6 citations · 25 across the 20 of their papers we have counts for
23 papers
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…
Projected gradient-tracking in multi-cluster games and its application to power management
Jan Zimmermann, Tatiana Tatarenko, Volker Willert +1
We are concerned with a distributed approach to solve multi-cluster games arising in multi-agent systems. In such games, agents are separated into distinct clusters. The agents bel…
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…
Gradient Play in -Cluster Games with Zero-Order Information
Tatiana Tatarenko, Jan Zimmermann, Jürgen Adamy
We study a distributed approach for seeking a Nash equilibrium in -cluster games with strictly monotone mappings. Each player within each cluster has access to the current value…
Gradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games
Jan Zimmermann, Tatiana Tatarenko, Volker Willert +1
We are concerned with finding Nash Equilibria in agent-based multi-cluster games, where agents are separated into distinct clusters. While the agents inside each cluster collaborat…
Revisiting Consensus-Based Energy-Management in Smart Grid with Transmission Losses and Directed Communication
Jan Zimmermann, Tatiana Tatarenko, Volker Willert +1
We discovered a deficiency in Algorithm 1 and Theorem 3 of [1]. The algorithm called CEMA aims to solve an energy management problem distributively. However, by means of a counter…
Bandit Learning in Convex Non-Strictly Monotone Games
Tatiana Tatarenko, Maryam Kamgarpour
We address learning Nash equilibria in convex games under the payoff information setting. We consider the case in which the game pseudo-gradient is monotone but not necessarily str…
Convergence Rate of a Penalty Method for Strongly Convex Problems with Linear Constraints
Angelia Nedich, Tatiana Tatarenko
We consider an optimization problem with strongly convex objective and linear inequalities constraints. To be able to deal with a large number of constraints we provide a penalty r…
Projected Push-Sum Gradient Descent-Ascent for Convex Optimizationwith Application to Economic Dispatch Problems
Jan Zimmermann, Tatiana Tatarenko, Volker Willert +1
We propose a novel algorithm for solving convex, constrained and distributed optimization problems defined on multi-agent-networks, where each agent has exclusive access to a part…
Geometric Convergence of Distributed Gradient Play in Games with Unconstrained Action Sets
Tatiana Tatarenko, Angelia Nedich
We provide a distributed algorithm to learn a Nash equilibrium in a class of non-cooperative games with strongly monotone mappings and unconstrained action sets. Each player has ac…
Penalized Push-Sum Algorithm for Constrained Distributed Optimization with Application to Energy Management in Smart Grid
Tatiana Tatarenko, Jan Zimmermann, Volker Willert andJürgen Adamy
We study distributed convex constrained optimization on a time-varying multi-agent network. Each agent has access to its own local cost function, its local constraints, and its ins…
Learning Nash Equilibria in Monotone Games
Tatiana Tatarenko, Maryam Kamgarpour
We consider multi-agent decision making where each agent's cost function depends on all agents' strategies. We propose a distributed algorithm to learn a Nash equilibrium, whereby…
Geometric Convergence of Gradient Play Algorithms for Distributed Nash Equilibrium Seeking
Tatiana Tatarenko, Wei Shi, Angelia Nedich
We study distributed algorithms for seeking a Nash equilibrium in a class of non-cooperative convex games with strongly monotone mappings. Each player has access to her own smooth…
A Smooth Inexact Penalty Reformulation of Convex Problems with Linear Constraints
Tatiana Tatarenko, Angelia Nedich
In this work, we consider a constrained convex problem with linear inequalities and provide an inexact penalty re-formulation of the problem. The novelty is in the choice of the pe…
Minimizing Regret of Bandit Online Optimization in Unconstrained Action Spaces
Tatiana Tatarenko, Maryam Kamgarpour
We consider online convex optimization with a zero-order oracle feedback. In particular, the decision maker does not know the explicit representation of the time-varying cost funct…
Stochastic Learning in Potential Games: Communication and Payoff-Based Approaches
Tatiana Tatarenko
Game theory serves as a powerful tool for distributed optimization in multi-agent systems in different applications. In this paper we consider multi-agent systems that can be model…
Learning Generalized Nash Equilibria in a Class of Convex Games
Tatiana Tatarenko, Maryam Kamgarpour
We consider multi-agent decision making where each agent optimizes its convex cost function subject to individual and coupling constraints. The constraint sets are compact convex s…
Payoff-Based Approach to Learning Nash Equilibria in Convex Games
Tatiana Tatarenko, Maryam Kamgarpour
We consider multi-agent decision making, where each agent optimizes its cost function subject to constraints. Agents' actions belong to a compact convex Euclidean space and the age…
Non-Convex Distributed Optimization
Tatiana Tatarenko, Behrouz Touri
We study distributed non-convex optimization on a time-varying multi-agent network. Each node has access to its own smooth local cost function, and the collective goal is to minimi…