Distributed Online Convex Optimization on Time-Varying Directed Graphs

By Mohammad Akbari, Bahman Gharesifard, Tamás Linder. 2017

178 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Shahin Shahrampour
2. Ali Jadbabaie
3. Jiyan Jiang
4. Wenpeng Zhang
5. Wenwu Zhu
6. Jinjie Gu
7. Wei Ren
8. Francesco Bullo

Citations in the corpus

• Decentralized Online Learning: Take Benefits from Others’ Data without Sharing Your Own to Track Global Trend (2019, neutral): “Additionally, many decentralized online optimization methods are proposed, for example, decentralized online multi-task learning [41], decentralized online ADMM [35], decentralized online gradient descent [2], decentralized continuous-time online saddle-point method [23], decentralized online Nester”
• Asynchronous Decentralized Online Learning (2021, methodology): “We compare AD-OGP with synchronous decentralized online gradient-push (D-OGP) [1], which synchronizes the local update and communication of all learners at each round.”
• Distributed Online Optimization in Dynamic Environments Using Mirror Descent (2016, background): “Finally, in [18], distributed online optimization is studied for strongly convex objective functions over time-varying networks.”