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<a name="Multimin-Algorithms-with-Derivatives"></a>
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<p>
Next: <a href="Multimin-Algorithms-without-Derivatives.html#Multimin-Algorithms-without-Derivatives" accesskey="n" rel="next">Multimin Algorithms without Derivatives</a>, Previous: <a href="Multimin-Stopping-Criteria.html#Multimin-Stopping-Criteria" accesskey="p" rel="previous">Multimin Stopping Criteria</a>, Up: <a href="Multidimensional-Minimization.html#Multidimensional-Minimization" accesskey="u" rel="up">Multidimensional Minimization</a> &nbsp; [<a href="Function-Index.html#Function-Index" title="Index" rel="index">Index</a>]</p>
</div>
<hr>
<a name="Algorithms-with-Derivatives"></a>
<h3 class="section">37.7 Algorithms with Derivatives</h3>

<p>There are several minimization methods available. The best choice of
algorithm depends on the problem.  The algorithms described in this
section use the value of the function and its gradient at each
evaluation point.
</p>
<dl>
<dt><a name="index-gsl_005fmultimin_005ffdfminimizer_005fconjugate_005ffr"></a>Minimizer: <strong>gsl_multimin_fdfminimizer_conjugate_fr</strong></dt>
<dd><a name="index-Fletcher_002dReeves-conjugate-gradient-algorithm_002c-minimization"></a>
<a name="index-Conjugate-gradient-algorithm_002c-minimization"></a>
<a name="index-minimization_002c-conjugate-gradient-algorithm"></a>
<p>This is the Fletcher-Reeves conjugate gradient algorithm. The conjugate
gradient algorithm proceeds as a succession of line minimizations. The
sequence of search directions is used to build up an approximation to the
curvature of the function in the neighborhood of the minimum.  
</p>
<p>An initial search direction <var>p</var> is chosen using the gradient, and line
minimization is carried out in that direction.  The accuracy of the line
minimization is specified by the parameter <var>tol</var>.  The minimum
along this line occurs when the function gradient <var>g</var> and the search direction
<var>p</var> are orthogonal.  The line minimization terminates when
<em>dot(p,g) &lt; tol |p| |g|</em>.  The
search direction is updated  using the Fletcher-Reeves formula
<em>p' = g' - \beta g</em> where <em>\beta=-|g'|^2/|g|^2</em>, and
the line minimization is then repeated for the new search
direction.
</p></dd></dl>

<dl>
<dt><a name="index-gsl_005fmultimin_005ffdfminimizer_005fconjugate_005fpr"></a>Minimizer: <strong>gsl_multimin_fdfminimizer_conjugate_pr</strong></dt>
<dd><a name="index-Polak_002dRibiere-algorithm_002c-minimization"></a>
<a name="index-minimization_002c-Polak_002dRibiere-algorithm"></a>
<p>This is the Polak-Ribiere conjugate gradient algorithm.  It is similar
to the Fletcher-Reeves method, differing only in the choice of the
coefficient <em>\beta</em>. Both methods work well when the evaluation
point is close enough to the minimum of the objective function that it
is well approximated by a quadratic hypersurface.
</p></dd></dl>

<dl>
<dt><a name="index-gsl_005fmultimin_005ffdfminimizer_005fvector_005fbfgs2"></a>Minimizer: <strong>gsl_multimin_fdfminimizer_vector_bfgs2</strong></dt>
<dt><a name="index-gsl_005fmultimin_005ffdfminimizer_005fvector_005fbfgs"></a>Minimizer: <strong>gsl_multimin_fdfminimizer_vector_bfgs</strong></dt>
<dd><a name="index-BFGS-algorithm_002c-minimization"></a>
<a name="index-minimization_002c-BFGS-algorithm"></a>
<p>These methods use the vector Broyden-Fletcher-Goldfarb-Shanno (BFGS)
algorithm.  This is a quasi-Newton method which builds up an approximation
to the second derivatives of the function <em>f</em> using the difference
between successive gradient vectors.  By combining the first and second
derivatives the algorithm is able to take Newton-type steps towards the
function minimum, assuming quadratic behavior in that region.
</p>
<p>The <code>bfgs2</code> version of this minimizer is the most efficient
version available, and is a faithful implementation of the line
minimization scheme described in Fletcher&rsquo;s <cite>Practical Methods of
Optimization</cite>, Algorithms 2.6.2 and 2.6.4.  It supersedes the original
<code>bfgs</code> routine and requires substantially fewer function and
gradient evaluations.  The user-supplied tolerance <var>tol</var>
corresponds to the parameter <em>\sigma</em> used by Fletcher.  A value
of 0.1 is recommended for typical use (larger values correspond to
less accurate line searches).
</p>
</dd></dl>

<dl>
<dt><a name="index-gsl_005fmultimin_005ffdfminimizer_005fsteepest_005fdescent"></a>Minimizer: <strong>gsl_multimin_fdfminimizer_steepest_descent</strong></dt>
<dd><a name="index-steepest-descent-algorithm_002c-minimization"></a>
<a name="index-minimization_002c-steepest-descent-algorithm"></a>
<p>The steepest descent algorithm follows the downhill gradient of the
function at each step. When a downhill step is successful the step-size
is increased by a factor of two.  If the downhill step leads to a higher
function value then the algorithm backtracks and the step size is
decreased using the parameter <var>tol</var>.  A suitable value of <var>tol</var>
for most applications is 0.1.  The steepest descent method is
inefficient and is included only for demonstration purposes.
</p></dd></dl>

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Next: <a href="Multimin-Algorithms-without-Derivatives.html#Multimin-Algorithms-without-Derivatives" accesskey="n" rel="next">Multimin Algorithms without Derivatives</a>, Previous: <a href="Multimin-Stopping-Criteria.html#Multimin-Stopping-Criteria" accesskey="p" rel="previous">Multimin Stopping Criteria</a>, Up: <a href="Multidimensional-Minimization.html#Multidimensional-Minimization" accesskey="u" rel="up">Multidimensional Minimization</a> &nbsp; [<a href="Function-Index.html#Function-Index" title="Index" rel="index">Index</a>]</p>
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