6 citations · 15 across the 13 of their papers we have counts for
13 papers · 1 filter
Learning Parametric Monotone Games
Alberto Bemporad, Tatiana Tatarenko
We study the problem of learning from data a parametric Nash equilibrium (NE) problem that is monotone (or strongly monotone) for all parameter values. In the presence of local and…
Solving Monotone Linear-Quadratic Generalized Nash Equilibrium Problems via Quadratic Programming
Alberto Bemporad, Tatiana Tatarenko
We consider generalized Nash equilibrium problems among players with convex quadratic costs and shared affine constraints, assuming only that the game's pseudogradient is merel…
Learning Approximate Solutions to Multiparametric Generalized Nash Equilibrium Problems
A. Bemporad, T. Tatarenko
We propose a learning-based approach for approximating solution mappings of multiparametric generalized Nash equilibrium problems (GNEPs) with coupling in both objectives and const…
Convergence Rate of Payoff-based Generalized Nash Equilibrium Learning
Tatiana Tatarenko, Maryam Kamgarpour
We consider generalized Nash equilibrium (GNE) problems in games with strongly monotone pseudo-gradients and jointly linear coupling constraints. We establish the convergence rate…
Convergence Rate of Learning a Strongly Variationally Stable Equilibrium
Tatiana Tatarenko, Maryam Kamgarpour
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 mono…
On the Rate of Convergence of Payoff-based Algorithms to Nash Equilibrium in Strongly Monotone Games
Tatiana Tatarenko, Maryam Kamgarpour
We derive the rate of convergence to Nash equilibria for the payoff-based algorithm proposed in \cite{tat_kam_TAC}. These rates are achieved under the standard assumption of convex…