On generalized successive overrelaxation methods for augmented linear systems

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• 作者：Zhong-Zhi Bai ; Beresford N. Parlett and Zeng-Qi Wang
• 关键词：Mathematics Subject Classification (2000) 65F10 ; 65F50 ; CR ; G1.3
• 刊名：Numerische Mathematik
• 出版年：2005
• 出版时间：November 2005
• 年：2005
• 卷：102
• 期：1
• 页码：1-38
• 全文大小：355 KB

文摘

For the augmented system of linear equations, Golub, Wu and Yuan recently studied an SOR-like method (BIT 41(2001)71–85). By further accelerating it with another parameter, in this paper we present a generalized SOR (GSOR) method for the augmented linear system. We prove its convergence under suitable restrictions on the iteration parameters, and determine its optimal iteration parameters and the corresponding optimal convergence factor. Theoretical analyses show that the GSOR method has faster asymptotic convergence rate than the SOR-like method. Also numerical results show that the GSOR method is more effective than the SOR-like method when they are applied to solve the augmented linear system. This GSOR method is further generalized to obtain a framework of the relaxed splitting iterative methods for solving both symmetric and nonsymmetric augmented linear systems by using the techniques of vector extrapolation, matrix relaxation and inexact iteration. Besides, we also demonstrate a complete version about the convergence theory of the SOR-like method.