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algorithms.html

@@ -400,7 +400,7 @@ <h2> Contents </h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="pygmo.scipy_optimize">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pygmo.</span></span><span class="sig-name descname"><span class="pre">scipy_optimize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">args=(),</span> <span class="pre">method:</span> <span class="pre">typing.Optional[str]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">tol:</span> <span class="pre">typing.Optional[float]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">callback:</span> <span class="pre">typing.Optional[typing.Callable[[typing.Any],</span> <span class="pre">typing.Any]]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">options:</span> <span class="pre">typing.Optional[typing.MutableMapping[str,</span> <span class="pre">typing.Any]]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">selection:</span> <span class="pre">pygmo.core.s_policy</span> <span class="pre">=</span> <span class="pre">Selection</span> <span class="pre">policy</span> <span class="pre">name:</span> <span class="pre">Select</span> <span class="pre">best</span> <span class="pre">	C++</span> <span class="pre">class</span> <span class="pre">name:</span> <span class="pre">pagmo::select_best</span>&#160; <span class="pre">Extra</span> <span class="pre">info:</span> <span class="pre">	Absolute</span> <span class="pre">migration</span> <span class="pre">rate:</span> <span class="pre">1</span> </em><span class="sig-paren">)</span><a class="headerlink" href="#pygmo.scipy_optimize" title="Permalink to this definition">#</a></dt>
-<dd><p>This class is a user defined algorithm (UDA) providing a wrapper around the function <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize" title="(in SciPy v1.10.1)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.optimize.minimize()</span></code></a>.</p>
+<dd><p>This class is a user defined algorithm (UDA) providing a wrapper around the function <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize" title="(in SciPy v1.11.1)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.optimize.minimize()</span></code></a>.</p>
 <p>This wraps several well-known local optimization algorithms:</p>
 <blockquote>
 <div><ul class="simple">
@@ -440,7 +440,7 @@ <h2> Contents </h2>
 </pre></div>
 </div>
 <p>The constructor initializes a wrapper instance for a specific algorithm.
-Construction arguments are those options of <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize" title="(in SciPy v1.10.1)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.optimize.minimize()</span></code></a> that are not problem-specific.
+Construction arguments are those options of <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize" title="(in SciPy v1.11.1)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.optimize.minimize()</span></code></a> that are not problem-specific.
 Problem-specific options, for example the bounds, constraints and the existence of a gradient and hessian,
 are deduced from the problem in the population given to the evolve function.</p>
 <dl class="field-list simple">
@@ -736,7 +736,7 @@ <h2>Algorithms exposed from C++<a class="headerlink" href="#algorithms-exposed-f
 <dt class="sig sig-object py" id="pygmo.maco.set_bfe">
 <span class="sig-name descname"><span class="pre">set_bfe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pygmo.maco.set_bfe" title="Permalink to this definition">#</a></dt>
 <dd><p>Set the batch function evaluation scheme.</p>
-<p>This method will set the batch function evaluation scheme to be used for <a class="reference internal" href="#pygmo.nsga2" title="pygmo.nsga2"><code class="xref py py-class docutils literal notranslate"><span class="pre">nsga2</span></code></a>.</p>
+<p>This method will set the batch function evaluation scheme to be used for <a class="reference internal" href="#pygmo.maco" title="pygmo.maco"><code class="xref py py-class docutils literal notranslate"><span class="pre">maco</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><p><strong>b</strong> (<a class="reference internal" href="bfe.html#pygmo.bfe" title="pygmo.bfe"><code class="xref py py-class docutils literal notranslate"><span class="pre">bfe</span></code></a>) – the batch function evaluation object</p>
@@ -1613,6 +1613,21 @@ <h2>Algorithms exposed from C++<a class="headerlink" href="#algorithms-exposed-f
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="pygmo.cmaes.set_bfe">
+<span class="sig-name descname"><span class="pre">set_bfe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#pygmo.cmaes.set_bfe" title="Permalink to this definition">#</a></dt>
+<dd><p>Set the batch function evaluation scheme.</p>
+<p>This method will set the batch function evaluation scheme to be used for <a class="reference internal" href="#pygmo.cmaes" title="pygmo.cmaes"><code class="xref py py-class docutils literal notranslate"><span class="pre">cmaes</span></code></a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>b</strong> (<a class="reference internal" href="bfe.html#pygmo.bfe" title="pygmo.bfe"><code class="xref py py-class docutils literal notranslate"><span class="pre">bfe</span></code></a>) – the batch function evaluation object</p>
+</dd>
+<dt class="field-even">Raises</dt>
+<dd class="field-even"><p><strong>unspecified</strong> – any exception thrown by the underlying C++ method</p>
+</dd>
+</dl>
+</dd></dl>
+
 </dd></dl>
 
 <hr class="docutils" />

generic_utils.html

@@ -402,7 +402,7 @@ <h1>Generic optimization utilities<a class="headerlink" href="#generic-optimizat
 <dd class="field-even"><p>a random decision vector within the problem’s bounds</p>
 </dd>
 <dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.25)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 <dt class="field-even">Raises</dt>
 <dd class="field-even"><ul class="simple">
@@ -439,7 +439,7 @@ <h1>Generic optimization utilities<a class="headerlink" href="#generic-optimizat
 <dd class="field-even"><p>a batch of random decision vectors within the problem’s bounds, laid out contiguously in a 1D array</p>
 </dd>
 <dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.25)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 <dt class="field-even">Raises</dt>
 <dd class="field-even"><ul class="simple">

genetic_op_utils.html

@@ -402,7 +402,7 @@ <h1>Generic optimization utilities<a class="headerlink" href="#generic-optimizat
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p>of <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>: containing the two crossovered chromosomes</p>
+<dd class="field-even"><p>of <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.25)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>: containing the two crossovered chromosomes</p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a></p>
@@ -438,7 +438,7 @@ <h1>Generic optimization utilities<a class="headerlink" href="#generic-optimizat
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p>of <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>: containing the two crossovered chromosomes</p>
+<dd class="field-even"><p>of <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.25)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>: containing the two crossovered chromosomes</p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.11)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a></p>

genindex.html

@@ -1206,9 +1206,11 @@ <h2 id="S">S</h2>
         <li><a href="topologies.html#pygmo.ring.set_all_weights">(pygmo.ring method)</a>
 </li>
       </ul></li>
-      <li><a href="algorithms.html#pygmo.gaco.set_bfe">set_bfe() (pygmo.gaco method)</a>
+      <li><a href="algorithms.html#pygmo.cmaes.set_bfe">set_bfe() (pygmo.cmaes method)</a>
 
       <ul>
+        <li><a href="algorithms.html#pygmo.gaco.set_bfe">(pygmo.gaco method)</a>
+</li>
         <li><a href="algorithms.html#pygmo.maco.set_bfe">(pygmo.maco method)</a>
 </li>
         <li><a href="algorithms.html#pygmo.moead_gen.set_bfe">(pygmo.moead_gen method)</a>

gh_utils.html

@@ -418,7 +418,7 @@ <h1>Gradient and Hessians utilities<a class="headerlink" href="#gradient-and-hes
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">pygmo</span> <span class="k">as</span> <span class="nn">pg</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">my_fun</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pg</span><span class="o">.</span><span class="n">estimate_sparsity</span><span class="p">(</span><span class="n">callable</span> <span class="o">=</span> <span class="n">my_fun</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">],</span> <span class="n">dx</span> <span class="o">=</span> <span class="mf">1e-8</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pg</span><span class="o">.</span><span class="n">estimate_sparsity</span><span class="p">(</span><span class="nb">callable</span> <span class="o">=</span> <span class="n">my_fun</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">],</span> <span class="n">dx</span> <span class="o">=</span> <span class="mf">1e-8</span><span class="p">)</span>
 <span class="go">array([[0, 0],</span>
 <span class="go">       [0, 3],</span>
 <span class="go">       [1, 2],</span>
@@ -464,7 +464,7 @@ <h1>Gradient and Hessians utilities<a class="headerlink" href="#gradient-and-hes
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">pygmo</span> <span class="k">as</span> <span class="nn">pg</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">my_fun</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pg</span><span class="o">.</span><span class="n">estimate_gradient</span><span class="p">(</span><span class="n">callable</span> <span class="o">=</span> <span class="n">my_fun</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span><span class="p">,</span> <span class="n">dx</span> <span class="o">=</span> <span class="mf">1e-8</span><span class="p">)</span> 
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pg</span><span class="o">.</span><span class="n">estimate_gradient</span><span class="p">(</span><span class="nb">callable</span> <span class="o">=</span> <span class="n">my_fun</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span><span class="p">,</span> <span class="n">dx</span> <span class="o">=</span> <span class="mf">1e-8</span><span class="p">)</span> 
 <span class="go">array([1., 0., 0., 1., 0., 0., 1., 0., 0., 1., 0., 0.])</span>
 </pre></div>
 </div>
@@ -510,7 +510,7 @@ <h1>Gradient and Hessians utilities<a class="headerlink" href="#gradient-and-hes
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">pygmo</span> <span class="k">as</span> <span class="nn">pg</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">my_fun</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="n">pg</span><span class="o">.</span><span class="n">estimate_gradient_h</span><span class="p">(</span><span class="n">callable</span> <span class="o">=</span> <span class="n">my_fun</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span><span class="p">,</span> <span class="n">dx</span> <span class="o">=</span> <span class="mf">1e-2</span><span class="p">)</span> 
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pg</span><span class="o">.</span><span class="n">estimate_gradient_h</span><span class="p">(</span><span class="nb">callable</span> <span class="o">=</span> <span class="n">my_fun</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="mi">4</span><span class="p">,</span> <span class="n">dx</span> <span class="o">=</span> <span class="mf">1e-2</span><span class="p">)</span> 
 <span class="go">array([1., 0., 0., 1., 0., 0., 1., 0., 0., 1., 0., 0.])</span>
 </pre></div>
 </div>

population.html

@@ -631,7 +631,7 @@ <h1>Population class<a class="headerlink" href="#population-class" title="Permal
 <dd class="field-odd"><p>a random decision vector within the problem’s bounds</p>
 </dd>
 <dt class="field-even">Return type</dt>
-<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
+<dd class="field-even"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.25)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></p>
 </dd>
 <dt class="field-odd">Raises</dt>
 <dd class="field-odd"><p><strong>unspecified</strong> – any exception thrown by <a class="reference internal" href="generic_utils.html#pygmo.random_decision_vector" title="pygmo.random_decision_vector"><code class="xref py py-func docutils literal notranslate"><span class="pre">pygmo.random_decision_vector()</span></code></a></p>