@Article{JCM-42-1605,
author = {Bai , JianchaoGuo , KeLiang , JunliJing , Yang and So , H.C.},
title = {Accelerated Symmetric ADMM and Its Applications in Large-Scale Signal Processing},
journal = {Journal of Computational Mathematics},
year = {2024},
volume = {42},
number = {6},
pages = {1605--1626},
abstract = {<p style="text-align: justify;">The alternating direction method of multipliers (ADMM) has been extensively investigated in the past decades for solving separable convex optimization problems, and surprisingly, it also performs efficiently for nonconvex programs. In this paper, we propose
a symmetric ADMM based on acceleration techniques for a family of potentially nonsmooth
and nonconvex programming problems with equality constraints, where the dual variables
are updated twice with different stepsizes. Under proper assumptions instead of the so-called Kurdyka-Lojasiewicz inequality, convergence of the proposed algorithm as well as
its pointwise iteration-complexity are analyzed in terms of the corresponding augmented
Lagrangian function and the primal-dual residuals, respectively. Performance of our algorithm is verified by numerical examples corresponding to signal processing applications in
sparse nonconvex/convex regularized minimization.</p>},
issn = {1991-7139},
doi = {https://doi.org/10.4208/jcm.2305-m2021-0107},
url = {http://global-sci.org/intro/article_detail/jcm/23509.html}
}