[Announcement] Sparse matrix Conjugate Gradient Solver

I'm happy to announce the first release of conjugateGradient, implementing the Conjugate Gradient algorithm for solving linear systems of equations over sparse matrices: http://hackage.haskell.org/package/conjugateGradient The conjugate gradient algorithm only applies to the cases when the input matrix is symmetric and positive definite. See: http://en.wikipedia.org/wiki/Conjugate_gradient_method The advantage of this method is that it can handle really large sparse systems quite well, when other direct methods (such as LU decomposition) are just not practical due to algorithmic complexity. Such large and sparse systems naturally arise in engineering applications, such as in ASIC placement algorithms and when solving partial differential equations. Bug reports, feedback, and improvements are always welcome. -Levent.

Nice! I will look at your implementation to see if I can learn something
from this. :)
On Sun, Apr 14, 2013 at 9:59 PM, Levent Erkok
I'm happy to announce the first release of conjugateGradient, implementing the Conjugate Gradient algorithm for solving linear systems of equations over sparse matrices:
http://hackage.haskell.org/package/conjugateGradient
The conjugate gradient algorithm only applies to the cases when the input matrix is symmetric and positive definite. See: http://en.wikipedia.org/wiki/Conjugate_gradient_method
The advantage of this method is that it can handle really large sparse systems quite well, when other direct methods (such as LU decomposition) are just not practical due to algorithmic complexity. Such large and sparse systems naturally arise in engineering applications, such as in ASIC placement algorithms and when solving partial differential equations.
Bug reports, feedback, and improvements are always welcome.
-Levent.
_______________________________________________ Haskell-Cafe mailing list Haskell-Cafe@haskell.org http://www.haskell.org/mailman/listinfo/haskell-cafe
-- E-mail sent by Daniel Díaz Casanueva let f x = x in x
participants (2)
-
Daniel Díaz Casanueva
-
Levent Erkok