mrpt::math::CLevenbergMarquardtTempl< NUMTYPE > Class Template Reference

An implementation of the Levenberg-Marquardt algorithm for least-square minimization. More...

#include <mrpt/math/CLevenbergMarquardt.h>

Inheritance diagram for mrpt::math::CLevenbergMarquardtTempl< NUMTYPE >:

List of all members.

Classes
struct	TResultInfo
Public Types
typedef void(*	TFunctor )(const std::vector< NUMTYPE > &x, const std::vector< NUMTYPE > &y, std::vector< NUMTYPE > &out)
	The type of the function passed to execute.
Static Public Member Functions
static void	execute (std::vector< NUMTYPE > &out_optimal_x, const std::vector< NUMTYPE > &x0, TFunctor functor, const std::vector< NUMTYPE > &increments, const std::vector< NUMTYPE > &userParam, TResultInfo &out_info, bool verbose=false, const size_t &maxIter=200, const NUMTYPE tau=1e-3, const NUMTYPE e1=1e-8, const NUMTYPE e2=1e-8)
	Executes the LM-method, with derivatives estimated from "functor" Is a user-provided function which takes as input two vectors, in this order: x: The parameters to be optimized.

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Detailed Description

template<typename NUMTYPE = double>
class mrpt::math::CLevenbergMarquardtTempl< NUMTYPE >

An implementation of the Levenberg-Marquardt algorithm for least-square minimization.

Refer to the wiki page for more details on the algorithm and usage.

Definition at line 47 of file CLevenbergMarquardt.h.

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Member Typedef Documentation

template<typename NUMTYPE = double>

typedef void(* mrpt::math::CLevenbergMarquardtTempl< NUMTYPE >::TFunctor)(const std::vector< NUMTYPE > &x, const std::vector< NUMTYPE > &y, std::vector< NUMTYPE > &out)

The type of the function passed to execute.

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Member Function Documentation

template<typename NUMTYPE = double>

static void mrpt::math::CLevenbergMarquardtTempl< NUMTYPE >::execute	(	std::vector< NUMTYPE > &	out_optimal_x,
		const std::vector< NUMTYPE > &	x0,
		TFunctor	functor,
		const std::vector< NUMTYPE > &	increments,
		const std::vector< NUMTYPE > &	userParam,
		TResultInfo &	out_info,
		bool	verbose = false,
		const size_t &	maxIter = 200,
		const NUMTYPE	tau = 1e-3,
		const NUMTYPE	e1 = 1e-8,
		const NUMTYPE	e2 = 1e-8
	)			[static]

Executes the LM-method, with derivatives estimated from "functor" Is a user-provided function which takes as input two vectors, in this order:

• x: The parameters to be optimized.

• userParam: The vector passed to the LM algorithm, unmodified. and must return the "error vector", or the error (not squared) in each measured dimension, so the sum of the square of that output is the overall square error.