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diff --git a/src/EigenUnsupported/NonLinearOptimization b/src/EigenUnsupported/NonLinearOptimization deleted file mode 100644 index 961f192..0000000 --- a/src/EigenUnsupported/NonLinearOptimization +++ /dev/null @@ -1,140 +0,0 @@ -// This file is part of Eigen, 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 "NumericalDiff" - -/** - * \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 a least-squares solution 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 renowned 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 modifying 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 needs a functor computing the Jacobian. It can be computed by - * hand, using auto-differentiation (see \ref AutoDiff_Module), or using numerical - * differences (see \ref NumericalDiff_Module). For instance: - *\code - * MyFunc func; - * NumericalDiff<MyFunc> func_with_num_diff(func); - * LevenbergMarquardt<NumericalDiff<MyFunc> > lm(func_with_num_diff); - * \endcode - * For HybridNonLinearSolver, the method solveNumericalDiff() does the above wrapping for - * you. - * - * 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 |