A relaxed-inertial forward-backward-forward algorithm for stochastic generalized Nash equilibrium seeking

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A relaxed-inertial forward-backward-forward algorithm for stochastic generalized Nash equilibrium seeking

Conference Paper (2021)

Author(s)

Shisheng Cui (The Pennsylvania State University)

B. Franci (TU Delft - Team Sergio Grammatico)

S. Grammatico (TU Delft - Team Bart De Schutter, TU Delft - Team Sergio Grammatico)

Uday V. Shanbhag (The Pennsylvania State University)

Mathias Staudigl (Maastricht University)

Research Group

Team Sergio Grammatico

DOI related publication

https://doi.org/10.1109/CDC45484.2021.9682905 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:876ce6a4-427a-4a35-bf78-9a8f4cd17674

More Info

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Publication Year

2021

Language

English

Research Group

Team Sergio Grammatico

Pages (from-to)

197-202

Publisher

IEEE

ISBN (print)

978-1-6654-3659-5

Event

60th IEEE Conference on Decision and Control (CDC 2021) (2021-12-14 - 2021-12-17), Austin, United States

Downloads counter

120

Abstract

We propose a new operator splitting algorithm for distributed Nash equilibrium seeking under stochastic uncertainty, featuring relaxation and inertial effects. The proposed algorithm is derived from a forward-backward-forward scheme for solving structured monotone inclusion problems with Lipschitz continuous and monotone pseudogradient operator. To the best of our knowledge, this is the first distributed generalized Nash equilibrium seeking algorithm featuring acceleration techniques in stochastic Nash equilibrium problems without assuming cocoercivity. Numerical examples illustrate the effect of inertia and relaxation on the performance of our proposed algorithm.