sparse-linear-algebra: Numerical computation in native Haskell

[ gpl, library, numeric ] [ Propose Tags ]

Overview

The sparse-linear-algebra library provides iterative linear solvers, matrix decompositions, eigenvalue computations and related utilities. The user interface is provided by the top-level module Numeric.LinearAlgebra.Sparse:

import           Numeric.LinearAlgebra.Sparse

Please refer to the README file for usage examples.


[Skip to Readme]
Versions [faq] 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, mtl (>=2.2.1), mwc-random, primitive (>=0.6.1.0), QuickCheck, vector [details]
License GPL-3.0-only
Copyright 2016 Marco Zocca
Author Marco Zocca
Maintainer zocca.marco gmail
Category Numeric
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 Tue Nov 15 14:53:35 UTC 2016
Distributions LTSHaskell:0.3.1, NixOS:0.3.1, Stackage:0.3.1
Executables sparse-linear-algebra
Downloads 6357 total (81 in the last 30 days)
Rating 2.0 (votes: 1) [estimated by rule of succession]
Your Rating
  • λ
  • λ
  • λ
Status Hackage Matrix CI
Docs available [build log]
Last success reported on 2016-11-16 [all 1 reports]

Modules

[Index]

Downloads

Maintainer's Corner

For package maintainers and hackage trustees


Readme for sparse-linear-algebra-0.2.2.0

[back to package description]

sparse-linear-algebra

Numerical computation in native Haskell

TravisCI : Build Status

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.

Contents :

  • Iterative linear solvers (linSolve)

    • Generalized Minimal Residual (GMRES) (non-Hermitian systems)

    • BiConjugate Gradient (BCG)

    • Conjugate Gradient Squared (CGS)

    • BiConjugate Gradient Stabilized (BiCGSTAB) (non-Hermitian systems)

    • Transpose-Free Quasi-Minimal Residual (TFQMR)

  • Direct linear solvers

    • LU-based (luSolve)
  • Matrix factorization algorithms

    • QR (qr)

    • LU (lu)

    • Cholesky (chol)

  • Eigenvalue algorithms

    • Arnoldi iteration (arnoldi)

    • QR (eigsQR)

    • Rayleigh quotient iteration (eigRayleigh)

  • Utilities : Vector and matrix norms, matrix condition number, Givens rotation, Householder reflection

  • Predicates : Matrix orthogonality test (A^T A ~= I)


Examples

The module Numeric.LinearAlgebra.Sparse contains the user interface.

Creation of sparse data

The fromListSM function creates a sparse matrix from an array of its entries we use :

fromListSM :: Foldable t => (Int, Int) -> t (IxRow, IxCol, a) -> SpMatrix a

e.g.

> amat = fromListSM (3,3) [(0,0,2),(1,0,4),(1,1,3),(1,2,2),(2,2,5)]

and similarly

fromListSV :: Int -> [(Int, a)] -> SpVector a

can be used to create sparse vectors. Alternatively, the user can copy the contents of a list to a (dense) SpVector using

fromListDenseSV :: Int -> [a] -> SpVector a

Displaying sparse data

Both sparse vectors and matrices can be pretty-printed using prd:

> prd amat
( 3 rows, 3 columns ) , 5 NZ ( sparsity 0.5555555555555556 )

[2,0,0]
[4,3,2]
[0,0,5]

The zeros are just added at printing time; sparse vectors and matrices should only contain non-zero entries.

Matrix operations

There are a few common matrix factorizations available; in the following example we compute the LU factorization of a matrix and verify it with the matrix-matrix product ## :

> (l, u) = lu amat
> prd $ l ## u
( 3 rows, 3 columns ) , 9 NZ ( sparsity 1.0 )

[2.0,0.0,0.0]
[4.0,3.0,2.0]
[0.0,0.0,5.0]

Notice that the result is dense, i.e. certain entries are numerically zero but have been inserted into the result along with all the others (thus taking up memory!). To preserve sparsity, we can use a sparsifying matrix-matrix product #~#, which filters out all the elements x for which |x| <= eps, where eps (defined in Numeric.Eps) depends on the numerical type used (e.g. it is 10^-6 for Floats and 10^-12 for Doubles).

> prd $ l #~# u
( 3 rows, 3 columns ) , 5 NZ ( sparsity 0.5555555555555556 )

[2.0,0.0,0.0]
[4.0,3.0,2.0]
[0.0,0.0,5.0]

A matrix is transposed using transposeSM.

Sometimes we need to compute matrix-matrix transpose products, which is why the library offers the infix operators #^# (M^T N) and ##^ (M N^T):

> amat' = amat #^# amat
> prd amat'
( 3 rows, 3 columns ) , 9 NZ ( sparsity 1.0 )

[20.0,12.0,8.0]
[12.0,9.0,6.0]
[8.0,6.0,29.0]

> l = chol amat'
> prd $ l ##^ l
( 3 rows, 3 columns ) , 9 NZ ( sparsity 1.0 )

[20.000000000000004,12.0,8.0]
[12.0,9.0,10.8]
[8.0,10.8,29.0]

In the above example we have also shown the Cholesky decomposition (M = L L^T where L is a lower-triangular matrix), which is only possible for symmetric positive-definite matrices.

Linear systems

Large sparse linear systems are best solved with iterative methods. sparse-linear-algebra provides a selection of these via the linSolve function, or alternatively <\> (which uses GMRES as default solver method) :

> b = fromListDenseSV 3 [3,2,5]
> x = amat <\> b
> prd x
( 3 elements ) ,  3 NZ ( sparsity 1.0 )

[1.4999999999999998,-1.9999999999999998,0.9999999999999998]

The result can be verified by computing the matrix-vector action amat #> x, which should (ideally) be very close to the right-hand side b :

> prd $ amat #> x
( 3 elements ) ,  3 NZ ( sparsity 1.0 )

[2.9999999999999996,1.9999999999999996,4.999999999999999]

The library also provides a forward-backward substitution solver (luSolve) based on a triangular factorization of the system matrix (usually LU). This should be the preferred for solving smaller, dense systems. Using the data defined above we can cross-verify the two solution methods:

> x' = luSolve l u b
> prd x'

( 3 elements ) ,  3 NZ ( sparsity 1.0 )

[1.5,-2.0,1.0]

This is also an experiment in principled scientific programming :

  • 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),

  • 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 Additive classes respectively). This allows to decouple the algorithms from the actual implementation of the backend,

  • implement the algorithms, following 1:1 the textbook [1, 2]

License

GPL3, see LICENSE

Credits

Inspired by

References

[1] : Y. Saad, Iterative Methods for Sparse Linear Systems, 2nd ed., 2000

[2] : L. N. Trefethen, D. Bau, Numerical Linear Algebra, SIAM, 1997