A smoothing proximal gradient algorithm for matrix rank minimization problem

In this paper, we study the low-rank matrix minimization problem, where the loss function is convex but nonsmooth and the penalty term is defined by the cardinality function. We first introduce an exact continuous relaxation, that is, both problems have the same minimizers and the same optimal value...

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Bibliographic Details
Published in:	Computational optimization and applications Vol. 81; no. 2; pp. 519 - 538
Main Authors:	Yu, Quan, Zhang, Xinzhen
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.03.2022 Springer Nature B.V
Subjects:	Algorithms Convex and Discrete Geometry Lower bounds Management Science Mathematics Mathematics and Statistics Operations Research Operations Research/Decision Theory Optimization Smoothing Statistics 15A83 90C30 65K05 Low-rank approximation Nonsmooth convex loss function Smoothing method 15A03
ISSN:	0926-6003, 1573-2894
Online Access:	Get full text
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