| Autor: |
Dohyun Kim, Daeyoung Park |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
ICT Express, Vol 7, Iss 3, Pp 398-401 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2405-9595 |
| DOI: |
10.1016/j.icte.2021.03.011 |
| Popis: |
In this paper, we propose an accelerated sparse recovery algorithm based on inexact alternating direction of multipliers. We formulate a sparse recovery problem with a concave regularizer and solve it with the relaxed and accelerated alternating method of multipliers (R-A-ADMM). We introduce learnable parameters to optimize the algorithm with given data sets. The derived algorithm is an accelerated version of LISTA-AT that controls the threshold for each entry according to the previously recovered estimate. Numerical results show that the proposed Accel-LISTA-AT algorithm converges much faster and recovers the sparse signals with lower mean squared errors than the other learning-based sparse recovery algorithms. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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