A Class of Weighted Low Rank Approximation of the Positive Semidefinite Hankel Matrix
Joint Authors
Bai, Jianchao
Cheng, Kexin
Zhang, Xuewei
Duan, Xue-Feng
Source
Journal of Applied Mathematics
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-03-19
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
We consider the weighted low rank approximation of the positive semidefinite Hankel matrix problem arising in signal processing.
By using the Vandermonde representation, we firstly transform the problem into an unconstrained optimization problem and then use the nonlinear conjugate gradient algorithm with the Armijo line search to solve the equivalent unconstrained optimization problem.
Numerical examples illustrate that the new method is feasible and effective.
American Psychological Association (APA)
Bai, Jianchao& Duan, Xue-Feng& Cheng, Kexin& Zhang, Xuewei. 2015. A Class of Weighted Low Rank Approximation of the Positive Semidefinite Hankel Matrix. Journal of Applied Mathematics،Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1067157
Modern Language Association (MLA)
Bai, Jianchao…[et al.]. A Class of Weighted Low Rank Approximation of the Positive Semidefinite Hankel Matrix. Journal of Applied Mathematics No. 2015 (2015), pp.1-7.
https://search.emarefa.net/detail/BIM-1067157
American Medical Association (AMA)
Bai, Jianchao& Duan, Xue-Feng& Cheng, Kexin& Zhang, Xuewei. A Class of Weighted Low Rank Approximation of the Positive Semidefinite Hankel Matrix. Journal of Applied Mathematics. 2015. Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1067157
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1067157
