EIC code displayed by LXR master/​include/​eigen3/​unsupported/​Eigen/​NonLinearOptimization	Source navigation Diff markup Identifier search General search
	Version: master test Revert   Change

Warning, /include/eigen3/unsupported/Eigen/NonLinearOptimization is written in an unsupported language. File is not indexed.

 // 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

This page was automatically generated by the 2.3.7 LXR engine. The LXR team