A relaxed-inertial forward-backward-forward algorithm for stochastic generalized Nash equilibrium seeking
Shisheng Cui, Barbara Franci, Sergio Grammatico, Uday V. Shanbhag, Mathias Staudigl · 2021 60th IEEE Conference on Decision and Control (CDC) · 2021
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.