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Commons Math example source code file (AbstractScalarDifferentiableOptimizer.java)

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

Java - Commons Math tags/keywords

abstractscalardifferentiableoptimizer, differentiablemultivariaterealfunction, functionevaluationexception, functionevaluationexception, goaltype, illegalargumentexception, maxevaluationsexceededexception, multivariatevectorialfunction, optimizationexception, optimizationexception, realconvergencechecker, realconvergencechecker, realpointvaluepair, realpointvaluepair

The Commons Math AbstractScalarDifferentiableOptimizer.java 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.math.optimization.general;

import org.apache.commons.math.FunctionEvaluationException;
import org.apache.commons.math.MaxEvaluationsExceededException;
import org.apache.commons.math.MaxIterationsExceededException;
import org.apache.commons.math.analysis.DifferentiableMultivariateRealFunction;
import org.apache.commons.math.analysis.MultivariateVectorialFunction;
import org.apache.commons.math.optimization.GoalType;
import org.apache.commons.math.optimization.OptimizationException;
import org.apache.commons.math.optimization.RealConvergenceChecker;
import org.apache.commons.math.optimization.DifferentiableMultivariateRealOptimizer;
import org.apache.commons.math.optimization.RealPointValuePair;
import org.apache.commons.math.optimization.SimpleScalarValueChecker;

/**
 * Base class for implementing optimizers for multivariate scalar functions.
 * <p>This base class handles the boilerplate methods associated to thresholds
 * settings, iterations and evaluations counting.</p>
 * @version $Revision: 925812 $ $Date: 2010-03-21 11:49:31 -0400 (Sun, 21 Mar 2010) $
 * @since 2.0
 */
public abstract class AbstractScalarDifferentiableOptimizer
    implements DifferentiableMultivariateRealOptimizer {

    /** Default maximal number of iterations allowed. */
    public static final int DEFAULT_MAX_ITERATIONS = 100;

    /** Convergence checker. */
    protected RealConvergenceChecker checker;

    /**
     * Type of optimization.
     * @since 2.1
     */
    protected GoalType goal;

    /** Current point set. */
    protected double[] point;

    /** Maximal number of iterations allowed. */
    private int maxIterations;

    /** Number of iterations already performed. */
    private int iterations;

    /** Maximal number of evaluations allowed. */
    private int maxEvaluations;

    /** Number of evaluations already performed. */
    private int evaluations;

    /** Number of gradient evaluations. */
    private int gradientEvaluations;

    /** Objective function. */
    private DifferentiableMultivariateRealFunction function;

    /** Objective function gradient. */
    private MultivariateVectorialFunction gradient;

    /** Simple constructor with default settings.
     * <p>The convergence check is set to a {@link SimpleScalarValueChecker}
     * and the maximal number of evaluation is set to its default value.</p>
     */
    protected AbstractScalarDifferentiableOptimizer() {
        setConvergenceChecker(new SimpleScalarValueChecker());
        setMaxIterations(DEFAULT_MAX_ITERATIONS);
        setMaxEvaluations(Integer.MAX_VALUE);
    }

    /** {@inheritDoc} */
    public void setMaxIterations(int maxIterations) {
        this.maxIterations = maxIterations;
    }

    /** {@inheritDoc} */
    public int getMaxIterations() {
        return maxIterations;
    }

    /** {@inheritDoc} */
    public int getIterations() {
        return iterations;
    }

    /** {@inheritDoc} */
    public void setMaxEvaluations(int maxEvaluations) {
        this.maxEvaluations = maxEvaluations;
    }

    /** {@inheritDoc} */
    public int getMaxEvaluations() {
        return maxEvaluations;
    }

    /** {@inheritDoc} */
    public int getEvaluations() {
        return evaluations;
    }

    /** {@inheritDoc} */
    public int getGradientEvaluations() {
        return gradientEvaluations;
    }

    /** {@inheritDoc} */
    public void setConvergenceChecker(RealConvergenceChecker convergenceChecker) {
        this.checker = convergenceChecker;
    }

    /** {@inheritDoc} */
    public RealConvergenceChecker getConvergenceChecker() {
        return checker;
    }

    /** Increment the iterations counter by 1.
     * @exception OptimizationException if the maximal number
     * of iterations is exceeded
     */
    protected void incrementIterationsCounter()
        throws OptimizationException {
        if (++iterations > maxIterations) {
            throw new OptimizationException(new MaxIterationsExceededException(maxIterations));
        }
    }

    /**
     * Compute the gradient vector.
     * @param evaluationPoint point at which the gradient must be evaluated
     * @return gradient at the specified point
     * @exception FunctionEvaluationException if the function gradient
     */
    protected double[] computeObjectiveGradient(final double[] evaluationPoint)
        throws FunctionEvaluationException {
        ++gradientEvaluations;
        return gradient.value(evaluationPoint);
    }

    /**
     * Compute the objective function value.
     * @param evaluationPoint point at which the objective function must be evaluated
     * @return objective function value at specified point
     * @exception FunctionEvaluationException if the function cannot be evaluated
     * or its dimension doesn't match problem dimension or the maximal number
     * of iterations is exceeded
     */
    protected double computeObjectiveValue(final double[] evaluationPoint)
        throws FunctionEvaluationException {
        if (++evaluations > maxEvaluations) {
            throw new FunctionEvaluationException(new MaxEvaluationsExceededException(maxEvaluations),
                                                  evaluationPoint);
        }
        return function.value(evaluationPoint);
    }

    /** {@inheritDoc} */
    public RealPointValuePair optimize(final DifferentiableMultivariateRealFunction f,
                                         final GoalType goalType,
                                         final double[] startPoint)
        throws FunctionEvaluationException, OptimizationException, IllegalArgumentException {

        // reset counters
        iterations          = 0;
        evaluations         = 0;
        gradientEvaluations = 0;

        // store optimization problem characteristics
        function = f;
        gradient = f.gradient();
        goal     = goalType;
        point    = startPoint.clone();

        return doOptimize();

    }

    /** Perform the bulk of optimization algorithm.
     * @return the point/value pair giving the optimal value for objective function
     * @exception FunctionEvaluationException if the objective function throws one during
     * the search
     * @exception OptimizationException if the algorithm failed to converge
     * @exception IllegalArgumentException if the start point dimension is wrong
     */
    protected abstract RealPointValuePair doOptimize()
        throws FunctionEvaluationException, OptimizationException, IllegalArgumentException;

}

Other Commons Math examples (source code examples)

Here is a short list of links related to this Commons Math AbstractScalarDifferentiableOptimizer.java source code file:

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