Generalized Rosenbrock benchmark function. More...

#include <shark/ObjectiveFunctions/Benchmarks/Rosenbrock.h>

Inheritance diagram for shark::benchmarks::Rosenbrock:

Public Member Functions
	Rosenbrock (std::size_t dimensions=23, double initialSpread=1.0)
	Constructs the problem.

std::string	name () const
	From INameable: return the class name.

std::size_t	numberOfVariables () const
	Accesses the number of variables.

bool	hasScalableDimensionality () const

void	setNumberOfVariables (std::size_t numberOfVariables)
	Adjusts the number of variables if the function is scalable.

SearchPointType	proposeStartingPoint () const
	Proposes a starting point in the feasible search space of the function.

double	eval (const SearchPointType &p) const
	Evaluates the objective function for the supplied argument.

virtual ResultType	evalDerivative (const SearchPointType &p, FirstOrderDerivative &derivative) const
	Evaluates the objective function and calculates its gradient.

virtual ResultType	evalDerivative (const SearchPointType &p, SecondOrderDerivative &derivative) const
	Evaluates the objective function and calculates its gradient.

Public Member Functions inherited from shark::AbstractObjectiveFunction< RealVector, double >
const Features &	features () const

virtual void	updateFeatures ()

bool	hasValue () const
	returns whether this function can calculate it's function value

bool	hasFirstDerivative () const
	returns whether this function can calculate the first derivative

bool	hasSecondDerivative () const
	returns whether this function can calculate the second derivative

bool	canProposeStartingPoint () const
	returns whether this function can propose a starting point.

bool	isConstrained () const
	returns whether this function can return

bool	hasConstraintHandler () const
	returns whether this function can return

bool	canProvideClosestFeasible () const
	Returns whether this function can calculate thee closest feasible to an infeasible point.

bool	isThreadSafe () const
	Returns true, when the function can be usd in parallel threads.

bool	isNoisy () const
	Returns true, when the function can be usd in parallel threads.

	AbstractObjectiveFunction ()
	Default ctor.

virtual	~AbstractObjectiveFunction ()
	Virtual destructor.

virtual void	init ()

void	setRng (random::rng_type *rng)
	Sets the Rng used by the objective function.

virtual std::size_t	numberOfObjectives () const

virtual bool	hasScalableObjectives () const

virtual void	setNumberOfObjectives (std::size_t numberOfObjectives)
	Adjusts the number of objectives if the function is scalable.

std::size_t	evaluationCounter () const
	Accesses the evaluation counter of the function.

AbstractConstraintHandler< SearchPointType > const &	getConstraintHandler () const
	Returns the constraint handler of the function if it has one.

virtual bool	isFeasible (const SearchPointType &input) const
	Tests whether a point in SearchSpace is feasible, e.g., whether the constraints are fulfilled.

virtual void	closestFeasible (SearchPointType &input) const
	If supported, the supplied point is repaired such that it satisfies all of the function's constraints.

ResultType	operator() (SearchPointType const &input) const
	Evaluates the function. Useful together with STL-Algorithms like std::transform.

Public Member Functions inherited from shark::INameable
virtual	~INameable ()

Additional Inherited Members
Public Types inherited from shark::AbstractObjectiveFunction< RealVector, double >
enum	Feature
	List of features that are supported by an implementation. More...

typedef RealVector	SearchPointType

typedef double	ResultType

typedef boost::mpl::if_< std::is_arithmetic< double >, SearchPointType, RealMatrix >::type	FirstOrderDerivative

typedef TypedFlags< Feature >	Features
	This statement declares the member m_features. See Core/Flags.h for details.

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

Protected Member Functions inherited from shark::AbstractObjectiveFunction< RealVector, double >
void	announceConstraintHandler (AbstractConstraintHandler< SearchPointType > const *handler)
	helper function which is called to announce the presence of an constraint handler.

Protected Attributes inherited from shark::AbstractObjectiveFunction< RealVector, double >
Features	m_features

std::size_t	m_evaluationCounter
	Evaluation counter, default value: 0.

AbstractConstraintHandler< SearchPointType > const *	m_constraintHandler

random::rng_type *	mep_rng

Detailed Description

Generalized Rosenbrock benchmark function.

This non-convex benchmark function for real-valued optimization is a generalization from two to multiple dimensions of a classic function first proposed in:

H. H. Rosenbrock. An automatic method for finding the greatest or least value of a function. The Computer Journal 3: 175-184, 1960

Definition at line 60 of file Rosenbrock.h.

Constructor & Destructor Documentation

◆ Rosenbrock()

shark::benchmarks::Rosenbrock::Rosenbrock	(	std::size_t	dimensions = `23`,
		double	initialSpread = `1.0`
	)

inline

Constructs the problem.

Parameters

dimensions	number of dimensions to optimize
initialSpread	spread of the initial starting point

Definition at line 66 of file Rosenbrock.h.

References shark::AbstractObjectiveFunction< RealVector, double >::CAN_PROPOSE_STARTING_POINT, shark::AbstractObjectiveFunction< RealVector, double >::HAS_FIRST_DERIVATIVE, shark::AbstractObjectiveFunction< RealVector, double >::HAS_SECOND_DERIVATIVE, and shark::AbstractObjectiveFunction< RealVector, double >::m_features.

Member Function Documentation

◆ eval()

double shark::benchmarks::Rosenbrock::eval ( const SearchPointType & input ) const

inlinevirtual

Evaluates the objective function for the supplied argument.

Parameters

[in] input The argument for which the function shall be evaluated.

Returns: The result of evaluating the function for the supplied argument.

Exceptions

FeatureNotAvailableException in the default implementation and if a function does not support this feature.

Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.

Definition at line 98 of file Rosenbrock.h.

References shark::AbstractObjectiveFunction< RealVector, double >::m_evaluationCounter, and shark::sqr().

Referenced by evalDerivative(), and evalDerivative().

◆ evalDerivative() [1/2]

virtual ResultType shark::benchmarks::Rosenbrock::evalDerivative	(	const SearchPointType &	input,
		FirstOrderDerivative &	derivative
	)		const

inlinevirtual

Evaluates the objective function and calculates its gradient.

Parameters

[in]	input	The argument to eval the function for.
[out]	derivative	The derivate is placed here.

Returns: The result of evaluating the function for the supplied argument.

Exceptions

FeatureNotAvailableException in the default implementation and if a function does not support this feature.

Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.

Definition at line 110 of file Rosenbrock.h.

References eval(), and shark::sqr().

◆ evalDerivative() [2/2]

virtual ResultType shark::benchmarks::Rosenbrock::evalDerivative	(	const SearchPointType &	input,
		SecondOrderDerivative &	derivative
	)		const

inlinevirtual

Evaluates the objective function and calculates its gradient.

Parameters

[in]	input	The argument to eval the function for.
[out]	derivative	The derivate and the Hessian are placed here.

Returns: The result of evaluating the function for the supplied argument.

Exceptions

FeatureNotAvailableException in the default implementation and if a function does not support this feature.

Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.

Definition at line 123 of file Rosenbrock.h.

References eval(), and shark::sqr().

◆ hasScalableDimensionality()

bool shark::benchmarks::Rosenbrock::hasScalableDimensionality ( ) const

inlinevirtual

Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.

Definition at line 81 of file Rosenbrock.h.

◆ name()

std::string shark::benchmarks::Rosenbrock::name ( ) const

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 74 of file Rosenbrock.h.

◆ numberOfVariables()

std::size_t shark::benchmarks::Rosenbrock::numberOfVariables ( ) const

inlinevirtual

Accesses the number of variables.

Implements shark::AbstractObjectiveFunction< RealVector, double >.

Definition at line 77 of file Rosenbrock.h.

Referenced by proposeStartingPoint(), and setNumberOfVariables().

◆ proposeStartingPoint()

SearchPointType shark::benchmarks::Rosenbrock::proposeStartingPoint ( ) const

inlinevirtual

Proposes a starting point in the feasible search space of the function.

Returns: The generated starting point.

Exceptions

FeatureNotAvailableException in the default implementation and if a function does not support this feature.

Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.

Definition at line 89 of file Rosenbrock.h.

References shark::AbstractObjectiveFunction< RealVector, double >::mep_rng, numberOfVariables(), and shark::random::uni().

◆ setNumberOfVariables()

void shark::benchmarks::Rosenbrock::setNumberOfVariables ( std::size_t numberOfVariables )

inlinevirtual

Adjusts the number of variables if the function is scalable.

Parameters

[in] numberOfVariables The new dimension.

Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.

Definition at line 85 of file Rosenbrock.h.

References numberOfVariables().

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

include/shark/ObjectiveFunctions/Benchmarks/Rosenbrock.h

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ Rosenbrock()

Member Function Documentation

◆ eval()

◆ evalDerivative() [1/2]

◆ evalDerivative() [2/2]

◆ hasScalableDimensionality()

◆ name()

◆ numberOfVariables()

◆ proposeStartingPoint()

◆ setNumberOfVariables()