conjugateGradient: Sparse matrix linear-equation solver

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Sparse matrix linear-equation solver, using the conjugate gradient algorithm. Note that the technique only applies to matrices that are symmetric and positive-definite. See http://en.wikipedia.org/wiki/Conjugate_gradient_method for details.

The conjugate gradient method can handle very large sparse matrices, where direct methods (such as LU decomposition) are way too expensive to be useful in practice. Such large sparse matrices arise naturally in many engineering problems, such as in ASIC placement algorithms and when solving partial differential equations.


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Versions [RSS] 1.0, 1.1, 1.2, 1.3, 1.4, 2.0, 2.1, 2.2
Dependencies base (>=4 && <5), containers (>=0.5), random [details]
License BSD-3-Clause
Copyright Levent Erkok, 2013
Author Levent Erkok
Maintainer Levent Erkok (erkokl@gmail.com)
Category Math
Home page http://github.com/LeventErkok/conjugateGradient
Bug tracker http://github.com/LeventErkok/conjugateGradient/issues
Source repo head: git clone git://github.com/LeventErkok/conjugateGradient.git
Uploaded by LeventErkok at 2013-04-20T06:34:41Z
Distributions NixOS:2.2
Reverse Dependencies 1 direct, 0 indirect [details]
Downloads 6536 total (6 in the last 30 days)
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Readme for conjugateGradient-2.2

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Conjugate Gradient Solver 
=========================

[![Build Status](https://secure.travis-ci.org/LeventErkok/conjugateGradient.png?branch=master)](http://travis-ci.org/LeventErkok/conjugateGradient)

Sparse matrix linear equation solver, using the Conjugate Gradient algorithm: http://en.wikipedia.org/wiki/Conjugate_gradient_method.

The method is applicable to matrices that are symmetric and positive definite.

On hackage: http://hackage.haskell.org/package/conjugateGradient