./wip/spai, Sparse Approximate Inverses

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Branch: CURRENT, Version: 3.2nb1, Package name: spai-3.2nb1, Maintainer: jihbed.research

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


Required to run:
[lang/g95] [shells/bash] [math/lapack] [math/blas] [parallel/mpi-ch]

Required to build:
[devel/libtool-fortran] [pkgtools/cwrappers]

Master sites:

RMD160: dee77a66e04e0515cac47a3840b0d06899fa70bf
Filesize: 552.608 KB

Version history: (Expand)

CVS history: (Expand)


   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