Abstract
Recently, there have been growing interests in solving distributed consensus optimization problems over directed networks that consist of multiple agents. In this paper, we develop a first-order (gradient-based) algorithm, referred to as Push-DIGing, for this class of problems. To run Push-DIGing, each agent in the network only needs to know its own out-degree and employs a fixed step-size. Under the strong convexity assumption, we prove that the introduced algorithm converges to the global minimizer at some R-linear (geometric) rate as long as the nonnegative step-size is no greater than some explicit bound.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 485-489 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781509045457 |
| DOIs | |
| State | Published - Apr 19 2017 |
| Externally published | Yes |
| Event | 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States Duration: Dec 7 2016 → Dec 9 2016 |
Other
| Other | 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 |
|---|---|
| Country/Territory | United States |
| City | Washington |
| Period | 12/7/16 → 12/9/16 |
Keywords
- Directed network
- Distributed optimization
- Linear convergence
- Small-gain theorem
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications