home | career | drupal | java | mac | mysql | perl | scala | uml | unix  

Java example source code file (MullerSolver.java)

This example Java source code file (MullerSolver.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

abstractunivariatesolver, default_absolute_accuracy, mullersolver, nobracketingexception, numberistoolargeexception, override, toomanyevaluationsexception

The MullerSolver.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.analysis.solvers;

import org.apache.commons.math3.exception.NoBracketingException;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
import org.apache.commons.math3.exception.TooManyEvaluationsException;
import org.apache.commons.math3.util.FastMath;

/**
 * This class implements the <a href="http://mathworld.wolfram.com/MullersMethod.html">
 * Muller's Method</a> for root finding of real univariate functions. For
 * reference, see <b>Elementary Numerical Analysis, ISBN 0070124477,
 * chapter 3.
 * <p>
 * Muller's method applies to both real and complex functions, but here we
 * restrict ourselves to real functions.
 * This class differs from {@link MullerSolver} in the way it avoids complex
 * operations.</p>

* Muller's original method would have function evaluation at complex point. * Since our f(x) is real, we have to find ways to avoid that. Bracketing * condition is one way to go: by requiring bracketing in every iteration, * the newly computed approximation is guaranteed to be real.</p> * <p> * Normally Muller's method converges quadratically in the vicinity of a * zero, however it may be very slow in regions far away from zeros. For * example, f(x) = exp(x) - 1, min = -50, max = 100. In such case we use * bisection as a safety backup if it performs very poorly.</p> * <p> * The formulas here use divided differences directly.</p> * * @since 1.2 * @see MullerSolver2 */ public class MullerSolver extends AbstractUnivariateSolver { /** Default absolute accuracy. */ private static final double DEFAULT_ABSOLUTE_ACCURACY = 1e-6; /** * Construct a solver with default accuracy (1e-6). */ public MullerSolver() { this(DEFAULT_ABSOLUTE_ACCURACY); } /** * Construct a solver. * * @param absoluteAccuracy Absolute accuracy. */ public MullerSolver(double absoluteAccuracy) { super(absoluteAccuracy); } /** * Construct a solver. * * @param relativeAccuracy Relative accuracy. * @param absoluteAccuracy Absolute accuracy. */ public MullerSolver(double relativeAccuracy, double absoluteAccuracy) { super(relativeAccuracy, absoluteAccuracy); } /** * {@inheritDoc} */ @Override protected double doSolve() throws TooManyEvaluationsException, NumberIsTooLargeException, NoBracketingException { final double min = getMin(); final double max = getMax(); final double initial = getStartValue(); final double functionValueAccuracy = getFunctionValueAccuracy(); verifySequence(min, initial, max); // check for zeros before verifying bracketing final double fMin = computeObjectiveValue(min); if (FastMath.abs(fMin) < functionValueAccuracy) { return min; } final double fMax = computeObjectiveValue(max); if (FastMath.abs(fMax) < functionValueAccuracy) { return max; } final double fInitial = computeObjectiveValue(initial); if (FastMath.abs(fInitial) < functionValueAccuracy) { return initial; } verifyBracketing(min, max); if (isBracketing(min, initial)) { return solve(min, initial, fMin, fInitial); } else { return solve(initial, max, fInitial, fMax); } } /** * Find a real root in the given interval. * * @param min Lower bound for the interval. * @param max Upper bound for the interval. * @param fMin function value at the lower bound. * @param fMax function value at the upper bound. * @return the point at which the function value is zero. * @throws TooManyEvaluationsException if the allowed number of calls to * the function to be solved has been exhausted. */ private double solve(double min, double max, double fMin, double fMax) throws TooManyEvaluationsException { final double relativeAccuracy = getRelativeAccuracy(); final double absoluteAccuracy = getAbsoluteAccuracy(); final double functionValueAccuracy = getFunctionValueAccuracy(); // [x0, x2] is the bracketing interval in each iteration // x1 is the last approximation and an interpolation point in (x0, x2) // x is the new root approximation and new x1 for next round // d01, d12, d012 are divided differences double x0 = min; double y0 = fMin; double x2 = max; double y2 = fMax; double x1 = 0.5 * (x0 + x2); double y1 = computeObjectiveValue(x1); double oldx = Double.POSITIVE_INFINITY; while (true) { // Muller's method employs quadratic interpolation through // x0, x1, x2 and x is the zero of the interpolating parabola. // Due to bracketing condition, this parabola must have two // real roots and we choose one in [x0, x2] to be x. final double d01 = (y1 - y0) / (x1 - x0); final double d12 = (y2 - y1) / (x2 - x1); final double d012 = (d12 - d01) / (x2 - x0); final double c1 = d01 + (x1 - x0) * d012; final double delta = c1 * c1 - 4 * y1 * d012; final double xplus = x1 + (-2.0 * y1) / (c1 + FastMath.sqrt(delta)); final double xminus = x1 + (-2.0 * y1) / (c1 - FastMath.sqrt(delta)); // xplus and xminus are two roots of parabola and at least // one of them should lie in (x0, x2) final double x = isSequence(x0, xplus, x2) ? xplus : xminus; final double y = computeObjectiveValue(x); // check for convergence final double tolerance = FastMath.max(relativeAccuracy * FastMath.abs(x), absoluteAccuracy); if (FastMath.abs(x - oldx) <= tolerance || FastMath.abs(y) <= functionValueAccuracy) { return x; } // Bisect if convergence is too slow. Bisection would waste // our calculation of x, hopefully it won't happen often. // the real number equality test x == x1 is intentional and // completes the proximity tests above it boolean bisect = (x < x1 && (x1 - x0) > 0.95 * (x2 - x0)) || (x > x1 && (x2 - x1) > 0.95 * (x2 - x0)) || (x == x1); // prepare the new bracketing interval for next iteration if (!bisect) { x0 = x < x1 ? x0 : x1; y0 = x < x1 ? y0 : y1; x2 = x > x1 ? x2 : x1; y2 = x > x1 ? y2 : y1; x1 = x; y1 = y; oldx = x; } else { double xm = 0.5 * (x0 + x2); double ym = computeObjectiveValue(xm); if (FastMath.signum(y0) + FastMath.signum(ym) == 0.0) { x2 = xm; y2 = ym; } else { x0 = xm; y0 = ym; } x1 = 0.5 * (x0 + x2); y1 = computeObjectiveValue(x1); oldx = Double.POSITIVE_INFINITY; } } } }

Other Java examples (source code examples)

Here is a short list of links related to this Java MullerSolver.java source code file:



my book on functional programming

 

new blog posts

 

Copyright 1998-2021 Alvin Alexander, alvinalexander.com
All Rights Reserved.

A percentage of advertising revenue from
pages under the /java/jwarehouse URI on this website is
paid back to open source projects.