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Volume 8 Issue 9
Sep.  2021

IEEE/CAA Journal of Automatica Sinica

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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
Citation: 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

Distributed Resource Allocation via Accelerated Saddle Point Dynamics

doi: 10.1109/JAS.2021.1004114
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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  • 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.

     

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    Highlights

    • 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.

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