Structure updated and intqpipopt files added

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diff --git a/help/intfminunc.xml b/help/intfminunc.xml
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-<?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}
-&amp;\mbox{min}_{x}
-&amp; 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>