some more documentation
diff --git a/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h b/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h index 4a84802..98872e0 100644 --- a/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h +++ b/unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
@@ -35,32 +35,15 @@ /** - * \brief asdf - * * This class allows you to add a method df() to your functor, which will * use numerical differentiation to compute an approximate of the * derivative for the functor. Of course, if you have an analytical form - * for the derivative, you should rather implement df() using it. + * for the derivative, you should rather implement df() by yourself. * * More information on * http://en.wikipedia.org/wiki/Numerical_differentiation * - * Currently only "Forward" and "Central" scheme are implemented. Those - * are basic methods, and there exist some more elaborated way of - * computing such approximates. They are implemented using both - * proprietary and free software, and usually requires linking to an - * external library. It is very easy for you to write a functor - * using such software, and the purpose is quite orthogonal to what we - * want to achieve with Eigen. - * - * This is why we will not provide wrappers for every great numerical - * differenciation software that exist, but should rather stick with those - * basic ones, that still are useful for testing. - * - * Also, the module "Non linear optimization" needs this in order to - * provide full features compatibility with the original (c)minpack - * package. - * + * Currently only "Forward" and "Central" scheme are implemented. */ template<typename Functor, NumericalDiffMode mode=Forward> class NumericalDiff : public Functor