diff --git a/help/en_US/scilab_en_US_help/qpipopt.html b/help/en_US/scilab_en_US_help/qpipopt.html index fba4521..6659f44 100644 --- a/help/en_US/scilab_en_US_help/qpipopt.html +++ b/help/en_US/scilab_en_US_help/qpipopt.html @@ -20,7 +20,7 @@ - qpipopt_mat >> + qpipoptmat >> @@ -38,6 +38,8 @@

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 @@

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)

+x0 = repmat(0,nbVar,1); +param = list("MaxIter", 300, "CpuTime", 100); +[xopt,fopt,exitflag,output,lambda]=qpipopt(nbVar,nbCon,Q,p,lb,ub,conMatrix,conLB,conUB,x0,param)

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 >>

diff --git a/help/en_US/scilab_en_US_help/qpipoptmat.html b/help/en_US/scilab_en_US_help/qpipoptmat.html new file mode 100644 index 0000000..5e7518e --- /dev/null +++ b/help/en_US/scilab_en_US_help/qpipoptmat.html @@ -0,0 +1,146 @@ + + + qpipoptmat + + + +

+ + + + +

+ << qpipopt + +

+ Symphony Toolbox + +

+ symphony >> + +

+ + + + Symphony Toolbox >> Symphony Toolbox > 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

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).

+ +

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 â€‹Andreas WÃ¤chter and â€‹Carl 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 â‰¤ 2
+// â€“x1 + 2x2 â‰¤ 2
+// 2x1 + x2 â‰¤ 3
+// 0 â‰¤ x1, 0 â‰¤ 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

Keyur Joshi, Saikiran, Iswarya, Harpreet Singh

+
+ +

+ + + + + + +

Report an issue
+ << qpipopt + +	+ 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 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 @@ -

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

- << 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 @@

- << qpipopt_mat + << qpipoptmat

@@ -20,7 +20,7 @@

- symphony_mat >> + symphonymat >>

@@ -102,7 +102,7 @@ find the minimum or maximum of f(x) such that

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);

+[x,f,status,output] = symphony(8,3,c,isInt,lb,ub,conMatrix,conlb,conub,1)

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)

+[x,f,status,output] = symphony(nbVar,nbCon,p,isInt,lb,ub,conMatrix,conLB,conUB,-1,options)

Authors

Keyur Joshi, Saikiran, Iswarya, Harpreet Singh

@@ -197,7 +197,7 @@ find the minimum or maximum of f(x) such that

Report an issue - << qpipopt_mat + << qpipoptmat @@ -205,7 +205,7 @@ find the minimum or maximum of f(x) such that

- symphony_mat >> + symphonymat >> diff --git a/help/en_US/scilab_en_US_help/symphonymat.html b/help/en_US/scilab_en_US_help/symphonymat.html new file mode 100644 index 0000000..23ff2c6 --- /dev/null +++ b/help/en_US/scilab_en_US_help/symphonymat.html @@ -0,0 +1,201 @@ + + + symphonymat + + + +

+ + + + +

+ << symphony + +

+ Symphony Toolbox + +

+ Symphony Native Functions >> + +

+ + + + Symphony Toolbox >> Symphony Toolbox > symphonymat + +

+

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

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 â‰¤ 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 â‰¤ 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 â‰¤ x â‰¤ ub.
ub : +: Upper bounds, specified as a vector or array of doubles. ub represents the upper bounds elementwise in lb â‰¤ x â‰¤ 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.

+ +

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 â€‹Ted Ralphs, â€‹Menal Guzelsoy and â€‹Ashutosh Mahajan.

+ +

Examples

// Objective function
+c = [350*5,330*3,310*4,280*6,500,450,400,100]
+// Lower Bound of variable
+lb = repmat(0,1,8);
+// Upper Bound of variables
+ub = [repmat(1,1,4) repmat(%inf,1,4)];
+// Constraint Matrix
+Aeq = [5,3,4,6,1,1,1,1;
+5*0.05,3*0.04,4*0.05,6*0.03,0.08,0.07,0.06,0.03;
+5*0.03,3*0.03,4*0.04,6*0.04,0.06,0.07,0.08,0.09;]
+beq = [ 25, 1.25, 1.25]
+intcon = [1 2 3 4];
+// Calling Symphony
+[x,f,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

Keyur Joshi, Saikiran, Iswarya, Harpreet Singh

+
+ +

+ + + + + + +

Report an issue
+ << symphony + +	+ Symphony Toolbox + +	+ Symphony Native Functions >> + +

+ + diff --git a/help/en_US/symphony.xml b/help/en_US/symphony.xml index 86ad4b7..c33b95c 100644 --- a/help/en_US/symphony.xml +++ b/help/en_US/symphony.xml @@ -114,7 +114,8 @@ 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); +[x,f,status,output] = symphony(8,3,c,isInt,lb,ub,conMatrix,conlb,conub,1) + ]]> @@ -193,7 +194,7 @@ isInt = repmat(%t,1,nbVar) 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 .. @@ -202,7 +203,7 @@ 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 .. // Optimal value fopt = [ 24381 ] // Calling Symphony -[x,f,iter]= symphony(nbVar,nbCon,p,isInt,lb,ub,conMatrix,conLB,conUB,-1,options) +[x,f,status,output] = symphony(nbVar,nbCon,p,isInt,lb,ub,conMatrix,conLB,conUB,-1,options) ]]> diff --git a/help/en_US/symphonymat.xml b/help/en_US/symphonymat.xml new file mode 100644 index 0000000..ca56363 --- /dev/null +++ b/help/en_US/symphonymat.xml @@ -0,0 +1,203 @@ + + + + + + + + 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 + + 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 â‰¤ 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 â‰¤ 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 â‰¤ x â‰¤ ub. + ub : + Upper bounds, specified as a vector or array of doubles. ub represents the upper bounds elementwise in lb â‰¤ x â‰¤ 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. + + + + + 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 \leq C(x) \leq conUB \\ +& & lb \leq x \leq ub \\ +\end{eqnarray} + + + +We are calling SYMPHONY written in C by gateway files for the actual computation. SYMPHONY was originally written by â€‹Ted Ralphs, â€‹Menal Guzelsoy and â€‹Ashutosh Mahajan. + + + + + + + Examples + + + + + Examples + + + + + Authors + + Keyur Joshi, Saikiran, Iswarya, Harpreet Singh + + + -- cgit