Distributed robust adaptive equilibrium computation for generalized convex games

TL;DR

This paper develops distributed algorithms for computing Nash equilibria in generalized convex games with unknown component functions, addressing challenges like delays and network changes, and verifies performance on a power system test case.

Contribution

It introduces two novel distributed algorithms for equilibrium computation in convex games with unknown functions, handling delays and dynamic networks.

Findings

Algorithms converge under delays and topology changes

Performance validated on IEEE 30-bus Test System

Integrates convex analysis, variational inequalities, and stochastic approximation

Abstract

This paper considers a class of generalized convex games where each player is associated with a convex objective function, a convex inequality constraint and a convex constraint set. The players aim to compute a Nash equilibrium through communicating with neighboring players. The particular challenge we consider is that the component functions are unknown a priori to associated players. We study two distributed computation algorithms and analyze their convergence properties in the presence of data transmission delays and dynamic changes of network topologies. The algorithm performance is verified through demand response on the IEEE 30-bus Test System. Our technical tools integrate convex analysis, variational inequalities and simultaneous perturbation stochastic approximation.