Paper Info
Title
Preconditioned AHSS-PU alternating splitting iterative methods for saddle point problems
Abstract
In order to solve large sparse saddle point problems (SPP) quickly and efficiently, Wang and Zhang recently studied the preconditioned accelerated Hermitian and skew-Hermitian splitting (PAHSS) methods. Through accelerating the PAHSS iteration algorithms by using parameterized Uzawa (PU) method, a preconditioned AHSS-PU alternating splitting iterative method (PAHSS-PU method) for solving saddle point problems is proposed in this paper. The convergence results of this new method are given under some suitable conditions. Moreover, we can obtain that if the parameters are suitable selected, then the PAHSS-PU algorithm will outperform the PAHSS algorithm and some Uzawa-type methods in the same precision condition. Numerical experiments are presented to illustrate the theoretical results and examine the numerical effectiveness of the PAHSS-PU method.
Year
DOI
Venue
2016
10.1016/j.amc.2015.09.073
Applied Mathematics and Computation
Keywords
Field
DocType
Saddle point problem,Alternating iterative,The PAHSS method,The parameterized Uzawa method
Convergence (routing),Mathematical optimization,Parameterized complexity,Saddle point,Iterative method,Hermitian matrix,Mathematics
Journal
Volume
ISSN
Citations 
273
0096-3003
0
PageRank 
References 
Authors
0.34
15
2
Name
Order
Citations
PageRank
Qingqing Zheng1182.31
Changfeng Ma210016.25