Szczegóły publikacji
Opis bibliograficzny
Event-triggering interaction scheme for discrete-time decentralized optimization with nonuniform step sizes / Yuming Feng, Wei Zhang, Jiang Xiong, Huaqing Li, Leszek RUTKOWSKI // IEEE Transactions on Cybernetics ; ISSN 2168-2267. — 2022 — vol. 52 no. 2, s. 748–757. — Bibliogr. s. 755-756, Abstr. — L. Rutkowski - dod. afiliacje: Czestochowa University of Technology ; University of Social Sciences, Łódz ; Polish Academy of Sciences, Warsaw
Autorzy (5)
- Feng Yuming
- Zhang Wei
- Xiong Jiang
- Li Huaqing
- AGHRutkowski Leszek
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 144534 |
|---|---|
| Data dodania do BaDAP | 2023-01-12 |
| Tekst źródłowy | URL |
| DOI | 10.1109/TCYB.2019.2963330 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | IEEE Transactions on Cybernetics |
Abstract
In this article, we study the discrete-time decentralized optimization problems of multiagent systems by an event-triggering interaction scheme, in which each agent privately knows its local convex cost function, and collectively minimizes the total cost functions. The underlying interaction and the corresponding weight matrix are required to be undirected connected and doubly stochastic, respectively. To resolve this optimization problem collaboratively, we propose a decentralized event-triggering algorithm (DETA) that is based on the consensus theory and inexact gradient tracking technique. DETA involves each agent interacting with its neighboring agents only at some independent event-triggering sampling time instants. Under the assumptions that the global convex cost function is coercive and has Lipschitz continuous gradient, we prove that DETA steers all agents' states to an optimal solution even with nonuniform constant step sizes. Moreover, our analysis also shows that DETA converges at a rate of O(1/root t) if the step sizes are uniform and do not exceed some upper bounds. We illustrate the effectiveness of DETA on a canonical simple decentralized parameter estimation problem.
