/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math3.optimization.general;
import org.apache.commons.math3.analysis.MultivariateVectorFunction;
import org.apache.commons.math3.analysis.differentiation.GradientFunction;
import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableFunction;
import org.apache.commons.math3.optimization.ConvergenceChecker;
import org.apache.commons.math3.optimization.GoalType;
import org.apache.commons.math3.optimization.OptimizationData;
import org.apache.commons.math3.optimization.InitialGuess;
import org.apache.commons.math3.optimization.PointValuePair;
import org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer;
/**
* Base class for implementing optimizers for multivariate scalar
* differentiable functions.
* It contains boiler-plate code for dealing with gradient evaluation.
*
* @deprecated As of 3.1 (to be removed in 4.0).
* @since 3.1
*/
@Deprecated
public abstract class AbstractDifferentiableOptimizer
extends BaseAbstractMultivariateOptimizer<MultivariateDifferentiableFunction> {
/**
* Objective function gradient.
*/
private MultivariateVectorFunction gradient;
/**
* @param checker Convergence checker.
*/
protected AbstractDifferentiableOptimizer(ConvergenceChecker<PointValuePair> checker) {
super(checker);
}
/**
* Compute the gradient vector.
*
* @param evaluationPoint Point at which the gradient must be evaluated.
* @return the gradient at the specified point.
*/
protected double[] computeObjectiveGradient(final double[] evaluationPoint) {
return gradient.value(evaluationPoint);
}
/**
* {@inheritDoc}
*
* @deprecated In 3.1. Please use
* {@link #optimizeInternal(int,MultivariateDifferentiableFunction,GoalType,OptimizationData[])}
* instead.
*/
@Override@Deprecated
protected PointValuePair optimizeInternal(final int maxEval,
final MultivariateDifferentiableFunction f,
final GoalType goalType,
final double[] startPoint) {
return optimizeInternal(maxEval, f, goalType, new InitialGuess(startPoint));
}
/** {@inheritDoc} */
@Override
protected PointValuePair optimizeInternal(final int maxEval,
final MultivariateDifferentiableFunction f,
final GoalType goalType,
final OptimizationData... optData) {
// Store optimization problem characteristics.
gradient = new GradientFunction(f);
// Perform optimization.
return super.optimizeInternal(maxEval, f, goalType, optData);
}
}