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Spencer Ogden >
Math-Function-Roots-0.065 >
Math::Function::Roots

Module Version: 0.065
Math::Function::Roots - Functions for finding roots of arbitrary functions

Version 0.065

This is a collection of functions (in the perl sense) to find the root of arbitrary functions (in the mathmatical sense). The Functions take a sub reference and a range or guess of the answer and return the root of the function.

use Math::Function::Roots qw(bisection epsilon max_iter); epsilon(0); # Set desired accuracy max_iter(50_000) # Put cap on runtime # Find the root of 2x+1 in the range (-5,5) my $result = bisection( sub {2*shift()+1}, -5, 5); # $result == -.5 # Alternative method of setting epsilon and max_iter my $result2 = bisection( sub {2*shift()+1}, -5, 5, epsilon=>.00001, max_iter=>20);

Numerical Analysis is the method of using algorithms, often iterative, to approximate the solution to a problem to which finding an exact solution would be difficult. Root Finding Algorithms are used to find the root of functions. They deal with continuous mathematical functions (one unique value of f(x) for every x). A root is anywhere the function evaluates to zero, i.e. where it crosses the x-axis. Different algortihms have different capacity for finding multiple roots, many can only find one root.

But enough of that, if you are here you probably know what a root finding algorithm is. I have begun implementing the following algorithms, which are described in detail below. The basic outline is algorithm( function, guess). Each function below is named after the underlying algorithm.

All of the algorithms have similar parameters, so I will describe them once. Always mandatory is the function we are finding the root of.

Functions are passed as code references. These can take the form of "\&Function" or "sub{...}". Simple function can be inlined with sub{}, with more complicated functions taking the reference is recommended.

# f(x) = 2x - 4 # sub{ 2*shift() - 4 used as my $root = bisection( sub{ 2*shift() - 4 }, -10, 10 );

Often you will have a function of multiple variables. This can be done with a wrapper function, such as:

sub foo{ my ($x1,$x2) = @_; return $x1**2 + $x2**2; } sub wrapper{ my $x2 = shift; return foo( 5, $x2 ); } my $result = bisection( \&wrapper, -10, 10 );

Whatever subroutine is passed, it will be called with one argument, and is expected to return a single result. Functions not fitting that description will need a wrapper.

This will find the root of f(x) = 5**2 + x**2. Different algorithms react differently to certain functions. There is some advice below on good algorithms for certain types of functions.

Most algorithms require an initial range or guesses. If referred to as 'guesses' then the solution (root) need not be in the range [guess1,guess2]. If a range or min and max are required, then to solution **must** lie within [min,max].

Epsilon (*e*) is used to set the desired accuracy. Less accurate answers take fewer iterations and are therefore quicker to compute. In general *e* referres to the maximum distance from the given solution to the actual solution. If you need 6 decimals of accuracy, then *e* = .000_000_1 is appropriate, this is the default. Calculating a few decimals beyond what you need is generally recommended to prevent compounding rounding errors. *epsilon* is a named parameter to set *e* for that particular run of the algorithm, it should always follow mandatory parameters:

my $result = bisection( sub{...}, -10, 10, epsilon => .01 );

The *epsilon*() function may be used to set *e* globally, be careful.

Similar to epsilon, the maximum number of iterations an algorithm should run for may be set with the *max_iter* named parameter, or globally with *max_iter*(i). This maximum is normally used to catch errors, i.e. when a given function doesn't converge, or when there is a bug (nah...). Do not use this to control run-time, if you need an answer faster, use a larger epsilon. The only reason to change this would be if you had a slowly converging function, and you were willing to wait for a good answer, then you could raise the maximum to allow the algorithm to continue working. Default is 5,000.

This will return the number of iterations used to find the last result. This might help to give an indication on how an algorithm performs on your data.

Below is a listing of availible algorithms. Many have restriction on the types of functions they work on, particularly the characteristics of the function near its root. Quick summary:

- bisection - Good for general purposes, you must provide a range in which one and only one root exists. Basically a binary search for the root.
- fixed_point - Only useful on a set of functions that can be converted to a fixed-point function with certain properties, see below. Fast when it can be used.
- secant - A fast converging algorithm which bases guesses on the slope of the function. Because slope is used, areas of the function where the slope is near horizontal (f'(x) == 0) should be avoided.

Uses the bisection algorithm. Average performance, but dependable. Min and max are used to specify a range which contains the root. To ensure this f(min) and f(max) must have opposite signs (meaning that there must be at least one root between them). Giving a range with multiple roots in it will not work in most cases. This method is dependable, because it does not care about the shape of the function. It is also a bit slower than som algorithms because it does not take hints from the shape.

The Fixed-Point Iteration algorithm is a fast robust method which, unfortunately, works on a limited domain of problems, and requires some algebra. The benefits are that it can converge rapidly, and the range the root is in does not need to be known, any guess will converge, eventually.

A fixed-point is where g(x) = x. The method is to find a function, g(x), which has a fixed-point where f(x) has a root. This can be done trivially by using g(x) = x - f(x). In more general cases it is done by factoring an x so that g(x) = x = ff(x), where x = ff(x) is some identity derived from f(x).

As was mentioned there is a restriction on you choice of g(x), it is that the absolute value of the derivative of g(x) must be less than 1. Or |g'(x)| < 1 (mathematical notation *is* handy sometimes). The closer g'(x) is to 0 the faster the rate of convergence.

Consider a range [a,b] which contains the fixed-point and within which |g'(x)| < 1 holds true. This might be an infinite range or a segment of the function. As long as your initial guess is within this range, the algorithm will converge.

*guess* is an approximation of the answer. The algorithm will converge regardless of the relationship of *guess* to the actual answer, just so long as *guess* is within the range [a,b].

Why go through all this hassle? Well, certain functions lend themselves to being transformed easily into fixed-point functions. Also, with a derivative near 0 the convergence is very fast, regardless of initial guess.

The secant method is a simplification of the Newton method, which uses the derivitive of the function to better predict the root of the function. The secant method uses a secant (line between two points on the function) as a substitute for knowing or calculating the derivative of the function.

As usual, provide the function, then provide two guesses. Unlike bisection, these do not need to bracket the solution. Local minimums or maximums, where the slope is near 0, are unfriendly to this algorithm. When the two guesses are near the solution however, this algorithm gives rapid convergence.

False Position is an algorithm similar to Secant, it uses secants of the function to pick better guesses. The difference is that this method incorporates the bracketing of the Bisection method, with the speed of the Secant method.

Bracketing is a desirable property because it makes the algorithm more dependable. Bracketing ensures that the algorithm will stay within the given range. This is useful with higer-order functions where you want to restrict your search to the area directly around the root.

The only restriction is that the functions derivative must not equal 0 within the range [min,max]. There must also only be one root within the range, which (as in Bisection) is ensured by requiring that f(min) and f(max) have opposite signs.

This a hybrid function which uses a combination of algorithms to find the root of the given function. Both *guess1* and *guess2* are optional. If one is provided, it is used as an approximate starting point. If both are given, then they are taken as a range, the root **must** be within this range.

It will most likely return the root nearest your guess, but no guarantees. Don't provide a range with more than one root in it, you might find one, you might not. More information will give higher performance and more control over which root is being found, but if you don't know anything about the function, give it a try without a guess. Settings from epsilon and maximum iterations apply as normal.

The first priority witll be adding more algorithms. Then it might be interesting to implement a mechanism where several algorithms could be tried on love data to choose the best algorithm for the domain. Lastly, using Inline::C or XS to rewrite the algos in C would be desirable for performance. Ideally I would like it to work so that if a C compiler is availible, then the C version is compiled and used, otherwise the Perl version is used. I've seen examples of this, but don't know how it is done at the moment, so this is a ways off.

Finish of test coverage.

Spencer Ogden, `<spencer@spencerogden.com>`

The find function is broken

Please report any bugs or feature requests to `bug-algorithm-bisection@rt.cpan.org`

, or through the web interface at http://rt.cpan.org. I will be notified, and then you'll automatically be notified of progress on your bug as I make changes.

Copyright 2005 Spencer Ogden, All Rights Reserved.

This program is free software; you can redistribute it and/or modify it under the same terms as Perl itself.

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