itk::LBFGSOptimizer Class Reference
[Numerics]

#include <itkLBFGSOptimizer.h>

Inheritance diagram for itk::LBFGSOptimizer:

[legend]Collaboration diagram for itk::LBFGSOptimizer:

[legend]List of all members.

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Detailed Description

Wrap of the vnl_lbfgs algorithm.

Optimizers

Definition at line 31 of file itkLBFGSOptimizer.h.

Public Types
typedef ReceptorMemberCommand< Self >	CommandType
typedef SmartPointer< const Self >	ConstPointer
typedef CostFunctionType::Pointer	CostFunctionPointer
typedef SingleValuedCostFunction	CostFunctionType
typedef CostFunctionType::DerivativeType	DerivativeType
typedef vnl_lbfgs	InternalOptimizerType
typedef vnl_vector< double >	InternalParametersType
typedef CostFunctionType::MeasureType	MeasureType
typedef Superclass::ParametersType	ParametersType
typedef SmartPointer< Self >	Pointer
typedef Superclass::ScalesType	ScalesType
typedef LBFGSOptimizer	Self
typedef SingleValuedNonLinearVnlOptimizer	Superclass
Public Member Functions
virtual LightObject::Pointer	CreateAnother () const
virtual void	DebugOff () const
virtual void	DebugOn () const
virtual void	Delete ()
virtual const ParametersType &	GetCachedCurrentPosition ()
virtual const DerivativeType &	GetCachedDerivative ()
Command *	GetCommand (unsigned long tag)
virtual const CostFunctionType *	GetCostFunction ()
virtual const ParametersType &	GetCurrentPosition ()
bool	GetDebug () const
virtual double	GetDefaultStepLength ()
virtual double	GetGradientConvergenceTolerance ()
virtual const ParametersType &	GetInitialPosition ()
virtual double	GetLineSearchAccuracy ()
Methods to define whether the cost function will be maximized or *minimized By default the VNL amoeba optimizer is only a minimizer *Maximization is implemented here by notifying the CostFunctionAdaptor *which in its turn will multiply the function values and its derivative by **virtual const bool &	GetMaximize ()
virtual unsigned int	GetMaximumNumberOfFunctionEvaluations ()
const MetaDataDictionary &	GetMetaDataDictionary (void) const
MetaDataDictionary &	GetMetaDataDictionary (void)
bool	GetMinimize () const
virtual unsigned long	GetMTime () const
virtual const char *	GetNameOfClass () const
vnl_lbfgs *	GetOptimizer (void)
virtual int	GetReferenceCount () const
virtual const ScalesType &	GetScales ()
virtual bool	GetTrace ()
MeasureType	GetValue (const ParametersType &parameters) const
Return Cached Values These method have the advantage of not triggering a *recomputation of the metric but it has the disadvantage of returning *a value that may not be the one corresponding to the current parameters For *GUI update this method is a good for mathematical *validation you should rather call	GetValue ().*/virtual const MeasureType &GetCachedValue()
MeasureType	GetValue ()
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)
virtual void	SetCostFunction (CostFunctionType *costFunction)
virtual void	SetCostFunction (SingleValuedCostFunction *costFunction)
void	SetDebug (bool debugFlag) const
Set Get the default step size This is a positive real number *with a default value of which determines the stpe size in the line *search *virtual void	SetDefaultStepLength (double stp)
Set Get the gradient convergence tolerance This is a positive *real number that determines the accuracy with which the solution is to *be found The optimization terminates X where denotes the Euclidean norm *virtual void	SetGradientConvergenceTolerance (double gtol)
virtual void	SetInitialPosition (const ParametersType &param)
virtual void	SetMaximize (bool _arg)
Set Get the maximum number of function evaluations allowed *virtual void	SetMaximumNumberOfFunctionEvaluations (unsigned int n)
void	SetMetaDataDictionary (const MetaDataDictionary &rhs)
void	SetMinimize (bool v)
virtual void	SetReferenceCount (int)
void	SetScales (const ScalesType &scales)
Set Get the optimizer trace flag If set to the optimizer *prints out information every iteration *virtual void	SetTrace (bool flag)
void	StartOptimization (void)
virtual void	TraceOff ()
virtual void	TraceOn ()
virtual void	UnRegister () const
Set Get the line search accuracy This is a positive real number *with a default value which controls the accuracy of the line *search If the function and gradient evalutions are inexpensive with *respect to the cost of the iterations it may be advantageous to set *the value to a small	value (say 0.1).*/virtual void SetLineSearchAccuracy(double tol)
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 line search accuracy This is a positive real number *with a default value	of
Return Cached Values These method have the advantage of not triggering a *recomputation of the metric but it has the disadvantage of returning *a value that may not be the one corresponding to the current parameters For *GUI update this method is a good	option
Return Cached Values These method have the advantage of not triggering a *recomputation of the metric but it has the disadvantage of returning *a value that may not be the one corresponding to the current parameters For *GUI update	purposes
Set Get the optimizer trace flag If set to	true
Return Cached Values These method have the advantage of not triggering a *recomputation of the metric	value
Set Get the gradient convergence tolerance This is a positive *real number that determines the accuracy with which the solution is to *be found The optimization terminates	when: * ||G|| < gtol max(1
Protected Types
typedef Superclass::CostFunctionAdaptorType	CostFunctionAdaptorType
Protected Member Functions
CostFunctionAdaptorType *	GetCostFunctionAdaptor (void)
const CostFunctionAdaptorType *	GetCostFunctionAdaptor (void) const
CostFunctionAdaptorType *	GetNonConstCostFunctionAdaptor (void) const
	LBFGSOptimizer ()
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
void	SetCostFunctionAdaptor (CostFunctionAdaptorType *adaptor)
virtual void	SetCurrentPosition (const ParametersType &param)
virtual	~LBFGSOptimizer ()
Protected Attributes
CostFunctionPointer	m_CostFunction
int	m_ReferenceCount
SimpleFastMutexLock	m_ReferenceCountLock
bool	m_ScalesInitialized
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 ReceptorMemberCommand< Self > itk::SingleValuedNonLinearVnlOptimizer::CommandType [inherited]

Command observer that will interact with the ITK-VNL cost-function adaptor in order to generate iteration events. This will allow to overcome the limitation of VNL optimizers not offering callbacks for every iteration Definition at line 48 of file itkSingleValuedNonLinearVnlOptimizer.h.

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

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer. Definition at line 39 of file itkLBFGSOptimizer.h.

typedef Superclass::CostFunctionAdaptorType itk::LBFGSOptimizer::CostFunctionAdaptorType [protected]

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer. Definition at line 110 of file itkLBFGSOptimizer.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 vnl_lbfgs itk::LBFGSOptimizer::InternalOptimizerType

Internal optimizer type. Definition at line 51 of file itkLBFGSOptimizer.h.

typedef vnl_vector<double> itk::LBFGSOptimizer::InternalParametersType

InternalParameters typedef. Definition at line 45 of file itkLBFGSOptimizer.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::LBFGSOptimizer::Pointer

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer. Definition at line 38 of file itkLBFGSOptimizer.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 LBFGSOptimizer itk::LBFGSOptimizer::Self

Standard "Self" typedef. Reimplemented from itk::SingleValuedNonLinearVnlOptimizer. Definition at line 36 of file itkLBFGSOptimizer.h.

typedef SingleValuedNonLinearVnlOptimizer itk::LBFGSOptimizer::Superclass

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer. Definition at line 37 of file itkLBFGSOptimizer.h.

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Constructor & Destructor Documentation

itk::LBFGSOptimizer::LBFGSOptimizer (   )  [protected]

virtual itk::LBFGSOptimizer::~LBFGSOptimizer (   )  [protected, virtual]

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Member Function Documentation

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

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

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 const ParametersType& itk::SingleValuedNonLinearVnlOptimizer::GetCachedCurrentPosition (   )  [virtual, inherited]

virtual const DerivativeType& itk::SingleValuedNonLinearVnlOptimizer::GetCachedDerivative (   )  [virtual, inherited]

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.

CostFunctionAdaptorType* itk::SingleValuedNonLinearVnlOptimizer::GetCostFunctionAdaptor (  void   )  [protected, inherited]

const CostFunctionAdaptorType* itk::SingleValuedNonLinearVnlOptimizer::GetCostFunctionAdaptor (  void   )  const [protected, inherited]

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::LBFGSOptimizer::GetDefaultStepLength (   )  [virtual]

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

virtual double itk::LBFGSOptimizer::GetGradientConvergenceTolerance (   )  [virtual]

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

Get the position to initialize the optimization.

virtual double itk::LBFGSOptimizer::GetLineSearchAccuracy (   )  [virtual]

Methods to define whether the cost function will be maximized or* minimized By default the VNL amoeba optimizer is only a minimizer* Maximization is implemented here by notifying the CostFunctionAdaptor* which in its turn will multiply the function values and its derivative by* * virtual const bool& itk::SingleValuedNonLinearVnlOptimizer::GetMaximize (   )  [virtual, inherited]

virtual unsigned int itk::LBFGSOptimizer::GetMaximumNumberOfFunctionEvaluations (   )  [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::SingleValuedNonLinearVnlOptimizer::GetMinimize (   )  const [inline, inherited]

Definition at line 72 of file itkSingleValuedNonLinearVnlOptimizer.h.

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::LBFGSOptimizer::GetNameOfClass (   )  const [virtual]

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

CostFunctionAdaptorType* itk::SingleValuedNonLinearVnlOptimizer::GetNonConstCostFunctionAdaptor (  void   )  const [protected, inherited]

The purpose of this method is to get around the lack of const-correctness in VNL cost-functions and optimizers

vnl_lbfgs* itk::LBFGSOptimizer::GetOptimizer (  void   )

Method for getting access to the internal optimizer.

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 bool itk::LBFGSOptimizer::GetTrace (   )  [virtual]

MeasureType itk::SingleValuedNonLinearOptimizer::GetValue (  const ParametersType &  parameters  )  const [inherited]

Get the cost function value at the given parameters. Reimplemented in itk::SPSAOptimizer.

Return Cached Values These method have the advantage of not triggering a* recomputation of the metric but it has the disadvantage of returning* a value that may not be the one corresponding to the current parameters For* GUI update this method is a good for mathematical* validation you should rather call itk::SingleValuedNonLinearVnlOptimizer::GetValue (   )  const [inherited]

Reimplemented in itk::AmoebaOptimizer, and itk::ConjugateGradientOptimizer.

MeasureType itk::LBFGSOptimizer::GetValue (   )

Return Current Value

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().

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::SingleValuedNonLinearVnlOptimizer::MaximizeOff (   )  [virtual, inherited]

virtual void itk::SingleValuedNonLinearVnlOptimizer::MaximizeOn (   )  [virtual, inherited]

void itk::SingleValuedNonLinearVnlOptimizer::MinimizeOff (  void   )  [inline, inherited]

Definition at line 78 of file itkSingleValuedNonLinearVnlOptimizer.h.

void itk::SingleValuedNonLinearVnlOptimizer::MinimizeOn (  void   )  [inline, inherited]

Definition at line 76 of file itkSingleValuedNonLinearVnlOptimizer.h.

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

Print out internal state Reimplemented from itk::SingleValuedNonLinearVnlOptimizer.

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.

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

Set the cost function.

virtual void itk::LBFGSOptimizer::SetCostFunction (  SingleValuedCostFunction *  costFunction  )  [virtual]

Plug in a Cost Function into the optimizer Implements itk::SingleValuedNonLinearVnlOptimizer.

void itk::SingleValuedNonLinearVnlOptimizer::SetCostFunctionAdaptor (  CostFunctionAdaptorType *  adaptor  )  [protected, inherited]

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 the default step size This is a positive real number* with a default value of which determines the stpe size in the line* search* virtual void itk::LBFGSOptimizer::SetDefaultStepLength (  double  stp  )  [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().

Set Get the gradient convergence tolerance This is a positive* real number that determines the accuracy with which the solution is to* be found The optimization terminates X where denotes the Euclidean norm* virtual void itk::LBFGSOptimizer::SetGradientConvergenceTolerance (  double  gtol  )  [virtual]

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

Set the position to initialize the optimization.

virtual void itk::SingleValuedNonLinearVnlOptimizer::SetMaximize (  bool  _arg  )  [virtual, inherited]

Set Get the maximum number of function evaluations allowed* virtual void itk::LBFGSOptimizer::SetMaximumNumberOfFunctionEvaluations (  unsigned int  n  )  [virtual]

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

Returns: Set the MetaDataDictionary

void itk::SingleValuedNonLinearVnlOptimizer::SetMinimize (  bool  v  )  [inline, inherited]

Definition at line 74 of file itkSingleValuedNonLinearVnlOptimizer.h.

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 the optimizer trace flag If set to the optimizer* prints out information every iteration* virtual void itk::LBFGSOptimizer::SetTrace (  bool  flag  )  [virtual]

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

Start optimization with an initial value. Reimplemented from itk::Optimizer.

virtual void itk::LBFGSOptimizer::TraceOff (   )  [virtual]

virtual void itk::LBFGSOptimizer::TraceOn (   )  [virtual]

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

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

Set Get the line search accuracy This is a positive real number* with a default value which controls the accuracy of the line* search If the function and gradient evalutions are inexpensive with* respect to the cost of the iterations it may be advantageous to set* the value to a small itk::LBFGSOptimizer::value (  say 0.  1  )

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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.

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.

Set Get the line search accuracy This is a positive real number* with a default value itk::LBFGSOptimizer::of

Definition at line 81 of file itkLBFGSOptimizer.h.

Return Cached Values These method have the advantage of not triggering a* recomputation of the metric but it has the disadvantage of returning* a value that may not be the one corresponding to the current parameters For* GUI update this method is a good itk::SingleValuedNonLinearVnlOptimizer::option [inherited]

Definition at line 83 of file itkSingleValuedNonLinearVnlOptimizer.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]

Return Cached Values These method have the advantage of not triggering a* recomputation of the metric but it has the disadvantage of returning* a value that may not be the one corresponding to the current parameters For* GUI update itk::SingleValuedNonLinearVnlOptimizer::purposes [inherited]

Definition at line 83 of file itkSingleValuedNonLinearVnlOptimizer.h.

Set Get the optimizer trace flag If set to itk::LBFGSOptimizer::true

Definition at line 62 of file itkLBFGSOptimizer.h.

Return Cached Values These method have the advantage of not triggering a* recomputation of the metric itk::SingleValuedNonLinearVnlOptimizer::value [inherited]

Definition at line 83 of file itkSingleValuedNonLinearVnlOptimizer.h.

Set Get the gradient convergence tolerance This is a positive* real number that determines the accuracy with which the solution is to* be found The optimization terminates itk::LBFGSOptimizer::when

Definition at line 72 of file itkLBFGSOptimizer.h.

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

• itkLBFGSOptimizer.h

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