On the convergence of a randomized block coordinate descent algorithm for a matrix least squares problem

DOI10.1016/j.aml.2021.107689zbMath1484.65121OpenAlexW3203858329MaRDI QIDQ2060942

Kui Du, Xiaohui Sun, Cheng-Chao Ruan

Publication date: 13 December 2021

Published in: Applied Mathematics Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.aml.2021.107689

zbMATH Keywords

linear convergencestrong convexitymatrix least squares problemrandomized block coordinate descent

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Inverse problems in linear algebra (15A29)

Cites Work

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• The university of Florida sparse matrix collection
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• Kronecker Products, Unitary Matrices and Signal Processing Applications
• Large Least Squares Problems Involving Kronecker Products
• Analysis of the Block Coordinate Descent Method for Linear Ill-Posed Problems
• On greedy randomized coordinate descent methods for solving large linear least‐squares problems
• Methods of conjugate gradients for solving linear systems

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