Numerical analysis: Difference between revisions

Revision as of 18:52, 17 January 2010

Revision as of 05:18, 18 January 2010

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Since the late twentieth century, most algorithms are implemented in a variety of programming languages. The [[Netlib]] repository contains various collections of software routines for numerical problems, mostly in [[Fortran]] and [[C (programming language)|C]]. Commercial products implementing many different numerical algorithms include the [[IMSL Numerical Libraries|IMSL]] and [[Numerical Algorithms Group|NAG]] libraries; a free alternative is the [[GNU Scientific Library]].

There are several popular numerical computing applications such as [[MATLAB]], [[S-PLUS]], [[LabVIEW]], and [[IDL (programming language)|IDL]], and [[VisSim]], as well as free and open source alternatives such as [[FreeMat]], [[Scilab]], [[GNU Octave]] (similar to Matlab), [[IT++]] (a C++ library), [[R (programming language)|R]] (similar to S-PLUS) and certain variants of [[Python (programming language)|Python]]. Performance varies widely: while vector and matrix operations are usually fast, scalar loops may vary in speed by more than an order of magnitude.<ref>[http://www.sciviews.org/benchmark/ Speed comparison of various number crunching packages]</ref><ref>[http://www.scientificweb.com/ncrunch/ncrunch5.pdf Comparison of mathematical programs for data analysis] Stefan Steinhaus, ScientificWeb.com</ref>

Many [[computer algebra system]]s such as [[Mathematica]] also benefit from the availability of [[arbitrary precision arithmetic]] which can provide more accurate results.