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Java example source code file (MultivariateFunctionPenaltyAdapter.java)

This example Java source code file (MultivariateFunctionPenaltyAdapter.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.

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Java - Java tags/keywords

deprecated, multivariatefunction, multivariatefunctionpenaltyadapter, numberistoosmallexception

The MultivariateFunctionPenaltyAdapter.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,
 * 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.analysis.MultivariateFunction;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.MathUtils;

 * <p>Adapter extending bounded {@link MultivariateFunction} to an unbouded
 * domain using a penalty function.</p>
 * <p>
 * This adapter can be used to wrap functions subject to simple bounds on
 * parameters so they can be used by optimizers that do <em>not directly
 * support simple bounds.
 * </p>
 * <p>
 * The principle is that the user function that will be wrapped will see its
 * parameters bounded as required, i.e when its {@code value} method is called
 * with argument array {@code point}, the elements array will fulfill requirement
 * {@code lower[i] <= point[i] <= upper[i]} for all i. Some of the components
 * may be unbounded or bounded only on one side if the corresponding bound is
 * set to an infinite value. The optimizer will not manage the user function by
 * itself, but it will handle this adapter and it is this adapter that will take
 * care the bounds are fulfilled. The adapter {@link #value(double[])} method will
 * be called by the optimizer with unbound parameters, and the adapter will check
 * if the parameters is within range or not. If it is in range, then the underlying
 * user function will be called, and if it is not the value of a penalty function
 * will be returned instead.
 * </p>
 * <p>
 * This adapter is only a poor man solution to simple bounds optimization constraints
 * that can be used with simple optimizers like {@link SimplexOptimizer} with {@link
 * NelderMeadSimplex} or {@link MultiDirectionalSimplex}. A better solution is to use
 * an optimizer that directly supports simple bounds like {@link CMAESOptimizer} or
 * {@link BOBYQAOptimizer}. One caveat of this poor man solution is that if start point
 * or start simplex is completely outside of the allowed range, only the penalty function
 * is used, and the optimizer may converge without ever entering the range.
 * </p>
 * @see MultivariateFunctionMappingAdapter
 * @deprecated As of 3.1 (to be removed in 4.0).
 * @since 3.0

public class MultivariateFunctionPenaltyAdapter implements MultivariateFunction {

    /** Underlying bounded function. */
    private final MultivariateFunction bounded;

    /** Lower bounds. */
    private final double[] lower;

    /** Upper bounds. */
    private final double[] upper;

    /** Penalty offset. */
    private final double offset;

    /** Penalty scales. */
    private final double[] scale;

    /** Simple constructor.
     * <p>
     * When the optimizer provided points are out of range, the value of the
     * penalty function will be used instead of the value of the underlying
     * function. In order for this penalty to be effective in rejecting this
     * point during the optimization process, the penalty function value should
     * be defined with care. This value is computed as:
     * <pre>
     *   penalty(point) = offset + ∑<sub>i[scale[i] * √|point[i]-boundary[i]|]
     * </pre>
     * where indices i correspond to all the components that violates their boundaries.
     * </p>
     * <p>
     * So when attempting a function minimization, offset should be larger than
     * the maximum expected value of the underlying function and scale components
     * should all be positive. When attempting a function maximization, offset
     * should be lesser than the minimum expected value of the underlying function
     * and scale components should all be negative.
     * minimization, and lesser than the minimum expected value of the underlying
     * function when attempting maximization.
     * </p>
     * <p>
     * These choices for the penalty function have two properties. First, all out
     * of range points will return a function value that is worse than the value
     * returned by any in range point. Second, the penalty is worse for large
     * boundaries violation than for small violations, so the optimizer has an hint
     * about the direction in which it should search for acceptable points.
     * </p>
     * @param bounded bounded function
     * @param lower lower bounds for each element of the input parameters array
     * (some elements may be set to {@code Double.NEGATIVE_INFINITY} for
     * unbounded values)
     * @param upper upper bounds for each element of the input parameters array
     * (some elements may be set to {@code Double.POSITIVE_INFINITY} for
     * unbounded values)
     * @param offset base offset of the penalty function
     * @param scale scale of the penalty function
     * @exception DimensionMismatchException if lower bounds, upper bounds and
     * scales are not consistent, either according to dimension or to bounadary
     * values
    public MultivariateFunctionPenaltyAdapter(final MultivariateFunction bounded,
                                                  final double[] lower, final double[] upper,
                                                  final double offset, final double[] scale) {

        // safety checks
        if (lower.length != upper.length) {
            throw new DimensionMismatchException(lower.length, upper.length);
        if (lower.length != scale.length) {
            throw new DimensionMismatchException(lower.length, scale.length);
        for (int i = 0; i < lower.length; ++i) {
            // note the following test is written in such a way it also fails for NaN
            if (!(upper[i] >= lower[i])) {
                throw new NumberIsTooSmallException(upper[i], lower[i], true);

        this.bounded = bounded;
        this.lower   = lower.clone();
        this.upper   = upper.clone();
        this.offset  = offset;
        this.scale   = scale.clone();


    /** Compute the underlying function value from an unbounded point.
     * <p>
     * This method simply returns the value of the underlying function
     * if the unbounded point already fulfills the bounds, and compute
     * a replacement value using the offset and scale if bounds are
     * violated, without calling the function at all.
     * </p>
     * @param point unbounded point
     * @return either underlying function value or penalty function value
    public double value(double[] point) {

        for (int i = 0; i < scale.length; ++i) {
            if ((point[i] < lower[i]) || (point[i] > upper[i])) {
                // bound violation starting at this component
                double sum = 0;
                for (int j = i; j < scale.length; ++j) {
                    final double overshoot;
                    if (point[j] < lower[j]) {
                        overshoot = scale[j] * (lower[j] - point[j]);
                    } else if (point[j] > upper[j]) {
                        overshoot = scale[j] * (point[j] - upper[j]);
                    } else {
                        overshoot = 0;
                    sum += FastMath.sqrt(overshoot);
                return offset + sum;

        // all boundaries are fulfilled, we are in the expected
        // domain of the underlying function
        return bounded.value(point);



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