Stochastic Event-Triggered Algorithm for Distributed Convex Optimisation

This paper investigates the problem of distributed convex optimisation under constrained communication. A novel stochastic event-triggering algorithm is shown to solve the problem asymptotically to any arbitrarily small error without exhibiting Zeno behaviour. A systematic design of the stochastic e...

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Veröffentlicht in:IEEE transactions on control of network systems Jg. 10; H. 3; S. 1 - 12
Hauptverfasser: Tsang, Kam Fai Elvis, Huang, Mengyu, Shi, Ling, Johansson, Karl Henrik
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.09.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:2325-5870, 2372-2533
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Abstract This paper investigates the problem of distributed convex optimisation under constrained communication. A novel stochastic event-triggering algorithm is shown to solve the problem asymptotically to any arbitrarily small error without exhibiting Zeno behaviour. A systematic design of the stochastic event processes is then derived from the analysis on optimality and communication rate with the help of a meta-optimisation problem. Lastly, a numerical example on distributed classification is provided to visualise the performance of the proposed algorithm in terms of convergence in optimisation error and average communication rate with comparison to other algorithms in the literature. We show that the proposed algorithm is highly effective in reducing communication rates compared with algorithms proposed in the literature.
AbstractList This article investigates the problem of distributed convex optimization under constrained communication. A novel stochastic event-triggering algorithm is shown to solve the problem asymptotically to any arbitrarily small error without exhibiting Zeno behavior. A systematic design of the stochastic event processes is then derived from the analysis on the optimality and communication rate with the help of a meta-optimization problem. Finally, a numerical example on distributed classification is provided to visualize the performance of the proposed algorithm in terms of convergence in optimization error and average communication rate with comparison to other algorithms in the literature. We show that the proposed algorithm is highly effective in reducing communication rates compared with algorithms proposed in the literature.
This paper investigates the problem of distributed convex optimisation under constrained communication. A novel stochastic event-triggering algorithm is shown to solve the problem asymptotically to any arbitrarily small error without exhibiting Zeno behaviour. A systematic design of the stochastic event processes is then derived from the analysis on optimality and communication rate with the help of a meta-optimisation problem. Lastly, a numerical example on distributed classification is provided to visualise the performance of the proposed algorithm in terms of convergence in optimisation error and average communication rate with comparison to other algorithms in the literature. We show that the proposed algorithm is highly effective in reducing communication rates compared with algorithms proposed in the literature.
Author Huang, Mengyu
Shi, Ling
Johansson, Karl Henrik
Tsang, Kam Fai Elvis
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Snippet This paper investigates the problem of distributed convex optimisation under constrained communication. A novel stochastic event-triggering algorithm is shown...
This article investigates the problem of distributed convex optimization under constrained communication. A novel stochastic event-triggering algorithm is...
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SubjectTerms Algorithms
Convexity
Distributed Optimisation
Event-Triggered Control
Networked Control Systems
Optimization
Title Stochastic Event-Triggered Algorithm for Distributed Convex Optimisation
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