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diff --git a/help/intfminunc.xml b/help/intfminunc.xml new file mode 100644 index 0000000..dd1ae3e --- /dev/null +++ b/help/intfminunc.xml @@ -0,0 +1,170 @@ +<?xml version="1.0" encoding="UTF-8"?> + +<!-- + * + * This help file was generated from intfminunc.sci using help_from_sci(). + * + --> + +<refentry version="5.0-subset Scilab" xml:id="intfminunc" xml:lang="en" + xmlns="http://docbook.org/ns/docbook" + xmlns:xlink="http://www.w3.org/1999/xlink" + xmlns:svg="http://www.w3.org/2000/svg" + xmlns:ns3="http://www.w3.org/1999/xhtml" + xmlns:mml="http://www.w3.org/1998/Math/MathML" + xmlns:scilab="http://www.scilab.org" + xmlns:db="http://docbook.org/ns/docbook"> + + <refnamediv> + <refname>intfminunc</refname> + <refpurpose>Solves a multi-variable unconstrainted optimization problem</refpurpose> + </refnamediv> + + +<refsynopsisdiv> + <title>Calling Sequence</title> + <synopsis> + 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(.....) + + </synopsis> +</refsynopsisdiv> + +<refsection> + <title>Parameters</title> + <variablelist> + <varlistentry><term>f :</term> + <listitem><para> a function, representing the objective function of the problem</para></listitem></varlistentry> + <varlistentry><term>x0 :</term> + <listitem><para> a vector of doubles, containing the starting of variables.</para></listitem></varlistentry> + <varlistentry><term>intcon :</term> + <listitem><para> a vector of integers, represents which variables are constrained to be integers</para></listitem></varlistentry> + <varlistentry><term>options:</term> + <listitem><para> a list, containing the option for user to specify. See below for details.</para></listitem></varlistentry> + <varlistentry><term>xopt :</term> + <listitem><para> a vector of doubles, the computed solution of the optimization problem.</para></listitem></varlistentry> + <varlistentry><term>fopt :</term> + <listitem><para> a scalar of double, the function value at x.</para></listitem></varlistentry> + <varlistentry><term>exitflag :</term> + <listitem><para> a scalar of integer, containing the flag which denotes the reason for termination of algorithm. See below for details.</para></listitem></varlistentry> + <varlistentry><term>gradient :</term> + <listitem><para> a vector of doubles, containing the Objective's gradient of the solution.</para></listitem></varlistentry> + <varlistentry><term>hessian :</term> + <listitem><para> a matrix of doubles, containing the Objective's hessian of the solution.</para></listitem></varlistentry> + </variablelist> +</refsection> + +<refsection> + <title>Description</title> + <para> +Search the minimum of an unconstrained optimization problem specified by : +Find the minimum of f(x) such that + </para> + <para> +<latex> +\begin{eqnarray} +&\mbox{min}_{x} +& f(x)\\ +\end{eqnarray} +</latex> + </para> + <para> +The routine calls Bonmin for solving the Un-constrained Optimization problem, Bonmin is a library written in C++. + </para> + <para> +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. +<itemizedlist> +<listitem>Syntax : options= list("IntegerTolerance", [---], "MaxNodes", [---], "CpuTime", [---], "AllowableGap", [---], "MaxIter", [---]);</listitem> +<listitem>IntegerTolerance : a Scalar, containing the Integer tolerance value that the solver should take.</listitem> +<listitem>MaxNodes : a Scalar, containing the maximum nodes that the solver should make.</listitem> +<listitem>MaxIter : a Scalar, containing the Maximum Number of Iteration that the solver should take.</listitem> +<listitem>AllowableGap : a Scalar, containing the allowable gap value that the solver should take.</listitem> +<listitem>CpuTime : a Scalar, containing the Maximum amount of CPU Time that the solver should take.</listitem> +<listitem>gradobj : a string, to turn on or off the user supplied objective gradient.</listitem> +<listitem>hessian : a Scalar, to turn on or off the user supplied objective hessian.</listitem> +<listitem>Default Values : options = list('integertolerance',1d-06,'maxnodes',2147483647,'cputime',1d10,'allowablegap',0,'maxiter',2147483647,'gradobj',"off",'hessian',"off")</listitem> +</itemizedlist> +</itemizedlist> + </para> + <para> +The exitflag allows to know the status of the optimization which is given back by Bonmin. +<itemizedlist> +<listitem>exitflag=0 : Optimal Solution Found. </listitem> +<listitem>exitflag=1 : InFeasible Solution.</listitem> +<listitem>exitflag=2 : Output is Continuous Unbounded.</listitem> +<listitem>exitflag=3 : Limit Exceeded.</listitem> +<listitem>exitflag=4 : User Interrupt.</listitem> +<listitem>exitflag=5 : MINLP Error.</listitem> +</itemizedlist> + </para> + <para> +For more details on exitflag see the Bonmin page, go to http://www.coin-or.org/Bonmin + </para> + <para> +</para> +</refsection> + +<refsection> + <title>Examples</title> + <programlisting role="example"><![CDATA[ +//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 + + ]]></programlisting> +</refsection> + +<refsection> + <title>Examples</title> + <programlisting role="example"><![CDATA[ +//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 + + ]]></programlisting> +</refsection> + +<refsection> + <title>Examples</title> + <programlisting role="example"><![CDATA[ +//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) + ]]></programlisting> +</refsection> +</refentry> |