LOW RANK MATRIX MINIMIZATION WITH A TRUNCATED DIFFERENCE OF NUCLEAR NORM AND FROBENIUS NORM REGULARIZATION

被引：4

作者：

Guo, Huiyuan ^{[1
]}

Yu, Quan ^{[1
]}

Zhang, Xinzhen ^{[1
]}

Cheng, Lulu ^{[1
]}

机构：

[1] Tianjin Univ, Sch Math, Tianjin 300350, Peoples R China

来源：

JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION | 2023年 / 19卷 / 04期

关键词：

Low rank matrix minimization; forward-backward splitting; REWEIGHTED LEAST-SQUARES; ALGORITHM; RECOVERY;

D O I：

10.3934/jimo.2022045

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper, we present a novel regularization with a truncated difference of nuclear norm and Frobenius norm of form L-t,L-*-alpha F with an integer t and parameter a for rank minimization problem. The forward-backward splitting (FBS) algorithm is proposed to solve such a regularization problem, whose subproblems are shown to have closed-form solutions. We show that any accumulation point of the sequence generated by the FBS algorithm is a first-order stationary point. In the end, the numerical results demonstrate that the proposed FBS algorithm outperforms the existing methods.

引用

页码：2354 / 2366

页数：13

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