ITK  4.13.0
Insight Segmentation and Registration Toolkit
Public Types | Public Member Functions | Static Public Member Functions | Protected Member Functions | Protected Attributes | List of all members
itk::GradientDescentOptimizerv4Template< TInternalComputationValueType > Class Template Reference

#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.
Examples:
Examples/RegistrationITKv4/MultiStageImageRegistration1.cxx, Examples/RegistrationITKv4/MultiStageImageRegistration2.cxx, and SphinxExamples/src/Registration/Metricsv4/PerformRegistrationOnVectorImages/Code.cxx.

Definition at line 77 of file itkGradientDescentOptimizerv4.h.

Public Types

typedef SmartPointer< const SelfConstPointer
 
typedef Superclass::DerivativeType DerivativeType
 
typedef Superclass::IndexRangeType IndexRangeType
 
typedef
TInternalComputationValueType 
InternalComputationValueType
 
typedef Superclass::MeasureType MeasureType
 
typedef Superclass::ParametersType ParametersType
 
typedef SmartPointer< SelfPointer
 
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 SelfConstPointer
 
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< SelfPointer
 
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 SelfConstPointer
 
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< SelfPointer
 
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 SelfConstPointer
 
typedef SmartPointer< SelfPointer
 
typedef Object Self
 
typedef LightObject Superclass
 
- Public Types inherited from itk::LightObject
typedef SmartPointer< const SelfConstPointer
 
typedef SmartPointer< SelfPointer
 
typedef LightObject Self
 

Public Member Functions

virtual ::itk::LightObject::Pointer CreateAnother () const
 
virtual void EstimateLearningRate ()
 
virtual const
TInternalComputationValueType & 
GetConvergenceValue () const
 
virtual const char * GetNameOfClass () const
 
virtual void ResumeOptimization () override
 
virtual void SetConvergenceWindowSize (SizeValueType _arg)
 
virtual void SetMinimumConvergenceValue (TInternalComputationValueType _arg)
 
virtual void StartOptimization (bool doOnlyInitialization=false) override
 
virtual void StopOptimization (void) override
 
virtual void SetLearningRate (TInternalComputationValueType _arg)
 
virtual const
TInternalComputationValueType & 
GetLearningRate () const
 
virtual void SetMaximumStepSizeInPhysicalUnits (TInternalComputationValueType _arg)
 
virtual const
TInternalComputationValueType & 
GetMaximumStepSizeInPhysicalUnits () const
 
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 override
 
virtual const DerivativeTypeGetGradient () const
 
virtual SizeValueType GetNumberOfIterations () const override
 
virtual const StopConditionTypeGetStopCondition () const
 
virtual const
StopConditionReturnStringType 
GetStopConditionDescription () const override
 
virtual void SetNumberOfIterations (const SizeValueType numberOfIterations) override
 
virtual void ModifyGradientByScales ()
 
virtual void ModifyGradientByLearningRate ()
 
- Public Member Functions inherited from itk::ObjectToObjectOptimizerBaseTemplate< TInternalComputationValueType >
virtual const MeasureTypeGetCurrentMetricValue () const
 
virtual const ParametersTypeGetCurrentPosition () const
 
virtual const ThreadIdTypeGetNumberOfThreads () const
 
virtual const ScalesTypeGetScales () const
 
virtual const bool & GetScalesAreIdentity () const
 
bool GetScalesInitialized () const
 
virtual const MeasureTypeGetValue () const
 
virtual const ScalesTypeGetWeights () 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 MetricTypeGetModifiableMetric ()
 
virtual const MetricTypeGetMetric () 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
 
CommandGetCommand (unsigned long tag)
 
bool GetDebug () const
 
MetaDataDictionaryGetMetaDataDictionary ()
 
const MetaDataDictionaryGetMetaDataDictionary () const
 
virtual ModifiedTimeType GetMTime () const
 
virtual const TimeStampGetTimeStamp () const
 
bool HasObserver (const EventObject &event) const
 
void InvokeEvent (const EventObject &)
 
void InvokeEvent (const EventObject &) const
 
virtual void Modified () const
 
virtual void Register () const override
 
void RemoveAllObservers ()
 
void RemoveObserver (unsigned long tag)
 
void SetDebug (bool debugFlag) const
 
void SetMetaDataDictionary (const MetaDataDictionary &rhs)
 
virtual void SetReferenceCount (int) override
 
virtual void UnRegister () const noexceptoverride
 
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 ()
 
 GradientDescentOptimizerv4Template ()
 
virtual void ModifyGradientByLearningRateOverSubRange (const IndexRangeType &subrange) override
 
virtual void ModifyGradientByScalesOverSubRange (const IndexRangeType &subrange) override
 
virtual void PrintSelf (std::ostream &os, Indent indent) const override
 
virtual ~GradientDescentOptimizerv4Template () override
 
- Protected Member Functions inherited from itk::GradientDescentOptimizerBasev4Template< TInternalComputationValueType >
 GradientDescentOptimizerBasev4Template ()
 
virtual ~GradientDescentOptimizerBasev4Template () override
 
- Protected Member Functions inherited from itk::ObjectToObjectOptimizerBaseTemplate< TInternalComputationValueType >
 ObjectToObjectOptimizerBaseTemplate ()
 
virtual ~ObjectToObjectOptimizerBaseTemplate () override
 
- Protected Member Functions inherited from itk::Object
 Object ()
 
bool PrintObservers (std::ostream &os, Indent indent) const
 
virtual void SetTimeStamp (const TimeStamp &time)
 
virtual ~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

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
AtomicInt< int > m_ReferenceCount
 

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 ( )
overrideprotectedvirtual

Destructor

Member Function Documentation

template<typename TInternalComputationValueType>
virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::AdvanceOneStep ( )
protectedvirtual
template<typename TInternalComputationValueType>
virtual::itk::LightObject::Pointer itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::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::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. WindowConvergenceMonitoringFunction always returns output convergence value in 'TInternalComputationValueType' precision.

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

Set/Get the learning rate to apply. It is overridden by automatic learning rate estimation if enabled. See main documentation.

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

Set/Get 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 const char* itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::GetNameOfClass ( ) const
virtual
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)
overrideprotectedvirtual
template<typename TInternalComputationValueType>
virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::ModifyGradientByScalesOverSubRange ( const IndexRangeType subrange)
overrideprotectedvirtual

Modify the gradient by scales and weights 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>
virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::PrintSelf ( std::ostream &  os,
Indent  indent 
) const
overrideprotectedvirtual
template<typename TInternalComputationValueType>
virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::ResumeOptimization ( )
overridevirtual
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/Get the learning rate to apply. It is overridden by automatic learning rate estimation if enabled. See main documentation.

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

Set/Get 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)
overridevirtual
template<typename TInternalComputationValueType>
virtual void itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::StopOptimization ( void  )
overridevirtual

Member Data Documentation

template<typename TInternalComputationValueType>
ParametersType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::m_BestParameters
protected

Definition at line 229 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>
TInternalComputationValueType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::m_ConvergenceValue
protected

Definition at line 225 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>
MeasureType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::m_CurrentBestValue
protected

Store the best value and related parameters.

Definition at line 228 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>
TInternalComputationValueType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::m_LearningRate
protected

Definition at line 223 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>
TInternalComputationValueType itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::m_MinimumConvergenceValue
protected

Definition at line 224 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 gradient direction. This is needed by the regular step gradient descent and Quasi Newton optimizers.

Definition at line 238 of file itkGradientDescentOptimizerv4.h.

template<typename TInternalComputationValueType>
bool itk::GradientDescentOptimizerv4Template< TInternalComputationValueType >::m_ReturnBestParametersAndValue
protected

Definition at line 231 of file itkGradientDescentOptimizerv4.h.


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