Year: 2019
Type: preprint
Abstract: Decentralized distributed optimization over time-varying graphs (networks) is nowadays a very popular branch of research in optimization theory and consensus theory. One of the motivations to consider such networks is an application to drone networks. However, the first theoretical results in this branch appeared only five years ago (Nedic, 2014). The first results about the possibility of geometric rates of convergence for strongly convex smooth optimization problems on such networks were obtained only two years ago (Nedic, 2017). In this paper, we improve the rate of geometric convergence in the latter paper for the considered class of problems, using an original penalty method trick and robustness of projected gradient descent.
Source: arXiv (Cornell University)
Authors: Rogozin, Alexander, Gasnikov, Alexander
Institution: Moscow Institute of Physics and Technology
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2990017479