Uses of Interface
de.jstacs.algorithms.optimization.termination.TerminationCondition

Packages that use 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.
de.jstacs.algorithms.optimization.termination   
 

Uses of TerminationCondition in de.jstacs.algorithms.optimization
 

Methods in de.jstacs.algorithms.optimization with parameters of type TerminationCondition
static int Optimizer.conjugateGradientsFR(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t)
          The conjugate gradient algorithm by Fletcher and Reeves.
static int Optimizer.conjugateGradientsPR(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t)
          The conjugate gradient algorithm by Polak and Ribière.
static int Optimizer.conjugateGradientsPRP(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream 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, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream 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, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream 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, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream 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, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t)
          The Broyden-Fletcher-Goldfarb-Shanno version of the quasi-Newton method.
static int Optimizer.quasiNewtonDFP(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t)
          The Davidon-Fletcher-Powell version of the quasi-Newton method.
static int Optimizer.steepestDescent(DifferentiableFunction f, double[] currentValues, TerminationCondition terminationMode, double linEps, StartDistanceForecaster startDistance, OutputStream out, Time t)
          The steepest descent.
 

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

Classes in de.jstacs.algorithms.optimization.termination that implement TerminationCondition
 class AbsoluteValueCondition
          Deprecated. use of the absolute value condition is not recommended and it may be removed in future releases
 class CombinedCondition
          This class allows to use many TerminationConditions at once.
 class IterationCondition
          This class will stop an optimization if the number of iteration reaches a given number.
 class SmallDifferenceOfFunctionEvaluationsCondition
          This class implements a TerminationCondition that stops an optimization if the difference of the current and the last function evaluations will be small, i.e., $f(\underline{x}_{i-1}) - f(\underline{x}_i) < \epsilon$.
 class SmallStepCondition
          This class implements a TerminationCondition that allows no further iteration in an optimization if the scalar product of the current and the last values of x will be small, i.e., $(\underline{x}_i-\underline{x}_{i-1})^T (\underline{x}_i-\underline{x}_{i-1}) < \epsilon$.
 class TimeCondition
          This class implements a TerminationCondition that stops the optimization if the elapsed time in seconds is greater than a given value.
 

Constructors in de.jstacs.algorithms.optimization.termination with parameters of type TerminationCondition
CombinedCondition(int threshold, TerminationCondition... condition)
          This constructor creates an instance that allows to use many TerminationConditions at one.