Distributed Resource Allocation via Accelerated Saddle Point Dynamics

Wen-Ting Lin; Yan-Wu Wang; Chaojie Li; Xinghuo Yu

doi:10.1109/JAS.2021.1004114

Volume 8 Issue 9

Sep. 2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2021 > 8(9): 1588-1599

W.-T. Lin, Y.-W. Wang, C. J. Li, and X. H. Yu, "Distributed Resource Allocation via Accelerated Saddle Point Dynamics," IEEE/CAA J. Autom. Sinica, vol. 8, no. 9, pp. 1588-1599, Sep. 2021. doi: 10.1109/JAS.2021.1004114

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W.-T. Lin, Y.-W. Wang, C. J. Li, and X. H. Yu, "Distributed Resource Allocation via Accelerated Saddle Point Dynamics," IEEE/CAA J. Autom. Sinica, vol. 8, no. 9, pp. 1588-1599, Sep. 2021. doi: 10.1109/JAS.2021.1004114

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Distributed Resource Allocation via Accelerated Saddle Point Dynamics

doi: 10.1109/JAS.2021.1004114

Wen-Ting Lin^{1, 2
,},
Yan-Wu Wang^{1, 2
,
,},
Chaojie Li^3
,,
Xinghuo Yu^4
,

1.
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
2.
Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, China
3.
School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney NSW 2052, Australia
4.
School of Engineering, Royal Melbourne Institute of Technology (RMIT University), Melbourne VIC 3000, Australia

Funds: This work was supported by the National Natural Science Foundation of China (61773172) and supported in part by the Australian Research Council (DP200101197, DE210100274)

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Author Bio:
Wen-Ting Lin received the B.S. degree in thermal energy and power engineering, the M.S. degree in refrigeration and cryogenics from Huazhong University of Science and Technology (HUST), Wuhan, China, in 2011 and 2014, respectively. She also received the M.S. degree in clean and renewable energy from ParisTech in 2014. She is currently a Ph.D. candidate at Huazhong University of Science and Technology (HUST), Wuhan, China. Her research interest is distributed optimization

Yan-Wu Wang (M’10–SM’13) received the B.S. degree in automatic control, the M.S. and the Ph.D. degrees in control theory and control engineering from Huazhong University of Science and Technology (HUST), Wuhan, China, in 1997, 2000, and 2003, respectively. Currently, she is a Professor with the School of Automation, HUST, and with the Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, China. Her research interests include hybrid systems, cooperative control, and multi-agent systems, with applications in smart grid. She was a recipient of several awards, including the first prize of Hubei Province Natural Science in 2014, the first prize of the Ministry of Education of China in 2005, and the Excellent Ph.D. Dissertation of Hubei Province in 2004, In 2008, she was awarded the title of “New Century Excellent Talents” by the Chinese Ministry of Education

Chaojie Li received the B.Eng. degree in electronic science and technology and the M.Eng. degree in computer science from Chongqing University, Chongqing, China, in 2007 and 2011, respectively, and the Ph.D. degree from RMIT University, Melbourne, VIC, Australia in 2017. Currently, he is with the School of Electrical Engineering and Telecommunications,University of New South Wales, Australia. His current research interests include distributed optimization and control in smart grid, neural networks, and their application

Xinghuo Yu (M’92–SM’98–F’08) received the B.Eng. and M.Eng. degrees in electrical and electronic engineering from the University of Science and Technology of China, Hefei, China in 1982 and 1984 respectively, and the Ph.D. degree in control science and engineering from Southeast University, Nanjing, China in 1988. He is an Associate Deputy Vice-Chancellor and a Distinguished Professor at Royal Melbourne Institute of Technology (RMIT University), Melbourne, Australia. He is also the Junior Past President of IEEE Industrial Electronics Society for 2020 and 2021. His research interests include control systems, complex and intelligent systems, and smart energy systems. He has served as an Associate Editor of IEEE Transactions on Automatic Control, IEEE Transactions on Circuits and Systems I: Regular Papers, IEEE Transactions on Industrial Electronics, and IEEE Transactions on Industrial Informatics. He received a number of awards and honors for his contributions, including 2013 Dr.-Ing. Eugene Mittelmann Achievement Award of IEEE Industrial Electronics Society, 2018 M A Sargent Medal from Engineers Australia and 2018 Austalasian AI Distinguished Research Contribution Award from Australian Computer Society
Corresponding author: Yan-Wu Wang, e-mail: wangyw@hust.edu.cn
Received Date: 2020-11-29
Revised Date: 2021-01-27
Accepted Date: 2021-05-07

Available Online: 2023-05-11

Abstract

Abstract

In this paper, accelerated saddle point dynamics is proposed for distributed resource allocation over a multi-agent network, which enables a hyper-exponential convergence rate. Specifically, an inertial fast-slow dynamical system with vanishing damping is introduced, based on which the distributed saddle point algorithm is designed. The dual variables are updated in two time scales, i.e., the fast manifold and the slow manifold. In the fast manifold, the consensus of the Lagrangian multipliers and the tracking of the constraints are pursued by the consensus protocol. In the slow manifold, the updating of the Lagrangian multipliers is accelerated by inertial terms. Hyper-exponential stability is defined to characterize a faster convergence of our proposed algorithm in comparison with conventional primal-dual algorithms for distributed resource allocation. The simulation of the application in the energy dispatch problem verifies the result, which demonstrates the fast convergence of the proposed saddle point dynamics.
- Accelerated saddle point dynamic,
- distributed resource allocation,
- hyper-exponential stability,
- power dispatch

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References

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Accelerated saddle point dynamics are firstly proposed for resource allocation.
The proposed algorithm is initialization-free.
An inertial fast-slow dynamical system is introduced for distributed algorithm design.

Distributed Resource Allocation via Accelerated Saddle Point Dynamics

doi: 10.1109/JAS.2021.1004114

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通讯作者: 陈斌, bchen63@163.com

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