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CVS Commit History:


   2012-11-23 23:33:55 by othyro | Files touched by this commit (43)
Log message:
Mostly whitespace and blank line fixing. Some files also got minor
formatting corrections as well as other corrections.
   2012-10-07 18:23:08 by Aleksej Saushev | Files touched by this commit (87)
Log message:
Drop superfluous PKG_DESTDIR_SUPPORT, "user-destdir" is default these days.
Mark packages that don't or might probably not have staged installation.
   2012-09-03 16:44:27 by Kamel Derouiche | Files touched by this commit (1)
Log message:
Update Makefile

   2011-05-09 23:29:38 by Kamel Derouiche | Files touched by this commit (4) | Imported package
Log message:
Import spai-3.2 as wip/spai.

Given a sparse matrix A the SPAI Algorithm computes a sparse approximate inverse
M by minimizing || AM - I || in the Frobenius norm. The approximate inverse is
computed explicitly and can then be applied as a preconditioner to an iterative
method.The sparsity pattern of the approximate inverse is either fixed a priori
or captured automatically:

 * Fixed sparsity: The sparsity pattern of M is either banded or a subset
   of the sparsity pattern of A.
 * Adaptive sparsity: The algorithm proceeds until the 2-norm of each column
   of AM-I is less than eps. By varying eps the user controls the quality and
    the cost of computing the preconditioner. Usually the optimal eps lies \ 
between 0.5 and 0.7.

A very sparse preconditioner is very cheap to compute but may not lead to much
improvement, while if M becomes rather dense it  becomes too expensive to
compute. The optimal preconditioner lies between these two extremes and is
problem and computer architecture dependent. The approximate inverse M can also
be used as a robust (parallel) smoother for (algebraic) multi-grid methods


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