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 $\mathbb{R} \rightarrow \mathbb{R}^m$ function, derivable n times ( $n \geq 2$). 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.

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:
gradientgradient will be store in this argument
argumentpoint where the gradient will be computed
functionIdevaluated 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:
gradientgradient will be store in this argument
argumentpoint where the gradient will be computed
functionIdevaluated 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:
gradientgradient will be store in this argument
argumentpoint where the gradient will be computed
functionIdevaluated 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:
hessianhessian will be stored here
argumentpoint where the hessian will be computed
functionIdevaluated 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:
hessianhessian will be stored here
argumentpoint where the hessian will be computed
functionIdevaluated 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:
jacobianjacobian will be store in this argument
argpoint 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:
jacobianjacobian will be store in this argument
argpoint where the jacobian will be computed

Reimplemented from roboptim::GenericDifferentiableFunction< T >.

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