Game Theory for Big Data Processing: Multileader Multifollower Game-Based ADMM - 2018


In this Project, tradeoff and convergence problems for incentive mechanisms are addressed by combining optimization and game theory. Specifically, a multiple-leader multiple-follower (MLMF) game-primarily based alternating direction method of multipliers (ADMM) is developed that incentivizes the agents to perform a cluster of controllers' tasks so as to satisfy their corresponding objectives. Each analytical and simulation results verify that the proposed methodology reaches a hierarchical social optimum and converges linearly. More importantly, the convergence rate is independent of the network size, that indicates that the MLMF game-primarily based ADMM will be employed in very large networks, like highly virtualized Communication networks with two-layer hierarchies, for giant information processing.

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