diff options
Diffstat (limited to 'newstructure/macros/cbcintlinprog.sci')
| -rw-r--r-- | newstructure/macros/cbcintlinprog.sci | 234 |
1 files changed, 234 insertions, 0 deletions
diff --git a/newstructure/macros/cbcintlinprog.sci b/newstructure/macros/cbcintlinprog.sci new file mode 100644 index 0000000..00e129f --- /dev/null +++ b/newstructure/macros/cbcintlinprog.sci @@ -0,0 +1,234 @@ +// Copyright (C) 2016 - IIT Bombay - FOSSEE +// +// 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: Pranav Deshpande and Akshay Miterani +// Organization: FOSSEE, IIT Bombay +// Email: toolbox@scilab.in + +function [xopt,fopt,exitflag,output] = cbcintlinprog (varargin) + // Solves a mixed integer linear programming constrained optimization problem in intlinprog format. + // + // Calling Sequence + // xopt = cbcintlinprog(c,intcon,A,b) + // xopt = cbcintlinprog(c,intcon,A,b,Aeq,beq) + // xopt = cbcintlinprog(c,intcon,A,b,Aeq,beq,lb,ub) + // xopt = cbcintlinprog(c,intcon,A,b,Aeq,beq,lb,ub,options) + // xopt = cbcintlinprog('path_to_mps_file') + // xopt = cbcintlinprog('path_to_mps_file',options) + // [xopt,fopt,status,output] = cbcintlinprog( ... ) + // + // 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 : a matrix of double, represents the linear coefficients in the inequality constraints A⋅x ≤ b. + // b : a vector of double, represents the linear coefficients in the inequality constraints A⋅x ≤ b. + // Aeq : a matrix of double, represents the linear coefficients in the equality constraints Aeq⋅x = beq. + // beq : a vector of double, represents the linear coefficients in the equality constraints Aeq⋅x = beq. + // 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 parameters to be set. + // xopt : a vector of double, the computed solution of the optimization problem. + // fopt : a double, the value of the function at x. + // status : status flag returned from symphony. See below for details. + // output : The output data structure contains detailed information about the optimization process. See below for details. + // + // 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 \!\, intcon\\ + // \end{eqnarray} + // </latex> + // + // 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] = cbcintlinprog(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; + // //Constraint 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('MaxTime', 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 cbc + // [x,f,status,output] = cbcintlinprog(c,intcon,A,b,[],[],lb,ub,options); + // Authors + // Akshay Miterani and Pranav Deshpande + + if(type(varargin(1))==1) then + + //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]"), "cbcintlinprog", rhs); + error(errmsg); + end + + c = []; + intcon = []; + A = []; + b = []; + Aeq = []; + beq = []; + lb = []; + ub = []; + options = list(); + + c = varargin(1) + intcon = varargin(2) + A = varargin(3) + b = varargin(4) + + if(size(c,2) == 0) then + errmsg = msprintf(gettext("%s: Cannot determine the number of variables because input objective coefficients is empty"),"cbcintlinprog"); + error(errmsg); + end + + if (size(c,2)~=1) then + errmsg = msprintf(gettext("%s: Objective Coefficients should be a column matrix"), "cbcintlinprog"); + error(errmsg); + end + + nbVar = size(c,1); + + if ( rhs<5 ) then + Aeq = [] + beq = [] + else + Aeq = varargin(5); + beq = varargin(6); + end + + if ( rhs<7 ) then + lb = repmat(-%inf,1,nbVar); + ub = repmat(%inf,1,nbVar); + else + lb = varargin(7); + ub = varargin(8); + end + + if (rhs<9|size(varargin(9))==0) then + options = list(); + else + options = varargin(9); + end + [xopt,fopt,exitflag,output]=cbcmatrixintlinprog(c,intcon,A,b,Aeq,beq,lb,ub,options); + elseif(type(varargin(1))==10) then + + [lhs , rhs] = argn(); + + //To check the number of argument given by user + if ( rhs < 1 | rhs > 2) then + errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be in the set of [1 2]"),"cbcintlinprog",rhs); + error(errmsg) + end + mpsFile = varargin(1); + if ( rhs<2 | size(varargin(2)) ==0 ) then + param = list(); + else + param =varargin(2); + end + + [xopt,fopt,exitflag,output]=cbcmpsintlinprog(mpsFile,param); + end + +endfunction |