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

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)

Report an issue
+ << intfminimax + +	+ FOSSEE Optimization Toolbox + +	+ intqpipopt >> + +

intfminunc

Calling Sequence

Parameters

Description

Examples

Examples

Examples