A matrix nullspace approach for solving equality-constrained multivariable polynomial least-squares problems

被引：6
|
作者：
Hoelze, Matthew S. [1 ,2 ]
Bernstein, Dennis S. [3 ]
机构：
[1] Univ Bremen, Parallel Comp Embedded Sensor Syst Res Grp, D-28359 Bremen, Germany
[2] German Aerosp Ctr DLR, D-28359 Bremen, Germany
[3] Univ Michigan, Dept Aerosp Engn, Ann Arbor, MI 48109 USA
来源：
AUTOMATICA | 2014年 / 50卷 / 12期
关键词：
Identification algorithms; Least squares; Multivariable polynomial;
D O I：
10.1016/j.automatica.2014.10.039
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We present an elimination theory-based method for solving equality-constrained multivariable polynomial least-squares problems in system identification. While most algorithms in elimination theory rely upon Groebner bases and symbolic multivariable polynomial division algorithms, we present an algorithm which is based on computing the nullspace of a large sparse matrix and the zeros of a scalar, univariate polynomial. (C) 2014 Elsevier Ltd. All rights reserved.
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页码：3030 / 3037
页数：8
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