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<h3 class="section">4.1 Direct user interface to default numerical gradient method of new frontends</h3>
<p><a name="index-dfpdp-75"></a>
<h4 class="subheading">Helptext:</h4>
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<pre class="verbatim">function jac = dfpdp (p, func[, hook])
Returns Jacobian of func (p) with respect to p with finite
differencing. The optional argument hook is a structure which can
contain the following fields at the moment:
hook.f: value of func(p) for p as given in the arguments
hook.diffp: positive vector of fractional steps from given p in
finite differencing (actual steps may be smaller if bounds are
given). The default is .001 * ones (size (p)).
hook.diff_onesided: logical vector, indexing elements of p for
which only one-sided differences should be computed (faster); even
if not one-sided, differences might not be exactly central if
bounds are given. The default is false (size (p)).
hook.fixed: logical vector, indexing elements of p for which zero
should be returned instead of the guessed partial derivatives
(useful in optimization if some parameters are not optimized, but
are 'fixed').
hook.lbound, hook.ubound: vectors of lower and upper parameter
bounds (or -Inf or +Inf, respectively) to be respected in finite
differencing. The consistency of bounds is not checked.
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