#include <itkSPSAOptimizer.h>

Detailed Description

An optimizer based on simultaneous perturbation...

This optimizer is an implementation of the Simultaneous Perturbation Stochastic Approximation method, described in [108].

Definition at line 60 of file itkSPSAOptimizer.h.

Inheritance diagram for itk::SPSAOptimizer:

Collaboration diagram for itk::SPSAOptimizer:

Public Types
using	ConstPointer = SmartPointer<const Self>

using	Pointer = SmartPointer<Self>

using	Self = SPSAOptimizer

using	StopConditionSPSAOptimizerEnum = SPSAOptimizerEnums::StopConditionSPSAOptimizer

using	Superclass = SingleValuedNonLinearOptimizer

Public Types inherited from itk::SingleValuedNonLinearOptimizer
using	ConstPointer = SmartPointer<const Self>

using	CostFunctionPointer = CostFunctionType::Pointer

using	CostFunctionType = SingleValuedCostFunction

using	DerivativeType = CostFunctionType::DerivativeType

using	MeasureType = CostFunctionType::MeasureType

using	ParametersType = Superclass::ParametersType

using	Pointer = SmartPointer<Self>

using	Self = SingleValuedNonLinearOptimizer

using	Superclass = NonLinearOptimizer

Public Types inherited from itk::NonLinearOptimizer
using	ConstPointer = SmartPointer<const Self>

using	ParametersType = Superclass::ParametersType

using	Pointer = SmartPointer<Self>

using	ScalesType = Superclass::ScalesType

using	Self = NonLinearOptimizer

using	Superclass = Optimizer

Public Types inherited from itk::Optimizer
using	ConstPointer = SmartPointer<const Self>

using	ParametersType = OptimizerParameters<double>

using	Pointer = SmartPointer<Self>

using	ScalesType = Array<double>

using	Self = Optimizer

using	Superclass = Object

Public Types inherited from itk::Object
using	ConstPointer = SmartPointer<const Self>

using	Pointer = SmartPointer<Self>

using	Self = Object

using	Superclass = LightObject

Public Types inherited from itk::LightObject
using	ConstPointer = SmartPointer<const Self>

using	Pointer = SmartPointer<Self>

using	Self = LightObject

Public Member Functions
virtual void	AdvanceOneStep ()

virtual::itk::LightObject::Pointer	CreateAnother () const

virtual SizeValueType	GetCurrentIteration () const

virtual const DerivativeType &	GetGradient () const

virtual double	GetGradientMagnitude () const

virtual double	GetLearningRate () const

const char *	GetNameOfClass () const override

virtual double	GetStateOfConvergence () const

virtual StopConditionSPSAOptimizerEnum	GetStopCondition () const

std::string	GetStopConditionDescription () const override

virtual MeasureType	GetValue () const

virtual MeasureType	GetValue (const ParametersType &parameters) const

virtual void	GuessParameters (SizeValueType numberOfGradientEstimates, double initialStepSize)

void	ResumeOptimization ()

void	StartOptimization () override

void	StopOptimization ()


virtual void	SetSa (double _arg)

virtual double	GetSa () const

void	Seta (double a)

double	Geta () const


virtual void	SetSc (double _arg)

virtual double	GetSc () const

void	Setc (double c)

double	Getc () const


virtual void	SetA (double _arg)

virtual double	GetA () const


virtual void	SetAlpha (double _arg)

virtual double	GetAlpha () const


virtual void	SetGamma (double _arg)

virtual double	GetGamma () const


virtual bool	GetMaximize () const

virtual void	SetMaximize (bool _arg)

virtual void	MaximizeOn ()

virtual void	MaximizeOff ()

bool	GetMinimize () const

void	SetMinimize (bool v)

void	MinimizeOn ()

void	MinimizeOff ()


virtual void	SetNumberOfPerturbations (SizeValueType _arg)

virtual SizeValueType	GetNumberOfPerturbations () const


virtual void	SetStateOfConvergenceDecayRate (double _arg)

virtual double	GetStateOfConvergenceDecayRate () const


virtual void	SetMinimumNumberOfIterations (SizeValueType _arg)

virtual SizeValueType	GetMinimumNumberOfIterations () const


virtual void	SetMaximumNumberOfIterations (SizeValueType _arg)

virtual SizeValueType	GetMaximumNumberOfIterations () const


virtual void	SetTolerance (double _arg)

virtual double	GetTolerance () const

Public Member Functions inherited from itk::SingleValuedNonLinearOptimizer
virtual const CostFunctionType *	GetCostFunction () const

virtual CostFunctionType *	GetModifiableCostFunction ()

MeasureType	GetValue (const ParametersType &parameters) const

virtual void	SetCostFunction (CostFunctionType *costFunction)

Public Member Functions inherited from itk::Optimizer
virtual const ParametersType &	GetCurrentPosition () const

virtual const ParametersType &	GetInitialPosition () const

virtual void	SetInitialPosition (const ParametersType &param)

void	SetScales (const ScalesType &scales)

virtual const ScalesType &	GetScales () const

virtual const ScalesType &	GetInverseScales () const

Public Member Functions inherited from itk::Object
unsigned long	AddObserver (const EventObject &event, Command *cmd) const

unsigned long	AddObserver (const EventObject &event, std::function< void(const EventObject &)> function) const

virtual void	DebugOff () const

virtual void	DebugOn () const

Command *	GetCommand (unsigned long tag)

bool	GetDebug () const

MetaDataDictionary &	GetMetaDataDictionary ()

const MetaDataDictionary &	GetMetaDataDictionary () const

virtual ModifiedTimeType	GetMTime () const

virtual const TimeStamp &	GetTimeStamp () const

bool	HasObserver (const EventObject &event) const

void	InvokeEvent (const EventObject &)

void	InvokeEvent (const EventObject &) const

virtual void	Modified () const

void	Register () const override

void	RemoveAllObservers ()

void	RemoveObserver (unsigned long tag) const

void	SetDebug (bool debugFlag) const

void	SetReferenceCount (int) override

void	UnRegister () const noexcept override

void	SetMetaDataDictionary (const MetaDataDictionary &rhs)

void	SetMetaDataDictionary (MetaDataDictionary &&rrhs)

virtual void	SetObjectName (std::string _arg)

virtual const std::string &	GetObjectName () const

Public Member Functions inherited from itk::LightObject
Pointer	Clone () const

virtual void	Delete ()

virtual int	GetReferenceCount () const

void	Print (std::ostream &os, Indent indent=0) const

Static Public Member Functions
static Pointer	New ()

Static Public Member Functions inherited from itk::SingleValuedNonLinearOptimizer
static Pointer	New ()

Static Public Member Functions inherited from itk::NonLinearOptimizer
static Pointer	New ()

Static Public Member Functions inherited from itk::Optimizer
static Pointer	New ()

Static Public Member Functions inherited from itk::Object
static bool	GetGlobalWarningDisplay ()

static void	GlobalWarningDisplayOff ()

static void	GlobalWarningDisplayOn ()

static Pointer	New ()

static void	SetGlobalWarningDisplay (bool val)

Static Public Member Functions inherited from itk::LightObject
static void	BreakOnError ()

static Pointer	New ()

Protected Member Functions
virtual double	Compute_a (SizeValueType k) const

virtual double	Compute_c (SizeValueType k) const

virtual void	ComputeGradient (const ParametersType &parameters, DerivativeType &gradient)

virtual void	GenerateDelta (const unsigned int spaceDimension)

void	PrintSelf (std::ostream &os, Indent indent) const override

	SPSAOptimizer ()

	~SPSAOptimizer () override=default

Protected Member Functions inherited from itk::SingleValuedNonLinearOptimizer
	SingleValuedNonLinearOptimizer ()

	~SingleValuedNonLinearOptimizer () override=default

Protected Member Functions inherited from itk::NonLinearOptimizer
	NonLinearOptimizer ()=default

	~NonLinearOptimizer () override

Protected Member Functions inherited from itk::Optimizer
	Optimizer ()

virtual void	SetCurrentPosition (const ParametersType &param)

	~Optimizer () override=default

Protected Member Functions inherited from itk::Object
	Object ()

bool	PrintObservers (std::ostream &os, Indent indent) const

virtual void	SetTimeStamp (const TimeStamp &timeStamp)

	~Object () override

Protected Member Functions inherited from itk::LightObject
virtual LightObject::Pointer	InternalClone () const

	LightObject ()

virtual void	PrintHeader (std::ostream &os, Indent indent) const

virtual void	PrintTrailer (std::ostream &os, Indent indent) const

virtual	~LightObject ()

Protected Attributes
SizeValueType	m_CurrentIteration {}

DerivativeType	m_Delta {}

Statistics::MersenneTwisterRandomVariateGenerator::Pointer	m_Generator {}

DerivativeType	m_Gradient {}

double	m_LearningRate {}

double	m_StateOfConvergence {}

bool	m_Stop { false }

StopConditionSPSAOptimizerEnum	m_StopCondition {}

Protected Attributes inherited from itk::SingleValuedNonLinearOptimizer
CostFunctionPointer	m_CostFunction {}

Protected Attributes inherited from itk::Optimizer
ParametersType	m_CurrentPosition {}

bool	m_ScalesInitialized { false }

Protected Attributes inherited from itk::LightObject
std::atomic< int >	m_ReferenceCount {}

Private Attributes
double	m_A {}

double	m_Alpha {}

double	m_Gamma {}

double	m_GradientMagnitude {}

bool	m_Maximize {}

SizeValueType	m_MaximumNumberOfIterations {}

SizeValueType	m_MinimumNumberOfIterations {}

SizeValueType	m_NumberOfPerturbations {}

double	m_Sa {}

double	m_Sc {}

double	m_StateOfConvergenceDecayRate {}

double	m_Tolerance {}

Member Typedef Documentation

◆ ConstPointer

using itk::SPSAOptimizer::ConstPointer = SmartPointer<const Self>

Definition at line 69 of file itkSPSAOptimizer.h.

◆ Pointer

using itk::SPSAOptimizer::Pointer = SmartPointer<Self>

Definition at line 68 of file itkSPSAOptimizer.h.

◆ Self

using itk::SPSAOptimizer::Self = SPSAOptimizer

Standard class type aliases.

Definition at line 66 of file itkSPSAOptimizer.h.

◆ StopConditionSPSAOptimizerEnum

using itk::SPSAOptimizer::StopConditionSPSAOptimizerEnum = SPSAOptimizerEnums::StopConditionSPSAOptimizer

Definition at line 77 of file itkSPSAOptimizer.h.

◆ Superclass

using itk::SPSAOptimizer::Superclass = SingleValuedNonLinearOptimizer

Definition at line 67 of file itkSPSAOptimizer.h.

Constructor & Destructor Documentation

◆ SPSAOptimizer()

itk::SPSAOptimizer::SPSAOptimizer ( )

protected

Referenced by GetNameOfClass().

◆ ~SPSAOptimizer()

itk::SPSAOptimizer::~SPSAOptimizer ( )

overrideprotecteddefault

Member Function Documentation

◆ AdvanceOneStep()

virtual void itk::SPSAOptimizer::AdvanceOneStep ( )

virtual

Advance one step following the gradient direction.

◆ Compute_a()

virtual double itk::SPSAOptimizer::Compute_a ( SizeValueType k ) const

protectedvirtual

Method to compute the learning rate at iteration k (a_k).

◆ Compute_c()

virtual double itk::SPSAOptimizer::Compute_c ( SizeValueType k ) const

protectedvirtual

Method to compute the gain factor for the perturbation at iteration k (c_k).

◆ ComputeGradient()

virtual void itk::SPSAOptimizer::ComputeGradient	(	const ParametersType &	parameters,
		DerivativeType &	gradient )

protectedvirtual

Compute the gradient at a position. m_NumberOfPerturbations are used, and scales are taken into account.

◆ CreateAnother()

virtual::itk::LightObject::Pointer itk::SPSAOptimizer::CreateAnother ( ) const

virtual

Create an object from an instance, potentially deferring to a factory. This method allows you to create an instance of an object that is exactly the same type as the referring object. This is useful in cases where an object has been cast back to a base class.

Reimplemented from itk::SingleValuedNonLinearOptimizer.

◆ GenerateDelta()

virtual void itk::SPSAOptimizer::GenerateDelta ( const unsigned int spaceDimension )

protectedvirtual

Generate a perturbation vector delta.

The elements are drawn from a Bernoulli distribution (+-1).

Takes scales into account.

◆ GetA()

virtual double itk::SPSAOptimizer::GetA ( ) const

virtual

Set/Get A.

◆ Geta()

double itk::SPSAOptimizer::Geta ( ) const

inline

Set/Get a.

Definition at line 151 of file itkSPSAOptimizer.h.

References GetSa().

◆ GetAlpha()

virtual double itk::SPSAOptimizer::GetAlpha ( ) const

virtual

Set/Get alpha.

◆ Getc()

double itk::SPSAOptimizer::Getc ( ) const

inline

Set/Get c.

Definition at line 167 of file itkSPSAOptimizer.h.

References GetSc().

◆ GetCurrentIteration()

virtual SizeValueType itk::SPSAOptimizer::GetCurrentIteration ( ) const

virtual

Get the current iteration number.

◆ GetGamma()

virtual double itk::SPSAOptimizer::GetGamma ( ) const

virtual

Set/Get gamma.

◆ GetGradient()

virtual const DerivativeType & itk::SPSAOptimizer::GetGradient ( ) const

virtual

Get the latest computed gradient

◆ GetGradientMagnitude()

virtual double itk::SPSAOptimizer::GetGradientMagnitude ( ) const

virtual

Get the GradientMagnitude of the latest computed gradient

◆ GetLearningRate()

virtual double itk::SPSAOptimizer::GetLearningRate ( ) const

virtual

Get the current LearningRate (a_k)

◆ GetMaximize()

virtual bool itk::SPSAOptimizer::GetMaximize ( ) const

virtual

Methods to configure the cost function.

◆ GetMaximumNumberOfIterations()

virtual SizeValueType itk::SPSAOptimizer::GetMaximumNumberOfIterations ( ) const

virtual

Set/Get the maximum number of iterations.

◆ GetMinimize()

bool itk::SPSAOptimizer::GetMinimize ( ) const

inline

Methods to configure the cost function.

Definition at line 193 of file itkSPSAOptimizer.h.

References m_Maximize.

◆ GetMinimumNumberOfIterations()

virtual SizeValueType itk::SPSAOptimizer::GetMinimumNumberOfIterations ( ) const

virtual

Set/Get the minimum number of iterations

◆ GetNameOfClass()

const char * itk::SPSAOptimizer::GetNameOfClass ( ) const

overridevirtual

See also: LightObject::GetNameOfClass()

Reimplemented from itk::SingleValuedNonLinearOptimizer.

References SPSAOptimizer().

◆ GetNumberOfPerturbations()

virtual SizeValueType itk::SPSAOptimizer::GetNumberOfPerturbations ( ) const

virtual

Set/Get the number of perturbation used to construct a gradient estimate g_k. q = NumberOfPerturbations g_k = 1/q sum_{j=1..q} g^(j)_k

◆ GetSa()

virtual double itk::SPSAOptimizer::GetSa ( ) const

virtual

Set/Get a.

Referenced by Geta().

◆ GetSc()

virtual double itk::SPSAOptimizer::GetSc ( ) const

virtual

Set/Get c.

Referenced by Getc().

◆ GetStateOfConvergence()

virtual double itk::SPSAOptimizer::GetStateOfConvergence ( ) const

virtual

Get the state of convergence in the last iteration. When the StateOfConvergence is lower than the Tolerance, and the minimum number of iterations has been performed, the optimization stops.

The state of convergence (SOC) is initialized with 0.0 and updated after each iteration as follows: SOC *= SOCDecayRate SOC += a_k * GradientMagnitude

◆ GetStateOfConvergenceDecayRate()

virtual double itk::SPSAOptimizer::GetStateOfConvergenceDecayRate ( ) const

virtual

Set/Get StateOfConvergenceDecayRate (number between 0 and 1).

◆ GetStopCondition()

virtual StopConditionSPSAOptimizerEnum itk::SPSAOptimizer::GetStopCondition ( ) const

virtual

Get Stop condition.

◆ GetStopConditionDescription()

std::string itk::SPSAOptimizer::GetStopConditionDescription ( ) const

overridevirtual

Get the reason for termination

Reimplemented from itk::Optimizer.

◆ GetTolerance()

virtual double itk::SPSAOptimizer::GetTolerance ( ) const

virtual

Set/Get Tolerance

◆ GetValue() [1/2]

virtual MeasureType itk::SPSAOptimizer::GetValue ( ) const

virtual

Get the cost function value at the current position.

◆ GetValue() [2/2]

virtual MeasureType itk::SPSAOptimizer::GetValue ( const ParametersType & parameters ) const

virtual

Get the cost function value at any position

◆ GuessParameters()

virtual void itk::SPSAOptimizer::GuessParameters	(	SizeValueType	numberOfGradientEstimates,
		double	initialStepSize )

virtual

Guess the parameters a and A. This function needs the number of GradientEstimates used for estimating a and A and and the expected initial step size (where step size is defined as the maximum of the absolute values of the parameter update). Make sure you set c, Alpha, Gamma, the MaximumNumberOfIterations, the Scales, and the the InitialPosition before calling this method.

Described in [107].

◆ MaximizeOff()

virtual void itk::SPSAOptimizer::MaximizeOff ( )

virtual

Methods to configure the cost function.

Referenced by MinimizeOn().

◆ MaximizeOn()

virtual void itk::SPSAOptimizer::MaximizeOn ( )

virtual

Methods to configure the cost function.

Referenced by MinimizeOff().

◆ MinimizeOff()

void itk::SPSAOptimizer::MinimizeOff ( )

inline

Methods to configure the cost function.

Definition at line 208 of file itkSPSAOptimizer.h.

References MaximizeOn().

◆ MinimizeOn()

void itk::SPSAOptimizer::MinimizeOn ( )

inline

Methods to configure the cost function.

Definition at line 203 of file itkSPSAOptimizer.h.

References MaximizeOff().

◆ New()

static Pointer itk::SPSAOptimizer::New ( )

static

Method for creation through the object factory.

◆ PrintSelf()

void itk::SPSAOptimizer::PrintSelf	(	std::ostream &	os,
		Indent	indent ) const

overrideprotectedvirtual

PrintSelf method.

Reimplemented from itk::SingleValuedNonLinearOptimizer.

◆ ResumeOptimization()

void itk::SPSAOptimizer::ResumeOptimization ( )

Resume previously stopped optimization with current parameters

See also: StopOptimization.

◆ SetA()

virtual void itk::SPSAOptimizer::SetA ( double _arg )

virtual

Set/Get A.

◆ Seta()

void itk::SPSAOptimizer::Seta ( double a )

inline

Set/Get a.

Definition at line 146 of file itkSPSAOptimizer.h.

References SetSa().

◆ SetAlpha()

virtual void itk::SPSAOptimizer::SetAlpha ( double _arg )

virtual

Set/Get alpha.

◆ Setc()

void itk::SPSAOptimizer::Setc ( double c )

inline

Set/Get c.

Definition at line 162 of file itkSPSAOptimizer.h.

References SetSc().

◆ SetGamma()

virtual void itk::SPSAOptimizer::SetGamma ( double _arg )

virtual

Set/Get gamma.

◆ SetMaximize()

virtual void itk::SPSAOptimizer::SetMaximize ( bool _arg )

virtual

Methods to configure the cost function.

Referenced by SetMinimize().

◆ SetMaximumNumberOfIterations()

virtual void itk::SPSAOptimizer::SetMaximumNumberOfIterations ( SizeValueType _arg )

virtual

Set/Get the maximum number of iterations.

◆ SetMinimize()

void itk::SPSAOptimizer::SetMinimize ( bool v )

inline

Methods to configure the cost function.

Definition at line 198 of file itkSPSAOptimizer.h.

References SetMaximize().

◆ SetMinimumNumberOfIterations()

virtual void itk::SPSAOptimizer::SetMinimumNumberOfIterations ( SizeValueType _arg )

virtual

Set/Get the minimum number of iterations

◆ SetNumberOfPerturbations()

virtual void itk::SPSAOptimizer::SetNumberOfPerturbations ( SizeValueType _arg )

virtual

Set/Get the number of perturbation used to construct a gradient estimate g_k. q = NumberOfPerturbations g_k = 1/q sum_{j=1..q} g^(j)_k

◆ SetSa()

virtual void itk::SPSAOptimizer::SetSa ( double _arg )

virtual

Set/Get a.

Referenced by Seta().

◆ SetSc()

virtual void itk::SPSAOptimizer::SetSc ( double _arg )

virtual

Set/Get c.

Referenced by Setc().

◆ SetStateOfConvergenceDecayRate()

virtual void itk::SPSAOptimizer::SetStateOfConvergenceDecayRate ( double _arg )

virtual

Set/Get StateOfConvergenceDecayRate (number between 0 and 1).

◆ SetTolerance()

virtual void itk::SPSAOptimizer::SetTolerance ( double _arg )

virtual

Set/Get Tolerance

◆ StartOptimization()

void itk::SPSAOptimizer::StartOptimization ( )

overridevirtual

Start optimization.

Reimplemented from itk::Optimizer.

◆ StopOptimization()

void itk::SPSAOptimizer::StopOptimization ( )

Stop optimization.

See also: ResumeOptimization

Member Data Documentation

◆ m_A

double itk::SPSAOptimizer::m_A {}

private

Definition at line 326 of file itkSPSAOptimizer.h.

◆ m_Alpha

double itk::SPSAOptimizer::m_Alpha {}

private

Definition at line 327 of file itkSPSAOptimizer.h.

◆ m_CurrentIteration

SizeValueType itk::SPSAOptimizer::m_CurrentIteration {}

protected

Definition at line 281 of file itkSPSAOptimizer.h.

◆ m_Delta

DerivativeType itk::SPSAOptimizer::m_Delta {}

protected

Definition at line 273 of file itkSPSAOptimizer.h.

◆ m_Gamma

double itk::SPSAOptimizer::m_Gamma {}

private

Definition at line 328 of file itkSPSAOptimizer.h.

◆ m_Generator

Statistics::MersenneTwisterRandomVariateGenerator::Pointer itk::SPSAOptimizer::m_Generator {}

protected

Random number generator

Definition at line 284 of file itkSPSAOptimizer.h.

◆ m_Gradient

DerivativeType itk::SPSAOptimizer::m_Gradient {}

protected

Variables updated during optimization

Definition at line 269 of file itkSPSAOptimizer.h.

◆ m_GradientMagnitude

double itk::SPSAOptimizer::m_GradientMagnitude {}

private

Definition at line 320 of file itkSPSAOptimizer.h.

◆ m_LearningRate

double itk::SPSAOptimizer::m_LearningRate {}

protected

Definition at line 271 of file itkSPSAOptimizer.h.

◆ m_Maximize

bool itk::SPSAOptimizer::m_Maximize {}

private

Definition at line 319 of file itkSPSAOptimizer.h.

Referenced by GetMinimize().

◆ m_MaximumNumberOfIterations

SizeValueType itk::SPSAOptimizer::m_MaximumNumberOfIterations {}

private

Definition at line 316 of file itkSPSAOptimizer.h.

◆ m_MinimumNumberOfIterations

SizeValueType itk::SPSAOptimizer::m_MinimumNumberOfIterations {}

private

Settings.

Definition at line 315 of file itkSPSAOptimizer.h.

◆ m_NumberOfPerturbations

SizeValueType itk::SPSAOptimizer::m_NumberOfPerturbations {}

private

Definition at line 321 of file itkSPSAOptimizer.h.

◆ m_Sa

double itk::SPSAOptimizer::m_Sa {}

private

Parameters, as described by Spall.

Definition at line 324 of file itkSPSAOptimizer.h.

◆ m_Sc

double itk::SPSAOptimizer::m_Sc {}

private

Definition at line 325 of file itkSPSAOptimizer.h.

◆ m_StateOfConvergence

double itk::SPSAOptimizer::m_StateOfConvergence {}

protected

Definition at line 279 of file itkSPSAOptimizer.h.

◆ m_StateOfConvergenceDecayRate

double itk::SPSAOptimizer::m_StateOfConvergenceDecayRate {}

private

Definition at line 317 of file itkSPSAOptimizer.h.

◆ m_Stop

bool itk::SPSAOptimizer::m_Stop { false }

protected

Definition at line 275 of file itkSPSAOptimizer.h.

◆ m_StopCondition

StopConditionSPSAOptimizerEnum itk::SPSAOptimizer::m_StopCondition {}

protected

Definition at line 277 of file itkSPSAOptimizer.h.

◆ m_Tolerance

double itk::SPSAOptimizer::m_Tolerance {}

private

Definition at line 318 of file itkSPSAOptimizer.h.

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

itkSPSAOptimizer.h

Detailed Description

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Protected Attributes

Private Attributes

Member Typedef Documentation

◆ ConstPointer

◆ Pointer

◆ Self

◆ StopConditionSPSAOptimizerEnum

◆ Superclass

Constructor & Destructor Documentation

◆ SPSAOptimizer()

◆ ~SPSAOptimizer()

Member Function Documentation

◆ AdvanceOneStep()

◆ Compute_a()

◆ Compute_c()

◆ ComputeGradient()

◆ CreateAnother()

◆ GenerateDelta()

◆ GetA()

◆ Geta()

◆ GetAlpha()

◆ Getc()

◆ GetCurrentIteration()

◆ GetGamma()

◆ GetGradient()

◆ GetGradientMagnitude()

◆ GetLearningRate()

◆ GetMaximize()

◆ GetMaximumNumberOfIterations()

◆ GetMinimize()

◆ GetMinimumNumberOfIterations()

◆ GetNameOfClass()

◆ GetNumberOfPerturbations()

◆ GetSa()

◆ GetSc()

◆ GetStateOfConvergence()

◆ GetStateOfConvergenceDecayRate()

◆ GetStopCondition()

◆ GetStopConditionDescription()

◆ GetTolerance()

◆ GetValue() [1/2]

◆ GetValue() [2/2]

◆ GuessParameters()

◆ MaximizeOff()

◆ MaximizeOn()

◆ MinimizeOff()

◆ MinimizeOn()

◆ New()

◆ PrintSelf()

◆ ResumeOptimization()

◆ SetA()

◆ Seta()

◆ SetAlpha()

◆ Setc()

◆ SetGamma()

◆ SetMaximize()

◆ SetMaximumNumberOfIterations()

◆ SetMinimize()

◆ SetMinimumNumberOfIterations()

◆ SetNumberOfPerturbations()

◆ SetSa()

◆ SetSc()

◆ SetStateOfConvergenceDecayRate()

◆ SetTolerance()

◆ StartOptimization()

◆ StopOptimization()

Member Data Documentation

◆ m_A

◆ m_Alpha

◆ m_CurrentIteration

◆ m_Delta

◆ m_Gamma

◆ m_Generator

◆ m_Gradient

◆ m_GradientMagnitude