On multi-step randomized extended Kaczmarz method for solving large sparse inconsistent linear systems

被引:7
|
作者
Bai, Zhong-Zhi [1 ,2 ]
Wang, Lu [1 ,2 ]
机构
[1] Chinese Acad Sci, Inst Computat Math & Sci Engn Comp, Acad Math & Syst Sci, State Key Lab Sci Engn Comp, POB 2719, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
关键词
System of linear equations; Randomized Kaczmarz method; Inconsistency; Multi -step iteration; Convergence property; CONVERGENCE RATE; GAUSS-SEIDEL; GREEDY; ALGORITHM;
D O I
10.1016/j.apnum.2023.06.008
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In order to improve the convergence property and computational behavior of the randomized extended Kaczmarz method, we propose a multi-step randomized extended Kaczmarz method, in which we repeatedly update the iterate several times at each iteration step, obtaining a nonstationary inner-outer iteration scheme for solving large-scale, sparse, and inconsistent system of linear equations. For this multi-step randomized extended Kaczmarz method, we prove its convergence, derive an upper bound for its convergence rate, and demonstrate that this upper bound can be smaller than that of the randomized extended Kaczmarz method for several typical choices of the numbers of inner iteration steps. Numerical experiments also show that the multi-step randomized extended Kaczmarz method can perform better than the randomized extended Kaczmarz method if we choose the numbers of inner iteration steps appropriately.& COPY; 2023 IMACS. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:197 / 213
页数:17
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