Towards Time-Varying Proximal Dynamics in Multi-Agent Network Games (I)

Carlo Cenedese, Yu Kawano, Sergio Grammatico, Ming Cao

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)
40 Downloads (Pure)


Distributed decision making in multi-agent networks has recently attracted significant research attention thanks to its wide applicability, e.g. in the management and optimization of computer networks, power systems, robotic teams, sensor networks and consumer markets. Distributed decision-making problems can be modeled by inter-dependent optimization problems, i.e., multi-agent game-equilibrium seeking problems, where noncooperative agents seek an equilibrium by communicating over a network. To achieve a network equilibrium, the agents may decide to update their decision variables via proximal dynamics, driven by the decision variables of the neighboring agents. In this paper, we first provide an operator-theoretic characterization of convergence with a time-invariant communication network. Then, for the time-varying case, we consider adjacency matrices that may switch subject to a dwell time. We illustrate our investigations with a distributed robotic exploration scenario.
Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE Conference on Decision and Control (CDC)
Number of pages6
ISBN (Print)978-1-5386-1395-5
Publication statusPublished - 2018
Event2018 IEEE Conference on Decision and Control (CDC) - Fontainebleau, Miami Beach, United States
Duration: 17-Dec-201819-Dec-2018


Conference2018 IEEE Conference on Decision and Control (CDC)
Country/TerritoryUnited States
CityFontainebleau, Miami Beach

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