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arXiv · 2004.03692

A novel greedy Gauss-Seidel method for solving large linear least squares problem

Yanjun Zhang·Hanyu Li
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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.

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Yanjun Zhang, Hanyu Li. 2020-04-08. A novel greedy Gauss-Seidel method for solving large linear least squares problem. https://arxiv.org/abs/2004.03692

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