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• W1936086639 abstract "This paper considers reconstructing a spectrally sparse signal from a small number of randomly observed time-domain samples. The signal of interest is a linear combination of complex sinusoids at $R$ distinct frequencies. The frequencies can assume any continuous values in the normalized frequency domain $[0,1)$. After converting the spectrally sparse signal recovery into a low rank structured matrix completion problem, we propose an efficient feasible point approach, named projected Wirtinger gradient descent (PWGD) algorithm, to efficiently solve this structured matrix completion problem. We further accelerate our proposed algorithm by a scheme inspired by FISTA. We give the convergence analysis of our proposed algorithms. Extensive numerical experiments are provided to illustrate the efficiency of our proposed algorithm. Different from earlier approaches, our algorithm can solve problems of very large dimensions very efficiently." @default.
• W1936086639 created "2016-06-24" @default.
• W1936086639 creator A5053943952 @default.
• W1936086639 creator A5057913701 @default.
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• W1936086639 date "2015-07-13" @default.
• W1936086639 modified "2023-09-26" @default.
• W1936086639 title "Projected Wirtinger Gradient Descent for Low-Rank Hankel Matrix Completion in Spectral Compressed Sensing" @default.
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• W1936086639 doi "https://doi.org/10.48550/arxiv.1507.03707" @default.
• W1936086639 hasPublicationYear "2015" @default.
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