Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals

Myung Cho; Jian-Feng Cai; Suhui Liu; Yonina C. Eldar; Weiyu Xu

doi:10.1109/icassp.2016.7472556

Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals

Myung Cho, Jian-Feng Cai, Suhui Liu, Yonina C. Eldar, Weiyu Xu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

20 Scopus citations

Abstract

We propose fast algorithms that speed up or improve the performance of recovering spectrally sparse signals from un-derdetermined measurements. Our algorithms are based on a non-convex approach of using alternating projected gradient descent for structured matrix recovery. We apply this approach to two formulations of structured matrix recovery: Hankel and Toeplitz mosaic structured matrix, and Hankel structured matrix. Our methods provide better recovery performance, and faster signal recovery than existing algorithms, including atomic norm minimization.

Original language	English (US)
Title of host publication	2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	4638-4642
Number of pages	5
ISBN (Print)	9781479999880
DOIs	https://doi.org/10.1109/icassp.2016.7472556
State	Published - Jun 24 2016
Externally published	Yes

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10.1109/icassp.2016.7472556

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@inproceedings{4a2be0968f3a4fceaa8b673fc9bed94b,

title = "Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals",

abstract = "We propose fast algorithms that speed up or improve the performance of recovering spectrally sparse signals from un-derdetermined measurements. Our algorithms are based on a non-convex approach of using alternating projected gradient descent for structured matrix recovery. We apply this approach to two formulations of structured matrix recovery: Hankel and Toeplitz mosaic structured matrix, and Hankel structured matrix. Our methods provide better recovery performance, and faster signal recovery than existing algorithms, including atomic norm minimization.",

author = "Myung Cho and Jian-Feng Cai and Suhui Liu and Eldar, {Yonina C.} and Weiyu Xu",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: The work of W. Xu was supported by Simons Foundation, Iowa Energy Center, KAUST, NIH 1R01EB020665-01. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2016",

month = jun,

day = "24",

doi = "10.1109/icassp.2016.7472556",

language = "English (US)",

isbn = "9781479999880",

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booktitle = "2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals. / Cho, Myung; Cai, Jian-Feng; Liu, Suhui et al.
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 4638-4642.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals

AU - Cho, Myung

AU - Cai, Jian-Feng

AU - Liu, Suhui

AU - Eldar, Yonina C.

AU - Xu, Weiyu

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledgements: The work of W. Xu was supported by Simons Foundation, Iowa Energy Center, KAUST, NIH 1R01EB020665-01. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2016/6/24

Y1 - 2016/6/24

N2 - We propose fast algorithms that speed up or improve the performance of recovering spectrally sparse signals from un-derdetermined measurements. Our algorithms are based on a non-convex approach of using alternating projected gradient descent for structured matrix recovery. We apply this approach to two formulations of structured matrix recovery: Hankel and Toeplitz mosaic structured matrix, and Hankel structured matrix. Our methods provide better recovery performance, and faster signal recovery than existing algorithms, including atomic norm minimization.

AB - We propose fast algorithms that speed up or improve the performance of recovering spectrally sparse signals from un-derdetermined measurements. Our algorithms are based on a non-convex approach of using alternating projected gradient descent for structured matrix recovery. We apply this approach to two formulations of structured matrix recovery: Hankel and Toeplitz mosaic structured matrix, and Hankel structured matrix. Our methods provide better recovery performance, and faster signal recovery than existing algorithms, including atomic norm minimization.

UR - http://hdl.handle.net/10754/623543

UR - http://ieeexplore.ieee.org/document/7472556/

UR - http://www.scopus.com/inward/record.url?scp=84973390724&partnerID=8YFLogxK

U2 - 10.1109/icassp.2016.7472556

DO - 10.1109/icassp.2016.7472556

M3 - Conference contribution

SN - 9781479999880

SP - 4638

EP - 4642

BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals

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