a 1 x 1 matrix of doubles, number of variables
a double, number of variables.
a 1 x 1 matrix of doubles, number of constraints
a double, number of constraints.
a 1 x n matrix of doubles, where n is number of variables, contains coefficients of the variables in the objective
a 1 x n matrix of doubles, where n is number of variables, represents coefficients of the variables in the objective.
a 1 x n matrix of boolean, where n is number of variables, representing wether a variable is constrained to be an integer
a vector of boolean, represents wether a variable is constrained to be an integer.
a 1 x n matrix of doubles, where n is number of variables, contains lower bounds of the variables. Bound can be negative infinity
a vector of doubles, represents lower bounds of the variables.
a 1 x n matrix of doubles, where n is number of variables, contains upper bounds of the variables. Bound can be infinity
a vector of doubles, represents upper bounds of the variables.
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
a matrix of doubles, represents matrix representing the constraint matrix.
a m x 1 matrix of doubles, where m is number of constraints, contains lower bounds of the constraints.
a vector of doubles, represents lower bounds of the constraints.
a m x 1 matrix of doubles, where m is number of constraints, contains upper bounds of the constraints
a vector of doubles, represents upper bounds of the constraints
The sense (maximization/minimization) of the objective. Use 1(sym_minimize ) or -1 (sym_maximize) here
The sense (maximization/minimization) of the objective. Use 1(sym_minimize ) or -1 (sym_maximize) here.
a 1xq marix of string, provided to set the paramters in symphony
a a list containing the the parameters to be set.
a 1xn matrix of doubles, the computed solution of the optimization problem
a vector of doubles, the computed solution of the optimization problem.
a 1x1 matrix of doubles, the function value at x
a double, the function value at x.
status flag from symphony
status flag from symphony.
The output data structure contains detailed informations about the optimization process.
//A basic case : // Objective function -c = [350*5,330*3,310*4,280*6,500,450,400,100] +c = [350*5,330*3,310*4,280*6,500,450,400,100]'; // Lower Bound of variable -lb = repmat(0,1,8); +lb = repmat(0,8,1); // Upper Bound of variables -ub = [repmat(1,1,4) repmat(%inf,1,4)]; +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; @@ -121,7 +121,7 @@ find the minimum or maximum of f(x) such that 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] +1162 653 814 625 599 476 767 954 906 904 649 873 565 853 1008 632]'; //Constraint Matrix conMatrix = [ //Constraint 1 @@ -168,9 +168,9 @@ find the minimum or maximum of f(x) such that nbCon = size(conMatrix,1) nbVar = size(conMatrix,2) // Lower Bound of variables -lb = repmat(0,1,nbVar) +lb = repmat(0,nbVar,1) // Upper Bound of variables -ub = repmat(1,1,nbVar) +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 -- cgit |