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/*
* 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.DifferentiableMultivariateFunction;
import org.apache.commons.math3.analysis.MultivariateVectorFunction;
import org.apache.commons.math3.analysis.FunctionUtils;
import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableFunction;
import org.apache.commons.math3.optimization.DifferentiableMultivariateOptimizer;
import org.apache.commons.math3.optimization.GoalType;
import org.apache.commons.math3.optimization.ConvergenceChecker;
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 2.0
*/
@Deprecated
public abstract class AbstractScalarDifferentiableOptimizer
extends BaseAbstractMultivariateOptimizer<DifferentiableMultivariateFunction>
implements DifferentiableMultivariateOptimizer {
/**
* Objective function gradient.
*/
private MultivariateVectorFunction gradient;
/**
* Simple constructor with default settings.
* The convergence check is set to a
* {@link org.apache.commons.math3.optimization.SimpleValueChecker
* SimpleValueChecker}.
* @deprecated See {@link org.apache.commons.math3.optimization.SimpleValueChecker#SimpleValueChecker()}
*/
@Deprecated
protected AbstractScalarDifferentiableOptimizer() {}
/**
* @param checker Convergence checker.
*/
protected AbstractScalarDifferentiableOptimizer(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.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the allowed number of evaluations is exceeded.
*/
protected double[] computeObjectiveGradient(final double[] evaluationPoint) {
return gradient.value(evaluationPoint);
}
/** {@inheritDoc} */
@Override
protected PointValuePair optimizeInternal(int maxEval,
final DifferentiableMultivariateFunction f,
final GoalType goalType,
final double[] startPoint) {
// Store optimization problem characteristics.
gradient = f.gradient();
return super.optimizeInternal(maxEval, f, goalType, startPoint);
}
/**
* Optimize an objective function.
*
* @param f Objective function.
* @param goalType Type of optimization goal: either
* {@link GoalType#MAXIMIZE} or {@link GoalType#MINIMIZE}.
* @param startPoint Start point for optimization.
* @param maxEval Maximum number of function evaluations.
* @return the point/value pair giving the optimal value for objective
* function.
* @throws org.apache.commons.math3.exception.DimensionMismatchException
* if the start point dimension is wrong.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the maximal number of evaluations is exceeded.
* @throws org.apache.commons.math3.exception.NullArgumentException if
* any argument is {@code null}.
*/
public PointValuePair optimize(final int maxEval,
final MultivariateDifferentiableFunction f,
final GoalType goalType,
final double[] startPoint) {
return optimizeInternal(maxEval,
FunctionUtils.toDifferentiableMultivariateFunction(f),
goalType,
startPoint);
}
}
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