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starting documentation

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This commit is contained in:
Thomas Capricelli 2009-11-09 03:07:36 +01:00
parent ecbcdafb0f
commit cddc83752c
4 changed files with 16 additions and 3 deletions
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3
unsupported/Eigen/AutoDiff
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@ -35,6 +35,9 @@ namespace Eigen {
* This module features forward automatic differentation via a simple * This module features forward automatic differentation via a simple
* templated scalar type wrapper AutoDiffScalar. * templated scalar type wrapper AutoDiffScalar.
* *
* Warning : this should NOT be confused with numerical differentiation, which
* is a different method and has its own module in Eigen.
*
* \code * \code
* #include <unsupported/Eigen/AutoDiff> * #include <unsupported/Eigen/AutoDiff>
* \endcode * \endcode

3
unsupported/Eigen/NonLinear
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@ -31,8 +31,7 @@
namespace Eigen { namespace Eigen {
/** \ingroup Unsupported_modules /** \ingroup Unsupported_modules
* \defgroup NonLinear Support for non linear optimization and non linear least * \defgroup NonLinearOptimization_Module Non linear optimization module
* square using minpack routines.
* *
* \code * \code
* #include <unsupported/Eigen/NonLinear> * #include <unsupported/Eigen/NonLinear>

5
unsupported/Eigen/NumericalDiff
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@ -30,9 +30,12 @@
namespace Eigen { namespace Eigen {
/** \ingroup Unsupported_modules /** \ingroup Unsupported_modules
* \defgroup NumericalDiff_Module Support for numerical differenciation. * \defgroup NumericalDiff_Module Numerical differenciation module
* See http://en.wikipedia.org/wiki/Numerical_differentiation * See http://en.wikipedia.org/wiki/Numerical_differentiation
* *
* Warning : this should NOT be confused with automatic differentiation, which
* is a different method and has its own module in Eigen.
*
* \code * \code
* #include <unsupported/Eigen/NumericalDiff> * #include <unsupported/Eigen/NumericalDiff>
* \endcode * \endcode

8
unsupported/Eigen/src/NonLinear/LevenbergMarquardt.h
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@ -1,4 +1,12 @@
/**
* \brief Performs non linear optimization over a non-linear function,
* using a variant of the Levenberg Marquardt algorithm.
*
* Check wikipedia for more information.
* http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm
*/
template<typename FunctorType, typename Scalar=double> template<typename FunctorType, typename Scalar=double>
class LevenbergMarquardt class LevenbergMarquardt
{ {