#include <itkGradientDescentOptimizerv4.h>

Inheritance diagram for itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >:

Collaboration diagram for itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >:

Detailed Description

template<typename TInternalComputationValueType>
class itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >

Gradient descent optimizer.

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

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

Optionally, the best metric value and matching parameters can be stored and retried via GetValue() and GetCurrentPosition(). See SetReturnBestParametersAndValue().

Gradient scales can be manually set or automatically estimated, as documented in the base class. The learing rate defaults to 1.0, and can be set in two ways: 1) manually, via SetLearningRate(). Or, 2) automatically, either at each iteration or only at the first iteration, by assigning a ScalesEstimator via SetScalesEstimator(). When a ScalesEstimator is assigned, the optimizer is enabled by default to estimate learning rate only once, during the first iteration. This behavior can be changed via SetDoEstimateLearningRateAtEveryIteration() and SetDoEstimateLearningRateOnce(). For learning rate to be estimated at each iteration, the user must call SetDoEstimateLearningRateAtEveryIteration(true) and SetDoEstimateLearningRateOnce(false). When enabled, the optimizer computes learning rate(s) such that at each step, each voxel's change in physical space will be less than m_MaximumStepSizeInPhysicalUnits.

 m_LearningRate =
   m_MaximumStepSizeInPhysicalUnits /
   m_ScalesEstimator->EstimateStepScale(scaledGradient)

where m_MaximumStepSizeInPhysicalUnits defaults to the voxel spacing returned by m_ScalesEstimator->EstimateMaximumStepSize() (which is typically 1 voxel), and can be set by the user via SetMaximumStepSizeInPhysicalUnits(). When SetDoEstimateLearningRateOnce is enabled, the voxel change may become being greater than m_MaximumStepSizeInPhysicalUnits in later iterations.

Note: Unlike the previous version of GradientDescentOptimizer, this version does not have a "maximize/minimize" option to modify the effect of the metric derivative. The assigned metric is assumed to return a parameter derivative result that "improves" the optimization when added to the current parameters via the metric::UpdateTransformParameters method, after the optimizer applies scales and a learning rate.

Definition at line 77 of file itkGradientDescentOptimizerv4.h.

Public Types
typedef SmartPointer< const Self >	ConstPointer

typedef Superclass::DerivativeType	DerivativeType

typedef Superclass::IndexRangeType	IndexRangeType

typedef TInternalComputationValueType	InternalComputationValueType

typedef Superclass::MeasureType	MeasureType

typedef Superclass::ParametersType	ParametersType

typedef SmartPointer< Self >	Pointer

typedef Superclass::ScalesType	ScalesType

typedef GradientDescentOptimizerv4Template	Self

typedef Superclass::StopConditionType	StopConditionType

typedef GradientDescentOptimizerBasev4Template < TInternalComputationValueType >	Superclass

Public Types inherited from itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >
typedef SmartPointer< const Self >	ConstPointer

typedef itk::Function::WindowConvergenceMonitoringFunction < TInternalComputationValueType >	ConvergenceMonitoringType

typedef Superclass::DerivativeType	DerivativeType

typedef ThreadedIndexedContainerPartitioner::IndexRangeType	IndexRangeType

typedef TInternalComputationValueType	InternalComputationValueType

typedef Superclass::MeasureType	MeasureType

typedef Superclass::MetricType	MetricType

typedef MetricType::Pointer	MetricTypePointer

typedef Superclass::ParametersType	ParametersType

typedef SmartPointer< Self >	Pointer

typedef Superclass::ScalesType	ScalesType

typedef GradientDescentOptimizerBasev4Template	Self

typedef Superclass::StopConditionDescriptionType	StopConditionDescriptionType

typedef Superclass::StopConditionReturnStringType	StopConditionReturnStringType

enum	StopConditionType { MAXIMUM_NUMBER_OF_ITERATIONS, COSTFUNCTION_ERROR, UPDATE_PARAMETERS_ERROR, STEP_TOO_SMALL, CONVERGENCE_CHECKER_PASSED, GRADIENT_MAGNITUDE_TOLEARANCE, OTHER_ERROR }

typedef ObjectToObjectOptimizerBaseTemplate < TInternalComputationValueType >	Superclass

Public Types inherited from itk::ObjectToObjectOptimizerBaseTemplate< TInternalComputationValueType >
typedef SmartPointer< const Self >	ConstPointer

typedef MetricType::DerivativeType	DerivativeType

typedef MetricType::MeasureType	MeasureType

typedef ObjectToObjectMetricBaseTemplate < TInternalComputationValueType >	MetricType

typedef MetricType::Pointer	MetricTypePointer

typedef MetricType::NumberOfParametersType	NumberOfParametersType

typedef OptimizerParameters < TInternalComputationValueType >	ParametersType

typedef SmartPointer< Self >	Pointer

typedef OptimizerParameterScalesEstimatorTemplate < TInternalComputationValueType >	ScalesEstimatorType

typedef OptimizerParameters < TInternalComputationValueType >	ScalesType

typedef ObjectToObjectOptimizerBaseTemplate	Self

typedef std::ostringstream	StopConditionDescriptionType

typedef std::string	StopConditionReturnStringType

typedef Object	Superclass

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

typedef SmartPointer< Self >	Pointer

typedef Object	Self

typedef LightObject	Superclass

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

typedef SmartPointer< Self >	Pointer

typedef LightObject	Self

Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother (void) const

virtual void	EstimateLearningRate ()

virtual const TInternalComputationValueType &	GetConvergenceValue () const

virtual const TInternalComputationValueType &	GetLearningRate () const

virtual const TInternalComputationValueType &	GetMaximumStepSizeInPhysicalUnits () const

virtual const char *	GetNameOfClass () const

virtual void	ResumeOptimization () ITK_OVERRIDE

virtual void	SetConvergenceWindowSize (SizeValueType _arg)

virtual void	SetLearningRate (TInternalComputationValueType _arg)

virtual void	SetMaximumStepSizeInPhysicalUnits (TInternalComputationValueType _arg)

virtual void	SetMinimumConvergenceValue (TInternalComputationValueType _arg)

virtual void	StartOptimization (bool doOnlyInitialization=false) ITK_OVERRIDE

virtual void	StopOptimization (void) ITK_OVERRIDE


virtual void	SetDoEstimateLearningRateAtEachIteration (bool _arg)

virtual const bool &	GetDoEstimateLearningRateAtEachIteration () const

virtual void	DoEstimateLearningRateAtEachIterationOn ()

virtual void	DoEstimateLearningRateAtEachIterationOff ()


virtual void	SetDoEstimateLearningRateOnce (bool _arg)

virtual const bool &	GetDoEstimateLearningRateOnce () const

virtual void	DoEstimateLearningRateOnceOn ()

virtual void	DoEstimateLearningRateOnceOff ()


virtual void	SetReturnBestParametersAndValue (bool _arg)

virtual const bool &	GetReturnBestParametersAndValue () const

virtual void	ReturnBestParametersAndValueOn ()

virtual void	ReturnBestParametersAndValueOff ()

Public Member Functions inherited from itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >
virtual SizeValueType	GetCurrentIteration () const

virtual const DerivativeType &	GetGradient () const

virtual SizeValueType	GetNumberOfIterations () const

virtual const StopConditionType &	GetStopCondition () const

virtual const StopConditionReturnStringType	GetStopConditionDescription () const ITK_OVERRIDE

virtual void	SetNumberOfIterations (SizeValueType _arg)

virtual void	ModifyGradientByScales ()

virtual void	ModifyGradientByLearningRate ()

Public Member Functions inherited from itk::ObjectToObjectOptimizerBaseTemplate< TInternalComputationValueType >
virtual const MeasureType &	GetCurrentMetricValue () const

virtual const ParametersType &	GetCurrentPosition () const

virtual const ThreadIdType &	GetNumberOfThreads () const

virtual const ScalesType &	GetScales () const

virtual const bool &	GetScalesAreIdentity () const

bool	GetScalesInitialized (void) const

virtual const MeasureType &	GetValue () const

virtual const ScalesType &	GetWeights () const

virtual const bool &	GetWeightsAreIdentity () const

virtual void	SetNumberOfThreads (ThreadIdType number)

virtual void	SetScalesEstimator (ScalesEstimatorType *_arg)

virtual void	SetWeights (ScalesType _arg)

virtual void	SetMetric (MetricType *_arg)

virtual MetricType *	GetModifiableMetric ()

virtual const MetricType *	GetMetric () const

virtual void	SetScales (const ScalesType &scales)

virtual void	SetDoEstimateScales (bool _arg)

virtual const bool &	GetDoEstimateScales () const

virtual void	DoEstimateScalesOn ()

virtual void	DoEstimateScalesOff ()

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

unsigned long	AddObserver (const EventObject &event, Command *) const

virtual void	DebugOff () const

virtual void	DebugOn () const

Command *	GetCommand (unsigned long tag)

bool	GetDebug () const

MetaDataDictionary &	GetMetaDataDictionary (void)

const MetaDataDictionary &	GetMetaDataDictionary (void) 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

virtual void	Register () const ITK_OVERRIDE

void	RemoveAllObservers ()

void	RemoveObserver (unsigned long tag)

void	SetDebug (bool debugFlag) const

void	SetMetaDataDictionary (const MetaDataDictionary &rhs)

virtual void	SetReferenceCount (int) ITK_OVERRIDE

virtual void	UnRegister () const ITK_OVERRIDE

virtual void	SetObjectName (std::string _arg)

virtual const std::string &	GetObjectName () const

Public Member Functions inherited from itk::LightObject
virtual void	Delete ()

virtual int	GetReferenceCount () const

	itkCloneMacro (Self)

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

Static Public Member Functions
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 flag)

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

static Pointer	New ()

Protected Member Functions
virtual void	AdvanceOneStep (void)

	GradientDescentOptimizerv4Template ()

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

virtual	~GradientDescentOptimizerv4Template ()


virtual void	ModifyGradientByScalesOverSubRange (const IndexRangeType &subrange) ITK_OVERRIDE

virtual void	ModifyGradientByLearningRateOverSubRange (const IndexRangeType &subrange) ITK_OVERRIDE

Protected Member Functions inherited from itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >
	GradientDescentOptimizerBasev4Template ()

virtual	~GradientDescentOptimizerBasev4Template ()

Protected Member Functions inherited from itk::ObjectToObjectOptimizerBaseTemplate< TInternalComputationValueType >
	ObjectToObjectOptimizerBaseTemplate ()

virtual	~ObjectToObjectOptimizerBaseTemplate ()

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

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

virtual void	SetTimeStamp (const TimeStamp &time)

virtual	~Object ()

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
ParametersType	m_BestParameters

TInternalComputationValueType	m_ConvergenceValue

MeasureType	m_CurrentBestValue

TInternalComputationValueType	m_LearningRate

TInternalComputationValueType	m_MinimumConvergenceValue

DerivativeType	m_PreviousGradient

bool	m_ReturnBestParametersAndValue

Protected Attributes inherited from itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >
ConvergenceMonitoringType::Pointer	m_ConvergenceMonitoring

SizeValueType	m_ConvergenceWindowSize

bool	m_DoEstimateLearningRateAtEachIteration

bool	m_DoEstimateLearningRateOnce

DerivativeType	m_Gradient

TInternalComputationValueType	m_MaximumStepSizeInPhysicalUnits

DomainThreader < ThreadedIndexedContainerPartitioner, Self >::Pointer	m_ModifyGradientByLearningRateThreader

DomainThreader < ThreadedIndexedContainerPartitioner, Self >::Pointer	m_ModifyGradientByScalesThreader

bool	m_Stop

StopConditionType	m_StopCondition

StopConditionDescriptionType	m_StopConditionDescription

bool	m_UseConvergenceMonitoring

Protected Attributes inherited from itk::ObjectToObjectOptimizerBaseTemplate< TInternalComputationValueType >
SizeValueType	m_CurrentIteration

MeasureType	m_CurrentMetricValue

bool	m_DoEstimateScales

MetricTypePointer	m_Metric

SizeValueType	m_NumberOfIterations

ThreadIdType	m_NumberOfThreads

ScalesType	m_Scales

bool	m_ScalesAreIdentity

ScalesEstimatorType::Pointer	m_ScalesEstimator

ScalesType	m_Weights

bool	m_WeightsAreIdentity

Protected Attributes inherited from itk::LightObject
InternalReferenceCountType	m_ReferenceCount

SimpleFastMutexLock	m_ReferenceCountLock

Private Member Functions
	GradientDescentOptimizerv4Template (const Self &)

void	operator= (const Self &)

Additional Inherited Members
Protected Types inherited from itk::LightObject
typedef int	InternalReferenceCountType

Member Typedef Documentation

template<typename TInternalComputationValueType>

typedef SmartPointer< const Self > itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::ConstPointer

Definition at line 85 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

typedef Superclass::DerivativeType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::DerivativeType

Derivative type

Definition at line 98 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

typedef Superclass::IndexRangeType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::IndexRangeType

Definition at line 102 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

typedef TInternalComputationValueType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::InternalComputationValueType

It should be possible to derive the internal computation type from the class object.

Definition at line 91 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

typedef Superclass::MeasureType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::MeasureType

Metric type over which this class is templated

Definition at line 101 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

typedef Superclass::ParametersType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::ParametersType

Definition at line 104 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

typedef SmartPointer< Self > itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::Pointer

Definition at line 84 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

typedef Superclass::ScalesType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::ScalesType

Definition at line 103 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

typedef GradientDescentOptimizerv4Template itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::Self

Standard class typedefs.

Definition at line 82 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

typedef Superclass::StopConditionType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::StopConditionType

Definition at line 105 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

typedef GradientDescentOptimizerBasev4Template<TInternalComputationValueType> itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::Superclass

Definition at line 83 of file itkGradientDescentOptimizerv4.h.

Constructor & Destructor Documentation

template<typename TInternalComputationValueType>

itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::GradientDescentOptimizerv4Template ( )

protected

Default constructor

template<typename TInternalComputationValueType>

virtual itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::~GradientDescentOptimizerv4Template ( )

protectedvirtual

Destructor

template<typename TInternalComputationValueType>

itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::GradientDescentOptimizerv4Template ( const Self & )

private

Member Function Documentation

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::AdvanceOneStep ( void )

protectedvirtual

Advance one Step following the gradient direction. Includes transform update.

Reimplemented in itk::QuasiNewtonOptimizerv4Template< TInternalComputationValueType >, itk::GradientDescentLineSearchOptimizerv4Template< TInternalComputationValueType >, itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >, and itk::ConjugateGradientLineSearchOptimizerv4Template< TInternalComputationValueType >.

template<typename TInternalComputationValueType>

virtual::itk::LightObject::Pointer itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::CreateAnother ( void ) 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::Object.

Reimplemented in itk::QuasiNewtonOptimizerv4Template< TInternalComputationValueType >, itk::MultiGradientOptimizerv4Template< TInternalComputationValueType >, and itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >.

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::DoEstimateLearningRateAtEachIterationOff ( )

virtual

Option to use ScalesEstimator for learning rate estimation at each iteration. The estimation overrides the learning rate set by SetLearningRate(). Default is false.

See Also: SetDoEstimateLearningRateOnce(); SetScalesEstimator()

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::DoEstimateLearningRateAtEachIterationOn ( )

virtual

Option to use ScalesEstimator for learning rate estimation at each iteration. The estimation overrides the learning rate set by SetLearningRate(). Default is false.

See Also: SetDoEstimateLearningRateOnce(); SetScalesEstimator()

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::DoEstimateLearningRateOnceOff ( )

virtual

Option to use ScalesEstimator for learning rate estimation only once, during first iteration. The estimation overrides the learning rate set by SetLearningRate(). Default is true.

See Also: SetDoEstimateLearningRateAtEachIteration(); SetScalesEstimator()

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::DoEstimateLearningRateOnceOn ( )

virtual

Option to use ScalesEstimator for learning rate estimation only once, during first iteration. The estimation overrides the learning rate set by SetLearningRate(). Default is true.

See Also: SetDoEstimateLearningRateAtEachIteration(); SetScalesEstimator()

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::EstimateLearningRate ( )

virtual

Estimate the learning rate based on the current gradient.

Reimplemented in itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >.

template<typename TInternalComputationValueType>

virtual const TInternalComputationValueType& itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::GetConvergenceValue ( ) const

virtual

Get current convergence value

template<typename TInternalComputationValueType>

virtual const bool& itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::GetDoEstimateLearningRateAtEachIteration ( ) const

virtual

Option to use ScalesEstimator for learning rate estimation at each iteration. The estimation overrides the learning rate set by SetLearningRate(). Default is false.

See Also: SetDoEstimateLearningRateOnce(); SetScalesEstimator()

template<typename TInternalComputationValueType>

virtual const bool& itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::GetDoEstimateLearningRateOnce ( ) const

virtual

Option to use ScalesEstimator for learning rate estimation only once, during first iteration. The estimation overrides the learning rate set by SetLearningRate(). Default is true.

See Also: SetDoEstimateLearningRateAtEachIteration(); SetScalesEstimator()

template<typename TInternalComputationValueType>

virtual const TInternalComputationValueType& itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::GetLearningRate ( ) const

virtual

Get the learning rate.

template<typename TInternalComputationValueType>

virtual const TInternalComputationValueType& itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::GetMaximumStepSizeInPhysicalUnits ( ) const

virtual

Get the maximum step size, in physical space units.

template<typename TInternalComputationValueType>

virtual const char* itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::GetNameOfClass ( ) const

virtual

Run-time type information (and related methods).

Reimplemented from itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >.

Reimplemented in itk::QuasiNewtonOptimizerv4Template< TInternalComputationValueType >, itk::GradientDescentLineSearchOptimizerv4Template< TInternalComputationValueType >, itk::ConjugateGradientLineSearchOptimizerv4Template< TInternalComputationValueType >, itk::MultiGradientOptimizerv4Template< TInternalComputationValueType >, and itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >.

template<typename TInternalComputationValueType>

virtual const bool& itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::GetReturnBestParametersAndValue ( ) const

virtual

Flag. Set to have the optimizer track and return the best best metric value and corresponding best parameters that were calculated during the optimization. This captures the best solution when the optimizer oversteps or osciallates near the end of an optimization. Results are stored in m_CurrentMetricValue and in the assigned metric's parameters, retrievable via optimizer->GetCurrentPosition(). This option requires additional memory to store the best parameters, which can be large when working with high-dimensional transforms such as DisplacementFieldTransform.

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::ModifyGradientByLearningRateOverSubRange ( const IndexRangeType & subrange )

protectedvirtual

Modify the gradient over a given index range.

Implements itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >.

Reimplemented in itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >.

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::ModifyGradientByScalesOverSubRange ( const IndexRangeType & subrange )

protectedvirtual

Modify the gradient over a given index range.

Implements itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >.

Reimplemented in itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >.

template<typename TInternalComputationValueType>

static Pointer itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::New ( )

static

New macro for creation of through a Smart Pointer

template<typename TInternalComputationValueType>

void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::operator= ( const Self & )

private

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::PrintSelf	(	std::ostream &	os,
		Indent	indent
	)		const

protectedvirtual

Methods invoked by Print() to print information about the object including superclasses. Typically not called by the user (use Print() instead) but used in the hierarchical print process to combine the output of several classes.

Reimplemented from itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >.

Reimplemented in itk::QuasiNewtonOptimizerv4Template< TInternalComputationValueType >, itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >, itk::MultiGradientOptimizerv4Template< TInternalComputationValueType >, itk::GradientDescentLineSearchOptimizerv4Template< TInternalComputationValueType >, and itk::ConjugateGradientLineSearchOptimizerv4Template< TInternalComputationValueType >.

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::ResumeOptimization ( )

virtual

Resume optimization. This runs the optimization loop, and allows continuation of stopped optimization

Implements itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >.

Reimplemented in itk::MultiGradientOptimizerv4Template< TInternalComputationValueType >.

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::ReturnBestParametersAndValueOff ( )

virtual

Flag. Set to have the optimizer track and return the best best metric value and corresponding best parameters that were calculated during the optimization. This captures the best solution when the optimizer oversteps or osciallates near the end of an optimization. Results are stored in m_CurrentMetricValue and in the assigned metric's parameters, retrievable via optimizer->GetCurrentPosition(). This option requires additional memory to store the best parameters, which can be large when working with high-dimensional transforms such as DisplacementFieldTransform.

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::ReturnBestParametersAndValueOn ( )

virtual

Flag. Set to have the optimizer track and return the best best metric value and corresponding best parameters that were calculated during the optimization. This captures the best solution when the optimizer oversteps or osciallates near the end of an optimization. Results are stored in m_CurrentMetricValue and in the assigned metric's parameters, retrievable via optimizer->GetCurrentPosition(). This option requires additional memory to store the best parameters, which can be large when working with high-dimensional transforms such as DisplacementFieldTransform.

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::SetConvergenceWindowSize ( SizeValueType _arg )

virtual

Window size for the convergence checker. The convergence checker calculates convergence value by fitting to a window of the energy (metric value) profile.

The default m_ConvergenceWindowSize is set to 50 to pass all tests. It is suggested to use 10 for less stringent convergence checking.

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::SetDoEstimateLearningRateAtEachIteration ( bool _arg )

virtual

Option to use ScalesEstimator for learning rate estimation at each iteration. The estimation overrides the learning rate set by SetLearningRate(). Default is false.

See Also: SetDoEstimateLearningRateOnce(); SetScalesEstimator()

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::SetDoEstimateLearningRateOnce ( bool _arg )

virtual

Option to use ScalesEstimator for learning rate estimation only once, during first iteration. The estimation overrides the learning rate set by SetLearningRate(). Default is true.

See Also: SetDoEstimateLearningRateAtEachIteration(); SetScalesEstimator()

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::SetLearningRate ( TInternalComputationValueType _arg )

virtual

Set the learning rate.

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::SetMaximumStepSizeInPhysicalUnits ( TInternalComputationValueType _arg )

virtual

Set the maximum step size, in physical space units.

Only relevant when m_ScalesEstimator is set by user, and automatic learning rate estimation is enabled. See main documentation.

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::SetMinimumConvergenceValue ( TInternalComputationValueType _arg )

virtual

Minimum convergence value for convergence checking. The convergence checker calculates convergence value by fitting to a window of the energy profile. When the convergence value reaches a small value, it would be treated as converged.

The default m_MinimumConvergenceValue is set to 1e-8 to pass all tests. It is suggested to use 1e-6 for less stringent convergence checking.

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::SetReturnBestParametersAndValue ( bool _arg )

virtual

Flag. Set to have the optimizer track and return the best best metric value and corresponding best parameters that were calculated during the optimization. This captures the best solution when the optimizer oversteps or osciallates near the end of an optimization. Results are stored in m_CurrentMetricValue and in the assigned metric's parameters, retrievable via optimizer->GetCurrentPosition(). This option requires additional memory to store the best parameters, which can be large when working with high-dimensional transforms such as DisplacementFieldTransform.

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::StartOptimization ( bool doOnlyInitialization = false )

virtual

Start and run the optimization

Reimplemented from itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >.

Reimplemented in itk::RegularStepGradientDescentOptimizerv4< TInternalComputationValueType >, itk::MultiGradientOptimizerv4Template< TInternalComputationValueType >, itk::QuasiNewtonOptimizerv4Template< TInternalComputationValueType >, and itk::ConjugateGradientLineSearchOptimizerv4Template< TInternalComputationValueType >.

template<typename TInternalComputationValueType>

virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::StopOptimization ( void )

virtual

Stop optimization. The object is left in a state so the optimization can be resumed by calling ResumeOptimization.

Reimplemented from itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >.

Reimplemented in itk::MultiGradientOptimizerv4Template< TInternalComputationValueType >.

Member Data Documentation

template<typename TInternalComputationValueType>

ParametersType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::m_BestParameters

protected

Definition at line 235 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

TInternalComputationValueType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::m_ConvergenceValue

protected

Current convergence value.

Definition at line 231 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

MeasureType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::m_CurrentBestValue

protected

Store the best value and related paramters

Definition at line 234 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

TInternalComputationValueType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::m_LearningRate

protected

Manual learning rate to apply. It is overridden by automatic learning rate estimation if enabled. See main documentation.

Definition at line 212 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

TInternalComputationValueType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::m_MinimumConvergenceValue

protected

Minimum convergence value for convergence checking. The convergence checker calculates convergence value by fitting to a window of the energy profile. When the convergence value reaches a small value, such as 1e-8, it would be treated as converged.

Definition at line 227 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

DerivativeType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::m_PreviousGradient

protected

Store the previous gradient value at each iteration, so we can detect the changes in geradient direction. This is needed by the regular step gradient descent and Quasi newton optimizers.

Definition at line 245 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>

bool itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::m_ReturnBestParametersAndValue

protected

Flag to control returning of best value and parameters.

Definition at line 238 of file itkGradientDescentOptimizerv4.h.

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