#include <itkStochasticGradientDescentOptimizer.h>

Detailed Description

Implement a gradient descent optimizer.

StochasticGradientDescentOptimizer implements a simple gradient descent optimizer. At each iteration the current position is updated according to

$p_{n+1} = p_n + \mbox{learningRate} \, \frac{\partial f(p_n) }{\partial p_n}$

The learning rate is a fixed scalar defined via SetLearningRate(). The optimizer steps through a user defined number of iterations; no convergence checking is done.

Additionally, user can scale each component of the $\partial f / \partial p$ but setting a scaling vector using method SetScale().

The difference of this class with the itk::GradientDescentOptimizer is that it's based on the ScaledSingleValuedNonLinearOptimizer

See also: ScaledSingleValuedNonLinearOptimizer

Definition at line 53 of file itkStochasticGradientDescentOptimizer.h.

Inheritance diagram for itk::StochasticGradientDescentOptimizer:

[legend]

Data Structures
struct	MultiThreaderParameterType

Public Types
using	ConstPointer = SmartPointer<const Self>

using	Pointer = SmartPointer<Self>

using	ScaledCostFunctionPointer

using	ScaledCostFunctionType

using	ScalesType

using	Self = StochasticGradientDescentOptimizer

enum	StopConditionType { MaximumNumberOfIterations , MetricError , MinimumStepSize , InvalidDiagonalMatrix , GradientMagnitudeTolerance , LineSearchError }

using	Superclass = ScaledSingleValuedNonLinearOptimizer

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

using	Pointer = SmartPointer<Self>

using	ScaledCostFunctionPointer = ScaledCostFunctionType::Pointer

using	ScaledCostFunctionType = ScaledSingleValuedCostFunction

using	ScalesType = NonLinearOptimizer::ScalesType

using	Self = ScaledSingleValuedNonLinearOptimizer

using	Superclass = SingleValuedNonLinearOptimizer

Public Member Functions
virtual void	AdvanceOneStep ()

virtual const char *	GetClassName () const

virtual unsigned int	GetCurrentInnerIteration () const

virtual unsigned int	GetCurrentIteration () const

virtual const DerivativeType &	GetGradient ()

virtual unsigned int	GetLBFGSMemory () const

virtual const double &	GetLearningRate ()

virtual const unsigned long &	GetNumberOfInnerIterations ()

virtual const unsigned long &	GetNumberOfIterations ()

virtual const DerivativeType &	GetPreviousGradient ()

virtual const ParametersType &	GetPreviousPosition ()

virtual const DerivativeType &	GetSearchDir ()

virtual const StopConditionType &	GetStopCondition ()

virtual const double &	GetValue ()

	ITK_DISALLOW_COPY_AND_MOVE (StochasticGradientDescentOptimizer)

virtual void	MetricErrorResponse (ExceptionObject &err)

virtual void	ResumeOptimization ()

virtual void	SetLearningRate (double _arg)

virtual void	SetNumberOfIterations (unsigned long _arg)

void	SetNumberOfWorkUnits (ThreadIdType numberOfThreads)

virtual void	SetPreviousGradient (DerivativeType _arg)

virtual void	SetPreviousPosition (ParametersType _arg)

virtual void	SetUseEigen (bool _arg)

virtual void	SetUseMultiThread (bool _arg)

void	StartOptimization () override

virtual void	StopOptimization ()

Public Member Functions inherited from itk::ScaledSingleValuedNonLinearOptimizer
const ParametersType &	GetCurrentPosition () const override

virtual bool	GetMaximize () const

virtual const ScaledCostFunctionType *	GetScaledCostFunction ()

virtual const ParametersType &	GetScaledCurrentPosition ()

bool	GetUseScales () const

virtual void	InitializeScales ()

	ITK_DISALLOW_COPY_AND_MOVE (ScaledSingleValuedNonLinearOptimizer)

virtual void	MaximizeOff ()

virtual void	MaximizeOn ()

void	SetCostFunction (CostFunctionType *costFunction) override

virtual void	SetMaximize (bool _arg)

virtual void	SetUseScales (bool arg)

Static Public Member Functions
static Pointer	New ()

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

Protected Types
using	ThreadInfoType = MultiThreaderBase::WorkUnitInfo

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

	StochasticGradientDescentOptimizer ()

	~StochasticGradientDescentOptimizer () override=default

Protected Member Functions inherited from itk::ScaledSingleValuedNonLinearOptimizer
virtual void	GetScaledDerivative (const ParametersType &parameters, DerivativeType &derivative) const

virtual MeasureType	GetScaledValue (const ParametersType &parameters) const

virtual void	GetScaledValueAndDerivative (const ParametersType &parameters, MeasureType &value, DerivativeType &derivative) const

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

	ScaledSingleValuedNonLinearOptimizer ()

void	SetCurrentPosition (const ParametersType &param) override

virtual void	SetScaledCurrentPosition (const ParametersType &parameters)

	~ScaledSingleValuedNonLinearOptimizer () override=default

Protected Attributes
unsigned long	m_CurrentInnerIteration {}

unsigned long	m_CurrentIteration { 0 }

DerivativeType	m_Gradient {}

unsigned long	m_LBFGSMemory { 0 }

double	m_LearningRate { 1.0 }

ParametersType	m_MeanSearchDir {}

unsigned long	m_NumberOfInnerIterations {}

unsigned long	m_NumberOfIterations { 100 }

DerivativeType	m_PrePreviousGradient {}

ParametersType	m_PrePreviousSearchDir {}

DerivativeType	m_PreviousGradient {}

ParametersType	m_PreviousPosition {}

ParametersType	m_PreviousSearchDir {}

ParametersType	m_SearchDir {}

bool	m_Stop { false }

StopConditionType	m_StopCondition { MaximumNumberOfIterations }

MultiThreaderBase::Pointer	m_Threader { MultiThreaderBase::New() }

double	m_Value { 0.0 }

Protected Attributes inherited from itk::ScaledSingleValuedNonLinearOptimizer
ScaledCostFunctionPointer	m_ScaledCostFunction {}

ParametersType	m_ScaledCurrentPosition {}

Private Member Functions
void	ThreadedAdvanceOneStep (ThreadIdType threadId, ParametersType &newPosition)

Static Private Member Functions
static ITK_THREAD_RETURN_FUNCTION_CALL_CONVENTION	AdvanceOneStepThreaderCallback (void *arg)

Private Attributes
bool	m_UseEigen { false }

bool	m_UseMultiThread { false }

Member Typedef Documentation

◆ ConstPointer

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

Definition at line 62 of file itkStochasticGradientDescentOptimizer.h.

◆ Pointer

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

Definition at line 61 of file itkStochasticGradientDescentOptimizer.h.

◆ ScaledCostFunctionPointer

using itk::ScaledSingleValuedNonLinearOptimizer::ScaledCostFunctionPointer

Definition at line 87 of file itkScaledSingleValuedNonLinearOptimizer.h.

◆ ScaledCostFunctionType

using itk::ScaledSingleValuedNonLinearOptimizer::ScaledCostFunctionType

Definition at line 86 of file itkScaledSingleValuedNonLinearOptimizer.h.

◆ ScalesType

using itk::ScaledSingleValuedNonLinearOptimizer::ScalesType

Definition at line 85 of file itkScaledSingleValuedNonLinearOptimizer.h.

◆ Self

using itk::StochasticGradientDescentOptimizer::Self = StochasticGradientDescentOptimizer

Standard class typedefs.

Definition at line 59 of file itkStochasticGradientDescentOptimizer.h.

◆ Superclass

using itk::StochasticGradientDescentOptimizer::Superclass = ScaledSingleValuedNonLinearOptimizer

Definition at line 60 of file itkStochasticGradientDescentOptimizer.h.

◆ ThreadInfoType

using itk::StochasticGradientDescentOptimizer::ThreadInfoType = MultiThreaderBase::WorkUnitInfo

protected

Typedef for multi-threading.

Definition at line 179 of file itkStochasticGradientDescentOptimizer.h.

Member Enumeration Documentation

◆ StopConditionType

enum itk::StochasticGradientDescentOptimizer::StopConditionType

Codes of stopping conditions The MinimumStepSize stopcondition never occurs, but may be implemented in inheriting classes

Enumerator
MaximumNumberOfIterations
MetricError
MinimumStepSize
InvalidDiagonalMatrix
GradientMagnitudeTolerance
LineSearchError

Definition at line 82 of file itkStochasticGradientDescentOptimizer.h.

Constructor & Destructor Documentation

◆ StochasticGradientDescentOptimizer()

itk::StochasticGradientDescentOptimizer::StochasticGradientDescentOptimizer ( )

protected

◆ ~StochasticGradientDescentOptimizer()

itk::StochasticGradientDescentOptimizer::~StochasticGradientDescentOptimizer ( )

overrideprotecteddefault

Member Function Documentation

◆ AdvanceOneStep()

virtual void itk::StochasticGradientDescentOptimizer::AdvanceOneStep ( )

virtual

Advance one step following the gradient direction.

Reimplemented in elastix::AdaptiveStochasticLBFGS< TElastix >, and itk::StandardStochasticGradientOptimizer.

◆ AdvanceOneStepThreaderCallback()

static ITK_THREAD_RETURN_FUNCTION_CALL_CONVENTION itk::StochasticGradientDescentOptimizer::AdvanceOneStepThreaderCallback ( void * arg )

staticprivate

The callback function.

◆ GetClassName()

virtual const char * itk::StochasticGradientDescentOptimizer::GetClassName ( ) const

virtual

Run-time type information (and related methods).

Reimplemented from itk::ScaledSingleValuedNonLinearOptimizer.

Reimplemented in elastix::AdaptiveStochasticLBFGS< TElastix >, itk::AdaptiveStochasticLBFGSOptimizer, and itk::StandardStochasticGradientOptimizer.

◆ GetCurrentInnerIteration()

virtual unsigned int itk::StochasticGradientDescentOptimizer::GetCurrentInnerIteration ( ) const

virtual

Get the current inner iteration number.

◆ GetCurrentIteration()

virtual unsigned int itk::StochasticGradientDescentOptimizer::GetCurrentIteration ( ) const

virtual

Get the current iteration number.

◆ GetGradient()

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

virtual

Get current gradient.

◆ GetLBFGSMemory()

virtual unsigned int itk::StochasticGradientDescentOptimizer::GetLBFGSMemory ( ) const

virtual

Get the inner LBFGSMemory.

◆ GetLearningRate()

virtual const double & itk::StochasticGradientDescentOptimizer::GetLearningRate ( )

virtual

Get the learning rate.

◆ GetNumberOfInnerIterations()

virtual const unsigned long & itk::StochasticGradientDescentOptimizer::GetNumberOfInnerIterations ( )

virtual

Get the number of inner loop iterations.

◆ GetNumberOfIterations()

virtual const unsigned long & itk::StochasticGradientDescentOptimizer::GetNumberOfIterations ( )

virtual

Get the number of iterations.

◆ GetPreviousGradient()

virtual const DerivativeType & itk::StochasticGradientDescentOptimizer::GetPreviousGradient ( )

virtual

Get the Previous gradient.

◆ GetPreviousPosition()

virtual const ParametersType & itk::StochasticGradientDescentOptimizer::GetPreviousPosition ( )

virtual

Get the Previous Position.

◆ GetSearchDir()

virtual const DerivativeType & itk::StochasticGradientDescentOptimizer::GetSearchDir ( )

virtual

Get current search direction.

◆ GetStopCondition()

virtual const StopConditionType & itk::StochasticGradientDescentOptimizer::GetStopCondition ( )

virtual

Get Stop condition.

◆ GetValue()

virtual const double & itk::StochasticGradientDescentOptimizer::GetValue ( )

virtual

Get the current value.

◆ ITK_DISALLOW_COPY_AND_MOVE()

itk::StochasticGradientDescentOptimizer::ITK_DISALLOW_COPY_AND_MOVE ( StochasticGradientDescentOptimizer )

◆ MetricErrorResponse()

virtual void itk::StochasticGradientDescentOptimizer::MetricErrorResponse ( ExceptionObject & err )

virtual

Stop optimisation and pass on exception.

◆ New()

static Pointer itk::StochasticGradientDescentOptimizer::New ( )

static

Method for creation through the object factory.

◆ PrintSelf()

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

overrideprotected

◆ ResumeOptimization()

virtual void itk::StochasticGradientDescentOptimizer::ResumeOptimization ( )

virtual

Resume previously stopped optimization with current parameters

See also: StopOptimization.

Reimplemented in elastix::AdaptiveStochasticLBFGS< TElastix >.

◆ SetLearningRate()

virtual void itk::StochasticGradientDescentOptimizer::SetLearningRate ( double _arg )

virtual

Set the learning rate.

◆ SetNumberOfIterations()

virtual void itk::StochasticGradientDescentOptimizer::SetNumberOfIterations ( unsigned long _arg )

virtual

Set the number of iterations.

◆ SetNumberOfWorkUnits()

void itk::StochasticGradientDescentOptimizer::SetNumberOfWorkUnits ( ThreadIdType numberOfThreads )

inline

Set the number of threads.

Definition at line 164 of file itkStochasticGradientDescentOptimizer.h.

◆ SetPreviousGradient()

virtual void itk::StochasticGradientDescentOptimizer::SetPreviousGradient ( DerivativeType _arg )

virtual

Set the Previous gradient.

◆ SetPreviousPosition()

virtual void itk::StochasticGradientDescentOptimizer::SetPreviousPosition ( ParametersType _arg )

virtual

Set the Previous Position.

◆ SetUseEigen()

virtual void itk::StochasticGradientDescentOptimizer::SetUseEigen ( bool _arg )

virtual

◆ SetUseMultiThread()

virtual void itk::StochasticGradientDescentOptimizer::SetUseMultiThread ( bool _arg )

virtual

◆ StartOptimization()

void itk::StochasticGradientDescentOptimizer::StartOptimization ( )

override

Start optimization.

◆ StopOptimization()

virtual void itk::StochasticGradientDescentOptimizer::StopOptimization ( )

virtual

Stop optimization.

See also: ResumeOptimization

Reimplemented in elastix::AdaptiveStochasticLBFGS< TElastix >.

◆ ThreadedAdvanceOneStep()

void itk::StochasticGradientDescentOptimizer::ThreadedAdvanceOneStep	(	ThreadIdType	threadId,
		ParametersType &	newPosition )

inlineprivate

The threaded implementation of AdvanceOneStep().

Field Documentation

◆ m_CurrentInnerIteration

unsigned long itk::StochasticGradientDescentOptimizer::m_CurrentInnerIteration {}

protected

Definition at line 199 of file itkStochasticGradientDescentOptimizer.h.

◆ m_CurrentIteration

unsigned long itk::StochasticGradientDescentOptimizer::m_CurrentIteration { 0 }

protected

Definition at line 198 of file itkStochasticGradientDescentOptimizer.h.

◆ m_Gradient

DerivativeType itk::StochasticGradientDescentOptimizer::m_Gradient {}

protected

Definition at line 183 of file itkStochasticGradientDescentOptimizer.h.

◆ m_LBFGSMemory

unsigned long itk::StochasticGradientDescentOptimizer::m_LBFGSMemory { 0 }

protected

Definition at line 200 of file itkStochasticGradientDescentOptimizer.h.

◆ m_LearningRate

double itk::StochasticGradientDescentOptimizer::m_LearningRate { 1.0 }

protected

Definition at line 188 of file itkStochasticGradientDescentOptimizer.h.

◆ m_MeanSearchDir

ParametersType itk::StochasticGradientDescentOptimizer::m_MeanSearchDir {}

protected

Definition at line 187 of file itkStochasticGradientDescentOptimizer.h.

◆ m_NumberOfInnerIterations

unsigned long itk::StochasticGradientDescentOptimizer::m_NumberOfInnerIterations {}

protected

Definition at line 197 of file itkStochasticGradientDescentOptimizer.h.

◆ m_NumberOfIterations

unsigned long itk::StochasticGradientDescentOptimizer::m_NumberOfIterations { 100 }

protected

Definition at line 196 of file itkStochasticGradientDescentOptimizer.h.

◆ m_PrePreviousGradient

DerivativeType itk::StochasticGradientDescentOptimizer::m_PrePreviousGradient {}

protected

Definition at line 191 of file itkStochasticGradientDescentOptimizer.h.

◆ m_PrePreviousSearchDir

ParametersType itk::StochasticGradientDescentOptimizer::m_PrePreviousSearchDir {}

protected

Definition at line 186 of file itkStochasticGradientDescentOptimizer.h.

◆ m_PreviousGradient

DerivativeType itk::StochasticGradientDescentOptimizer::m_PreviousGradient {}

protected

Definition at line 190 of file itkStochasticGradientDescentOptimizer.h.

◆ m_PreviousPosition

ParametersType itk::StochasticGradientDescentOptimizer::m_PreviousPosition {}

protected

Definition at line 192 of file itkStochasticGradientDescentOptimizer.h.

◆ m_PreviousSearchDir

ParametersType itk::StochasticGradientDescentOptimizer::m_PreviousSearchDir {}

protected

Definition at line 185 of file itkStochasticGradientDescentOptimizer.h.

◆ m_SearchDir

ParametersType itk::StochasticGradientDescentOptimizer::m_SearchDir {}

protected

Definition at line 184 of file itkStochasticGradientDescentOptimizer.h.

◆ m_Stop

bool itk::StochasticGradientDescentOptimizer::m_Stop { false }

protected

Definition at line 195 of file itkStochasticGradientDescentOptimizer.h.

◆ m_StopCondition

StopConditionType itk::StochasticGradientDescentOptimizer::m_StopCondition { MaximumNumberOfIterations }

protected

Definition at line 189 of file itkStochasticGradientDescentOptimizer.h.

◆ m_Threader

MultiThreaderBase::Pointer itk::StochasticGradientDescentOptimizer::m_Threader { MultiThreaderBase::New() }

protected

Definition at line 193 of file itkStochasticGradientDescentOptimizer.h.

◆ m_UseEigen

bool itk::StochasticGradientDescentOptimizer::m_UseEigen { false }

private

Definition at line 211 of file itkStochasticGradientDescentOptimizer.h.

◆ m_UseMultiThread

bool itk::StochasticGradientDescentOptimizer::m_UseMultiThread { false }

private

Definition at line 204 of file itkStochasticGradientDescentOptimizer.h.

◆ m_Value

double itk::StochasticGradientDescentOptimizer::m_Value { 0.0 }

protected

Definition at line 182 of file itkStochasticGradientDescentOptimizer.h.

Detailed Description

Data Structures

Public Types

Public Member Functions

Static Public Member Functions

Protected Types

Protected Member Functions

Protected Attributes

Private Member Functions

Static Private Member Functions

Private Attributes

Member Typedef Documentation

◆ ConstPointer

◆ Pointer

◆ ScaledCostFunctionPointer

◆ ScaledCostFunctionType

◆ ScalesType

◆ Self

◆ Superclass

◆ ThreadInfoType

Member Enumeration Documentation

◆ StopConditionType

Constructor & Destructor Documentation

◆ StochasticGradientDescentOptimizer()

◆ ~StochasticGradientDescentOptimizer()

Member Function Documentation

◆ AdvanceOneStep()

◆ AdvanceOneStepThreaderCallback()

◆ GetClassName()

◆ GetCurrentInnerIteration()

◆ GetCurrentIteration()

◆ GetGradient()

◆ GetLBFGSMemory()

◆ GetLearningRate()

◆ GetNumberOfInnerIterations()

◆ GetNumberOfIterations()

◆ GetPreviousGradient()

◆ GetPreviousPosition()

◆ GetSearchDir()

◆ GetStopCondition()

◆ GetValue()

◆ ITK_DISALLOW_COPY_AND_MOVE()

◆ MetricErrorResponse()

◆ New()

◆ PrintSelf()

◆ ResumeOptimization()

◆ SetLearningRate()

◆ SetNumberOfIterations()

◆ SetNumberOfWorkUnits()

◆ SetPreviousGradient()

◆ SetPreviousPosition()

◆ SetUseEigen()

◆ SetUseMultiThread()

◆ StartOptimization()

◆ StopOptimization()

◆ ThreadedAdvanceOneStep()

Field Documentation

◆ m_CurrentInnerIteration

◆ m_CurrentIteration

◆ m_Gradient

◆ m_LBFGSMemory

◆ m_LearningRate

◆ m_MeanSearchDir

◆ m_NumberOfInnerIterations

◆ m_NumberOfIterations

◆ m_PrePreviousGradient

◆ m_PrePreviousSearchDir

◆ m_PreviousGradient

◆ m_PreviousPosition

◆ m_PreviousSearchDir

◆ m_SearchDir

◆ m_Stop

◆ m_StopCondition

◆ m_Threader

◆ m_UseEigen

◆ m_UseMultiThread

◆ m_Value