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Group by: No Grouping | Item Type | Date | Language
Number of items: 18.

Article

Zimmermann, Jan and Tatarenko, Tatiana (2019):
Optimales Energie-Management über verteilte, beschränkte Gradientenverfahren.
In: at - Automatisierungstechnik, 67 (11), pp. 922-935. ISSN 2196-677X (Online) bzw. 0178-2312 (Print),
DOI: 10.1515/auto-2019-0064,
[Article]

Zimmermann, Jan and Tatarenko, Tatiana and Willert, Volker and Adamy, Jürgen (2019):
Optimales Energie-Management über verteilte, beschränkte Gradientenverfahren. (Publisher's Version)
In: at - Automatisierungstechnik, 67 (11), pp. 922-935. De Gruyter, ISSN 0178-2312,
DOI: 10.25534/tuprints-00014291,
[Article]

Tatarenko, Tatiana and Touri, Behrouz (2017):
Non-Convex Distributed Optimization.
In: IEEE Transactions on Automatic Control, 62 (8), pp. 3744-3757. [Article]

Tatarenko, Tatiana (2017):
Independent Log-Linear Learning in Potential Games with Continuous Actions.
In: IEEE Transactions on Control of Network Systems, 5 (3), pp. 913-923. [Article]

Zubkov, A. M. and Tatarenko, Tatiana (2013):
The Equivalence between the Schur Order and the Stochastic Orders Generated by the Scheme of Allocation of Particles into Cells.
In: Mathematical Notes, 93 (1), pp. 69-74. [Article]

Conference or Workshop Item

Tatarenko, Tatiana and Zimmermann, Jan and Willert, Volker and Adamy, Jürgen (2019):
Penalized Push-Sum Algorithm for Constrained Distributed Optimization with Application to Energy Management in Smart Grid.
58th Conference on Decision and Control (CDC), Nice, France, December 11.-13., 2019, [Conference or Workshop Item]

Nedić, A. and Tatarenko, Tatiana (2017):
On stochastic proximal-point method for convex-composite optimization.
55th Annual Allerton Conference on Communication, Control, and Computing, Allerton, USA, October, 2017, [Conference or Workshop Item]

Tatarenko, Tatiana and Kamgarpour, M. (2017):
Payoff-Based Approach to Learning Nash Equilibria in Convex Games.
20th IFAC World Congress, Toulouse, France, July, 2017, [Conference or Workshop Item]

Tatarenko, Tatiana (2016):
Stochastic Payoff-Based Learning in Multi-Agent Systems Modeled by Means of Potential Games.
Conference on Decision and Control, Las Vegas, USA, December 12-15, 2016, [Conference or Workshop Item]

Tatarenko, Tatiana and Touri, Behrouz (2016):
On Local Analysis of Distributed Optimization.
American Control Conference, Boston, MA, USA, July 6-8, 2016, [Conference or Workshop Item]

Tatarenko, Tatiana (2016):
Stochastic Stability of Potential Function Maximizers in Continuous Version of Independent Log-Linear Learning.
European Control Conference, Aalborg, Denmark, June 29-July 1, 2016, [Conference or Workshop Item]

Tatarenko, Tatiana (2015):
1-Recall Reinforcement Learning Leading to an Optimal Equilibrium in Potential Games with Discrete and Continuous Actions.
IEEE Conference on Decision and Control 2015, Osaka, Japan, Dezember 14-17, [Conference or Workshop Item]

Tatarenko, Tatiana (2015):
Synchronous learning of efficient Nash equilibria in potential games with uncoupled dynamics and memoryless players.
European Control Conference 2015, Linz, Österreich, Juli 15-17, [Conference or Workshop Item]

Tatarenko, Tatiana and Popov, Ivan (2014):
Nash Equilibrium Flow in a Routing Game with Random Queues.
26th Chinese Control and Decision Conference, Changsha, China, 2014, May 31 - June 2, [Conference or Workshop Item]

Tatarenko, Tatiana (2014):
Proving Convergence of Log-Linear Learning in Potential Games.
American Control Conference, Portland, Oregon, USA, 2014, June 4 - 6, [Conference or Workshop Item]

Tatarenko, Tatiana (2014):
A Game Theoretic and Control Theoretic Approach to Incentive-Based Demand Management in Smart Grids.
22nd Mediterranean Conference on Control & Automation, Palermo, Italy, June 16 - 19, [Conference or Workshop Item]

Tatarenko, Tatiana (2013):
On Convergence of Log-Linear Learning in Potential Games.
UECE Meetings on Game Theory and Applications, Lisbon, Portugal, 7.-9. Nov. 2013, [Conference or Workshop Item]

Ph.D. Thesis

Tatarenko, Tatiana (2017):
Game-theoretic learning and distributed optimization in memoryless multi-agent systems.
Darmstadt, TU Darmstadt,
[Ph.D. Thesis]

This list was generated on Tue Mar 2 01:32:38 2021 CET.