From 6cc967755bb135d656fba0523b4eb206581492ca Mon Sep 17 00:00:00 2001
From: Harpreet
Date: Fri, 4 Dec 2015 10:00:51 +0530
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-// 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] = symphony_mat (varargin)
- // Solves a mixed integer linear programming constrained optimization problem in intlinprog format.
- //
- // Calling Sequence
- // xopt = symphony_mat(f,intcon,A,b)
- // xopt = symphony_mat(f,intcon,A,b,Aeq,beq)
- // xopt = symphony_mat(f,intcon,A,b,Aeq,beq,lb,ub)
- // xopt = symphony_mat(f,intcon,A,b,Aeq,beq,lb,ub,options)
- // [xopt,fopt,status,output] = symphony_mat( ... )
- //
- // 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 1xq marix of string, provided to set the paramters in symphony
- // 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,iter] = symphony_mat(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
- // // 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);
- //
- // 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<9) then
- options = [];
- 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
--
cgit