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Optimization in open networks via dual averaging

Yu-Guan Hsieh 1 Franck Iutzeler 1 Jérôme Malick 1 Panayotis Mertikopoulos 2, 3 
1 DAO - Données, Apprentissage et Optimisation
LJK - Laboratoire Jean Kuntzmann
2 POLARIS - Performance analysis and optimization of LARge Infrastructures and Systems
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : In networks of autonomous agents (e.g., fleets of vehicles, scattered sensors), the problem of minimizing the sum of the agents' local functions has received a lot of interest. We tackle here this distributed optimization problem in the case of open networks when agents can join and leave the network at any time. Leveraging recent online optimization techniques, we propose and analyze the convergence of a decentralized asynchronous optimization method for open networks.
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Contributor : Panayotis Mertikopoulos Connect in order to contact the contributor
Submitted on : Monday, September 13, 2021 - 12:21:28 PM
Last modification on : Wednesday, July 6, 2022 - 4:19:47 AM


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  • HAL Id : hal-03342395, version 1


Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos. Optimization in open networks via dual averaging. CDC 2021 - 60th IEEE Annual Conference on Decision and Control, Dec 2021, Austin, United States. pp.1-7. ⟨hal-03342395⟩



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