From ae76c977041e7584324738a3075d4a926508dd90 Mon Sep 17 00:00:00 2001 From: Thomas Capricelli Date: Tue, 10 Nov 2009 21:33:36 +0100 Subject: documentation fixes --- unsupported/Eigen/NonLinearOptimization | 32 +++++++++++++++----------------- 1 file changed, 15 insertions(+), 17 deletions(-) (limited to 'unsupported/Eigen/NonLinearOptimization') diff --git a/unsupported/Eigen/NonLinearOptimization b/unsupported/Eigen/NonLinearOptimization index b73935b3b..601b1abc7 100644 --- a/unsupported/Eigen/NonLinearOptimization +++ b/unsupported/Eigen/NonLinearOptimization @@ -1,4 +1,4 @@ -// This file is part of Eigen, a lightweight C++ template library +// This file is part of Eugenio, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2009 Thomas Capricelli @@ -59,15 +59,14 @@ namespace Eigen { * * 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, "good" norms). + * 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, with the same - * number of iterations. + * beginning, which ensure that the same results are found. * * \section Tests Tests * - * The tests are placed in the directory unsupported/test/NonLinear.cpp. + * 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', @@ -81,32 +80,31 @@ namespace Eigen { * 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 perform VERY well compared to those, both in accuracy and speed. + * (c)minpack performs VERY well compared to those, both in accuracy and speed. * - * The documentation for running the test is on the wiki + * The documentation for running the tests is on the wiki * http://eigen.tuxfamily.org/index.php?title=Developer%27s_Corner#Running_the_unit_tests * * \section API API : overview of methods * - * All algorithms can use either the jacobian (provided by the user) or compute - * an approximation by themselves (or rather, using Eigen \ref NumericalDiff_Module) - * The part of API referring to the latter use 'NumericalDiff' in the method name + * 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 but if you port a code that was - * previously using minpack. They just define a 'simple' API with default values + * 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 you init the algorithm, and use '*OneStep()' as much as you want : + * - 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 you just call a method (optimize() or solve()) which will - * basically do exactly the same : init + loop until a stop condition is met. - * Those are provided for convenience. + * - 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.minimizeNumericalDiff() is * implemented as follow : -- cgit v1.2.3