@Article{EAJAM-12-4,
author = {Yang, Ai-Li and Xue-Qi, Chen},
title = {A Partially Greedy Randomized Extended Gauss-Seidel Method for Solving Large Linear Systems},
journal = {East Asian Journal on Applied Mathematics},
year = {2022},
volume = {12},
number = {4},
pages = {874--890},
abstract = {<p style="text-align: justify;">A greedy Gauss-Seidel based on the greedy Kaczmarz algorithm and aimed
to find approximations of the solution $A^†b$ of systems of linear algebraic equations with
a full column-rank coefficient matrix $A$ is proposed. Developing this approach, we introduce a partially greedy randomized extended Gauss-Seidel method for finding approximate least-norm least-squares solutions of column-rank deficient linear systems. The
convergence of the methods is studied. Numerical experiments show that the proposed
methods are robust and efficient.</p>},
issn = {2079-7370},
doi = {https://doi.org/10.4208/eajam.300921.170422},
url = {https://global-sci.com/article/82496/a-partially-greedy-randomized-extended-gauss-seidel-method-for-solving-large-linear-systems}
}