From 29e8e8bbd43892c7fa146c165fdf128f786d6a7b Mon Sep 17 00:00:00 2001
From: Harpreet
Date: Mon, 2 Nov 2015 16:20:08 +0530
Subject: README.rst added

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+<?xml version="1.0" encoding="UTF-8"?>
+
+<!--
+ *
+ * This help file was generated from qpipoptmat.sci using help_from_sci().
+ *
+ -->
+
+<refentry version="5.0-subset Scilab" xml:id="qpipoptmat" 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>qpipoptmat</refname>
+    <refpurpose>Solves a linear quadratic problem.</refpurpose>
+  </refnamediv>
+
+
+<refsynopsisdiv>
+   <title>Calling Sequence</title>
+   <synopsis>
+   x = qpipoptmat(H,f)
+   x = qpipoptmat(H,f,A,b)
+   x = qpipoptmat(H,f,A,b,Aeq,beq)
+   x = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub)
+   x = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0)
+   x = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0,param)
+   [xopt,fopt,exitflag,output,lamda] = qpipoptmat( ... )
+   
+   </synopsis>
+</refsynopsisdiv>
+
+<refsection>
+   <title>Parameters</title>
+   <variablelist>
+   <varlistentry><term>H :</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>f :</term>
+      <listitem><para> a n x 1 matrix of doubles, where n is number of variables, represents coefficients of linear in the quadratic problem</para></listitem></varlistentry>
+   <varlistentry><term>A :</term>
+      <listitem><para> a m x n matrix of doubles, represents the linear coefficients in the inequality constraints</para></listitem></varlistentry>
+   <varlistentry><term>b :</term>
+      <listitem><para> a column vector of doubles, represents the linear coefficients in the inequality constraints</para></listitem></varlistentry>
+   <varlistentry><term>Aeq :</term>
+      <listitem><para> a meq x n matrix of doubles, represents the linear coefficients in the equality constraints</para></listitem></varlistentry>
+   <varlistentry><term>beq :</term>
+      <listitem><para> a vector of doubles, represents the linear coefficients in the equality constraints</para></listitem></varlistentry>
+   <varlistentry><term>LB :</term>
+      <listitem><para> a n x 1 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 n x 1 matrix of doubles, where n is number of variables, contains upper bounds of the variables.</para></listitem></varlistentry>
+   <varlistentry><term>x0 :</term>
+      <listitem><para> a m x 1 matrix of doubles, where m is number of constraints, contains initial guess of variables.</para></listitem></varlistentry>
+   <varlistentry><term>param :</term>
+      <listitem><para> a list containing the the parameters to be set.</para></listitem></varlistentry>
+   <varlistentry><term>xopt :</term>
+      <listitem><para> a nx1 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'*H*x + f'*x  \\
+&amp; \text{subject to} &amp; A.x \leq b \\
+&amp; &amp; Aeq.x \leq beq \\
+&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:
+
+Aeq= [1,-1,1,0,3,1;
+-1,0,-3,-4,5,6;
+2,5,3,0,1,0];
+beq=[1; 2; 3];
+A= [0,1,0,1,2,-1;
+-1,0,2,1,1,0];
+b = [-1; 2.5];
+lb=[-1000; -10000; 0; -1000; -1000; -1000];
+ub=[10000; 100; 1.5; 100; 100; 1000];
+x0 = repmat(0,6,1);
+param = list("MaxIter", 300, "CpuTime", 100);
+//and minimize 0.5*x'*Q*x + p'*x with
+f=[1; 2; 3; 4; 5; 6]; H=eye(6,6);
+[xopt,fopt,exitflag,output,lambda]=qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,[],param);
+clear H f A b Aeq beq lb ub;
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//Find the value of x that minimize following function
+// f(x) = 0.5*x1^2 + x2^2 - x1*x2 - 2*x1 - 6*x2
+// Subject to:
+// x1 + x2 ≤ 2
+// –x1 + 2x2 ≤ 2
+// 2x1 + x2 ≤ 3
+// 0 ≤ x1, 0 ≤ x2.
+H = [1 -1; -1 2];
+f = [-2; -6];
+A = [1 1; -1 2; 2 1];
+b = [2; 2; 3];
+lb = [0; 0];
+ub = [%inf; %inf];
+[xopt,fopt,exitflag,output,lambda] = qpipoptmat(H,f,A,b,[],[],lb,ub)
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Authors</title>
+   <simplelist type="vert">
+   <member>Keyur Joshi, Saikiran, Iswarya, Harpreet Singh</member>
+   </simplelist>
+</refsection>
+</refentry>
-- 
cgit