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  <div class="section" id="the-descentminimizer-class">
<h1>The <code class="docutils literal"><span class="pre">DescentMinimizer</span></code> class – …<a class="headerlink" href="#the-descentminimizer-class" title="Permalink to this headline"></a></h1>
<dl class="class">
<dt id="nifty.DescentMinimizer">
<em class="property">class </em><code class="descclassname">nifty.</code><code class="descname">DescentMinimizer</code><a class="reference internal" href="_modules/nifty/minimization/descent_minimizer.html#DescentMinimizer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#nifty.DescentMinimizer" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal"><span class="pre">keepers.logging.loggable.Loggable</span></code>, <code class="xref py py-class docutils literal"><span class="pre">object</span></code></p>
<p>A base class used by gradient methods to find a local minimum.</p>
<p>Descent minimization methods are used to find a local minimum of a scalar
function by following a descent direction. This class implements the
minimization procedure once a descent direction is known. The descent
direction has to be implemented separately.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>line_searcher</strong> : callable <em>optional</em></p>
<blockquote>
<div><p>Function which infers the step size in the descent direction
(default : LineSearchStrongWolfe()).</p>
</div></blockquote>
<p><strong>callback</strong> : callable <em>optional</em></p>
<blockquote>
<div><p>Function f(energy, iteration_number) supplied by the user to perform
in-situ analysis at every iteration step. When being called the
current energy and iteration_number are passed. (default: None)</p>
</div></blockquote>
<p><strong>convergence_tolerance</strong> : float <em>optional</em></p>
<blockquote>
<div><p>Tolerance specifying the case of convergence. (default: 1E-4)</p>
</div></blockquote>
<p><strong>convergence_level</strong> : integer <em>optional</em></p>
<blockquote>
<div><p>Number of times the tolerance must be undershot before convergence
is reached. (default: 3)</p>
</div></blockquote>
<p><strong>iteration_limit</strong> : integer <em>optional</em></p>
<blockquote class="last">
<div><p>Maximum number of iterations performed (default: None).</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Notes</p>
<p>The callback function can be used to externally stop the minimization by
raising a <cite>StopIteration</cite> exception.
Check <cite>get_descent_direction</cite> of a derived class for information on the
concrete minization scheme.</p>
<p class="rubric">Attributes</p>
<table border="1" class="docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td>convergence_tolerance</td>
<td>(float) Tolerance specifying the case of convergence.</td>
</tr>
<tr class="row-even"><td>convergence_level</td>
<td>(integer) Number of times the tolerance must be undershot before convergence is reached. (default: 3)</td>
</tr>
<tr class="row-odd"><td>iteration_limit</td>
<td>(integer) Maximum number of iterations performed.</td>
</tr>
<tr class="row-even"><td>line_searcher</td>
<td>(LineSearch) Function which infers the optimal step size for functional minization given a descent direction.</td>
</tr>
<tr class="row-odd"><td>callback</td>
<td>(function) Function f(energy, iteration_number) supplied by the user to perform in-situ analysis at every iteration step. When being called the current energy and iteration_number are passed.</td>
</tr>
</tbody>
</table>
<p class="rubric">Methods</p>
<table border="1" class="longtable docutils">
<colgroup>
<col width="10%" />
<col width="90%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><code class="xref py py-obj docutils literal"><span class="pre">__call__</span></code>(energy)</td>
<td>Performs the minimization of the provided Energy functional.</td>
</tr>
<tr class="row-even"><td><code class="xref py py-obj docutils literal"><span class="pre">get_descend_direction</span></code>(energy)</td>
<td></td>
</tr>
</tbody>
</table>
</dd></dl>

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