TY - GEN
T1 - Distributed Computation of Nash Equilibria for Monotone Aggregative Games via Iterative Regularization
AU - Lei, Jinlong
AU - Shanbhag, Uday V.
AU - Chen, Jie
N1 - Funding Information:
The work was partially sponsored by Shanghai Sailing Program (No. 20YF1452800) and the Fundamental Research Funds for the Central Universities (No. 22120200047).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/14
Y1 - 2020/12/14
N2 - This work considers an aggregative game over time-varying graphs, where each player's cost function depends on its own strategy and the aggregate of its competitors' strategies. Though the aggregate is unknown to any given player, each player may interact with its neighbors to construct an estimate of the aggregate. We design a distributed iterative Tikhonov regularization method in which each player may independently choose its steplengths and regularization parameters while meeting some overall coordination requirements. Under a monotonicity assumption on the concatenated player-specific gradient map, we prove that the generated sequence converges to the least-norm Nash equilibrium (i.e., a Nash equilibrium with the smallest two-norm) and validate the proposed method on a networked Nash-Cournot equilibrium problem.
AB - This work considers an aggregative game over time-varying graphs, where each player's cost function depends on its own strategy and the aggregate of its competitors' strategies. Though the aggregate is unknown to any given player, each player may interact with its neighbors to construct an estimate of the aggregate. We design a distributed iterative Tikhonov regularization method in which each player may independently choose its steplengths and regularization parameters while meeting some overall coordination requirements. Under a monotonicity assumption on the concatenated player-specific gradient map, we prove that the generated sequence converges to the least-norm Nash equilibrium (i.e., a Nash equilibrium with the smallest two-norm) and validate the proposed method on a networked Nash-Cournot equilibrium problem.
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U2 - 10.1109/CDC42340.2020.9303804
DO - 10.1109/CDC42340.2020.9303804
M3 - Conference contribution
AN - SCOPUS:85099881840
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 2285
EP - 2290
BT - 2020 59th IEEE Conference on Decision and Control, CDC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 59th IEEE Conference on Decision and Control, CDC 2020
Y2 - 14 December 2020 through 18 December 2020
ER -