@Article{CSIAM-AM-3-626,
author = {Wu , ShuangshuangTang , Yuchao and Zeng , Tieyong},
title = {Relaxed Alternating Minimization Algorithm for Separable Convex Programming with Applications to Imaging},
journal = {CSIAM Transactions on Applied Mathematics},
year = {2022},
volume = {3},
number = {4},
pages = {626--661},
abstract = {<p style="text-align: justify;">We propose a relaxed alternating minimization algorithm for solving two-block separable convex minimization problems with linear equality constraints, where
one block in the objective functions is strongly convex. We prove that the proposed
algorithm converges to the optimal primal-dual solution of the original problem. Furthermore, the convergence rates of the proposed algorithm in both ergodic and nonergodic senses have also been studied. We apply the proposed algorithm to solve several
composite convex minimization problems arising in image denoising and evaluate the
numerical performance of the proposed algorithm on a novel image denoising model.
Numerical results for both artificial and real noisy images demonstrate the efficiency
and effectiveness of the proposed algorithm.</p>},
issn = {2708-0579},
doi = {https://doi.org/10.4208/csiam-am.SO-2021-0039},
url = {http://global-sci.org/intro/article_detail/csiam-am/21151.html}
}