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diff --git a/unsupported/Eigen/NonLinearOptimization b/unsupported/Eigen/NonLinearOptimization new file mode 100644 index 000000000..cf6ca58f8 --- /dev/null +++ b/unsupported/Eigen/NonLinearOptimization @@ -0,0 +1,134 @@ +// This file is part of Eugenio, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_NONLINEAROPTIMIZATION_MODULE +#define EIGEN_NONLINEAROPTIMIZATION_MODULE + +#include <vector> + +#include <Eigen/Core> +#include <Eigen/Jacobi> +#include <Eigen/QR> +#include <unsupported/Eigen/NumericalDiff> + +/** \ingroup Unsupported_modules + * \defgroup NonLinearOptimization_Module Non linear optimization module + * + * \code + * #include <unsupported/Eigen/NonLinearOptimization> + * \endcode + * + * 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 an 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 minpack (http://en.wikipedia.org/wiki/MINPACK). + * Minpack is a very famous, old, robust and well-reknown package, written in + * fortran. Those implementations have been carefully tuned, tested, and used + * for several decades. + * + * The original fortran code was automatically translated using f2c (http://en.wikipedia.org/wiki/F2c) in C, + * then c++, and then cleaned by several different authors. + * The last one of those cleanings being our starting point : + * 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, stable 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. + * + * \section Tests Tests + * + * The tests are placed in the file unsupported/test/NonLinear.cpp. + * + * There are two kinds of tests : those that come from examples bundled with cminpack. + * They guaranty we get the same results as the original algorithms (value for 'x', + * for the number of evaluations of the function, and for the number of evaluations + * of the jacobian if ever). + * + * Other tests were added by myself at the very beginning of the + * process and check the results for levenberg-marquardt using the reference data + * on http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml. Since then i've + * carefully checked that the same results were obtained when modifiying the + * code. Please note that we do not always get the exact same decimals as they do, + * but this is ok : they use 128bits float, and we do the tests using the C type 'double', + * which is 64 bits on most platforms (x86 and amd64, at least). + * I've performed those tests on several other implementations of levenberg-marquardt, and + * (c)minpack performs VERY well compared to those, both in accuracy and speed. + * + * The documentation for running the tests is on the wiki + * http://eigen.tuxfamily.org/index.php?title=Tests + * + * \section API API : overview of methods + * + * Both algorithms can use either the jacobian (provided by the user) or compute + * an approximation by themselves (actually using Eigen \ref NumericalDiff_Module). + * The part of API referring to the latter use 'NumericalDiff' in the method names + * (exemple: LevenbergMarquardt.minimizeNumericalDiff() ) + * + * The methods LevenbergMarquardt.lmder1()/lmdif1()/lmstr1() and + * HybridNonLinearSolver.hybrj1()/hybrd1() are specific methods from the original + * minpack package that you probably should NOT use until you are porting a code that + * was previously using minpack. They just define a 'simple' API with default values + * for some parameters. + * + * All algorithms are provided using Two APIs : + * - one where the user inits the algorithm, and uses '*OneStep()' as much as he wants : + * this way the caller have control over the steps + * - one where the user just calls a method (optimize() or solve()) which will + * handle the loop: init + loop until a stop condition is met. Those are provided for + * convenience. + * + * As an example, the method LevenbergMarquardt::minimize() is + * implemented as follow : + * \code + * Status LevenbergMarquardt<FunctorType,Scalar>::minimize(FVectorType &x, const int mode) + * { + * Status status = minimizeInit(x, mode); + * do { + * status = minimizeOneStep(x, mode); + * } while (status==Running); + * return status; + * } + * \endcode + * + * \section examples Examples + * + * The easiest way to understand how to use this module is by looking at the many examples in the file + * unsupported/test/NonLinearOptimization.cpp. + */ + +#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/fdjac1.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_NONLINEAROPTIMIZATION_MODULE |