diff options
Diffstat (limited to 'macros')
| -rw-r--r-- | macros/lsqlin.bin | bin | 51508 -> 52068 bytes | |||
| -rw-r--r-- | macros/lsqlin.sci | 25 | ||||
| -rw-r--r-- | macros/lsqnonneg.bin | bin | 23408 -> 23608 bytes | |||
| -rw-r--r-- | macros/lsqnonneg.sci | 13 | ||||
| -rw-r--r-- | macros/qpipopt.bin | bin | 49368 -> 49652 bytes | |||
| -rw-r--r-- | macros/qpipopt.sci | 8 | ||||
| -rw-r--r-- | macros/qpipoptmat.bin | bin | 51224 -> 51408 bytes | |||
| -rw-r--r-- | macros/qpipoptmat.sci | 160 | ||||
| -rw-r--r-- | macros/symphony.bin | bin | 54708 -> 54824 bytes | |||
| -rw-r--r-- | macros/symphony.sci | 299 | ||||
| -rw-r--r-- | macros/symphonymat.bin | bin | 60820 -> 60900 bytes | |||
| -rw-r--r-- | macros/symphonymat.sci | 13 |
12 files changed, 259 insertions, 259 deletions
diff --git a/macros/lsqlin.bin b/macros/lsqlin.bin Binary files differindex 801025f..d7fccb3 100644 --- a/macros/lsqlin.bin +++ b/macros/lsqlin.bin diff --git a/macros/lsqlin.sci b/macros/lsqlin.sci index 4a5fa2d..1dc1fd5 100644 --- a/macros/lsqlin.sci +++ b/macros/lsqlin.sci @@ -14,11 +14,11 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin (varargin) // Solves a linear quadratic problem. // // Calling Sequence - // x = lsqlin(C,d,A,b) - // x = lsqlin(C,d,A,b,Aeq,beq) - // x = lsqlin(C,d,A,b,Aeq,beq,lb,ub) - // x = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0) - // x = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0,param) + // xopt = lsqlin(C,d,A,b) + // xopt = lsqlin(C,d,A,b,Aeq,beq) + // xopt = lsqlin(C,d,A,b,Aeq,beq,lb,ub) + // xopt = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0) + // xopt = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0,param) // [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin( ... ) // // Parameters @@ -36,8 +36,8 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin (varargin) // resnorm : a double, objective value returned as the scalar value norm(C*x-d)^2. // residual : a vector of doubles, solution residuals returned as the vector C*x-d. // 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). + // output : Structure containing information about the optimization. Right now it contains number of iteration. + // lambda : Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints. // // Description // Search the minimum of a constrained linear least square problem specified by : @@ -46,13 +46,13 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin (varargin) // \begin{eqnarray} // &\mbox{min}_{x} // & 1/2||C*x - d||_2^2 \\ - // & \text{subject to} & A.x \leq b \\ - // & & Aeq.x \leq beq \\ + // & \text{subject to} & A*x \leq b \\ + // & & Aeq*x = beq \\ // & & lb \leq x \leq ub \\ // \end{eqnarray} // </latex> // - // We are calling IPOpt for solving the linear least square problem, IPOpt is a library written in C++. The code has been written by Andreas Wächter and Carl Laird. + // We are calling IPOpt for solving the linear least square problem, IPOpt is a library written in C++. // // Examples // //A simple linear least square example @@ -73,8 +73,10 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin (varargin) // 0.2026 // 0.6721]; // [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b) + // // Press ENTER to continue // - // Examples + // Examples + // //A basic example for equality, inequality and bounds // C = [0.9501 0.7620 0.6153 0.4057 // 0.2311 0.4564 0.7919 0.9354 // 0.6068 0.0185 0.9218 0.9169 @@ -96,7 +98,6 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin (varargin) // lb = -0.1*ones(4,1); // ub = 2*ones(4,1); // [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b,Aeq,beq,lb,ub) - // // Authors // Harpreet Singh diff --git a/macros/lsqnonneg.bin b/macros/lsqnonneg.bin Binary files differindex cd8a04a..84e307b 100644 --- a/macros/lsqnonneg.bin +++ b/macros/lsqnonneg.bin diff --git a/macros/lsqnonneg.sci b/macros/lsqnonneg.sci index 77e5e44..b8694b4 100644 --- a/macros/lsqnonneg.sci +++ b/macros/lsqnonneg.sci @@ -14,8 +14,8 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg (varargin) // Solves nonnegative least-squares curve fitting problems. // // Calling Sequence - // x = lsqnonneg(C,d) - // x = lsqnonneg(C,d,param) + // xopt = lsqnonneg(C,d) + // xopt = lsqnonneg(C,d,param) // [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg( ... ) // // Parameters @@ -25,8 +25,8 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg (varargin) // resnorm : a double, objective value returned as the scalar value norm(C*x-d)^2. // residual : a vector of doubles, solution residuals returned as the vector C*x-d. // 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). + // output : Structure containing information about the optimization. Right now it contains number of iteration. + // lambda : Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints. // // Description // Solves nonnegative least-squares curve fitting problems specified by : @@ -39,10 +39,10 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg (varargin) // \end{eqnarray} // </latex> // - // We are calling IPOpt for solving the nonnegative least-squares curve fitting problems, IPOpt is a library written in C++. The code has been written by Andreas Wächter and Carl Laird. + // We are calling IPOpt for solving the nonnegative least-squares curve fitting problems, IPOpt is a library written in C++. // // Examples - // A basic lsqnonneg problem + // // A basic lsqnonneg problem // C = [ // 0.0372 0.2869 // 0.6861 0.7071 @@ -54,7 +54,6 @@ function [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg (varargin) // 0.0747 // 0.8405]; // [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg(C,d) - // // Authors // Harpreet Singh diff --git a/macros/qpipopt.bin b/macros/qpipopt.bin Binary files differindex 2fd432e..584f327 100644 --- a/macros/qpipopt.bin +++ b/macros/qpipopt.bin diff --git a/macros/qpipopt.sci b/macros/qpipopt.sci index 8b7cecd..affd061 100644 --- a/macros/qpipopt.sci +++ b/macros/qpipopt.sci @@ -34,8 +34,8 @@ function [xopt,fopt,exitflag,output,lambda] = qpipopt (varargin) // xopt : a vector of doubles, the computed solution of the optimization problem. // fopt : a double, 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). + // output : Structure containing information about the optimization. Right now it contains number of iteration. + // lambda : Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints. // // Description // Search the minimum of a constrained linear quadratic optimization problem specified by : @@ -50,7 +50,7 @@ function [xopt,fopt,exitflag,output,lambda] = qpipopt (varargin) // \end{eqnarray} // </latex> // - // We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. The code has been written by Andreas Wächter and Carl Laird. + // We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. // // Examples // //Find x in R^6 such that: @@ -70,6 +70,7 @@ function [xopt,fopt,exitflag,output,lambda] = qpipopt (varargin) // 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) + // // Press ENTER to continue // // Examples // //Find the value of x that minimize following function @@ -89,7 +90,6 @@ function [xopt,fopt,exitflag,output,lambda] = qpipopt (varargin) // nbVar = 2; // nbCon = 3; // [xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,Q,p,lb,ub,conMatrix,conLB,conUB) - // // Authors // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh diff --git a/macros/qpipoptmat.bin b/macros/qpipoptmat.bin Binary files differindex 7a37d9a..ad893f2 100644 --- a/macros/qpipoptmat.bin +++ b/macros/qpipoptmat.bin diff --git a/macros/qpipoptmat.sci b/macros/qpipoptmat.sci index 3f58e70..eec93ce 100644 --- a/macros/qpipoptmat.sci +++ b/macros/qpipoptmat.sci @@ -11,87 +11,85 @@ function [xopt,fopt,exitflag,output,lambda] = qpipoptmat (varargin) - // 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 symmetric matrix of doubles, represents coefficients of quadratic in the quadratic problem. - // f : a vector of doubles, represents coefficients of linear in the quadratic problem - // A : a vector of doubles, represents the linear coefficients in the inequality constraints - // b : a vector of doubles, represents the linear coefficients in the inequality constraints - // Aeq : a 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 vector of doubles, contains lower bounds of the variables. - // UB : a vector of doubles, contains upper bounds of the variables. - // x0 : a vector of doubles, contains initial guess of variables. - // param : a list containing the the parameters to be set. - // xopt : a vector of doubles, the computed solution of the optimization problem. - // fopt : a double, 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 - // - // <latex> - // \begin{eqnarray} - // &\mbox{min}_{x} - // & 1/2*x'*H*x + f'*x \\ - // & \text{subject to} & A.x \leq b \\ - // & & Aeq.x \leq beq \\ - // & & lb \leq x \leq ub \\ - // \end{eqnarray} - // </latex> - // - // We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. The code has been written by Andreas Wächter and 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 ≤ 2 - // // –x1 + 2x2 ≤ 2 - // // 2x1 + x2 ≤ 3 - // // 0 ≤ x1, 0 ≤ 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 + // Solves a linear quadratic problem. + // + // Calling Sequence + // xopt = qpipoptmat(H,f) + // xopt = qpipoptmat(H,f,A,b) + // xopt = qpipoptmat(H,f,A,b,Aeq,beq) + // xopt = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub) + // xopt = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0) + // xopt = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0,param) + // [xopt,fopt,exitflag,output,lamda] = qpipoptmat( ... ) + // + // Parameters + // H : a symmetric matrix of doubles, represents coefficients of quadratic in the quadratic problem. + // f : a vector of doubles, represents coefficients of linear in the quadratic problem + // A : a vector of doubles, represents the linear coefficients in the inequality constraints + // b : a vector of doubles, represents the linear coefficients in the inequality constraints + // Aeq : a 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 vector of doubles, contains lower bounds of the variables. + // UB : a vector of doubles, contains upper bounds of the variables. + // x0 : a vector of doubles, contains initial guess of variables. + // param : a list containing the the parameters to be set. + // xopt : a vector of doubles, the computed solution of the optimization problem. + // fopt : a double, the function value at x. + // exitflag : Integer identifying the reason the algorithm terminated. + // output : Structure containing information about the optimization. Right now it contains number of iteration. + // lambda : Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints. + // + // Description + // Search the minimum of a constrained linear quadratic optimization problem specified by : + // find the minimum of f(x) such that + // + // <latex> + // \begin{eqnarray} + // &\mbox{min}_{x} + // & 1/2*x'*H*x + f'*x \\ + // & \text{subject to} & A*x \leq b \\ + // & & Aeq*x = beq \\ + // & & lb \leq x \leq ub \\ + // \end{eqnarray} + // </latex> + // + // We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. + // + // 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 ≤ 2 + // // –x1 + 2x2 ≤ 2 + // // 2x1 + x2 ≤ 3 + // // 0 ≤ x1, 0 ≤ 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) + // // Press ENTER to continue + // + // 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) + // Authors + // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh //To check the number of input and output argument diff --git a/macros/symphony.bin b/macros/symphony.bin Binary files differindex 3dab926..4bca695 100644 --- a/macros/symphony.bin +++ b/macros/symphony.bin diff --git a/macros/symphony.sci b/macros/symphony.sci index eba9e64..b1a6f28 100644 --- a/macros/symphony.sci +++ b/macros/symphony.sci @@ -10,155 +10,156 @@ // http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt function [xopt,fopt,status,output] = 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,status,output] = symphony( ... ) - // - // Parameters - // nbVar : a double, number of variables. - // nbCon : a double, number of constraints. - // objCoeff : a vector of doubles, represents coefficients of the variables in the objective. - // isInt : a vector of boolean, represents wether a variable is constrained to be an integer. - // LB : a vector of doubles, represents lower bounds of the variables. - // UB : a vector of doubles, represents upper bounds of the variables. - // conMatrix : a matrix of doubles, represents matrix representing the constraint matrix. - // conLB : a vector of doubles, represents lower bounds of the constraints. - // conUB : a vector of doubles, represents upper bounds of the constraints - // objSense : The sense (maximization/minimization) of the objective. Use 1(sym_minimize ) or -1 (sym_maximize) here. - // options : a a list containing the the parameters to be set. - // xopt : a vector of doubles, the computed solution of the optimization problem. - // fopt : a double, the function value at x. - // status : status flag from symphony. - // output : The output data structure contains detailed informations about the optimization process. - // - // Description - // Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by : - // find the minimum or maximum of f(x) such that - // - // <latex> - // \begin{eqnarray} - // &\mbox{min}_{x} - // & f(x) \\ - // & \text{subject to} & conLB \leq C(x) \leq conUB \\ - // & & lb \leq x \leq ub \\ - // \end{eqnarray} - // </latex> - // - // We are calling SYMPHONY written in C by gateway files for the actual computation. SYMPHONY was originally written by Ted Ralphs, Menal Guzelsoy and Ashutosh Mahajan. - // - // Examples - // //A basic case : - // // Objective function - // c = [350*5,330*3,310*4,280*6,500,450,400,100]'; - // // Lower Bound of variable - // lb = repmat(0,8,1); - // // Upper Bound of variables - // ub = [repmat(1,4,1);repmat(%inf,4,1)]; - // // 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,status,output] = 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,nbVar,1) - // // Upper Bound of variables - // ub = repmat(1,nbVar,1) - // // 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 = 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] = symphony(nbVar,nbCon,p,isInt,lb,ub,conMatrix,conLB,conUB,-1,options) - // - // Authors - // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh + // 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,status,output] = symphony( ... ) + // + // Parameters + // nbVar : a double, number of variables. + // nbCon : a double, number of constraints. + // objCoeff : a vector of doubles, represents coefficients of the variables in the objective. + // isInt : a vector of boolean, represents wether a variable is constrained to be an integer. + // LB : a vector of doubles, represents lower bounds of the variables. + // UB : a vector of doubles, represents upper bounds of the variables. + // conMatrix : a matrix of doubles, represents matrix representing the constraint matrix. + // conLB : a vector of doubles, represents lower bounds of the constraints. + // conUB : a vector of doubles, represents upper bounds of the constraints + // objSense : The sense (maximization/minimization) of the objective. Use 1(sym_minimize ) or -1 (sym_maximize) here. + // options : a a list containing the the parameters to be set. + // xopt : a vector of doubles, the computed solution of the optimization problem. + // fopt : a double, the function value at x. + // status : status flag from symphony. + // output : The output data structure contains detailed informations about the optimization process. Right now it contains number of iteration. + // + // Description + // Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by : + // find the minimum or maximum of f(x) such that + // + // <latex> + // \begin{eqnarray} + // &\mbox{min}_{x} + // & f^T*x \\ + // & \text{subject to} & conLB \leq C*x \leq conUB \\ + // & & lb \leq x \leq ub \\ + // & & x_i \in \!\, \mathbb{Z}, i \in \!\, I + // \end{eqnarray} + // </latex> + // + // We are calling SYMPHONY written in C by gateway files for the actual computation. + // + // 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(0,8,1); + // // Upper Bound of variables + // ub = [repmat(1,4,1);repmat(%inf,4,1)]; + // // 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,status,output] = symphony(8,3,c,isInt,lb,ub,conMatrix,conlb,conub,1) + // // Press ENTER to continue + // + // 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,nbVar,1) + // // Upper Bound of variables + // ub = repmat(1,nbVar,1) + // // 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 = 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] = 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(); diff --git a/macros/symphonymat.bin b/macros/symphonymat.bin Binary files differindex 8d42926..08b1616 100644 --- a/macros/symphonymat.bin +++ b/macros/symphonymat.bin diff --git a/macros/symphonymat.sci b/macros/symphonymat.sci index 5aab6e5..40b07eb 100644 --- a/macros/symphonymat.sci +++ b/macros/symphonymat.sci @@ -32,7 +32,7 @@ function [xopt,fopt,status,iter] = symphonymat (varargin) // xopt : a vector of double, the computed solution of the optimization problem // fopt : a doubles, the function value at x // status : status flag from symphony. - // output : The output data structure contains detailed informations about the optimization process. + // output : The output data structure contains detailed informations about the optimization process. Right now it contains number of iteration. // // Description // Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by : @@ -41,14 +41,15 @@ function [xopt,fopt,status,iter] = symphonymat (varargin) // <latex> // \begin{eqnarray} // &\mbox{min}_{x} - // & f(x) \\ - // & \text{subject to} & A.x \leq b \\ - // & & Aeq.x \leq beq \\ + // & f^T*x \\ + // & \text{subject to} & A*x \leq b \\ + // & & Aeq*x = beq \\ // & & lb \leq x \leq ub \\ + // & & x_i \in \!\, \mathbb{Z}, i \in \!\, I // \end{eqnarray} // </latex> // - // We are calling SYMPHONY written in C by gateway files for the actual computation. SYMPHONY was originally written by Ted Ralphs, Menal Guzelsoy and Ashutosh Mahajan. + // We are calling SYMPHONY written in C by gateway files for the actual computation. // // Examples // // Objective function @@ -65,6 +66,7 @@ function [xopt,fopt,status,iter] = symphonymat (varargin) // intcon = [1 2 3 4]; // // Calling Symphony // [x,f,status,output] = symphonymat(c,intcon,[],[],Aeq,beq,lb,ub) + // // Press ENTER to continue // // Examples // // An advanced case where we set some options in symphony @@ -147,7 +149,6 @@ function [xopt,fopt,status,iter] = symphonymat (varargin) // fopt = [ 24381 ] // // Calling Symphony // [x,f,status,output] = symphonymat(objCoef,intcon,conMatrix,conUB,[],[],lb,ub,options); - // // Authors // Keyur Joshi, Saikiran, Iswarya, Harpreet Singh |