| Non-convex distributed optimization T Tatarenko, B Touri IEEE Transactions on Automatic Control 62 (8), 3744-3757, 2017 | 168 | 2017 |
| 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 | 82 | 2020 |
| Learning generalized Nash equilibria in a class of convex games T Tatarenko, M Kamgarpour IEEE Transactions on Automatic Control 64 (4), 1426-1439, 2018 | 62 | 2018 |
| 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 | 36 | 2018 |
| Learning Nash equilibria in monotone games T Tatarenko, M Kamgarpour 2019 IEEE 58th Conference on Decision and Control (CDC), 3104-3109, 2019 | 33 | 2019 |
| Proving convergence of log-linear learning in potential games T Tatarenko 2014 American Control Conference, 972-977, 2014 | 32 | 2014 |
| Bandit online learning of nash equilibria in monotone games T Tatarenko, M Kamgarpour | 20 | 2021 |
| Log-linear learning: Convergence in discrete and continuous strategy potential games T Tatarenko 53rd IEEE Conference on Decision and Control, 426-432, 2014 | 20 | 2014 |
| Geometric convergence of distributed gradient play in games with unconstrained action sets T Tatarenko, A Nedić IFAC-PapersOnLine 53 (2), 3367-3372, 2020 | 18 | 2020 |
| Stochastic learning in multi-agent optimization: Communication and payoff-based approaches T Tatarenko Automatica 99, 1-12, 2019 | 16 | 2019 |
| Solving leaderless multi-cluster games over directed graphs J Zimmermann, T Tatarenko, V Willert, J Adamy European Journal of Control 62, 14-21, 2021 | 14 | 2021 |
| Payoff-based approach to learning nash equilibria in convex games T Tatarenko, M Kamgarpour IFAC-PapersOnLine 50 (1), 1508-1513, 2017 | 12 | 2017 |
| 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 | 11 | 2014 |
| 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 | 10 | 2022 |
| 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 | 10 | 2020 |
| 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 | 9 | 2020 |
| 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 | 8 | 2021 |
| 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 | 8 | 2016 |
| Bandit learning in convex non-strictly monotone games T Tatarenko, M Kamgarpour arXiv preprint arXiv:2009.04258, 2020 | 7 | 2020 |
| Game-theoretic learning and distributed optimization in memoryless multi-agent systems T Tatarenko Springer International Publishing, 2017 | 7 | 2017 |
Angelia NedichProfessor, Arizona State University在 asu.edu 的电子邮件经过验证
Angelia NedichProfessor, Arizona State University在 asu.edu 的电子邮件经过验证
Behrouz TouriUniversity of California San Diego在 ucsd.edu 的电子邮件经过验证