A two-step randomized Gauss-Seidel method for solving large-scale linear least squares problems

被引：2
作者：
不详
机构：
[1] School of Mathematics and Statistics, Sichuan University of Science and Engineering, Zigong
[2] Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things, Bridge Non-destruction Detecting and Engineering Computing Key Laboratory, Zigong
来源：
ELECTRONIC RESEARCH ARCHIVE | 2022年 / 30卷 / 02期
关键词：
linear least-squares problem; two-step iterative method; convergence property; Gauss-Seidel; COORDINATE-DESCENT; EXTENDED KACZMARZ; ITERATIVE METHODS; OPTIMIZATION; TOMOGRAPHY; ALGORITHMS;
D O I：
10.3934/era.2022040
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
A two-step randomized Gauss-Seidel (TRGS) method is presented for large linear least squares problem with tall and narrow coefficient matrix. The TRGS method projects the approximate solution onto the solution space by given two random columns and is proved to be convergent when the coefficient matrix is of full rank. Several numerical examples show the effectiveness of the TRGS method among all methods compared.
引用
收藏
页码：755 / 779
页数：25
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