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Java example source code file (NewtonSolver.java)

This example Java source code file (NewtonSolver.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

abstractdifferentiableunivariatesolver, default_absolute_accuracy, deprecated, differentiableunivariatefunction, newtonsolver, override, toomanyevaluationsexception

The NewtonSolver.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.analysis.solvers;

import org.apache.commons.math3.analysis.DifferentiableUnivariateFunction;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.exception.TooManyEvaluationsException;

 * Implements <a href="http://mathworld.wolfram.com/NewtonsMethod.html">
 * Newton's Method</a> for finding zeros of real univariate functions.
 * <p>
 * The function should be continuous but not necessarily smooth.</p>
 * @deprecated as of 3.1, replaced by {@link NewtonRaphsonSolver}
public class NewtonSolver extends AbstractDifferentiableUnivariateSolver {
    /** Default absolute accuracy. */
    private static final double DEFAULT_ABSOLUTE_ACCURACY = 1e-6;

     * Construct a solver.
    public NewtonSolver() {
     * Construct a solver.
     * @param absoluteAccuracy Absolute accuracy.
    public NewtonSolver(double absoluteAccuracy) {

     * Find a zero near the midpoint of {@code min} and {@code max}.
     * @param f Function to solve.
     * @param min Lower bound for the interval.
     * @param max Upper bound for the interval.
     * @param maxEval Maximum number of evaluations.
     * @return the value where the function is zero.
     * @throws org.apache.commons.math3.exception.TooManyEvaluationsException
     * if the maximum evaluation count is exceeded.
     * @throws org.apache.commons.math3.exception.NumberIsTooLargeException
     * if {@code min >= max}.
    public double solve(int maxEval, final DifferentiableUnivariateFunction f,
                        final double min, final double max)
        throws TooManyEvaluationsException {
        return super.solve(maxEval, f, UnivariateSolverUtils.midpoint(min, max));

     * {@inheritDoc}
    protected double doSolve()
        throws TooManyEvaluationsException {
        final double startValue = getStartValue();
        final double absoluteAccuracy = getAbsoluteAccuracy();

        double x0 = startValue;
        double x1;
        while (true) {
            x1 = x0 - (computeObjectiveValue(x0) / computeDerivativeObjectiveValue(x0));
            if (FastMath.abs(x1 - x0) <= absoluteAccuracy) {
                return x1;

            x0 = x1;

Other Java examples (source code examples)

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my book on functional programming


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