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<div class="title">Numerical differentiation module</div> </div>
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<div class="fragment"><div class="line">* #include <unsupported/Eigen/NumericalDiff></div>
<div class="line">* </div>
</div><!-- fragment --><p>See <a href="http://en.wikipedia.org/wiki/Numerical_differentiation">http://en.wikipedia.org/wiki/Numerical_differentiation</a></p>
<p>Warning : this should NOT be confused with automatic differentiation, which is a different method and has its own module in <a class="el" href="namespaceEigen.html" title="iterative scaling algorithm to equilibrate rows and column norms in matrices ">Eigen</a> : <a class="el" href="group__AutoDiff__Module.html">Auto Diff module</a>.</p>
<p>Currently only "Forward" and "Central" schemes are implemented. Those are basic methods, and there exist some more elaborated way of computing such approximates. They are implemented using both proprietary and free software, and usually requires linking to an external library. It is very easy for you to write a functor using such software, and the purpose is quite orthogonal to what we want to achieve with <a class="el" href="namespaceEigen.html" title="iterative scaling algorithm to equilibrate rows and column norms in matrices ">Eigen</a>.</p>
<p>This is why we will not provide wrappers for every great numerical differentiation software that exist, but should rather stick with those basic ones, that still are useful for testing.</p>
<p>Also, the <a class="el" href="group__NonLinearOptimization__Module.html">Non linear optimization module</a> needs this in order to provide full features compatibility with the original (c)minpack package. </p>
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