Package org.hipparchus.optim.nonlinear.scalar.noderiv

Class BOBYQAOptimizer

java.lang.Object

org.hipparchus.optim.BaseOptimizer<P>

org.hipparchus.optim.BaseMultivariateOptimizer<PointValuePair>

org.hipparchus.optim.nonlinear.scalar.MultivariateOptimizer

org.hipparchus.optim.nonlinear.scalar.noderiv.BOBYQAOptimizer

public class BOBYQAOptimizer
extends MultivariateOptimizer

Powell's BOBYQA algorithm. This implementation is translated and adapted from the Fortran version available here. See this paper for an introduction.
BOBYQA is particularly well suited for high dimensional problems where derivatives are not available. In most cases it outperforms the PowellOptimizer significantly. Stochastic algorithms like CMAESOptimizer succeed more often than BOBYQA, but are more expensive. BOBYQA could also be considered as a replacement of any derivative-based optimizer when the derivatives are approximated by finite differences.

Field Summary

Fields

Modifier and Type	Field	Description
static double	DEFAULT_INITIAL_RADIUS	Default value for initialTrustRegionRadius: 10.0 .
static double	DEFAULT_STOPPING_RADIUS	Default value for stoppingTrustRegionRadius: 1.0E-8 .
static int	MINIMUM_PROBLEM_DIMENSION	Minimum dimension of the problem: 2

Fields inherited from class org.hipparchus.optim.BaseOptimizer

evaluations, iterations

Constructor Summary

Constructors

Constructor	Description
BOBYQAOptimizer(int numberOfInterpolationPoints)
BOBYQAOptimizer(int numberOfInterpolationPoints, double initialTrustRegionRadius, double stoppingTrustRegionRadius)

Method Summary

Modifier and Type	Method	Description
protected PointValuePair	doOptimize()	Performs the bulk of the optimization algorithm.

Methods inherited from class org.hipparchus.optim.nonlinear.scalar.MultivariateOptimizer

computeObjectiveValue, getGoalType, optimize, parseOptimizationData

Methods inherited from class org.hipparchus.optim.BaseMultivariateOptimizer

getLowerBound, getStartPoint, getUpperBound

Methods inherited from class org.hipparchus.optim.BaseOptimizer

getConvergenceChecker, getEvaluations, getIterations, getMaxEvaluations, getMaxIterations, incrementEvaluationCount, incrementIterationCount, optimize

Methods inherited from class java.lang.Object

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

Field Detail

MINIMUM_PROBLEM_DIMENSION

public static final int MINIMUM_PROBLEM_DIMENSION

Minimum dimension of the problem: 2

See Also:

Constant Field Values

DEFAULT_INITIAL_RADIUS

public static final double DEFAULT_INITIAL_RADIUS

Default value for initialTrustRegionRadius: 10.0 .

See Also:

Constant Field Values

DEFAULT_STOPPING_RADIUS

public static final double DEFAULT_STOPPING_RADIUS

Default value for stoppingTrustRegionRadius: 1.0E-8 .

See Also:

Constant Field Values

Constructor Detail

BOBYQAOptimizer

public BOBYQAOptimizer(int numberOfInterpolationPoints)

Parameters:

numberOfInterpolationPoints - Number of interpolation conditions. For a problem of dimension n, its value must be in the interval [n+2, (n+1)(n+2)/2]. Choices that exceed 2n+1 are not recommended.

BOBYQAOptimizer

public BOBYQAOptimizer(int numberOfInterpolationPoints,
                       double initialTrustRegionRadius,
                       double stoppingTrustRegionRadius)

Parameters:

numberOfInterpolationPoints - Number of interpolation conditions. For a problem of dimension n, its value must be in the interval [n+2, (n+1)(n+2)/2]. Choices that exceed 2n+1 are not recommended.

initialTrustRegionRadius - Initial trust region radius.

stoppingTrustRegionRadius - Stopping trust region radius.

Method Detail

doOptimize

protected PointValuePair doOptimize()

Performs the bulk of the optimization algorithm.

Specified by:

doOptimize in class BaseOptimizer<PointValuePair>

Returns:

the point/value pair giving the optimal value of the objective function.