Generalized Multi-cluster Game under Partial-decision Information with Applications to Management of Energy Internet
Yue Chen, Peng Yi

TL;DR
This paper develops a distributed algorithm for multi-cluster games with partial decision information, enabling agents in large-scale networks to find Nash equilibria efficiently despite limited communication.
Contribution
It introduces a novel primal-dual based distributed algorithm for generalized Nash equilibrium seeking under partial information in multi-cluster games.
Findings
Algorithm converges under certain conditions.
Operators are monotone with sufficient communication strength.
Applicable to Energy Internet management scenarios.
Abstract
The decision making and management of many engineering networks involves multiple parties with conflicting interests, while each party is constituted with multiple agents. Such problems can be casted as a multi-cluster game. Each cluster is treated as a self-interested player in a non-cooperative game where agents in the same cluster cooperate together to optimize the payoff function of the cluster. In a large-scale network, the information of agents in a cluster can not be available immediately for agents beyond this cluster, which raise challenges to the existing Nash equilibrium seeking algorithms. Hence, we consider a partial-decision information scenario in generalized Nash equilibrium seeking for multi-cluster games in a distributed manner. We reformulate the problem as finding zeros of the sum of preconditioned monotone operators by the primal-dual analysis and graph Laplacian…
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Taxonomy
TopicsMathematical and Theoretical Epidemiology and Ecology Models · Quantum Information and Cryptography
