Package | Description |
---|---|
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.
|
Modifier and Type | Method and Description |
---|---|
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.
|