A Novel Greedy Block Gauss-Seidel Method for Solving Large Linear Least-Squares Problems

被引:0
作者
Sun, Chao [1 ]
Guo, Xiao-Xia [1 ]
机构
[1] Ocean Univ China, Sch Math Sci, Qingdao 266100, Shandong, Peoples R China
基金
中国国家自然科学基金;
关键词
Greedy strategy; Linear least-squares problem; Block Gauss-Seidel method; Convergence property; COORDINATE DESCENT METHOD; TOMOGRAPHY;
D O I
10.1007/s42967-024-00417-7
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this paper, we present a new convergence upper bound for the greedy Gauss-Seidel (GGS) method proposed by Zhang and Li [38]. The new convergence upper bound improves the upper bound of the GGS method. In addition, we also propose a novel greedy block Gauss-Seidel (RDBGS) method based on the greedy strategy of the GGS method for solving large linear least-squares problems. It is proved that the RDBGS method converges to the unique solution of the linear least-squares problem. Numerical experiments demonstrate that the RDBGS method has superior performance in terms of iteration steps and computation time.
引用
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页数:18
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