com.webcab.ejb.math.optimization.multidimensional
Class AnnealingAlgorithmTypes

java.lang.Object
  |
  +--com.webcab.ejb.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 (ie MultiDimensionalSolver.derivBFGS(com.webcab.ejb.math.optimization.ExtremumTypes, double[], com.webcab.ejb.math.optimization.unidimensional.BracketingAlgorithm, com.webcab.ejb.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.
static AnnealingAlgorithmTypes	FLETCHER_POWELL This constant refers to using the Fletcher-Powell algorithm (ie MultiDimensionalSolver.derivFletcherPowell(com.webcab.ejb.math.optimization.ExtremumTypes, double[], com.webcab.ejb.math.optimization.unidimensional.BracketingAlgorithm, com.webcab.ejb.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.
static AnnealingAlgorithmTypes	FLETCHER_REEVES This constant refers to using the Fletcher-Reeves algorithm (ie MultiDimensionalSolver.derivFletcherReeves(com.webcab.ejb.math.optimization.ExtremumTypes, double[], com.webcab.ejb.math.optimization.unidimensional.BracketingAlgorithm, com.webcab.ejb.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.
static AnnealingAlgorithmTypes	POLAK_RIVIERE This constant refers to using the Polak-Riviere algorithm (ie MultiDimensionalSolver.derivPolakRiviere(com.webcab.ejb.math.optimization.ExtremumTypes, double[], com.webcab.ejb.math.optimization.unidimensional.BracketingAlgorithm, com.webcab.ejb.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.
static AnnealingAlgorithmTypes	STEEPEST_DESCENT This constant refers to using the Steepest-Descent algorithm (ie MultiDimensionalSolver.derivSteepestDescent(com.webcab.ejb.math.optimization.ExtremumTypes, double[], com.webcab.ejb.math.optimization.unidimensional.BracketingAlgorithm, com.webcab.ejb.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.

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 (ie MultiDimensionalSolver.derivSteepestDescent(com.webcab.ejb.math.optimization.ExtremumTypes, double[], com.webcab.ejb.math.optimization.unidimensional.BracketingAlgorithm, com.webcab.ejb.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 (ie MultiDimensionalSolver.derivFletcherPowell(com.webcab.ejb.math.optimization.ExtremumTypes, double[], com.webcab.ejb.math.optimization.unidimensional.BracketingAlgorithm, com.webcab.ejb.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 (ie MultiDimensionalSolver.derivBFGS(com.webcab.ejb.math.optimization.ExtremumTypes, double[], com.webcab.ejb.math.optimization.unidimensional.BracketingAlgorithm, com.webcab.ejb.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 (ie MultiDimensionalSolver.derivPolakRiviere(com.webcab.ejb.math.optimization.ExtremumTypes, double[], com.webcab.ejb.math.optimization.unidimensional.BracketingAlgorithm, com.webcab.ejb.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 (ie MultiDimensionalSolver.derivFletcherReeves(com.webcab.ejb.math.optimization.ExtremumTypes, double[], com.webcab.ejb.math.optimization.unidimensional.BracketingAlgorithm, com.webcab.ejb.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.