A fast proximal iteratively reweighted nuclear norm algorithm for nonconvex low-rank matrix minimization problems

In this paper, we propose a fast proximal iteratively reweighted nuclear norm algorithm with extrapolation for solving a class of nonconvex low-rank matrix minimization problems. The proposed method incorporates two different extrapolation steps with respect to the previous iterations into the backw...

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Bibliographic Details

Published in:	Applied numerical mathematics Vol. 179; pp. 66 - 86
Main Authors:	Ge, Zhili, Zhang, Xin, Wu, Zhongming
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.09.2022
Subjects:	Convergence analysis Extrapolation Low-rank minimization Nonconvex optimization Proximal iteratively reweighted method Extrapolation Nonconvex optimization Proximal iteratively reweighted method Low-rank minimization Convergence analysis
ISSN:	0168-9274, 1873-5460
Online Access:	Get full text
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