A Distributed Online Convex Optimization Algorithm with Improved Dynamic Regret

Journal Article

In this paper, we consider the problem of distributed online convex optimization, where a network of local agents aim to jointly optimize a convex function over a period of multiple time steps. The agents do not have any information about the future. Existing algorithms have established dynamic regret bounds that have explicit dependence on the number of time steps. In this work, we show that we can remove this dependence assuming that the local objective functions are strongly convex. More precisely, we propose a gradient tracking algorithm where agents jointly communicate and descend based on corrected gradient steps. We verify our theoretical results through numerical experiments.

Full Text

Duke Authors

Cited Authors

  • Zhang, Y; Ravier, RJ; Zavlanos, MM; Tarokh, V

Published Date

  • December 1, 2019

Published In

Volume / Issue

  • 2019-December /

Start / End Page

  • 2449 - 2454

Electronic International Standard Serial Number (EISSN)

  • 2576-2370

International Standard Serial Number (ISSN)

  • 0743-1546

Digital Object Identifier (DOI)

  • 10.1109/CDC40024.2019.9029474

Citation Source

  • Scopus