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+<?xml version="1.0" encoding="UTF-8"?>
+
+<!--
+ *
+ * This help file was generated from intfminbnd.sci using help_from_sci().
+ *
+ -->
+
+<refentry version="5.0-subset Scilab" xml:id="intfminbnd" 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>intfminbnd</refname>
+    <refpurpose>Solves a multi-variable optimization problem on a bounded interval</refpurpose>
+  </refnamediv>
+
+
+<refsynopsisdiv>
+   <title>Calling Sequence</title>
+   <synopsis>
+   xopt = intfminbnd(f,intcon,x1,x2)
+   xopt = intfminbnd(f,intcon,x1,x2,options)
+   [xopt,fopt] = intfminbnd(.....)
+   [xopt,fopt,exitflag]= intfminbnd(.....)
+   [xopt,fopt,exitflag,output]=intfminbnd(.....)
+   [xopt,fopt,exitflag,gradient,hessian]=intfminbnd(.....)
+   
+   </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>x1 :</term>
+      <listitem><para> a vector, containing the lower bound of the variables.</para></listitem></varlistentry>
+   <varlistentry><term>x2 :</term>
+      <listitem><para> a vector, containing the upper bound of the 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, containing the the computed solution of the optimization problem.</para></listitem></varlistentry>
+   <varlistentry><term>fopt :</term>
+      <listitem><para> a scalar of double, containing the 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 a multi-variable function on bounded interval specified by :
+Find the minimum of f(x) such that
+   </para>
+   <para>
+<latex>
+\begin{eqnarray}
+&amp;\mbox{min}_{x}
+&amp; f(x)\\
+&amp; \text{subject to} &amp; x1 \ &lt; x \ &lt; x2 \\
+\end{eqnarray}
+</latex>
+   </para>
+   <para>
+The routine calls Bonmin for solving the Bounded 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",[---], "MaxIter", [---], "AllowableGap",[---] "CpuTime", [---],"gradobj", "off", "hessian", "off" );</listitem>
+<listitem>IntegerTolerance : a Scalar, a number with that value of an integer is considered integer..</listitem>
+<listitem>MaxNodes : a Scalar, containing the Maximum Number of Nodes that the solver should search.</listitem>
+<listitem>CpuTime : a Scalar, containing the Maximum amount of CPU Time that the solver should take.</listitem>
+<listitem>AllowableGap : a Scalar, to stop the tree search when the gap between the objective value of the best known solution is reached.</listitem>
+<listitem>MaxIter : a Scalar, containing the Maximum Number of Iteration 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>
+   </para>
+   <para>
+The exitflag allows to know the status of the optimization which is given back by Ipopt.
+<itemizedlist>
+<listitem>exitflag=0 : Optimal Solution Found </listitem>
+<listitem>exitflag=1 : Maximum Number of Iterations Exceeded. Output may not be optimal.</listitem>
+<listitem>exitflag=2 : Maximum CPU Time exceeded. Output may not be optimal.</listitem>
+<listitem>exitflag=3 : Stop at Tiny Step.</listitem>
+<listitem>exitflag=4 : Solved To Acceptable Level.</listitem>
+<listitem>exitflag=5 : Converged to a point of local infeasibility.</listitem>
+</itemizedlist>
+   </para>
+   <para>
+For more details on exitflag see the Bonmin documentation, go to http://www.coin-or.org/Bonmin
+   </para>
+   <para>
+</para>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//Find x in R^6 such that it minimizes:
+//f(x)= sin(x1) + sin(x2) + sin(x3) + sin(x4) + sin(x5) + sin(x6)
+//-2 <= x1,x2,x3,x4,x5,x6 <= 2
+//Objective function to be minimised
+function y=f(x)
+y=0
+for i =1:6
+y=y+sin(x(i));
+end
+endfunction
+//Variable bounds
+x1 = [-2, -2, -2, -2, -2, -2];
+x2 = [2, 2, 2, 2, 2, 2];
+intcon = [2 3 4]
+//Options
+options=list("MaxIter",[1500],"CpuTime", [100])
+[x,fval] =intfminbnd(f ,intcon, x1, x2, options)
+// Press ENTER to continue
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//Find x in R such that it minimizes:
+//f(x)= 1/x^2
+//0 <= x <= 1000
+//Objective function to be minimised
+function y=f(x)
+y=1/x^2;
+endfunction
+//Variable bounds
+x1 = [0];
+x2 = [1000];
+intcon = [1];
+[x,fval,exitflag,output,lambda] =intfminbnd(f,intcon , x1, x2)
+// 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 it minimizes:
+//f(x)= -[(x1-1)^2 + (x2-1)^2]
+//-inf <= x1,x2 <= inf
+//Objective function to be minimised
+function y=f(x)
+y=-((x(1)-1)^2+(x(2)-1)^2);
+endfunction
+//Variable bounds
+x1 = [-%inf , -%inf];
+x2 = [ %inf , %inf];
+//Options
+options=list("MaxIter",[1500],"CpuTime", [100])
+[x,fval,exitflag,output,lambda] =intfminbnd(f,intcon, x1, x2, options)
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Authors</title>
+   <simplelist type="vert">
+   <member>Harpreet Singh</member>
+   </simplelist>
+</refsection>
+</refentry>