@Article{NMTMA-13-1,
author = {Jianchao, Bai and Zhie, Wu and Li, Jicheng and Zhie, Wu},
title = {Several Variants of the Primal-Dual Hybrid Gradient Algorithm with Applications},
journal = {Numerical Mathematics: Theory, Methods and Applications},
year = {2020},
volume = {13},
number = {1},
pages = {176--199},
abstract = {<p style="text-align: justify;">By reviewing the primal-dual hybrid gradient algorithm (PDHG) proposed by He, You and Yuan (SIAM J. Image Sci., 7(4) (2014), pp. 2526-2537), in this paper we introduce four improved schemes for solving a class of saddle-point problems. Convergence properties of the proposed algorithms are ensured based on weak assumptions, where none of the objective functions are assumed to be strongly convex but the step-sizes in the primal-dual updates are more flexible than the previous. By making use of variational analysis, the global convergence and sublinear convergence rate in the ergodic/nonergodic sense are established, and the&nbsp; numerical efficiency of our algorithms is verified by testing an image deblurring problem compared with several existing algorithms.</p>},
issn = {2079-7338},
doi = {https://doi.org/10.4208/nmtma.OA-2019-0030},
url = {https://global-sci.com/article/90341/several-variants-of-the-primal-dual-hybrid-gradient-algorithm-with-applications}
}