Abstract
This paper is concerned with the development of a computationally efficient optimization algorithm for off-grid direction finding using a sparse observation model. The optimization problem can be formulated as one smooth plus two nonsmooth functions. We propose two accelerated smoothing proximal gradient algorithms. The Nesterov smoothing methodology is utilized to reformulate nonsmooth functions into smooth ones, and the accelerated proximal gradient algorithm is adopted to solve the smoothed optimization problem. The computational efficiency and efficacy of the proposed algorithms are demonstrated numerically.
Original language | English (US) |
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Title of host publication | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 3121-3125 |
Number of pages | 5 |
ISBN (Electronic) | 9781509041176 |
DOIs | |
State | Published - Jun 16 2017 |
Event | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States Duration: Mar 5 2017 → Mar 9 2017 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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ISSN (Print) | 1520-6149 |
Other
Other | 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 |
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Country/Territory | United States |
City | New Orleans |
Period | 3/5/17 → 3/9/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Keywords
- Accelerated proximal gradient
- Group sparsity
- Nondifferentiable
- Nonsmooth function
- Smoothing