A damped forward-backward algorithm for stochastic generalized Nash equilibrium seeking

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A damped forward-backward algorithm for stochastic generalized Nash equilibrium seeking

Conference Paper (2020)

Author(s)

Barbara Franci (TU Delft - Team Bart De Schutter)

Sergio Grammatico (TU Delft - Team Bart De Schutter)

Research Group

Team Bart De Schutter

DOI related publication

https://doi.org/10.23919/ECC51009.2020.9143966 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:ce6352cc-f5b4-4c28-a14e-8dac80254209

More Info

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Publication Year

2020

Language

English

Research Group

Team Bart De Schutter

Pages (from-to)

1117-1122

ISBN (print)

978-3-907144-02-2

ISBN (electronic)

978-3-907144-01-5

Event

18th European Control Conference, ECC 2020 (2020-05-12 - 2020-05-15), Saint Petersburg, Russian Federation

Downloads counter

100

Abstract

We consider a stochastic generalized Nash equilibrium problem (GNEP) with expected-value cost functions. Inspired by Yi and Pavel (Automatica, 2019), we propose a distributed GNE seeking algorithm by exploiting the forward- backward operator splitting and a suitable preconditioning matrix. Specifically, we apply this method to the stochastic GNEP, where, at each iteration, the expected value of the pseudo-gradient is approximated via a number of random samples. Our main contribution is to show almost sure convergence of our proposed algorithm if the sample size grows large enough.