itk::SPSAOptimizer Class Reference
[Optimizers]

#include <itkSPSAOptimizer.h>

Inheritance diagram for itk::SPSAOptimizer:

[legend]Collaboration diagram for itk::SPSAOptimizer:

[legend]List of all members.

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Detailed Description

An optimizer based on simultaneous perturbation...

This optimizer is an implementation of the Simultaneous Perturbation Stochastic Approximation method, described in:

• http://www.jhuapl.edu/SPSA/

• Spall, J.C. (1998), "An Overview of the Simultaneous Perturbation Method for Efficient Optimization," Johns Hopkins APL Technical Digest, vol. 19, pp. 482-492

Definition at line 27 of file itkSPSAOptimizer.h.

Public Types
typedef SmartPointer< const Self >	ConstPointer
typedef CostFunctionType::Pointer	CostFunctionPointer
typedef SingleValuedCostFunction	CostFunctionType
typedef CostFunctionType::DerivativeType	DerivativeType
typedef CostFunctionType::MeasureType	MeasureType
typedef Superclass::ParametersType	ParametersType
typedef SmartPointer< Self >	Pointer
typedef Superclass::ScalesType	ScalesType
typedef SPSAOptimizer	Self
enum	StopConditionType {   Unknown,   MaximumNumberOfIterations,   BelowTolerance,   MetricError }
typedef SingleValuedNonLinearOptimizer	Superclass
Public Member Functions
virtual void	AdvanceOneStep (void)
virtual LightObject::Pointer	CreateAnother () const
virtual void	DebugOff () const
virtual void	DebugOn () const
virtual void	Delete ()
virtual double	GetA () const
virtual double	Geta () const
virtual double	GetAlpha () const
virtual double	Getc () const
Command *	GetCommand (unsigned long tag)
virtual const CostFunctionType *	GetCostFunction ()
virtual unsigned long	GetCurrentIteration () const
virtual const ParametersType &	GetCurrentPosition ()
bool	GetDebug () const
virtual double	GetGamma () const
virtual const DerivativeType &	GetGradient ()
virtual double	GetGradientMagnitude () const
virtual const ParametersType &	GetInitialPosition ()
virtual double	GetLearningRate () const
Methods to configure the cost function *virtual bool	GetMaximize () const
virtual unsigned long	GetMaximumNumberOfIterations () const
const MetaDataDictionary &	GetMetaDataDictionary (void) const
MetaDataDictionary &	GetMetaDataDictionary (void)
bool	GetMinimize () const
virtual unsigned long	GetMinimumNumberOfIterations () const
virtual unsigned long	GetMTime () const
virtual const char *	GetNameOfClass () const
virtual unsigned long	GetNumberOfPerturbations () const
virtual int	GetReferenceCount () const
virtual const ScalesType &	GetScales ()
virtual double	GetStateOfConvergence () const
virtual double	GetStateOfConvergenceDecayRate () const
virtual StopConditionType	GetStopCondition () const
virtual double	GetTolerance () const
virtual MeasureType	GetValue (const ParametersType &parameters) const
virtual MeasureType	GetValue (void) const
virtual void	GuessParameters (unsigned long numberOfGradientEstimates, double initialStepSize)
bool	HasObserver (const EventObject &event) const
void	InvokeEvent (const EventObject &) const
void	InvokeEvent (const EventObject &)
virtual void	MaximizeOff ()
virtual void	MaximizeOn ()
void	MinimizeOff ()
void	MinimizeOn ()
virtual void	Modified () const
void	Print (std::ostream &os, Indent indent=0) const
virtual void	Register () const
void	RemoveAllObservers ()
void	RemoveObserver (unsigned long tag)
void	ResumeOptimization (void)
Set Get A *virtual void	SetA (double _arg)
Set Get a *virtual void	Seta (double _arg)
Set Get alpha *virtual void	SetAlpha (double _arg)
Set Get c *virtual void	Setc (double _arg)
virtual void	SetCostFunction (CostFunctionType *costFunction)
void	SetDebug (bool debugFlag) const
Set Get gamma *virtual void	SetGamma (double _arg)
virtual void	SetInitialPosition (const ParametersType &param)
virtual void	SetMaximize (bool _arg)
Set Get the maximum number of iterations *virtual void	SetMaximumNumberOfIterations (unsigned long _arg)
void	SetMetaDataDictionary (const MetaDataDictionary &rhs)
void	SetMinimize (bool v)
Set Get the minimum number of iterations *virtual void	SetMinimumNumberOfIterations (unsigned long _arg)
virtual void	SetReferenceCount (int)
void	SetScales (const ScalesType &scales)
Set Get Tolerance *virtual void	SetTolerance (double _arg)
void	StartOptimization (void)
Set Get	StateOfConvergenceDecayRate (number between 0 and 1).*/virtual void SetStateOfConvergenceDecayRate(double _arg)
void	StopOptimization (void)
virtual void	UnRegister () const
Static Public Member Functions
static void	BreakOnError ()
static bool	GetGlobalWarningDisplay ()
static void	GlobalWarningDisplayOff ()
static void	GlobalWarningDisplayOn ()
static Pointer	New ()
This is a global flag that controls whether any warning *or error messages are displayed *static void	SetGlobalWarningDisplay (bool flag)
Public Attributes
Allow people to add remove invoke observers(callbacks) to any ITK *object.This is an implementation of the subject/observer design *pattern.An observer is added by specifying an event to respond to *and an itk unsigned lon	AddObserver )(const EventObject &event, Command *) const
This is a global flag that controls whether any	debug
Set Get the number of perturbation used to construct *a gradient estimate g_k *	q
Protected Member Functions
virtual double	Compute_a (unsigned long k) const
virtual double	Compute_c (unsigned long k) const
virtual void	ComputeGradient (const ParametersType &parameters, DerivativeType &gradient)
virtual void	GenerateDelta (const unsigned int spaceDimension)
bool	PrintObservers (std::ostream &os, Indent indent) const
void	PrintSelf (std::ostream &os, Indent indent) const
virtual void	PrintTrailer (std::ostream &os, Indent indent) const
virtual void	SetCurrentPosition (const ParametersType &param)
	SPSAOptimizer ()
virtual	~SPSAOptimizer ()
Protected Attributes
CostFunctionPointer	m_CostFunction
unsigned long	m_CurrentIteration
DerivativeType	m_Delta
Statistics::MersenneTwisterRandomVariateGenerator::Pointer	m_Generator
DerivativeType	m_Gradient
double	m_LearningRate
int	m_ReferenceCount
SimpleFastMutexLock	m_ReferenceCountLock
bool	m_ScalesInitialized
double	m_StateOfConvergence
bool	m_Stop
StopConditionType	m_StopCondition
Methods invoked by virtual 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.*/virtual void PrintSelf(std voi	PrintHeader )(std::ostream &os, Indent indent) const

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Member Typedef Documentation

typedef SmartPointer<const Self> itk::SPSAOptimizer::ConstPointer

Reimplemented from itk::SingleValuedNonLinearOptimizer. Definition at line 36 of file itkSPSAOptimizer.h.

typedef CostFunctionType::Pointer itk::SingleValuedNonLinearOptimizer::CostFunctionPointer [inherited]

Reimplemented in itk::FRPROptimizer, itk::LBFGSBOptimizer, itk::OnePlusOneEvolutionaryOptimizer, itk::PowellOptimizer, and itk::RegularStepGradientDescentOptimizer. Definition at line 56 of file itkSingleValuedNonLinearOptimizer.h.

typedef SingleValuedCostFunction itk::SingleValuedNonLinearOptimizer::CostFunctionType [inherited]

Type of the Cost Function Reimplemented in itk::FRPROptimizer, itk::LBFGSBOptimizer, itk::OnePlusOneEvolutionaryOptimizer, itk::PowellOptimizer, and itk::RegularStepGradientDescentOptimizer. Definition at line 55 of file itkSingleValuedNonLinearOptimizer.h.

typedef CostFunctionType::DerivativeType itk::SingleValuedNonLinearOptimizer::DerivativeType [inherited]

Derivative type. It defines a type used to return the cost function derivative. Definition at line 64 of file itkSingleValuedNonLinearOptimizer.h.

typedef CostFunctionType::MeasureType itk::SingleValuedNonLinearOptimizer::MeasureType [inherited]

Measure type. It defines a type used to return the cost function value. Reimplemented in itk::LBFGSBOptimizer. Definition at line 60 of file itkSingleValuedNonLinearOptimizer.h.

typedef Superclass::ParametersType itk::SingleValuedNonLinearOptimizer::ParametersType [inherited]

Parameters type. It defines a position in the optimization search space. Reimplemented from itk::NonLinearOptimizer. Reimplemented in itk::AmoebaOptimizer, itk::FRPROptimizer, itk::PowellOptimizer, and itk::QuaternionRigidTransformGradientDescentOptimizer. Definition at line 48 of file itkSingleValuedNonLinearOptimizer.h.

typedef SmartPointer<Self> itk::SPSAOptimizer::Pointer

Reimplemented from itk::SingleValuedNonLinearOptimizer. Definition at line 35 of file itkSPSAOptimizer.h.

typedef Superclass::ScalesType itk::NonLinearOptimizer::ScalesType [inherited]

Scale type. This array defines scale to be applied to parameters before being evaluated in the cost function. This allows to map to a more convenient space. In particular this is used to normalize parameter spaces in which some parameters have a different dynamic range. Reimplemented from itk::Optimizer. Definition at line 52 of file itkNonLinearOptimizer.h.

typedef SPSAOptimizer itk::SPSAOptimizer::Self

Standard class typedefs. Reimplemented from itk::SingleValuedNonLinearOptimizer. Definition at line 33 of file itkSPSAOptimizer.h.

typedef SingleValuedNonLinearOptimizer itk::SPSAOptimizer::Superclass

Reimplemented from itk::SingleValuedNonLinearOptimizer. Definition at line 34 of file itkSPSAOptimizer.h.

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Member Enumeration Documentation

enum itk::SPSAOptimizer::StopConditionType

Codes of stopping conditions Enumeration values: Unknown  MaximumNumberOfIterations  BelowTolerance  MetricError  Definition at line 45 of file itkSPSAOptimizer.h.

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Constructor & Destructor Documentation

itk::SPSAOptimizer::SPSAOptimizer (   )  [protected]

virtual itk::SPSAOptimizer::~SPSAOptimizer (   )  [inline, protected, virtual]

Definition at line 188 of file itkSPSAOptimizer.h.

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Member Function Documentation

virtual void itk::SPSAOptimizer::AdvanceOneStep (  void   )  [virtual]

Advance one step following the gradient direction.

static void itk::LightObject::BreakOnError (   )  [static, inherited]

This method is called when itkExceptionMacro executes. It allows the debugger to break on error.

virtual double itk::SPSAOptimizer::Compute_a (  unsigned long  k  )  const [protected, virtual]

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

virtual double itk::SPSAOptimizer::Compute_c (  unsigned long  k  )  const [protected, virtual]

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

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

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

virtual LightObject::Pointer itk::Object::CreateAnother (   )  const [virtual, inherited]

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::LightObject.

virtual void itk::Object::DebugOff (   )  const [virtual, inherited]

Turn debugging output off.

virtual void itk::Object::DebugOn (   )  const [virtual, inherited]

Turn debugging output on.

virtual void itk::LightObject::Delete (   )  [virtual, inherited]

Delete an itk object. This method should always be used to delete an object when the new operator was used to create it. Using the C delete method will not work with reference counting.

virtual void itk::SPSAOptimizer::GenerateDelta (  const unsigned int  spaceDimension  )  [protected, virtual]

Method to generate a perturbation vector. Takes scales into account.

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

virtual double itk::SPSAOptimizer::Geta (   )  const [virtual]

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

virtual double itk::SPSAOptimizer::Getc (   )  const [virtual]

Command* itk::Object::GetCommand (  unsigned long  tag  )  [inherited]

Get the command associated with the given tag. NOTE: This returns a pointer to a Command, but it is safe to asign this to a Command::Pointer. Since Command inherits from LightObject, at this point in the code, only a pointer or a reference to the Command can be used.

virtual const CostFunctionType* itk::SingleValuedNonLinearOptimizer::GetCostFunction (   )  [virtual, inherited]

Get the cost function.

virtual unsigned long itk::SPSAOptimizer::GetCurrentIteration (   )  const [virtual]

Get the current iteration number.

virtual const ParametersType& itk::Optimizer::GetCurrentPosition (   )  [virtual, inherited]

Get current position of the optimization.

bool itk::Object::GetDebug (   )  const [inherited]

Get the value of the debug flag.

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

static bool itk::Object::GetGlobalWarningDisplay (   )  [static, inherited]

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

Get the latest computed gradient

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

Get the GradientMagnitude of the latest computed gradient

virtual const ParametersType& itk::Optimizer::GetInitialPosition (   )  [virtual, inherited]

Get the position to initialize the optimization.

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

Get the current LearningRate (a_k)

Methods to configure the cost function* virtual bool itk::SPSAOptimizer::GetMaximize (   )  const [virtual]

virtual unsigned long itk::SPSAOptimizer::GetMaximumNumberOfIterations (   )  const [virtual]

const MetaDataDictionary& itk::Object::GetMetaDataDictionary (  void   )  const [inherited]

Returns: A constant reference to this objects MetaDataDictionary.

MetaDataDictionary& itk::Object::GetMetaDataDictionary (  void   )  [inherited]

Returns: A reference to this objects MetaDataDictionary. Warning: This reference may be changed.

bool itk::SPSAOptimizer::GetMinimize (   )  const [inline]

Definition at line 132 of file itkSPSAOptimizer.h.

virtual unsigned long itk::SPSAOptimizer::GetMinimumNumberOfIterations (   )  const [virtual]

virtual unsigned long itk::Object::GetMTime (   )  const [virtual, inherited]

Return this objects modified time. Reimplemented in itk::ImageRegistrationMethod< TFixedImage, TMovingImage >, itk::DeformationFieldSource< TOutputImage >, itk::InverseDeformationFieldImageFilter< TInputImage, TOutputImage >, itk::ResampleImageFilter< TInputImage, TOutputImage, TInterpolatorPrecisionType >, itk::VectorResampleImageFilter< TInputImage, TOutputImage, TInterpolatorPrecisionType >, itk::BoundingBox< TPointIdentifier, VPointDimension, TCoordRep, TPointsContainer >, itk::SceneSpatialObject< SpaceDimension >, and itk::SceneSpatialObject< NDimensions >.

virtual const char* itk::SPSAOptimizer::GetNameOfClass (   )  const [virtual]

Run-time type information (and related methods). Reimplemented from itk::SingleValuedNonLinearOptimizer.

virtual unsigned long itk::SPSAOptimizer::GetNumberOfPerturbations (   )  const [virtual]

virtual int itk::LightObject::GetReferenceCount (   )  const [inline, virtual, inherited]

Gets the reference count on this object. Definition at line 98 of file itkLightObject.h.

virtual const ScalesType& itk::Optimizer::GetScales (   )  [virtual, inherited]

Get current parameters scaling.

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

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

virtual StopConditionType itk::SPSAOptimizer::GetStopCondition (   )  const [virtual]

Get Stop condition.

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

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

Get the cost function value at any position Reimplemented from itk::SingleValuedNonLinearOptimizer.

virtual MeasureType itk::SPSAOptimizer::GetValue (  void   )  const [virtual]

Get the cost function value at the current position.

static void itk::Object::GlobalWarningDisplayOff (   )  [inline, static, inherited]

Definition at line 100 of file itkObject.h. References itk::Object::SetGlobalWarningDisplay().

static void itk::Object::GlobalWarningDisplayOn (   )  [inline, static, inherited]

Definition at line 98 of file itkObject.h. References itk::Object::SetGlobalWarningDisplay().

virtual void itk::SPSAOptimizer::GuessParameters (  unsigned long  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: Spall, J.C. (1998), "Implementation of the Simultaneous Perturbation Algorithm for Stochastic Optimization", IEEE Trans. Aerosp. Electron. Syst. 34(3), 817-823.

bool itk::Object::HasObserver (  const EventObject &  event  )  const [inherited]

Return true if an observer is registered for this event.

void itk::Object::InvokeEvent (  const EventObject &   )  const [inherited]

Call Execute on all the Commands observing this event id. The actions triggered by this call doesn't modify this object.

void itk::Object::InvokeEvent (  const EventObject &   )  [inherited]

Call Execute on all the Commands observing this event id.

virtual void itk::SPSAOptimizer::MaximizeOff (   )  [virtual]

Referenced by MinimizeOn().

virtual void itk::SPSAOptimizer::MaximizeOn (   )  [virtual]

Referenced by MinimizeOff().

void itk::SPSAOptimizer::MinimizeOff (  void   )  [inline]

Definition at line 138 of file itkSPSAOptimizer.h. References MaximizeOn().

void itk::SPSAOptimizer::MinimizeOn (  void   )  [inline]

Definition at line 136 of file itkSPSAOptimizer.h. References MaximizeOff().

virtual void itk::Object::Modified (   )  const [virtual, inherited]

Update the modification time for this object. Many filters rely on the modification time to determine if they need to recompute their data. Referenced by itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetCenter(), itk::HistogramAlgorithmBase< TInputHistogram >::SetInputHistogram(), itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetMatrix(), itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetOffset(), itk::ThresholdLabelerImageFilter< TInputImage, TOutputImage >::SetRealThresholds(), itk::CollidingFrontsImageFilter< TInputImage, TOutputImage >::SetSeedPoints1(), itk::CollidingFrontsImageFilter< TInputImage, TOutputImage >::SetSeedPoints2(), itk::NonUniformBSpline< TDimension >::SetSplineOrder(), itk::ThresholdLabelerImageFilter< TInputImage, TOutputImage >::SetThresholds(), itk::Statistics::GoodnessOfFitFunctionBase< typename ComponentType::HistogramType >::SetTotalObservedScale(), and itk::MatrixOffsetTransformBase< TScalarType, 3, 3 >::SetTranslation().

static Pointer itk::SPSAOptimizer::New (   )  [static]

Method for creation through the object factory. Reimplemented from itk::SingleValuedNonLinearOptimizer.

void itk::LightObject::Print (  std::ostream &  os, Indent  indent = 0 )  const [inherited]

Cause the object to print itself out.

bool itk::Object::PrintObservers (  std::ostream &  os, Indent  indent )  const [protected, inherited]

void itk::SPSAOptimizer::PrintSelf (  std::ostream &  os, Indent  indent )  const [protected, virtual]

PrintSelf method. Reimplemented from itk::SingleValuedNonLinearOptimizer.

virtual void itk::LightObject::PrintTrailer (  std::ostream &  os, Indent  indent )  const [protected, virtual, inherited]

virtual void itk::Object::Register (   )  const [virtual, inherited]

Increase the reference count (mark as used by another object). Reimplemented from itk::LightObject.

void itk::Object::RemoveAllObservers (   )  [inherited]

Remove all observers .

void itk::Object::RemoveObserver (  unsigned long  tag  )  [inherited]

Remove the observer with this tag value.

void itk::SPSAOptimizer::ResumeOptimization (  void   )

Resume previously stopped optimization with current parameters See also: StopOptimization.

Set Get A* virtual void itk::SPSAOptimizer::SetA (  double  _arg  )  [virtual]

Set Get a* virtual void itk::SPSAOptimizer::Seta (  double  _arg  )  [virtual]

Set Get alpha* virtual void itk::SPSAOptimizer::SetAlpha (  double  _arg  )  [virtual]

Set Get c* virtual void itk::SPSAOptimizer::Setc (  double  _arg  )  [virtual]

virtual void itk::SingleValuedNonLinearOptimizer::SetCostFunction (  CostFunctionType *  costFunction  )  [virtual, inherited]

Set the cost function.

virtual void itk::Optimizer::SetCurrentPosition (  const ParametersType &  param  )  [protected, virtual, inherited]

Set the current position.

void itk::Object::SetDebug (  bool  debugFlag  )  const [inherited]

Set the value of the debug flag. A non-zero value turns debugging on.

Set Get gamma* virtual void itk::SPSAOptimizer::SetGamma (  double  _arg  )  [virtual]

This is a global flag that controls whether any warning* or error messages are displayed* static void itk::Object::SetGlobalWarningDisplay (  bool  flag  )  [static, inherited]

Referenced by itk::Object::GlobalWarningDisplayOff(), and itk::Object::GlobalWarningDisplayOn().

virtual void itk::Optimizer::SetInitialPosition (  const ParametersType &  param  )  [virtual, inherited]

Set the position to initialize the optimization.

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

Referenced by SetMinimize().

Set Get the maximum number of iterations* virtual void itk::SPSAOptimizer::SetMaximumNumberOfIterations (  unsigned long  _arg  )  [virtual]

void itk::Object::SetMetaDataDictionary (  const MetaDataDictionary &  rhs  )  [inherited]

Returns: Set the MetaDataDictionary

void itk::SPSAOptimizer::SetMinimize (  bool  v  )  [inline]

Definition at line 134 of file itkSPSAOptimizer.h. References SetMaximize().

Set Get the minimum number of iterations* virtual void itk::SPSAOptimizer::SetMinimumNumberOfIterations (  unsigned long  _arg  )  [virtual]

virtual void itk::Object::SetReferenceCount (  int   )  [virtual, inherited]

Sets the reference count (use with care) Reimplemented from itk::LightObject.

void itk::Optimizer::SetScales (  const ScalesType &  scales  )  [inherited]

Set current parameters scaling. Reimplemented in itk::LBFGSBOptimizer.

Set Get Tolerance* virtual void itk::SPSAOptimizer::SetTolerance (  double  _arg  )  [virtual]

void itk::SPSAOptimizer::StartOptimization (  void   )  [virtual]

Start optimization. Reimplemented from itk::Optimizer.

Set Get itk::SPSAOptimizer::StateOfConvergenceDecayRate (  number between 0 and  1  )

void itk::SPSAOptimizer::StopOptimization (  void   )

Stop optimization. See also: ResumeOptimization

virtual void itk::Object::UnRegister (   )  const [virtual, inherited]

Decrease the reference count (release by another object). Reimplemented from itk::LightObject.

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Member Data Documentation

Allow people to add remove invoke observers (callbacks) to any ITK * object. This is an implementation of the subject/observer design * pattern. An observer is added by specifying an event to respond to * and an itk unsigned lon itk::Object::AddObserver)(const EventObject &event, Command *) const [inherited]

This is a global flag that controls whether any itk::Object::debug [inherited]

Definition at line 94 of file itkObject.h.

CostFunctionPointer itk::SingleValuedNonLinearOptimizer::m_CostFunction [protected, inherited]

Definition at line 80 of file itkSingleValuedNonLinearOptimizer.h.

unsigned long itk::SPSAOptimizer::m_CurrentIteration [protected]

Definition at line 200 of file itkSPSAOptimizer.h.

DerivativeType itk::SPSAOptimizer::m_Delta [protected]

Definition at line 196 of file itkSPSAOptimizer.h.

Statistics::MersenneTwisterRandomVariateGenerator::Pointer itk::SPSAOptimizer::m_Generator [protected]

Random number generator Definition at line 203 of file itkSPSAOptimizer.h.

DerivativeType itk::SPSAOptimizer::m_Gradient [protected]

Variables updated during optimization Definition at line 194 of file itkSPSAOptimizer.h.

double itk::SPSAOptimizer::m_LearningRate [protected]

Definition at line 195 of file itkSPSAOptimizer.h.

int itk::LightObject::m_ReferenceCount [mutable, protected, inherited]

Number of uses of this object by other objects. Definition at line 119 of file itkLightObject.h.

SimpleFastMutexLock itk::LightObject::m_ReferenceCountLock [mutable, protected, inherited]

Mutex lock to protect modification to the reference count Definition at line 122 of file itkLightObject.h.

bool itk::Optimizer::m_ScalesInitialized [protected, inherited]

Definition at line 90 of file itkOptimizer.h.

double itk::SPSAOptimizer::m_StateOfConvergence [protected]

Definition at line 199 of file itkSPSAOptimizer.h.

bool itk::SPSAOptimizer::m_Stop [protected]

Definition at line 197 of file itkSPSAOptimizer.h.

StopConditionType itk::SPSAOptimizer::m_StopCondition [protected]

Definition at line 198 of file itkSPSAOptimizer.h.

Methods invoked by virtual 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. */ virtual void PrintSelf(std voi itk::LightObject::PrintHeader)(std::ostream &os, Indent indent) const [protected, inherited]

Set Get the number of perturbation used to construct* a gradient estimate g_k* itk::SPSAOptimizer::q

Initial value: NumberOfPerturbations * g_k = 1/q sum_{j=1..q} g^(j)_k */ virtual void SetNumberOfPerturbations ( unsigned long _arg) Definition at line 144 of file itkSPSAOptimizer.h.

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

• itkSPSAOptimizer.h

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