Mathematical functions

Classes
class	roboptim::GenericConstantFunction< T >
	Constant function. More...
class	roboptim::Cos< T >
	Cos function. More...
class	roboptim::GenericIdentityFunction< T >
	Identity function. More...
class	roboptim::Polynomial< T >
	Polynomial function. More...
class	roboptim::Sin< T >
	Sin function. More...
class	roboptim::NTimesDerivableFunction< 2 >
	Explicit specialization for the stop case of NTimesDerivable class. More...
class	roboptim::NTimesDerivableFunction< DerivabilityOrder >
	Define a function, derivable n times ( ). More...
class	roboptim::GenericNumericLinearFunction< T >
	Build a linear function from a vector and a matrix. More...
class	roboptim::GenericNumericQuadraticFunction< T >
	Build a quadratic function from a matrix and a vector. More...
Functions
void	roboptim::Cos< T >::impl_gradient (gradient_ref gradient, const_argument_ref x, size_type) const
	Gradient evaluation.
void	roboptim::Cos< T >::impl_jacobian (jacobian_ref jacobian, const_argument_ref x) const
	Jacobian evaluation.
void	roboptim::Cos< T >::impl_hessian (hessian_ref hessian, const_argument_ref x, size_type) const
	Hessian evaluation.
void	roboptim::GenericIdentityFunction< T >::impl_gradient (gradient_ref gradient, const_argument_ref, size_type idFunction) const
	Gradient evaluation.
void	roboptim::Sin< T >::impl_gradient (gradient_ref gradient, const_argument_ref x, size_type) const
	Gradient evaluation.
void	roboptim::Sin< T >::impl_jacobian (jacobian_ref jacobian, const_argument_ref x) const
	Jacobian evaluation.
void	roboptim::Sin< T >::impl_hessian (hessian_ref hessian, const_argument_ref x, size_type) const
	Hessian evaluation.

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Function Documentation

template<typename T >

void roboptim::Cos< T >::impl_gradient	(	gradient_ref	gradient,
		const_argument_ref	argument,
		size_type	functionId
	)		const [protected, virtual]

Gradient evaluation.

Compute the gradient, has to be implemented in concrete classes. The gradient is computed for a specific sub-function which id is passed through the functionId argument.

Warning:

Do not call this function directly, call gradient instead.

Parameters:

gradient	gradient will be store in this argument
argument	point where the gradient will be computed
functionId	evaluated function id in the split representation

Implements roboptim::GenericDifferentiableFunction< T >.

template<typename T >

void roboptim::Sin< T >::impl_gradient	(	gradient_ref	gradient,
		const_argument_ref	argument,
		size_type	functionId
	)		const [protected, virtual]

Gradient evaluation.

Compute the gradient, has to be implemented in concrete classes. The gradient is computed for a specific sub-function which id is passed through the functionId argument.

Warning:

Do not call this function directly, call gradient instead.

Parameters:

gradient	gradient will be store in this argument
argument	point where the gradient will be computed
functionId	evaluated function id in the split representation

Implements roboptim::GenericDifferentiableFunction< T >.

template<typename T >

void roboptim::GenericIdentityFunction< T >::impl_gradient	(	gradient_ref	gradient,
		const_argument_ref	argument,
		size_type	functionId
	)		const [protected, virtual]

Gradient evaluation.

Compute the gradient, has to be implemented in concrete classes. The gradient is computed for a specific sub-function which id is passed through the functionId argument.

Warning:

Do not call this function directly, call gradient instead.

Parameters:

gradient	gradient will be store in this argument
argument	point where the gradient will be computed
functionId	evaluated function id in the split representation

Implements roboptim::GenericDifferentiableFunction< T >.

template<typename T >

void roboptim::Sin< T >::impl_hessian	(	hessian_ref	hessian,
		const_argument_ref	argument,
		size_type	functionId
	)		const [protected, virtual]

Hessian evaluation.

Compute the hessian, has to be implemented in concrete classes. The hessian is computed for a specific sub-function which id is passed through the functionId argument.

Warning:

Do not call this function directly, call hessian instead.

Parameters:

hessian	hessian will be stored here
argument	point where the hessian will be computed
functionId	evaluated function id in the split representation

Implements roboptim::GenericTwiceDifferentiableFunction< T >.

template<typename T >

void roboptim::Cos< T >::impl_hessian	(	hessian_ref	hessian,
		const_argument_ref	argument,
		size_type	functionId
	)		const [protected, virtual]

Hessian evaluation.

Compute the hessian, has to be implemented in concrete classes. The hessian is computed for a specific sub-function which id is passed through the functionId argument.

Warning:

Do not call this function directly, call hessian instead.

Parameters:

hessian	hessian will be stored here
argument	point where the hessian will be computed
functionId	evaluated function id in the split representation

Implements roboptim::GenericTwiceDifferentiableFunction< T >.

template<typename T >

void roboptim::Cos< T >::impl_jacobian	(	jacobian_ref	jacobian,
		const_argument_ref	arg
	)		const [protected, virtual]

Jacobian evaluation.

Computes the jacobian, can be overridden by concrete classes. The default behavior is to compute the jacobian from the gradient.

Warning:

Do not call this function directly, call jacobian instead.

Parameters:

jacobian	jacobian will be store in this argument
arg	point where the jacobian will be computed

Reimplemented from roboptim::GenericDifferentiableFunction< T >.

template<typename T >

void roboptim::Sin< T >::impl_jacobian	(	jacobian_ref	jacobian,
		const_argument_ref	arg
	)		const [protected, virtual]

Jacobian evaluation.

Computes the jacobian, can be overridden by concrete classes. The default behavior is to compute the jacobian from the gradient.

Warning:

Do not call this function directly, call jacobian instead.

Parameters:

jacobian	jacobian will be store in this argument
arg	point where the jacobian will be computed

Reimplemented from roboptim::GenericDifferentiableFunction< T >.