Thanks for all the replies,
It sounds like there is enough interest and even some potential
collaborators out there. I have created a few data structures to represent
sparse vectors and matrices. The vector was a simple binary tree and the
matrix a quad tree. As I suspected a standard IntMap was around 3X as fast
as my binary tree, so I have switched to the IntMap for now. I was hoping to
hold on to my quad tree for the matrix rep. because I like the elegance of
the recursive algorithms like Strassen’s for multiplication. In the end it
will most likely be best to have a few different matrix representations
anyway. For instance, I know that compressed row form is most efficient for
certain algorithms. I have a Gauss iterative solver working currently and am
thinking of moving on to a parallel Conjugate gradient or direct solver
using LU decomposition. I’m no expert in Haskell or numeric methods but I do
my best. I’ll clean up what I have and open up the project on Github or
Bitbucket. Any other thoughts or ideas are welcome.
I’m currently using the Matrix Market files for testing. It would be nice to
benchmark this against an industry standard solver in C or Fortran, without
having to do a lot of work to set it up. Does anyone know of an easy way to
do this? I’m just trying to get results to compare in orders of magnitude
for now. It would be motivating to see these comparisons.
--
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