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Java example source code file (BaseAbstractMultivariateOptimizer.java)

This example Java source code file (BaseAbstractMultivariateOptimizer.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

baseabstractmultivariateoptimizer, convergencechecker, deprecated, func, goaltype, incrementor, initialguess, multivariatefunction, numberistoolargeexception, numberistoosmallexception, optimizationdata, pointvaluepair, simplebounds, toomanyevaluationsexception

The BaseAbstractMultivariateOptimizer.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.direct;

import org.apache.commons.math3.util.Incrementor;
import org.apache.commons.math3.exception.MaxCountExceededException;
import org.apache.commons.math3.exception.TooManyEvaluationsException;
import org.apache.commons.math3.analysis.MultivariateFunction;
import org.apache.commons.math3.optimization.BaseMultivariateOptimizer;
import org.apache.commons.math3.optimization.OptimizationData;
import org.apache.commons.math3.optimization.GoalType;
import org.apache.commons.math3.optimization.InitialGuess;
import org.apache.commons.math3.optimization.SimpleBounds;
import org.apache.commons.math3.optimization.ConvergenceChecker;
import org.apache.commons.math3.optimization.PointValuePair;
import org.apache.commons.math3.optimization.SimpleValueChecker;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.exception.NumberIsTooLargeException;

/**
 * Base class for implementing optimizers for multivariate scalar functions.
 * This base class handles the boiler-plate methods associated to thresholds,
 * evaluations counting, initial guess and simple bounds settings.
 *
 * @param <FUNC> Type of the objective function to be optimized.
 *
 * @deprecated As of 3.1 (to be removed in 4.0).
 * @since 2.2
 */
@Deprecated
public abstract class BaseAbstractMultivariateOptimizer<FUNC extends MultivariateFunction>
    implements BaseMultivariateOptimizer<FUNC> {
    /** Evaluations counter. */
    protected final Incrementor evaluations = new Incrementor();
    /** Convergence checker. */
    private ConvergenceChecker<PointValuePair> checker;
    /** Type of optimization. */
    private GoalType goal;
    /** Initial guess. */
    private double[] start;
    /** Lower bounds. */
    private double[] lowerBound;
    /** Upper bounds. */
    private double[] upperBound;
    /** Objective function. */
    private MultivariateFunction function;

    /**
     * Simple constructor with default settings.
     * The convergence check is set to a {@link SimpleValueChecker}.
     * @deprecated See {@link SimpleValueChecker#SimpleValueChecker()}
     */
    @Deprecated
    protected BaseAbstractMultivariateOptimizer() {
        this(new SimpleValueChecker());
    }
    /**
     * @param checker Convergence checker.
     */
    protected BaseAbstractMultivariateOptimizer(ConvergenceChecker<PointValuePair> checker) {
        this.checker = checker;
    }

    /** {@inheritDoc} */
    public int getMaxEvaluations() {
        return evaluations.getMaximalCount();
    }

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

    /** {@inheritDoc} */
    public ConvergenceChecker<PointValuePair> getConvergenceChecker() {
        return checker;
    }

    /**
     * Compute the objective function value.
     *
     * @param point Point at which the objective function must be evaluated.
     * @return the objective function value at the specified point.
     * @throws TooManyEvaluationsException if the maximal number of
     * evaluations is exceeded.
     */
    protected double computeObjectiveValue(double[] point) {
        try {
            evaluations.incrementCount();
        } catch (MaxCountExceededException e) {
            throw new TooManyEvaluationsException(e.getMax());
        }
        return function.value(point);
    }

    /**
     * {@inheritDoc}
     *
     * @deprecated As of 3.1. Please use
     * {@link #optimize(int,MultivariateFunction,GoalType,OptimizationData[])}
     * instead.
     */
    @Deprecated
    public PointValuePair optimize(int maxEval, FUNC f, GoalType goalType,
                                   double[] startPoint) {
        return optimizeInternal(maxEval, f, goalType, new InitialGuess(startPoint));
    }

    /**
     * Optimize an objective function.
     *
     * @param maxEval Allowed number of evaluations of the objective function.
     * @param f Objective function.
     * @param goalType Optimization type.
     * @param optData Optimization data. The following data will be looked for:
     * <ul>
     *  <li>{@link InitialGuess}
     *  <li>{@link SimpleBounds}
     * </ul>
     * @return the point/value pair giving the optimal value of the objective
     * function.
     * @since 3.1
     */
    public PointValuePair optimize(int maxEval,
                                   FUNC f,
                                   GoalType goalType,
                                   OptimizationData... optData) {
        return optimizeInternal(maxEval, f, goalType, optData);
    }

    /**
     * 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}.
     * @deprecated As of 3.1. Please use
     * {@link #optimize(int,MultivariateFunction,GoalType,OptimizationData[])}
     * instead.
     */
    @Deprecated
    protected PointValuePair optimizeInternal(int maxEval, FUNC f, GoalType goalType,
                                              double[] startPoint) {
        return optimizeInternal(maxEval, f, goalType, new InitialGuess(startPoint));
    }

    /**
     * Optimize an objective function.
     *
     * @param maxEval Allowed number of evaluations of the objective function.
     * @param f Objective function.
     * @param goalType Optimization type.
     * @param optData Optimization data. The following data will be looked for:
     * <ul>
     *  <li>{@link InitialGuess}
     *  <li>{@link SimpleBounds}
     * </ul>
     * @return the point/value pair giving the optimal value of the objective
     * function.
     * @throws TooManyEvaluationsException if the maximal number of
     * evaluations is exceeded.
     * @since 3.1
     */
    protected PointValuePair optimizeInternal(int maxEval,
                                              FUNC f,
                                              GoalType goalType,
                                              OptimizationData... optData)
        throws TooManyEvaluationsException {
        // Set internal state.
        evaluations.setMaximalCount(maxEval);
        evaluations.resetCount();
        function = f;
        goal = goalType;
        // Retrieve other settings.
        parseOptimizationData(optData);
        // Check input consistency.
        checkParameters();
        // Perform computation.
        return doOptimize();
    }

    /**
     * Scans the list of (required and optional) optimization data that
     * characterize the problem.
     *
     * @param optData Optimization data. The following data will be looked for:
     * <ul>
     *  <li>{@link InitialGuess}
     *  <li>{@link SimpleBounds}
     * </ul>
     */
    private void parseOptimizationData(OptimizationData... optData) {
        // The existing values (as set by the previous call) are reused if
        // not provided in the argument list.
        for (OptimizationData data : optData) {
            if (data instanceof InitialGuess) {
                start = ((InitialGuess) data).getInitialGuess();
                continue;
            }
            if (data instanceof SimpleBounds) {
                final SimpleBounds bounds = (SimpleBounds) data;
                lowerBound = bounds.getLower();
                upperBound = bounds.getUpper();
                continue;
            }
        }
    }

    /**
     * @return the optimization type.
     */
    public GoalType getGoalType() {
        return goal;
    }

    /**
     * @return the initial guess.
     */
    public double[] getStartPoint() {
        return start == null ? null : start.clone();
    }
    /**
     * @return the lower bounds.
     * @since 3.1
     */
    public double[] getLowerBound() {
        return lowerBound == null ? null : lowerBound.clone();
    }
    /**
     * @return the upper bounds.
     * @since 3.1
     */
    public double[] getUpperBound() {
        return upperBound == null ? null : upperBound.clone();
    }

    /**
     * Perform the bulk of the optimization algorithm.
     *
     * @return the point/value pair giving the optimal value of the
     * objective function.
     */
    protected abstract PointValuePair doOptimize();

    /**
     * Check parameters consistency.
     */
    private void checkParameters() {
        if (start != null) {
            final int dim = start.length;
            if (lowerBound != null) {
                if (lowerBound.length != dim) {
                    throw new DimensionMismatchException(lowerBound.length, dim);
                }
                for (int i = 0; i < dim; i++) {
                    final double v = start[i];
                    final double lo = lowerBound[i];
                    if (v < lo) {
                        throw new NumberIsTooSmallException(v, lo, true);
                    }
                }
            }
            if (upperBound != null) {
                if (upperBound.length != dim) {
                    throw new DimensionMismatchException(upperBound.length, dim);
                }
                for (int i = 0; i < dim; i++) {
                    final double v = start[i];
                    final double hi = upperBound[i];
                    if (v > hi) {
                        throw new NumberIsTooLargeException(v, hi, true);
                    }
                }
            }

            // If the bounds were not specified, the allowed interval is
            // assumed to be [-inf, +inf].
            if (lowerBound == null) {
                lowerBound = new double[dim];
                for (int i = 0; i < dim; i++) {
                    lowerBound[i] = Double.NEGATIVE_INFINITY;
                }
            }
            if (upperBound == null) {
                upperBound = new double[dim];
                for (int i = 0; i < dim; i++) {
                    upperBound[i] = Double.POSITIVE_INFINITY;
                }
            }
        }
    }
}

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