#include <shark/Algorithms/DirectSearch/VDCMA.h>

Inheritance diagram for shark::VDCMA:

Public Member Functions
	VDCMA (random::rng_type &rng=random::globalRng)
	Default c'tor.

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

std::size_t	suggestLambda (std::size_t dimension)
	Calculates lambda for the supplied dimensionality n.

std::size_t	suggestMu (std::size_t lambda)
	Calculates mu for the supplied lambda and the recombination strategy.

void	init (ObjectiveFunctionType const &function, SearchPointType const &p)
	initializes the optimizer using a predefined starting point

void	init (ObjectiveFunctionType const &function, SearchPointType const &initialSearchPoint, std::size_t lambda, std::size_t mu, double initialSigma)
	Initializes the algorithm for the supplied objective function.

void	step (ObjectiveFunctionType const &function)
	Executes one iteration of the algorithm.

double	sigma () const
	Accesses the current step size.

void	setSigma (double sigma)
	Accesses the current step size.

void	setInitialSigma (double initialSigma)
	set the initial step size of the algorithm.

RealVector const &	mean () const
	Accesses the current population mean.

RealVector const &	weights () const
	Accesses the current weighting vector.

RealVector const &	evolutionPath () const
	Accesses the evolution path for the covariance matrix update.

RealVector const &	evolutionPathSigma () const
	Accesses the evolution path for the step size update.

std::size_t	mu () const
	Returns the size of the parent population \(\mu\).

std::size_t &	mu ()
	Returns a mutabl reference to the size of the parent population \(\mu\).

std::size_t	lambda () const
	Returns a immutable reference to the size of the offspring population \(\mu\).

std::size_t &	lambda ()
	Returns a mutable reference to the size of the offspring population \(\mu\).

Public Member Functions inherited from shark::AbstractSingleObjectiveOptimizer< RealVector >
std::size_t	numInitPoints () const
	By default most single objective optimizers only require a single point.

virtual void	init (ObjectiveFunctionType const &function, std::vector< SearchPointType > const &initPoints)
	Initialize the optimizer for the supplied objective function using a set of initialisation points.

virtual const SolutionType &	solution () const
	returns the current solution of the optimizer

Public Member Functions inherited from shark::AbstractOptimizer< PointType, ResultT, SolutionTypeT >
const Features &	features () const

virtual void	updateFeatures ()

bool	requiresValue () const

bool	requiresFirstDerivative () const

bool	requiresSecondDerivative () const

bool	canSolveConstrained () const

bool	requiresClosestFeasible () const

virtual	~AbstractOptimizer ()

virtual void	init (ObjectiveFunctionType const &function)
	Initialize the optimizer for the supplied objective function.

virtual void	init (ObjectiveFunctionType const &function, std::vector< SearchPointType > const &initPoints)=0
	Initialize the optimizer for the supplied objective function using a set of initialisation points.

virtual void	step (ObjectiveFunctionType const &function)=0
	Carry out one step of the optimizer for the supplied objective function.

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

Public Member Functions inherited from shark::ISerializable
virtual	~ISerializable ()
	Virtual d'tor.

virtual void	read (InArchive &archive)
	Read the component from the supplied archive.

virtual void	write (OutArchive &archive) const
	Write the component to the supplied archive.

void	load (InArchive &archive, unsigned int version)
	Versioned loading of components, calls read(...).

void	save (OutArchive &archive, unsigned int version) const
	Versioned storing of components, calls write(...).

	BOOST_SERIALIZATION_SPLIT_MEMBER ()

Additional Inherited Members
Public Types inherited from shark::AbstractSingleObjectiveOptimizer< RealVector >
typedef base_type::SearchPointType	SearchPointType

typedef base_type::SolutionType	SolutionType

typedef base_type::ResultType	ResultType

typedef base_type::ObjectiveFunctionType	ObjectiveFunctionType

Public Types inherited from shark::AbstractOptimizer< PointType, ResultT, SolutionTypeT >
enum	Feature { REQUIRES_VALUE = 1 , REQUIRES_FIRST_DERIVATIVE = 2 , REQUIRES_SECOND_DERIVATIVE = 4 , CAN_SOLVE_CONSTRAINED = 8 , REQUIRES_CLOSEST_FEASIBLE = 16 }
	Models features that the optimizer requires from the objective function. More...

typedef PointType	SearchPointType

typedef ResultT	ResultType

typedef SolutionTypeT	SolutionType

typedef AbstractObjectiveFunction< PointType, ResultType >	ObjectiveFunctionType

typedef TypedFlags< Feature >	Features

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

Protected Member Functions inherited from shark::AbstractOptimizer< PointType, ResultT, SolutionTypeT >
void	checkFeatures (ObjectiveFunctionType const &objectiveFunction)
	Convenience function that checks whether the features of the supplied objective function match with the required features of the optimizer.

Protected Attributes inherited from shark::AbstractSingleObjectiveOptimizer< RealVector >
SolutionType	m_best
	Current solution of the optimizer.

Protected Attributes inherited from shark::AbstractOptimizer< PointType, ResultT, SolutionTypeT >
Features	m_features

Detailed Description

Definition at line 52 of file VDCMA.h.

Constructor & Destructor Documentation

◆ VDCMA()

shark::VDCMA::VDCMA ( random::rng_type & rng = random::globalRng )

inline

Default c'tor.

Definition at line 61 of file VDCMA.h.

References shark::AbstractOptimizer< PointType, ResultT, SolutionTypeT >::m_features, and shark::AbstractOptimizer< PointType, ResultT, SolutionTypeT >::REQUIRES_VALUE.

Member Function Documentation

◆ evolutionPath()

RealVector const & shark::VDCMA::evolutionPath ( ) const

inline

Accesses the evolution path for the covariance matrix update.

Definition at line 205 of file VDCMA.h.

◆ evolutionPathSigma()

RealVector const & shark::VDCMA::evolutionPathSigma ( ) const

inline

Accesses the evolution path for the step size update.

Definition at line 210 of file VDCMA.h.

◆ init() [1/2]

void shark::VDCMA::init	(	ObjectiveFunctionType const &	function,
		SearchPointType const &	initialSearchPoint,
		std::size_t	lambda,
		std::size_t	mu,
		double	initialSigma
	)

inline

Initializes the algorithm for the supplied objective function.

Definition at line 99 of file VDCMA.h.

References lambda(), shark::AbstractSingleObjectiveOptimizer< RealVector >::m_best, mu(), shark::sqr(), and shark::random::uni().

◆ init() [2/2]

void shark::VDCMA::init	(	ObjectiveFunctionType const &	function,
		SearchPointType const &	startingPoint
	)

inlinevirtual

initializes the optimizer using a predefined starting point

Implements shark::AbstractSingleObjectiveOptimizer< RealVector >.

Definition at line 81 of file VDCMA.h.

References shark::AbstractOptimizer< PointType, ResultT, SolutionTypeT >::checkFeatures(), init(), lambda(), mu(), sigma(), suggestLambda(), and suggestMu().

Referenced by init().

◆ lambda() [1/2]

std::size_t & shark::VDCMA::lambda ( )

inline

Returns a mutable reference to the size of the offspring population \(\mu\).

Definition at line 230 of file VDCMA.h.

◆ lambda() [2/2]

std::size_t shark::VDCMA::lambda ( ) const

inline

Returns a immutable reference to the size of the offspring population \(\mu\).

Definition at line 225 of file VDCMA.h.

Referenced by init(), init(), and suggestMu().

◆ mean()

RealVector const & shark::VDCMA::mean ( ) const

inline

Accesses the current population mean.

Definition at line 195 of file VDCMA.h.

◆ mu() [1/2]

std::size_t & shark::VDCMA::mu ( )

inline

Returns a mutabl reference to the size of the parent population \(\mu\).

Definition at line 220 of file VDCMA.h.

◆ mu() [2/2]

std::size_t shark::VDCMA::mu ( ) const

inline

Returns the size of the parent population \(\mu\).

Definition at line 215 of file VDCMA.h.

Referenced by init(), and init().

◆ name()

std::string shark::VDCMA::name ( ) const

inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 66 of file VDCMA.h.

◆ setInitialSigma()

void shark::VDCMA::setInitialSigma ( double initialSigma )

inline

set the initial step size of the algorithm.

Sets the initial sigma at init to a given value. If this is 0, which it is by default, the default initialisation will be sigma= 1/sqrt(N) where N is the number of variables to optimize.

this method is the prefered one instead of init()

Definition at line 189 of file VDCMA.h.

◆ setSigma()

void shark::VDCMA::setSigma ( double sigma )

inline

Accesses the current step size.

Definition at line 178 of file VDCMA.h.

References sigma().

◆ sigma()

double shark::VDCMA::sigma ( ) const

inline

Accesses the current step size.

Definition at line 173 of file VDCMA.h.

Referenced by init(), and setSigma().

◆ step()

void shark::VDCMA::step ( ObjectiveFunctionType const & function )

inline

Executes one iteration of the algorithm.

Definition at line 150 of file VDCMA.h.

References shark::AbstractSingleObjectiveOptimizer< RealVector >::m_best, and shark::searchPoint().

◆ suggestLambda()

std::size_t shark::VDCMA::suggestLambda ( std::size_t dimension )

inline

Calculates lambda for the supplied dimensionality n.

Definition at line 70 of file VDCMA.h.

Referenced by init().

◆ suggestMu()

std::size_t shark::VDCMA::suggestMu ( std::size_t lambda )

inline

Calculates mu for the supplied lambda and the recombination strategy.

Definition at line 75 of file VDCMA.h.

References lambda().

Referenced by init().

◆ weights()

RealVector const & shark::VDCMA::weights ( ) const

inline

Accesses the current weighting vector.

Definition at line 200 of file VDCMA.h.

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

include/shark/Algorithms/DirectSearch/VDCMA.h

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ VDCMA()

Member Function Documentation

◆ evolutionPath()

◆ evolutionPathSigma()

◆ init() [1/2]

◆ init() [2/2]

◆ lambda() [1/2]

◆ lambda() [2/2]

◆ mean()

◆ mu() [1/2]

◆ mu() [2/2]

◆ name()

◆ setInitialSigma()

◆ setSigma()

◆ sigma()

◆ step()

◆ suggestLambda()

◆ suggestMu()

◆ weights()