diff options
Diffstat (limited to 'unsupported/Eigen/NonLinearOptimization')
| -rw-r--r-- | unsupported/Eigen/NonLinearOptimization | 44 |
1 files changed, 25 insertions, 19 deletions
diff --git a/unsupported/Eigen/NonLinearOptimization b/unsupported/Eigen/NonLinearOptimization index 600ab4c12..961f192b5 100644 --- a/unsupported/Eigen/NonLinearOptimization +++ b/unsupported/Eigen/NonLinearOptimization @@ -12,10 +12,10 @@ #include <vector> -#include <Eigen/Core> -#include <Eigen/Jacobi> -#include <Eigen/QR> -#include <unsupported/Eigen/NumericalDiff> +#include "../../Eigen/Core" +#include "../../Eigen/Jacobi" +#include "../../Eigen/QR" +#include "NumericalDiff" /** * \defgroup NonLinearOptimization_Module Non linear optimization module @@ -30,12 +30,12 @@ * 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 + * 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-reknown package, written in + * 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. * @@ -58,35 +58,41 @@ * 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). + * 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 + * 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 + * 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 + * 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 + * \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() ) + * 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 + * was previously using minpack. They just define a 'simple' API with default values * for some parameters. * - * All algorithms are provided using Two APIs : + * 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 @@ -94,7 +100,7 @@ * convenience. * * As an example, the method LevenbergMarquardt::minimize() is - * implemented as follow : + * implemented as follow: * \code * Status LevenbergMarquardt<FunctorType,Scalar>::minimize(FVectorType &x, const int mode) * { |