// 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