Linear Convergence of Generalized Proximal Point Algorithms for Monotone Inclusion Problems
Optimization and Control
2022-03-29 v2
Abstract
We focus on the linear convergence of generalized proximal point algorithms for solving monotone inclusion problems. Under the assumption that the associated monotone operator is metrically subregular or that the inverse of the monotone operator is Lipschitz continuous, we provide Q-linear and R-linear convergence results on generalized proximal point algorithms. Comparisons between our results and related ones in the literature are presented in remarks of this work.
Cite
@article{arxiv.2203.10720,
title = {Linear Convergence of Generalized Proximal Point Algorithms for Monotone Inclusion Problems},
author = {Hui Ouyang},
journal= {arXiv preprint arXiv:2203.10720},
year = {2022}
}
Comments
30 pages. arXiv admin note: text overlap with arXiv:2203.04527