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Updated version number and release date
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<h1>Py-BOBYQA: Derivative-Free Optimizer for Bound-Constrained Minimization<a class="headerlink" href="#py-bobyqa-derivative-free-optimizer-for-bound-constrained-minimization" title="Link to this heading"></a></h1>
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<p><strong>Release:</strong> 1.5</p>
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<p><strong>Release:</strong> 1.5.0</p>
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<p><strong>Date:</strong> 16 September 2024</p>
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<p><strong>Author:</strong> <a class="reference external" href="mailto:lindon&#46;roberts&#37;&#52;&#48;sydney&#46;edu&#46;au">Lindon Roberts</a></p>
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<p>Py-BOBYQA is a flexible package for finding local solutions to nonlinear, nonconvex minimization problems (with optional bound and other convex constraints), without requiring any derivatives of the objective. Py-BOBYQA is a Python implementation of the <a class="reference external" href="http://mat.uc.pt/~zhang/software.html#powell_software">BOBYQA</a> solver by Powell (documentation <a class="reference external" href="http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2009_06.pdf">here</a>). It is particularly useful when evaluations of the objective function are expensive and/or noisy.</p>
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<p>Py-BOBYQA is a type of <em>trust-region</em> method, a common category of optimization algorithms for nonconvex problems. Given a current estimate of the solution <span class="math notranslate nohighlight">\(x_k\)</span>, we compute a model which approximates the objective <span class="math notranslate nohighlight">\(m_k(s)\approx f(x_k+s)\)</span> (for small steps <span class="math notranslate nohighlight">\(s\)</span>), and maintain a value <span class="math notranslate nohighlight">\(\Delta_k&gt;0\)</span> (called the <em>trust region radius</em>) which measures the size of <span class="math notranslate nohighlight">\(s\)</span> for which the approximation is good.</p>
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<p>At each step, we compute a trial step <span class="math notranslate nohighlight">\(s_k\)</span> designed to make our approximation <span class="math notranslate nohighlight">\(m_k(s)\)</span> small (this task is called the <em>trust region subproblem</em>). We evaluate the objective at this new point, and if this provided a good decrease in the objective, we take the step (<span class="math notranslate nohighlight">\(x_{k+1}=x_k+s_k\)</span>), otherwise we stay put (<span class="math notranslate nohighlight">\(x_{k+1}=x_k\)</span>). Based on this information, we choose a new value <span class="math notranslate nohighlight">\(\Delta_{k+1}\)</span>, and repeat the process.</p>
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<p>In Py-BOBYQA, we construct our approximation <span class="math notranslate nohighlight">\(m_k(s)\)</span> by interpolating a linear or quadratic approximation for <span class="math notranslate nohighlight">\(f(x)\)</span> at several points close to <span class="math notranslate nohighlight">\(x_k\)</span>. To make sure our interpolated model is accurate, we need to regularly check that the points are well-spaced, and move them if they aren’t (i.e. improve the geometry of our interpolation points).</p>
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<p>Py-BOBYQA is a Python implementation of the BOBYQA solver by Powell <a class="reference internal" href="#powell2009" id="id1"><span>[Powell2009]</span></a>. More details about Py-BOBYQA algorithm are given in our paper <a class="reference internal" href="#cfmr2018" id="id2"><span>[CFMR2018]</span></a>.</p>
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<p>Py-BOBYQA is a Python implementation of the BOBYQA solver by Powell <a class="reference internal" href="#powell2009" id="id1"><span>[Powell2009]</span></a>. More details about Py-BOBYQA algorithm are given in our paper <a class="reference internal" href="userguide.html#cfmr2018" id="id2"><span>[CFMR2018]</span></a>.</p>
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<title>Using Py-BOBYQA &mdash; Py-BOBYQA v1.5 documentation</title>
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‎docs/build/latex/Py-BOBYQA.aux

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