#include <itkGradientDescentOptimizer.h>

Inheritance diagram for itk::GradientDescentOptimizer:

Collaboration diagram for itk::GradientDescentOptimizer:

Public Types
typedef SmartPointer< const Self >	ConstPointer
typedef SmartPointer< Self >	Pointer
typedef GradientDescentOptimizer	Self
enum	StopConditionType { MaximumNumberOfIterations, MetricError }
typedef SingleValuedNonLinearOptimizer	Superclass
Public Member Functions
virtual void	AdvanceOneStep (void)
virtual ::itk::LightObject::Pointer	CreateAnother (void) const
virtual SizeValueType	GetCurrentIteration () const
virtual const double &	GetLearningRate ()
virtual const char *	GetNameOfClass () const
virtual const SizeValueType &	GetNumberOfIterations ()
virtual const double &	GetValue ()
void	ResumeOptimization (void)
virtual void	SetLearningRate (double _arg)
virtual void	SetNumberOfIterations (SizeValueType _arg)
void	StartOptimization (void)
void	StopOptimization (void)

virtual const bool &	GetMaximize ()
virtual void	SetMaximize (bool _arg)
virtual void	MaximizeOn ()
virtual void	MaximizeOff ()
bool	GetMinimize () const
void	SetMinimize (bool v)
void	MinimizeOn ()
void	MinimizeOff ()

virtual const StopConditionType &	GetStopCondition ()
const std::string	GetStopConditionDescription () const
Static Public Member Functions
static Pointer	New ()
Protected Attributes
DerivativeType	m_Gradient
double	m_LearningRate
bool	m_Maximize
Private Member Functions
	GradientDescentOptimizer (const Self &)
void	operator= (const Self &)
Private Attributes
SizeValueType	m_CurrentIteration
SizeValueType	m_NumberOfIterations
bool	m_Stop
StopConditionType	m_StopCondition
std::ostringstream	m_StopConditionDescription
double	m_Value
virtual const DerivativeType &	GetGradient ()
	GradientDescentOptimizer ()
virtual	~GradientDescentOptimizer ()
void	PrintSelf (std::ostream &os, Indent indent) const

Detailed Description

Implement a gradient descent optimizer.

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

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

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

Additionally, user can scale each component of the df / dp but setting a scaling vector using method SetScale().

See also:: RegularStepGradientDescentOptimizer

Definition at line 50 of file itkGradientDescentOptimizer.h.

Member Typedef Documentation

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

Reimplemented from itk::SingleValuedNonLinearOptimizer.

Reimplemented in itk::QuaternionRigidTransformGradientDescentOptimizer.

Definition at line 58 of file itkGradientDescentOptimizer.h.

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

Reimplemented from itk::SingleValuedNonLinearOptimizer.

Reimplemented in itk::QuaternionRigidTransformGradientDescentOptimizer.

Definition at line 57 of file itkGradientDescentOptimizer.h.

typedef GradientDescentOptimizer itk::GradientDescentOptimizer::Self

Standard class typedefs.

Reimplemented from itk::SingleValuedNonLinearOptimizer.

Reimplemented in itk::QuaternionRigidTransformGradientDescentOptimizer.

Definition at line 55 of file itkGradientDescentOptimizer.h.

typedef SingleValuedNonLinearOptimizer itk::GradientDescentOptimizer::Superclass

Reimplemented from itk::SingleValuedNonLinearOptimizer.

Reimplemented in itk::QuaternionRigidTransformGradientDescentOptimizer.

Definition at line 56 of file itkGradientDescentOptimizer.h.

Member Enumeration Documentation

enum itk::GradientDescentOptimizer::StopConditionType

Codes of stopping conditions

Enumerator:

MaximumNumberOfIterations
MetricError

Definition at line 67 of file itkGradientDescentOptimizer.h.

Constructor & Destructor Documentation

itk::GradientDescentOptimizer::GradientDescentOptimizer ( ) [protected]

Get Gradient condition.

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

Get Gradient condition.

Definition at line 127 of file itkGradientDescentOptimizer.h.

itk::GradientDescentOptimizer::GradientDescentOptimizer ( const Self & ) [private]

Member Function Documentation

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

Advance one step following the gradient direction.

Reimplemented in itk::QuaternionRigidTransformGradientDescentOptimizer.

virtual::itk::LightObject::Pointer itk::GradientDescentOptimizer::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::SingleValuedNonLinearOptimizer.

Reimplemented in itk::QuaternionRigidTransformGradientDescentOptimizer.

virtual SizeValueType itk::GradientDescentOptimizer::GetCurrentIteration ( ) const [virtual]

Get the current iteration number.

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

Get Gradient condition.

virtual const double& itk::GradientDescentOptimizer::GetLearningRate ( ) [virtual]

Get the learning rate.

virtual const bool& itk::GradientDescentOptimizer::GetMaximize ( ) [virtual]

Methods to configure the cost function.

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

Methods to configure the cost function.

Definition at line 76 of file itkGradientDescentOptimizer.h.

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

Run-time type information (and related methods).

Reimplemented from itk::SingleValuedNonLinearOptimizer.

Reimplemented in itk::QuaternionRigidTransformGradientDescentOptimizer.

virtual const SizeValueType& itk::GradientDescentOptimizer::GetNumberOfIterations ( ) [virtual]

Get the number of iterations.

virtual const StopConditionType& itk::GradientDescentOptimizer::GetStopCondition ( ) [virtual]

Get Stop condition.

const std::string itk::GradientDescentOptimizer::GetStopConditionDescription ( ) const [virtual]

Get Stop condition.

Reimplemented from itk::Optimizer.

virtual const double& itk::GradientDescentOptimizer::GetValue ( ) [virtual]

Get the current value.

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

Methods to configure the cost function.

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

Methods to configure the cost function.

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

Methods to configure the cost function.

Definition at line 82 of file itkGradientDescentOptimizer.h.

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

Methods to configure the cost function.

Definition at line 80 of file itkGradientDescentOptimizer.h.

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

Method for creation through the object factory.

Reimplemented from itk::SingleValuedNonLinearOptimizer.

Reimplemented in itk::QuaternionRigidTransformGradientDescentOptimizer.

void itk::GradientDescentOptimizer::operator= ( const Self & ) [private]

Types inherited from the superclass

Reimplemented from itk::SingleValuedNonLinearOptimizer.

Reimplemented in itk::QuaternionRigidTransformGradientDescentOptimizer.

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

Get Gradient condition.

Reimplemented from itk::SingleValuedNonLinearOptimizer.

void itk::GradientDescentOptimizer::ResumeOptimization ( void )

Resume previously stopped optimization with current parameters

See also:: StopOptimization.

virtual void itk::GradientDescentOptimizer::SetLearningRate ( double _arg ) [virtual]

Set the learning rate.

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

Methods to configure the cost function.

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

Methods to configure the cost function.

Definition at line 78 of file itkGradientDescentOptimizer.h.

virtual void itk::GradientDescentOptimizer::SetNumberOfIterations ( SizeValueType _arg ) [virtual]

Set the number of iterations.

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

Start optimization.

Reimplemented from itk::Optimizer.

void itk::GradientDescentOptimizer::StopOptimization ( void )

Stop optimization.

See also:: ResumeOptimization

Member Data Documentation

SizeValueType itk::GradientDescentOptimizer::m_CurrentIteration [private]

Definition at line 145 of file itkGradientDescentOptimizer.h.

DerivativeType itk::GradientDescentOptimizer::m_Gradient [protected]

Definition at line 132 of file itkGradientDescentOptimizer.h.

double itk::GradientDescentOptimizer::m_LearningRate [protected]

Definition at line 136 of file itkGradientDescentOptimizer.h.

bool itk::GradientDescentOptimizer::m_Maximize [protected]

Definition at line 134 of file itkGradientDescentOptimizer.h.

SizeValueType itk::GradientDescentOptimizer::m_NumberOfIterations [private]

Definition at line 144 of file itkGradientDescentOptimizer.h.

bool itk::GradientDescentOptimizer::m_Stop [private]

Definition at line 141 of file itkGradientDescentOptimizer.h.

StopConditionType itk::GradientDescentOptimizer::m_StopCondition [private]

Definition at line 143 of file itkGradientDescentOptimizer.h.

std::ostringstream itk::GradientDescentOptimizer::m_StopConditionDescription [private]

Definition at line 146 of file itkGradientDescentOptimizer.h.

double itk::GradientDescentOptimizer::m_Value [private]

Definition at line 142 of file itkGradientDescentOptimizer.h.

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

itkGradientDescentOptimizer.h

Public Types

Public Member Functions

Static Public Member Functions

Protected Attributes

Private Member Functions

Private Attributes

Detailed Description

Member Typedef Documentation

Member Enumeration Documentation

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation