Symphony
+-
-
- Symphony Toolbox
-
- sym_addConstr — Add a new constraint +
- symphony — Solves a mixed integer linear programming constrained optimization problem. + + +
- symphony_mat — Solves a mixed integer linear programming constrained optimization problem. + +
- Symphony Gateway Functions
+
- sym_addConstr — Add a new constraint
- sym_addVar — Add a new variable @@ -261,6 +266,9 @@
- sym_solve — To solve the currently loaded MILP problem from scratch.
diff --git a/help/en_US/scilab_en_US_help/index.html~ b/help/en_US/scilab_en_US_help/index.html~ index 3aaf941..e31c03b 100644 --- a/help/en_US/scilab_en_US_help/index.html~ +++ b/help/en_US/scilab_en_US_help/index.html~ @@ -28,12 +28,17 @@
-Symphony Toolbox
--
+
- Symphony Toolbox
-
- sym_addConstr — Add a new constraint +
- Symphony Gateway Functions + +
- symphony — Solves a mixed integer linear programming constrained optimization problem. + + +
- symphony_mat — Solves a mixed integer linear programming constrained optimization problem. +
- sym_addConstr — Add a new constraint
- sym_addVar — Add a new variable @@ -261,6 +266,9 @@
- sym_solve — To solve the currently loaded MILP problem from scratch.
- sym_addConstr — Add a new constraint @@ -270,7 +270,7 @@
- sym_addConstr — Add a new constraint + + + +
- sym_addVar — Add a new variable + + + +
- sym_close — Close the Symphony environment + + + +
- sym_deleteConstrs — This routine is used to delete rows from the original constraint matrix. + + + +
- sym_deleteVars — This routine is used to delete columns from the original problem description. + + + +
- sym_getConstrActivity — Get the activity of the constraints in the solution + + + +
- sym_getConstrLower — To get the lower bounds of the constraints. + + + +
- sym_getConstrRange — To to get the constraint ranges. + + + +
- sym_getConstrSense — To get the row senses. + + + +
- sym_getConstrUpper — To get the upper bounds of the constraints. + + + +
- sym_getDblParam — This routine is used to get the value of a double type parameter. + + + +
- sym_getInfinity — Get Symphony's infinity value + + + +
- sym_getIntParam — This routine is used to get the value of an integer type parameter. + + + +
- sym_getIterCount — To get the number of the analyzed nodes of the branching tree after solving the problem. + + + +
- sym_getMatrix — To get the constraint matrix. + + + +
- sym_getNumConstr — To get the number of the constraints of the current problem. + + + +
- sym_getNumElements — To get the number of non-zero entries of the constraint matrix of the current problem. + + + +
- sym_getNumVar — To get the number of the variables of the current problem. + + + +
- sym_getObjCoeff — To get the objective vector. + + + +
- sym_getObjSense — Get the objective sense + + + +
- sym_getObjVal — Get the optimized objective value + + + +
- sym_getPrimalBound — Get the primal bound of the problem + + + +
- sym_getRhs — To to get the right hand side vector(column vector). + + + +
- sym_getStatus — To get status of the problem solver. + + + +
- sym_getStrParam — This routine is used to get the value of a string type parameter. + + + +
- sym_getVarLower — To get the lower bounds of the variables. + + + +
- sym_getVarSoln — Get the solution for the problem + + + +
- sym_getVarUpper — To get the upper bounds of the variables. + + + +
- sym_isAbandoned — To check whether the problem was abandoned for some reason. + + + +
- sym_isBinary — Check if a variable is constrained to be binary + + + +
- sym_isContinuous — Check if a variable is continuous + + + +
- sym_isEnvActive — Check if Symphony environment is active + + + +
- sym_isInfeasible — To check whether the problem was proven to be infeasible. + + + +
- sym_isInteger — Check if a variable is constrained to be an integer + + + +
- sym_isIterLimitReached — To know whether the iteration limit (node limit) was reached. + + + +
- sym_isOptimal — To check whether the problem was solved to optimality. + + + +
- sym_isTargetGapAchieved — To know whether the target gap was reached. + + + +
- sym_isTimeLimitReached — To know whether the time limit was reached. + + + +
- sym_loadMPS — This routine is used to load an instance from an MPS file. + + + +
- sym_loadProblem — Load a problem into Symphony + + + +
- sym_loadProblemBasic — Load a problem into Symphony (basic version) + + + +
- sym_open — Open the Symphony environment + + + +
- sym_resetParams — This routine sets all the environment variables and parameters to their default values. + + + +
- sym_setConstrLower — Set the lower bound of a constraint + + + +
- sym_setConstrType — Set the type of a constraint + + + +
- sym_setConstrUpper — Set the upper bound of a constraint + + + +
- sym_setContinuous — This routine is used to set the type of a variable to be continuous. + + + +
- sym_setDblParam — This routine is used to set a double type parameter. + + + +
- sym_setIntParam — This routine is used to set an integer type parameter. + + + +
- sym_setInteger — This routine is used to set the type of a variable to be integer. + + + +
- sym_setObjCoeff — Set coefficient of a variable in the objective + + + +
- sym_setObjSense — Set the objective sense + + + +
- sym_setPrimalBound — Set the primal bound of the problem + + + +
- sym_setStrParam — This routine is used to set a string type parameter. + + + +
- sym_setVarLower — Set lower bound of a variable + + + +
- sym_setVarSoln — Set a solution for the problem + + + +
- sym_setVarUpper — Set upper bound of a variable + + + +
- sym_solve — To solve the currently loaded MILP problem from scratch. + + + + + + +
- symphony — Solves a mixed integer linear programming constrained optimization problem. + + + + + +
- symphony_mat — Solves a mixed integer linear programming constrained optimization problem.
- 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
- Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
- 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
- Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
- 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
- Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
Symphony
+-
-
diff --git a/help/en_US/scilab_en_US_help/jhelpmap.jhm b/help/en_US/scilab_en_US_help/jhelpmap.jhm index 9e9f18d..f6f9c31 100644 --- a/help/en_US/scilab_en_US_help/jhelpmap.jhm +++ b/help/en_US/scilab_en_US_help/jhelpmap.jhm @@ -61,4 +61,6 @@+ + \ No newline at end of file diff --git a/help/en_US/scilab_en_US_help/jhelpset.hs b/help/en_US/scilab_en_US_help/jhelpset.hs index 94c2e7e..33aed4e 100644 --- a/help/en_US/scilab_en_US_help/jhelpset.hs +++ b/help/en_US/scilab_en_US_help/jhelpset.hs @@ -2,7 +2,7 @@ - Symphony Toolbox +Symphony top diff --git a/help/en_US/scilab_en_US_help/jhelptoc.xml b/help/en_US/scilab_en_US_help/jhelptoc.xml index 4e62ec7..a02863a 100644 --- a/help/en_US/scilab_en_US_help/jhelptoc.xml +++ b/help/en_US/scilab_en_US_help/jhelptoc.xml @@ -1,8 +1,8 @@ - \ No newline at end of file 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 4d29dfb..4af7cd8 100644 --- a/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html +++ b/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html @@ -15,7 +15,7 @@- + + @@ -61,6 +61,8 @@ + + - Symphony Toolbox + Symphony @@ -27,10 +27,10 @@ - Symphony Toolbox >> Symphony Toolbox + Symphony >> Symphony Gateway Functions
-Symphony Toolbox
+Symphony Gateway Functions
- Symphony Toolbox + Symphony diff --git a/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html~ b/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html~ new file mode 100644 index 0000000..482abc6 --- /dev/null +++ b/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html~ @@ -0,0 +1,296 @@ + + + + + + + + + + + Symphony >> Symphony Gateway Functions + +
+Symphony Gateway Functions
+
+ + + + diff --git a/help/en_US/scilab_en_US_help/setOptions.html b/help/en_US/scilab_en_US_help/setOptions.html new file mode 100644 index 0000000..0343da3 --- /dev/null +++ b/help/en_US/scilab_en_US_help/setOptions.html @@ -0,0 +1,59 @@ + + +setOptions + + + + + + + + Symphony Toolbox >> Symphony Toolbox > setOptions + +
++setOptions
+
+ + + + diff --git a/help/en_US/scilab_en_US_help/sym_addConstr.html b/help/en_US/scilab_en_US_help/sym_addConstr.html index 75f86e3..be380a9 100644 --- a/help/en_US/scilab_en_US_help/sym_addConstr.html +++ b/help/en_US/scilab_en_US_help/sym_addConstr.html @@ -12,11 +12,11 @@- Symphony Toolbox + Symphony - + symphony >> +
diff --git a/help/en_US/scilab_en_US_help/symphony.html b/help/en_US/scilab_en_US_help/symphony.html index e69de29..5c99038 100644 --- a/help/en_US/scilab_en_US_help/symphony.html +++ b/help/en_US/scilab_en_US_help/symphony.html @@ -0,0 +1,214 @@ + + +symphony + + + + + + + + Symphony >> Symphony > symphony + +
++ + +symphony
+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
++ +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
+
+ + +
+ +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
+
+ + + + diff --git a/help/en_US/scilab_en_US_help/symphony_call.html b/help/en_US/scilab_en_US_help/symphony_call.html new file mode 100644 index 0000000..2830c0c --- /dev/null +++ b/help/en_US/scilab_en_US_help/symphony_call.html @@ -0,0 +1,59 @@ + + +symphony_call + + + + + + + + Symphony Toolbox >> Symphony Toolbox > symphony_call + +
++symphony_call
+
+ + + + diff --git a/help/en_US/scilab_en_US_help/symphony_mat.html b/help/en_US/scilab_en_US_help/symphony_mat.html new file mode 100644 index 0000000..10ae133 --- /dev/null +++ b/help/en_US/scilab_en_US_help/symphony_mat.html @@ -0,0 +1,197 @@ + + +symphony_mat + + + + + + + + Symphony >> Symphony > symphony_mat + +
++ + +symphony_mat
+Solves a mixed integer linear programming constrained optimization problem.
+ +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,iter] = symphony_mat( ... )
+ +Parameters
++ +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
+
+
+ +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
+
+ + + + diff --git a/help/en_US/scilab_en_US_help/symphony_mat.html~ b/help/en_US/scilab_en_US_help/symphony_mat.html~ new file mode 100644 index 0000000..f3d56a4 --- /dev/null +++ b/help/en_US/scilab_en_US_help/symphony_mat.html~ @@ -0,0 +1,197 @@ + + +symphony_mat + + + + + + + + Symphony >> Symphony > symphony_mat + +
++ + +symphony_mat
+Solves a mixed integer linear programming constrained optimization problem.
+ +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,iter] = symphony_mat( ... )
+ +Parameters
++ +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
+
++ +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
+
+ + + + diff --git a/help/en_US/symphony.xml b/help/en_US/symphony.xml new file mode 100644 index 0000000..f71920f --- /dev/null +++ b/help/en_US/symphony.xml @@ -0,0 +1,215 @@ + + + + ++ + diff --git a/help/en_US/symphony_mat.xml b/help/en_US/symphony_mat.xml new file mode 100644 index 0000000..f9685f2 --- /dev/null +++ b/help/en_US/symphony_mat.xml @@ -0,0 +1,201 @@ + + + + ++ + + +symphony +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 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 + ++ ++\begin{eqnarray} +&\mbox{min}_{x} +& f(x) \\ +& \text{subject to} & conLB \geq C(x) \leq conUB \\ +& & lb \geq x \leq ub \\ +\end{eqnarray} + ++ ++ ++ ++ + +Examples ++ + + +Examples ++ + +Authors ++ +Keyur Joshi, Saikiran, Iswarya, Harpreet Singh ++ + diff --git a/jar/scilab_en_US_help.jar b/jar/scilab_en_US_help.jar index 22e9445..e64bbc7 100644 Binary files a/jar/scilab_en_US_help.jar and b/jar/scilab_en_US_help.jar differ diff --git a/macros/setOptions.bin b/macros/setOptions.bin index f591286..c0bb197 100644 Binary files a/macros/setOptions.bin and b/macros/setOptions.bin differ 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 Binary files a/macros/symphony.bin and b/macros/symphony.bin differ 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 // //+ + + +symphony_mat +Solves a mixed integer linear programming constrained optimization problem. ++ + +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,iter] = 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 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 + ++ ++\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} + ++ ++ + +Examples ++ + + +Examples ++ + +Authors ++ +Keyur Joshi, Saikiran, Iswarya, Harpreet Singh +// \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} // // // - // - // - // - - + // + // 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 Binary files a/macros/symphony_call.bin and b/macros/symphony_call.bin differ diff --git a/macros/symphony_mat.bin b/macros/symphony_mat.bin index fdaf7e6..21ebf8c 100644 Binary files a/macros/symphony_mat.bin and b/macros/symphony_mat.bin differ 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 // //- // \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} - // + // + // + // 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); diff --git a/tests/unit_tests/symphony_mat_base.dia.ref b/tests/unit_tests/symphony_mat_base.dia.ref new file mode 100644 index 0000000..3bf286d --- /dev/null +++ b/tests/unit_tests/symphony_mat_base.dia.ref @@ -0,0 +1,83 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// Author: Keyur Joshi 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 + +// <-- 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 + +// 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); + +//In Symphony Library for optimal solution status = 227 +status = sym_getStatus(); + +assert_close ( x , [1 1 0 1 7.25 0 0.25 3.5] , 1.e-7 ); +assert_close ( f , [ 8495] , 1.e-7 ); + +assert_checkequal( status , 227 ); diff --git a/tests/unit_tests/symphony_mat_base.tst b/tests/unit_tests/symphony_mat_base.tst new file mode 100644 index 0000000..3bf286d --- /dev/null +++ b/tests/unit_tests/symphony_mat_base.tst @@ -0,0 +1,83 @@ +// Copyright (C) 2015 - IIT Bombay - FOSSEE +// +// Author: Keyur Joshi 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 + +// <-- 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 + +// 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); + +//In Symphony Library for optimal solution status = 227 +status = sym_getStatus(); + +assert_close ( x , [1 1 0 1 7.25 0 0.25 3.5] , 1.e-7 ); +assert_close ( f , [ 8495] , 1.e-7 ); + +assert_checkequal( status , 227 ); -- cgit