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< DerivabilityOrder >
	Define a \(\mathbb{R} \rightarrow \mathbb{R}^m\) function, derivable n times ( \(n \geq 2\)). More...
class	roboptim::NTimesDerivableFunction< 2 >
	Explicit specialization for the stop case of NTimesDerivable class. 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. More...
void	roboptim::Cos< T >::impl_jacobian (jacobian_ref jacobian, const_argument_ref x) const
	Jacobian evaluation. More...
void	roboptim::Cos< T >::impl_hessian (hessian_ref hessian, const_argument_ref x, size_type) const
	Hessian evaluation. More...
void	roboptim::GenericIdentityFunction< T >::impl_gradient (gradient_ref gradient, const_argument_ref, size_type idFunction) const
	Gradient evaluation. More...
void	roboptim::Sin< T >::impl_gradient (gradient_ref gradient, const_argument_ref x, size_type) const
	Gradient evaluation. More...
void	roboptim::Sin< T >::impl_jacobian (jacobian_ref jacobian, const_argument_ref x) const
	Jacobian evaluation. More...
void	roboptim::Sin< T >::impl_hessian (hessian_ref hessian, const_argument_ref x, size_type) const
	Hessian evaluation. More...

Detailed Description

Function Documentation

template<typename T >

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

protectedvirtual

Gradient evaluation.

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

Warning

Do not call this function directly, call gradient instead.

Parameters

gradient	gradient will be stored in this vector
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

protectedvirtual

Gradient evaluation.

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

Warning

Do not call this function directly, call gradient instead.

Parameters

gradient	gradient will be stored in this vector
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

protectedvirtual

Gradient evaluation.

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

Warning

Do not call this function directly, call gradient instead.

Parameters

gradient	gradient will be stored in this vector
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

protectedvirtual

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

protectedvirtual

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

protectedvirtual

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

protectedvirtual

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 >.