135 lines
5.6 KiB
Plaintext
135 lines
5.6 KiB
Plaintext
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_NONLINEAROPTIMIZATION_MODULE
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#define EIGEN_NONLINEAROPTIMIZATION_MODULE
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#include <vector>
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#include <Eigen/Core>
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#include <Eigen/Jacobi>
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#include <Eigen/QR>
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#include <unsupported/Eigen/NumericalDiff>
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/**
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* \defgroup NonLinearOptimization_Module Non linear optimization module
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*
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* \code
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* #include <unsupported/Eigen/NonLinearOptimization>
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* \endcode
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*
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* This module provides implementation of two important algorithms in non linear
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* optimization. In both cases, we consider a system of non linear functions. Of
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* course, this should work, and even work very well if those functions are
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* actually linear. But if this is so, you should probably better use other
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* methods more fitted to this special case.
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*
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* One algorithm allows to find an extremum of such a system (Levenberg
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* Marquardt algorithm) and the second one is used to find
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* a zero for the system (Powell hybrid "dogleg" method).
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*
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* This code is a port of minpack (http://en.wikipedia.org/wiki/MINPACK).
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* Minpack is a very famous, old, robust and well-reknown package, written in
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* fortran. Those implementations have been carefully tuned, tested, and used
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* for several decades.
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*
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* The original fortran code was automatically translated using f2c (http://en.wikipedia.org/wiki/F2c) in C,
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* then c++, and then cleaned by several different authors.
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* The last one of those cleanings being our starting point :
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* http://devernay.free.fr/hacks/cminpack.html
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*
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* Finally, we ported this code to Eigen, creating classes and API
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* coherent with Eigen. When possible, we switched to Eigen
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* implementation, such as most linear algebra (vectors, matrices, stable norms).
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*
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* Doing so, we were very careful to check the tests we setup at the very
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* beginning, which ensure that the same results are found.
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*
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* \section Tests Tests
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*
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* The tests are placed in the file unsupported/test/NonLinear.cpp.
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*
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* There are two kinds of tests : those that come from examples bundled with cminpack.
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* They guaranty we get the same results as the original algorithms (value for 'x',
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* for the number of evaluations of the function, and for the number of evaluations
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* of the jacobian if ever).
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*
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* Other tests were added by myself at the very beginning of the
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* process and check the results for levenberg-marquardt using the reference data
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* on http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml. Since then i've
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* carefully checked that the same results were obtained when modifiying the
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* code. Please note that we do not always get the exact same decimals as they do,
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* but this is ok : they use 128bits float, and we do the tests using the C type 'double',
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* which is 64 bits on most platforms (x86 and amd64, at least).
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* I've performed those tests on several other implementations of levenberg-marquardt, and
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* (c)minpack performs VERY well compared to those, both in accuracy and speed.
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*
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* The documentation for running the tests is on the wiki
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* http://eigen.tuxfamily.org/index.php?title=Tests
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*
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* \section API API : overview of methods
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*
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* Both algorithms can use either the jacobian (provided by the user) or compute
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* an approximation by themselves (actually using Eigen \ref NumericalDiff_Module).
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* The part of API referring to the latter use 'NumericalDiff' in the method names
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* (exemple: LevenbergMarquardt.minimizeNumericalDiff() )
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*
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* The methods LevenbergMarquardt.lmder1()/lmdif1()/lmstr1() and
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* HybridNonLinearSolver.hybrj1()/hybrd1() are specific methods from the original
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* minpack package that you probably should NOT use until you are porting a code that
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* was previously using minpack. They just define a 'simple' API with default values
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* for some parameters.
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*
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* All algorithms are provided using Two APIs :
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* - one where the user inits the algorithm, and uses '*OneStep()' as much as he wants :
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* this way the caller have control over the steps
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* - one where the user just calls a method (optimize() or solve()) which will
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* handle the loop: init + loop until a stop condition is met. Those are provided for
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* convenience.
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*
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* As an example, the method LevenbergMarquardt::minimize() is
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* implemented as follow :
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* \code
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* Status LevenbergMarquardt<FunctorType,Scalar>::minimize(FVectorType &x, const int mode)
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* {
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* Status status = minimizeInit(x, mode);
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* do {
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* status = minimizeOneStep(x, mode);
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* } while (status==Running);
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* return status;
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* }
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* \endcode
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*
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* \section examples Examples
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*
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* The easiest way to understand how to use this module is by looking at the many examples in the file
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* unsupported/test/NonLinearOptimization.cpp.
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*/
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#ifndef EIGEN_PARSED_BY_DOXYGEN
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#include "src/NonLinearOptimization/qrsolv.h"
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#include "src/NonLinearOptimization/r1updt.h"
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#include "src/NonLinearOptimization/r1mpyq.h"
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#include "src/NonLinearOptimization/rwupdt.h"
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#include "src/NonLinearOptimization/fdjac1.h"
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#include "src/NonLinearOptimization/lmpar.h"
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#include "src/NonLinearOptimization/dogleg.h"
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#include "src/NonLinearOptimization/covar.h"
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#include "src/NonLinearOptimization/chkder.h"
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#endif
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#include "src/NonLinearOptimization/HybridNonLinearSolver.h"
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#include "src/NonLinearOptimization/LevenbergMarquardt.h"
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#endif // EIGEN_NONLINEAROPTIMIZATION_MODULE
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