/usr/include/eigen3/unsupported/Eigen/NonLinearOptimization is in libeigen3-dev 3.2.2-3.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | // 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 <unsupported/Eigen/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 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
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