@Article{NMTMA-13-200,
author = {Luo , Gang and Yang , Qingzhi},
title = {A Fast Symmetric Alternating Direction Method of Multipliers},
journal = {Numerical Mathematics: Theory, Methods and Applications},
year = {2019},
volume = {13},
number = {1},
pages = {200--219},
abstract = {<p style="text-align: justify;">In recent years, alternating direction method of multipliers (ADMM) and its variants are popular for the extensive use in image processing and statistical learning. A variant of ADMM: symmetric ADMM, which updates the Lagrange multiplier twice in one iteration, is always faster whenever it converges. In this paper, combined with Nesterov&#39;s accelerating strategy, an accelerated symmetric ADMM is proposed. We prove its $\mathcal{O}(\frac{1}{k^2})$ convergence rate under strongly convex condition. For the general situation, an accelerated method with a restart rule is proposed. Some preliminary numerical experiments show the efficiency of our algorithms.</p>},
issn = {2079-7338},
doi = {https://doi.org/10.4208/nmtma.OA-2018-0108},
url = {http://global-sci.org/intro/article_detail/nmtma/13437.html}
}