On hybrid preconditioning methods for large sparse saddle-point problems

被引：4
|
作者：
Wang, Zeng-Qi [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Dept Math, Shanghai 200240, Peoples R China
来源：
LINEAR ALGEBRA AND ITS APPLICATIONS | 2011年 / 434卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Saddle-point problem; Block symmetric Gauss-Seidel iteration; Matrix preconditioning; Eigenvalue clustering; CONJUGATE-GRADIENT METHODS; BLOCK SSOR PRECONDITIONERS; DEFINITE LINEAR-SYSTEMS; EQUATIONS; MATRICES;
D O I：
10.1016/j.laa.2010.06.035
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Based on the block-triangular product approximation to a 2-by-2 block matrix, a class of hybrid preconditioning methods is designed for accelerating the MINRES method for solving saddle-point problems. The appropriate values for the parameters involved in the new preconditioners are estimated, so that the numerical conditioning and the spectral property of the saddle-point matrix of the linear system can be substantially improved. Several practical hybrid preconditioners and the corresponding preconditioning iterative methods are constructed and studied, too. (C) 2010 Elsevier Inc. All rights reserved.
引用
收藏
页码：2353 / 2366
页数：14
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