Numer. Math. Theor. Meth. Appl., 14 (2021), pp. 714-737.
Published online: 2021-06
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The variants of randomized Kaczmarz and randomized Gauss-Seidel algorithms are two effective stochastic iterative methods for solving ridge regression problems. For solving ordinary least squares regression problems, the greedy randomized Gauss-Seidel (GRGS) algorithm always performs better than the randomized Gauss-Seidel algorithm (RGS) when the system is overdetermined. In this paper, inspired by the greedy modification technique of the GRGS algorithm, we extend the variant of the randomized Gauss-Seidel algorithm, obtaining a variant of greedy randomized Gauss-Seidel (VGRGS) algorithm for solving ridge regression problems. In addition, we propose a relaxed VGRGS algorithm and the corresponding convergence theorem is established. Numerical experiments show that our algorithms outperform the VRK-type and the VRGS algorithms when $m > n$.
}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.OA-2020-0095}, url = {http://global-sci.org/intro/article_detail/nmtma/19195.html} }The variants of randomized Kaczmarz and randomized Gauss-Seidel algorithms are two effective stochastic iterative methods for solving ridge regression problems. For solving ordinary least squares regression problems, the greedy randomized Gauss-Seidel (GRGS) algorithm always performs better than the randomized Gauss-Seidel algorithm (RGS) when the system is overdetermined. In this paper, inspired by the greedy modification technique of the GRGS algorithm, we extend the variant of the randomized Gauss-Seidel algorithm, obtaining a variant of greedy randomized Gauss-Seidel (VGRGS) algorithm for solving ridge regression problems. In addition, we propose a relaxed VGRGS algorithm and the corresponding convergence theorem is established. Numerical experiments show that our algorithms outperform the VRK-type and the VRGS algorithms when $m > n$.