Haskell Linear Algebra Matrix Library for Arbitrary Element Types

Posted by Johannes Weiß on Stack Overflow See other posts from Stack Overflow or by Johannes Weiß
Published on 2012-09-05T19:37:26Z Indexed on 2012/09/08 15:38 UTC
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linear-algebra

I'm looking for a Haskell linear algebra library that has the following features:

Matrix multiplication
Matrix addition
Matrix transposition
Rank calculation
Matrix inversion is a plus

and has the following properties:

arbitrary element (scalar) types (in particular element types that are not Storable instances). My elements are an instance of Num, additionally the multiplicative inverse can be calculated. The elements mathematically form a finite field (??_2²⁵⁶). That should be enough to implement the features mentioned above.
arbitrary matrix sizes (I'll probably need something like 100x100, but the matrix sizes will depend on the user's input so it should not be limited by anything else but the memory or the computational power available)
as fast as possible, but I'm aware that a library for arbitrary elements will probably not perform like a C/Fortran library that does the work (interfaced via FFI) because of the indirection of arbitrary (non Int, Double or similar) types. At least one pointer gets dereferenced when an element is touched
(written in Haskell, this is not a real requirement for me, but since my elements are no Storable instances the library has to be written in Haskell)

I already tried very hard and evaluated everything that looked promising (most of the libraries on Hackage directly state that they wont work for me). In particular I wrote test code using:

hmatrix, assumes Storable elements
Vec, but the documentation states:

Low Dimension : Although the dimensionality is limited only by what GHC will handle, the library is meant for 2,3 and 4 dimensions. For general linear algebra, check out the excellent hmatrix library and blas bindings

I looked into the code and the documentation of many more libraries but nothing seems to suit my needs :-(.

Update

Since there seems to be nothing, I started a project on GitHub which aims to develop such a library. The current state is very minimalistic, not optimized for speed at all and only the most basic functions have tests and therefore should work. But should you be interested in using or helping out developing it: Contact me (you'll find my mail address on my web site) or send pull requests.

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