2008
DOI: 10.1002/nla.619
|View full text |Cite
|
Sign up to set email alerts
|

On parallel multisplitting methods for symmetric positive semidefinite linear systems

Abstract: In this paper we propose some parallel multisplitting methods for solving consistent symmetric positive semidefinite linear systems, based on modified diagonally compensated reduction. The semiconvergence of the parallel multisplitting method is discussed. The results here generalize some known results for the nonsingular linear systems to the singular systems. It is well known that if B is nonsingular, then a splitting B = M − N is convergent if and only if (M −1 N )<1. However, for the singular matrix B we h… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
4
0

Year Published

2011
2011
2014
2014

Publication Types

Select...
2
1

Relationship

1
2

Authors

Journals

citations
Cited by 3 publications
(4 citation statements)
references
References 32 publications
0
4
0
Order By: Relevance
“…In 14, we give a modified diagonal compensated reduction method, which is analogous to the method of diagonal compensated reduction proposed in 13.…”
Section: Semiconvergence Of Parallel Multisplittingsmentioning
confidence: 99%
See 1 more Smart Citation
“…In 14, we give a modified diagonal compensated reduction method, which is analogous to the method of diagonal compensated reduction proposed in 13.…”
Section: Semiconvergence Of Parallel Multisplittingsmentioning
confidence: 99%
“…In 13 a diagonally compensated reduction method for singular systems is proposed. Recently, we present in 14 a modified diagonal compensated reduction method. Using this idea, we investigate semiconvergence of the parallel multisplitting method.…”
Section: Introductionmentioning
confidence: 99%
“…Later, this technique was further studied by many authors; see e.g. [3], [14], [15], [23], [34], [35], [37], [16], [7], [8], [22], [12], [27], [28], [29], [20], [9], [39], [40], [41] [32], [33], [10], etc.…”
mentioning
confidence: 99%
“…Conditions on the splittings (1.2) and on the weighting matrices which ensure the convergence of Algorithm 1.1 in some important cases where given by O'Leary and White [24], Nabben [22], Neumann and Plemmons [23], Frommer et al [14], [15], Song et al [27], [28], [29], Li et el [20], Hadjidimos and Yeyios [16], Cao and Song [7], etc. They showed that Algorithm 1.1 (semi)converges when • A is Hermitian (or symmetric) positive definite and the splittings (1.2) are P −regular; • A is monotone and the splittings (1.2) are (weak) regular;…”
mentioning
confidence: 99%