ITK
4.3.0
Insight Segmentation and Registration Toolkit
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Classes | |
class | itk::AmoebaOptimizer |
Wrap of the vnl_amoeba algorithm. More... | |
class | itk::ConjugateGradientOptimizer |
Wrap of the vnl_conjugate_gradient. More... | |
class | itk::CostFunction |
Base class for cost functions intended to be used with Optimizers. More... | |
class | itk::CumulativeGaussianCostFunction |
Cost function for the Cumulative Gaussian Optimizer. More... | |
class | itk::CumulativeGaussianOptimizer |
This is an optimizer specific to estimating the parameters of Cumulative Gaussian sampled data. More... | |
class | itk::ExhaustiveOptimizer |
Optimizer that fully samples a grid on the parametric space. More... | |
class | itk::FRPROptimizer |
Implements Fletch-Reeves & Polak-Ribiere optimization using dBrent line search. More... | |
class | itk::GradientDescentOptimizer |
Implement a gradient descent optimizer. More... | |
class | itk::InitializationBiasedParticleSwarmOptimizer |
Implementation of a biased/regularized Particle Swarm Optimization (PSO) algorithm. More... | |
class | itk::LBFGSBOptimizer |
Limited memory Broyden Fletcher Goldfarb Shannon minimization with simple bounds. More... | |
class | LBFGSBOptimizerHelper |
Wrapper helper around vnl_lbfgsb. More... | |
class | itk::LBFGSOptimizer |
Wrap of the vnl_lbfgs algorithm. More... | |
class | itk::LevenbergMarquardtOptimizer |
Wrap of the vnl_levenberg_marquardt algorithm. More... | |
class | itk::MultipleValuedCostFunction |
This class is a base for the CostFunctions returning a multiple values. More... | |
class | itk::MultipleValuedNonLinearOptimizer |
This class is a base for the Optimization methods that optimize a multiple valued function. More... | |
class | itk::MultipleValuedNonLinearVnlOptimizer |
This class is a base for the Optimization methods that optimize a multi-valued function. More... | |
class | itk::MultipleValuedVnlCostFunctionAdaptor |
This class is an Adaptor that allows to pass itk::MultipleValuedCostFunctions to vnl_optimizers expecting a vnl_cost_function. More... | |
class | itk::NonLinearOptimizer |
Wrap of the vnl_nonlinear_minimizer to be adapted. More... | |
class | itk::OnePlusOneEvolutionaryOptimizer |
1+1 evolutionary strategy optimizer More... | |
class | itk::Optimizer |
Generic representation for an optimization method. More... | |
class | itk::ParticleSwarmOptimizer |
Implementation of a Particle Swarm Optimization (PSO) algorithm. More... | |
class | itk::ParticleSwarmOptimizerBase |
Abstract implementation of a Particle Swarm Optimization (PSO) algorithm. More... | |
class | itk::PowellOptimizer |
Implements Powell optimization using Brent line search. More... | |
class | itk::QuaternionRigidTransformGradientDescentOptimizer |
Implement a gradient descent optimizer. More... | |
class | itk::RegularStepGradientDescentBaseOptimizer |
Implement a gradient descent optimizer. More... | |
class | itk::RegularStepGradientDescentOptimizer |
Implement a gradient descent optimizer. More... | |
class | itk::SingleValuedCostFunction |
This class is a base for the CostFunctions returning a single value. More... | |
class | itk::SingleValuedNonLinearOptimizer |
This class is a base for the Optimization methods that optimize a single valued function. More... | |
class | itk::SingleValuedNonLinearVnlOptimizer |
This class is a base for the Optimization methods that optimize a single valued function. More... | |
class | itk::SingleValuedVnlCostFunctionAdaptor |
This class is an Adaptor that allows to pass itk::SingleValuedCostFunctions to vnl_optimizers expecting a vnl_cost_function. More... | |
class | itk::SPSAOptimizer |
An optimizer based on simultaneous perturbation... More... | |
class | itk::VersorRigid3DTransformOptimizer |
Implement a gradient descent optimizer for the VersorRigid3DTransform parameter space. More... | |
class | itk::VersorTransformOptimizer |
Implement a gradient descent optimizer. More... | |
This module contains ITK classes than encapsulate numerical optimizers. A set of base classes categorize the type of cost function an optimizer is capable of operating on, and the concrete classes provide implementations of specific algorithms.