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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_NONLINEAR_MODULE_H
#define EIGEN_NONLINEAR_MODULE_H
#include <Eigen/Core>
#include <unsupported/Eigen/NumericalDiff>
namespace Eigen {
/** \ingroup Unsupported_modules
* \defgroup NonLinearOptimization_Module Non linear optimization module
*
* This module provides implementation of two important algorithms in non linear
* optimization. In both cases, we consider a system of non linear functions. Of
* course, this should work, and even work very well if those functions are
* actually linear. But if this is so, you should probably better use other
* methods more fitted to this special case.
*
* One algorithm allows to find the extremum of such a system (Levenberg
* Marquardt algorithm) and the second one is used to find
* a zero for the system (Powell hybrid "dogleg" method).
*
* This code is a port of a reknown implementation for both algorithms,
* called minpack (http://en.wikipedia.org/wiki/MINPACK). Those
* implementations have been carefully tuned, tested, and used for several
* decades.
* The original fortran code was automatically translated in C and then c++,
* and then cleaned by several authors
* (check http://devernay.free.fr/hacks/cminpack.html).
*
* Finally, we ported this code to Eigen, creating classes and API
* coherent with Eigen. When possible, we switched to Eigen
* implementation, such as most linear algebra (vectors, matrices, "good" norms).
*
* Doing so, we were very careful to check the tests we setup at the very
* beginning, which ensure that the same results are found, with the same
* number of iterations.
*
* \code
* #include <unsupported/Eigen/NonLinearOptimization>
* \endcode
*/
//@{
#ifndef EIGEN_PARSED_BY_DOXYGEN
#include "src/NonLinearOptimization/qrsolv.h"
#include "src/NonLinearOptimization/r1updt.h"
#include "src/NonLinearOptimization/r1mpyq.h"
#include "src/NonLinearOptimization/rwupdt.h"
#include "src/NonLinearOptimization/qrfac.h"
#include "src/NonLinearOptimization/fdjac1.h"
#include "src/NonLinearOptimization/qform.h"
#include "src/NonLinearOptimization/lmpar.h"
#include "src/NonLinearOptimization/dogleg.h"
#include "src/NonLinearOptimization/covar.h"
#include "src/NonLinearOptimization/chkder.h"
#endif
#include "src/NonLinearOptimization/HybridNonLinearSolver.h"
#include "src/NonLinearOptimization/LevenbergMarquardt.h"
//@}
}
#endif // EIGEN_NONLINEAR_MODULE_H
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