001/*
002 * Licensed to the Apache Software Foundation (ASF) under one or more
003 * contributor license agreements.  See the NOTICE file distributed with
004 * this work for additional information regarding copyright ownership.
005 * The ASF licenses this file to You under the Apache License, Version 2.0
006 * (the "License"); you may not use this file except in compliance with
007 * the License.  You may obtain a copy of the License at
008 *
009 *      http://www.apache.org/licenses/LICENSE-2.0
010 *
011 * Unless required by applicable law or agreed to in writing, software
012 * distributed under the License is distributed on an "AS IS" BASIS,
013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014 * See the License for the specific language governing permissions and
015 * limitations under the License.
016 */
017
018package org.apache.commons.math3.optimization.general;
019
020import org.apache.commons.math3.analysis.DifferentiableMultivariateFunction;
021import org.apache.commons.math3.analysis.MultivariateVectorFunction;
022import org.apache.commons.math3.analysis.FunctionUtils;
023import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableFunction;
024import org.apache.commons.math3.optimization.DifferentiableMultivariateOptimizer;
025import org.apache.commons.math3.optimization.GoalType;
026import org.apache.commons.math3.optimization.ConvergenceChecker;
027import org.apache.commons.math3.optimization.PointValuePair;
028import org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer;
029
030/**
031 * Base class for implementing optimizers for multivariate scalar
032 * differentiable functions.
033 * It contains boiler-plate code for dealing with gradient evaluation.
034 *
035 * @version $Id: AbstractScalarDifferentiableOptimizer.java 1422230 2012-12-15 12:11:13Z erans $
036 * @deprecated As of 3.1 (to be removed in 4.0).
037 * @since 2.0
038 */
039@Deprecated
040public abstract class AbstractScalarDifferentiableOptimizer
041    extends BaseAbstractMultivariateOptimizer<DifferentiableMultivariateFunction>
042    implements DifferentiableMultivariateOptimizer {
043    /**
044     * Objective function gradient.
045     */
046    private MultivariateVectorFunction gradient;
047
048    /**
049     * Simple constructor with default settings.
050     * The convergence check is set to a
051     * {@link org.apache.commons.math3.optimization.SimpleValueChecker
052     * SimpleValueChecker}.
053     * @deprecated See {@link org.apache.commons.math3.optimization.SimpleValueChecker#SimpleValueChecker()}
054     */
055    @Deprecated
056    protected AbstractScalarDifferentiableOptimizer() {}
057
058    /**
059     * @param checker Convergence checker.
060     */
061    protected AbstractScalarDifferentiableOptimizer(ConvergenceChecker<PointValuePair> checker) {
062        super(checker);
063    }
064
065    /**
066     * Compute the gradient vector.
067     *
068     * @param evaluationPoint Point at which the gradient must be evaluated.
069     * @return the gradient at the specified point.
070     * @throws org.apache.commons.math3.exception.TooManyEvaluationsException
071     * if the allowed number of evaluations is exceeded.
072     */
073    protected double[] computeObjectiveGradient(final double[] evaluationPoint) {
074        return gradient.value(evaluationPoint);
075    }
076
077    /** {@inheritDoc} */
078    @Override
079    protected PointValuePair optimizeInternal(int maxEval,
080                                              final DifferentiableMultivariateFunction f,
081                                              final GoalType goalType,
082                                              final double[] startPoint) {
083        // Store optimization problem characteristics.
084        gradient = f.gradient();
085
086        return super.optimizeInternal(maxEval, f, goalType, startPoint);
087    }
088
089    /**
090     * Optimize an objective function.
091     *
092     * @param f Objective function.
093     * @param goalType Type of optimization goal: either
094     * {@link GoalType#MAXIMIZE} or {@link GoalType#MINIMIZE}.
095     * @param startPoint Start point for optimization.
096     * @param maxEval Maximum number of function evaluations.
097     * @return the point/value pair giving the optimal value for objective
098     * function.
099     * @throws org.apache.commons.math3.exception.DimensionMismatchException
100     * if the start point dimension is wrong.
101     * @throws org.apache.commons.math3.exception.TooManyEvaluationsException
102     * if the maximal number of evaluations is exceeded.
103     * @throws org.apache.commons.math3.exception.NullArgumentException if
104     * any argument is {@code null}.
105     */
106    public PointValuePair optimize(final int maxEval,
107                                   final MultivariateDifferentiableFunction f,
108                                   final GoalType goalType,
109                                   final double[] startPoint) {
110        return optimizeInternal(maxEval,
111                                FunctionUtils.toDifferentiableMultivariateFunction(f),
112                                goalType,
113                                startPoint);
114    }
115}