Help and Symphony equivalent to intlinprog

7 files changed, 269 insertions, 67 deletions

diff --git a/macros/setOptions.bin b/macros/setOptions.bin
index f591286..c0bb197 100644
--- a/macros/setOptions.bin
+++ b/macros/setOptions.bin
diff --git a/macros/setOptions.sci b/macros/setOptions.sci
index 9d1be61..4fe4ac1 100644
--- a/macros/setOptions.sci
+++ b/macros/setOptions.sci
@@ -14,9 +14,6 @@ function setOptions(varagin)
     options = varagin(1);
     nbOpt = size(options,2);
     
-
-    
-    
     value = strtod(options)
 
     if (nbOpt~=0) then
diff --git a/macros/symphony.bin b/macros/symphony.bin
index d096d76..3d3d03a 100644
--- a/macros/symphony.bin
+++ b/macros/symphony.bin
diff --git a/macros/symphony.sci b/macros/symphony.sci
index 9b74898..01c93e1 100644
--- a/macros/symphony.sci
+++ b/macros/symphony.sci
@@ -10,54 +10,154 @@
 // http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
 
 function [xopt,fopt,iter] = 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,iter] = symphony( ... )
   //   
   //   Parameters
-  //   
-  //   nbVar = a 1 x 1 matrix of doubles, number of variables
-  //   nbCon = a 1 x 1 matrix of doubles, number of constraints
-  //   objCoeff = a 1 x n matrix of doubles, where n is number of variables, contains coefficients of the variables in the objective 
-  //   isInt = a 1 x n matrix of boolean, where n is number of variables, representing wether a variable is constrained to be an integer 
-  //   LB = a 1 x n matrix of doubles, where n is number of variables, contains lower bounds of the variables. Bound can be negative infinity
-  //   UB = a 1 x n matrix of doubles, where n is number of variables, contains upper bounds of the variables. Bound can be infinity
-  //   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
-  //   objSense = The sense (maximization/minimization) of the objective. Use 1(sym_minimize ) or -1 (sym_maximize) here
-  
-  //   xopt = a 1xn matrix of doubles, the computed solution of the optimization problem
-  //   fopt = a 1x1 matrix of doubles, the function value at x
-  //   iter = a 1x1 matrix of doubles, contains the number od iterations done by symphony
+  //   nbVar : a 1 x 1 matrix of doubles, number of variables
+  //   nbCon : a 1 x 1 matrix of doubles, number of constraints
+  //   objCoeff : a 1 x n matrix of doubles, where n is number of variables, contains coefficients of the variables in the objective 
+  //   isInt : a 1 x n matrix of boolean, where n is number of variables, representing wether a variable is constrained to be an integer 
+  //   LB : a 1 x n matrix of doubles, where n is number of variables, contains lower bounds of the variables. Bound can be negative infinity
+  //   UB : a 1 x n matrix of doubles, where n is number of variables, contains upper bounds of the variables. Bound can be infinity
+  //   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
+  //   objSense : The sense (maximization/minimization) of the objective. Use 1(sym_minimize ) or -1 (sym_maximize) here
+  //   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
+  //   iter : a 1x1 matrix of doubles, contains the number od iterations done by symphony
   //   
   //   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 \geq C(x) \leq conUB \\
-  //    & & & lb \geq x \leq ub \\
+  //    &\mbox{min}_{x}
+  //    & f(x) \\
+  //    & \text{subject to} & conLB \geq C(x) \leq conUB \\
+  //    & & lb \geq x \leq ub \\
   //    \end{eqnarray}
   //   </latex>
   //   
   //   
-  //   
-  //   
-  //   
-
-
+  //
+  //   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(0,1,8);
+  //    // Upper Bound of variables
+  //    ub = [repmat(1,1,4) repmat(%inf,1,4)];
+  //    // 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,iter] = 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(0,1,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 = ["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,iter]= 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();
 	
@@ -123,3 +223,4 @@ function [xopt,fopt,iter] = symphony (varargin)
    [xopt,fopt,iter] = 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
index 27d7f4a..49ff7cb 100644
--- a/macros/symphony_call.bin
+++ b/macros/symphony_call.bin
diff --git a/macros/symphony_mat.bin b/macros/symphony_mat.bin
index fdaf7e6..21ebf8c 100644
--- a/macros/symphony_mat.bin
+++ b/macros/symphony_mat.bin
diff --git a/macros/symphony_mat.sci b/macros/symphony_mat.sci
index 377fe90..a0fa895 100644
--- a/macros/symphony_mat.sci
+++ b/macros/symphony_mat.sci
@@ -10,8 +10,6 @@
 // http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
 
 function [xopt,fopt,iter] = symphony_mat (varargin)
-    
-
   // Solves a mixed integer linear programming constrained optimization problem.
   //
   //   Calling Sequence
@@ -22,36 +20,135 @@ function [xopt,fopt,iter] = symphony_mat (varargin)
   //   [xopt,fopt,iter] = symphony_mat( ... )
   //   
   //   Parameters
-  //   f = a nx1 matrix of doubles, 
-  //   intcon = 
-  //   A = 
-  //   b = 
-  //   Aeq = 
-  //   beq = 
-  //   lb = 
-  //   ub = 
-  //   options = 
-  //   
-  //   xopt = a 1xn matrix of doubles, the computed solution of the optimization problem
-  //   fopt = a 1x1 matrix of doubles, the function value at x
-  //   iter = a 1x1 matrix of doubles, contains the number od iterations done by symphony
+  //   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
+  //   iter : a 1x1 matrix of doubles, contains the number od iterations done by symphony
   //   
   //   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 \geq C(x) \leq conUB \\
-  //    & & & lb \geq x \leq ub \\
+  //   \begin{eqnarray}
+  //   \mbox{min}_{x}    & f(x) \\
+  //   \mbox{subject to} & c(x) \leq 0 \\
+  //                      & c_{eq}(x) = 0 \\
+  //                      & Ax \leq b \\
+  //                      & A_{eq} x = b_{eq} \\
+  //                      & lb \leq x \leq ub
   //    \end{eqnarray}
-  //   </latex>
+  //    </latex>
+  //
+  // 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();
 	
@@ -67,7 +164,7 @@ function [xopt,fopt,iter] = symphony_mat (varargin)
    A = varargin(3)
    b = varargin(4)
    
-   nbVar = size(f,2);
+   nbVar = size(objCoef,2);
    nbCon = size(A,1);
    
    if ( rhs<4 ) then
@@ -77,18 +174,20 @@ function [xopt,fopt,iter] = symphony_mat (varargin)
       Aeq = varargin(5);
       beq = varargin(6);
       
-      //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
+      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
       
-      //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
    
    if ( rhs<6 ) then
@@ -130,6 +229,11 @@ function [xopt,fopt,iter] = symphony_mat (varargin)
     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,iter] = symphony_call(nbVar,nbCon,objCoef,isInt,lb,ub,conMatrix,conLB,conUB,objSense,options);