Publication: Iteratively Reweighted ℓ1Minimization with Nonzero Index Update
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Issued Date
2021
Resource Type
Language
eng
File Type
application/pdf
Other identifier(s)
2-s2.0-85123455489
Rights Holder(s)
Scopus
Bibliographic Citation
Proceedings of the 2021 International Conference on Electrical Engineering and Photonics, EExPolytech 2021. Vol , No. (2021), p.10-14
Suggested Citation
Tausiesakul B. Iteratively Reweighted ℓ1Minimization with Nonzero Index Update. Proceedings of the 2021 International Conference on Electrical Engineering and Photonics, EExPolytech 2021. Vol , No. (2021), p.10-14. doi:10.1109/EExPolytech53083.2021.9614871 Retrieved from: https://hdl.handle.net/20.500.14740/7922
Author(s)
Abstract
The acquisition of a discrete-Time signal is an important part of compressive sensing. Instead of ℓ0-norm optimization, much attention is paid to ℓ1-norm problem formulation due to its computability at comparable accuracy. Iteratively reweighted ℓ1 (IRL1) minimization is known to be an improved algorithm of typical ℓ1-norm criterion. In this work, an alternative enhancement of the IRL1-norm criterion is presented. The proposed method invokes a descending sort of the absolute values of all elements in the solution and updates the nonzero indices in each iteration without any additional matrix factorization and matrix inverse. Numerical examples illustrate that for a large number of nonzero elements in the data the proposed nonzero index update can help the IRL1 minimization to perform noticeably better. © 2021 IEEE.