// Copyright (C) 2015 - IIT Bombay - FOSSEE // // 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 // Author: Harpreet Singh, Pranav Deshpande and Akshay Miterani // Organization: FOSSEE, IIT Bombay // Email: toolbox@scilab.in function [xopt,fopt,exitflag,gradient,hessian] = intfminunc (varargin) // Solves an unconstrainted multi-variable mixed integer non linear programming optimization problem // // Calling Sequence // xopt = intfminunc(f,x0) // xopt = intfminunc(f,x0,intcon) // xopt = intfminunc(f,x0,intcon,options) // [xopt,fopt] = intfminunc(.....) // [xopt,fopt,exitflag]= intfminunc(.....) // [xopt,fopt,exitflag,gradient,hessian]= intfminunc(.....) // // Parameters // f : a function, representing the objective function of the problem // x0 : a vector of doubles, containing the starting of variables. // intcon : a vector of integers, represents which variables are constrained to be integers // options: a list, containing the option for user to specify. See below for details. // xopt : a vector of doubles, the computed solution of the optimization problem. // fopt : a scalar of double, the function value at x. // exitflag : a scalar of integer, containing the flag which denotes the reason for termination of algorithm. See below for details. // gradient : a vector of doubles, containing the Objective's gradient of the solution. // hessian : a matrix of doubles, containing the Objective's hessian of the solution. // // Description // Search the minimum of a multi-variable mixed integer non linear programming unconstrained optimization problem specified by : // Find the minimum of f(x) such that // // // \begin{eqnarray} // &\mbox{min}_{x} // & f(x) // & x_i \in \!\, \mathbb{Z}, i \in \!\, I // \end{eqnarray} // // // The routine calls Bonmin for solving the Un-constrained Optimization problem, Bonmin is a library written in C++. // // The options allows the user to set various parameters of the Optimization problem. // It should be defined as type "list" and contains the following fields. // // Syntax : options= list("IntegerTolerance", [---], "MaxNodes", [---], "CpuTime", [---], "AllowableGap", [---], "MaxIter", [---]); // IntegerTolerance : a Scalar, containing the Integer tolerance value that the solver should take. // MaxNodes : a Scalar, containing the maximum nodes that the solver should make. // MaxIter : a Scalar, containing the Maximum Number of Iteration that the solver should take. // AllowableGap : a Scalar, containing the allowable gap value that the solver should take. // CpuTime : a Scalar, containing the Maximum amount of CPU Time that the solver should take. // gradobj : a string, to turn on or off the user supplied objective gradient. // hessian : a Scalar, to turn on or off the user supplied objective hessian. // Default Values : options = list('integertolerance',1d-06,'maxnodes',2147483647,'cputime',1d10,'allowablegap',0,'maxiter',2147483647,'gradobj',"off",'hessian',"off") // // // // The exitflag allows to know the status of the optimization which is given back by Bonmin. // // exitflag=0 : Optimal Solution Found. // exitflag=1 : InFeasible Solution. // exitflag=2 : Output is Continuous Unbounded. // exitflag=3 : Limit Exceeded. // exitflag=4 : User Interrupt. // exitflag=5 : MINLP Error. // // // For more details on exitflag see the Bonmin page, go to http://www.coin-or.org/Bonmin // // Examples // //Find x in R^2 such that it minimizes the Rosenbrock function // //f = 100*(x2 - x1^2)^2 + (1-x1)^2 // //Objective function to be minimised // function y= f(x) // y= 100*(x(2) - x(1)^2)^2 + (1-x(1))^2; // endfunction // //Starting point // x0=[-1,2]; // intcon = [2] // //Options // options=list("MaxIter", [1500], "CpuTime", [500]); // //Calling // [xopt,fopt,exitflag,gradient,hessian]=intfminunc(f,x0,intcon,options) // // Press ENTER to continue // // Examples // //Find x in R^2 such that the below function is minimum // //f = x1^2 + x2^2 // //Objective function to be minimised // function y= f(x) // y= x(1)^2 + x(2)^2; // endfunction // //Starting point // x0=[2,1]; // intcon = [1]; // [xopt,fopt]=intfminunc(f,x0,intcon) // // Press ENTER to continue // // Examples // //The below problem is an unbounded problem: // //Find x in R^2 such that the below function is minimum // //f = - x1^2 - x2^2 // //Objective function to be minimised // function [y,g,h] = f(x) // y = -x(1)^2 - x(2)^2; // g = [-2*x(1),-2*x(2)]; // h = [-2,0;0,-2]; // endfunction // //Starting point // x0=[2,1]; // intcon = [1] // options = list("gradobj","ON","hessian","on"); // [xopt,fopt,exitflag,gradient,hessian]=intfminunc(f,x0,intcon,options) //To check the number of input and output arguments [lhs , rhs] = argn(); //To check the number of arguments given by the user if ( rhs<2 | rhs>4 ) then errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be int [2 3 4] "), "intfminunc", rhs); error(errmsg); end //Storing the 1st and 2nd Input Parameters fun = varargin(1); x0 = varargin(2); //To add intcon intcon=[]; if ( rhs >=3 ) then intcon = varargin(3); end param = list(); //To check whether options has been entered by user if ( rhs>=4 ) then param =varargin(4); end nbvar = size(x0,"*"); ///////////////// To check whether the Input arguments ///////////////// Checktype("intfminunc", fun, "fun", 1, "function"); Checktype("intfminunc", x0, "x0", 2, "constant"); Checktype("intfminunc", intcon, "intcon", 3, "constant"); Checktype("intfminunc", param, "options", 4, "list"); ///////////////// To check x0 ///////////////// Checkvector("intfminunc", x0, "x0", 2, nbvar) x0 = x0(:); Checkvector("intfminbnd", intcon, "intcon", 3, size(intcon,"*")) intcon = intcon(:); //Error Checks for intcon for i=1:size(intcon,1) if(intcon(i)>nbvar) then errmsg = msprintf(gettext("%s: The values inside intcon should be less than the number of variables"), "intfminunc"); error(errmsg); end if (intcon(i)<0) then errmsg = msprintf(gettext("%s: The values inside intcon should be greater than 0 "), "intfminunc"); error(errmsg); end if(modulo(intcon(i),1)) then errmsg = msprintf(gettext("%s: The values inside intcon should be an integer "), "intfminunc"); error(errmsg); end end //If options has been entered, then check whether an even number of entires has been entered if (modulo(size(param),2)) then errmsg = msprintf(gettext("%s: Size of parameters should be even"), "intfminunc"); error(errmsg); end intconSize = length(intcon); options = list('integertolerance',1d-06,'maxnodes',2147483647,'cputime',1d10,'allowablegap',0,'maxiter',2147483647,'gradobj',"off",'hessian', "off") //Pushing param into default value for i = 1:(size(param))/2 select convstr(param(2*i-1),'l') case 'integertolerance' then Checktype("intfminbnd_options", param(2*i), param(2*i-1), 2*i, "constant"); options(2) = param(2*i); case 'maxnodes' then Checktype("intfminbnd_options", param(2*i), param(2*i-1), 2*i, "constant"); options(4) = options(2*i); case 'cputime' then Checktype("intfminbnd_options", param(2*i), param(2*i-1), 2*i, "constant"); options(6) = options(2*i); case 'allowablegap' then Checktype("intfminbnd_options", param(2*i), param(2*i-1), 2*i, "constant"); options(8) = options(2*i); case 'maxiter' then Checktype("intfminbnd_options", param(2*i), param(2*i-1), 2*i, "constant"); options(10) = options(2*i); case 'gradobj' then Checktype("intfminbnd_options", param(2*i), param(2*i-1), 2*i, "string"); if(convstr(param(2*i),'l') == "on") then options(12) = "on" elseif(convstr(param(2*i),'l') == "off") then options(12) = "off" else error(999, 'Unknown string passed in gradobj.'); end case 'hessian' then Checktype("intfminbnd_options", param(2*i), param(2*i-1), 2*i, "string"); if(convstr(param(2*i),'l') == "on") then options(14) = "on"; elseif(convstr(param(2*i),'l') == "off") then options(14) = "off"; else error(999, 'Unknown string passed in hessian.'); end else error(999, 'Unknown string argument passed.'); end end ///////////////// Functions Check ///////////////// //To check the match between fun (1st Parameter) and x0 (2nd Parameter) if(execstr('init=fun(x0)','errcatch')==21) then errmsg = msprintf(gettext("%s: Objective function and x0 did not match"), "intfminunc"); error(errmsg); end if(options(12) == "on") then if(execstr('[grad_y,grad_dy]=fun(x0)','errcatch')==59) then errmsg = msprintf(gettext("%s: Gradient of objective function is not provided"), "intfminunc"); error(errmsg); end Checkvector("intfminunc_options", grad_dy, "dy", 12, nbvar); end if(options(14) == "on") then if(execstr('[hessian_y,hessian_dy,hessian]=fun(x0)','errcatch')==59) then errmsg = msprintf(gettext("%s: Gradient of objective function is not provided"), "intfminunc"); error(errmsg); end if ( ~isequal(size(hessian),[nbvar nbvar]) ) then errmsg = msprintf(gettext("%s: Size of hessian should be nbvar X nbvar"), "intfminunc"); error(errmsg); end end //Converting the User defined Objective function into Required form (Error Detectable) function [y,check] = _f(x) try y=fun(x) [y,check] = checkIsreal(y) catch y=0; check=1; end endfunction //Defining an inbuilt Objective gradient function function [dy,check] = _gradf(x) if (options(12) =="on") then try [y,dy]=fun(x); [dy,check] = checkIsreal(dy); catch dy = 0; check=1; end else try dy=numderivative(fun,x); [dy,check] = checkIsreal(dy); catch dy=0; check=1; end end endfunction //Defining a function to calculate Hessian if the respective user entry is OFF function [hessy,check]=_gradhess(x) if (options(14) == "on") then try [obj,dy,hessy] = fun(x) [hessy,check] = checkIsreal(hessy) catch hessy = 0; check=1; end else try [dy,hessy]=numderivative(fun,x) [hessy,check] = checkIsreal(hessy) catch hessy=0; check=1; end end endfunction //Calling the bonmin function for solving the above problem [xopt,fopt,exitflag] = inter_fminunc(_f,_gradf,_gradhess,x0,intcon,options,intconSize,nbvar); //In the cases of the problem not being solved, return NULL to the output matrices if( exitflag~=0 & exitflag~=3 ) then gradient = []; hessian = []; else [ gradient, hessian] = numderivative(_f, xopt, [], [], "blockmat"); end //To print output message select exitflag case 0 then printf("\nOptimal Solution Found.\n"); case 1 then printf("\nInFeasible Solution.\n"); case 2 then printf("\nObjective Function is Continuous Unbounded.\n"); case 3 then printf("\Limit Exceeded.\n"); case 4 then printf("\nUser Interrupt.\n"); case 5 then printf("\nMINLP Error.\n"); else printf("\nInvalid status returned. Notify the Toolbox authors\n"); break; end endfunction function [y, check] = checkIsreal(x) if ((~isreal(x))) then y = 0 check=1; else y = x; check=0; end endfunction