Группа :: Науки/Математика
Пакет: spai
Главная Изменения Спек Патчи Sources Загрузить Gear Bugs and FR Repocop
Текущая версия: 3.2-alt3
Время сборки: 4 сентября 2009, 22:34 ( 759.8 недели назад )
Размер архива: 672.43 Kb
Домашняя страница: http://www.computational.unibas.ch/software/spa…
Лицензия: GPL v2
О пакете: SParse Approximate Inverse Preconditioner
Описание:
Список всех майнтейнеров, принимавших участие
в данной и/или предыдущих сборках пакета: Список rpm-пакетов, предоставляемый данным srpm-пакетом:
ACL:
Время сборки: 4 сентября 2009, 22:34 ( 759.8 недели назад )
Размер архива: 672.43 Kb
Домашняя страница: http://www.computational.unibas.ch/software/spa…
Лицензия: GPL v2
О пакете: SParse Approximate Inverse Preconditioner
Описание:
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.
Текущий майнтейнер: 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.
Список всех майнтейнеров, принимавших участие
в данной и/или предыдущих сборках пакета: Список rpm-пакетов, предоставляемый данным srpm-пакетом:
- libspai
- libspai-devel
- libspai-devel-doc
- libspai-devel-static
- spai