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<h2 class="chapter">4 Functions for numerical approximation of gradients and Hessians</h2>
<p><a name="index-gradient-functions-74"></a>
You should not usually need to use these functions directly or pass them
as arguments. They should be chosen and used by optimizer functions,
possibly subject to their configuration options.
<ul class="menu">
<li><a accesskey="1" href="dfpdp.html#dfpdp">dfpdp</a>: Direct user interface to default numerical
gradient method of new frontends.
<li><a accesskey="2" href="deriv.html#deriv">deriv</a>: Higher order numerical derivatives.
<li><a accesskey="3" href="numgradient.html#numgradient">numgradient</a>: Another numerical gradient function.
<li><a accesskey="4" href="numhessian.html#numhessian">numhessian</a>: Numerical Hessian function.
<li><a accesskey="5" href="cdiff.html#cdiff">cdiff</a>: A string, yielding the numerical gradient if
evaluated.
<li><a accesskey="6" href="jacobs.html#jacobs">jacobs</a>: Complex step derivatives.
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