Convergence Rate of Learning a Strongly Variationally Stable Equilibrium

2023/04/05 by Tatiana Tatarenko, Tatarenko, Tatiana, Maryam Kamgarpour +1 · 1 citation
Computer Science · Economics, Econometrics and Finance · Social Sciences · #Adaptive Dynamic Programming Control #Economic theories and models #Experimental Behavioral Economics Studies #FOS: Mathematics #Optimization and Control (math.OC)

paper · pdf · doi:10.48550/arxiv.2304.02355

openalex publication_date 2023/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We derive the rate of convergence to the strongly variationally stable Nash equilibrium in a convex game, for a zeroth-order learning algorithm. Though we do not assume strong monotonicity of the game, our rates for the one-point feedback and for the two-point feedback match the best known rates for strongly monotone games under zeroth-order information.

Cited by

• Convergence Rate of Generalized Nash Equilibrium Learning in Strongly Monotone Games with Linear Constraints

Related

• Convergence Rate of Generalized Nash Equilibrium Learning in Strongly Monotone Games with Linear Constraints
• On the Rate of Convergence of Payoff-based Algorithms to Nash Equilibrium in Strongly Monotone Games
• Convergence Rate of Payoff-based Generalized Nash Equilibrium Learning
• Convergence Analysis of Gradient-Based Learning with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings
• Bandit Learning in Convex Non-Strictly Monotone Games