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Java example source code file (AbstractDifferentiableOptimizer.java)

This example Java source code file (AbstractDifferentiableOptimizer.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

abstractdifferentiableoptimizer, baseabstractmultivariateoptimizer, goaltype, gradientfunction, initialguess, multivariatedifferentiablefunction, multivariatevectorfunction, optimizationdata, override@deprecated, pointvaluepair

The AbstractDifferentiableOptimizer.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.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);
    }
}

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