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<li class="navelem"><a class="el" href="namespace_quant_lib.html">QuantLib</a></li><li class="navelem"><a class="el" href="class_quant_lib_1_1_non_linear_least_square.html">NonLinearLeastSquare</a></li> </ul>
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<a href="#pub-methods">Public Member Functions</a> |
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<div class="title">NonLinearLeastSquare Class Reference</div> </div>
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<p>Non-linear least-square method.
<a href="class_quant_lib_1_1_non_linear_least_square.html#details">More...</a></p>
<p><code>#include <ql/math/optimization/leastsquare.hpp></code></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a97ef06e4c5f0246dc4628a5e4d397444"><td class="memItemLeft" align="right" valign="top"><a id="a97ef06e4c5f0246dc4628a5e4d397444"></a>
 </td><td class="memItemRight" valign="bottom"><a class="el" href="class_quant_lib_1_1_non_linear_least_square.html#a97ef06e4c5f0246dc4628a5e4d397444">NonLinearLeastSquare</a> (<a class="el" href="class_quant_lib_1_1_constraint.html">Constraint</a> &c, <a class="el" href="group__types.html#ga4bdf4bfe76b9ffa6fa64c47d8bfa0c78">Real</a> accuracy=1e-4, <a class="el" href="group__types.html#gaf38bdb4c54463b1f456655efa95b5c77">Size</a> maxiter=100)</td></tr>
<tr class="memdesc:a97ef06e4c5f0246dc4628a5e4d397444"><td class="mdescLeft"> </td><td class="mdescRight">Default constructor. <br /></td></tr>
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<tr class="memitem:ae2998502b85c7e6c2c92431bf284d7b9"><td class="memItemLeft" align="right" valign="top"><a id="ae2998502b85c7e6c2c92431bf284d7b9"></a>
 </td><td class="memItemRight" valign="bottom"><a class="el" href="class_quant_lib_1_1_non_linear_least_square.html#ae2998502b85c7e6c2c92431bf284d7b9">NonLinearLeastSquare</a> (<a class="el" href="class_quant_lib_1_1_constraint.html">Constraint</a> &c, <a class="el" href="group__types.html#ga4bdf4bfe76b9ffa6fa64c47d8bfa0c78">Real</a> accuracy, <a class="el" href="group__types.html#gaf38bdb4c54463b1f456655efa95b5c77">Size</a> maxiter, const ext::shared_ptr< <a class="el" href="class_quant_lib_1_1_optimization_method.html">OptimizationMethod</a> > &om)</td></tr>
<tr class="memdesc:ae2998502b85c7e6c2c92431bf284d7b9"><td class="mdescLeft"> </td><td class="mdescRight">Default constructor. <br /></td></tr>
<tr class="separator:ae2998502b85c7e6c2c92431bf284d7b9"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a20fd9ab60a7cce2d6c0acad31c0038ce"><td class="memItemLeft" align="right" valign="top"><a id="a20fd9ab60a7cce2d6c0acad31c0038ce"></a>
 </td><td class="memItemRight" valign="bottom"><a class="el" href="class_quant_lib_1_1_non_linear_least_square.html#a20fd9ab60a7cce2d6c0acad31c0038ce">~NonLinearLeastSquare</a> ()</td></tr>
<tr class="memdesc:a20fd9ab60a7cce2d6c0acad31c0038ce"><td class="mdescLeft"> </td><td class="mdescRight">Destructor. <br /></td></tr>
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<a class="el" href="class_quant_lib_1_1_array.html">Array</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="class_quant_lib_1_1_non_linear_least_square.html#a067968a223168798fa3f64b6051f4354">perform</a> (<a class="el" href="class_quant_lib_1_1_least_square_problem.html">LeastSquareProblem</a> &lsProblem)</td></tr>
<tr class="memdesc:a067968a223168798fa3f64b6051f4354"><td class="mdescLeft"> </td><td class="mdescRight">Solve least square problem using numerix solver. <br /></td></tr>
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void </td><td class="memItemRight" valign="bottom"><b>setInitialValue</b> (const <a class="el" href="class_quant_lib_1_1_array.html">Array</a> &initialValue)</td></tr>
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<tr class="memitem:a1cbd190434a8924319877d9a566a7362"><td class="memItemLeft" align="right" valign="top"><a id="a1cbd190434a8924319877d9a566a7362"></a>
<a class="el" href="class_quant_lib_1_1_array.html">Array</a> & </td><td class="memItemRight" valign="bottom"><a class="el" href="class_quant_lib_1_1_non_linear_least_square.html#a1cbd190434a8924319877d9a566a7362">results</a> ()</td></tr>
<tr class="memdesc:a1cbd190434a8924319877d9a566a7362"><td class="mdescLeft"> </td><td class="mdescRight">return the results <br /></td></tr>
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<a class="el" href="group__types.html#ga4bdf4bfe76b9ffa6fa64c47d8bfa0c78">Real</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="class_quant_lib_1_1_non_linear_least_square.html#ab12209a008e22f49e05231300e3aeead">residualNorm</a> () const</td></tr>
<tr class="memdesc:ab12209a008e22f49e05231300e3aeead"><td class="mdescLeft"> </td><td class="mdescRight">return the least square residual norm <br /></td></tr>
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<a class="el" href="group__types.html#ga4bdf4bfe76b9ffa6fa64c47d8bfa0c78">Real</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="class_quant_lib_1_1_non_linear_least_square.html#a41dcabed89ac75b85696b3b3ce92bee3">lastValue</a> () const</td></tr>
<tr class="memdesc:a41dcabed89ac75b85696b3b3ce92bee3"><td class="mdescLeft"> </td><td class="mdescRight">return last function value <br /></td></tr>
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<tr class="memitem:a09297bd56f438d544473a442aef004dc"><td class="memItemLeft" align="right" valign="top"><a id="a09297bd56f438d544473a442aef004dc"></a>
<a class="el" href="group__types.html#gab9c87440c314438e51a899a03d2442d0">Integer</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="class_quant_lib_1_1_non_linear_least_square.html#a09297bd56f438d544473a442aef004dc">exitFlag</a> () const</td></tr>
<tr class="memdesc:a09297bd56f438d544473a442aef004dc"><td class="mdescLeft"> </td><td class="mdescRight">return exit flag <br /></td></tr>
<tr class="separator:a09297bd56f438d544473a442aef004dc"><td class="memSeparator" colspan="2"> </td></tr>
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<a class="el" href="group__types.html#gab9c87440c314438e51a899a03d2442d0">Integer</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="class_quant_lib_1_1_non_linear_least_square.html#af5c912b2b85c9d53e1036eff777c5cd9">iterationsNumber</a> () const</td></tr>
<tr class="memdesc:af5c912b2b85c9d53e1036eff777c5cd9"><td class="mdescLeft"> </td><td class="mdescRight">return the performed number of iterations <br /></td></tr>
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</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Non-linear least-square method. </p>
<p>Using a given optimization algorithm (default is conjugate gradient),</p>
<p class="formulaDsp">
\[ min \{ r(x) : x in R^n \} \]
</p>
<p>where \( r(x) = |f(x)|^2 \) is the Euclidean norm of \( f(x) \) for some vector-valued function \( f \) from \( R^n \) to \( R^m \), </p><p class="formulaDsp">
\[ f = (f_1, ..., f_m) \]
</p>
<p> with \( f_i(x) = b_i - \phi(x,t_i) \) where \( b \) is the vector of target data and \( phi \) is a scalar function.</p>
<p>Assuming the differentiability of \( f \), the gradient of \( r \) is defined by </p><p class="formulaDsp">
\[ grad r(x) = f'(x)^t.f(x) \]
</p>
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