1 research outputs found
A novel greedy Gauss-Seidel method for solving large linear least squares problem
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.Comment: arXiv admin note: substantial text overlap with arXiv:2004.0206