/symphony.bin +++ b/macros/symphony.bin diff --git a/macros/symphony.sci b/macros/symphony.sci index 4b11ae8..c5b1cdf 100644 --- a/macros/symphony.sci +++ b/macros/symphony.sci @@ -206,11 +206,11 @@ function [xopt,fopt,status,output] = symphony (varargin) UB = UB'; end - if (size(conLB,2)== [nbVar]) then + if (size(conLB,2)== [nbCon]) then conLB = conLB'; end - if (size(conUB,2)== [nbVar]) then + if (size(conUB,2)== [nbCon]) then conUB = conUB'; end @@ -277,6 +277,36 @@ function [xopt,fopt,status,output] = symphony (varargin) error(errmsg); end + //Check if the user gives a matrix instead of a vector + + if ((size(isInt,1)~=1)& (size(isInt,2)~=1)) then + errmsg = msprintf(gettext("%s: isInt should be a vector"), "qpipopt"); + error(errmsg); + end + + if (size(LB,1)~=1)& (size(LB,2)~=1) then + errmsg = msprintf(gettext("%s: Lower Bound should be a vector"), "qpipopt"); + error(errmsg); + end + + if (size(UB,1)~=1)& (size(UB,2)~=1) then + errmsg = msprintf(gettext("%s: Upper Bound should be a vector"), "qpipopt"); + error(errmsg); + end + + if (nbCon) then + if ((size(conLB,1)~=1)& (size(conLB,2)~=1)) then + errmsg = msprintf(gettext("%s: Constraint Lower Bound should be a vector"), "qpipopt"); + error(errmsg); + end + + if (size(conUB,1)~=1)& (size(conUB,2)~=1) then + errmsg = msprintf(gettext("%s: Constraint Upper Bound should be a vector"), "qpipopt"); + error(errmsg); + end + end + + LB = LB'; UB = UB'; isInt = isInt'; diff --git a/macros/symphony.sci~ b/macros/symphony.sci~ deleted file mode 100644 index 4b11ae8..0000000 --- a/macros/symphony.sci~ +++ /dev/null @@ -1,287 +0,0 @@ -// Copyright (C) 2015 - IIT Bombay - FOSSEE -// -// Author: Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: harpreet.mertia@gmail.com -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt - -function [xopt,fopt,status,output] = symphony (varargin) - // Solves a mixed integer linear programming constrained optimization problem. - // - // Calling Sequence - // xopt = symphony(nbVar,nbCon,objCoef,isInt,LB,UB,conMatrix,conLB,conUB) - // xopt = symphony(nbVar,nbCon,objCoef,isInt,LB,UB,conMatrix,conLB,conUB,objSense) - // xopt = symphony(nbVar,nbCon,objCoef,isInt,LB,UB,conMatrix,conLB,conUB,objSense,options) - // [xopt,fopt,status,output] = symphony( ... ) - // - // Parameters - // nbVar : a double, number of variables. - // nbCon : a double, number of constraints. - // objCoeff : a 1 x n matrix of doubles, where n is number of variables, represents coefficients of the variables in the objective. - // isInt : a vector of boolean, represents wether a variable is constrained to be an integer. - // LB : a vector of doubles, represents lower bounds of the variables. - // UB : a vector of doubles, represents upper bounds of the variables. - // conMatrix : a matrix of doubles, represents matrix representing the constraint matrix. - // conLB : a vector of doubles, represents lower bounds of the constraints. - // conUB : a vector of doubles, represents upper bounds of the constraints - // objSense : The sense (maximization/minimization) of the objective. Use 1(sym_minimize ) or -1 (sym_maximize) here. - // options : a a list containing the the parameters to be set. - // xopt : a vector of doubles, the computed solution of the optimization problem. - // fopt : a double, the function value at x. - // status : status flag from symphony. - // output : The output data structure contains detailed informations about the optimization process. - // - // Description - // Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by : - // find the minimum or maximum of f(x) such that - // - // <latex> - // \begin{eqnarray} - // &\mbox{min}_{x} - // & f(x) \\ - // & \text{subject to} & conLB \leq C(x) \leq conUB \\ - // & & lb \leq x \leq ub \\ - // \end{eqnarray} - // </latex> - // - // We are calling SYMPHONY written in C by gateway files for the actual computation. SYMPHONY was originally written by Ted Ralphs, Menal Guzelsoy and Ashutosh Mahajan. - // - // Examples - // //A basic case : - // // Objective function - // c = [350*5,330*3,310*4,280*6,500,450,400,100]'; - // // Lower Bound of variable - // lb = repmat(0,8,1); - // // Upper Bound of variables - // ub = [repmat(1,4,1);repmat(%inf,4,1)]; - // // Constraint Matrix - // conMatrix = [5,3,4,6,1,1,1,1; - // 5*0.05,3*0.04,4*0.05,6*0.03,0.08,0.07,0.06,0.03; - // 5*0.03,3*0.03,4*0.04,6*0.04,0.06,0.07,0.08,0.09;] - // // Lower Bound of constrains - // conlb = [ 25; 1.25; 1.25] - // // Upper Bound of constrains - // conub = [ 25; 1.25; 1.25] - // // Row Matrix for telling symphony that the is integer or not - // isInt = [repmat(%t,1,4) repmat(%f,1,4)]; - // xopt = [1 1 0 1 7.25 0 0.25 3.5] - // fopt = [8495] - // // Calling Symphony - // [x,f,status,output] = symphony(8,3,c,isInt,lb,ub,conMatrix,conlb,conub,1) - // - // Examples - // // An advanced case where we set some options in symphony - // // This problem is taken from - // // P.C.Chu and J.E.Beasley - // // "A genetic algorithm for the multidimensional knapsack problem", - // // Journal of Heuristics, vol. 4, 1998, pp63-86. - // // The problem to be solved is: - // // Max sum{j=1,...,n} p(j)x(j) - // // st sum{j=1,...,n} r(i,j)x(j) <= b(i) i=1,...,m - // // x(j)=0 or 1 - // // The function to be maximize i.e. P(j) - // p = [ 504 803 667 1103 834 585 811 856 690 832 846 813 868 793 .. - // 825 1002 860 615 540 797 616 660 707 866 647 746 1006 608 .. - // 877 900 573 788 484 853 942 630 591 630 640 1169 932 1034 .. - // 957 798 669 625 467 1051 552 717 654 388 559 555 1104 783 .. - // 959 668 507 855 986 831 821 825 868 852 832 828 799 686 .. - // 510 671 575 740 510 675 996 636 826 1022 1140 654 909 799 .. - // 1162 653 814 625 599 476 767 954 906 904 649 873 565 853 1008 632]'; - // //Constraint Matrix - // conMatrix = [ - // //Constraint 1 - // 42 41 523 215 819 551 69 193 582 375 367 478 162 898 .. - // 550 553 298 577 493 183 260 224 852 394 958 282 402 604 .. - // 164 308 218 61 273 772 191 117 276 877 415 873 902 465 .. - // 320 870 244 781 86 622 665 155 680 101 665 227 597 354 .. - // 597 79 162 998 849 136 112 751 735 884 71 449 266 420 .. - // 797 945 746 46 44 545 882 72 383 714 987 183 731 301 .. - // 718 91 109 567 708 507 983 808 766 615 554 282 995 946 651 298; - // //Constraint 2 - // 509 883 229 569 706 639 114 727 491 481 681 948 687 941 .. - // 350 253 573 40 124 384 660 951 739 329 146 593 658 816 .. - // 638 717 779 289 430 851 937 289 159 260 930 248 656 833 .. - // 892 60 278 741 297 967 86 249 354 614 836 290 893 857 .. - // 158 869 206 504 799 758 431 580 780 788 583 641 32 653 .. - // 252 709 129 368 440 314 287 854 460 594 512 239 719 751 .. - // 708 670 269 832 137 356 960 651 398 893 407 477 552 805 881 850; - // //Constraint 3 - // 806 361 199 781 596 669 957 358 259 888 319 751 275 177 .. - // 883 749 229 265 282 694 819 77 190 551 140 442 867 283 .. - // 137 359 445 58 440 192 485 744 844 969 50 833 57 877 .. - // 482 732 968 113 486 710 439 747 174 260 877 474 841 422 .. - // 280 684 330 910 791 322 404 403 519 148 948 414 894 147 .. - // 73 297 97 651 380 67 582 973 143 732 624 518 847 113 .. - // 382 97 905 398 859 4 142 110 11 213 398 173 106 331 254 447 ; - // //Constraint 4 - // 404 197 817 1000 44 307 39 659 46 334 448 599 931 776 .. - // 263 980 807 378 278 841 700 210 542 636 388 129 203 110 .. - // 817 502 657 804 662 989 585 645 113 436 610 948 919 115 .. - // 967 13 445 449 740 592 327 167 368 335 179 909 825 614 .. - // 987 350 179 415 821 525 774 283 427 275 659 392 73 896 .. - // 68 982 697 421 246 672 649 731 191 514 983 886 95 846 .. - // 689 206 417 14 735 267 822 977 302 687 118 990 323 993 525 322; - // //Constrain 5 - // 475 36 287 577 45 700 803 654 196 844 657 387 518 143 .. - // 515 335 942 701 332 803 265 922 908 139 995 845 487 100 .. - // 447 653 649 738 424 475 425 926 795 47 136 801 904 740 .. - // 768 460 76 660 500 915 897 25 716 557 72 696 653 933 .. - // 420 582 810 861 758 647 237 631 271 91 75 756 409 440 .. - // 483 336 765 637 981 980 202 35 594 689 602 76 767 693 .. - // 893 160 785 311 417 748 375 362 617 553 474 915 457 261 350 635 ; - // ]; - // nbCon = size(conMatrix,1) - // nbVar = size(conMatrix,2) - // // Lower Bound of variables - // lb = repmat(0,nbVar,1) - // // Upper Bound of variables - // ub = repmat(1,nbVar,1) - // // Row Matrix for telling symphony that the is integer or not - // isInt = repmat(%t,1,nbVar) - // // Lower Bound of constrains - // conLB=repmat(0,nbCon,1); - // // Upper Bound of constraints - // conUB=[11927 13727 11551 13056 13460 ]'; - // options = list("time_limit", 25); - // // The expected solution : - // // Output variables - // xopt = [0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 1 0 1 .. - // 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 .. - // 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0] - // // Optimal value - // fopt = [ 24381 ] - // // Calling Symphony - // [x,f,status,output] = symphony(nbVar,nbCon,p,isInt,lb,ub,conMatrix,conLB,conUB,-1,options) - // - // Authors - // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh - -//To check the number of input and output argument - [lhs , rhs] = argn(); - -//To check the number of argument given by user - if ( rhs < 9 | rhs > 11 ) then - errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be in the set [9 10 11]"), "Symphony", rhs); - error(errmsg) - end - - nbVar = varargin(1); - nbCon = varargin(2); - objCoef = varargin(3); - isInt = varargin(4); - LB = varargin(5); - UB = varargin(6); - conMatrix = varargin(7); - conLB = varargin(8); - conUB = varargin(9); - - if ( rhs<10 ) then - objSense = 1; - else - objSense = varargin(10); - end - - if (rhs<11|size(varargin(11))==0) then - options = list(); - else - options = varargin(11); - end - -// Check if the user gives row vector -// and Changing it to a column matrix - - if (size(isInt,2)== [nbVar]) then - isInt = isInt'; - end - - if (size(LB,2)== [nbVar]) then - LB = LB'; - end - - if (size(UB,2)== [nbVar]) then - UB = UB'; - end - - if (size(conLB,2)== [nbVar]) then - conLB = conLB'; - end - - if (size(conUB,2)== [nbVar]) then - conUB = conUB'; - end - - - if (size(objCoef,2)~=1) then - errmsg = msprintf(gettext("%s: Objective Coefficients should be a column matrix"), "Symphony"); - error(errmsg); - end - - if (size(objCoef,1)~=nbVar) then - errmsg = msprintf(gettext("%s: Number of variables in Objective Coefficients is not equal to number of variables given"), "Symphony"); - error(errmsg); - end - - //Check the size of isInt which should equal to the number of variables - if(size(isInt,1)~=nbVar) then - errmsg = msprintf(gettext("%s: The size of isInt is not equal to the number of variables"), "Symphony"); - error(errmsg); - end - - //Check the size of lower bound of inequality constraint which should equal to the number of constraints - if ( size(conLB,1) ~= nbCon) then - errmsg = msprintf(gettext("%s: The Lower Bound of constraint is not equal to the number of constraint"), "Symphony"); - error(errmsg); - end - - //Check the size of lower bound of inequality constraint which should equal to the number of constraints - if ( size(conUB,1) ~= nbCon) then - errmsg = msprintf(gettext("%s: The Upper Bound of constraint is not equal to the number of constraint"), "Symphony"); - error(errmsg); - end - - //Check the row of constraint which should equal to the number of constraints - if ( size(conMatrix,1) ~= nbCon) then - errmsg = msprintf(gettext("%s: The number of rows in constraint should be equal to the number of constraints"), "Symphony"); - error(errmsg); - end - - //Check the column of constraint which should equal to the number of variables - if ( size(conMatrix,2) ~= nbVar) then - errmsg = msprintf(gettext("%s: The number of columns in constraint should equal to the number of variables"), "Symphony"); - error(errmsg); - end - - //Check the size of Lower Bound which should equal to the number of variables - if ( size(LB,1) ~= nbVar) then - errmsg = msprintf(gettext("%s: The Lower Bound is not equal to the number of variables"), "Symphony"); - error(errmsg); - end - - //Check the size of Upper Bound which should equal to the number of variables - if ( size(UB,1) ~= nbVar) then - errmsg = msprintf(gettext("%s: The Upper Bound is not equal to the number of variables"), "Symphony"); - error(errmsg); - end - - if (type(options) ~= 15) then - errmsg = msprintf(gettext("%s: Options should be a list "), "Symphony"); - error(errmsg); - end - - if (modulo(size(options),2)) then - errmsg = msprintf(gettext("%s: Size of parameters should be even"), "Symphony"); - error(errmsg); - end - - LB = LB'; - UB = UB'; - isInt = isInt'; - objCoef = objCoef'; - - [xopt,fopt,status,output] = symphony_call(nbVar,nbCon,objCoef,isInt,LB,UB,conMatrix,conLB,conUB,objSense,options); - -endfunction diff --git a/macros/symphony_call.bin b/macros/symphony_call.bin Binary files differindex 5008236..53671fa 100644 --- a/macros/symphony_call.bin +++ b/macros/symphony_call.bin diff --git a/macros/symphony_call.sci b/macros/symphony_call.sci index c8323fc..cfe73ae 100644 --- a/macros/symphony_call.sci +++ b/macros/symphony_call.sci @@ -11,6 +11,11 @@ function [xopt,fopt,status,output] = symphony_call(nbVar,nbCon,objCoef,isInt,LB,UB,conMatrix,conLB,conUB,objSense,options) + xopt = []; + fopt = []; + status = []; + output = []; + //Opening Symphony environment sym_open(); @@ -27,14 +32,9 @@ function [xopt,fopt,status,output] = symphony_call(nbVar,nbCon,objCoef,isInt,LB, end op = sym_solve(); - disp(op); - xopt = []; - fopt = []; - status = []; - output = []; - - if (~op) then + status = sym_getStatus(); + if (status == 228 | status == 227 | status == 229 | status == 230 | status == 231 | status == 232 | status == 233) then xopt = sym_getVarSoln(); // Symphony gives a row matrix converting it to column matrix xopt = xopt'; @@ -46,7 +46,7 @@ function [xopt,fopt,status,output] = symphony_call(nbVar,nbCon,objCoef,isInt,LB, output = struct("Iterations" , []); - output.Iterations = sym_getIterCount(); + output.Iterations = sym_getIterCount(); endfunction diff --git a/macros/symphony_call.sci~ b/macros/symphony_call.sci~ deleted file mode 100644 index 057ba63..0000000 --- a/macros/symphony_call.sci~ +++ /dev/null @@ -1,52 +0,0 @@ -// Copyright (C) 2015 - IIT Bombay - FOSSEE -// -// Author: Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: harpreet.mertia@gmail.com -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt - -function [xopt,fopt,status,output] = symphony_call(nbVar,nbCon,objCoef,isInt,LB,UB,conMatrix,conLB,conUB,objSense,options) - - //Opening Symphony environment - sym_open(); - - //Setting Options for the Symphpony -// setOptions(options); - - //Choosing to launch basic or advanced version - if(~issparse(conMatrix)) then - sym_loadProblemBasic(nbVar,nbCon,LB,UB,objCoef,isInt,objSense,conMatrix,conLB,conUB); - else - // Changing to Constraint Matrix into sparse matrix - conMatrix_advanced=sparse(conMatrix); - sym_loadProblem(nbVar,nbCon,LB,UB,objCoef,isInt,objSense,conMatrix_advanced,conLB,conUB); - end - - op = sym_solve(); - disp(op); - - xopt = []; - fopt = []; - status = []; - output = []; - - if (~op) then - xopt = sym_getVarSoln(); - // Symphony gives a row matrix converting it to column matrix - xopt = xopt'; - - fopt = sym_getObjVal(); - end - - status = sym_getStatus(); - - output = struct("Iterations" , []); - - output.Iterations = sym_getIterCount(); - - -endfunction diff --git a/macros/symphonymat.bin b/macros/symphonymat.bin Binary files differindex 01460d6..95bba1a 100644 --- a/macros/symphonymat.bin +++ b/macros/symphonymat.bin diff --git a/macros/symphonymat.sci b/macros/symphonymat.sci index 87427e1..9e1ffaf 100644 --- a/macros/symphonymat.sci +++ b/macros/symphonymat.sci @@ -196,6 +196,20 @@ function [xopt,fopt,status,iter] = symphonymat (varargin) options = varargin(9); end +// Check if the user gives empty matrix + if (size(lb,2)==0) then + lb = repmat(-%inf,nbVar,1); + end + + if (size(intcon,2)==0) then + intcon = 0; + end + + if (size(ub,2)==0) then + ub = repmat(%inf,nbVar,1); + end + +// Calculating the size of equality and inequality constraints nbConInEq = size(A,1); nbConEq = size(Aeq,1); @@ -283,6 +297,37 @@ function [xopt,fopt,status,iter] = symphonymat (varargin) error(errmsg); end + //Check if the user gives a matrix instead of a vector + + if ((size(intcon,1)~=1)& (size(intcon,2)~=1)) then + errmsg = msprintf(gettext("%s: intcon should be a vector"), "symphonymat"); + error(errmsg); + end + + if (size(lb,1)~=1)& (size(lb,2)~=1) then + errmsg = msprintf(gettext("%s: Lower Bound should be a vector"), "symphonymat"); + error(errmsg); + end + + if (size(ub,1)~=1)& (size(ub,2)~=1) then + errmsg = msprintf(gettext("%s: Upper Bound should be a vector"), "symphonymat"); + error(errmsg); + end + + if (nbConInEq) then + if ((size(b,1)~=1)& (size(b,2)~=1)) then + errmsg = msprintf(gettext("%s: Constraint Lower Bound should be a vector"), "symphonymat"); + error(errmsg); + end + end + + if (nbConEq) then + if (size(beq,1)~=1)& (size(beq,2)~=1) then + errmsg = msprintf(gettext("%s: Constraint Upper Bound should be a vector"), "symphonymat"); + error(errmsg); + end + end + //Changing the inputs in symphony's format conMatrix = [A;Aeq] @@ -291,7 +336,9 @@ function [xopt,fopt,status,iter] = symphonymat (varargin) conUB = [b;beq] ; isInt = repmat(%f,1,nbVar); - for i=1:size(intcon,2) + // Changing intcon into column vector + intcon = intcon(:); + for i=1:size(intcon,1) isInt(intcon(i)) = %t end diff --git a/macros/symphonymat.sci~ b/macros/symphonymat.sci~ deleted file mode 100644 index 455dd67..0000000 --- a/macros/symphonymat.sci~ +++ /dev/null @@ -1,242 +0,0 @@ -// Copyright (C) 2015 - IIT Bombay - FOSSEE -// -// Author: Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: harpreet.mertia@gmail.com -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt - -function [xopt,fopt,status,iter] = symphonymat (varargin) - // Solves a mixed integer linear programming constrained optimization problem in intlinprog format. - // - // Calling Sequence - // xopt = symphonymat(f,intcon,A,b) - // xopt = symphonymat(f,intcon,A,b,Aeq,beq) - // xopt = symphonymat(f,intcon,A,b,Aeq,beq,lb,ub) - // xopt = symphonymat(f,intcon,A,b,Aeq,beq,lb,ub,options) - // [xopt,fopt,status,output] = symphonymat( ... ) - // - // Parameters - // f : a 1xn matrix of doubles, where n is number of variables, contains coefficients of the variables in the objective - // intcon : Vector of integer constraints, specified as a vector of positive integers. The values in intcon indicate the components of the decision variable x that are integer-valued. intcon has values from 1 through number of variable - // A : Linear inequality constraint matrix, specified as a matrix of doubles. A represents the linear coefficients in the constraints A*x ≤ b. A has size M-by-N, where M is the number of constraints and N is number of variables - // b : Linear inequality constraint vector, specified as a vector of doubles. b represents the constant vector in the constraints A*x ≤ b. b has length M, where A is M-by-N - // Aeq : Linear equality constraint matrix, specified as a matrix of doubles. Aeq represents the linear coefficients in the constraints Aeq*x = beq. Aeq has size Meq-by-N, where Meq is the number of constraints and N is number of variables - // beq : Linear equality constraint vector, specified as a vector of doubles. beq represents the constant vector in the constraints Aeq*x = beq. beq has length Meq, where Aeq is Meq-by-N. - // lb : Lower bounds, specified as a vector or array of doubles. lb represents the lower bounds elementwise in lb ≤ x ≤ ub. - // ub : Upper bounds, specified as a vector or array of doubles. ub represents the upper bounds elementwise in lb ≤ x ≤ ub. - // options : a list containing the the parameters to be set. - // xopt : a 1xn matrix of doubles, the computed solution of the optimization problem - // fopt : a 1x1 matrix of doubles, the function value at x - // output : The output data structure contains detailed informations about the optimization process. - // - // Description - // Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by : - // find the minimum or maximum of f(x) such that - // - // <latex> - // \begin{eqnarray} - // &\mbox{min}_{x} - // & f(x) \\ - // & \text{subject to} & conLB \leq C(x) \leq conUB \\ - // & & lb \leq x \leq ub \\ - // \end{eqnarray} - // </latex> - // - // We are calling SYMPHONY written in C by gateway files for the actual computation. SYMPHONY was originally written by Ted Ralphs, Menal Guzelsoy and Ashutosh Mahajan. - // - // Examples - // // Objective function - // c = [350*5,330*3,310*4,280*6,500,450,400,100] - // // Lower Bound of variable - // lb = repmat(0,1,8); - // // Upper Bound of variables - // ub = [repmat(1,1,4) repmat(%inf,1,4)]; - // // Constraint Matrix - // Aeq = [5,3,4,6,1,1,1,1; - // 5*0.05,3*0.04,4*0.05,6*0.03,0.08,0.07,0.06,0.03; - // 5*0.03,3*0.03,4*0.04,6*0.04,0.06,0.07,0.08,0.09;] - // beq = [ 25, 1.25, 1.25] - // intcon = [1 2 3 4]; - // // Calling Symphony - // [x,f,status,output] = symphonymat(c,intcon,[],[],Aeq,beq,lb,ub) - // - // Examples - // // An advanced case where we set some options in symphony - // // This problem is taken from - // // P.C.Chu and J.E.Beasley - // // "A genetic algorithm for the multidimensional knapsack problem", - // // Journal of Heuristics, vol. 4, 1998, pp63-86. - // // The problem to be solved is: - // // Max sum{j=1,...,n} p(j)x(j) - // // st sum{j=1,...,n} r(i,j)x(j) <= b(i) i=1,...,m - // // x(j)=0 or 1 - // // The function to be maximize i.e. P(j) - // objCoef = -1*[ 504 803 667 1103 834 585 811 856 690 832 846 813 868 793 .. - // 825 1002 860 615 540 797 616 660 707 866 647 746 1006 608 .. - // 877 900 573 788 484 853 942 630 591 630 640 1169 932 1034 .. - // 957 798 669 625 467 1051 552 717 654 388 559 555 1104 783 .. - // 959 668 507 855 986 831 821 825 868 852 832 828 799 686 .. - // 510 671 575 740 510 675 996 636 826 1022 1140 654 909 799 .. - // 1162 653 814 625 599 476 767 954 906 904 649 873 565 853 1008 632] - // //Constraint Matrix - // conMatrix = [ //Constraint 1 - // 42 41 523 215 819 551 69 193 582 375 367 478 162 898 .. - // 550 553 298 577 493 183 260 224 852 394 958 282 402 604 .. - // 164 308 218 61 273 772 191 117 276 877 415 873 902 465 .. - // 320 870 244 781 86 622 665 155 680 101 665 227 597 354 .. - // 597 79 162 998 849 136 112 751 735 884 71 449 266 420 .. - // 797 945 746 46 44 545 882 72 383 714 987 183 731 301 .. - // 718 91 109 567 708 507 983 808 766 615 554 282 995 946 651 298; - // //Constraint 2 - // 509 883 229 569 706 639 114 727 491 481 681 948 687 941 .. - // 350 253 573 40 124 384 660 951 739 329 146 593 658 816 .. - // 638 717 779 289 430 851 937 289 159 260 930 248 656 833 .. - // 892 60 278 741 297 967 86 249 354 614 836 290 893 857 .. - // 158 869 206 504 799 758 431 580 780 788 583 641 32 653 .. - // 252 709 129 368 440 314 287 854 460 594 512 239 719 751 .. - // 708 670 269 832 137 356 960 651 398 893 407 477 552 805 881 850; - // //Constraint 3 - // 806 361 199 781 596 669 957 358 259 888 319 751 275 177 .. - // 883 749 229 265 282 694 819 77 190 551 140 442 867 283 .. - // 137 359 445 58 440 192 485 744 844 969 50 833 57 877 .. - // 482 732 968 113 486 710 439 747 174 260 877 474 841 422 .. - // 280 684 330 910 791 322 404 403 519 148 948 414 894 147 .. - // 73 297 97 651 380 67 582 973 143 732 624 518 847 113 .. - // 382 97 905 398 859 4 142 110 11 213 398 173 106 331 254 447 ; - // //Constraint 4 - // 404 197 817 1000 44 307 39 659 46 334 448 599 931 776 .. - // 263 980 807 378 278 841 700 210 542 636 388 129 203 110 .. - // 817 502 657 804 662 989 585 645 113 436 610 948 919 115 .. - // 967 13 445 449 740 592 327 167 368 335 179 909 825 614 .. - // 987 350 179 415 821 525 774 283 427 275 659 392 73 896 .. - // 68 982 697 421 246 672 649 731 191 514 983 886 95 846 .. - // 689 206 417 14 735 267 822 977 302 687 118 990 323 993 525 322; - // //Constrain 5 - // 475 36 287 577 45 700 803 654 196 844 657 387 518 143 .. - // 515 335 942 701 332 803 265 922 908 139 995 845 487 100 .. - // 447 653 649 738 424 475 425 926 795 47 136 801 904 740 .. - // 768 460 76 660 500 915 897 25 716 557 72 696 653 933 .. - // 420 582 810 861 758 647 237 631 271 91 75 756 409 440 .. - // 483 336 765 637 981 980 202 35 594 689 602 76 767 693 .. - // 893 160 785 311 417 748 375 362 617 553 474 915 457 261 350 635 ; - // ]; - // nbVar = size(objCoef,2) - // conUB=[11927 13727 11551 13056 13460 ]; - // // Lower Bound of variables - // lb = repmat(0,1,nbVar) - // // Upper Bound of variables - // ub = repmat(1,1,nbVar) - // // Lower Bound of constrains - // intcon = [] - // for i = 1:nbVar - // intcon = [intcon i]; - // end - // options = list("time_limit", 25); - // // The expected solution : - // // Output variables - // xopt = [0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 1 0 1 .. - // 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 .. - // 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0] - // // Optimal value - // fopt = [ 24381 ] - // // Calling Symphony - // [x,f,status,output] = symphonymat(objCoef,intcon,conMatrix,conUB,[],[],lb,ub,options); - // - // Authors - // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh - - -//To check the number of input and output argument - [lhs , rhs] = argn(); - -//To check the number of argument given by user - if ( rhs < 4 | rhs == 5 | rhs == 7 | rhs > 9 ) then - errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be in the set [4 6 8 9]"), "Symphony", rhs); - error(errmsg) - end - - - objCoef = varargin(1) - intcon = varargin(2) - A = varargin(3) - b = varargin(4) - - nbVar = size(objCoef,2); - nbCon = size(A,1); - - if ( rhs<4 ) then - Aeq = [] - beq = [] - else - Aeq = varargin(5); - beq = varargin(6); - - if (size(Aeq,1)~=0) then - //Check the size of equality constraint which should equal to the number of inequality constraints - if ( size(Aeq,2) ~= nbVar) then - errmsg = msprintf(gettext("%s: The size of equality constraint is not equal to the number of variables"), "Symphony"); - error(errmsg); - end - - //Check the size of upper bound of inequality constraint which should equal to the number of constraints - if ( size(beq,2) ~= size(Aeq,1)) then - errmsg = msprintf(gettext("%s: The equality constraint upper bound is not equal to the number of equality constraint"), "Symphony"); - error(errmsg); - end - end - - end - - if ( rhs<6 ) then - lb = repmat(-%inf,1,nbVar); - ub = repmat(%inf,1,nbVar); - else - lb = varargin(7); - ub = varargin(8); - end - - if (rhs<8) then - options = list(); - else - options = varargin(9); - end - - -//Check the size of lower bound of inequality constraint which should equal to the number of constraints - if ( size(b,2) ~= size(A,1)) then - errmsg = msprintf(gettext("%s: The Lower Bound of inequality constraint is not equal to the number of constraint"), "Symphony"); - error(errmsg); - end - -//Check the size of Lower Bound which should equal to the number of variables - if ( size(lb,2) ~= nbVar) then - errmsg = msprintf(gettext("%s: The Lower Bound is not equal to the number of variables"), "Symphony"); - error(errmsg); - end - -//Check the size of Upper Bound which should equal to the number of variables - if ( size(ub,2) ~= nbVar) then - errmsg = msprintf(gettext("%s: The Upper Bound is not equal to the number of variables"), "Symphony"); - error(errmsg); - end - - //Changing the inputs in symphony's format - conMatrix = [A;Aeq] - nbCon = size(conMatrix,1); - conLB = [repmat(-%inf,1,size(A,1)), beq]'; - conUB = [b,beq]' ; - - isInt = repmat(%f,1,nbVar); - for i=1:size(intcon,2) - isInt(intcon(i)) = %t - end - - objSense = 1; - - [xopt,fopt,status,iter] = symphony_call(nbVar,nbCon,objCoef,isInt,lb,ub,conMatrix,conLB,conUB,objSense,options); - -endfunction diff --git a/sci_gateway/cpp/QuadNLP.hpp~ b/sci_gateway/cpp/QuadNLP.hpp~ deleted file mode 100644 index 5e1047f..0000000 --- a/sci_gateway/cpp/QuadNLP.hpp~ +++ /dev/null @@ -1,134 +0,0 @@ -/* - * Quadratic Programming Toolbox for Scilab using IPOPT library - * Authors : - Sai Kiran - Keyur Joshi - Iswarya - - - * Optimizing (minimizing) the quadratic objective function having any number of variables and linear constraints. - * -*/ - -#ifndef __QuadNLP_HPP__ -#define __QuadNLP_HPP__ - -#include "IpTNLP.hpp" -extern "C"{ -#include <sciprint.h> - -} -using namespace Ipopt; - -class QuadNLP : public TNLP -{ - private: - Index numVars_; // Number of variables. - - Index numConstr_; // Number of constraints. - - const Number *qMatrix_ = NULL; //qMatrix_ is a pointer to matrix of size numVars X numVars_ - // with coefficents of quadratic terms in objective function. - - const Number *lMatrix_ = NULL;//lMatrix_ is a pointer to matrix of size 1*numVars_ - // with coefficents of linear terms in objective function. - - const Number *conMatrix_ = NULL;//conMatrix_ is a pointer to matrix of size numConstr X numVars - // with coefficients of terms in a each objective in each row. - - const Number *conUB_= NULL; //conUB_ is a pointer to a matrix of size of 1*numConstr_ - // with upper bounds of all constraints. - - const Number *conLB_ = NULL; //conLB_ is a pointer to a matrix of size of 1*numConstr_ - // with lower bounds of all constraints. - - const Number *varUB_= NULL; //varUB_ is a pointer to a matrix of size of 1*numVar_ - // with upper bounds of all variables. - - const Number *varLB_= NULL; //varLB_ is a pointer to a matrix of size of 1*numVar_ - // with lower bounds of all variables. - - const Number *varGuess_= NULL; //varGuess_ is a pointer to a matrix of size of 1*numVar_ - // with initial guess of all variables. - - Number *finalX_= NULL; //finalX_ is a pointer to a matrix of size of 1*numVar_ - // with final value for the primal variables. - - Number *finalZl_= NULL; //finalZl_ is a pointer to a matrix of size of 1*numVar_ - // with final values for the lower bound multipliers - - Number *finalZu_= NULL; //finalZu_ is a pointer to a matrix of size of 1*numVar_ - // with final values for the upper bound multipliers - - Number *finalLambda_= NULL; //finalLambda_ is a pointer to a matrix of size of 1*numConstr_ - // with final values for the upper bound multipliers - - Number finalObjVal_; //finalObjVal_ is a scalar with the final value of the objective. - - int iter_; //Number of iteration. - - int status_; //Solver return status - - QuadNLP(const QuadNLP&); - QuadNLP& operator=(const QuadNLP&); - public: - /* - * Constructor - */ - QuadNLP(Index nV, Index nC, Number *qM, Number *lM, Number *cM, Number *cUB, Number *cLB, Number *vUB, Number *vLB): - numVars_(nV),numConstr_(nC),qMatrix_(qM),lMatrix_(lM),conMatrix_(cM),conUB_(cUB),conLB_(cLB),varUB_(vUB),varLB_(vLB),finalX_(0), finalZl_(0), finalZu_(0), finalObjVal_(1e20){ } - - - /* Go to : - - http://www.coin-or.org/Ipopt/documentation/node23.html#SECTION00053130000000000000 - For details about these below methods. - */ - virtual ~QuadNLP(); - virtual bool get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, - Index& nnz_h_lag, IndexStyleEnum& index_style); - virtual bool get_bounds_info(Index n, Number* x_l, Number* x_u, - Index m, Number* g_l, Number* g_u); - 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); - virtual bool eval_f(Index n, const Number* x, bool new_x, Number& obj_value); - virtual bool eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f); - virtual bool eval_g(Index n, const Number* x, bool new_x, Index m, Number* g); - virtual bool eval_jac_g(Index n, const Number* x, bool new_x, - Index m, Index nele_jac, Index* iRow, Index *jCol, - Number* values); - 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); - 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); - - const double * getX(); //Returns a pointer to a matrix of size of 1*numVar - // with final value for the primal variables. - - const double * getZu(); //Returns a pointer to a matrix of size of 1*numVars - // with final values for the upper bound multipliers - - const double * getZl(); //Returns a pointer to a matrix of size of 1*numVars - // with final values for the upper bound multipliers - - const double * getLambda(); //Returns a pointer to a matrix of size of 1*numConstr - // with final values for the constraint multipliers - - - double getObjVal(); //Returns the output of the final value of the objective. - - double iterCount(); //Returns the iteration count - - int returnStatus(); //Returns the status count - - -}; - -#endif __QuadNLP_HPP__ diff --git a/sci_gateway/cpp/README.rst~ b/sci_gateway/cpp/README.rst~ deleted file mode 100644 index e69de29..0000000 --- a/sci_gateway/cpp/README.rst~ +++ /dev/null diff --git a/sci_gateway/cpp/builder_gateway_cpp.sce~ b/sci_gateway/cpp/builder_gateway_cpp.sce~ deleted file mode 100644 index 225edd8..0000000 --- a/sci_gateway/cpp/builder_gateway_cpp.sce~ +++ /dev/null @@ -1,149 +0,0 @@ -// Copyright (C) 2015 - IIT Bombay - FOSSEE -// -// Author: Keyur Joshi, Sai Kiran, Iswarya and Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: harpreet.mertia@gmail.com -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt - -mode(-1) -lines(0) - -toolbox_title = "FAMOS"; - -[a, opt] = getversion(); -Version = opt(2); - -path_builder = get_absolute_file_path('builder_gateway_cpp.sce'); - -tools_path = path_builder + "../../thirdparty/linux/"; - -C_Flags=["-w -fpermissive -I"+tools_path+"include/coin -Wl,-rpath="+tools_path+"lib/"+Version+filesep()+" "] - -Linker_Flag = ["-L"+tools_path+"lib/"+Version+filesep()+"libSym"+" "+"-L"+tools_path+"lib/"+Version+filesep()+"libipopt" ] - - -//Name of All the Functions -Function_Names = [ - //for opening/closing environment and checking if it is open/close - "sym_open","sci_sym_open"; - "sym_close","sci_sym_close"; - "sym_isEnvActive","sci_sym_isEnvActive"; - - //run time parameters - "sym_resetParams","sci_sym_set_defaults"; - "sym_setIntParam","sci_sym_set_int_param"; - "sym_getIntParam","sci_sym_get_int_param"; - "sym_setDblParam","sci_sym_set_dbl_param"; - "sym_getDblParam","sci_sym_get_dbl_param"; - "sym_setStrParam","sci_sym_set_str_param"; - "sym_getStrParam","sci_sym_get_str_param"; - "sym_getInfinity","sci_sym_getInfinity"; - - //problem loaders - "sym_loadProblemBasic","sci_sym_loadProblemBasic"; - "sym_loadProblem","sci_sym_loadProblem"; - "sym_loadMPS","sci_sym_load_mps"; - - //basic data - "sym_getNumConstr","sci_sym_get_num_int"; - "sym_getNumVar","sci_sym_get_num_int"; - "sym_getNumElements","sci_sym_get_num_int"; - - //variable and objective data - "sym_isContinuous","sci_sym_isContinuous"; - "sym_isBinary","sci_sym_isBinary"; - "sym_isInteger","sci_sym_isInteger"; - "sym_setContinuous","sci_sym_set_continuous"; - "sym_setInteger","sci_sym_set_integer"; - "sym_getVarLower","sci_sym_get_dbl_arr"; - "sym_getVarUpper","sci_sym_get_dbl_arr"; - "sym_setVarLower","sci_sym_setVarBound"; - "sym_setVarUpper","sci_sym_setVarBound"; - "sym_getObjCoeff","sci_sym_get_dbl_arr"; - "sym_setObjCoeff","sci_sym_setObjCoeff"; - "sym_getObjSense","sci_sym_getObjSense"; - "sym_setObjSense","sci_sym_setObjSense"; - - //constraint data - "sym_getRhs","sci_sym_get_dbl_arr"; - "sym_getConstrRange","sci_sym_get_dbl_arr"; - "sym_getConstrLower","sci_sym_get_dbl_arr"; - "sym_getConstrUpper","sci_sym_get_dbl_arr"; - "sym_setConstrLower","sci_sym_setConstrBound"; - "sym_setConstrUpper","sci_sym_setConstrBound"; - "sym_setConstrType","sci_sym_setConstrType"; - "sym_getMatrix","sci_sym_get_matrix"; - "sym_getConstrSense","sci_sym_get_row_sense"; - - //add/remove variables and constraints - "sym_addConstr","sci_sym_addConstr"; - "sym_addVar","sci_sym_addVar"; - "sym_deleteVars","sci_sym_delete_cols"; - "sym_deleteConstrs","sci_sym_delete_rows"; - - //primal bound - "sym_getPrimalBound","sci_sym_getPrimalBound"; - "sym_setPrimalBound","sci_sym_setPrimalBound"; - - //set preliminary solution - "sym_setVarSoln","sci_sym_setColSoln"; - - //solve - "sym_solve","sci_sym_solve"; - - //post solve functions - "sym_getStatus","sci_sym_get_status"; - "sym_isOptimal","sci_sym_get_solver_status"; - "sym_isInfeasible","sci_sym_get_solver_status"; - "sym_isAbandoned","sci_sym_get_solver_status"; - "sym_isIterLimitReached","sci_sym_get_solver_status"; - "sym_isTimeLimitReached","sci_sym_get_solver_status"; - "sym_isTargetGapAchieved","sci_sym_get_solver_status"; - "sym_getVarSoln","sci_sym_getVarSoln"; - "sym_getObjVal","sci_sym_getObjVal"; - "sym_getIterCount","sci_sym_get_iteration_count"; - "sym_getConstrActivity","sci_sym_getRowActivity"; - - //QP function - "solveqp","sci_solveqp" - ]; - -//Name of all the files to be compiled -Files = [ - "globals.cpp", - "sci_iofunc.hpp", - "sci_iofunc.cpp", - "sci_sym_openclose.cpp", - "sci_solver_status_query_functions.cpp", - "sci_sym_solve.cpp", - "sci_sym_loadproblem.cpp", - "sci_sym_isenvactive.cpp", - "sci_sym_load_mps.cpp", - "sci_vartype.cpp", - "sci_sym_getinfinity.cpp", - "sci_sym_solution.cpp", - "sym_data_query_functions.cpp" - "sci_sym_set_variables.cpp", - "sci_sym_setobj.cpp", - "sci_sym_varbounds.cpp", - "sci_sym_rowmod.cpp", - "sci_sym_set_indices.cpp", - "sci_sym_addrowcol.cpp", - "sci_sym_primalbound.cpp", - "sci_sym_setcolsoln.cpp", - "sci_sym_getrowact.cpp", - "sci_sym_getobjsense.cpp", - "sci_sym_remove.cpp", - "sci_QuadNLP.cpp", - "QuadNLP.hpp", - "sci_ipopt.cpp" - - ] - -tbx_build_gateway(toolbox_title,Function_Names,Files,get_absolute_file_path("builder_gateway_cpp.sce"), [], Linker_Flag, C_Flags, [], "g++"); - -clear WITHOUT_AUTO_PUTLHSVAR toolbox_title Function_Names Files Linker_Flag C_Flags; diff --git a/sci_gateway/cpp/libFAMOS.so b/sci_gateway/cpp/libFAMOS.so Binary files differindex 4a210ee..ec45532 100755 --- a/sci_gateway/cpp/libFAMOS.so +++ b/sci_gateway/cpp/libFAMOS.so diff --git a/sci_gateway/cpp/sci_QuadNLP.cpp b/sci_gateway/cpp/sci_QuadNLP.cpp index 99987a2..50e8109 100644 --- a/sci_gateway/cpp/sci_QuadNLP.cpp +++ b/sci_gateway/cpp/sci_QuadNLP.cpp @@ -209,10 +209,11 @@ void QuadNLP::finalize_solution(SolverReturn status, finalLambda_[i] = lambda[i]; } - iter_ = ip_data->iter_count(); finalObjVal_ = obj_value; status_ = status; - + if (status_ == 0 | status_ == 1 | status_ == 2){ + iter_ = ip_data->iter_count(); + } } const double * QuadNLP::getX() diff --git a/sci_gateway/cpp/sci_QuadNLP.cpp~ b/sci_gateway/cpp/sci_QuadNLP.cpp~ deleted file mode 100644 index 99987a2..0000000 --- a/sci_gateway/cpp/sci_QuadNLP.cpp~ +++ /dev/null @@ -1,253 +0,0 @@ -/* - * Quadratic Programming Toolbox for Scilab using IPOPT library - * Authors : - Sai Kiran - Keyur Joshi - Iswarya - */ - -#include "QuadNLP.hpp" -#include "IpIpoptData.hpp" - -extern "C"{ -#include <api_scilab.h> -#include <Scierror.h> -#include <BOOL.h> -#include <localization.h> -#include <sciprint.h> - - -double x_static,i, *op_obj_x = NULL,*op_obj_value = NULL; - -using namespace Ipopt; - -QuadNLP::~QuadNLP() - { - free(finalX_); - free(finalZl_); - free(finalZu_);} - -//get NLP info such as number of variables,constraints,no.of elements in jacobian and hessian to allocate memory -bool QuadNLP::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, Index& nnz_h_lag, IndexStyleEnum& index_style){ - n=numVars_; // Number of variables - m=numConstr_; // Number of constraints - nnz_jac_g = n*m; // No. of elements in Jacobian of constraints - nnz_h_lag = n*(n+1)/2; // No. of elements in lower traingle of Hessian of the Lagrangian. - index_style=C_STYLE; // Index style of matrices - return true; - } - -//get variable and constraint bound info -bool QuadNLP::get_bounds_info(Index n, Number* x_l, Number* x_u, Index m, Number* g_l, Number* g_u){ - - unsigned int i; - for(i=0;i<n;i++){ - x_l[i]=varLB_[i]; - x_u[i]=varUB_[i]; - } - - for(i=0;i<m;i++){ - g_l[i]=conLB_[i]; - g_u[i]=conUB_[i]; - } - return true; - } - -//get value of objective function at vector x -bool QuadNLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value){ - unsigned int i,j; - obj_value=0; - - for (i=0;i<=n;i++){ - for (j=0;j<=n;j++){ - obj_value+=0.5*x[i]*x[j]*qMatrix_[n*i+j]; - } - obj_value+=x[i]*lMatrix_[i]; - } - return true; - } - -//get value of gradient of objective function at vector x. -bool QuadNLP::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f){ - unsigned int i,j; - for(i=0;i<n;i++) - { - grad_f[i]=lMatrix_[i]; - for(j=0;j<n;j++) - { - grad_f[i]+=(qMatrix_[n*i+j])*x[j]; - } - } - return true; -} - -//Get the values of constraints at vector x. -bool QuadNLP::eval_g(Index n, const Number* x, bool new_x, Index m, Number* g){ - unsigned int i,j; - for(i=0;i<m;i++) - { - g[i]=0; - for(j=0;j<n;j++) - { - g[i]+=x[j]*conMatrix_[i+j*m]; - } - } - return true; -} - -// This method sets initial values for required vectors . For now we are assuming 0 to all values. -bool QuadNLP::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){ - if (init_x == true){ //we need to set initial values for vector x - for (Index var=0;var<n;var++) - x[var]=varGuess_[var];//initialize with 0 or we can change. - } - - if (init_z == true){ //we need to provide initial values for vector bound multipliers - for (Index var=0;var<n;++var){ - z_L[var]=0.0; //initialize with 0 or we can change. - z_U[var]=0.0;//initialize with 0 or we can change. - } - } - - if (init_lambda == true){ //we need to provide initial values for lambda values. - for (Index var=0;var<m;++var){ - lambda[var]=0.0; //initialize with 0 or we can change. - } - } - - return true; - } -/* Return either the sparsity structure of the Jacobian of the constraints, or the values for the Jacobian of the constraints at the point x. - -*/ -bool QuadNLP::eval_jac_g(Index n, const Number* x, bool new_x, - Index m, Index nele_jac, Index* iRow, Index *jCol, - Number* values){ - - //It asked for structure of jacobian. - if (values==NULL){ //Structure of jacobian (full structure) - int index=0; - for (int var=0;var<m;++var)//no. of constraints - for (int flag=0;flag<n;++flag){//no. of variables - iRow[index]=var; - jCol[index]=flag; - index++; - } - } - //It asked for values - else { - int index=0; - for (int var=0;var<m;++var) - for (int flag=0;flag<n;++flag) - values[index++]=conMatrix_[var+flag*m]; - } - return true; - } - -/* - * Return either the sparsity structure of the Hessian of the Lagrangian, - * or the values of the Hessian of the Lagrangian for the given values for - * x,lambda,obj_factor. -*/ -bool QuadNLP::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){ - - if (values==NULL){ - Index idx=0; - for (Index row = 0; row < n; row++) { - for (Index col = 0; col <= row; col++) { - iRow[idx] = row; - jCol[idx] = col; - idx++; - } - } - } - else { - Index index=0; - for (Index row=0;row < n;++row){ - for (Index col=0; col <= row; ++col){ - values[index++]=obj_factor*(qMatrix_[n*row+col]); - } - } - } - return true; - } - - -void QuadNLP::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){ - - finalX_ = (double*)malloc(sizeof(double) * numVars_ * 1); - for (Index i=0; i<n; i++) - { - finalX_[i] = x[i]; - } - - finalZl_ = (double*)malloc(sizeof(double) * numVars_ * 1); - for (Index i=0; i<n; i++) - { - finalZl_[i] = z_L[i]; - } - - finalZu_ = (double*)malloc(sizeof(double) * numVars_ * 1); - for (Index i=0; i<n; i++) - { - finalZu_[i] = z_U[i]; - } - - finalLambda_ = (double*)malloc(sizeof(double) * numConstr_ * 1); - for (Index i=0; i<m; i++) - { - finalLambda_[i] = lambda[i]; - } - - iter_ = ip_data->iter_count(); - finalObjVal_ = obj_value; - status_ = status; - - } - - const double * QuadNLP::getX() - { - return finalX_; - } - - const double * QuadNLP::getZl() - { - return finalZl_; - } - - const double * QuadNLP::getZu() - { - return finalZu_; - } - - const double * QuadNLP::getLambda() - { - return finalLambda_; - } - - double QuadNLP::getObjVal() - { - return finalObjVal_; - } - - double QuadNLP::iterCount() - { - return (double)iter_; - } - - int QuadNLP::returnStatus() - { - return status_; - } - -} diff --git a/sci_gateway/cpp/sci_ipopt.cpp b/sci_gateway/cpp/sci_ipopt.cpp index 4168488..98ed153 100644 --- a/sci_gateway/cpp/sci_ipopt.cpp +++ b/sci_gateway/cpp/sci_ipopt.cpp @@ -324,46 +324,45 @@ int sci_solveqp(char *fname) return 0; } - using namespace Ipopt; - - SmartPtr<QuadNLP> Prob = new QuadNLP(nVars,nCons,QItems,PItems,ConItems,conUB,conLB,varUB,varLB,init_guess); - SmartPtr<IpoptApplication> app = IpoptApplicationFactory(); - app->RethrowNonIpoptException(true); - - // Change some options - // Note: The following choices are only examples, they might not be - // suitable for your optimization problem. - app->Options()->SetNumericValue("tol", 1e-7); - app->Options()->SetIntegerValue("max_iter", (int)*max_iter); - app->Options()->SetNumericValue("max_cpu_time", *cpu_time); - app->Options()->SetStringValue("mu_strategy", "adaptive"); - - // Indicates whether all equality constraints are linear - app->Options()->SetStringValue("jac_c_constant", "yes"); - // Indicates whether all inequality constraints are linear - app->Options()->SetStringValue("jac_d_constant", "yes"); - // Indicates whether the problem is a quadratic problem - app->Options()->SetStringValue("hessian_constant", "yes"); + using namespace Ipopt; + + SmartPtr<QuadNLP> Prob = new QuadNLP(nVars,nCons,QItems,PItems,ConItems,conUB,conLB,varUB,varLB,init_guess); + SmartPtr<IpoptApplication> app = IpoptApplicationFactory(); + app->RethrowNonIpoptException(true); + + // Change some options + // Note: The following choices are only examples, they might not be + // suitable for your optimization problem. + app->Options()->SetNumericValue("tol", 1e-7); + app->Options()->SetIntegerValue("max_iter", (int)*max_iter); + app->Options()->SetNumericValue("max_cpu_time", *cpu_time); + app->Options()->SetStringValue("mu_strategy", "adaptive"); + + // Indicates whether all equality constraints are linear + app->Options()->SetStringValue("jac_c_constant", "yes"); + // Indicates whether all inequality constraints are linear + app->Options()->SetStringValue("jac_d_constant", "yes"); + // Indicates whether the problem is a quadratic problem + app->Options()->SetStringValue("hessian_constant", "yes"); + + // Initialize the IpoptApplication and process the options + ApplicationReturnStatus status; + status = app->Initialize(); + if (status != Solve_Succeeded) { + sciprint("\n*** Error during initialization!\n"); + return (int) status; + } + // Ask Ipopt to solve the problem - // Initialize the IpoptApplication and process the options - ApplicationReturnStatus status; - status = app->Initialize(); - if (status != Solve_Succeeded) { - sciprint("\n*** Error during initialization!\n"); - return0toScilab(); - return (int) status; - } - // Ask Ipopt to solve the problem - - status = app->OptimizeTNLP(Prob); + status = app->OptimizeTNLP(Prob); + + int stats = Prob->returnStatus(); + if (stats == 0 | stats == 1 | stats == 2){ double *fX = Prob->getX(); double ObjVal = Prob->getObjVal(); - double *Zl = Prob->getZl(); - double *Zu = Prob->getZu(); - double *Lambda = Prob->getLambda(); double iteration = Prob->iterCount(); - int stats = Prob->returnStatus(); + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, nVars, fX); if (sciErr.iErr) { @@ -392,41 +391,121 @@ int sci_solveqp(char *fname) return 0; } - sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 5, 1, nVars, Zl); + AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; + AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; + AssignOutputVariable(pvApiCtx, 3) = nbInputArgument(pvApiCtx) + 3; + AssignOutputVariable(pvApiCtx, 4) = nbInputArgument(pvApiCtx) + 4; + } + + else + { + double *fX = NULL; + double ObjVal = 0; + int stats = Prob->returnStatus(); + double iteration = 0; + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 0, 0, fX); if (sciErr.iErr) { printError(&sciErr, 0); return 0; } - sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 6, 1, nVars, Zu); + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 2,1,1,&ObjVal); if (sciErr.iErr) { printError(&sciErr, 0); return 0; } - - sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 7, 1, nCons, Lambda); + + sciErr = createMatrixOfInteger32(pvApiCtx, nbInputArgument(pvApiCtx) + 3,1,1,&stats); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 4,1,1,&iteration); if (sciErr.iErr) { printError(&sciErr, 0); return 0; } - AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; AssignOutputVariable(pvApiCtx, 3) = nbInputArgument(pvApiCtx) + 3; AssignOutputVariable(pvApiCtx, 4) = nbInputArgument(pvApiCtx) + 4; + } + + + if(stats == 0){ + + double *Zl = Prob->getZl(); + double *Zu = Prob->getZu(); + double *Lambda = Prob->getLambda(); + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 5, 1, nVars, Zl); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 6, 1, nVars, Zu); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 7, 1, nCons, Lambda); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + AssignOutputVariable(pvApiCtx, 5) = nbInputArgument(pvApiCtx) + 5; AssignOutputVariable(pvApiCtx, 6) = nbInputArgument(pvApiCtx) + 6; - AssignOutputVariable(pvApiCtx, 7) = nbInputArgument(pvApiCtx) + 7; + AssignOutputVariable(pvApiCtx, 7) = nbInputArgument(pvApiCtx) + 7; + } - // As the SmartPtrs go out of scope, the reference count - // will be decremented and the objects will automatically - // be deleted. + else{ + double *Zl = NULL; + double *Zu = NULL; + double *Lambda = NULL; + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 5, 0, 0, Zl); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 6, 0, 0, Zu); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 7, 0, 0, Lambda); + if (sciErr.iErr) + { + printError(&sciErr, 0); + return 0; + } + + AssignOutputVariable(pvApiCtx, 5) = nbInputArgument(pvApiCtx) + 5; + AssignOutputVariable(pvApiCtx, 6) = nbInputArgument(pvApiCtx) + 6; + AssignOutputVariable(pvApiCtx, 7) = nbInputArgument(pvApiCtx) + 7; + } + // As the SmartPtrs go out of scope, the reference count + // will be decremented and the objects will automatically + // be deleted. + return 0; } diff --git a/sci_gateway/cpp/sci_ipopt.cpp~ b/sci_gateway/cpp/sci_ipopt.cpp~ deleted file mode 100644 index 8d62b21..0000000 --- a/sci_gateway/cpp/sci_ipopt.cpp~ +++ /dev/null @@ -1,409 +0,0 @@ -/* - * Quadratic Programming Toolbox for Scilab using IPOPT library - * Authors : - Sai Kiran - Keyur Joshi - Iswarya - */ - - -#include "sci_iofunc.hpp" -#include "IpIpoptApplication.hpp" -#include "QuadNLP.hpp" - -extern "C"{ -#include <api_scilab.h> -#include <Scierror.h> -#include <BOOL.h> -#include <localization.h> -#include <sciprint.h> - -int j; -double *op_x, *op_obj,*p; - -bool readSparse(int arg,int *iRows,int *iCols,int *iNbItem,int** piNbItemRow, int** piColPos, double** pdblReal){ - SciErr sciErr; - int* piAddr = NULL; - int iType = 0; - int iRet = 0; - sciErr = getVarAddressFromPosition(pvApiCtx, arg, &piAddr); - if(sciErr.iErr) { - printError(&sciErr, 0); - return false; - } - sciprint("\ndone\n"); - if(isSparseType(pvApiCtx, piAddr)){ - sciprint("done\n"); - sciErr =getSparseMatrix(pvApiCtx, piAddr, iRows, iCols, iNbItem, piNbItemRow, piColPos, pdblReal); - if(sciErr.iErr) { - printError(&sciErr, 0); - return false; - } - } - - else { - sciprint("\nSparse matrix required\n"); - return false; - } - return true; - } - -int sci_solveqp(char *fname) -{ - - CheckInputArgument(pvApiCtx, 10, 10); // We need total 10 input arguments. - CheckOutputArgument(pvApiCtx, 7, 7); - - // Error management variable - SciErr sciErr; - int retVal=0, *piAddressVarQ = NULL,*piAddressVarP = NULL,*piAddressVarCM = NULL,*piAddressVarCUB = NULL,*piAddressVarCLB = NULL, *piAddressVarLB = NULL,*piAddressVarUB = NULL,*piAddressVarG = NULL; - double *QItems=NULL,*PItems=NULL,*ConItems=NULL,*conUB=NULL,*conLB=NULL,*varUB=NULL,*varLB=NULL,*init_guess = NULL,x,f,iter; - static unsigned int nVars = 0,nCons = 0; - unsigned int temp1 = 0,temp2 = 0; - - - ////////// Manage the input argument ////////// - - - //Number of Variables - getIntFromScilab(1,&nVars); - - //Number of Constraints - getIntFromScilab(2,&nCons); - - temp1 = nVars; - temp2 = nCons; - - //Q matrix from scilab - /* get Address of inputs */ - sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddressVarQ); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - /* Check that the first input argument is a real matrix (and not complex) */ - if ( !isDoubleType(pvApiCtx, piAddressVarQ) || isVarComplex(pvApiCtx, piAddressVarQ) ) - { - Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 3); - return 0; - } - - /* get matrix */ - sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarQ, &temp1, &temp1, &QItems); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - //P matrix from scilab - /* get Address of inputs */ - sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddressVarP); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - /* Check that the first input argument is a real matrix (and not complex) */ - if ( !isDoubleType(pvApiCtx, piAddressVarP) || isVarComplex(pvApiCtx, piAddressVarP) ) - { - Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 4); - return 0; - } - - temp1 = 1; - temp2 = nVars; - /* get matrix */ - sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarP, &temp1,&temp2, &PItems); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - if (nCons!=0) - { - //conMatrix matrix from scilab - /* get Address of inputs */ - sciErr = getVarAddressFromPosition(pvApiCtx, 5, &piAddressVarCM); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - /* Check that the first input argument is a real matrix (and not complex) */ - if ( !isDoubleType(pvApiCtx, piAddressVarCM) || isVarComplex(pvApiCtx, piAddressVarCM) ) - { - Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 5); - return 0; - } - temp1 = nCons; - temp2 = nVars; - - /* get matrix */ - sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarCM,&temp1, &temp2, &ConItems); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - - //conLB matrix from scilab - /* get Address of inputs */ - sciErr = getVarAddressFromPosition(pvApiCtx, 6, &piAddressVarCLB); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - /* Check that the first input argument is a real matrix (and not complex) */ - if ( !isDoubleType(pvApiCtx, piAddressVarCLB) || isVarComplex(pvApiCtx, piAddressVarCLB) ) - { - Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 6); - return 0; - } - temp1 = nCons; - temp2 = 1; - - /* get matrix */ - sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarCLB,&temp1, &temp2, &conLB); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - //conUB matrix from scilab - /* get Address of inputs */ - sciErr = getVarAddressFromPosition(pvApiCtx, 7, &piAddressVarCUB); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - /* Check that the first input argument is a real matrix (and not complex) */ - if ( !isDoubleType(pvApiCtx, piAddressVarCUB) || isVarComplex(pvApiCtx, piAddressVarCUB) ) - { - Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 7); - return 0; - } - - temp1 = nCons; - temp2 = 1; - - /* get matrix */ - sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarCUB,&temp1, &temp2, &conUB); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - } - - //varLB matrix from scilab - /* get Address of inputs */ - sciErr = getVarAddressFromPosition(pvApiCtx, 8, &piAddressVarLB); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - /* Check that the first input argument is a real matrix (and not complex) */ - if ( !isDoubleType(pvApiCtx, piAddressVarLB) || isVarComplex(pvApiCtx, piAddressVarLB) ) - { - Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 8); - return 0; - } - temp1 = 1; - temp2 = nVars; - - /* get matrix */ - sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarLB, &temp1,&temp2, &varLB); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - //varUB matrix from scilab - /* get Address of inputs */ - sciErr = getVarAddressFromPosition(pvApiCtx, 9, &piAddressVarUB); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - /* Check that the first input argument is a real matrix (and not complex) */ - if ( !isDoubleType(pvApiCtx, piAddressVarUB) || isVarComplex(pvApiCtx, piAddressVarUB) ) - { - Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 9); - return 0; - } - - temp1 = 1; - temp2 = nVars; - - /* get matrix */ - sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarUB, &temp1,&temp2, &varUB); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - /* get matrix */ - sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarLB, &temp1,&temp2, &varLB); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - //Initial Value of variables from scilab - /* get Address of inputs */ - sciErr = getVarAddressFromPosition(pvApiCtx, 10, &piAddressVarG); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - /* Check that the first input argument is a real matrix (and not complex) */ - if ( !isDoubleType(pvApiCtx, piAddressVarG) || isVarComplex(pvApiCtx, piAddressVarG) ) - { - Scierror(999, "%s: Wrong type for input argument #%d: A real matrix expected.\n", fname, 10); - return 0; - } - - temp1 = 1; - temp2 = nVars; - - /* get matrix */ - sciErr = getMatrixOfDouble(pvApiCtx, piAddressVarG, &temp1,&temp2, &init_guess); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - using namespace Ipopt; - - SmartPtr<QuadNLP> Prob = new QuadNLP(nVars,nCons,QItems,PItems,ConItems,conUB,conLB,varUB,varLB); - SmartPtr<IpoptApplication> app = IpoptApplicationFactory(); - app->RethrowNonIpoptException(true); - - // Change some options - // Note: The following choices are only examples, they might not be - // suitable for your optimization problem. - app->Options()->SetNumericValue("tol", 1e-7); - app->Options()->SetStringValue("mu_strategy", "adaptive"); - - // Indicates whether all equality constraints are linear - app->Options()->SetStringValue("jac_c_constant", "yes"); - // Indicates whether all inequality constraints are linear - app->Options()->SetStringValue("jac_d_constant", "yes"); - // Indicates whether the problem is a quadratic problem - app->Options()->SetStringValue("hessian_constant", "yes"); - - // Initialize the IpoptApplication and process the options - ApplicationReturnStatus status; - status = app->Initialize(); - if (status != Solve_Succeeded) { - sciprint("\n*** Error during initialization!\n"); - return0toScilab(); - return (int) status; - } - // Ask Ipopt to solve the problem - - status = app->OptimizeTNLP(Prob); - - double *fX = Prob->getX(); - double ObjVal = Prob->getObjVal(); - double *Zl = Prob->getZl(); - double *Zu = Prob->getZu(); - double *Lambda = Prob->getLambda(); - double iteration = Prob->iterCount(); - int stats = Prob->returnStatus(); - sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, nVars, fX); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 2,1,1,&ObjVal); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - sciErr = createMatrixOfInteger32(pvApiCtx, nbInputArgument(pvApiCtx) + 3,1,1,&stats); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 4,1,1,&iteration); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 5, 1, nVars, Zl); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 6, 1, nVars, Zu); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 7, 1, nCons, Lambda); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 0; - } - - - AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1; - AssignOutputVariable(pvApiCtx, 2) = nbInputArgument(pvApiCtx) + 2; - AssignOutputVariable(pvApiCtx, 3) = nbInputArgument(pvApiCtx) + 3; - AssignOutputVariable(pvApiCtx, 4) = nbInputArgument(pvApiCtx) + 4; - AssignOutputVariable(pvApiCtx, 5) = nbInputArgument(pvApiCtx) + 5; - AssignOutputVariable(pvApiCtx, 6) = nbInputArgument(pvApiCtx) + 6; - AssignOutputVariable(pvApiCtx, 7) = nbInputArgument(pvApiCtx) + 7; - - // As the SmartPtrs go out of scope, the reference count - // will be decremented and the objects will automatically - // be deleted. - - - return 0; - } - -} - -/* -hessian_constan -jacobian _constant - -j_s_d constant : yes -*/ - diff --git a/sci_gateway/cpp/sci_sym_solve.cpp~ b/sci_gateway/cpp/sci_sym_solve.cpp~ deleted file mode 100644 index 4abb268..0000000 --- a/sci_gateway/cpp/sci_sym_solve.cpp~ +++ /dev/null @@ -1,49 +0,0 @@ -/* - * Implementation Symphony Tool Box for Scilab - * Contains sym_solve function - * Author : Sai Kiran - */ - -#include <symphony.h> -#include <sci_iofunc.hpp> -extern sym_environment* global_sym_env;//defined in globals.cpp - -extern "C" { -#include <api_scilab.h> -#include <Scierror.h> -#include <BOOL.h> -#include <localization.h> -#include <sciprint.h> -#include <stdio.h> -int process_ret_val(int); - -int sci_sym_solve(char *fname, unsigned long fname_len){ - - int status=0; - - //check whether we have no input and one output argument or not - CheckInputArgument(pvApiCtx, 0, 0) ;//no input argument - CheckOutputArgument(pvApiCtx, 1, 1) ;//one output argument - - // Check environment - if(global_sym_env==NULL) - sciprint("Error: Symphony environment is not initialized.\n"); - else {// There is an environment opened - double time_limit = -1.0; - status = sym_get_dbl_param(global_sym_env,"time_limit",&time_limit); - - if (status == FUNCTION_TERMINATED_NORMALLY) { - if ( time_limit < 0.0 ) - sciprint("\nNote: There is no limit on time.\n"); - else sciprint("\nNote: Time limit has been set to %lf.\n",time_limit); - status=process_ret_val(sym_solve(global_sym_env));// Call function - } - else { - sciprint("\nUnable to read time limit.\n"); - status = 1; //Error state - } - } - // Return result to scilab - return returnDoubleToScilab(status); - } -} diff --git a/tests/unit_tests/README.rst~ b/tests/unit_tests/README.rst~ deleted file mode 100644 index e69de29..0000000 --- a/tests/unit_tests/README.rst~ +++ /dev/null diff --git a/tests/unit_tests/qpipopt_base.tst~ b/tests/unit_tests/qpipopt_base.tst~ deleted file mode 100644 index 9de0d6b..0000000 --- a/tests/unit_tests/qpipopt_base.tst~ +++ /dev/null @@ -1,76 +0,0 @@ -// Copyright (C) 2015 - IIT Bombay - FOSSEE -// -// Author: Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: harpreet.mertia@gmail.com -// -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt - -// <-- JVM NOT MANDATORY --> -// <-- ENGLISH IMPOSED --> - - -// -// assert_close -- -// Returns 1 if the two real matrices computed and expected are close, -// i.e. if the relative distance between computed and expected is lesser than epsilon. -// Arguments -// computed, expected : the two matrices to compare -// epsilon : a small number -// -function flag = assert_close ( computed, expected, epsilon ) - if expected==0.0 then - shift = norm(computed-expected); - else - shift = norm(computed-expected)/norm(expected); - end -// if shift < epsilon then -// flag = 1; -// else -// flag = 0; -// end -// if flag <> 1 then pause,end - flag = assert_checktrue ( shift < epsilon ); -endfunction -// -// assert_equal -- -// Returns 1 if the two real matrices computed and expected are equal. -// Arguments -// computed, expected : the two matrices to compare -// epsilon : a small number -// -//function flag = assert_equal ( computed , expected ) -// if computed==expected then -// flag = 1; -// else -// flag = 0; -// end -// if flag <> 1 then pause,end -//endfunction - -///Find the value of x that minimize following function -// f(x) = 0.5*x1^2 + x2^2 - x1*x2 - 2*x1 - 6*x2 -// Subject to: -// x1 + x2 ≤ 2 -// –x1 + 2x2 ≤ 2 -// 2x1 + x2 ≤ 3 -// 0 ≤ x1, 0 ≤ x2. -Q = [1 -1; -1 2]; -p = [-2; -6]; -conMatrix = [1 1; -1 2; 2 1]; -conUB = [2; 2; 3]; -conLB = [-%inf; -%inf; -%inf]; -lb = [0; 0]; -ub = [%inf; %inf]; -nbVar = 2; -nbCon = 3; -[xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,Q,p,lb,ub,conMatrix,conLB,conUB) - -assert_close ( x , [0.6666667 1.3333333]' , 1.e-7 ); -assert_close ( f , [ - 8.2222223] , 1.e-7 ); - -assert_checkequal( exitflag , 0 ); diff --git a/tests/unit_tests/qpipoptmat_base .tst~ b/tests/unit_tests/qpipoptmat_base .tst~ deleted file mode 100644 index 9de0d6b..0000000 --- a/tests/unit_tests/qpipoptmat_base .tst~ +++ /dev/null @@ -1,76 +0,0 @@ -// Copyright (C) 2015 - IIT Bombay - FOSSEE -// -// Author: Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: harpreet.mertia@gmail.com -// -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt - -// <-- JVM NOT MANDATORY --> -// <-- ENGLISH IMPOSED --> - - -// -// assert_close -- -// Returns 1 if the two real matrices computed and expected are close, -// i.e. if the relative distance between computed and expected is lesser than epsilon. -// Arguments -// computed, expected : the two matrices to compare -// epsilon : a small number -// -function flag = assert_close ( computed, expected, epsilon ) - if expected==0.0 then - shift = norm(computed-expected); - else - shift = norm(computed-expected)/norm(expected); - end -// if shift < epsilon then -// flag = 1; -// else -// flag = 0; -// end -// if flag <> 1 then pause,end - flag = assert_checktrue ( shift < epsilon ); -endfunction -// -// assert_equal -- -// Returns 1 if the two real matrices computed and expected are equal. -// Arguments -// computed, expected : the two matrices to compare -// epsilon : a small number -// -//function flag = assert_equal ( computed , expected ) -// if computed==expected then -// flag = 1; -// else -// flag = 0; -// end -// if flag <> 1 then pause,end -//endfunction - -///Find the value of x that minimize following function -// f(x) = 0.5*x1^2 + x2^2 - x1*x2 - 2*x1 - 6*x2 -// Subject to: -// x1 + x2 ≤ 2 -// –x1 + 2x2 ≤ 2 -// 2x1 + x2 ≤ 3 -// 0 ≤ x1, 0 ≤ x2. -Q = [1 -1; -1 2]; -p = [-2; -6]; -conMatrix = [1 1; -1 2; 2 1]; -conUB = [2; 2; 3]; -conLB = [-%inf; -%inf; -%inf]; -lb = [0; 0]; -ub = [%inf; %inf]; -nbVar = 2; -nbCon = 3; -[xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,Q,p,lb,ub,conMatrix,conLB,conUB) - -assert_close ( x , [0.6666667 1.3333333]' , 1.e-7 ); -assert_close ( f , [ - 8.2222223] , 1.e-7 ); - -assert_checkequal( exitflag , 0 ); |