The revisited total least squares problems with linear equality constraint

被引：6
|
作者：
Liu, Qiaohua [1 ]
Jin, Shufang [1 ]
Yao, Lei [1 ]
Shen, Dongmei [2 ]
机构：
[1] Shanghai Univ, Dept Math, Shanghai 200444, Peoples R China
[2] Shanghai Lixin Univ Accounting & Finance, Sch Stat & Math, Shanghai 201209, Peoples R China
来源：
APPLIED NUMERICAL MATHEMATICS | 2020年 / 152卷
基金：
中国国家自然科学基金;
关键词：
Total least squares; Linear equality constraints; The method of weighting; Weighting factor; Inverse iteration method;
D O I：
10.1016/j.apnum.2019.11.021
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The total least squares problem with linear equality constraint is proved to be approximated by an unconstrained total least squares problem with a large weight on the constraint. A criterion for choosing the weighting factor is given, and a QR-based inverse (QR-INV) iteration method is presented. Numerical results show that the QR-INV method is more efficient than the standard QR-SVD procedure and Schaffrin's inverse iteration method, especially for large and sparse matrices. (C) 2019 IMACS. Published by Elsevier B.V. All rights reserved.
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页码：275 / 284
页数：10
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