Abstract
StruMF is an algebraic structured preconditioner for the interative solution of large sparse linear systems. The preconditioner corresponds to a multifrontal variant of sparse LU factorization in which some dense blocks of the factors are approximated with low-rank matrices. It is algebraic in that it only requires the linear system itself, and the approximation threshold that determines the accuracy of individual low-rank approximations. Favourable rank properties are obtained using a block partitioning which is a refinement of the partitioning induced by nested dissection ordering.
- Developers:
- Release Date:
- 2014-05-01
- Project Type:
- Open Source, No Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
C compilers
- Licenses:
-
Other (Commercial or Open-Source): https://ipo.lbl.gov/marketplace
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:AC02-05CH11231
- Code ID:
- 57172
- Site Accession Number:
- 5185; 2014-104
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Country of Origin:
- United States
Citation Formats
Napov, Artem, and Li, Xiaoye S.
Structured Multifrontal Sparse Solver.
Computer Software.
USDOE.
01 May. 2014.
Web.
doi:10.11578/dc.20210521.67.
Napov, Artem, & Li, Xiaoye S.
(2014, May 01).
Structured Multifrontal Sparse Solver.
[Computer software].
https://doi.org/10.11578/dc.20210521.67.
Napov, Artem, and Li, Xiaoye S.
"Structured Multifrontal Sparse Solver." Computer software.
May 01, 2014.
https://doi.org/10.11578/dc.20210521.67.
@misc{
doecode_57172,
title = {Structured Multifrontal Sparse Solver},
author = {Napov, Artem and Li, Xiaoye S.},
abstractNote = {StruMF is an algebraic structured preconditioner for the interative solution of large sparse linear systems. The preconditioner corresponds to a multifrontal variant of sparse LU factorization in which some dense blocks of the factors are approximated with low-rank matrices. It is algebraic in that it only requires the linear system itself, and the approximation threshold that determines the accuracy of individual low-rank approximations. Favourable rank properties are obtained using a block partitioning which is a refinement of the partitioning induced by nested dissection ordering.},
doi = {10.11578/dc.20210521.67},
url = {https://doi.org/10.11578/dc.20210521.67},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20210521.67}},
year = {2014},
month = {may}
}