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A Multi-Scale Method for Distributed Convex Optimization with Constraints

Author

Listed:
  • Wei Ni

    (Nanchang University)

  • Xiaoli Wang

    (Harbin Institute of Technology at Weihai)

Abstract

This paper proposes a multi-scale method to design a continuous-time distributed algorithm for constrained convex optimization problems by using multi-agents with Markov switched network dynamics and noisy inter-agent communications. Unlike most previous work which mainly puts emphasis on dealing with fixed network topology, this paper tackles the challenging problem of investigating the joint effects of stochastic networks and the inter-agent communication noises on the distributed optimization dynamics, which has not been systemically studied in the past literature. Also, in sharp contrast to previous work in constrained optimization, we depart from the use of projected gradient flow which is non-smooth and hard to analyze; instead, we design a smooth optimization dynamics which leads to easier convergence analysis and more efficient numerical simulations. Moreover, the multi-scale method presented in this paper generalizes previously known distributed convex optimization algorithms from the fixed network topology to the switching case and the stochastic averaging obtained in this paper is a generalization of the existing deterministic averaging.

Suggested Citation

  • Wei Ni & Xiaoli Wang, 2022. "A Multi-Scale Method for Distributed Convex Optimization with Constraints," Journal of Optimization Theory and Applications, Springer, vol. 192(1), pages 379-400, January.
    • Handle: RePEc:spr:joptap:v:192:y:2022:i:1:d:10.1007_s10957-021-01982-0
    DOI: 10.1007/s10957-021-01982-0
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    References listed on IDEAS

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    1. S. Sundhar Ram & A. Nedić & V. V. Veeravalli, 2010. "Distributed Stochastic Subgradient Projection Algorithms for Convex Optimization," Journal of Optimization Theory and Applications, Springer, vol. 147(3), pages 516-545, December.
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    Cited by:

    1. Li, Jingwang & An, Qing & Su, Housheng, 2023. "Proximal nested primal-dual gradient algorithms for distributed constraint-coupled composite optimization," Applied Mathematics and Computation, Elsevier, vol. 444(C).
    2. Zhou, Xu & Ma, Zhongjing & Zou, Suli & Zhang, Jinhui, 2022. "Consensus-based distributed economic dispatch for Multi Micro Energy Grid systems under coupled carbon emissions," Applied Energy, Elsevier, vol. 324(C).

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