org.apache.commons.math3.optimization.general
Class AbstractDifferentiableOptimizer

java.lang.Object
  org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer<MultivariateDifferentiableFunction>
      org.apache.commons.math3.optimization.general.AbstractDifferentiableOptimizer

All Implemented Interfaces:

BaseMultivariateOptimizer<MultivariateDifferentiableFunction>, BaseOptimizer<PointValuePair>

Deprecated. As of 3.1 (to be removed in 4.0).

@Deprecated
public abstract class AbstractDifferentiableOptimizer
extends BaseAbstractMultivariateOptimizer<MultivariateDifferentiableFunction>

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

Since:

3.1

Version:

$Id: AbstractDifferentiableOptimizer.java 7721 2013-02-14 14:07:13Z CardosoP $

Field Summary

Fields inherited from class org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer

evaluations

Constructor Summary

protected

AbstractDifferentiableOptimizer(ConvergenceChecker<PointValuePair> checker) Deprecated.

Method Summary

protected double[]	computeObjectiveGradient(double[] evaluationPoint) Deprecated. Compute the gradient vector.
protected PointValuePair	optimizeInternal(int maxEval, MultivariateDifferentiableFunction f, GoalType goalType, double[] startPoint) Deprecated. In 3.1. Please use optimizeInternal(int,MultivariateDifferentiableFunction,GoalType,OptimizationData[]) instead.
protected PointValuePair	optimizeInternal(int maxEval, MultivariateDifferentiableFunction f, GoalType goalType, OptimizationData... optData) Deprecated. Optimize an objective function.

Methods inherited from class org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer

computeObjectiveValue, doOptimize, getConvergenceChecker, getEvaluations, getGoalType, getLowerBound, getMaxEvaluations, getStartPoint, getUpperBound, optimize, optimize

Methods inherited from class java.lang.Object

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

Constructor Detail

AbstractDifferentiableOptimizer

protected AbstractDifferentiableOptimizer(ConvergenceChecker<PointValuePair> checker)

Deprecated.

Parameters:

checker - Convergence checker.

Method Detail

computeObjectiveGradient

protected double[] computeObjectiveGradient(double[] evaluationPoint)

Deprecated.

Compute the gradient vector.

Parameters:

evaluationPoint - Point at which the gradient must be evaluated.

Returns:

the gradient at the specified point.

optimizeInternal

@Deprecated
protected PointValuePair optimizeInternal(int maxEval,
                                                     MultivariateDifferentiableFunction f,
                                                     GoalType goalType,
                                                     double[] startPoint)

Deprecated. In 3.1. Please use optimizeInternal(int,MultivariateDifferentiableFunction,GoalType,OptimizationData[]) instead.

Optimize an objective function.

Overrides:

optimizeInternal in class BaseAbstractMultivariateOptimizer<MultivariateDifferentiableFunction>

Parameters:

maxEval - Maximum number of function evaluations.

f - Objective function.

goalType - Type of optimization goal: either GoalType.MAXIMIZE or GoalType.MINIMIZE.

startPoint - Start point for optimization.

Returns:

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

optimizeInternal

protected PointValuePair optimizeInternal(int maxEval,
                                          MultivariateDifferentiableFunction f,
                                          GoalType goalType,
                                          OptimizationData... optData)

Deprecated.

Optimize an objective function.

Overrides:

optimizeInternal in class BaseAbstractMultivariateOptimizer<MultivariateDifferentiableFunction>

Parameters:

maxEval - Allowed number of evaluations of the objective function.

f - Objective function.

goalType - Optimization type.

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

• InitialGuess
• SimpleBounds

Returns:

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