#include <itkAmoebaOptimizer.h>

Inheritance diagram for itk::AmoebaOptimizer:

Collaboration diagram for itk::AmoebaOptimizer:

Public Types
typedef SmartPointer< const Self >	ConstPointer
typedef vnl_vector< double >	InternalParametersType
typedef unsigned int	NumberOfIterationsType
typedef Superclass::ParametersType	ParametersType
typedef SmartPointer< Self >	Pointer
typedef AmoebaOptimizer	Self
typedef SingleValuedNonLinearVnlOptimizer	Superclass
Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother (void) const
virtual const char *	GetNameOfClass () const
vnl_amoeba *	GetOptimizer (void) const
const std::string	GetStopConditionDescription () const
MeasureType	GetValue () const
virtual void	SetCostFunction (SingleValuedCostFunction *costFunction)
void	StartOptimization (void)

virtual void	SetMaximumNumberOfIterations (NumberOfIterationsType _arg)
virtual NumberOfIterationsType	GetMaximumNumberOfIterations () const

virtual void	SetAutomaticInitialSimplex (bool _arg)
virtual void	AutomaticInitialSimplexOn ()
virtual void	AutomaticInitialSimplexOff ()
virtual bool	GetAutomaticInitialSimplex () const

virtual void	SetOptimizeWithRestarts (bool _arg)
virtual void	OptimizeWithRestartsOn ()
virtual void	OptimizeWithRestartsOff ()
virtual bool	GetOptimizeWithRestarts () const

void	SetInitialSimplexDelta (ParametersType initialSimplexDelta, bool automaticInitialSimplex=false)
virtual ParametersType	GetInitialSimplexDelta () const

virtual void	SetParametersConvergenceTolerance (double _arg)
virtual double	GetParametersConvergenceTolerance () const

virtual void	SetFunctionConvergenceTolerance (double _arg)
virtual double	GetFunctionConvergenceTolerance () const
Static Public Member Functions
static Pointer	New ()
Protected Types
typedef Superclass::CostFunctionAdaptorType	CostFunctionAdaptorType
Protected Member Functions
	AmoebaOptimizer ()
void	PrintSelf (std::ostream &os, Indent indent) const
virtual	~AmoebaOptimizer ()
Private Member Functions

void	ValidateSettings ()
	AmoebaOptimizer (const Self &)
void	operator= (const Self &)
Private Attributes
bool	m_AutomaticInitialSimplex
CostFunctionType::MeasureType	m_FunctionConvergenceTolerance
ParametersType	m_InitialSimplexDelta
NumberOfIterationsType	m_MaximumNumberOfIterations
bool	m_OptimizeWithRestarts
ParametersType::ValueType	m_ParametersConvergenceTolerance
std::ostringstream	m_StopConditionDescription
vnl_amoeba *	m_VnlOptimizer

Detailed Description

Wrap of the vnl_amoeba algorithm.

AmoebaOptimizer is a wrapper around the vnl_amoeba algorithm which is an implementation of the Nelder-Meade downhill simplex problem. For most problems, it is a few times slower than a Levenberg-Marquardt algorithm but does not require derivatives of its cost function. It works by creating a simplex (n+1 points in ND space). The cost function is evaluated at each corner of the simplex. The simplex is then modified (by reflecting a corner about the opposite edge, by shrinking the entire simplex, by contracting one edge of the simplex, or by expanding the simplex) in searching for the minimum of the cost function.

The methods AutomaticInitialSimplex() and SetInitialSimplexDelta() control whether the optimizer defines the initial simplex automatically (by constructing a very small simplex around the initial position) or uses a user supplied simplex size.

The method SetOptimizeWithRestarts() indicates that the amoeabe algorithm should be rerun after if converges. This heuristic increases the chances of escaping from a local optimum. Each time the simplex is initialized with the best solution obtained by the previous runs. The edge length is half of that from the previous iteration. The heuristic is terminated if the total number of iterations is greater-equal than the maximal number of iterations (SetMaximumNumberOfIterations) or the difference between the current function value and the best function value is less than a threshold (SetFunctionConvergenceTolerance) and max(|best_parameters_i - current_parameters_i|) is less than a threshold (SetParametersConvergenceTolerance).

Definition at line 61 of file itkAmoebaOptimizer.h.

Member Typedef Documentation

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

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer.

Definition at line 69 of file itkAmoebaOptimizer.h.

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

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer.

Definition at line 159 of file itkAmoebaOptimizer.h.

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

InternalParameters typedef.

Definition at line 83 of file itkAmoebaOptimizer.h.

typedef unsigned int itk::AmoebaOptimizer::NumberOfIterationsType

Definition at line 70 of file itkAmoebaOptimizer.h.

typedef Superclass::ParametersType itk::AmoebaOptimizer::ParametersType

Parameters type. It defines a position in the optimization search space.

Reimplemented from itk::SingleValuedNonLinearOptimizer.

Definition at line 76 of file itkAmoebaOptimizer.h.

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

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer.

Definition at line 68 of file itkAmoebaOptimizer.h.

typedef AmoebaOptimizer itk::AmoebaOptimizer::Self

Standard "Self" typedef.

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer.

Definition at line 66 of file itkAmoebaOptimizer.h.

typedef SingleValuedNonLinearVnlOptimizer itk::AmoebaOptimizer::Superclass

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer.

Definition at line 67 of file itkAmoebaOptimizer.h.

Constructor & Destructor Documentation

itk::AmoebaOptimizer::AmoebaOptimizer ( ) [protected]

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

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

Check that the settings are valid. If not throw an exception.

Member Function Documentation

virtual void itk::AmoebaOptimizer::AutomaticInitialSimplexOff ( ) [virtual]

Set/Get the mode which determines how the amoeba algorithm defines the initial simplex. Default is AutomaticInitialSimplexOn. If AutomaticInitialSimplex is on, the initial simplex is created with a default size. If AutomaticInitialSimplex is off, then InitialSimplexDelta will be used to define the initial simplex, setting the ith corner of the simplex as [x0[0], x0[1], ..., x0[i]+InitialSimplexDelta[i], ..., x0[d-1]].

virtual void itk::AmoebaOptimizer::AutomaticInitialSimplexOn ( ) [virtual]

Set/Get the mode which determines how the amoeba algorithm defines the initial simplex. Default is AutomaticInitialSimplexOn. If AutomaticInitialSimplex is on, the initial simplex is created with a default size. If AutomaticInitialSimplex is off, then InitialSimplexDelta will be used to define the initial simplex, setting the ith corner of the simplex as [x0[0], x0[1], ..., x0[i]+InitialSimplexDelta[i], ..., x0[d-1]].

virtual::itk::LightObject::Pointer itk::AmoebaOptimizer::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.

virtual bool itk::AmoebaOptimizer::GetAutomaticInitialSimplex ( ) const [virtual]

Set/Get the mode which determines how the amoeba algorithm defines the initial simplex. Default is AutomaticInitialSimplexOn. If AutomaticInitialSimplex is on, the initial simplex is created with a default size. If AutomaticInitialSimplex is off, then InitialSimplexDelta will be used to define the initial simplex, setting the ith corner of the simplex as [x0[0], x0[1], ..., x0[i]+InitialSimplexDelta[i], ..., x0[d-1]].

virtual double itk::AmoebaOptimizer::GetFunctionConvergenceTolerance ( ) const [virtual]

The optimization algorithm will terminate when the simplex diameter and the difference in cost function values at the corners of the simplex falls below user specified thresholds. The cost function convergence threshold is set via SetFunctionConvergenceTolerance().

virtual ParametersType itk::AmoebaOptimizer::GetInitialSimplexDelta ( ) const [virtual]

Set/Get the deltas that are used to define the initial simplex when AutomaticInitialSimplex is off.

virtual NumberOfIterationsType itk::AmoebaOptimizer::GetMaximumNumberOfIterations ( ) const [virtual]

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

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

Run-time type information (and related methods).

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer.

vnl_amoeba* itk::AmoebaOptimizer::GetOptimizer ( void ) const

Method for getting access to the internal optimizer.

virtual bool itk::AmoebaOptimizer::GetOptimizeWithRestarts ( ) const [virtual]

Set/Get the mode that determines if we want to use multiple runs of the Amoeba optimizer. If true, then the optimizer is rerun after it converges. The additional runs are performed using a simplex initialized with the best solution obtained by the previous runs. The edge length is half of that from the previous iteration.

virtual double itk::AmoebaOptimizer::GetParametersConvergenceTolerance ( ) const [virtual]

The optimization algorithm will terminate when the simplex diameter and the difference in cost function values at the corners of the simplex falls below user specified thresholds. The simplex diameter threshold is set via SetParametersConvergenceTolerance().

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

Report the reason for stopping.

Reimplemented from itk::Optimizer.

MeasureType itk::AmoebaOptimizer::GetValue ( ) const

Return Current Value

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

Method for creation through the object factory.

Reimplemented from itk::SingleValuedNonLinearOptimizer.

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

Check that the settings are valid. If not throw an exception.

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer.

virtual void itk::AmoebaOptimizer::OptimizeWithRestartsOff ( ) [virtual]

Set/Get the mode that determines if we want to use multiple runs of the Amoeba optimizer. If true, then the optimizer is rerun after it converges. The additional runs are performed using a simplex initialized with the best solution obtained by the previous runs. The edge length is half of that from the previous iteration.

virtual void itk::AmoebaOptimizer::OptimizeWithRestartsOn ( ) [virtual]

Set/Get the mode that determines if we want to use multiple runs of the Amoeba optimizer. If true, then the optimizer is rerun after it converges. The additional runs are performed using a simplex initialized with the best solution obtained by the previous runs. The edge length is half of that from the previous iteration.

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

Print out internal state

Reimplemented from itk::SingleValuedNonLinearVnlOptimizer.

virtual void itk::AmoebaOptimizer::SetAutomaticInitialSimplex ( bool _arg ) [virtual]

Set/Get the mode which determines how the amoeba algorithm defines the initial simplex. Default is AutomaticInitialSimplexOn. If AutomaticInitialSimplex is on, the initial simplex is created with a default size. If AutomaticInitialSimplex is off, then InitialSimplexDelta will be used to define the initial simplex, setting the ith corner of the simplex as [x0[0], x0[1], ..., x0[i]+InitialSimplexDelta[i], ..., x0[d-1]].

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

Plug in a Cost Function into the optimizer

Implements itk::SingleValuedNonLinearVnlOptimizer.

virtual void itk::AmoebaOptimizer::SetFunctionConvergenceTolerance ( double _arg ) [virtual]

The optimization algorithm will terminate when the simplex diameter and the difference in cost function values at the corners of the simplex falls below user specified thresholds. The cost function convergence threshold is set via SetFunctionConvergenceTolerance().

void itk::AmoebaOptimizer::SetInitialSimplexDelta	(	ParametersType	initialSimplexDelta,
		bool	automaticInitialSimplex = `false`
	)

Set/Get the deltas that are used to define the initial simplex when AutomaticInitialSimplex is off.

virtual void itk::AmoebaOptimizer::SetMaximumNumberOfIterations ( NumberOfIterationsType _arg ) [virtual]

Set/Get the maximum number of iterations. The optimization algorithm will terminate after the maximum number of iterations has been reached. The default value is defined as DEFAULT_MAXIMAL_NUMBER_OF_ITERATIONS.

virtual void itk::AmoebaOptimizer::SetOptimizeWithRestarts ( bool _arg ) [virtual]

Set/Get the mode that determines if we want to use multiple runs of the Amoeba optimizer. If true, then the optimizer is rerun after it converges. The additional runs are performed using a simplex initialized with the best solution obtained by the previous runs. The edge length is half of that from the previous iteration.

virtual void itk::AmoebaOptimizer::SetParametersConvergenceTolerance ( double _arg ) [virtual]

The optimization algorithm will terminate when the simplex diameter and the difference in cost function values at the corners of the simplex falls below user specified thresholds. The simplex diameter threshold is set via SetParametersConvergenceTolerance().

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

Start optimization with an initial value.

Reimplemented from itk::Optimizer.

void itk::AmoebaOptimizer::ValidateSettings ( ) [private]

Check that the settings are valid. If not throw an exception.