- sym_addConstr — Add a new constraint diff --git a/help/en_US/scilab_en_US_help/jhelpmap.jhm b/help/en_US/scilab_en_US_help/jhelpmap.jhm index 1601f23..9dfdea5 100644 --- a/help/en_US/scilab_en_US_help/jhelpmap.jhm +++ b/help/en_US/scilab_en_US_help/jhelpmap.jhm @@ -4,9 +4,9 @@
- Q :
a n x n symmetric matrix of doubles, where n is number of variables, represents coefficients of quadratic in the quadratic problem.
- p : -
a 1 x n matrix of doubles, where n is number of variables, represents coefficients of linear in the quadratic problem
+ a n x 1 matrix of doubles, where n is number of variables, represents coefficients of linear in the quadratic problem
- LB : -
a 1 x n matrix of doubles, where n is number of variables, contains lower bounds of the variables.
+ a n x 1 matrix of doubles, where n is number of variables, contains lower bounds of the variables.
- UB : -
a 1 x n matrix of doubles, where n is number of variables, contains upper bounds of the variables.
+ a n x 1 matrix of doubles, where n is number of variables, contains upper bounds of the variables.
- 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.
+ - x0 : +
a m x 1 matrix of doubles, where m is number of constraints, contains initial guess of variables.
+ - param : +
a list containing the the parameters to be set.
- xopt :
a 1xn matrix of doubles, the computed solution of the optimization problem.
- fopt :
@@ -92,7 +98,9 @@ find the minimum of f(x) such that
p=[1; 2; 3; 4; 5; 6]; Q=eye(6,6);
nbVar = 6;
nbCon = 5;
-[xopt,fopt,exitflag,output,lambda]=qpipopt(nbVar,nbCon,Q,p,lb,ub,conMatrix,conLB,conUB)
- H : +
a n x n matrix of doubles, where n is number of variables, represents coefficients of quadratic in the quadratic problem.
+ - f : +
a n x 1 matrix of doubles, where n is number of variables, represents coefficients of linear in the quadratic problem
+ - A : +
a m x n matrix of doubles, represents the linear coefficients in the inequality constraints
+ - b : +
a column vector of doubles, represents the linear coefficients in the inequality constraints
+ - Aeq : +
a meq x n matrix of doubles, represents the linear coefficients in the equality constraints
+ - beq : +
a vector of doubles, represents the linear coefficients in the equality constraints
+ - LB : +
a n x 1 matrix of doubles, where n is number of variables, contains lower bounds of the variables.
+ - UB : +
a n x 1 matrix of doubles, where n is number of variables, contains upper bounds of the variables.
+ - x0 : +
a m x 1 matrix of doubles, where m is number of constraints, contains initial guess of variables.
+ - param : +
a list containing the the parameters to be set.
+ - xopt : +
a nx1 matrix of doubles, the computed solution of the optimization problem.
+ - fopt : +
a 1x1 matrix of doubles, the function value at x.
+ - exitflag : +
Integer identifying the reason the algorithm terminated.
+ - output : +
Structure containing information about the optimization.
+ - lambda : +
Structure containing the Lagrange multipliers at the solution x (separated by constraint type).
- Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
- qpipopt_mat — Solves a linear quadratic problem. +
- qpipoptmat — Solves a linear quadratic problem. @@ -49,7 +49,7 @@ -
- symphony_mat — Solves a mixed integer linear programming constrained optimization problem in intlinprog format. +
- symphonymat — Solves a mixed integer linear programming constrained optimization problem in intlinprog format.
- Symphony Native Functions
- sym_addConstr — Add a new constraint diff --git a/help/en_US/scilab_en_US_help/section_508f0b211d17ea6769714cc144e6b731.html b/help/en_US/scilab_en_US_help/section_508f0b211d17ea6769714cc144e6b731.html index 1d1b6d9..cf8c746 100644 --- a/help/en_US/scilab_en_US_help/section_508f0b211d17ea6769714cc144e6b731.html +++ b/help/en_US/scilab_en_US_help/section_508f0b211d17ea6769714cc144e6b731.html @@ -12,7 +12,7 @@
- << symphony_mat + << symphonymat @@ -268,7 +268,7 @@ Report an issue - << symphony_mat + << symphonymat diff --git a/help/en_US/scilab_en_US_help/symphony.html b/help/en_US/scilab_en_US_help/symphony.html index 0af9d1b..a1e3eea 100644 --- a/help/en_US/scilab_en_US_help/symphony.html +++ b/help/en_US/scilab_en_US_help/symphony.html @@ -12,7 +12,7 @@ - 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 тЄ 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 тЄ 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 тЄ x тЄ ub.
+ - ub : +
Upper bounds, specified as a vector or array of doubles. ub represents the upper bounds elementwise in lb тЄ x тЄ ub.
+ - options : +
a list containing the the parameters to be set.
+ - 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.
- Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
Calling Sequence
xopt = qpipopt(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB) +xopt = qpipopt(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB,x0) +xopt = qpipopt(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB,x0,param) [xopt,fopt,exitflag,output,lamda] = qpipopt( ... )
Parameters
@@ -48,17 +50,21 @@Examples
//Find the value of x that minimize following function
@@ -130,7 +138,7 @@ find the minimum of f(x) such that
|
- qpipopt_mat >> + qpipoptmat >> |
+
qpipoptmat
+Solves a linear quadratic problem.
Calling Sequence
+x = qpipoptmat(H,f) +x = qpipoptmat(H,f,A,b) +x = qpipoptmat(H,f,A,b,Aeq,beq) +x = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub) +x = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0) +x = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0,param) +[xopt,fopt,exitflag,output,lamda] = qpipoptmat( ... )
Parameters
+Description
+Search the minimum of a constrained linear quadratic optimization problem specified by : +find the minimum of f(x) such that
+
We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. The code has been written by тAndreas WУЄchter and тCarl Laird.
+Examples
+//Find x in R^6 such that: + +Aeq= [1,-1,1,0,3,1; +-1,0,-3,-4,5,6; +2,5,3,0,1,0]; +beq=[1; 2; 3]; +A= [0,1,0,1,2,-1; +-1,0,2,1,1,0]; +b = [-1; 2.5]; +lb=[-1000; -10000; 0; -1000; -1000; -1000]; +ub=[10000; 100; 1.5; 100; 100; 1000]; +x0 = repmat(0,6,1); +param = list("MaxIter", 300, "CpuTime", 100); +//and minimize 0.5*x'*Q*x + p'*x with +f=[1; 2; 3; 4; 5; 6]; H=eye(6,6); +[xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,[],param); +clear H f A b Aeq beq lb ub; | | |
Examples
+//Find the value of x that minimize following function +// f(x) = 0.5*x1^2 + x2^2 - x1*x2 - 2*x1 - 6*x2 +// Subject to: +// x1 + x2 тЄ 2 +// тx1 + 2x2 тЄ 2 +// 2x1 + x2 тЄ 3 +// 0 тЄ x1, 0 тЄ x2. +H = [1 -1; -1 2]; +f = [-2; -6]; +A = [1 1; -1 2; 2 1]; +b = [2; 2; 3]; +lb = [0; 0]; +ub = [%inf; %inf]; +[xopt,fopt,exitflag,output,lambda] = qpipoptmat(H,f,A,b,[],[],lb,ub) | | |
Authors
++ + + + 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 ed07ab6..1e5e538 100644 --- a/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html +++ b/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html @@ -37,7 +37,7 @@ -
Examples
// An advanced case where we set some options in symphony @@ -177,7 +177,7 @@ find the minimum or maximum of f(x) such that conLB=repmat(0,nbCon,1); // Upper Bound of constraints conUB=[11927 13727 11551 13056 13460 ]'; -options = ["time_limit" "25"] +options = list("time_limit", 25); // The expected solution : // Output variables xopt = [0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 1 0 1 .. @@ -186,7 +186,7 @@ find the minimum or maximum of f(x) such that // Optimal value fopt = [ 24381 ] // Calling Symphony -[x,f,iter]= symphony(nbVar,nbCon,p,isInt,lb,ub,conMatrix,conLB,conUB,-1,options) | | |
Authors
+
symphonymat
+Solves a mixed integer linear programming constrained optimization problem in intlinprog format.
Calling Sequence
+xopt = symphonymat(f,intcon,A,b) +xopt = symphonymat(f,intcon,A,b,Aeq,beq) +xopt = symphonymat(f,intcon,A,b,Aeq,beq,lb,ub) +xopt = symphonymat(f,intcon,A,b,Aeq,beq,lb,ub,options) +[xopt,fopt,status,output] = symphonymat( ... )
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
+
We are calling SYMPHONY written in C by gateway files for the actual computation. SYMPHONY was originally written by тTed Ralphs, тMenal Guzelsoy and тAshutosh 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,status,output] = symphonymat(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 +options = list("time_limit", 25); +// The expected solution : +// Output variables +xopt = [0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 1 0 1 .. +0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 .. +0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0] +// Optimal value +fopt = [ 24381 ] +// Calling Symphony +[x,f,status,output] = symphonymat(objCoef,intcon,conMatrix,conUB,[],[],lb,ub,options); | | |
Authors
++ + + + -- cgit