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Cited by 13
Analysis of Structured Low Rank Approximation as an Optimization Problem

2013 IEEE International Conference on Acoustics, Speech and Signal Processing
L. Condat, A. Hirabayashi, Y. Hironaga
https://doi.org/10.1109/ICASSP.2013.6638819
Conference  (2013), p. 6014
2015 23rd European Signal Processing Conference (EUSIPCO)
Laurent Condat, Akira Hirabayashi
https://doi.org/10.1109/EUSIPCO.2015.7362425
Conference  (2015), p. 459
AIP Conference Proceedings
Jonathan Gillard, Dmitri Kvasov, Anatoly Zhigljavsky
https://doi.org/10.1063/1.4965338
Conference  Volume 1776 (2016), p. 060004
Computation of the nearest non-prime polynomial matrix: Structured low-rank approximation approach
Tanay Saha, Swanand R. Khare
https://doi.org/10.1016/j.laa.2020.12.034
Journal  Linear Algebra and its Applications Volume 616 (2021), p. 84
Global Optimization Challenges in Structured Low Rank Approximation
Jonathan Gillard, Anatoly Zhigljavsky
https://doi.org/10.1007/978-3-319-69404-7_26
Book  Lecture Notes in Computer Science (Learning and Intelligent Optimization) Volume 10556 (2017), p. 326
Low rank approximation of the symmetric positive semidefinite matrix
Xuefeng Duan, Jiaofen Li, Qingwen Wang, Xinjun Zhang
https://doi.org/10.1016/j.cam.2013.09.080
Journal  Journal of Computational and Applied Mathematics Volume 260 (2014), p. 236
Novel alternating update method for low rank approximation of structured matrices
Jianchao Bai, Jicheng Li, Pingfan Dai
https://doi.org/10.1016/j.apnum.2017.07.001
Journal  Applied Numerical Mathematics Volume 121 (2017), p. 223
On the Least-Squares Fitting of Data by Sinusoids
Yaroslav D. Sergeyev, Dmitri E. Kvasov, Marat S. Mukhametzhanov
https://doi.org/10.1007/978-3-319-29975-4_11
Book  Springer Optimization and Its Applications (Advances in Stochastic and Deterministic Global Optimization) Volume 107 (2016), p. 209
Optimal rank-1 Hankel approximation of matrices: Frobenius norm and spectral norm and Cadzow's algorithm
Hanna Knirsch, Markus Petz, Gerlind Plonka
https://doi.org/10.1016/j.laa.2021.07.004
Journal  Linear Algebra and its Applications Volume 629 (2021), p. 1
Optimization challenges in the structured low rank approximation problem
Jonathan Gillard, Anatoly Zhigljavsky
https://doi.org/10.1007/s10898-012-9962-8
Journal  Journal of Global Optimization Volume 57, Issue 3 (2013), p. 733
Parameter Estimation, Forecasting, Gap Filling
Nina Golyandina, Anton Korobeynikov, Anatoly Zhigljavsky
https://doi.org/10.1007/978-3-662-57380-8_3
Book  Use R! (Singular Spectrum Analysis with R) (2018), p. 121
Stochastic algorithms for solving structured low-rank matrix approximation problems
J.W. Gillard, A.A. Zhigljavsky
https://doi.org/10.1016/j.cnsns.2014.08.023
Journal  Communications in Nonlinear Science and Numerical Simulation Volume 21, Issue 1-3 (2015), p. 70
Weighted norms in subspace‐based methods for time series analysis
J. W. Gillard, A. A. Zhigljavsky
https://doi.org/10.1002/nla.2062
Journal  Numerical Linear Algebra with Applications Volume 23, Issue 5 (2016), p. 947


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