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## 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 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.

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