Uses of Class
de.jstacs.algorithms.optimization.Optimizer.TerminationCondition

Packages that use Optimizer.TerminationCondition
de.jstacs.algorithms.optimization Provides classes for different types of algorithms that are not directly linked to the modelling components of Jstacs: Algorithms on graphs, algorithms for numerical optimization, and a basic alignment algorithm.
 

Uses of Optimizer.TerminationCondition in de.jstacs.algorithms.optimization
 

Methods in de.jstacs.algorithms.optimization that return Optimizer.TerminationCondition
static Optimizer.TerminationCondition Optimizer.TerminationCondition.valueOf(String name)
          Returns the enum constant of this type with the specified name.
static Optimizer.TerminationCondition[] Optimizer.TerminationCondition.values()
          Returns an array containing the constants of this enum type, in the order they're declared.
 

Methods in de.jstacs.algorithms.optimization with parameters of type Optimizer.TerminationCondition
static int Optimizer.conjugateGradientsFR(DifferentiableFunction f, double[] currentValues, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, Time t)
          The conjugate gradient algorithm by Fletcher and Reeves.
static int Optimizer.conjugateGradientsPR(DifferentiableFunction f, double[] currentValues, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, Time t)
          The conjugate gradient algorithm by Polak and Ribière.
static int Optimizer.conjugateGradientsPRP(DifferentiableFunction f, double[] currentValues, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, Time t)
          The conjugate gradient algorithm by Polak and Ribière called "Polak-Ribière-Positive".
static int Optimizer.limitedMemoryBFGS(DifferentiableFunction f, double[] currentValues, byte m, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, Time t)
          The Broyden-Fletcher-Goldfarb-Shanno version of limited memory quasi-Newton methods.
static int Optimizer.optimize(byte algorithm, DifferentiableFunction f, double[] currentValues, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out)
          This method enables you to use all different implemented optimization algorithms by only one method.
static int Optimizer.optimize(byte algorithm, DifferentiableFunction f, double[] currentValues, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, Time t)
          This method enables you to use all different implemented optimization algorithms by only one method.
static int Optimizer.quasiNewtonBFGS(DifferentiableFunction f, double[] currentValues, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, Time t)
          The Broyden-Fletcher-Goldfarb-Shanno version of the quasi-Newton method.
static int Optimizer.quasiNewtonDFP(DifferentiableFunction f, double[] currentValues, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, Time t)
          The Davidon-Fletcher-Powell version of the quasi-Newton method.
static int Optimizer.steepestDescent(DifferentiableFunction f, double[] currentValues, Optimizer.TerminationCondition terminationMode, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, Time t)
          The steepest descent.