sparse-linear-algebra: Sparse linear algebra datastructures and algorithms
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| Versions [RSS] | 0.1.0.0, 0.1.0.1, 0.1.0.2, 0.1.0.3, 0.2.0.0, 0.2.0.1, 0.2.0.2, 0.2.0.3, 0.2.0.4, 0.2.0.5, 0.2.0.7, 0.2.0.8, 0.2.0.9, 0.2.1.0, 0.2.1.1, 0.2.2.0, 0.2.9, 0.2.9.1, 0.2.9.2, 0.2.9.3, 0.2.9.4, 0.2.9.5, 0.2.9.6, 0.2.9.7, 0.2.9.8, 0.2.9.9, 0.3, 0.3.1 (info) |
|---|---|
| Dependencies | base (>=4.7 && <5), containers, hspec, monad-loops, mtl (>=2.2.1), mwc-random, primitive (>=0.6.1.0), QuickCheck, sparse-linear-algebra, transformers (>=0.5.2.0) [details] |
| Tested with | ghc ==8.0.1 |
| License | BSD-3-Clause |
| Copyright | 2016 Marco Zocca |
| Author | Marco Zocca |
| Maintainer | zocca.marco gmail |
| Category | Math |
| Home page | https://github.com/ocramz/sparse-linear-algebra |
| Source repo | head: git clone https://github.com/ocramz/sparse-linear-algebra |
| Uploaded | by ocramz at 2016-10-16T21:59:23Z |
| Distributions | |
| Reverse Dependencies | 8 direct, 0 indirect [details] |
| Executables | sparse-linear-algebra |
| Downloads | 12820 total (7 in the last 30 days) |
| Rating | 2.0 (votes: 1) [estimated by Bayesian average] |
| Your Rating | |
| Status | Docs available [build log] Last success reported on 2016-10-16 [all 1 reports] |
Readme for sparse-linear-algebra-0.1.0.0
[back to package description]sparse-linear-algebra
Sparse linear algebra datastructures and algorithms in Haskell
TravisCI : 
This library provides common numerical analysis functionality, without requiring any external bindings. It is not optimized for performance (yet), but it serves as an experimental platform for scientific computation in a purely functional setting.
Algorithms :
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Iterative linear solvers
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Conjugate Gradient Squared (CGS)
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BiConjugate Gradient Stabilized (BiCGSTAB) (non-Hermitian systems)
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Matrix decompositions
- QR factorization
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Eigenvalue algorithms
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QR algorithm
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Rayleigh quotient iteration
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Utilities : Vector and matrix norms, matrix condition number, Givens rotation, Householder reflection
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Predicates : Matrix orthogonality test (A^T A ~= I)
This is also an experiment in principled scientific programming :
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set the stage by declaring typeclasses and some useful generic operations (normed linear vector spaces, i.e. finite-dimensional spaces equipped with an inner product that induces a distance function),
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define appropriate data structures, and how they relate to those properties (sparse vectors and matrices, defined internally via
Data.IntMap, are made instances of the VectorSpace and AdditiveGroup classes respectively). This allows to decouple the algorithms from the actual implementation of the backend, -
implement the algorithms, following 1:1 the textbook [1]
License
GPL3, see LICENSE
Credits
Inspired by
linear: https://hackage.haskell.org/package/linearsparse-lin-alg: https://github.com/laughedelic/sparse-lin-alg
References
[1] : Y. Saad, Iterative Methods for Sparse Linear Systems, 2nd ed., 2000