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<span class="path"><a href="index.html">Symphony Toolbox</a> >> <a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a> > lsqnonneg</span>
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<div class="refnamediv"><h1 class="refname">lsqnonneg</h1>
<p class="refpurpose">Solves nonnegative least-squares curve fitting problems.</p></div>
<div class="refsynopsisdiv"><h3 class="title">Calling Sequence</h3>
<div class="synopsis"><pre><span class="default">xopt</span><span class="default"> = </span><span class="functionid">lsqnonneg</span><span class="default">(</span><span class="default">C</span><span class="default">,</span><span class="default">d</span><span class="default">)</span>
<span class="default">xopt</span><span class="default"> = </span><span class="functionid">lsqnonneg</span><span class="default">(</span><span class="default">C</span><span class="default">,</span><span class="default">d</span><span class="default">,</span><span class="default">param</span><span class="default">)</span>
<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">resnorm</span><span class="default">,</span><span class="default">residual</span><span class="default">,</span><span class="default">exitflag</span><span class="default">,</span><span class="default">output</span><span class="default">,</span><span class="default">lambda</span><span class="default">] = </span><span class="functionid">lsqnonneg</span><span class="default">( ... )</span></pre></div></div>
<div class="refsection"><h3 class="title">Parameters</h3>
<dl><dt><span class="term">C :</span>
<dd><p class="para">a matrix of double, represents the multiplier of the solution x in the expression C⋅x - d. Number of columns in C is equal to the number of elements in x.</p></dd></dt>
<dt><span class="term">d :</span>
<dd><p class="para">a vector of double, represents the additive constant term in the expression C⋅x - d. Number of elements in d is equal to the number of rows in C matrix.</p></dd></dt>
<dt><span class="term">xopt :</span>
<dd><p class="para">a vector of double, the computed solution of the optimization problem.</p></dd></dt>
<dt><span class="term">resnorm :</span>
<dd><p class="para">a double, objective value returned as the scalar value norm(C⋅x-d)^2.</p></dd></dt>
<dt><span class="term">residual :</span>
<dd><p class="para">a vector of double, solution residuals returned as the vector d-C⋅x.</p></dd></dt>
<dt><span class="term">exitflag :</span>
<dd><p class="para">The exit status. See below for details.</p></dd></dt>
<dt><span class="term">output :</span>
<dd><p class="para">The structure consist of statistics about the optimization. See below for details.</p></dd></dt>
<dt><span class="term">lambda :</span>
<dd><p class="para">The structure consist of the Lagrange multipliers at the solution of problem. See below for details.</p></dd></dt></dl></div>
<div class="refsection"><h3 class="title">Description</h3>
<p class="para">Solves nonnegative least-squares curve fitting problems specified by :</p>
<p class="para"><span><img src='./_LaTeX_lsqnonneg.xml_1.png' style='position:relative;top:19px;width:193px;height:46px'/></span></p>
<p class="para">The routine calls Ipopt for solving the nonnegative least-squares curve fitting problems, Ipopt is a library written in C++.</p>
<p class="para">The exitflag allows to know the status of the optimization which is given back by Ipopt.
<ul class="itemizedlist"><li>exitflag=0 : Optimal Solution Found</li>
<li>exitflag=1 : Maximum Number of Iterations Exceeded. Output may not be optimal.</li>
<li>exitflag=2 : Maximum CPU Time exceeded. Output may not be optimal.</li>
<li>exitflag=3 : Stop at Tiny Step.</li>
<li>exitflag=4 : Solved To Acceptable Level.</li>
<li>exitflag=5 : Converged to a point of local infeasibility.</li></ul></p>
<p class="para">For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/</p>
<p class="para">The output data structure contains detailed informations about the optimization process.
It has type "struct" and contains the following fields.
<ul class="itemizedlist"><li>output.iterations: The number of iterations performed during the search</li>
<li>output.constrviolation: The max-norm of the constraint violation.</li></ul></p>
<p class="para">The lambda data structure contains the Lagrange multipliers at the end
of optimization. In the current version the values are returned only when the the solution is optimal.
It has type "struct" and contains the following fields.
<ul class="itemizedlist"><li>lambda.lower: The Lagrange multipliers for the lower bound constraints.</li>
<li>lambda.upper: The Lagrange multipliers for the upper bound constraints.</li></ul></p>
<p class="para"></p></div>
<div class="refsection"><h3 class="title">Examples</h3>
<div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">// A basic lsqnonneg problem</span>
<span class="scilabid">C</span> <span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilabnumber">1</span> <span class="scilabnumber">1</span> <span class="scilabnumber">1</span><span class="scilabdefault">;</span>
<span class="scilabnumber">1</span> <span class="scilabnumber">1</span> <span class="scilabnumber">0</span><span class="scilabdefault">;</span>
<span class="scilabnumber">0</span> <span class="scilabnumber">1</span> <span class="scilabnumber">1</span><span class="scilabdefault">;</span>
<span class="scilabnumber">1</span> <span class="scilabnumber">0</span> <span class="scilabnumber">0</span><span class="scilabdefault">;</span>
<span class="scilabnumber">0</span> <span class="scilabnumber">0</span> <span class="scilabnumber">1</span><span class="scilabopenclose">]</span>
<span class="scilabid">d</span> <span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilabnumber">89</span><span class="scilabdefault">;</span>
<span class="scilabnumber">67</span><span class="scilabdefault">;</span>
<span class="scilabnumber">53</span><span class="scilabdefault">;</span>
<span class="scilabnumber">35</span><span class="scilabdefault">;</span>
<span class="scilabnumber">20</span><span class="scilabdefault">;</span><span class="scilabopenclose">]</span>
<span class="scilabopenclose">[</span><span class="scilabid">xopt</span><span class="scilabdefault">,</span><span class="scilabid">resnorm</span><span class="scilabdefault">,</span><span class="scilabid">residual</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabdefault">,</span><span class="scilabid">lambda</span><span class="scilabopenclose">]</span> <span class="scilaboperator">=</span> <span class="scilabid">lsqnonneg</span><span class="scilabopenclose">(</span><span class="scilabid">C</span><span class="scilabdefault">,</span><span class="scilabid">d</span><span class="scilabopenclose">)</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
<div class="refsection"><h3 class="title">Authors</h3>
<ul class="itemizedlist"><li class="member">Harpreet Singh</li></ul></div>
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