General interface class for a Derivatives. More...

#include <Derivatives.h>

Public Types
typedef Eigen::Matrix< double, IN_DIM, 1 >	IN_TYPE
	function input vector type More...

typedef Eigen::Matrix< double, OUT_DIM, 1 >	OUT_TYPE
	function output vector type More...

typedef Eigen::Matrix< double, OUT_DIM, IN_DIM >	JAC_TYPE

typedef Eigen::Matrix< double, IN_DIM, IN_DIM >	HES_TYPE

Public Member Functions
	Derivatives ()

virtual	~Derivatives ()
	default destructor More...

virtual Derivatives< IN_DIM, OUT_DIM, SCALAR > *	clone () const =0
	deep copy for derived classes More...

virtual OUT_TYPE	forwardZero (const Eigen::VectorXd &x)
	Evaluates the method itself. More...

virtual JAC_TYPE	jacobian (const Eigen::VectorXd &x)
	Evaluates the jacobian with respect to the input. More...

virtual void	sparseJacobian (const Eigen::VectorXd &x, Eigen::VectorXd &jac, Eigen::VectorXi &iRow, Eigen::VectorXi &jCol)
	Returns the evaluated jacobian in sparse format. More...

virtual Eigen::VectorXd	sparseJacobianValues (const Eigen::VectorXd &x)
	Returns the non zero values of the jacobian. More...

virtual HES_TYPE	hessian (const Eigen::VectorXd &x, const Eigen::VectorXd &lambda)
	Evaluates the hessian (2nd order derivatives with respect to input) of the method. In case of a vector valued function, the method returns the weighted sum of the hessians with weights w. More...

virtual void	sparseHessian (const Eigen::VectorXd &x, const Eigen::VectorXd &lambda, Eigen::VectorXd &hes, Eigen::VectorXi &iRow, Eigen::VectorXi &jCol)
	Returns the weighted sum of hessian of the problem in sparse format. More...

virtual Eigen::VectorXd	sparseHessianValues (const Eigen::VectorXd &x, const Eigen::VectorXd &lambda)
	Returns the non zero elements of the hessian of the problem. More...

Detailed Description

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>
class ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >

General interface class for a Derivatives.

Interface for a general Derivatives of a vector-valued function. Can be used either with fixed size or dynamic size data types

Template Parameters

IN_DIM	input dimension of function (use Eigen::Dynamic (-1) for dynamic size)
OUT_DIM	output dimension of function (use Eigen::Dynamic (-1) for dynamic size)
SCALAR	scalar data type

Member Typedef Documentation

◆ IN_TYPE

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>

typedef Eigen::Matrix<double, IN_DIM, 1> ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >::IN_TYPE

function input vector type

◆ OUT_TYPE

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>

typedef Eigen::Matrix<double, OUT_DIM, 1> ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >::OUT_TYPE

function output vector type

◆ JAC_TYPE

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>

typedef Eigen::Matrix<double, OUT_DIM, IN_DIM> ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >::JAC_TYPE

◆ HES_TYPE

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>

typedef Eigen::Matrix<double, IN_DIM, IN_DIM> ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >::HES_TYPE

Constructor & Destructor Documentation

◆ Derivatives()

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>

ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >::Derivatives ( )

inline

◆ ~Derivatives()

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>

virtual ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >::~Derivatives ( )

inlinevirtual

default destructor

Member Function Documentation

◆ clone()

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>

virtual Derivatives<IN_DIM, OUT_DIM, SCALAR>* ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >::clone ( ) const

pure virtual

deep copy for derived classes

Implemented in ct::core::DerivativesNumDiff< IN_DIM, OUT_DIM >, ct::core::generated::TestForwardZero, ct::core::generated::TestHessian, and ct::core::generated::TestJacobian.

Referenced by ct::core::Derivatives< 3, 2, double >::~Derivatives().

◆ forwardZero()

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>

virtual OUT_TYPE ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >::forwardZero ( const Eigen::VectorXd & x )

inlinevirtual

Evaluates the method itself.

Parameters

[in] x The point of evaluation

Returns: The evalution of the method

Reimplemented in ct::core::DerivativesNumDiff< IN_DIM, OUT_DIM >, and ct::core::generated::TestForwardZero.

◆ jacobian()

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>

virtual JAC_TYPE ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >::jacobian ( const Eigen::VectorXd & x )

inlinevirtual

Evaluates the jacobian with respect to the input.

Parameters

[in] x The point of evaluation

Returns: The evaluated jacobian

Reimplemented in ct::core::DerivativesNumDiff< IN_DIM, OUT_DIM >, and ct::core::generated::TestJacobian.

◆ sparseJacobian()

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>

virtual void ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >::sparseJacobian	(	const Eigen::VectorXd &	x,
		Eigen::VectorXd &	jac,
		Eigen::VectorXi &	iRow,
		Eigen::VectorXi &	jCol
	)

inlinevirtual

Returns the evaluated jacobian in sparse format.

Parameters

[in]	x	The point of evaluation
[out]	jac	The non zero values of the jacobian
[out]	iRow	The row indices of the non zero values
[out]	jCol	The column indices of the non zero values

◆ sparseJacobianValues()

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>

virtual Eigen::VectorXd ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >::sparseJacobianValues ( const Eigen::VectorXd & x )

inlinevirtual

Returns the non zero values of the jacobian.

Parameters

[in] x The point of evaluation

Returns: The non zeros values of the jacobian

◆ hessian()

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>

virtual HES_TYPE ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >::hessian	(	const Eigen::VectorXd &	x,
		const Eigen::VectorXd &	lambda
	)

inlinevirtual

Evaluates the hessian (2nd order derivatives with respect to input) of the method. In case of a vector valued function, the method returns the weighted sum of the hessians with weights w.

Parameters

[in]	x	The point of evaluation
[in]	lambda	The weights of the sum

Returns: The evaluated hessian

Reimplemented in ct::core::generated::TestHessian.

◆ sparseHessian()

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>

virtual void ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >::sparseHessian	(	const Eigen::VectorXd &	x,
		const Eigen::VectorXd &	lambda,
		Eigen::VectorXd &	hes,
		Eigen::VectorXi &	iRow,
		Eigen::VectorXi &	jCol
	)

inlinevirtual

Returns the weighted sum of hessian of the problem in sparse format.

Parameters

[in]	x	The point of evaluation
[in]	lambda	The weights of the sum of the hessian
	hes	The non zero values of the hessian
	iRow	The row indices of the non zero values
	jCol	The column indices of the non zero values

◆ sparseHessianValues()

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>

virtual Eigen::VectorXd ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >::sparseHessianValues	(	const Eigen::VectorXd &	x,
		const Eigen::VectorXd &	lambda
	)

inlinevirtual

Returns the non zero elements of the hessian of the problem.

Parameters

[in]	x	The point of evaluation
[in]	lambda	The weights of the sum of the hessian

Returns: { description_of_the_return_value }

The documentation for this class was generated from the following file:

/home/gim2rng/ct_devel_ws/src/control-toolbox/ct_core/include/ct/core/math/Derivatives.h

Public Types

Public Member Functions

Detailed Description

template<int IN_DIM, int OUT_DIM, typename SCALAR = double> class ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >

Member Typedef Documentation

◆ IN_TYPE

◆ OUT_TYPE

◆ JAC_TYPE

◆ HES_TYPE

Constructor & Destructor Documentation

◆ Derivatives()

◆ ~Derivatives()

Member Function Documentation

◆ clone()

◆ forwardZero()

◆ jacobian()

◆ sparseJacobian()

◆ sparseJacobianValues()

◆ hessian()

◆ sparseHessian()

◆ sparseHessianValues()

template<int IN_DIM, int OUT_DIM, typename SCALAR = double>
class ct::core::Derivatives< IN_DIM, OUT_DIM, SCALAR >