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Tatiana Tatarenko
Tatiana Tatarenko
Postdoc
Verified email at rmr.tu-darmstadt.de
Title
Cited by
Cited by
Year
Non-convex distributed optimization
T Tatarenko, B Touri
IEEE Transactions on Automatic Control 62 (8), 3744-3757, 2017
1652017
Geometric convergence of gradient play algorithms for distributed Nash equilibrium seeking
T Tatarenko, W Shi, A Nedić
IEEE Transactions on Automatic Control 66 (11), 5342-5353, 2020
712020
Learning generalized Nash equilibria in a class of convex games
T Tatarenko, M Kamgarpour
IEEE Transactions on Automatic Control 64 (4), 1426-1439, 2018
592018
Accelerated gradient play algorithm for distributed Nash equilibrium seeking
T Tatarenko, W Shi, A Nedić
2018 IEEE Conference on Decision and Control (CDC), 3561-3566, 2018
342018
Learning Nash equilibria in monotone games
T Tatarenko, M Kamgarpour
2019 IEEE 58th Conference on Decision and Control (CDC), 3104-3109, 2019
332019
Proving convergence of log-linear learning in potential games
T Tatarenko
2014 American Control Conference, 972-977, 2014
322014
Bandit online learning of nash equilibria in monotone games
T Tatarenko, M Kamgarpour
202021
Log-linear learning: Convergence in discrete and continuous strategy potential games
T Tatarenko
53rd IEEE Conference on Decision and Control, 426-432, 2014
202014
Geometric convergence of distributed gradient play in games with unconstrained action sets
T Tatarenko, A Nedić
IFAC-PapersOnLine 53 (2), 3367-3372, 2020
182020
Stochastic learning in multi-agent optimization: Communication and payoff-based approaches
T Tatarenko
Automatica 99, 1-12, 2019
162019
Payoff-based approach to learning nash equilibria in convex games
T Tatarenko, M Kamgarpour
IFAC-PapersOnLine 50 (1), 1508-1513, 2017
122017
Solving leaderless multi-cluster games over directed graphs
J Zimmermann, T Tatarenko, V Willert, J Adamy
European Journal of Control 62, 14-21, 2021
112021
A game theoretic and control theoretic approach to incentive-based demand management in smart grids
T Tatarenko, L Garcia-Moreno
22nd Mediterranean Conference on Control and Automation, 634-639, 2014
112014
Projected push-sum gradient descent-ascent for convex optimization with application to economic dispatch problems
J Zimmermann, T Tatarenko, V Willert, J Adamy
2020 59th IEEE Conference on Decision and Control (CDC), 542-547, 2020
102020
Convergence rate of a penalty method for strongly convex problems with linear constraints
A Nedić, T Tatarenko
2020 59th IEEE Conference on Decision and Control (CDC), 372-377, 2020
92020
On the rate of convergence of payoff-based algorithms to Nash equilibrium in strongly monotone games
T Tatarenko, M Kamgarpour
arXiv preprint arXiv:2202.11147, 2022
82022
Stochastic payoff-based learning in multi-agent systems modeled by means of potential games
T Tatarenko
2016 IEEE 55th Conference on Decision and Control (CDC), 5298-5303, 2016
82016
Gradient play in n-cluster games with zero-order information
T Tatarenko, J Zimmermann, J Adamy
2021 60th IEEE Conference on Decision and Control (CDC), 3104-3109, 2021
72021
Game-theoretic learning and distributed optimization in memoryless multi-agent systems
T Tatarenko
Springer International Publishing, 2017
72017
Independent log-linear learning in potential games with continuous actions
T Tatarenko
IEEE Transactions on Control of Network Systems 5 (3), 913-923, 2017
72017
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