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.

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}
}