@Article{EAJAM-12-2,
author = {Zhong-Zhi, Bai and Wang, Lu and V., Muratova, Galina},
title = {On Relaxed Greedy Randomized Augmented Kaczmarz Methods for Solving Large Sparse Inconsistent Linear Systems},
journal = {East Asian Journal on Applied Mathematics},
year = {2022},
volume = {12},
number = {2},
pages = {323--332},
abstract = {<p style="text-align: justify;">For solving large-scale sparse inconsistent linear systems by iteration methods, we introduce a relaxation parameter in the probability criterion of the greedy
randomized augmented Kaczmarz method, obtaining a class of relaxed greedy randomized augmented Kaczmarz methods. We prove the convergence of these methods and
estimate upper bounds for their convergence rates. Theoretical analysis and numerical
experiments show that these methods can perform better than the greedy randomized
augmented Kaczmarz method if the relaxation parameter is chosen appropriately.</p>},
issn = {2079-7370},
doi = {https://doi.org/10.4208/eajam.100821.251121},
url = {https://global-sci.com/article/82468/on-relaxed-greedy-randomized-augmented-kaczmarz-methods-for-solving-large-sparse-inconsistent-linear-systems}
}