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| author | Harpreet | 2015-12-31 16:03:57 +0530 |
|---|---|---|
| committer | Harpreet | 2015-12-31 16:03:57 +0530 |
| commit | d5356061fbd3a9b3052dee25bd9c82c375c42e22 (patch) | |
| tree | 72a37d5161eb0f4b895513c46c68e031d1200520 /macros/symphonymat.sci | |
| parent | eb9ca1191c94059cd7adcf69805906c809fe9712 (diff) | |
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Macros example updated
Diffstat (limited to 'macros/symphonymat.sci')
| -rw-r--r-- | macros/symphonymat.sci | 295 |
1 files changed, 147 insertions, 148 deletions
diff --git a/macros/symphonymat.sci b/macros/symphonymat.sci index 2c0c18d..67e64c5 100644 --- a/macros/symphonymat.sci +++ b/macros/symphonymat.sci @@ -1,162 +1,162 @@ // 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 +// Author: Harpreet Singh +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in function [xopt,fopt,status,iter] = symphonymat (varargin) - // Solves a mixed integer linear programming constrained optimization problem in intlinprog format. - // - // Calling Sequence - // xopt = symphonymat(c,intcon,A,b) - // xopt = symphonymat(c,intcon,A,b,Aeq,beq) - // xopt = symphonymat(c,intcon,A,b,Aeq,beq,lb,ub) - // xopt = symphonymat(c,intcon,A,b,Aeq,beq,lb,ub,options) - // [xopt,fopt,status,output] = symphonymat( ... ) - // - // Parameters - // c : a vector of double, 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 double. A represents the linear coefficients in the constraints A*x ≤ b. A has the size where columns equals to the number of variables. - // b : Linear inequality constraint vector, specified as a vector of double. b represents the constant vector in the constraints A*x ≤ b. b has size equals to the number of rows in A. - // Aeq : Linear equality constraint matrix, specified as a matrix of double. Aeq represents the linear coefficients in the constraints Aeq*x = beq. Aeq has the size where columns equals to the number of variables. - // beq : Linear equality constraint vector, specified as a vector of double. beq represents the constant vector in the constraints Aeq*x = beq. beq has size equals to the number of rows in Aeq. - // lb : Lower bounds, specified as a vector or array of double. lb represents the lower bounds elementwise in lb ≤ x ≤ ub. - // ub : Upper bounds, specified as a vector or array of double. ub represents the upper bounds elementwise in lb ≤ x ≤ ub. - // options : a list containing the the parameters to be set. - // xopt : a vector of double, the computed solution of the optimization problem. - // fopt : a double, the function value at x - // status : status flag from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded. - // output : The output data structure contains detailed information about the optimization process. This version only contains number of iterations. - // - // Description - // Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by : - // find the minimum or maximum of C'⋅x such that - // - // <latex> - // \begin{eqnarray} - // &\mbox{min}_{x} - // & C^T⋅x \\ - // & \text{subject to} & A⋅x \leq b \\ - // & & Aeq⋅x = beq \\ - // & & lb \leq x \leq ub \\ - // & & x_i \in \!\, \mathbb{Z}, i \in \!\, I - // \end{eqnarray} - // </latex> - // - // The routine calls SYMPHONY written in C by gateway files for the actual computation. - // - // 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) - // // Press ENTER to continue - // - // 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) - // c = -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 - // A = [ //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(c,1) - // b=[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(c,intcon,A,b,[],[],lb,ub,options); - // Authors - // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh + // Solves a mixed integer linear programming constrained optimization problem in intlinprog format. + // + // Calling Sequence + // xopt = symphonymat(c,intcon,A,b) + // xopt = symphonymat(c,intcon,A,b,Aeq,beq) + // xopt = symphonymat(c,intcon,A,b,Aeq,beq,lb,ub) + // xopt = symphonymat(c,intcon,A,b,Aeq,beq,lb,ub,options) + // [xopt,fopt,status,output] = symphonymat( ... ) + // + // Parameters + // c : a vector of double, 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 double. A represents the linear coefficients in the constraints A*x ≤ b. A has the size where columns equals to the number of variables. + // b : Linear inequality constraint vector, specified as a vector of double. b represents the constant vector in the constraints A*x ≤ b. b has size equals to the number of rows in A. + // Aeq : Linear equality constraint matrix, specified as a matrix of double. Aeq represents the linear coefficients in the constraints Aeq*x = beq. Aeq has the size where columns equals to the number of variables. + // beq : Linear equality constraint vector, specified as a vector of double. beq represents the constant vector in the constraints Aeq*x = beq. beq has size equals to the number of rows in Aeq. + // lb : Lower bounds, specified as a vector or array of double. lb represents the lower bounds elementwise in lb ≤ x ≤ ub. + // ub : Upper bounds, specified as a vector or array of double. ub represents the upper bounds elementwise in lb ≤ x ≤ ub. + // options : a list containing the the parameters to be set. + // xopt : a vector of double, the computed solution of the optimization problem. + // fopt : a double, the function value at x + // status : status flag returned from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded. + // output : The output data structure contains detailed information about the optimization process. This version only contains number of iterations. + // + // Description + // Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by : + // + // <latex> + // \begin{eqnarray} + // &\mbox{min}_{x} + // & C^T⋅x \\ + // & \text{subject to} & A⋅x \leq b \\ + // & & Aeq⋅x = beq \\ + // & & lb \leq x \leq ub \\ + // & & x_i \in \!\, \mathbb{Z}, i \in \!\, I + // \end{eqnarray} + // </latex> + // + // The routine calls SYMPHONY written in C by gateway files for the actual computation. + // + // Examples + // // Objective function + // // Reference: Westerberg, Carl-Henrik, Bengt Bjorklund, and Eskil Hultman. "An application of mixed integer programming in a Swedish steel mill." Interfaces 7, no. 2 (1977): 39-43. + // 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) + // // Press ENTER to continue + // + // 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) + // c = -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 + // A = [ //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(c,1) + // b=[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(c,intcon,A,b,[],[],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 input and output argument + [lhs , rhs] = argn(); -//To check the number of argument given by user + //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); @@ -171,7 +171,6 @@ function [xopt,fopt,status,iter] = symphonymat (varargin) lb = []; ub = []; - c = varargin(1) intcon = varargin(2) A = varargin(3) |