A truncated approximate difference algorithm for sparse signal recovery
Digital Signal Processing, ISSN: 1051-2004, Vol: 141, Page: 104191
2023
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
In this paper, we study the regularization lp -norm minimization problem to recover the sparse signals. We first prove that every global optimal solution to the regularization lp -norm minimization problem also solves the l0 -norm minimization problem if the certain conditions are satisfied, and then generate a truncated approximated difference algorithm to recover the sparse signals. At last, we provide some numerical simulations to test the performance of the truncated approximated difference algorithm, and the numerical results show that the proposed algorithm performs effectively in recovering the sparse signals compared with some state-of-art methods.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1051200423002865; http://dx.doi.org/10.1016/j.dsp.2023.104191; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85169058638&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1051200423002865; https://dx.doi.org/10.1016/j.dsp.2023.104191
Elsevier BV
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