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Statistical sampling library for use in virtdata libraries, based
on apache commons math 4
/*
* 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.math4.analysis.solvers;
import org.apache.commons.math4.exception.NoBracketingException;
import org.apache.commons.math4.exception.NumberIsTooLargeException;
import org.apache.commons.math4.exception.TooManyEvaluationsException;
import org.apache.commons.math4.util.FastMath;
/**
* This class implements the
* Muller's Method for root finding of real univariate functions. For
* reference, see Elementary Numerical Analysis, ISBN 0070124477,
* chapter 3.
*
* 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.
* Except for the initial [min, max], it does not require bracketing
* condition, e.g. f(x0), f(x1), f(x2) can have the same sign. If a complex
* number arises in the computation, we simply use its modulus as a real
* approximation.
*
* Because the interval may not be bracketing, the bisection alternative is
* not applicable here. However in practice our treatment usually works
* well, especially near real zeroes where the imaginary part of the complex
* approximation is often negligible.
*
* The formulas here do not use divided differences directly.
*
* @since 1.2
* @see MullerSolver
*/
public class MullerSolver2 extends AbstractUnivariateSolver {
/** Default absolute accuracy. */
private static final double DEFAULT_ABSOLUTE_ACCURACY = 1e-6;
/**
* Construct a solver with default accuracy (1e-6).
*/
public MullerSolver2() {
this(DEFAULT_ABSOLUTE_ACCURACY);
}
/**
* Construct a solver.
*
* @param absoluteAccuracy Absolute accuracy.
*/
public MullerSolver2(double absoluteAccuracy) {
super(absoluteAccuracy);
}
/**
* Construct a solver.
*
* @param relativeAccuracy Relative accuracy.
* @param absoluteAccuracy Absolute accuracy.
*/
public MullerSolver2(double relativeAccuracy,
double absoluteAccuracy) {
super(relativeAccuracy, absoluteAccuracy);
}
/**
* {@inheritDoc}
*/
@Override
protected double doSolve()
throws TooManyEvaluationsException,
NumberIsTooLargeException,
NoBracketingException {
final double min = getMin();
final double max = getMax();
verifyInterval(min, max);
final double relativeAccuracy = getRelativeAccuracy();
final double absoluteAccuracy = getAbsoluteAccuracy();
final double functionValueAccuracy = getFunctionValueAccuracy();
// x2 is the last root approximation
// x is the new approximation and new x2 for next round
// x0 < x1 < x2 does not hold here
double x0 = min;
double y0 = computeObjectiveValue(x0);
if (FastMath.abs(y0) < functionValueAccuracy) {
return x0;
}
double x1 = max;
double y1 = computeObjectiveValue(x1);
if (FastMath.abs(y1) < functionValueAccuracy) {
return x1;
}
if(y0 * y1 > 0) {
throw new NoBracketingException(x0, x1, y0, y1);
}
double x2 = 0.5 * (x0 + x1);
double y2 = computeObjectiveValue(x2);
double oldx = Double.POSITIVE_INFINITY;
while (true) {
// quadratic interpolation through x0, x1, x2
final double q = (x2 - x1) / (x1 - x0);
final double a = q * (y2 - (1 + q) * y1 + q * y0);
final double b = (2 * q + 1) * y2 - (1 + q) * (1 + q) * y1 + q * q * y0;
final double c = (1 + q) * y2;
final double delta = b * b - 4 * a * c;
double x;
final double denominator;
if (delta >= 0.0) {
// choose a denominator larger in magnitude
double dplus = b + FastMath.sqrt(delta);
double dminus = b - FastMath.sqrt(delta);
denominator = FastMath.abs(dplus) > FastMath.abs(dminus) ? dplus : dminus;
} else {
// take the modulus of (B +/- FastMath.sqrt(delta))
denominator = FastMath.sqrt(b * b - delta);
}
if (denominator != 0) {
x = x2 - 2.0 * c * (x2 - x1) / denominator;
// perturb x if it exactly coincides with x1 or x2
// the equality tests here are intentional
while (x == x1 || x == x2) {
x += absoluteAccuracy;
}
} else {
// extremely rare case, get a random number to skip it
x = min + FastMath.random() * (max - min);
oldx = Double.POSITIVE_INFINITY;
}
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;
}
// prepare the next iteration
x0 = x1;
y0 = y1;
x1 = x2;
y1 = y2;
x2 = x;
y2 = y;
oldx = x;
}
}
}