webcab.lib.math.optimization.multidimensional
Class AnnealingAlgorithmTypes

java.lang.Object
  |
  +--webcab.lib.math.optimization.multidimensional.AnnealingAlgorithmTypes

public final class AnnealingAlgorithmTypes

extends Object

Here we provide the constants class of the Multi-Dimensional algorithms which are used in the Simulated Annealing approach for Differential Object functions.

Field Summary

static AnnealingAlgorithmTypes	BFGS This constant refers to using the BFGS algorithm (i.e.
static AnnealingAlgorithmTypes	FLETCHER_POWELL This constant refers to using the Fletcher-Powell algorithm (i.e.
static AnnealingAlgorithmTypes	FLETCHER_REEVES This constant refers to using the Fletcher-Reeves algorithm (i.e.
static AnnealingAlgorithmTypes	POLAK_RIVIERE This constant refers to using the Polak-Riviere algorithm (i.e.
static AnnealingAlgorithmTypes	STEEPEST_DESCENT This constant refers to using the Steepest-Descent algorithm (i.e.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

STEEPEST_DESCENT

public static final AnnealingAlgorithmTypes STEEPEST_DESCENT

This constant refers to using the Steepest-Descent algorithm (i.e. MultiDimensionalSolver.derivSteepestDescent(webcab.lib.math.optimization.ExtremumTypes, double[], webcab.lib.math.optimization.unidimensional.BracketingAlgorithm, webcab.lib.math.optimization.unidimensional.LocateAlgorithm, double, double, double, int, double)) which is then used in conjunction with the Simulated Annealing approach in order to find the global extremum of a multi-dimensional optimization problem with a differentiable object function.

FLETCHER_POWELL

public static final AnnealingAlgorithmTypes FLETCHER_POWELL

This constant refers to using the Fletcher-Powell algorithm (i.e. MultiDimensionalSolver.derivFletcherPowell(webcab.lib.math.optimization.ExtremumTypes, double[], webcab.lib.math.optimization.unidimensional.BracketingAlgorithm, webcab.lib.math.optimization.unidimensional.LocateAlgorithm, double, double, double, int, double)) which is then used in conjunction with the Simulated Annealing approach in order to find the global extremum of a multi-dimensioanl optimization problem with a differentiable object function.

BFGS

public static final AnnealingAlgorithmTypes BFGS

This constant refers to using the BFGS algorithm (i.e. MultiDimensionalSolver.derivBFGS(webcab.lib.math.optimization.ExtremumTypes, double[], webcab.lib.math.optimization.unidimensional.BracketingAlgorithm, webcab.lib.math.optimization.unidimensional.LocateAlgorithm, double, double, double, int, double)) which is then used in conjunction with the Simulated Annealing approach in order to find the global extremum of a multi-dimensional optimization problem with a differentiable object function.

POLAK_RIVIERE

public static final AnnealingAlgorithmTypes POLAK_RIVIERE

This constant refers to using the Polak-Riviere algorithm (i.e. MultiDimensionalSolver.derivPolakRiviere(webcab.lib.math.optimization.ExtremumTypes, double[], webcab.lib.math.optimization.unidimensional.BracketingAlgorithm, webcab.lib.math.optimization.unidimensional.LocateAlgorithm, double, double, double, int, double)) which is then used in conjunction with the Simulated Annealing approach in order to find the global extremum of a multi-dimensional optimization problem with a differentiable object function.

FLETCHER_REEVES

public static final AnnealingAlgorithmTypes FLETCHER_REEVES

This constant refers to using the Fletcher-Reeves algorithm (i.e. MultiDimensionalSolver.derivFletcherReeves(webcab.lib.math.optimization.ExtremumTypes, double[], webcab.lib.math.optimization.unidimensional.BracketingAlgorithm, webcab.lib.math.optimization.unidimensional.LocateAlgorithm, double, double, double, int, double)) which is then used in conjunction with the Simulated Annealing approach in order to find the global extremum of a multi-dimensional optimization problem with a differentiable object function.