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<a name="math_toolkit.gd_opt"></a><a class="link" href="gd_opt.html" title="Gradient Based Optimizers">Gradient Based Optimizers</a>
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<dt><span class="section"><a href="gd_opt/introduction.html">Introduction</a></span></dt>
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<dt><span class="section"><a href="gd_opt/gradient_descent.html">Gradient Desccent</a></span></dt>
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<dt><span class="section"><a href="gd_opt/nesterov.html">Nesterov Gradient Desccent</a></span></dt>
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<p>
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Gradient based optimizers are algorithms that use the gradient of a funciton
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to iteratively find locally extreme points of functions over a set of parameters.
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This sections provides a description of a set of gradient optimizers. The optimizers
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are written with <code class="computeroutput"><span class="identifier">boost</span><span class="special">::</span><span class="identifier">math</span><span class="special">::</span><span class="identifier">differentiation</span><span class="special">::</span><span class="identifier">reverse_mode</span><span class="special">::</span><span class="identifier">rvar</span></code> in
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mind, however if a way to evaluate the funciton and its gradient is provided,
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the optimizers should work in exactly the same way.
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<div class="copyright-footer">Copyright © 2006-2021 Nikhar Agrawal, Anton Bikineev, Matthew Borland,
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Paul A. Bristow, Marco Guazzone, Christopher Kormanyos, Hubert Holin, Bruno
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Lalande, John Maddock, Evan Miller, Jeremy Murphy, Matthew Pulver, Johan Råde,
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Gautam Sewani, Benjamin Sobotta, Nicholas Thompson, Thijs van den Berg, Daryle
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Walker and Xiaogang Zhang<p>
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Distributed under the Boost Software License, Version 1.0. (See accompanying
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file LICENSE_1_0.txt or copy at <a href="http://www.boost.org/LICENSE_1_0.txt" target="_top">http://www.boost.org/LICENSE_1_0.txt</a>)
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