| Title | Bridge the Gap Between ADMM and Stackelberg Game: Incentive Mechanism Design for Big Data Networks |
| Authors | Zheng, Zijie Song, Lingyang Han, Zhu |
| Affiliation | Peking Univ, Sch Elect Engn & Comp Sci, State Key Lab Adv Opt Commun Syst & Networks, Beijing 1000871, Peoples R China. Univ Houston, Dept Elect & Comp Engn, Houston, TX 77004 USA. |
| Keywords | ADMM big data game theory large-scale network |
| Issue Date | 2017 |
| Publisher | IEEE SIGNAL PROCESSING LETTERS |
| Citation | IEEE SIGNAL PROCESSING LETTERS. 2017, 24(2), 191-195. |
| Abstract | Alternating directionmethod 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://hdl.handle.net/20.500.11897/501216 |
| ISSN | 1070-9908 |
| DOI | 10.1109/LSP.2017.2649545 |
| Indexed | SCI(E) EI |
| Appears in Collections: | 信息科学技术学院 区域光纤通信网与新型光通信系统国家重点实验室 |
