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::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
	roboptim::ROBOPTIM_GENERATE_TRAITS_REFS_T (derivative, traits_t)
	Explicit specialization for the stop case of NTimesDerivable class.
virtual size_type	roboptim::derivabilityOrderMax () const
	Returns the maximum derivability order (relevant for N>2 only).
virtual	roboptim::~NTimesDerivableFunction ()
size_type	roboptim::derivativeSize () const
	Return the size of the derivative vector.
bool	roboptim::isValidDerivative (const_derivative_ref derivative) const
	Check if a derivative is valid (check sizes).
result_t	roboptim::operator() (value_type argument) const
	Evaluate the function at a specified point.
void	roboptim::operator() (result_ref result, value_type argument) const
	Evaluate the function at a specified point.
derivative_t	roboptim::derivative (value_type argument, size_type order=1) const
	Compute the derivative of the function.
void	roboptim::derivative (derivative_ref derivative, value_type argument, size_type order=1) const
	Compute the derivative of the function.
virtual std::ostream &	roboptim::print (std::ostream &o) const
	Display the function on the specified output stream.
	roboptim::NTimesDerivableFunction (size_type outputSize=1, std::string name=std::string())
	Concrete class constructor should call this constructor.
void	roboptim::impl_compute (result_ref result, const_argument_ref argument) const
	Function evaluation.
virtual void	roboptim::impl_compute (result_ref result, value_type t) const =0
	Function evaluation.
void	roboptim::impl_gradient (gradient_ref gradient, const_argument_ref argument, size_type functionId=0) const
	Gradient evaluation.
virtual void	roboptim::impl_derivative (derivative_ref derivative, value_type argument, size_type order=1) const =0
	Derivative evaluation.
void	roboptim::impl_hessian (hessian_ref hessian, const_argument_ref argument, size_type functionId=0) const
	Hessian evaluation.
void	roboptim::Cos< T >::impl_gradient (gradient_ref gradient, const_argument_ref x, size_type) const
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
void	roboptim::GenericIdentityFunction< T >::impl_gradient (gradient_ref gradient, const_argument_ref, size_type idFunction) const
void	roboptim::Sin< T >::impl_gradient (gradient_ref gradient, const_argument_ref x, size_type) const
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

Variables
static const size_type	roboptim::derivabilityOrder = 2
	Function derivability order.

Detailed Description

Function Documentation

◆ derivabilityOrderMax()

virtual size_type roboptim::derivabilityOrderMax ( ) const

virtual

Returns the maximum derivability order (relevant for N>2 only).

Referenced by derivative().

◆ derivative() [1/2]

void roboptim::derivative	(	derivative_ref	derivative,
		value_type	argument,
		size_type	order = 1 ) const

Compute the derivative of the function.

Derivative is computed for a certain order, at a given point.

Parameters

derivative	derivative will be stored in this vector
argument	point at which the derivative will be computed
order	derivative order (if 0 then function is evaluated)

References derivabilityOrderMax(), derivative(), impl_derivative(), and isValidDerivative().

◆ derivative() [2/2]

derivative_t roboptim::derivative	(	value_type	argument,
		size_type	order = 1 ) const

Compute the derivative of the function.

Derivative is computed for a certain order, at a given point.

Parameters

argument	point at which the derivative will be computed
order	derivative order (if 0 then function is evaluated)

Returns: derivative vector

References derivative().

Referenced by roboptim::CachedFunction< T >::cachedFunctionDerivative(), derivative(), derivative(), roboptim::CachedFunction< T >::impl_derivative(), impl_derivative(), roboptim::Split< T >::impl_derivative(), impl_gradient(), impl_hessian(), isValidDerivative(), ROBOPTIM_GENERATE_TRAITS_REFS_T(), roboptim::GenericFunctionTraits< EigenMatrixDense >::ROBOPTIM_GENERATE_TYPEDEFS_EIGEN_REF(), and roboptim::GenericFunctionTraits< EigenMatrixSparse >::ROBOPTIM_GENERATE_TYPEDEFS_EIGEN_REF().

◆ derivativeSize()

size_type roboptim::derivativeSize ( ) const

Return the size of the derivative vector.

Returns: derivative vector size

Referenced by isValidDerivative().

◆ impl_compute() [1/2]

void roboptim::impl_compute	(	result_ref	result,
		const_argument_ref	argument ) const

protected

Function evaluation.

Implement generic function evaluation, as required by Function, using this class evaluation method (using a double instead of a vector).

Warning: Do not call this function directly, call #operator()(result_ref, const_argument_ref) const instead.

Parameters

result	result will be stored in this vector
argument	point at which the function will be evaluated

Examples: result.cc, visualization-gnuplot-function.cc, and visualization-matplotlib-function.cc.

Referenced by operator()(), and roboptim::ParametrizedFunction< F >::operator()().

◆ impl_compute() [2/2]

virtual void roboptim::impl_compute	(	result_ref	result,
		value_type	t ) const

protectedpure virtual

Function evaluation.

Evaluate the function, has to be implemented in concrete classes.

Warning: Do not call this function directly, call #operator()(double) const instead.

Parameters

result	result will be stored in this vector
t	point at which the function will be evaluated

◆ impl_derivative()

virtual void roboptim::impl_derivative	(	derivative_ref	derivative,
		value_type	argument,
		size_type	order = 1 ) const

protectedpure virtual

Derivative evaluation.

Compute the derivative, has to be implemented in concrete classes.

Warning: Do not call this function directly, call derivative instead.

Parameters

derivative	derivative will be store in this argument
argument	point where the gradient will be computed
order	derivative order (if 0 evaluates the function)

References derivative().

Referenced by derivative().

◆ impl_gradient() [1/4]

template<typename T>

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

protected

References roboptim::GenericDifferentiableFunction< T >::gradient().

◆ impl_gradient() [2/4]

template<typename T>

void roboptim::GenericIdentityFunction< T >::impl_gradient	(	gradient_ref	gradient,
		const_argument_ref	,
		size_type	idFunction ) const

protected

References roboptim::GenericDifferentiableFunction< T >::gradient().

◆ impl_gradient() [3/4]

void roboptim::impl_gradient	(	gradient_ref	gradient,
		const_argument_ref	argument,
		size_type	functionId = 0 ) const

protected

Gradient evaluation.

Implement the gradient computation, as required by DerivableFunction. 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 or derivative 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

ROBOPTIM_DO_NOT_CHECK_ALLOCATION

References derivative(), is_malloc_allowed(), and set_is_malloc_allowed().

◆ impl_gradient() [4/4]

template<typename T>

void roboptim::Sin< T >::impl_gradient	(	gradient_ref	gradient,
		const_argument_ref	x,
		size_type	) const

protected

References roboptim::GenericDifferentiableFunction< T >::gradient().

◆ impl_hessian() [1/3]

template<typename T>

void roboptim::Cos< T >::impl_hessian	(	hessian_ref	hessian,
		const_argument_ref	x,
		size_type	) const

protected

References roboptim::GenericTwiceDifferentiableFunction< T >::hessian().

◆ impl_hessian() [2/3]

void roboptim::impl_hessian	(	hessian_ref	hessian,
		const_argument_ref	argument,
		size_type	functionId = 0 ) const

protected

Hessian evaluation.

Implement the hessian computation, as required by the TwiceDerivableFunction class using the derivative method. 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

ROBOPTIM_DO_NOT_CHECK_ALLOCATION

References derivative(), is_malloc_allowed(), and set_is_malloc_allowed().

◆ impl_hessian() [3/3]

template<typename T>

void roboptim::Sin< T >::impl_hessian	(	hessian_ref	hessian,
		const_argument_ref	x,
		size_type	) const

protected

References roboptim::GenericTwiceDifferentiableFunction< T >::hessian().

◆ impl_jacobian() [1/2]

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 >.

References roboptim::GenericDifferentiableFunction< T >::jacobian().

◆ impl_jacobian() [2/2]

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 >.

References roboptim::GenericDifferentiableFunction< T >::jacobian().

◆ isValidDerivative()

bool roboptim::isValidDerivative ( const_derivative_ref derivative ) const

Check if a derivative is valid (check sizes).

Parameters

derivative derivative vector to be checked

Returns: true if valid, false if not

References derivative(), and derivativeSize().

Referenced by derivative().

◆ NTimesDerivableFunction()

roboptim::NTimesDerivableFunction	(	size_type	outputSize = 1,
		std::string	name = std::string () )

protected

Concrete class constructor should call this constructor.

Parameters

outputSize	output size (result size)
name	function's name

◆ operator()() [1/2]

void roboptim::operator()	(	result_ref	result,
		value_type	argument ) const

Evaluate the function at a specified point.

The program will abort if the argument does not have the expected size.

Parameters

result	result will be stored in this vector
argument	point at which the function will be evaluated

Returns: computed result

References impl_compute().

◆ operator()() [2/2]

result_t roboptim::operator() ( value_type argument ) const

Evaluate the function at a specified point.

The program will abort if the argument does not have the expected size.

Parameters

argument point at which the function will be evaluated

Returns: computed result

◆ print()

virtual std::ostream & roboptim::print ( std::ostream & o ) const

virtual

Display the function on the specified output stream.

Parameters

o	output stream used for display

Returns: output stream

References roboptim::NTimesDerivableFunction< DerivabilityOrder >::derivabilityOrderMax().

◆ ROBOPTIM_GENERATE_TRAITS_REFS_T()

roboptim::ROBOPTIM_GENERATE_TRAITS_REFS_T	(	derivative	,
		traits_t	)

Explicit specialization for the stop case of NTimesDerivable class.

This specialization defines the interface of a ``n times derivable function'' and implements generic methods required by upper classes using this class specific interface. */ template <> class NTimesDerivableFunction<2> : public TwiceDifferentiableFunction { public:

/ Parent type. typedef TwiceDifferentiableFunction parent_t;

/ Traits type. typedef parent_t::traits_t traits_t;

/** Derivative type.

Derivatives are column vectors.

References derivative().

◆ ~NTimesDerivableFunction()

virtual roboptim::~NTimesDerivableFunction ( )

virtual

Variable Documentation

◆ derivabilityOrder

const size_type roboptim::derivabilityOrder = 2

static

Function derivability order.

One static const variable per class in inheritance structure.

Classes

Functions

Variables

Detailed Description

Function Documentation

◆ derivabilityOrderMax()

◆ derivative() [1/2]

◆ derivative() [2/2]

◆ derivativeSize()

◆ impl_compute() [1/2]

◆ impl_compute() [2/2]

◆ impl_derivative()

◆ impl_gradient() [1/4]

◆ impl_gradient() [2/4]

◆ impl_gradient() [3/4]

◆ impl_gradient() [4/4]

◆ impl_hessian() [1/3]

◆ impl_hessian() [2/3]

◆ impl_hessian() [3/3]

◆ impl_jacobian() [1/2]

◆ impl_jacobian() [2/2]

◆ isValidDerivative()

◆ NTimesDerivableFunction()

◆ operator()() [1/2]

◆ operator()() [2/2]

◆ print()

◆ ROBOPTIM_GENERATE_TRAITS_REFS_T()

◆ ~NTimesDerivableFunction()

Variable Documentation

◆ derivabilityOrder