Accelerated low rank matrix approximate algorithms for matrix completion

被引：2

作者：

Wang, Jin ^{[1
]}

Wang, Yan-Ping ^{[2
]}

Xu, Zhi ^{[3
]}

Wang, Chuan-Long ^{[3
]}

机构：

[1] Inst Appl Phys & Computat Math, Beijing 100094, Peoples R China

[2] Taiyuan Normal Univ, Jinzhong 030619, Peoples R China

[3] Taiyuan Normal Univ, Higher Educ Key Lab Engn & Sci Comp Shanxi Prov, Jinzhong 030619, Peoples R China

来源：

COMPUTERS & MATHEMATICS WITH APPLICATIONS | 2019年 / 77卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Matrix completion; Over-relaxation acceleration; Low-rank matrix; Convergence;

D O I：

10.1016/j.camwa.2018.09.037

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we propose two accelerated algorithms for the low-rank approximate method in Wang et al. (0000) for matrix completion. The main idea is to use the successive over-relaxation technique. Based on the successive over-relaxation method for the feasible matrices or projection matrices, the low-rank matrix approximate method is modified and accelerated. Meanwhile, we discuss the convergence of the over-relaxation algorithm for the feasible matrix. Finally, the numerical experiments show them to be effective. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：334 / 341

页数：8

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