Group :: Sciences/Mathematics
RPM: spai
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Current version: 3.2-alt9
Build date: 22 september 2021, 14:38 ( 131.1 weeks ago )
Size: 675.35 Kb
Home page: http://www.computational.unibas.ch/software/spa…
License: GPLv2
Summary: SParse Approximate Inverse Preconditioner
Description:
List of contributors List of rpms provided by this srpm:
ACL:
Build date: 22 september 2021, 14:38 ( 131.1 weeks ago )
Size: 675.35 Kb
Home page: http://www.computational.unibas.ch/software/spa…
License: GPLv2
Summary: SParse Approximate Inverse Preconditioner
Description:
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.
Current maintainer: Eugeny A. Rostovtsev (REAL) 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.
List of contributors List of rpms provided by this srpm:
- libspai
- libspai-debuginfo
- libspai-devel
- libspai-devel-doc
- spai
- spai-debuginfo