MRILDU: An Improvement to ILUT Based on Incomplete LDU Factorization and Dropping in Multiple Rows
We provide an improvement MRILDU to ILUT for general sparse linear systems in the paper. The improvement is based on the consideration that relatively large elements should be kept down as much as possible. To do so, two schemes are used. Firstly, incomplete LDU factorization is used instead of inc...
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doaj-75f9535f1f5840b194107ec66ac2c7f02020-11-24T23:58:08ZengHindawi LimitedJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/467672467672MRILDU: An Improvement to ILUT Based on Incomplete LDU Factorization and Dropping in Multiple RowsJian-Ping Wu0Huai-Fa Ma1College of Computer, National University of Defense Technology, Changsha 410073, ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaWe provide an improvement MRILDU to ILUT for general sparse linear systems in the paper. The improvement is based on the consideration that relatively large elements should be kept down as much as possible. To do so, two schemes are used. Firstly, incomplete LDU factorization is used instead of incomplete LU. Besides, multiple rows are computed at a time, and then dropping is applied to these rows to extract the relatively large elements in magnitude. Incomplete LDU is not only fairer when there are large differences between the elements of factors L and U, but also more natural for the latter dropping in multiple rows. And the dropping in multiple rows is more profitable, for there may be large differences between elements in different rows in each factor. The provided MRILDU is comparable to ILUT in storage requirement and computational complexity. And the experiments for spare linear systems from UF Sparse Matrix Collection, inertial constrained fusion simulation, numerical weather prediction, and concrete sample simulation show that it is more effective than ILUT in most cases and is not as sensitive as ILUT to the parameter p, the maximum number of nonzeros allowed in each row of a factor.http://dx.doi.org/10.1155/2014/467672 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jian-Ping Wu Huai-Fa Ma |
spellingShingle |
Jian-Ping Wu Huai-Fa Ma MRILDU: An Improvement to ILUT Based on Incomplete LDU Factorization and Dropping in Multiple Rows Journal of Applied Mathematics |
author_facet |
Jian-Ping Wu Huai-Fa Ma |
author_sort |
Jian-Ping Wu |
title |
MRILDU: An Improvement to ILUT Based on Incomplete LDU Factorization and Dropping in Multiple Rows |
title_short |
MRILDU: An Improvement to ILUT Based on Incomplete LDU Factorization and Dropping in Multiple Rows |
title_full |
MRILDU: An Improvement to ILUT Based on Incomplete LDU Factorization and Dropping in Multiple Rows |
title_fullStr |
MRILDU: An Improvement to ILUT Based on Incomplete LDU Factorization and Dropping in Multiple Rows |
title_full_unstemmed |
MRILDU: An Improvement to ILUT Based on Incomplete LDU Factorization and Dropping in Multiple Rows |
title_sort |
mrildu: an improvement to ilut based on incomplete ldu factorization and dropping in multiple rows |
publisher |
Hindawi Limited |
series |
Journal of Applied Mathematics |
issn |
1110-757X 1687-0042 |
publishDate |
2014-01-01 |
description |
We provide an improvement MRILDU to ILUT for general sparse linear systems in the paper.
The improvement is based on the consideration that relatively large elements should be kept down
as much as possible. To do so, two schemes are used. Firstly, incomplete LDU factorization is used instead of incomplete LU. Besides, multiple rows are computed at a time, and then dropping is applied to these rows to extract the relatively large elements in magnitude. Incomplete LDU is not only fairer when there are large differences between the elements of factors L and U, but also more natural
for the latter dropping in multiple rows. And the dropping in multiple rows is more profitable, for
there may be large differences between elements in different rows in each factor. The provided
MRILDU is comparable to ILUT in storage requirement and computational complexity. And the
experiments for spare linear systems from UF Sparse Matrix Collection, inertial constrained
fusion simulation, numerical weather prediction, and concrete sample simulation show that it is
more effective than ILUT in most cases and is not as sensitive as ILUT to the parameter p, the
maximum number of nonzeros allowed in each row of a factor. |
url |
http://dx.doi.org/10.1155/2014/467672 |
work_keys_str_mv |
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