Distributed Optimization for Second-Order Multi-Agent Systems with Dynamic Event-Triggered Communication

Yi, Xinlei; Yao, Lisha; Yang, Tao; George, Jemin; Johansson, Karl Henrik

doi:10.1109/cdc.2018.8618989

Cited by 75 publications

(26 citation statements)

References 33 publications

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“…Proof : From Lemmas 1 and 2 in the online version of [43], we know that all the results except (22b)-(22c) hold.…”

Section: Discussionmentioning

confidence: 87%

Communication Compression for Distributed Nonconvex Optimization

Yi¹,

Zhang²,

Yang³

et al. 2022

Preprint

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This paper considers decentralized nonconvex optimization with the cost functions being distributed over agents. Noting that information compression is a key tool to reduce the heavy communication load for decentralized algorithms as agents iteratively communicate with neighbors, we propose three decentralized primal-dual algorithms with compressed communication. The first two algorithms are applicable to a general class of compressors with bounded relative compression error and the third algorithm is suitable for two general classes of compressors with bounded absolute compression error.We show that the proposed decentralized algorithms with compressed communication have comparable convergence properties as state-of-the-art algorithms without communication compression. Specifically, we show that they can find first-order stationary points with sublinear convergence rate O(1/T ) when each local cost function is smooth, where T is the total number of iterations, and find global optima with linear convergence rate under an additional condition that the global cost function satisfies the Polyak-Łojasiewicz condition. Numerical simulations are provided to illustrate the effectiveness of the theoretical results.

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“…Proof : From Lemmas 1 and 2 in the online version of [43], we know that all the results except (22b)-(22c) hold.…”

Section: Discussionmentioning

confidence: 87%

Communication Compression for Distributed Nonconvex Optimization

Yi¹,

Zhang²,

Yang³

et al. 2022

Preprint

Self Cite

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“…Compared with References 32-35, the dynamic event-triggered communication scheme can save communication resources. Different from the existing results, 11,20,27 the designed algorithms are more applicable and closer to the actuals. Specifically, the algorithms can be applied to the cases, where the distributed optimization requires signal estimation/function computation/data integration under power and bandwidth constraints and demands communication of large volumes of data.…”

mentioning

confidence: 75%

Dynamic event‐triggered communication based distributed optimization

Zhang

Lunze

Sun

et al. 2021

Intl J Robust & Nonlinear

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This article presents distributed continuous-time algorithms with dynamic event-triggered communication, called the dynamic event-triggered algorithms, to solve a convex optimization problem in a multiagent network. Firstly, a new dynamic event-triggered communication scheme is introduced and it is shown that the optimization problem is solved and occurrence of Zeno behavior is prevented. Secondly, under the dynamic event-triggered communication implementations, both uniform and logarithmic quantized information algorithms are given to solve the optimization problem. Finally, numerical simulations are given to illustrate the effectiveness of the derived results.

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“…Under event-triggered mechanism, the unnecessary reduntant communication can be mitigated since it emphasizes that the information broadcasting is executed only when it is needed. In continuous-time optimization problem by event-triggered method, paper [28,29,[32][33][34] have not considered the Demand Constraint and Generation Constraint. Paper [30,31,35] researched the same optimization problem with Demand Constraint.…”

Section: Problem Formulationmentioning

confidence: 99%

Distributed optimization for economic power dispatch with event‐triggered communication

Shi

Zheng

Lin

et al. 2019

Asian Journal of Control

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This paper focuses on the economic power dispatch problem in a smart grid by using the distributed consensus with event-triggered communication mechanism based on continuous-time multi-agent systems over balanced directed networks, where there are many generation units working cooperatively to achieve an optimal solution. The event-triggered communication mechanism means that the information transmission between agents is triggered by event instead of time. The triggering condition and algorithm for each agent are fully decentralized. At each instant time, each agent updates its state by employing the states collected from itself and its neighboring agents at their last triggering time. Finally, two examples are given to analyze the feasibility of above proposed algorithm.

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Distributed Optimization for Second-Order Multi-Agent Systems with Dynamic Event-Triggered Communication

Cited by 75 publications

References 33 publications

Communication Compression for Distributed Nonconvex Optimization

Communication Compression for Distributed Nonconvex Optimization

Dynamic event‐triggered communication based distributed optimization

Distributed optimization for economic power dispatch with event‐triggered communication

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