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## Uses of Interface
de.jstacs.algorithms.optimization.StartDistanceForecaster

Packages that use StartDistanceForecaster
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.motifDiscovery | This package provides the framework including the interface for any de novo motif discoverer

Uses of StartDistanceForecaster in de.jstacs.algorithms.optimization

Classes in de.jstacs.algorithms.optimization that implement StartDistanceForecaster
`class` | `ConstantStartDistance` The most simple `StartDistanceForecaster` that returns always the same value.
`class` | `LimitedMedianStartDistance` This class implements a `StartDistanceForecaster` that returns the median of a limited memory over the last values.

Methods in de.jstacs.algorithms.optimization with parameters of type StartDistanceForecaster
`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.

Uses of StartDistanceForecaster in de.jstacs.motifDiscovery

Methods in de.jstacs.motifDiscovery with parameters of type StartDistanceForecaster
`static double[][]` | `MutableMotifDiscovererToolbox.optimize(ScoringFunction[] funs, OptimizableFunction opt, byte algorithm, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History[][] hist, int[][] minimalNewLength, OptimizableFunction.KindOfParameter plugIn)` This method tries to optimize the problem at hand as good as possible.
`static double[][]` | `MutableMotifDiscovererToolbox.optimize(ScoringFunction[] funs, OptimizableFunction opt, byte algorithm, double eps, double linEps, StartDistanceForecaster startDistance, SafeOutputStream out, boolean breakOnChanged, History template, OptimizableFunction.KindOfParameter plugIn)` This method tries to optimize the problem at hand as good as possible.

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