activity
20182022
most citedGradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games

6 citations · 15 across the 8 of their papers we have counts for

collaborators

14 papers

eess.SY20222 cited

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…

math.OC20224 cited

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…

cs.GT2021

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…

eess.SY20216 cited

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…

eess.SY2020

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…

math.OC20203 cited

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…