Online Distributed Optimization With Nonconvex Objective Functions Via Dynamic Regrets

In this paper, the problem of online distributed optimization subject to a convex set is studied by employing a network of agents, where the objective functions allocated to agents are nonconvex. Each agent only has access to its own objective function information at the previous time, and can only...

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Vydané v:	IEEE transactions on automatic control Ročník 68; číslo 11; s. 1 - 16
Hlavní autori:	Lu, Kaihong, Wang, Long
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.11.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Benchmark testing Convexity Deviation Distributed algorithms Dynamic regrets Graph theory Heuristic algorithms Linear programming Mirrors multi-agent networks nonconvex optimization online distributed optimization Optimization Power system dynamics
ISSN:	0018-9286, 1558-2523
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