Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/124011
Title: Bridge the Gap Between ADMM and Stackelberg Game: Incentive Mechanism Design for Big Data Networks
Authors: Zijie Zheng;Lingyang Song;Zhu Han
Year: 2017
Publisher: IEEE
Abstract: Alternating direction method of multipliers (ADMM) has been well recognized as an efficient optimization approach due to its fast convergence speed and variable decomposition property. However, in big data networks, the agents may not feedback the variables as the centralized controller expects. In this paper, we model the problem as a Stackelberg game and design a Stackelberg game based ADMM to deal with the contradiction between the centralized objective of the controller and the individual objectives from the agents. The Stackelberg game based ADMM can converge linearly, which is not dependent on the number of agents. The case study verifies the fast convergence of our game-based incentive mechanism.
URI: http://localhost/handle/Hannan/124011
volume: 24
issue: 2
More Information: 191,
195
Appears in Collections:2017

Files in This Item:
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7808985.pdf372.59 kBAdobe PDF
Title: Bridge the Gap Between ADMM and Stackelberg Game: Incentive Mechanism Design for Big Data Networks
Authors: Zijie Zheng;Lingyang Song;Zhu Han
Year: 2017
Publisher: IEEE
Abstract: Alternating direction method of multipliers (ADMM) has been well recognized as an efficient optimization approach due to its fast convergence speed and variable decomposition property. However, in big data networks, the agents may not feedback the variables as the centralized controller expects. In this paper, we model the problem as a Stackelberg game and design a Stackelberg game based ADMM to deal with the contradiction between the centralized objective of the controller and the individual objectives from the agents. The Stackelberg game based ADMM can converge linearly, which is not dependent on the number of agents. The case study verifies the fast convergence of our game-based incentive mechanism.
URI: http://localhost/handle/Hannan/124011
volume: 24
issue: 2
More Information: 191,
195
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7808985.pdf372.59 kBAdobe PDF
Title: Bridge the Gap Between ADMM and Stackelberg Game: Incentive Mechanism Design for Big Data Networks
Authors: Zijie Zheng;Lingyang Song;Zhu Han
Year: 2017
Publisher: IEEE
Abstract: Alternating direction method of multipliers (ADMM) has been well recognized as an efficient optimization approach due to its fast convergence speed and variable decomposition property. However, in big data networks, the agents may not feedback the variables as the centralized controller expects. In this paper, we model the problem as a Stackelberg game and design a Stackelberg game based ADMM to deal with the contradiction between the centralized objective of the controller and the individual objectives from the agents. The Stackelberg game based ADMM can converge linearly, which is not dependent on the number of agents. The case study verifies the fast convergence of our game-based incentive mechanism.
URI: http://localhost/handle/Hannan/124011
volume: 24
issue: 2
More Information: 191,
195
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7808985.pdf372.59 kBAdobe PDF