Uses of Class
de.jstacs.algorithms.optimization.TerminationException

Packages that use TerminationException
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 TerminationException in de.jstacs.algorithms.optimization
 

Methods in de.jstacs.algorithms.optimization that throw TerminationException
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 Ribiere.
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 Ribiere called Polak-Ribiere-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 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 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.