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<a name="Nonlinear-Least_002dSquares-Initialization"></a>
<div class="header">
<p>
Next: <a href="Nonlinear-Least_002dSquares-Function-Definition.html#Nonlinear-Least_002dSquares-Function-Definition" accesskey="n" rel="next">Nonlinear Least-Squares Function Definition</a>, Previous: <a href="Nonlinear-Least_002dSquares-Tunable-Parameters.html#Nonlinear-Least_002dSquares-Tunable-Parameters" accesskey="p" rel="previous">Nonlinear Least-Squares Tunable Parameters</a>, Up: <a href="Nonlinear-Least_002dSquares-Fitting.html#Nonlinear-Least_002dSquares-Fitting" accesskey="u" rel="up">Nonlinear Least-Squares Fitting</a> [<a href="Function-Index.html#Function-Index" title="Index" rel="index">Index</a>]</p>
</div>
<hr>
<a name="Initializing-the-Solver-3"></a>
<h3 class="section">39.5 Initializing the Solver</h3>
<dl>
<dt><a name="index-gsl_005fmultifit_005fnlinear_005falloc"></a>Function: <em>gsl_multifit_nlinear_workspace *</em> <strong>gsl_multifit_nlinear_alloc</strong> <em>(const gsl_multifit_nlinear_type * <var>T</var>, const gsl_multifit_nlinear_parameters * <var>params</var>, const size_t <var>n</var>, const size_t <var>p</var>)</em></dt>
<dt><a name="index-gsl_005fmultilarge_005fnlinear_005falloc"></a>Function: <em>gsl_multilarge_nlinear_workspace *</em> <strong>gsl_multilarge_nlinear_alloc</strong> <em>(const gsl_multilarge_nlinear_type * <var>T</var>, const gsl_multilarge_nlinear_parameters * <var>params</var>, const size_t <var>n</var>, const size_t <var>p</var>)</em></dt>
<dd><a name="index-gsl_005fmultifit_005fnlinear_005falloc-1"></a>
<a name="index-gsl_005fmultifit_005fnlinear_005ftype"></a>
<p>These functions return a pointer to a newly allocated instance of a
derivative solver of type <var>T</var> for <var>n</var> observations and <var>p</var>
parameters. The <var>params</var> input specifies a tunable set of
parameters which will affect important details in each iteration
of the trust region subproblem algorithm. It is recommended to start
with the suggested default parameters (see
<code>gsl_multifit_nlinear_default_parameters</code> and
<code>gsl_multilarge_nlinear_default_parameters</code>) and then tune
the parameters once the code is working correctly. See
<a href="Nonlinear-Least_002dSquares-Tunable-Parameters.html#Nonlinear-Least_002dSquares-Tunable-Parameters">Nonlinear Least-Squares Tunable Parameters</a>
for descriptions of the various parameters.
For example, the following code creates an instance of a
Levenberg-Marquardt solver for 100 data points and 3 parameters,
using suggested defaults:
</p>
<div class="example">
<pre class="example">const gsl_multifit_nlinear_type * T
= gsl_multifit_nlinear_lm;
gsl_multifit_nlinear_parameters params
= gsl_multifit_nlinear_default_parameters();
gsl_multifit_nlinear_workspace * w
= gsl_multifit_nlinear_alloc (T, &params, 100, 3);
</pre></div>
<p>The number of observations <var>n</var> must be greater than or equal to
parameters <var>p</var>.
</p>
<p>If there is insufficient memory to create the solver then the function
returns a null pointer and the error handler is invoked with an error
code of <code>GSL_ENOMEM</code>.
</p></dd></dl>
<dl>
<dt><a name="index-gsl_005fmultifit_005fnlinear_005fdefault_005fparameters"></a>Function: <em>gsl_multifit_nlinear_parameters</em> <strong>gsl_multifit_nlinear_default_parameters</strong> <em>(void)</em></dt>
<dt><a name="index-gsl_005fmultilarge_005fnlinear_005fdefault_005fparameters"></a>Function: <em>gsl_multilarge_nlinear_parameters</em> <strong>gsl_multilarge_nlinear_default_parameters</strong> <em>(void)</em></dt>
<dd><p>These functions return a set of recommended default parameters
for use in solving nonlinear least squares problems. The user
can tune each parameter to improve the performance on their
particular problem, see
<a href="Nonlinear-Least_002dSquares-Tunable-Parameters.html#Nonlinear-Least_002dSquares-Tunable-Parameters">Nonlinear Least-Squares Tunable Parameters</a>.
</p></dd></dl>
<dl>
<dt><a name="index-gsl_005fmultifit_005fnlinear_005finit"></a>Function: <em>int</em> <strong>gsl_multifit_nlinear_init</strong> <em>(const gsl_vector * <var>x</var>, gsl_multifit_nlinear_fdf * <var>fdf</var>, gsl_multifit_nlinear_workspace * <var>w</var>)</em></dt>
<dt><a name="index-gsl_005fmultifit_005fnlinear_005fwinit"></a>Function: <em>int</em> <strong>gsl_multifit_nlinear_winit</strong> <em>(const gsl_vector * <var>x</var>, const gsl_vector * <var>wts</var>, gsl_multifit_nlinear_fdf * <var>fdf</var>, gsl_multifit_nlinear_workspace * <var>w</var>)</em></dt>
<dt><a name="index-gsl_005fmultilarge_005fnlinear_005finit"></a>Function: <em>int</em> <strong>gsl_multilarge_nlinear_init</strong> <em>(const gsl_vector * <var>x</var>, gsl_multilarge_nlinear_fdf * <var>fdf</var>, gsl_multilarge_nlinear_workspace * <var>w</var>)</em></dt>
<dt><a name="index-gsl_005fmultilarge_005fnlinear_005fwinit"></a>Function: <em>int</em> <strong>gsl_multilarge_nlinear_winit</strong> <em>(const gsl_vector * <var>x</var>, const gsl_vector * <var>wts</var>, gsl_multilarge_nlinear_fdf * <var>fdf</var>, gsl_multilarge_nlinear_workspace * <var>w</var>)</em></dt>
<dd><p>These functions initialize, or reinitialize, an existing workspace <var>w</var>
to use the system <var>fdf</var> and the initial guess
<var>x</var>. See <a href="Nonlinear-Least_002dSquares-Function-Definition.html#Nonlinear-Least_002dSquares-Function-Definition">Nonlinear Least-Squares Function Definition</a>
for a description of the <var>fdf</var> structure.
</p>
<p>Optionally, a weight vector <var>wts</var> can be given to perform
a weighted nonlinear regression. Here, the weighting matrix is
<em>W = diag(w_1,w_2,...,w_n)</em>.
</p></dd></dl>
<dl>
<dt><a name="index-gsl_005fmultifit_005fnlinear_005ffree"></a>Function: <em>void</em> <strong>gsl_multifit_nlinear_free</strong> <em>(gsl_multifit_nlinear_workspace * <var>w</var>)</em></dt>
<dt><a name="index-gsl_005fmultilarge_005fnlinear_005ffree"></a>Function: <em>void</em> <strong>gsl_multilarge_nlinear_free</strong> <em>(gsl_multilarge_nlinear_workspace * <var>w</var>)</em></dt>
<dd><p>These functions free all the memory associated with the workspace <var>w</var>.
</p></dd></dl>
<dl>
<dt><a name="index-gsl_005fmultifit_005fnlinear_005fname"></a>Function: <em>const char *</em> <strong>gsl_multifit_nlinear_name</strong> <em>(const gsl_multifit_nlinear_workspace * <var>w</var>)</em></dt>
<dt><a name="index-gsl_005fmultilarge_005fnlinear_005fname"></a>Function: <em>const char *</em> <strong>gsl_multilarge_nlinear_name</strong> <em>(const gsl_multilarge_nlinear_workspace * <var>w</var>)</em></dt>
<dd><p>These functions return a pointer to the name of the solver. For example,
</p>
<div class="example">
<pre class="example">printf ("w is a '%s' solver\n",
gsl_multifit_nlinear_name (w));
</pre></div>
<p>would print something like <code>w is a 'trust-region' solver</code>.
</p></dd></dl>
<dl>
<dt><a name="index-gsl_005fmultifit_005fnlinear_005ftrs_005fname"></a>Function: <em>const char *</em> <strong>gsl_multifit_nlinear_trs_name</strong> <em>(const gsl_multifit_nlinear_workspace * <var>w</var>)</em></dt>
<dt><a name="index-gsl_005fmultilarge_005fnlinear_005ftrs_005fname"></a>Function: <em>const char *</em> <strong>gsl_multilarge_nlinear_trs_name</strong> <em>(const gsl_multilarge_nlinear_workspace * <var>w</var>)</em></dt>
<dd><p>These functions return a pointer to the name of the trust region subproblem
method. For example,
</p>
<div class="example">
<pre class="example">printf ("w is a '%s' solver\n",
gsl_multifit_nlinear_trs_name (w));
</pre></div>
<p>would print something like <code>w is a 'levenberg-marquardt' solver</code>.
</p></dd></dl>
<hr>
<div class="header">
<p>
Next: <a href="Nonlinear-Least_002dSquares-Function-Definition.html#Nonlinear-Least_002dSquares-Function-Definition" accesskey="n" rel="next">Nonlinear Least-Squares Function Definition</a>, Previous: <a href="Nonlinear-Least_002dSquares-Tunable-Parameters.html#Nonlinear-Least_002dSquares-Tunable-Parameters" accesskey="p" rel="previous">Nonlinear Least-Squares Tunable Parameters</a>, Up: <a href="Nonlinear-Least_002dSquares-Fitting.html#Nonlinear-Least_002dSquares-Fitting" accesskey="u" rel="up">Nonlinear Least-Squares Fitting</a> [<a href="Function-Index.html#Function-Index" title="Index" rel="index">Index</a>]</p>
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