A New Adaptive Randomized Equilibrium Search for Generalized Nash Equilibrium Problems
This paper introduces Adaptive Randomized Equilibrium Search (ARES), a practical local search heuristic that combines best-response information with local action sampling and adaptive trust-region mechanisms to robustly solve Generalized Nash Equilibrium Problems, particularly demonstrating superior ability to avoid cycling and discover diverse equilibria in nonconvex and non-shared constraint settings compared to deterministic methods.