Minimum rank positive semidefinite solution to the matrix approximation problem in the spectral norm

被引：3

作者：

Liu, Xifu ^{[1
]}

Luo, Le ^{[1
]}

机构：

[1] Chongqing Normal Univ, Sch Math Sci, Chongqing 401331, Peoples R China

来源：

APPLIED MATHEMATICS LETTERS | 2020年 / 107卷

基金：

中国国家自然科学基金;

关键词：

Matrix approximation; Positive semidefinite solution; Minimum rank; Spectral norm; LEAST-SQUARES SOLUTIONS; CONSTRAINED MATRIX;

D O I：

10.1016/j.aml.2020.106408

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we discuss the following minimum rank matrix approximation problem in the spectral norm: min(X >= 0) r(X) subject to parallel to A - BXB *parallel to(2) = min, where A epsilon C->=(mxm). and B epsilon C-mxn. By using the positive-semidefinite-type generalized singular value decomposition, we derive the expressions of the minimum rank and the minimum rank positive semidefinite solution to the above matrix approximation problem. (C) 2020 Elsevier Ltd. All rights reserved.

引用

页数：8

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