Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2015, Special Issue (2015), Article ID 937573, 7 pages.

A Class of Weighted Low Rank Approximation of the Positive Semidefinite Hankel Matrix

Jianchao Bai, Xuefeng Duan, Kexin Cheng, and Xuewei Zhang

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Abstract

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.

Article information

Source
J. Appl. Math., Volume 2015, Special Issue (2015), Article ID 937573, 7 pages.

Dates
First available in Project Euclid: 15 April 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1429105416

Digital Object Identifier
doi:10.1155/2015/937573

Mathematical Reviews number (MathSciNet)
MR3326675

Zentralblatt MATH identifier
1347.65105

Citation

Bai, Jianchao; Duan, Xuefeng; Cheng, Kexin; Zhang, Xuewei. A Class of Weighted Low Rank Approximation of the Positive Semidefinite Hankel Matrix. J. Appl. Math. 2015, Special Issue (2015), Article ID 937573, 7 pages. doi:10.1155/2015/937573. https://projecteuclid.org/euclid.jam/1429105416


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