Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/17380
Title: Iteratively Reweighted ℓ1Minimization with Nonzero Index Update
Authors: Tausiesakul B.
Keywords: Compressed sensing
Factorization
Inverse problems
Matrix algebra
1-norm minimizations
Compressive sensing
Discrete-time signals
Improved * algorithm
Index update
Iteratively reweighted ℓ1-norm minimization
Minimisation
Optimisations
Problem formulation
Sparsity support
Iterative methods
Issue Date: 2021
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.
URI: https://ir.swu.ac.th/jspui/handle/123456789/17380
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123455489&doi=10.1109%2fEExPolytech53083.2021.9614871&partnerID=40&md5=c79f030aaaf8e45ddaaf92980c481c5c
Appears in Collections:Scopus 1983-2021

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