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 proceedingConference contribution

6 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 languageEnglish (US)
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4638-4642
Number of pages5
ISBN (Print)9781479999880
DOIs
StatePublished - Jun 24 2016
Externally publishedYes

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