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Event-Triggered Discrete-Time Distributed Consensus Optimization over Time-Varying Graphs
2017
Complexity
This paper focuses on a class of event-triggered discrete-time distributed consensus optimization algorithms, with a set of agents whose communication topology is depicted by a sequence of time-varying networks. The communication process is steered by independent trigger conditions observed by agents and is decentralized and just rests with each agent's own state. At each time, each agent only has access to its privately local Lipschitz convex objective function. At the next time step, every
doi:10.1155/2017/5385708
fatcat:pqusujozc5fy7abrim52222pqm