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dc.contributor.author
Bianchi, Mattia
dc.contributor.author
Belgioioso, Giuseppe
dc.contributor.author
Grammatico, Sergio
dc.date.accessioned
2021-02-19T10:26:55Z
dc.date.available
2021-02-03T03:48:41Z
dc.date.available
2021-02-19T10:26:55Z
dc.date.issued
2021
dc.identifier.isbn
978-1-7281-7447-1
en_US
dc.identifier.isbn
978-1-7281-7446-4
en_US
dc.identifier.isbn
978-1-7281-7448-8
en_US
dc.identifier.other
10.1109/CDC42340.2020.9304145
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/467411
dc.description.abstract
We address the Nash equilibrium problem in a partial-decision information scenario, where each agent can only observe the actions of some neighbors, while its cost possibly depends on the strategies of other agents. Our main contribution is the design of a fully-distributed, single-layer, fixed-step algorithm, based on a proximal best-response augmented with consensus terms. To derive our algorithm, we follow an operator-theoretic approach. First, we recast the Nash equilibrium problem as that of finding a zero of a monotone operator. Then, we demonstrate that the resulting inclusion can be solved in a fully-distributed way via a proximal-point method, thanks to the use of a novel preconditioning matrix. Under strong monotonicity and Lipschitz continuity of the game mapping, we prove linear convergence of our algorithm to a Nash equilibrium. Furthermore, we show that our method outperforms the fastest known gradient-based schemes, both in terms of guaranteed convergence rate, via theoretical analysis, and in practice, via numerical simulations. © 2020 IEEE.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.title
A fully-distributed proximal-point algorithm for Nash equilibrium seeking with linear convergence rate
en_US
dc.type
Conference Paper
dc.date.published
2021-01-11
ethz.book.title
2020 59th IEEE Conference on Decision and Control (CDC)
en_US
ethz.pages.start
2303
en_US
ethz.pages.end
2308
en_US
ethz.event
59th IEEE Conference on Decision and Control (CDC 2020) (virtual)
en_US
ethz.event.location
Jeju, South Korea
en_US
ethz.event.date
December 14-18, 2020
en_US
ethz.notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.
en_US
ethz.identifier.scopus
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2021-02-03T03:48:54Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-02-19T10:27:07Z
ethz.rosetta.lastUpdated
2021-02-19T10:27:07Z
ethz.rosetta.versionExported
true
ethz.COinS
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