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+<?xml version="1.0" encoding="UTF-8"?>
+
+<!--
+ *
+ * This help file was generated from qpipopt.sci using help_from_sci().
+ *
+ -->
+
+<refentry version="5.0-subset Scilab" xml:id="qpipopt" 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>qpipopt</refname>
+    <refpurpose>Solves a linear quadratic problem.</refpurpose>
+  </refnamediv>
+
+
+<refsynopsisdiv>
+   <title>Calling Sequence</title>
+   <synopsis>
+   xopt = qpipopt(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB)
+   [xopt,fopt,exitflag,output,lamda] = qpipopt( ... )
+   
+   </synopsis>
+</refsynopsisdiv>
+
+<refsection>
+   <title>Parameters</title>
+   <variablelist>
+   <varlistentry><term>nbVar :</term>
+      <listitem><para> a 1 x 1 matrix of doubles, number of variables</para></listitem></varlistentry>
+   <varlistentry><term>nbCon :</term>
+      <listitem><para> a 1 x 1 matrix of doubles, number of constraints</para></listitem></varlistentry>
+   <varlistentry><term>Q :</term>
+      <listitem><para> a n x n matrix of doubles, where n is number of variables, represents coefficients of quadratic in the quadratic problem.</para></listitem></varlistentry>
+   <varlistentry><term>p :</term>
+      <listitem><para> a 1 x n matrix of doubles, where n is number of variables, represents coefficients of linear in the quadratic problem</para></listitem></varlistentry>
+   <varlistentry><term>LB :</term>
+      <listitem><para> a 1 x n matrix of doubles, where n is number of variables, contains lower bounds of the variables.</para></listitem></varlistentry>
+   <varlistentry><term>UB :</term>
+      <listitem><para> a 1 x n matrix of doubles, where n is number of variables, contains upper bounds of the variables.</para></listitem></varlistentry>
+   <varlistentry><term>conMatrix :</term>
+      <listitem><para> a m x n matrix of doubles, where n is number of variables and m is number of constraints, contains  matrix representing the constraint matrix</para></listitem></varlistentry>
+   <varlistentry><term>conLB :</term>
+      <listitem><para> a m x 1 matrix of doubles, where m is number of constraints, contains lower bounds of the constraints.</para></listitem></varlistentry>
+   <varlistentry><term>conUB :</term>
+      <listitem><para> a m x 1 matrix of doubles, where m is number of constraints, contains upper bounds of the constraints.</para></listitem></varlistentry>
+   <varlistentry><term>xopt :</term>
+      <listitem><para> a 1xn matrix of doubles, the computed solution of the optimization problem.</para></listitem></varlistentry>
+   <varlistentry><term>fopt :</term>
+      <listitem><para> a 1x1 matrix of doubles, the function value at x.</para></listitem></varlistentry>
+   <varlistentry><term>exitflag :</term>
+      <listitem><para> Integer identifying the reason the algorithm terminated.</para></listitem></varlistentry>
+   <varlistentry><term>output :</term>
+      <listitem><para> Structure containing information about the optimization.</para></listitem></varlistentry>
+   <varlistentry><term>lambda :</term>
+      <listitem><para> Structure containing the Lagrange multipliers at the solution x (separated by constraint type).</para></listitem></varlistentry>
+   </variablelist>
+</refsection>
+
+<refsection>
+   <title>Description</title>
+   <para>
+Search the minimum of a constrained linear quadratic optimization problem specified by :
+find the minimum of f(x) such that
+   </para>
+   <para>
+<latex>
+\begin{eqnarray}
+&amp;\mbox{min}_{x}
+&amp; 1/2*x'*Q*x + p'*x  \\
+&amp; \text{subject to} &amp; conLB \leq C(x) \leq conUB \\
+&amp; &amp; lb \leq x \leq ub \\
+\end{eqnarray}
+</latex>
+   </para>
+   <para>
+We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. The code has been written by ​Andreas Wächter and ​Carl Laird.
+   </para>
+   <para>
+</para>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//Find x in R^6 such that:
+
+conMatrix= [1,-1,1,0,3,1;
+-1,0,-3,-4,5,6;
+2,5,3,0,1,0
+0,1,0,1,2,-1;
+-1,0,2,1,1,0];
+conLB=[1 2 3 -%inf -%inf]';
+conUB = [1 2 3 -1 2.5]';
+//with  x between ci and cs:
+lb=[-1000 -10000 0 -1000 -1000 -1000];
+ub=[10000 100 1.5 100 100 1000];
+//and minimize 0.5*x'*Q*x + p'*x with
+p=[1;2;3;4;5;6]; Q=eye(6,6);
+nbVar = 6;
+nbCon = 5;
+[xopt,fopt,exitflag,output,lambda]=qpipopt(nbVar,nbCon,Q,p,lb,ub,conMatrix,conLB,conUB)
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//min. -8*x1 -16*x2 + x1^2 + 4* x2^2
+//  such that
+//     x1 + x2 <= 5,
+//     x1 <= 3,
+//     x1 >= 0,
+//     x2 >= 0
+conMatrix= [1 1];
+conLB=[-%inf];
+conUB = [5];
+//with  x between ci and cs:
+lb=[0,0];
+ub=[3,%inf];
+//and minimize 0.5*x'*Q*x + p'*x with
+p=[-8,-16];
+Q=[1,0;0,4];
+nbVar = 2;
+nbCon = 1;
+[xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,Q,p,lb,ub,conMatrix,conLB,conUB)
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Authors</title>
+   <simplelist type="vert">
+   <member>Keyur Joshi, Saikiran, Iswarya, Harpreet Singh</member>
+   </simplelist>
+</refsection>
+</refentry>