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<h2 class="chapter">24 Simulated Annealing</h2>
<p><a name="index-simulated-annealing-2010"></a><a name="index-combinatorial-optimization-2011"></a><a name="index-optimization_002c-combinatorial-2012"></a><a name="index-energy-function-2013"></a><a name="index-cost-function-2014"></a>Stochastic search techniques are used when the structure of a space is
not well understood or is not smooth, so that techniques like Newton's
method (which requires calculating Jacobian derivative matrices) cannot
be used. In particular, these techniques are frequently used to solve
combinatorial optimization problems, such as the traveling salesman
problem.
<p>The goal is to find a point in the space at which a real valued
<dfn>energy function</dfn> (or <dfn>cost function</dfn>) is minimized. Simulated
annealing is a minimization technique which has given good results in
avoiding local minima; it is based on the idea of taking a random walk
through the space at successively lower temperatures, where the
probability of taking a step is given by a Boltzmann distribution.
<p>The functions described in this chapter are declared in the header file
<samp><span class="file">gsl_siman.h</span></samp>.
<ul class="menu">
<li><a accesskey="1" href="Simulated-Annealing-algorithm.html">Simulated Annealing algorithm</a>
<li><a accesskey="2" href="Simulated-Annealing-functions.html">Simulated Annealing functions</a>
<li><a accesskey="3" href="Examples-with-Simulated-Annealing.html">Examples with Simulated Annealing</a>
<li><a accesskey="4" href="Simulated-Annealing-References-and-Further-Reading.html">Simulated Annealing References and Further Reading</a>
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