A novel greedy Gauss-Seidel method for solving large linear least squares problem
Numerical Analysis
2020-04-09 v1 Numerical Analysis
Abstract
We present a novel greedy Gauss-Seidel method for solving large linear least squares problem. This method improves the greedy randomized coordinate descent (GRCD) method proposed recently by Bai and Wu [Bai ZZ, and Wu WT. On greedy randomized coordinate descent methods for solving large linear least-squares problems. Numer Linear Algebra Appl. 2019;26(4):1--15], which in turn improves the popular randomized Gauss-Seidel method. Convergence analysis of the new method is provided. Numerical experiments show that, for the same accuracy, our method outperforms the GRCD method in term of the computing time.
Cite
@article{arxiv.2004.03692,
title = {A novel greedy Gauss-Seidel method for solving large linear least squares problem},
author = {Yanjun Zhang and Hanyu Li},
journal= {arXiv preprint arXiv:2004.03692},
year = {2020}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2004.02062