Year: 2022
Author: Jia-Qi Chen, Zheng-Da Huang
East Asian Journal on Applied Mathematics, Vol. 12 (2022), Iss. 2 : pp. 406–420
Abstract
A fast block coordinate descent method for solving linear least-squares problems is proposed. The method is based on a greedy criterion of the column selection used at each iteration. It is proven that if the coefficient matrix of the corresponding system has full column rank, the method converges to the unique solution of the linear least-squares problem. Numerical experiments show the advantage of this approach over similar methods in terms of CPU time and computational cost, does not matter whether the coefficient matrix is of full column rank or not.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/eajam.160721.160122
East Asian Journal on Applied Mathematics, Vol. 12 (2022), Iss. 2 : pp. 406–420
Published online: 2022-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 15
Keywords: Linear least-squares problem Kaczmarz method coordinate descent method greedy blocks convergence.
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