15-16 July 2021
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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
A Self-adaptive Algorithm for Solving Basis Pursuit Denoising Problem
Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.01206103
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 6, 2021.
Abstract: In this paper, we further consider a method for solving the basis pursuit denoising problem (BPDP), which has received considerable attention in signal processing and statistical inference. To this end, a new self-adaptive algorithm is proposed, its global convergence results is established. Furthermore, we also show that the method is sublinearly convergent rate of O( 1/k). Finally, the availability of given method is shown via somek numerical examples.
Keywords: Basis pursuit denoising problem; algorithm; global convergence; sublinearly convergent rate; sparse signal recovery
Mengkai Zhu, Xu Zhang, Bing Xue and Hongchun Sun, “A Self-adaptive Algorithm for Solving Basis Pursuit Denoising Problem” International Journal of Advanced Computer Science and Applications(IJACSA), 12(6), 2021. http://dx.doi.org/10.14569/IJACSA.2021.01206103
@article{Zhu2021,
title = {A Self-adaptive Algorithm for Solving Basis Pursuit Denoising Problem},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.01206103},
url = {http://dx.doi.org/10.14569/IJACSA.2021.01206103},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {6},
author = {Mengkai Zhu and Xu Zhang and Bing Xue and Hongchun Sun}
}
