On convergence of residual-based extended Kaczmarz method for solving inconsistent linear systems

IF 2.6 3区 数学 Q2 MATHEMATICS, APPLIED
Fang Chen, Chen-Xiao Gao
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引用次数: 0

Abstract

The Kaczmarz-type methods, also called as the algebraic reconstruction techniques, have been studied and applied due to their high computational efficiency and good convergence property for solving very large systems of linear equations. Recently, the residual-based extended Kaczmarz method was proposed by Bao et al. [9] to solve the inconsistent linear systems. In theoretical framework, based on the condition that the \(i_{k-1}\)-th element of the residual vector \(r_k\) in the k-th iteration step is equal to zero, the authors obtained the convergence theorem. However, this element is not always equal to zero, making the basic hypothesis of the convergence theorem be not satisfied, so that the convergence conclusion does not hold true. According to this meticulous observation, we give a correct and detailed analysis for the convergence property of the residual-based extended Kaczmarz method. As a result, we provide a corrected convergence theory for this iteration method. Also, we examine these results by a numerical example.

求解不一致线性系统的基于残差的扩展Kaczmarz方法的收敛性
kaczmarz型方法又称代数重构技术,由于其在求解大型线性方程组时具有较高的计算效率和较好的收敛性而得到了广泛的研究和应用。最近,Bao等人提出了基于残差的扩展Kaczmarz方法来求解不一致线性系统。在理论框架中,基于第k次迭代步骤中残差向量\(r_k\)的\(i_{k-1}\) -th元素等于零的条件,得到了收敛定理。然而,这个元素并不总是等于零,使得收敛定理的基本假设不被满足,从而收敛结论不成立。根据这一细致的观察,我们对基于残差的扩展Kaczmarz方法的收敛性给出了正确而详细的分析。最后给出了该迭代方法的收敛性修正理论。并通过一个数值算例对这些结果进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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