Stochastic Gradient Optimization Techniques
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This paper proposes and analyzes two block Gauss‐Seidel methods, one randomized and one deterministic, to solve large linear least squares problems and proves the convergence of the proposed methods for full‐rank overdetermined systems.
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ABSTRACT In this paper, we propose and analyze two block Gauss‐Seidel methods, one randomized and one deterministic, to solve large linear least squares problems. We prove the convergence of the proposed methods for full‐rank overdetermined systems and provide numerical experiments to show their effectiveness compared to existing methods. In contrast to other block Kaczmarz and block coordinate descent algorithms, the proposed methods are especially advantageous when it comes to their implementation.
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@article{Mustafa2026Fast,
title = {On Fast Randomized and Deterministic Block Gauss‐Seidel Methods for Solving Least Squares Problems},
author = {Ashif Mustafa and Manideepa Saha},
journal = {Numerical Linear Algebra with Applications},
year = {2026},
doi = {10.1002/nla.70103},
url = {https://doi.org/10.1002/nla.70103}
}FAQ
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