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Java example source code file (AbstractScalarDifferentiableOptimizer.java)

This example Java source code file (AbstractScalarDifferentiableOptimizer.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

abstractscalardifferentiableoptimizer, baseabstractmultivariateoptimizer, deprecated, differentiablemultivariatefunction, differentiablemultivariateoptimizer, goaltype, multivariatedifferentiablefunction, multivariatevectorfunction, override, pointvaluepair

The AbstractScalarDifferentiableOptimizer.java Java example source code

/*
 * 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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