Package org.apache.commons.math4.legacy.optim.nonlinear.scalar

Class GradientMultivariateOptimizer

java.lang.Object

org.apache.commons.math4.legacy.optim.BaseOptimizer<PAIR>

org.apache.commons.math4.legacy.optim.BaseMultivariateOptimizer<PointValuePair>

org.apache.commons.math4.legacy.optim.nonlinear.scalar.MultivariateOptimizer

org.apache.commons.math4.legacy.optim.nonlinear.scalar.GradientMultivariateOptimizer

Direct Known Subclasses:

NonLinearConjugateGradientOptimizer

public abstract class GradientMultivariateOptimizer
extends MultivariateOptimizer

Base class for implementing optimizers for multivariate scalar differentiable functions. It contains boiler-plate code for dealing with gradient evaluation.

Since:

3.1

Constructor Summary

Constructors

Modifier	Constructor	Description
protected	GradientMultivariateOptimizer(ConvergenceChecker<PointValuePair> checker)

Method Summary

Modifier and Type	Method	Description
protected double[]	computeObjectiveGradient(double[] params)	Compute the gradient vector.
PointValuePair	optimize(OptimizationData... optData)	Stores data and performs the optimization.
protected void	parseOptimizationData(OptimizationData... optData)	Scans the list of (required and optional) optimization data that characterize the problem.

Methods inherited from class org.apache.commons.math4.legacy.optim.nonlinear.scalar.MultivariateOptimizer

computeObjectiveValue, createLineSearch, getGoalType, getObjectiveFunction, lineSearch

Methods inherited from class org.apache.commons.math4.legacy.optim.BaseMultivariateOptimizer

getLowerBound, getStartPoint, getUpperBound

Methods inherited from class org.apache.commons.math4.legacy.optim.BaseOptimizer

doOptimize, getAbsoluteTolerance, getConvergenceChecker, getEvaluations, getIterations, getMaxEvaluations, getMaxIterations, getRelativeTolerance, incrementEvaluationCount, incrementIterationCount, optimize

Methods inherited from class java.lang.Object

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

Constructor Detail

GradientMultivariateOptimizer

protected GradientMultivariateOptimizer(ConvergenceChecker<PointValuePair> checker)

Parameters:

checker - Convergence checker.

Method Detail

computeObjectiveGradient

protected double[] computeObjectiveGradient(double[] params)

Compute the gradient vector.

Parameters:

params - Point at which the gradient must be evaluated.

Returns:

the gradient at the specified point.

optimize

public PointValuePair optimize(OptimizationData... optData)
                        throws TooManyEvaluationsException

Stores data and performs the optimization.

The list of parameters is open-ended so that sub-classes can extend it with arguments specific to their concrete implementations.

When the method is called multiple times, instance data is overwritten only when actually present in the list of arguments: when not specified, data set in a previous call is retained (and thus is optional in subsequent calls).

Important note: Subclasses must override BaseOptimizer.parseOptimizationData(OptimizationData[]) if they need to register their own options; but then, they must also call super.parseOptimizationData(optData) within that method.

Overrides:

optimize in class MultivariateOptimizer

Parameters:

optData - Optimization data. In addition to those documented in MultivariateOptimizer, this method will register the following data:

• ObjectiveFunctionGradient

Returns:

a point/value pair that satisfies the convergence criteria.

Throws:

TooManyEvaluationsException - if the maximal number of evaluations (of the objective function) is exceeded.

parseOptimizationData

protected void parseOptimizationData(OptimizationData... optData)

Scans the list of (required and optional) optimization data that characterize the problem.

Overrides:

parseOptimizationData in class MultivariateOptimizer

Parameters:

optData - Optimization data. The following data will be looked for:

• ObjectiveFunctionGradient